From 07c24bf3322ec9d389c36b982ca82c8e8568a04a Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 13:13:01 +0900 Subject: [PATCH 01/23] Created using Colaboratory --- .../species-distribution-modeling.ipynb | 141 ++++++++++++++++++ 1 file changed, 141 insertions(+) create mode 100644 tutorials/species-distribution-modeling/species-distribution-modeling.ipynb diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb new file mode 100644 index 000000000..d2441b560 --- /dev/null +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -0,0 +1,141 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Earth Engine Community Tutorial Template", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "8kdsGkYJXXKc" + }, + "source": [ + "#@title Copyright 2023 The Earth Engine Community Authors { display-mode: \"form\" }\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l18M9_r5XmAQ" + }, + "source": [ + "# Tutorial title\n", + "Author: github-username\n", + "\n", + "This notebook serves as a template for creating your own Earth Engine Community Tutorial using Python and Colab. Before proceeding, please be sure you've read [Writing your own tutorial](https://developers.google.com/earth-engine/tutorials/community/write), as well as the [Earth Engine Community Tutorials Style Guide](https://developers.google.com/earth-engine/tutorials/community/styleguide).\n", + "\n", + "To get started, replace `Tutorial title` and `github-username` above with your own. Then, replace the text in this cell with a few sentences describing what the user is going to learn in this tutorial. Be sure to include _concise_ background information; only include what's helpful and relevant. When in doubt, leave it out!\n", + "\n", + "IMPORTANT: Be sure not to modify the copyright header at the beginning of this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U7i55vr_aKCB" + }, + "source": [ + "### Run me first\n", + "\n", + "Run the following cell to initialize the API. The output will contain instructions on how to grant this notebook access to Earth Engine using your account." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XeFsiSp2aDL6" + }, + "source": [ + "import ee\n", + "\n", + "# Trigger the authentication flow.\n", + "ee.Authenticate()\n", + "\n", + "# Initialize the library.\n", + "ee.Initialize(project='my-project')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VOf_UnIcZKBJ" + }, + "source": [ + "## Additional instructions\n", + "\n", + "Break up your tutorial into sections, one section per task, using text cells to describe each code block. Code should be split into small chunks that can be executed together and that can be easily understood by the reader.\n", + "\n", + "You can find useful examples of how to render images, charts, and interactive maps in the [Earth Engine Python API Colab Setup](\n", + "https://colab.research.google.com/github/google/earthengine-community/blob/master/guides/linked/ee-api-colab-setup.ipynb) notebook.\n", + "\n", + "### Getting started\n", + "\n", + "Before starting work on your tutorial:\n", + "\n", + "1. If you haven't already, [Join GitHub](https://github.com/join).\n", + "\n", + "1. File a [Tutorial proposal](TODO) to discuss your idea with the maintainers.\n", + "\n", + "1. Once your proposal is approved, [fork the Earth Engine Community repository](https://github.com/google/earthengine-community/fork) to your personal account.\n", + "\n", + "1. In Colab, click \"File > Save a copy in GitHub\", granting Colab permission to write to your personal repo as necessary.\n", + "\n", + " > Note: Pull requests are linked to the branch from which they were created. If you plan to have more than one tutorial out for review at a time, you will need to create a separate branch for each pull request ([instructions](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-and-deleting-branches-within-your-repository)).\n", + "\n", + "1. In the \"Copy to GitHub\" dialog that appears, select the `master` branch of the fork created above.\n", + "\n", + "1. Enter the \"File path\" as follows:\n", + "\n", + " ```\n", + " tutorials/your-tutorial-name/index.ipynb\n", + " ```\n", + "\n", + " Replace `your-tutorial-name` with a short filename, using only lowercase letters, numbers, and \"-\", preserving the \".ipynb\" extension.\n", + "\n", + "1. Write your tutorial directly in the notebook, leaving the copyright cell intact, and updating the title cell.\n", + "\n", + "### Adding images\n", + "\n", + "Images should be uploaded to your fork in GitHub under the `tutorials/your-tutorial-name` folder created above. Images must be directly under that directory; nested subdirectories are not allowed (e.g., \"`tutorials/your-tutorial-name/`~`img/`~`cool-viz.jpg`\").\n", + "\n", + "To refer to images uploaded in your tutorial, use Markdown's image syntax, referring to the filename of the image, without specifying its absolute path or full URL. For example:\n", + "\n", + "```md\n", + "![alt text](my-awesome-viz.jpg)\n", + "```\n", + "\n", + "### Submitting tutorial for review\n", + "\n", + "Once your tutorial is ready for review:\n", + "\n", + "1. Follow GitHub's instructions on [creating a pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork) to request a review.\n", + "\n", + "1. Reviewer(s) will be assigned to review your notebook. Work with them to finalize its content. Once ready, the Earth Engine Community maintainers will approve, merge, and publish your tutorial in the [Earth Engine developers documentation](https://developers.google.com/earth-engine/tutorials/community)." + ] + } + ] +} \ No newline at end of file From dd609d27cdb34772687c929737dd88f5562706e7 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 13:46:20 +0900 Subject: [PATCH 02/23] Update species-distribution-modeling.ipynb --- .../species-distribution-modeling.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index d2441b560..98c86bcc3 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -3,7 +3,7 @@ "nbformat_minor": 0, "metadata": { "colab": { - "name": "Earth Engine Community Tutorial Template", + "name": "Species Distribution Modeling", "provenance": [] }, "kernelspec": { @@ -138,4 +138,4 @@ ] } ] -} \ No newline at end of file +} From d6b09753aab628bce8d52768956b44d1546d6b7e Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 14:23:31 +0900 Subject: [PATCH 03/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 42 ++++++++++++------- 1 file changed, 26 insertions(+), 16 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 98c86bcc3..9bb1e6d6e 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -32,7 +32,7 @@ "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], - "execution_count": null, + "execution_count": 1, "outputs": [] }, { @@ -41,14 +41,10 @@ "id": "l18M9_r5XmAQ" }, "source": [ - "# Tutorial title\n", - "Author: github-username\n", - "\n", - "This notebook serves as a template for creating your own Earth Engine Community Tutorial using Python and Colab. Before proceeding, please be sure you've read [Writing your own tutorial](https://developers.google.com/earth-engine/tutorials/community/write), as well as the [Earth Engine Community Tutorials Style Guide](https://developers.google.com/earth-engine/tutorials/community/styleguide).\n", + "# Species Distribtuion Modeling\n", + "Author: Byeong-Hyeok Yu\n", "\n", - "To get started, replace `Tutorial title` and `github-username` above with your own. Then, replace the text in this cell with a few sentences describing what the user is going to learn in this tutorial. Be sure to include _concise_ background information; only include what's helpful and relevant. When in doubt, leave it out!\n", - "\n", - "IMPORTANT: Be sure not to modify the copyright header at the beginning of this tutorial." + "In this tutorial, the methodology of Species Distribution Modeling using Google Earth Engine will be introduced. A brief overview of Species Distribution Modeling will be provided, followed by the process of predicting and analyzing the habitat of an endangered bird species known as the Fairy pitta (scientific name: *Pitta nympha*)." ] }, { @@ -74,9 +70,10 @@ "ee.Authenticate()\n", "\n", "# Initialize the library.\n", - "ee.Initialize(project='my-project')" + "# ee.Initialize(project='my-project')\n", + "ee.Initialize(project='ee-foss4g')" ], - "execution_count": null, + "execution_count": 2, "outputs": [] }, { @@ -85,14 +82,18 @@ "id": "VOf_UnIcZKBJ" }, "source": [ - "## Additional instructions\n", + "## A brief overview of Species Distribution Modeling\n", + "\n", + "Let's explore what species distribution models are, the advantages of using Google Earth Engine for their processing, the required data for the models, and how the workflow is structured.\n", + "\n", + "### What is Species Distribution Modeling?\n", + "\n", + "Species Distribution Modeling (SDM below) is the most common methodology used to estimate the actual or potential geographic distribution of a species. It involves characterizing the environmental conditions suitable for a particular species and then identifying where these suitable conditions are distributed geographically.\n", "\n", - "Break up your tutorial into sections, one section per task, using text cells to describe each code block. Code should be split into small chunks that can be executed together and that can be easily understood by the reader.\n", + "SDM has emerged as a crucial component of conservation planning in recent years, and various modeling techniques have been developed for this purpose. Implementing SDM in Google Earth Engine provides easy access to large-scale environmental data, along with powerful computing capabilities and support for machine learning algorithms, allowing for rapid modeling.\n", "\n", - "You can find useful examples of how to render images, charts, and interactive maps in the [Earth Engine Python API Colab Setup](\n", - "https://colab.research.google.com/github/google/earthengine-community/blob/master/guides/linked/ee-api-colab-setup.ipynb) notebook.\n", + "In this tutorial, we will explore the implementation of species distribution modeling using Google Earth Engine.\n", "\n", - "### Getting started\n", "\n", "Before starting work on your tutorial:\n", "\n", @@ -136,6 +137,15 @@ "\n", "1. Reviewer(s) will be assigned to review your notebook. Work with them to finalize its content. Once ready, the Earth Engine Community maintainers will approve, merge, and publish your tutorial in the [Earth Engine developers documentation](https://developers.google.com/earth-engine/tutorials/community)." ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "4jbM03uIrjST" + }, + "execution_count": null, + "outputs": [] } ] -} +} \ No newline at end of file From dcdb016be07368331a096ea49626cd945310e685 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 18:08:30 +0900 Subject: [PATCH 04/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 142 +++++++++++++----- 1 file changed, 106 insertions(+), 36 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 9bb1e6d6e..b0bfaab3a 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -32,7 +32,7 @@ "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], - "execution_count": 1, + "execution_count": null, "outputs": [] }, { @@ -73,7 +73,7 @@ "# ee.Initialize(project='my-project')\n", "ee.Initialize(project='ee-foss4g')" ], - "execution_count": 2, + "execution_count": 1, "outputs": [] }, { @@ -90,59 +90,129 @@ "\n", "Species Distribution Modeling (SDM below) is the most common methodology used to estimate the actual or potential geographic distribution of a species. It involves characterizing the environmental conditions suitable for a particular species and then identifying where these suitable conditions are distributed geographically.\n", "\n", - "SDM has emerged as a crucial component of conservation planning in recent years, and various modeling techniques have been developed for this purpose. Implementing SDM in Google Earth Engine provides easy access to large-scale environmental data, along with powerful computing capabilities and support for machine learning algorithms, allowing for rapid modeling.\n", - "\n", - "In this tutorial, we will explore the implementation of species distribution modeling using Google Earth Engine.\n", - "\n", - "\n", - "Before starting work on your tutorial:\n", - "\n", - "1. If you haven't already, [Join GitHub](https://github.com/join).\n", - "\n", - "1. File a [Tutorial proposal](TODO) to discuss your idea with the maintainers.\n", - "\n", - "1. Once your proposal is approved, [fork the Earth Engine Community repository](https://github.com/google/earthengine-community/fork) to your personal account.\n", - "\n", - "1. In Colab, click \"File > Save a copy in GitHub\", granting Colab permission to write to your personal repo as necessary.\n", + "SDM has emerged as a crucial component of conservation planning in recent years, and various modeling techniques have been developed for this purpose. Implementing SDM in Google Earth Engine (GEE below) provides easy access to large-scale environmental data, along with powerful computing capabilities and support for machine learning algorithms, allowing for rapid modeling.\n", "\n", - " > Note: Pull requests are linked to the branch from which they were created. If you plan to have more than one tutorial out for review at a time, you will need to create a separate branch for each pull request ([instructions](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-and-deleting-branches-within-your-repository)).\n", + " > Note: Conservation biologist Dr. Ramiro D. Crego implemented SDM using the GEE JavaScript Code Editor and published his research findings [(Crego et al, 2022)](https://onlinelibrary.wiley.com/doi/10.1111/ddi.13491). The methodology of SDM introduced here has been translated and modified from the [JavaScript source code](https://smithsonian.github.io/SDMinGEE/) he shared into the Python language.\n", "\n", - "1. In the \"Copy to GitHub\" dialog that appears, select the `master` branch of the fork created above.\n", + "### Data Required for SDM\n", "\n", - "1. Enter the \"File path\" as follows:\n", + "SDM typically utilizes the relationship between known species occurrence records and environmental variables to identify the conditions under which a population can sustain. In other words, two types of model input data are required:\n", "\n", - " ```\n", - " tutorials/your-tutorial-name/index.ipynb\n", - " ```\n", + "1. Occurrence records of known species\n", + "1. Various environmental variables\n", "\n", - " Replace `your-tutorial-name` with a short filename, using only lowercase letters, numbers, and \"-\", preserving the \".ipynb\" extension.\n", + "These data are input into algorithms to identify environmental conditions associated with the presence of species.\n", "\n", - "1. Write your tutorial directly in the notebook, leaving the copyright cell intact, and updating the title cell.\n", + "### Workflow of SDM using GEE\n", "\n", - "### Adding images\n", + "The workflow for SDM using GEE is as follows:\n", "\n", - "Images should be uploaded to your fork in GitHub under the `tutorials/your-tutorial-name` folder created above. Images must be directly under that directory; nested subdirectories are not allowed (e.g., \"`tutorials/your-tutorial-name/`~`img/`~`cool-viz.jpg`\").\n", - "\n", - "To refer to images uploaded in your tutorial, use Markdown's image syntax, referring to the filename of the image, without specifying its absolute path or full URL. For example:\n", + "1. Collection and preprocessing of species occurrence data\n", + "1. Definition of the area of interest\n", + "1. Addition of GEE environmental variables\n", + "1. Generation of pseudo-absence data\n", + "1. Model fitting and prediction\n", + "1. Variable importance and accuracy assessment" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Habitat Prediction and Analysis Using GEE\n", "\n", - "```md\n", - "![alt text](my-awesome-viz.jpg)\n", - "```\n", + "The [Fairy pitta (*Pitta nympha*)](https://datazone.birdlife.org/species/factsheet/22698684) will be used as a case study to demonstrate the application of GEE-based SDM. While this specific species has been selected for one example, researchers can apply the methodology to any target species of interest with slight modifications to the provided source code.\n", "\n", - "### Submitting tutorial for review\n", + "The Fairy pitta is a rare summer migrant and passage migrant in South Korea, whose distribution area is expanding due to recent climate warming on the Korean Peninsula. It is classified as a rare species, endangered wildlife of class II, Natural Monument No. 204, evaluated as Regionally Extinct (RE) in the National Red List, and Vulnerable (VU) according to the IUCN categories.\n", "\n", - "Once your tutorial is ready for review:\n", + "Conducting SDM for the conservation planning of the Fairy pitta appears to be quite valuable. Now, let's proceed with habitat prediction and analysis through GEE." + ], + "metadata": { + "id": "tjomxWfVcTmN" + } + }, + { + "cell_type": "markdown", + "source": [ + "First, the Python libraries are imported.The `import` statement brings in the entire contents of a module, while the `from import` statement allows for the importation of specific objects from a module." + ], + "metadata": { + "id": "pViK9PM-gLjh" + } + }, + { + "cell_type": "code", + "source": [ + "# Import libraries\n", + "import geemap\n", "\n", - "1. Follow GitHub's instructions on [creating a pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request-from-a-fork) to request a review.\n", + "import geemap.colormaps as cm\n", + "import pandas as pd, geopandas as gpd\n", + "import numpy as np, matplotlib.pyplot as plt\n", + "import os, requests, math, random\n", "\n", - "1. Reviewer(s) will be assigned to review your notebook. Work with them to finalize its content. Once ready, the Earth Engine Community maintainers will approve, merge, and publish your tutorial in the [Earth Engine developers documentation](https://developers.google.com/earth-engine/tutorials/community)." + "from ipyleaflet import TileLayer\n", + "from statsmodels.stats.outliers_influence import variance_inflation_factor" + ], + "metadata": { + "id": "4jbM03uIrjST", + "outputId": "baa5449b-af0f-4c39-f086-535c984f0509", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } ] }, + { + "cell_type": "markdown", + "source": [ + "### Collection and Preprocessing of Species Occurrence Data" + ], + "metadata": { + "id": "SRrwa4ROghr9" + } + }, { "cell_type": "code", "source": [], "metadata": { - "id": "4jbM03uIrjST" + "id": "oHtKaH0FgXTz" }, "execution_count": null, "outputs": [] From a1f911a9183e35cd407c2ea4229dd3eac854c425 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 19:05:44 +0900 Subject: [PATCH 05/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 1211 ++++++++++++++++- 1 file changed, 1203 insertions(+), 8 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index b0bfaab3a..0980403f9 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -15,7 +15,12 @@ { "cell_type": "code", "metadata": { - "id": "8kdsGkYJXXKc" + "id": "8kdsGkYJXXKc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "ea49ceb7-2fdf-4929-a5bf-a3bd2536fe8f" }, "source": [ "#@title Copyright 2023 The Earth Engine Community Authors { display-mode: \"form\" }\n", @@ -32,8 +37,43 @@ "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], - "execution_count": null, - "outputs": [] + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] }, { "cell_type": "markdown", @@ -41,7 +81,7 @@ "id": "l18M9_r5XmAQ" }, "source": [ - "# Species Distribtuion Modeling\n", + "# Species Distribution Modeling\n", "Author: Byeong-Hyeok Yu\n", "\n", "In this tutorial, the methodology of Species Distribution Modeling using Google Earth Engine will be introduced. A brief overview of Species Distribution Modeling will be provided, followed by the process of predicting and analyzing the habitat of an endangered bird species known as the Fairy pitta (scientific name: *Pitta nympha*)." @@ -155,11 +195,11 @@ ], "metadata": { "id": "4jbM03uIrjST", - "outputId": "baa5449b-af0f-4c39-f086-535c984f0509", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "outputId": "baa5449b-af0f-4c39-f086-535c984f0509" }, "execution_count": 3, "outputs": [ @@ -202,17 +242,1172 @@ { "cell_type": "markdown", "source": [ - "### Collection and Preprocessing of Species Occurrence Data" + "### Collection and Preprocessing of Species Occurrence Data\n", + "\n", + "Now, let's collect occurrence data for the Fairy pitta. Even if you don't currently have access to occurrence data for the species of interest, you can obtain observational data about specific species through the GBIF API. The [GBIF API](https://techdocs.gbif.org/en/openapi/) is an interface that allows access to the species distribution data provided by GBIF, enabling users to search, filter, and download data, as well as acquire various information related to species.\n", + "\n", + "In the code below, the `species_name` variable is assigned the scientific name of the species (e.g. *Pitta nympha* for Fairy pitta), and the `country_code` variable is assigned the country code (e.g. KR for South Korea). The `base_url` variable stores the address of the GBIF API. `params` is a dictionary containing parameters to be used in the API request:\n", + "\n", + "* `scientificName`: Sets the scientific name of the species to be searched.\n", + "* `country`: Limits the search to a specific country.\n", + "* `hasCoordinate`: Ensures only data with coordinates (true) are searched.\n", + "* `basisOfRecord`: Chooses only records of human observation (`HUMAN_OBSERVATION`).\n", + "* `limit`: Sets the maximum number of results returned to 10000." ], "metadata": { "id": "SRrwa4ROghr9" } }, + { + "cell_type": "code", + "source": [ + "def get_gbif_species_data(species_name, country_code):\n", + " \"\"\"\n", + " Retrieves observational data for a specific species using the GBIF API and returns it as a pandas DataFrame.\n", + "\n", + " Parameters:\n", + " species_name (str): The scientific name of the species to query.\n", + " country_code (str): The country code of the where the observation data will be queried.\n", + "\n", + " Returns:\n", + " pd.DataFrame: A pandas DataFrame containing the observational data.\n", + " \"\"\"\n", + " base_url = \"https://api.gbif.org/v1/occurrence/search\"\n", + " params = {\n", + " \"scientificName\": species_name,\n", + " \"country\": country_code,\n", + " \"hasCoordinate\": \"true\",\n", + " \"basisOfRecord\": \"HUMAN_OBSERVATION\",\n", + " \"limit\": 10000,\n", + " }\n", + "\n", + " try:\n", + " response = requests.get(base_url, params=params)\n", + " response.raise_for_status() # Raises an exception for a response error.\n", + " data = response.json()\n", + " occurrences = data.get(\"results\", [])\n", + "\n", + " if occurrences: # If data is present\n", + " df = pd.json_normalize(occurrences)\n", + " return df\n", + " else:\n", + " print(\"No data found for the given species and country code.\")\n", + " return pd.DataFrame() # Returns an empty DataFrame\n", + " except requests.RequestException as e:\n", + " print(f\"Request failed: {e}\")\n", + " return pd.DataFrame() # Returns an empty DataFrame in case of an exception" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "oHtKaH0FgXTz", + "outputId": "35aaf721-029e-4c34-b35c-06608f1b5250" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Using the parameters set previously, we query the GBIF API for observational records of the Fairy pitta (*Pitta nympha*), and load the results into a DataFrame to check the first row. A DataFrame is a data structure for handling table-formatted data, consisting of rows and columns. If necessary, the DataFrame can be saved as a CSV file and read back in." + ], + "metadata": { + "id": "Zs5ZUfZUnjZ2" + } + }, + { + "cell_type": "code", + "source": [ + "# Retrieve Fairy Pitta data\n", + "df = get_gbif_species_data(\"Pitta nympha\", \"KR\")\n", + "\"\"\"\n", + "# Save DataFrame to CSV and read back in.\n", + "df.to_csv(\"pitta_nympha_data.csv\", index=False)\n", + "df = pd.read_csv(\"pitta_nympha_data.csv\")\n", + "\"\"\"\n", + "df.head(1) # Display the first row of the DataFrame" + ], + "metadata": { + "id": "Mx-DjtGNnUXk", + "outputId": "95a3b0ee-25cb-4547-cb3b-8da17eed9fff", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 182 + } + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " key datasetKey \\\n", + "0 4126765284 50c9509d-22c7-4a22-a47d-8c48425ef4a7 \n", + "\n", + " publishingOrgKey installationKey \\\n", + "0 28eb1a3f-1c15-4a95-931a-4af90ecb574d 997448a8-f762-11e1-a439-00145eb45e9a \n", + "\n", + " hostingOrganizationKey publishingCountry protocol \\\n", + "0 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE \n", + "\n", + " lastCrawled lastParsed crawlId ... \\\n", + "0 2024-01-23T16:28:21.693+00:00 2024-01-25T09:13:47.069+00:00 431 ... \n", + "\n", + " nomenclaturalCode fieldNotes behavior verbatimElevation \\\n", + "0 NaN NaN NaN NaN \n", + "\n", + " higherClassification extensions.http://rs.tdwg.org/ac/terms/Multimedia \\\n", + "0 NaN NaN \n", + "\n", + " distanceFromCentroidInMeters associatedTaxa lifeStage occurrenceRemarks \n", + "0 NaN NaN NaN NaN \n", + "\n", + "[1 rows x 110 columns]" + ], + "text/html": [ + "\n", + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The data from 1995 is very sparse, with significant gaps compared to other years, and the months of August and September also have limited samples and exhibit different seasonal characteristics compared to other periods. Excluding this data could contribute to improving the stability and predictive power of the model.\n", + "\n", + "However, it's important to note that excluding data may enhance the model's generalization ability, but it could also lead to the loss of valuable information relevant to the research objectives. Therefore, such decisions should be made with careful consideration." + ], + "metadata": { + "id": "vIj9CO1RrCSs" + } + }, + { + "cell_type": "code", + "source": [ + "# Filtering data by year and month\n", + "filtered_gdf = gdf[\n", + " (~gdf['year'].eq(1995)) &\n", + " (~gdf['month'].between(8, 9))\n", + "]\n", + "\n", + "# Visualize the filtered data distribution\n", + "plot_data_distribution(filtered_gdf)" + ], + "metadata": { + "id": "k1PpbNsGq9qh", + "outputId": "1c90e189-5c7d-4a60-cef6-6096603f7e97", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + } + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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hQwdJUlxcnBo2bKidO3fqnnvuuZ7oAAAAprD3+AkAAKA8ua7ClCTVqlVLn3zyiX7//XcdOXJEhmGobt26qlKlyg0Fyc3N1dKlS5WZmanQ0FAlJibq0qVLCgsLs/Zp0KCBatasqR07dlCYAgAAZUZxjZ8AAADKuusuTF1WpUoV3XXXXTccYN++fQoNDVVWVpbc3d21YsUKNWrUSHv37pWLi4sqV65s09/X11cpKSmFbi87O1vZ2dnW1xkZGTecEQAAwB7sNX4CAAAoL65r8vPiUL9+fe3du1e7du3Sk08+qaioKB04cKDI25syZYq8vLysS0BAgB3TAgAAAAAAwF5ML0y5uLjotttuU0hIiKZMmaJmzZrprbfekp+fn3JycpSWlmbTPzU1VX5+foVuLyYmRunp6dbl5MmTxXwEAAAA5pk6daosFotGjhxpbePJxgAAoKwwvTB1pby8PGVnZyskJETOzs7atGmTdV1SUpJOnDih0NDQQt/v6uoqT09PmwUAAKA82r17t9599101bdrUpn3UqFFas2aNli5dqm3btun06dPq2bOnSSkBAAAKV+Q5puwhJiZG4eHhqlmzps6fP6/4+Hht3bpVCQkJ8vLy0uDBgzV69GhVrVpVnp6eGj58uEJDQ5n4HAAA3PQuXLigfv36ad68eZo0aZK1PT09nScbAwCAMsPUK6bOnj2r/v37q379+urYsaN2796thIQEderUSZI0ffp0de3aVZGRkWrTpo38/Py0fPlyMyMDAACUCtHR0YqIiLB5grGkf3yycUGys7OVkZFhswAAAJQEU6+Ymj9//lXXu7m5afbs2Zo9e3YJJQIAACj9lixZom+++Ua7d+/Oty4lJeW6n2w8ZcoUTZgwoTiiAgAAXFWpm2MKAAAAhTt58qSefvppLV68WG5ubnbZJg+PAQAAZqEwBQAAUIYkJibq7NmzuvPOO+Xk5CQnJydt27ZNM2fOlJOTk3x9fa/7ycY8PAYAAJjF1Fv5AAAAcH06duyoffv22bQNHDhQDRo00PPPP6+AgADrk40jIyMlXduTjQEAAMxAYQoAAKAM8fDwUJMmTWzaKlWqJG9vb2s7TzYGAABlBYUpAACAcmb69OlycHBQZGSksrOz1aVLF73zzjtmxwIAAMiHwhQAAEAZt3XrVpvXPNkYAACUFUx+DgAAAAAAAFNQmAIAAAAAAIApKEwBAAAAAADAFBSmAAAAAAAAYAoKUwAAAAAAADAFhSkAAAAAAACYgsIUAAAAAAAATEFhCgAAAAAAAKagMAUAAAAAAABTUJgCAAAAAACAKShMAQAAAAAAwBQUpgAAAAAAQJkWGxurpk2bytPTU56engoNDdX69eut67OyshQdHS1vb2+5u7srMjJSqampJibGZRSmAAAAAABAmVajRg1NnTpViYmJ2rNnjzp06KDu3bvrhx9+kCSNGjVKa9as0dKlS7Vt2zadPn1aPXv2NDk1JMnJ7AAAAAAAAAA3olu3bjavJ0+erNjYWO3cuVM1atTQ/PnzFR8frw4dOkiS4uLi1LBhQ+3cuVP33HOPGZHx/3HFFAAAAAAAKDdyc3O1ZMkSZWZmKjQ0VImJibp06ZLCwsKsfRo0aKCaNWtqx44dJiaFxBVTAAAAAACgHNi3b59CQ0OVlZUld3d3rVixQo0aNdLevXvl4uKiypUr2/T39fVVSkqKOWFhRWEKAAAAAACUefXr19fevXuVnp6uZcuWKSoqStu2bTM7Fv4BhSkAAAAAAFDmubi46LbbbpMkhYSEaPfu3XrrrbfUp08f5eTkKC0tzeaqqdTUVPn5+ZmUFpcxxxQAAAAAACh38vLylJ2drZCQEDk7O2vTpk3WdUlJSTpx4oRCQ0NNTAiJK6YAAAAAAEAZFxMTo/DwcNWsWVPnz59XfHy8tm7dqoSEBHl5eWnw4MEaPXq0qlatKk9PTw0fPlyhoaE8ka8UoDAFAAAAAADKtLNnz6p///46c+aMvLy81LRpUyUkJKhTp06SpOnTp8vBwUGRkZHKzs5Wly5d9M4775icGhKFKQAAAAAAUMbNnz//quvd3Nw0e/ZszZ49u4QS4VoxxxQAAAAAAABMQWEKAGCazz//XN26dZO/v78sFotWrlxpsz41NVUDBgyQv7+/KlasqPvuu0+HDx82JywAAAAAu+NWPgCAaTIzM9WsWTMNGjRIPXv2tFlnGIZ69OghZ2dnrVq1Sp6ennrzzTcVFhamAwcOqFKlSialBgAAKN0Cx6wzOwLKgONTI8yOIInCFADAROHh4QoPDy9w3eHDh7Vz507t379fjRs3liTFxsbKz89PH3zwgYYMGVKSUQEAAAAUA27lAwCUStnZ2ZL+mqjyMgcHB7m6umr79u1mxQIAAABgR6YWpqZMmaK77rpLHh4eqlatmnr06KGkpCSbPllZWYqOjpa3t7fc3d0VGRmp1NRUkxIDAEpKgwYNVLNmTcXExOj3339XTk6Opk2bpp9//llnzpwxOx4AAAAAOzC1MLVt2zZFR0dr586d2rhxoy5duqTOnTsrMzPT2mfUqFFas2aNli5dqm3btun06dP55iEBAJQ/zs7OWr58uQ4dOqSqVauqYsWK2rJli8LDw+XgwAW/AAAAQHlg6hxTGzZssHm9YMECVatWTYmJiWrTpo3S09M1f/58xcfHq0OHDpKkuLg4NWzYUDt37tQ999xjRmwAQAkJCQnR3r17lZ6erpycHPn4+KhFixZq3ry52dEAAAAA2EGp+pNzenq6JKlq1aqSpMTERF26dElhYWHWPpdv7dixY0eB28jOzlZGRobNAgAo27y8vOTj46PDhw9rz5496t69u9mRAAAAANhBqSlM5eXlaeTIkWrVqpWaNGkiSUpJSZGLi4sqV65s09fX11cpKSkFbmfKlCny8vKyLgEBAcUdHQBKpc8//1zdunWTv7+/LBaLVq5cabPeYrEUuLz22msllvHChQvau3ev9u7dK0lKTk7W3r17deLECUnS0qVLtXXrVh07dkyrVq1Sp06d1KNHD3Xu3LnEMgIAAAAoPqWmMBUdHa39+/dryZIlN7SdmJgYpaenW5eTJ0/aKSEAlC2ZmZlq1qyZZs+eXeD6M2fO2CzvvfeeLBaLIiMjSyzjnj17FBwcrODgYEnS6NGjFRwcrLFjx1ozPvbYY2rQoIFGjBihxx57TB988EGJ5QMAAABQvEydY+qyYcOGae3atfr8889Vo0YNa7ufn59ycnKUlpZmc9VUamqq/Pz8CtyWq6urXF1dizsyAJR64eHhCg8PL3T9lb9HV61apfbt26t27drFHc2qXbt2Mgyj0PUjRozQiBEjSiwPAAAAgJJl6hVThmFo2LBhWrFihTZv3qygoCCb9SEhIXJ2dtamTZusbUlJSTpx4oRCQ0NLOi4AlFupqalat26dBg8ebHYUAAAAADcRU6+Yio6OVnx8vFatWiUPDw/rvFFeXl6qUKGCvLy8NHjwYI0ePVpVq1aVp6enhg8frtDQUJ7IBwB2tHDhQnl4eKhnz55mRwEAAABwEzG1MBUbGyvpr1s5/i4uLk4DBgyQJE2fPl0ODg6KjIxUdna2unTponfeeaeEkwJA+fbee++pX79+cnNzs9s2A8ess9u2iur41AizIwAAAAC4ClMLU1ebV+QyNzc3zZ49u9DJewEAN+aLL75QUlKSPvzwQ7OjAAAAALjJlJqn8gEAzDF//nyFhISoWbNmZkcBAAAAcJMpFU/lAwDY34ULF3TkyBHr6+TkZO3du1dVq1ZVzZo1JUkZGRlaunSp3njjDbNiAgAAALiJUZgCgHJqz549at++vfX16NGjJUlRUVFasGCBJGnJkiUyDEN9+/Y1IyIAAACAmxyFKQAop9q1a/ePc/kNHTpUQ4cOLaFEAAAAAGCLOaYAAAAAAABgCgpTAAAAAAAAMAW38gFAGRQ4Zp3ZEXR8aoTZEQAAAACUcVwxBQAAAAAAAFNQmAIAAAAAAIApKEwBAAAAAADAFBSmAAAAAAAAYAoKUwAAAAAAADAFhSkAAAAAAACYgsIUAABAGTJlyhTddddd8vDwULVq1dSjRw8lJSXZ9MnKylJ0dLS8vb3l7u6uyMhIpaammpQYAACgcBSmAAAAypBt27YpOjpaO3fu1MaNG3Xp0iV17txZmZmZ1j6jRo3SmjVrtHTpUm3btk2nT59Wz549TUwNAABQMCezAwAAAODabdiwweb1ggULVK1aNSUmJqpNmzZKT0/X/PnzFR8frw4dOkiS4uLi1LBhQ+3cuVP33HOPGbEBAAAKxBVTAAAAZVh6erokqWrVqpKkxMREXbp0SWFhYdY+DRo0UM2aNbVjx44Ct5Gdna2MjAybBQAAoCRQmAIAACij8vLyNHLkSLVq1UpNmjSRJKWkpMjFxUWVK1e26evr66uUlJQCtzNlyhR5eXlZl4CAgOKODgAAIInCFAAAQJkVHR2t/fv3a8mSJTe0nZiYGKWnp1uXkydP2ikhAADA1VGYQpny+eefq1u3bvL395fFYtHKlStt1i9fvlydO3eWt7e3LBaL9u7da0pOAACK27Bhw7R27Vpt2bJFNWrUsLb7+fkpJydHaWlpNv1TU1Pl5+dX4LZcXV3l6elpswAAAJQEClMoUzIzM9WsWTPNnj270PWtW7fWtGnTSjgZAAAlwzAMDRs2TCtWrNDmzZsVFBRksz4kJETOzs7atGmTtS0pKUknTpxQaGhoSccFAAC4Kp7KhzIlPDxc4eHhha5/7LHHJEnHjx8voUQAAJSs6OhoxcfHa9WqVfLw8LDOG+Xl5aUKFSrIy8tLgwcP1ujRo1W1alV5enpq+PDhCg0N5Yl8AACg1KEwBQAAUIbExsZKktq1a2fTHhcXpwEDBkiSpk+fLgcHB0VGRio7O1tdunTRO++8U8JJAQAA/hmFKQAAgDLEMIx/7OPm5qbZs2cXeus7AABAacEcUwAAAAAAADAFhSkAAAAAAACYgsIUAAAAAAAATMEcUyhTLly4oCNHjlhfJycna+/evapatapq1qyp3377TSdOnNDp06cl/fV4bEny8/OTn5+fKZkBAAAAAEDBuGIKZcqePXsUHBys4OBgSdLo0aMVHByssWPHSpJWr16t4OBgRURESJIefvhhBQcHa86cOaZlBgAAAAAABeOKKZQp7dq1u+rTiAYMGGB9VDYAAAAAACjduGIKAAAAAAAApqAwBQAAAAAAAFOYeivf559/rtdee02JiYk6c+aMVqxYoR49eljXG4ahcePGad68eUpLS1OrVq0UGxurunXrmhcaxSZwzDpT9398aoSp+0fhcnNzNX78eC1atEgpKSny9/fXgAED9OKLL8pisZgdDwAAAABQRKZeMZWZmalmzZpp9uzZBa5/9dVXNXPmTM2ZM0e7du1SpUqV1KVLF2VlZZVwUgBmmjZtmmJjY/X222/r4MGDmjZtml599VXNmjXL7GgAAAAAgBtg6hVT4eHhCg8PL3CdYRiaMWOGXnzxRXXv3l2S9P7778vX11crV67Uww8/XJJRAZjoq6++Uvfu3a1PWwwMDNQHH3ygr7/+2uRkAAAAAIAbUWrnmEpOTlZKSorCwsKsbV5eXmrRooV27NhR6Puys7OVkZFhswAo21q2bKlNmzbp0KFDkqTvvvtO27dvL7SwDQAAAAAoG0y9YupqUlJSJEm+vr427b6+vtZ1BZkyZYomTJhQrNkAlKwxY8YoIyNDDRo0kKOjo3JzczV58mT169fP7GgAAAAAgBtQaq+YKqqYmBilp6dbl5MnT5odCcAN+uijj7R48WLFx8frm2++0cKFC/X6669r4cKFZkcDAAAAANyAUnvFlJ+fnyQpNTVV1atXt7anpqbqjjvuKPR9rq6ucnV1Le54AErQs88+qzFjxljnlrv99tv1008/acqUKYqKijI5HQAAAACgqErtFVNBQUHy8/PTpk2brG0ZGRnatWuXQkNDTUwGoKRdvHhRDg62v64cHR2Vl5dnUiIAAAAAgD2YesXUhQsXdOTIEevr5ORk7d27V1WrVlXNmjU1cuRITZo0SXXr1lVQUJBeeukl+fv7q0ePHuaFBlDiunXrpsmTJ6tmzZpq3Lixvv32W7355psaNGiQ2dEAAAAAADfA1MLUnj171L59e+vr0aNHS5KioqK0YMECPffcc8rMzNTQoUOVlpam1q1ba8OGDXJzczMrMgATzJo1Sy+99JKeeuopnT17Vv7+/nriiSc0duxYs6PZOHXqlJ5//nmtX79eFy9e1G233aa4uDg1b97c7GgAAAAAUCqZWphq166dDMModL3FYtHEiRM1ceLEEkwFoLTx8PDQjBkzNGPGDLOjFOr3339Xq1at1L59e61fv14+Pj46fPiwqlSpYnY0AAAAACi1Su3k5wBQlkybNk0BAQGKi4uztgUFBZmYCAAAAABKv1I7+TkAlCWrV69W8+bN1atXL1WrVk3BwcGaN2+e2bEAAAAAoFTjiikAJSpwzDqzI+j41Ai7b/PYsWOKjY3V6NGj9Z///Ee7d+/WiBEj5OLioqioKLvvDwAAAADKAwpTAGAHeXl5at68uV555RVJUnBwsPbv3685c+ZQmAIAAACAQnArHwDYQfXq1dWoUSObtoYNG+rEiRMmJQIAAACA0o/CFADYQatWrZSUlGTTdujQIdWqVcukRAAAAABQ+lGYAgA7GDVqlHbu3KlXXnlFR44cUXx8vObOnavo6GizowGl3tSpU2WxWDRy5EizowAAAKCEUZgCADu46667tGLFCn3wwQdq0qSJXn75Zc2YMUP9+vUzOxpQqu3evVvvvvuumjZtanYUAAAAmIDJzwHATrp27aquXbuaHQMoMy5cuKB+/fpp3rx5mjRpktlxAAAAYAKumAIAAKaIjo5WRESEwsLCzI4CAAAAk3DFFAAAKHFLlizRN998o927d5sdBQAAACaiMAVAsbGxio2N1fHjxyVJjRs31tixYxUeHm5uMJMEjllndgQdnxphdgSg2Jw8eVJPP/20Nm7cKDc3N7PjAAAAwETcygdANWrU0NSpU5WYmKg9e/aoQ4cO6t69u3744QezowEohxITE3X27FndeeedcnJykpOTk7Zt26aZM2fKyclJubm5ZkcEAABACeGKKQDq1q2bzevJkycrNjZWO3fuVOPGjU1KBaC86tixo/bt22fTNnDgQDVo0EDPP/+8HB0dTUoGAACAkkZhCoCN3NxcLV26VJmZmQoNDTU7DoByyMPDQ02aNLFpq1Spkry9vfO1AwAAoHzjVj4UasqUKbrrrrvk4eGhatWqqUePHkpKSjI7VplTVs7jvn375O7uLldXV/3rX//SihUr1KhRI7NjAQAAAADKMQpTKNS2bdsUHR2tnTt3auPGjbp06ZI6d+6szMxMs6OVKWXlPNavX1979+7Vrl279OSTTyoqKkoHDhwwOxaAm8TWrVs1Y8YMs2MAAACghHErHwq1YcMGm9cLFixQtWrVlJiYqDZt2piUquwpK+fRxcVFt912myQpJCREu3fv1ltvvaV3333X5GQAAAAAgPKKK6ZwzdLT0yVJVatWNTlJ2VZWzmNeXp6ys7PNjgEAAAAAKMe4Ysokn3/+uV577TUlJibqzJkzWrFihXr06GF2rELl5eVp5MiRatWqFRPT3oDSeh5jYmIUHh6umjVr6vz584qPj9fWrVuVkJBgdjQAZUDgmHWm7v/41AhT9w8AAICiozBlkszMTDVr1kyDBg1Sz549zY7zj6Kjo7V//35t377d7ChlWmk9j2fPnlX//v115swZeXl5qWnTpkpISFCnTp3MjgYAAAAAKMcoTJkkPDxc4eHhZse4JsOGDdPatWv1+eefq0aNGmbHKbNK83mcP3++2REAAAAAADchClMolGEYGj58uFasWKGtW7cqKCjI7EhlEucRAAAAAICCUZhCoaKjoxUfH69Vq1bJw8NDKSkpkiQvLy9VqFDB5HRlB+cRAAAAAICCUZhCoWJjYyVJ7dq1s2mPi4vTgAEDSj5QGVXS55FJiAEAAAAAZQWFKRTKMAyzI5QLnEcAAAAAAArmYHYAAAAAAAAA3Jy4YsokFy5c0JEjR6yvk5OTtXfvXlWtWlU1a9Y0MRkAAAAAAEDJoDBlkj179qh9+/bW16NHj5YkRUVFacGCBXbfn9nzDknlY+4hs89jeTiHAAAAAABcRmHKJO3atWPuIQAAAAAAcFNjjikAAAAAAACYgsIUAAAAAAAATFEmbuWbPXu2XnvtNaWkpKhZs2aaNWuW7r77brNjWZk975DE3EMAACC/0j6GAgAAKPVXTH344YcaPXq0xo0bp2+++UbNmjVTly5ddPbsWbOjAQAAlFqMoQAAQFlQ6gtTb775ph5//HENHDhQjRo10pw5c1SxYkW99957ZkcDAAAotRhDAQCAsqBUF6ZycnKUmJiosLAwa5uDg4PCwsK0Y8cOE5MBAACUXoyhAABAWVGq55g6d+6ccnNz5evra9Pu6+urH3/8scD3ZGdnKzs72/o6PT1dkpSRkVFsOfOyLxbbtq/VPx0fGf/ZtXxGyPjPSvu/s0RGeyGjfZDxxhXnd/zft28YRrHux56udwxlxvhJMv+zg9KvuD+D14PPK65FafnM8nnFtSjOz+v1jJ9KdWGqKKZMmaIJEybkaw8ICDAhTcnxmmF2gn9W2jOW9nwSGe2FjPZBRvsg440rqXznz5+Xl5dXyeyshN2s4yeUfqX99w9wJT6zKEtK4vN6LeOnUl2YuuWWW+To6KjU1FSb9tTUVPn5+RX4npiYGI0ePdr6Oi8vT7/99pu8vb1lsViKNW9RZGRkKCAgQCdPnpSnp6fZccoszqN9cB7tg/N44ziH9sF5tA/DMHT+/Hn5+/ubHeWaXe8YqqyNn8oj/ntFWcNnFmUJn9eSdz3jp1JdmHJxcVFISIg2bdqkHj16SPproLRp0yYNGzaswPe4urrK1dXVpq1y5crFnPTGeXp68h+IHXAe7YPzaB+cxxvHObQPzuONK2tXSl3vGKqsjp/KI/57RVnDZxZlCZ/XknWt46dSXZiSpNGjRysqKkrNmzfX3XffrRkzZigzM1MDBw40OxoAAECpxRgKAACUBaW+MNWnTx/98ssvGjt2rFJSUnTHHXdow4YN+SbzBAAAwP9hDAUAAMqCUl+YkqRhw4YVeuteWefq6qpx48blu3we14fzaB+cR/vgPN44zqF9cB5RnsdQ5Q3/vaKs4TOLsoTPa+lmMcrSs48BAAAAAABQbjiYHQAAAAAAAAA3JwpTAAAAAAAAMAWFKQAAAAAAAJiCwpQdTJkyRXfddZc8PDxUrVo19ejRQ0lJSTZ9srKyFB0dLW9vb7m7uysyMlKpqak2fUaMGKGQkBC5urrqjjvuyLefrVu3qnv37qpevboqVaqkO+64Q4sXLy7OQytRJXUe/+7IkSPy8PBQ5cqV7Xw05ijJc2gYhl5//XXVq1dPrq6uuvXWWzV58uTiOrQSVZLnMSEhQffcc488PDzk4+OjyMhIHT9+vJiOrGTZ4zx+99136tu3rwICAlShQgU1bNhQb731Vr59bd26VXfeeadcXV112223acGCBcV9eCWmpM7j8uXL1alTJ/n4+MjT01OhoaFKSEgokWMEbmbjx4+XxWKxWRo0aGB2LKBQp06d0qOPPipvb29VqFBBt99+u/bs2WN2LKBAgYGB+X7HWiwWRUdHmx0Nf0Nhyg62bdum6Oho7dy5Uxs3btSlS5fUuXNnZWZmWvuMGjVKa9as0dKlS7Vt2zadPn1aPXv2zLetQYMGqU+fPgXu56uvvlLTpk318ccf6/vvv9fAgQPVv39/rV27ttiOrSSV1Hm87NKlS+rbt6/uvfdeux+LWUryHD799NP673//q9dff10//vijVq9erbvvvrtYjqukldR5TE5OVvfu3dWhQwft3btXCQkJOnfuXIHbKYvscR4TExNVrVo1LVq0SD/88INeeOEFxcTE6O2337b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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Additionally, the use of a heatmap allows us to quickly grasp the frequency of species occurrence by year and month, providing an intuitive visualization of the temporal changes and patterns within the data." + ], + "metadata": { + "id": "-r2fND5nr0dU" + } + }, + { + "cell_type": "code", + "source": [ + "# Yearly and monthly data distribution heatmap\n", + "def plot_heatmap(gdf, h_size=8):\n", + "\n", + " statistics = gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)\n", + "\n", + " # Heatmap\n", + " plt.figure(figsize=(h_size, h_size-6))\n", + " heatmap = plt.imshow(statistics.values, cmap=\"YlOrBr\", origin=\"upper\", aspect=\"auto\")\n", + "\n", + " # Display values above each pixel\n", + " for i in range(len(statistics.index)):\n", + " for j in range(len(statistics.columns)):\n", + " plt.text(j, i, statistics.values[i, j], ha=\"center\", va=\"center\", color=\"black\")\n", + "\n", + " plt.colorbar(heatmap, label=\"Count\")\n", + " plt.title(\"Monthly Species Count by Year\")\n", + " plt.xlabel(\"Year\")\n", + " plt.ylabel(\"Month\")\n", + " plt.xticks(range(len(statistics.columns)), statistics.columns)\n", + " plt.yticks(range(len(statistics.index)), statistics.index)\n", + " plt.tight_layout()\n", + " plt.savefig('heatmap_plot.png')\n", + " plt.show()\n", + " print(gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)) # Display the data statistics" + ], + "metadata": { + "id": "Iju-dNZErzkJ", + "outputId": "2d39d407-8d38-4e88-87f2-10124086e104", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plot_heatmap(filtered_gdf)" + ], + "metadata": { + "id": "dlW1nIQZrjx3", + "outputId": 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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "year 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023\n", + "month \n", + "5 0 0 5 0 2 1 5 4 5 22 8 1\n", + "6 0 2 6 1 1 6 0 6 13 35 20 3\n", + "7 1 0 0 1 0 1 1 7 1 10 8 0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now, the filtered GeoDataFrame is converted into a Google Earth Engine object." + ], + "metadata": { + "id": "jy0BBd-6sdLz" + } + }, + { + "cell_type": "code", + "source": [ + "# Convert GeoDataFrame to Earth Engine object\n", + "data_raw = geemap.geopandas_to_ee(filtered_gdf)" + ], + "metadata": { + "id": "Fxu0OsBksMKM", + "outputId": "5e7004a8-e0ee-4b27-93ec-3a7a359b0a5d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Next, we will define the raster pixel size of the SDM results as 1km resolution." + ], + "metadata": { + "id": "55p_GfB6sv3U" + } + }, + { + "cell_type": "code", + "source": [ + "# Spatial resolution setting (meters)\n", + "GrainSize = 1000" + ], + "metadata": { + "id": "FsTbNQ17s1l-", + "outputId": "b18c9435-8539-4ff0-8b50-fa8201baf69d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "When multiple occurrence points are present within the same 1km resolution raster pixel, there is a high likelihood that they share the same environmental conditions at the same geographic location. Using such data directly in the analysis can introduce bias into the results.\n", + "\n", + "In other words, we need to limit the potential impact of geographic sampling bias. To achieve this, we will retain only one location within each 1km pixel and remove all others, allowing the model to more objectively reflect the environmental conditions." + ], + "metadata": { + "id": "A20gAGUZtN6S" + } + }, + { + "cell_type": "code", + "source": [ + "def remove_duplicates(data, GrainSize):\n", + " # Select one occurrence record per pixel at the chosen spatial resolution (1km)\n", + " random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", + " rand_point_vals = random_raster.sampleRegions(collection=ee.FeatureCollection(data), scale=10, geometries=True)\n", + " return rand_point_vals.distinct('random')\n", + "\n", + "Data = remove_duplicates(data_raw, GrainSize)\n", + "\n", + "# Before selection and after selection\n", + "print('Original data size:', data_raw.size().getInfo())\n", + "print('Final data size:', Data.size().getInfo())" + ], + "metadata": { + "id": "dHtyQyMQs82v", + "outputId": "a3cb25be-98e7-4c7d-9515-2c96226f1cf9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + } + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Original data size: 176\n", + "Final data size: 111\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The visualization of geographic sampling bias before preprocessing (blue) and after preprocessing (red) is shown below. To facilitate comparison, the map has been centered on an area with a high concentration of Fairy pitta occurrence coordinates." + ], + "metadata": { + "id": "Rhu4b4BxuMHE" + } + }, { "cell_type": "code", "source": [], "metadata": { - "id": "oHtKaH0FgXTz" + "id": "d9pFgpUztsB-" }, "execution_count": null, "outputs": [] From df853fa709625a54e3692f35053d70e49d5801fb Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 19:10:48 +0900 Subject: [PATCH 06/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 1147 ++++++++++++++++- 1 file changed, 1115 insertions(+), 32 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 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"DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } } }, "cells": [ @@ -365,12 +1331,12 @@ "df.head(1) # Display the first row of the DataFrame" ], "metadata": { - "id": "Mx-DjtGNnUXk", - "outputId": "95a3b0ee-25cb-4547-cb3b-8da17eed9fff", "colab": { "base_uri": "https://localhost:8080/", "height": 182 - } + }, + "id": "Mx-DjtGNnUXk", + "outputId": "95a3b0ee-25cb-4547-cb3b-8da17eed9fff" }, "execution_count": 8, "outputs": [ @@ -625,12 +1591,12 @@ "gdf.head(1) # Display the first row of the GeoDataFrame" ], "metadata": { - "id": "qjt0jgJCpALg", - "outputId": "f8850f22-66b4-4cd9-a082-71b919df2ee0", "colab": { "base_uri": "https://localhost:8080/", "height": 81 - } + }, + "id": "qjt0jgJCpALg", + "outputId": "f8850f22-66b4-4cd9-a082-71b919df2ee0" }, "execution_count": 9, "outputs": [ @@ -852,12 +1818,12 @@ " plt.show()" ], "metadata": { - "id": "N1fS3YuOqOQQ", - "outputId": "9764ac5e-06f6-4547-ccce-cd4e0f6c8c21", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "N1fS3YuOqOQQ", + "outputId": "9764ac5e-06f6-4547-ccce-cd4e0f6c8c21" }, "execution_count": 10, "outputs": [ @@ -903,12 +1869,12 @@ "plot_data_distribution(gdf)" ], "metadata": { - "id": "SWirf5m2q9Mx", - "outputId": "6092a17c-eb71-4710-a967-9412ca8e85ee", "colab": { "base_uri": "https://localhost:8080/", "height": 407 - } + }, + "id": "SWirf5m2q9Mx", + "outputId": "6092a17c-eb71-4710-a967-9412ca8e85ee" }, "execution_count": 11, "outputs": [ @@ -982,12 +1948,12 @@ "plot_data_distribution(filtered_gdf)" ], "metadata": { - "id": "k1PpbNsGq9qh", - "outputId": "1c90e189-5c7d-4a60-cef6-6096603f7e97", "colab": { "base_uri": "https://localhost:8080/", "height": 407 - } + }, + "id": "k1PpbNsGq9qh", + "outputId": "1c90e189-5c7d-4a60-cef6-6096603f7e97" }, "execution_count": 12, "outputs": [ @@ -1075,12 +2041,12 @@ " print(gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)) # Display the data statistics" ], "metadata": { - "id": "Iju-dNZErzkJ", - "outputId": "2d39d407-8d38-4e88-87f2-10124086e104", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "Iju-dNZErzkJ", + "outputId": "2d39d407-8d38-4e88-87f2-10124086e104" }, "execution_count": 13, "outputs": [ @@ -1126,12 +2092,12 @@ "plot_heatmap(filtered_gdf)" ], "metadata": { - "id": "dlW1nIQZrjx3", - "outputId": "e1b92df6-edab-4d3b-d5d6-d7eccc985aaf", "colab": { "base_uri": "https://localhost:8080/", "height": 298 - } + }, + "id": "dlW1nIQZrjx3", + "outputId": "e1b92df6-edab-4d3b-d5d6-d7eccc985aaf" }, "execution_count": 14, "outputs": [ @@ -1208,12 +2174,12 @@ "data_raw = geemap.geopandas_to_ee(filtered_gdf)" ], "metadata": { - "id": "Fxu0OsBksMKM", - "outputId": "5e7004a8-e0ee-4b27-93ec-3a7a359b0a5d", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "Fxu0OsBksMKM", + "outputId": "5e7004a8-e0ee-4b27-93ec-3a7a359b0a5d" }, "execution_count": 15, "outputs": [ @@ -1269,12 +2235,12 @@ "GrainSize = 1000" ], "metadata": { - "id": "FsTbNQ17s1l-", - "outputId": "b18c9435-8539-4ff0-8b50-fa8201baf69d", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "FsTbNQ17s1l-", + "outputId": "b18c9435-8539-4ff0-8b50-fa8201baf69d" }, "execution_count": 16, "outputs": [ @@ -1341,12 +2307,12 @@ "print('Final data size:', Data.size().getInfo())" ], "metadata": { - "id": "dHtyQyMQs82v", - "outputId": "a3cb25be-98e7-4c7d-9515-2c96226f1cf9", "colab": { "base_uri": "https://localhost:8080/", "height": 53 - } + }, + "id": "dHtyQyMQs82v", + "outputId": "a3cb25be-98e7-4c7d-9515-2c96226f1cf9" }, "execution_count": 17, "outputs": [ @@ -1397,17 +2363,134 @@ { "cell_type": "markdown", "source": [ - "The visualization of geographic sampling bias before preprocessing (blue) and after preprocessing (red) is shown below. To facilitate comparison, the map has been centered on an area with a high concentration of Fairy pitta occurrence coordinates." + "The visualization comparing geographic sampling bias before preprocessing (in blue) and after preprocessing (in red) is shown below. To facilitate comparison, the map has been centered on the area with a high concentration of Fairy pitta occurrence coordinates in Hallasan National Park." ], "metadata": { "id": "Rhu4b4BxuMHE" } }, + { + "cell_type": "code", + "source": [ + "# Visualization of geographic sampling bias before (blue) and after (red) preprocessing\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "\n", + "# Add the random raster layer\n", + "random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", + "Map.addLayer(random_raster, {'min': 0, 'max': 1, 'palette': ['black', 'white'], 'opacity': 0.5}, 'Random Raster')\n", + "\n", + "# Add the original data layer in blue\n", + "Map.addLayer(data_raw, {'color': 'blue'}, 'Original data')\n", + "\n", + "# Add the final data layer in red\n", + "Map.addLayer(Data, {'color': 'red'}, 'Final data')\n", + "\n", + "# Set the center of the map to the coordinates\n", + "Map.setCenter(126.712, 33.516, 15)\n", + "Map" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 421, + "referenced_widgets": [ + "f3d0ca373f104661aa279cb395ac8188", + "89f60785d92b4dd386d3601d9b96e66a", + "dca49dd326184943b1bdb6ad7832ef76", + "bbcf69d829d34ace94b8bcdcac53e371", + "de1504f3d74c49a0b68fb2f673620379", + "7dfa92ebf190485f8f95f4d08adf18e9", + "10d039e80ad9453397502cccab498285", + "13ef8158e75648c69f9e5de5ff5bc2f1", + "ac6576c4eaf84ef2912453c5a65e6628", + "7cc88edee72d4a42a33c399c82b0713f", + "c23ece520ef543f9a855b76fd937b7b7", + "68f428d255ae4e8d8c5bde1df22cc4a1", + "9f1da3d10e644976acb177cee781995b", + "befcee4020a348be881e1fa7b74d0604", + "4d85624b75604d0b829d4b6303350bbb", + "7c63263d856d4b57b203518b77d1d039", + "289c4680b22f471da8dd7b8ddd494d2d", + "0a61ee090d5f474797ed50cd722282e3", + "d1e213a21c8a45179976981f574cebad", + "5a20045e1a5449869a946eb03bbf32a5", + "faab6b1bcfb24a39804006bc0a755c64", + "14f2f3808bd84d37bbb58f76bee82b78", + "9927fcaab331411d9d22b6a6d520f1a4", + 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+ } + } + } + ] + }, { "cell_type": "code", "source": [], "metadata": { - "id": "d9pFgpUztsB-" + "id": "XIyhhdzUvyZw" }, "execution_count": null, "outputs": [] From b80e6087389075149710a1b8ef2ce519b0e83ac3 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 20:58:25 +0900 Subject: [PATCH 07/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 6951 +++++++++++++++-- 1 file changed, 6230 insertions(+), 721 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 1f32cedda..8d7ac8c44 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -12,7 +12,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "f3d0ca373f104661aa279cb395ac8188": { + "efab02307cff46e3ad2b1cf108b25aa7": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -25,33 +25,33 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 3364750, + "bottom": 1786, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 33.516, - 126.712 + 37.54457732085584, + 108.17138671875001 ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_89f60785d92b4dd386d3601d9b96e66a", - "IPY_MODEL_dca49dd326184943b1bdb6ad7832ef76", - "IPY_MODEL_bbcf69d829d34ace94b8bcdcac53e371", - "IPY_MODEL_de1504f3d74c49a0b68fb2f673620379", - "IPY_MODEL_7dfa92ebf190485f8f95f4d08adf18e9", - "IPY_MODEL_10d039e80ad9453397502cccab498285", - 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"keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_68f428d255ae4e8d8c5bde1df22cc4a1", - "IPY_MODEL_9f1da3d10e644976acb177cee781995b", - "IPY_MODEL_befcee4020a348be881e1fa7b74d0604", - "IPY_MODEL_4d85624b75604d0b829d4b6303350bbb" + "IPY_MODEL_3f35fcb1b3344b898d5d2abf6c01ac70", + "IPY_MODEL_357da287cd30446cbdde93021cfed110", + "IPY_MODEL_cc24bb56a5ac4d039f40bbfdd2c6b1ae", + "IPY_MODEL_79cbf087f6c04b9cb8a660c732ffe162" ], - "layout": "IPY_MODEL_7c63263d856d4b57b203518b77d1d039", - "left": 7146508, + "layout": "IPY_MODEL_3477ccc2ddd44453910981689fe08dbf", + "left": 2879, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 33.52315036278108, + "north": 50.17689812200107, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -102,24 +102,24 @@ ], "panes": {}, "prefer_canvas": false, - "right": 7147308, + "right": 3679, "scroll_wheel_zoom": true, - "south": 33.508838409451315, - "style": "IPY_MODEL_289c4680b22f471da8dd7b8ddd494d2d", + "south": 22.43134015636062, + "style": "IPY_MODEL_377111f79365487b80b229301d7d2cac", "tap": true, "tap_tolerance": 15, - "top": 3364350, + "top": 1386, "touch_zoom": true, - "west": 126.69485092163087, + "west": 73.03710937500001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 15, + "zoom": 4, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 } }, - "89f60785d92b4dd386d3601d9b96e66a": { + "5fdf7951a18a4dad874e612b06afd99f": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -141,10 +141,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_0a61ee090d5f474797ed50cd722282e3" + "widget": "IPY_MODEL_7c182f4aedcf43e5b81f1e29b16431a1" } }, - "dca49dd326184943b1bdb6ad7832ef76": { + "f81b89aa37fc4957afcaa7c9516a89f2": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -170,7 +170,7 @@ "zoom_out_title": "Zoom out" } }, - "bbcf69d829d34ace94b8bcdcac53e371": { + "c9dd146878df458c9a2d5e2e2896ffe5": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -188,7 +188,7 @@ "position": "topleft" } }, - "de1504f3d74c49a0b68fb2f673620379": { + "e8454329e19546c491b79019dc9028a4": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -227,7 +227,7 @@ "remove": true } }, - "7dfa92ebf190485f8f95f4d08adf18e9": { + "84e3257c97214d6e96d6a06ff71f232e": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -253,7 +253,7 @@ "update_when_idle": false } }, - "10d039e80ad9453397502cccab498285": { + "16dda2a123a348da98ef01457d510c03": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -294,7 +294,7 @@ "secondary_length_unit": null } }, - "13ef8158e75648c69f9e5de5ff5bc2f1": { + "d838762362394200a14e6c4b11a99038": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -316,10 +316,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_d1e213a21c8a45179976981f574cebad" + "widget": "IPY_MODEL_fa4824b4fe794ec39fb8d6943df059df" } }, - "ac6576c4eaf84ef2912453c5a65e6628": { + "57304b09f95a4568b3e2ef27cd57b889": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -339,7 +339,7 @@ "prefix": "ipyleaflet" } }, - "7cc88edee72d4a42a33c399c82b0713f": { + "377111f79365487b80b229301d7d2cac": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -354,7 +354,7 @@ "cursor": "grab" } }, - "c23ece520ef543f9a855b76fd937b7b7": { + 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true, "zoom_offset": 0 } }, - "befcee4020a348be881e1fa7b74d0604": { + "357da287cd30446cbdde93021cfed110": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -520,12 +520,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/b8c6626abab342d31a0a20518f122c13-cbad527e11f29ab63066fd9e01b3ad74/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/b8c6626abab342d31a0a20518f122c13-3bbcc2576d8528bc9ea5eb455e21f7dd/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "4d85624b75604d0b829d4b6303350bbb": { + "cc24bb56a5ac4d039f40bbfdd2c6b1ae": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -572,12 +572,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": 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"base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "ea49ceb7-2fdf-4929-a5bf-a3bd2536fe8f" + }, + "source": [ + "#@title Copyright 2023 The Earth Engine Community Authors { display-mode: \"form\" }\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l18M9_r5XmAQ" + }, + "source": [ + "# Species Distribution Modeling\n", + "Author: Byeong-Hyeok Yu\n", + "\n", + "In this tutorial, the methodology of Species Distribution Modeling using Google Earth Engine will be introduced. A brief overview of Species Distribution Modeling will be provided, followed by the process of predicting and analyzing the habitat of an endangered bird species known as the Fairy pitta (scientific name: *Pitta nympha*)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U7i55vr_aKCB" + }, + "source": [ + "### Run me first\n", + "\n", + "Run the following cell to initialize the API. The output will contain instructions on how to grant this notebook access to Earth Engine using your account." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XeFsiSp2aDL6" + }, + "source": [ + "import ee\n", + "\n", + "# Trigger the authentication flow.\n", + "ee.Authenticate()\n", + "\n", + "# Initialize the library.\n", + "# ee.Initialize(project='my-project')\n", + "ee.Initialize(project='ee-foss4g')" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VOf_UnIcZKBJ" + }, + "source": [ + "## A brief overview of Species Distribution Modeling\n", + "\n", + "Let's explore what species distribution models are, the advantages of using Google Earth Engine for their processing, the required data for the models, and how the workflow is structured.\n", + "\n", + "### What is Species Distribution Modeling?\n", + "\n", + "Species Distribution Modeling (SDM below) is the most common methodology used to estimate the actual or potential geographic distribution of a species. It involves characterizing the environmental conditions suitable for a particular species and then identifying where these suitable conditions are distributed geographically.\n", + "\n", + "SDM has emerged as a crucial component of conservation planning in recent years, and various modeling techniques have been developed for this purpose. Implementing SDM in Google Earth Engine (GEE below) provides easy access to large-scale environmental data, along with powerful computing capabilities and support for machine learning algorithms, allowing for rapid modeling.\n", + "\n", + " > Note: Conservation biologist Dr. Ramiro D. Crego implemented SDM using the GEE JavaScript Code Editor and published his research findings [(Crego et al, 2022)](https://onlinelibrary.wiley.com/doi/10.1111/ddi.13491). The methodology of SDM introduced here has been translated and modified from the [JavaScript source code](https://smithsonian.github.io/SDMinGEE/) he shared into the Python language.\n", + "\n", + "### Data Required for SDM\n", + "\n", + "SDM typically utilizes the relationship between known species occurrence records and environmental variables to identify the conditions under which a population can sustain. In other words, two types of model input data are required:\n", + "\n", + "1. Occurrence records of known species\n", + "1. Various environmental variables\n", + "\n", + "These data are input into algorithms to identify environmental conditions associated with the presence of species.\n", + "\n", + "### Workflow of SDM using GEE\n", + "\n", + "The workflow for SDM using GEE is as follows:\n", + "\n", + "1. Collection and preprocessing of species occurrence data\n", + "1. Definition of the Area of Interest\n", + "1. Addition of GEE environmental variables\n", + "1. Generation of pseudo-absence data\n", + "1. Model fitting and prediction\n", + "1. Variable importance and accuracy assessment" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Habitat Prediction and Analysis Using GEE\n", + "\n", + "The [Fairy pitta (*Pitta nympha*)](https://datazone.birdlife.org/species/factsheet/22698684) will be used as a case study to demonstrate the application of GEE-based SDM. While this specific species has been selected for one example, researchers can apply the methodology to any target species of interest with slight modifications to the provided source code.\n", + "\n", + "The Fairy pitta is a rare summer migrant and passage migrant in South Korea, whose distribution area is expanding due to recent climate warming on the Korean Peninsula. It is classified as a rare species, endangered wildlife of class II, Natural Monument No. 204, evaluated as Regionally Extinct (RE) in the National Red List, and Vulnerable (VU) according to the IUCN categories.\n", + "\n", + "Conducting SDM for the conservation planning of the Fairy pitta appears to be quite valuable. Now, let's proceed with habitat prediction and analysis through GEE." + ], + "metadata": { + "id": "tjomxWfVcTmN" + } + }, + { + "cell_type": "markdown", + "source": [ + "First, the Python libraries are imported.The `import` statement brings in the entire contents of a module, while the `from import` statement allows for the importation of specific objects from a module." + ], + "metadata": { + "id": "pViK9PM-gLjh" + } + }, + { + "cell_type": "code", + "source": [ + "# Import libraries\n", + "import geemap\n", + "\n", + "import geemap.colormaps as cm\n", + "import pandas as pd, geopandas as gpd\n", + "import numpy as np, matplotlib.pyplot as plt\n", + "import os, requests, math, random\n", + "\n", + "from ipyleaflet import TileLayer\n", + "from statsmodels.stats.outliers_influence import variance_inflation_factor" + ], + "metadata": { + "id": "4jbM03uIrjST", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "baa5449b-af0f-4c39-f086-535c984f0509" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Collection and Preprocessing of Species Occurrence Data\n", + "\n", + "Now, let's collect occurrence data for the Fairy pitta. Even if you don't currently have access to occurrence data for the species of interest, you can obtain observational data about specific species through the GBIF API. The [GBIF API](https://techdocs.gbif.org/en/openapi/) is an interface that allows access to the species distribution data provided by GBIF, enabling users to search, filter, and download data, as well as acquire various information related to species.\n", + "\n", + "In the code below, the `species_name` variable is assigned the scientific name of the species (e.g. *Pitta nympha* for Fairy pitta), and the `country_code` variable is assigned the country code (e.g. KR for South Korea). The `base_url` variable stores the address of the GBIF API. `params` is a dictionary containing parameters to be used in the API request:\n", + "\n", + "* `scientificName`: Sets the scientific name of the species to be searched.\n", + "* `country`: Limits the search to a specific country.\n", + "* `hasCoordinate`: Ensures only data with coordinates (true) are searched.\n", + "* `basisOfRecord`: Chooses only records of human observation (`HUMAN_OBSERVATION`).\n", + "* `limit`: Sets the maximum number of results returned to 10000." + ], + "metadata": { + "id": "SRrwa4ROghr9" + } + }, + { + "cell_type": "code", + "source": [ + "def get_gbif_species_data(species_name, country_code):\n", + " \"\"\"\n", + " Retrieves observational data for a specific species using the GBIF API and returns it as a pandas DataFrame.\n", + "\n", + " Parameters:\n", + " species_name (str): The scientific name of the species to query.\n", + " country_code (str): The country code of the where the observation data will be queried.\n", + "\n", + " Returns:\n", + " pd.DataFrame: A pandas DataFrame containing the observational data.\n", + " \"\"\"\n", + " base_url = \"https://api.gbif.org/v1/occurrence/search\"\n", + " params = {\n", + " \"scientificName\": species_name,\n", + " \"country\": country_code,\n", + " \"hasCoordinate\": \"true\",\n", + " \"basisOfRecord\": \"HUMAN_OBSERVATION\",\n", + " \"limit\": 10000,\n", + " }\n", + "\n", + " try:\n", + " response = requests.get(base_url, params=params)\n", + " response.raise_for_status() # Raises an exception for a response error.\n", + " data = response.json()\n", + " occurrences = data.get(\"results\", [])\n", + "\n", + " if occurrences: # If data is present\n", + " df = pd.json_normalize(occurrences)\n", + " return df\n", + " else:\n", + " print(\"No data found for the given species and country code.\")\n", + " return pd.DataFrame() # Returns an empty DataFrame\n", + " except requests.RequestException as e:\n", + " print(f\"Request failed: {e}\")\n", + " return pd.DataFrame() # Returns an empty DataFrame in case of an exception" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "oHtKaH0FgXTz", + "outputId": "35aaf721-029e-4c34-b35c-06608f1b5250" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Using the parameters set previously, we query the GBIF API for observational records of the Fairy pitta (*Pitta nympha*), and load the results into a DataFrame to check the first row. A DataFrame is a data structure for handling table-formatted data, consisting of rows and columns. 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ll1/q9OnTOnjwoAzDUIMGDVStWrUbCiQvL0+LFy/WuXPnFBISouTkZF28eFGhoaHmNo0aNVJQUJC2bNlCUgoAAJQZJTV+AgAAKMuuOylVoFq1arrttttuOIA9e/YoJCRE2dnZcnd317Jly9SkSRPt3r1bzs7O8vLysmjv5+entLS0y/aXk5OjnJwc8+OsrKwbjhEAAMAarDV+AgAAKA+ua6HzktCwYUPt3r1b27Zt01NPPaWoqCjt27ev2P1NnDhRnp6e5i0wMNCK0QIAAAAAAMAa7J6UcnZ2Vv369dW6dWtNnDhRLVq00HvvvSd/f3/l5uYqIyPDon16err8/f0v29/o0aOVmZlp3o4dO1bCRwAAAAAAAIDrZfek1L/l5+crJydHrVu3lpOTk9auXWuuS0lJ0dGjRxUSEnLZ/V1cXOTh4WGxAQAAlFeTJk2SyWTSiBEjzGXZ2dmKjo6Wj4+P3N3dFRkZqfT0dPsFCQAAUIRiryllDaNHj1Z4eLiCgoJ05swZJSQkaP369UpKSpKnp6cGDx6smJgYeXt7y8PDQ8OGDVNISAiLnAMAAEjavn27Zs2apebNm1uUjxw5UqtWrdLixYvl6empoUOHqlevXvruu+/sFCkAAEBhdk1KnTp1SgMGDNDJkyfl6emp5s2bKykpSffcc48kacqUKXJwcFBkZKRycnLUtWtXzZgxw54hAwAAlApnz55Vv379NGfOHL3++uvm8szMTM2dO1cJCQnq0qWLJCk+Pl6NGzfW1q1b+XEPAACUGnZNSs2dO/eK9a6uroqNjVVsbKyNIgIAACgboqOjFRERodDQUIukVHJysi5evKjQ0FBzWaNGjRQUFKQtW7YUmZTi7sUAAMAe7JqUAgAAwPVbtGiRdu7cqe3btxeqS0tLk7Ozs7y8vCzK/fz8lJaWVmR/EydO1KuvvloSoQIAAFxWqVvoHAAAAJd37NgxPfPMM1q4cKFcXV2t0id3LwYAAPZAUgoAAKAMSU5O1qlTp9SqVStVqlRJlSpV0oYNG/T++++rUqVK8vPzU25urjIyMiz2S09Pl7+/f5F9cvdiAABgD1y+BwAAUIbcfffd2rNnj0XZY489pkaNGumFF15QYGCgnJyctHbtWkVGRkqSUlJSdPToUYWEhNgjZAAAgCKRlAIAAChDqlatqmbNmlmUValSRT4+PubywYMHKyYmRt7e3vLw8NCwYcMUEhLCnfcAAECpQlIKAACgnJkyZYocHBwUGRmpnJwcde3aVTNmzLB3WAAAABZISgEAAJRx69evt3js6uqq2NhYxcbG2icgAACAa8BC5wAAAAAAALA5klIAAAAAAACwOZJSAAAAAAAAsDmSUgAAAAAAALA5klIAAAAAAACwOZJSAAAAAAAAsDmSUgAAAAAAALA5klIAAAAAAACwOZJSAAAAAAAAsDmSUgAAAAAAALA5klIAAAAAAACwOZJSAAAAAIBC4uLi1Lx5c3l4eMjDw0MhISFavXq1uT47O1vR0dHy8fGRu7u7IiMjlZ6ebseIAZQ1JKUAAAAAAIXUqlVLkyZNUnJysnbs2KEuXbqoR48e+umnnyRJI0eO1BdffKHFixdrw4YNOnHihHr16mXnqAGUJZXsHQAAAAAAoPTp3r27xeMJEyYoLi5OW7duVa1atTR37lwlJCSoS5cukqT4+Hg1btxYW7du1R133GGPkAGUMcyUAgAAAABcUV5enhYtWqRz584pJCREycnJunjxokJDQ81tGjVqpKCgIG3ZssWOkQIoS5gpBQAAAAAo0p49exQSEqLs7Gy5u7tr2bJlatKkiXbv3i1nZ2d5eXlZtPfz81NaWpp9ggVQ5pCUAgAAAAAUqWHDhtq9e7cyMzP12WefKSoqShs2bLB3WADKCZJSAAAAAIAiOTs7q379+pKk1q1ba/v27XrvvffUp08f5ebmKiMjw2K2VHp6uvz9/e0ULYCyhjWlAAAAAADXJD8/Xzk5OWrdurWcnJy0du1ac11KSoqOHj2qkJAQO0YIoCxhphQAAAAAoJDRo0crPDxcQUFBOnPmjBISErR+/XolJSXJ09NTgwcPVkxMjLy9veXh4aFhw4YpJCSEO+8BuGYkpQAAAAAAhZw6dUoDBgzQyZMn5enpqebNmyspKUn33HOPJGnKlClycHBQZGSkcnJy1LVrV82YMcPOUQMoS0hKAQAAAAAKmTt37hXrXV1dFRsbq9jYWBtFBKC8YU0pAADKuI0bN6p79+4KCAiQyWTS8uXLLerT09M1cOBABQQEqHLlyrr33nt14MAB+wQLAAAA/H8kpQAAKOPOnTunFi1aFPlLtWEY6tmzpw4fPqzPP/9cu3btUnBwsEJDQ3Xu3Dk7RAsAAABcwuV7AACUceHh4QoPDy+y7sCBA9q6dav27t2rpk2bSpLi4uLk7++vjz/+WI8//rgtQwUAXIfaL66ydwhl2pFJEfYOAcBVMFMKAIByLCcnR9KldT8KODg4yMXFRZs2bbJXWAAAAIB9k1ITJ07UbbfdpqpVq6pGjRrq2bOnUlJSLNpkZ2crOjpaPj4+cnd3V2RkpNLT0+0UMQAA1nW19aDOnj2roUOHqlatWnJzc1OTJk00c+bMa+6/UaNGCgoK0ujRo3X69Gnl5uZq8uTJ+u2333Ty5EkrHw0AAABw7eyalNqwYYOio6O1detWrVmzRhcvXlRYWJjFGhcjR47UF198ocWLF2vDhg06ceKEevXqZceoAQCwniutByVJMTExSkxM1IIFC7R//36NGDFCQ4cO1YoVK66pfycnJy1dulS//PKLvL29VblyZa1bt07h4eFycGDCNAAAAOzHrmtKJSYmWjyeN2+eatSooeTkZHXs2FGZmZmaO3euEhIS1KVLF0lSfHy8GjdurK1bt+qOO+6wR9gAAFjNldaDkqTNmzcrKipKnTp1kiQNGTJEs2bN0vfff6/777//mp6jdevW2r17tzIzM5WbmytfX1+1bdtWbdq0scYhAAAAAMVSqn4izczMlCR5e3tLkpKTk3Xx4kWFhoaa2xRchrBly5Yi+8jJyVFWVpbFBgBAWdWuXTutWLFCx48fl2EYWrdunX755ReFhYVdd1+enp7y9fXVgQMHtGPHDvXo0aMEIgYAAACuTam5+15+fr5GjBih9u3bq1mzZpKktLQ0OTs7y8vLy6Ktn5+f0tLSiuxn4sSJevXVV0s6XAAAbGLatGkaMmSIatWqpUqVKsnBwUFz5sxRx44dzW3Onj2rgwcPmh+npqZq9+7d8vb2VlBQkBYvXixfX18FBQVpz549euaZZ9SzZ89iJbYAAAAAayk1Sano6Gjt3bv3hu8ENHr0aMXExJgfZ2VlKTAw8EbDAwDALqZNm6atW7dqxYoVCg4O1saNGxUdHa2AgADzTOIdO3aoc+fO5n0KvgejoqI0b948nTx5UjExMUpPT1fNmjU1YMAAjRkzxi7HAwAAABQoFUmpoUOHauXKldq4caNq1aplLvf391dubq4yMjIsZkulp6fL39+/yL5cXFzk4uJS0iEDAFDiLly4oP/7v//TsmXLFBERIUlq3ry5du/erbffftuclOrUqZMMw7hsP8OHD9fw4cNtEjMAAABwrey6ppRhGBo6dKiWLVumb775RnXq1LGob926tZycnLR27VpzWUpKio4ePaqQkBBbhwsAgE1dvHhRFy9eLHSXPEdHR+Xn59spKgAAAMA67DpTKjo6WgkJCfr8889VtWpV8zpRnp6ecnNzk6enpwYPHqyYmBh5e3vLw8NDw4YNU0hICHfeAwCUC1dbD+quu+7SqFGj5ObmpuDgYG3YsEEffvih3n33XTtGDQAAANw4uyal4uLiJMl8m+sC8fHxGjhwoCRpypQpcnBwUGRkpHJyctS1a1fNmDHDxpECAFAyrrYe1KJFizR69Gj169dPf/31l4KDgzVhwgT95z//sein9ourrvu5j0yKuLHgAQAAgBtg16TUlda/KODq6qrY2FjFxsbaICIAAGzrautB+fv7Kz4+3oYRAQAAALZh1zWlAAAAAAAAUDGRlAIAAAAAAIDN2fXyPQAAwHpQAAAAqJiYKQUAAAAAAACbIykFAAAAAAAAmyMpBQAAAAAAAJsjKQUAAAAAAACbIykFAAAAAAAAmyMpBQAAAAAAAJsjKQUAAAAAAACbIykFAAAAAAAAmyMpBQAAAAAAAJsjKQUAAAAAAACbIykFAAAAAAAAmyMpBQAAUIZMnDhRt912m6pWraoaNWqoZ8+eSklJsWiTnZ2t6Oho+fj4yN3dXZGRkUpPT7dTxAAAAEUjKQUAAFCGbNiwQdHR0dq6davWrFmjixcvKiwsTOfOnTO3GTlypL744gstXrxYGzZs0IkTJ9SrVy87Rg0AAFBYJXsHAAAAgGuXmJho8XjevHmqUaOGkpOT1bFjR2VmZmru3LlKSEhQly5dJEnx8fFq3Lixtm7dqjvuuMMeYQMAABTCTCkAAIAyLDMzU5Lk7e0tSUpOTtbFixcVGhpqbtOoUSMFBQVpy5YtRfaRk5OjrKwsiw0AAKCkkZQCAAAoo/Lz8zVixAi1b99ezZo1kySlpaXJ2dlZXl5eFm39/PyUlpZWZD8TJ06Up6eneQsMDCzp0AEAAEhKAQCu38aNG9W9e3cFBATIZDJp+fLlFvVLly5VWFiYfHx8ZDKZtHv3brvECZR30dHR2rt3rxYtWnRD/YwePVqZmZnm7dixY1aKEAAA4PJISgEArtu5c+fUokULxcbGXra+Q4cOmjx5so0jAyqOoUOHauXKlVq3bp1q1aplLvf391dubq4yMjIs2qenp8vf37/IvlxcXOTh4WGxAQAAlDQWOgcAXLfw8HCFh4dftr5///6SpCNHjtgoIqDiMAxDw4YN07Jly7R+/XrVqVPHor5169ZycnLS2rVrFRkZKUlKSUnR0aNHFRISYo+QAQAAikRSCgAAoAyJjo5WQkKCPv/8c1WtWtW8TpSnp6fc3Nzk6empwYMHKyYmRt7e3vLw8NCwYcMUEhLCnfcAAECpQlIKAACgDImLi5MkderUyaI8Pj5eAwcOlCRNmTJFDg4OioyMVE5Ojrp27aoZM2bYOFIAAIArIykFAABQhhiGcdU2rq6uio2Nvey6bwAAAKUBC50DAAAAAADA5khKAQAAAAAAwOa4fA8AcN3Onj2rgwcPmh+npqZq9+7d8vb2VlBQkP766y8dPXpUJ06ckHTpzl/SpVvVX+6W9AAAAAAqFmZKAQCu244dO9SyZUu1bNlSkhQTE6OWLVvqlVdekSStWLFCLVu2VEREhCTp4YcfVsuWLTVz5ky7xQwAAACgdGGmFADgunXq1OmKiy0PHDjQfBcwAAAAACgKM6UAAAAAAABgcySlAAAAAAAAYHN2vXxv48aNeuutt5ScnKyTJ09q2bJl6tmzp7neMAyNHTtWc+bMUUZGhtq3b6+4uDg1aNDAfkEDAFT7xVXF2u/IpAgrR2JdeXl5GjdunBYsWKC0tDQFBARo4MCBevnll2UymWzWBwAAAFAR2HWm1Llz59SiRQvFxsYWWf/mm2/q/fff18yZM7Vt2zZVqVJFXbt2VXZ2to0jBQBUBJMnT1ZcXJymT5+u/fv3a/LkyXrzzTc1bdo0m/YBAAAAVAR2nSkVHh6u8PDwIusMw9DUqVP18ssvq0ePHpKkDz/8UH5+flq+fLkefvhhW4YKAKgANm/erB49epjvGli7dm19/PHH+v77723aBwAAAFARlNo1pVJTU5WWlqbQ0FBzmaenp9q2bastW7Zcdr+cnBxlZWVZbAAAXIt27dpp7dq1+uWXXyRJP/zwgzZt2nTZH1BKqg8AAACgIrDrTKkrSUtLkyT5+flZlPv5+ZnrijJx4kS9+uqrJRobAKB8evHFF5WVlaVGjRrJ0dFReXl5mjBhgvr162fTPgAAAICKoNTOlCqu0aNHKzMz07wdO3bM3iEBAMqITz/9VAsXLlRCQoJ27typ+fPn6+2339b8+fNt2gcAAABQEZTamVL+/v6SpPT0dNWsWdNcnp6erltvvfWy+7m4uMjFxaWkwwMAlEOjRo3Siy++aF638JZbbtGvv/6qiRMnKioqymZ9AAAAABVBqZ0pVadOHfn7+2vt2rXmsqysLG3btk0hISF2jAwAUF6dP39eDg6WX42Ojo7Kz8+3aR8AAABARWDXmVJnz57VwYMHzY9TU1O1e/dueXt7KygoSCNGjNDrr7+uBg0aqE6dOhozZowCAgLUs2dP+wUNACi3unfvrgkTJigoKEhNmzbVrl279O6772rQoEE27QMAAACoCOyalNqxY4c6d+5sfhwTEyNJioqK0rx58/T888/r3LlzGjJkiDIyMtShQwclJibK1dXVXiEDAMqxadOmacyYMXr66ad16tQpBQQE6Mknn9Qrr7xi0z4AAACAisCuSalOnTrJMIzL1ptMJo0fP17jx4+3YVQAgIqqatWqmjp1qqZOnWrXPgAAAICKoNSuKQUAAAAAAIDyi6QUAAAAAAAAbM6ul+8BAGBPtV9cVaz9jkyKsGofKNq4ceP06quvWpQ1bNhQP//8s50iAgAAgDWRlAIAAKVW06ZN9fXXX5sfV6rE0AUAAKC8YGQHAABKrUqVKsnf39/eYQAAAKAEsKYUAAAotQ4cOKCAgADVrVtX/fr109GjR+0dEgAAAKyEpBQAACiV2rZtq3nz5ikxMVFxcXFKTU3VnXfeqTNnztg7NAAAAFgBl+8BAIBSKTw83Pzv5s2bq23btgoODtann36qwYMH2zEyAAAAWAMzpQAAQJng5eWlm2++WQcPHrR3KAAAALACklIAAKBMOHv2rA4dOqSaNWvaOxQAAABYAUkpAABQKj333HPasGGDjhw5os2bN+uBBx6Qo6Oj+vbta+/QAAAAYAUkpQAA5UJcXJyaN28uDw8PeXh4KCQkRKtXr7Z3WLgBv/32m/r27auGDRuqd+/e8vHx0datW+Xr62vv0AAAAK5q0qRJMplMGjFihL1DKbVY6BwAUC7UqlVLkyZNUoMGDWQYhubPn68ePXpo165datq0qb3DQzEsWrTI3iEAAAAUy/bt2zVr1iw1b97c3qGUasyUAgCUC927d9d9992nBg0a6Oabb9aECRPk7u6urVu32js0AAAAVCBnz55Vv379NGfOHFWrVs3e4ZRqJKUAAOVOXl6eFi1apHPnzikkJMTe4QAAAKACiY6OVkREhEJDQ+0dSqnH5XsAUMFNnDhRS5cu1c8//yw3Nze1a9dOkydPVsOGDctcDHv27FFISIiys7Pl7u6uZcuWqUmTJiUUNUpC7RdXXfc+RyZFlEAkAAAA12/RokXauXOntm/fbu9QygRmSgFABbdhwwZFR0dr69atWrNmjS5evKiwsDCdO3euzMXQsGFD7d69W9u2bdNTTz2lqKgo7du3r4SiBgAAAP7n2LFjeuaZZ7Rw4UK5urraO5wygZlSAFDBJSYmWjyeN2+eatSooeTkZHXs2LFMxeDs7Kz69etLklq3bq3t27frvffe06xZs6waLwAAAPBvycnJOnXqlFq1amUuy8vL08aNGzV9+nTl5OTI0dHRjhGWPsyUAgBYyMzMlCR5e3uX+Rjy8/OVk5NjjZAAAABKnYkTJ+q2225T1apVVaNGDfXs2VMpKSn2DqvCuvvuu7Vnzx7t3r3bvLVp00b9+vXT7t27SUgVgaRUCdi4caO6d++ugIAAmUwmLV++3N4hASjHrPmZk5+frxEjRqh9+/Zq1qyZ9YK0QQyjR4/Wxo0bdeTIEe3Zs0ejR4/W+vXr1a9fvxKMFgAAwH5KwzIM+J+qVauqWbNmFluVKlXk4+Njt7F1acfleyXg3LlzatGihQYNGqRevXrZOxwA5Zw1P3Oio6O1d+9ebdq0yUrR2S6GU6dOacCAATp58qQ8PT3VvHlzJSUl6Z577imhSAEAAOyrNCzDANwIklIlIDw8XOHh4fYOA0AFYa3PnKFDh2rlypXauHGjatWqZYXIbBvD3LlzSygqAACAsqE0LMMAS+vXr7d3CKUaSSkAqOAMw9CwYcO0bNkyrV+/XnXq1KmQMQAAAJRlpWEZBuB6kZQCgAouOjpaCQkJ+vzzz1W1alWlpaVJkjw9PeXm5lZhYgAAACjLSsMyDMD1IikFABVcXFycJKlTp04W5fHx8Ro4cGCpjqH2i6uu+7mOTIq47n0AAABKs9KwDIOtFWccCEulYVxMUgoAKjjDMOwdQqmIAQAAoKxhCQSUdSSlAAAAAAAog1gCAWUdSakScPbsWR08eND8ODU1Vbt375a3t7eCgoLsGBmA8ojPHAAAgIqpNCzDANwIklIlYMeOHercubP5cUxMjCQpKipK8+bNs1NUAMqr6/3MKe7199a+5pz1oAAAAG4MSyCgrCMpVQI6derEhwMAm+EzBwAAAEBZ5GDvAAAAAAAAAFDxMFMKAAAAAICrKO4SCLiEZRhQlDKRlIqNjdVbb72ltLQ0tWjRQtOmTdPtt99u77DMWBcFgC3xmQPgWpX2MRQAAKjYSv3le5988oliYmI0duxY7dy5Uy1atFDXrl116tQpe4cGAABQajGGAgAApV2pT0q9++67euKJJ/TYY4+pSZMmmjlzpipXrqwPPvjA3qEBAACUWoyhAABAaVeqk1K5ublKTk5WaGiouczBwUGhoaHasmWLHSMDAAAovRhDAQCAsqBUryn1xx9/KC8vT35+fhblfn5++vnnn4vcJycnRzk5OebHmZmZkqSsrKwSizM/5/x171OS8QAo3270M6c4+5eWPv792cm5+B/ORcko6DsrK0tVq1aVyWQqseeypusdQ9lj/CQV/z2DS6z9+vB63JiS+HvhNbkx/I2ULrwepY8txlCGYVy5oVGKHT9+3JBkbN682aJ81KhRxu23317kPmPHjjUksbGxsbGxsbFZdcvMzLTF8McqrncMxfiJjY2NjY2NrSS2Y8eOXXHMUqpnSlWvXl2Ojo5KT0+3KE9PT5e/v3+R+4wePVoxMTHmx/n5+frrr7/k4+Nj8183s7KyFBgYqGPHjsnDw8Omz13ecC6ti/NpXZxP6+J8Wg/n8sYZhqEzZ86oatWqqlq1qr3DuWbXO4YqTeOn0oK/n9KF16P04TUpXXg9Sp+K/poUjKECAgKu2K5UJ6WcnZ3VunVrrV27Vj179pR0aZC0du1aDR06tMh9XFxc5OLiYlHm5eVVwpFemYeHR4V8E5YEzqV1cT6ti/NpXZxP6+Fc3hhPT097h3DdrncMVRrHT6UFfz+lC69H6cNrUrrwepQ+Ffk1uZYxVKlOSklSTEyMoqKi1KZNG91+++2aOnWqzp07p8cee8zeoQEAAJRajKEAAEBpV+qTUn369NHvv/+uV155RWlpabr11luVmJhYaOFOAAAA/A9jKAAAUNqV+qSUJA0dOvSyl+uVZi4uLho7dmyh6fC4fpxL6+J8Whfn07o4n9bDuURZHUOVBvz9lC68HqUPr0npwutR+vCaXBuTYVzt/nwAAAAAAACAdTnYOwAAAAAAAABUPCSlAAAAAAAAYHMkpQAAAAAAAGBzJKWuYuPGjerevbsCAgJkMpm0fPlyi/r09HQNHDhQAQEBqly5su69914dOHDAos2hQ4f0wAMPyNfXVx4eHurdu7fS09Mt2tSuXVsmk8limzRpUkkfnk1NnDhRt912m6pWraoaNWqoZ8+eSklJsWiTnZ2t6Oho+fj4yN3dXZGRkYXO1dGjRxUREaHKlSurRo0aGjVqlP7++2+LNuvXr1erVq3k4uKi+vXra968eSV9eDZnq/O5fv36Qu9Nk8mktLQ0mxynLVjrXA4fPlytW7eWi4uLbr311iKf68cff9Sdd94pV1dXBQYG6s033yypw7IbW53PI0eOFPne3Lp1a0kens1Z43z+8MMP6tu3rwIDA+Xm5qbGjRvrvffeK/RcFeGzE7iScePGFfpMadSokb3DqvCOHz+uRx99VD4+PnJzc9Mtt9y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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The data from 1995 is very sparse, with significant gaps compared to other years, and the months of August and September also have limited samples and exhibit different seasonal characteristics compared to other periods. Excluding this data could contribute to improving the stability and predictive power of the model.\n", + "\n", + "However, it's important to note that excluding data may enhance the model's generalization ability, but it could also lead to the loss of valuable information relevant to the research objectives. Therefore, such decisions should be made with careful consideration." + ], + "metadata": { + "id": "vIj9CO1RrCSs" + } + }, + { + "cell_type": "code", + "source": [ + "# Filtering data by year and month\n", + "filtered_gdf = gdf[\n", + " (~gdf['year'].eq(1995)) &\n", + " (~gdf['month'].between(8, 9))\n", + "]\n", + "\n", + "# Visualize the filtered data distribution\n", + "plot_data_distribution(filtered_gdf)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + }, + "id": "k1PpbNsGq9qh", + "outputId": "1c90e189-5c7d-4a60-cef6-6096603f7e97" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Additionally, the use of a heatmap allows us to quickly grasp the frequency of species occurrence by year and month, providing an intuitive visualization of the temporal changes and patterns within the data." + ], + "metadata": { + "id": "-r2fND5nr0dU" + } + }, + { + "cell_type": "code", + "source": [ + "# Yearly and monthly data distribution heatmap\n", + "def plot_heatmap(gdf, h_size=8):\n", + "\n", + " statistics = gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)\n", + "\n", + " # Heatmap\n", + " plt.figure(figsize=(h_size, h_size-6))\n", + " heatmap = plt.imshow(statistics.values, cmap=\"YlOrBr\", origin=\"upper\", aspect=\"auto\")\n", + "\n", + " # Display values above each pixel\n", + " for i in range(len(statistics.index)):\n", + " for j in range(len(statistics.columns)):\n", + " plt.text(j, i, statistics.values[i, j], ha=\"center\", va=\"center\", color=\"black\")\n", + "\n", + " plt.colorbar(heatmap, label=\"Count\")\n", + " plt.title(\"Monthly Species Count by Year\")\n", + " plt.xlabel(\"Year\")\n", + " plt.ylabel(\"Month\")\n", + " plt.xticks(range(len(statistics.columns)), statistics.columns)\n", + " plt.yticks(range(len(statistics.index)), statistics.index)\n", + " plt.tight_layout()\n", + " plt.savefig('heatmap_plot.png')\n", + " plt.show()\n", + " print(gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)) # Display the data statistics" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "Iju-dNZErzkJ", + "outputId": "2d39d407-8d38-4e88-87f2-10124086e104" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plot_heatmap(filtered_gdf)" + ], + "metadata": { + "colab": { + "base_uri": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "year 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023\n", + "month \n", + "5 0 0 5 0 2 1 5 4 5 22 8 1\n", + "6 0 2 6 1 1 6 0 6 13 35 20 3\n", + "7 1 0 0 1 0 1 1 7 1 10 8 0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now, the filtered GeoDataFrame is converted into a Google Earth Engine object." + ], + "metadata": { + "id": "jy0BBd-6sdLz" + } + }, + { + "cell_type": "code", + "source": [ + "# Convert GeoDataFrame to Earth Engine object\n", + "data_raw = geemap.geopandas_to_ee(filtered_gdf)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "Fxu0OsBksMKM", + "outputId": "5e7004a8-e0ee-4b27-93ec-3a7a359b0a5d" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Next, we will define the raster pixel size of the SDM results as 1km resolution." + ], + "metadata": { + "id": "55p_GfB6sv3U" + } + }, + { + "cell_type": "code", + "source": [ + "# Spatial resolution setting (meters)\n", + "GrainSize = 1000" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "FsTbNQ17s1l-", + "outputId": "b18c9435-8539-4ff0-8b50-fa8201baf69d" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} } + ] + }, + { + "cell_type": "markdown", + "source": [ + "When multiple occurrence points are present within the same 1km resolution raster pixel, there is a high likelihood that they share the same environmental conditions at the same geographic location. Using such data directly in the analysis can introduce bias into the results.\n", + "\n", + "In other words, we need to limit the potential impact of geographic sampling bias. To achieve this, we will retain only one location within each 1km pixel and remove all others, allowing the model to more objectively reflect the environmental conditions." + ], + "metadata": { + "id": "A20gAGUZtN6S" } - } - }, - "cells": [ + }, { "cell_type": "code", + "source": [ + "def remove_duplicates(data, GrainSize):\n", + " # Select one occurrence record per pixel at the chosen spatial resolution (1km)\n", + " random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", + " rand_point_vals = random_raster.sampleRegions(collection=ee.FeatureCollection(data), scale=10, geometries=True)\n", + " return rand_point_vals.distinct('random')\n", + "\n", + "Data = remove_duplicates(data_raw, GrainSize)\n", + "\n", + "# Before selection and after selection\n", + "print('Original data size:', data_raw.size().getInfo())\n", + "print('Final data size:', Data.size().getInfo())" + ], "metadata": { - "id": "8kdsGkYJXXKc", "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 53 }, - "outputId": "ea49ceb7-2fdf-4929-a5bf-a3bd2536fe8f" + "id": "dHtyQyMQs82v", + "outputId": "a3cb25be-98e7-4c7d-9515-2c96226f1cf9" }, - "source": [ - "#@title Copyright 2023 The Earth Engine Community Authors { display-mode: \"form\" }\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ], - "execution_count": 4, + "execution_count": 17, "outputs": [ { "output_type": "display_data", @@ -1038,136 +6376,209 @@ ] }, "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Original data size: 176\n", + "Final data size: 111\n" + ] } ] }, { "cell_type": "markdown", - "metadata": { - "id": "l18M9_r5XmAQ" - }, "source": [ - "# Species Distribution Modeling\n", - "Author: Byeong-Hyeok Yu\n", - "\n", - "In this tutorial, the methodology of Species Distribution Modeling using Google Earth Engine will be introduced. A brief overview of Species Distribution Modeling will be provided, followed by the process of predicting and analyzing the habitat of an endangered bird species known as the Fairy pitta (scientific name: *Pitta nympha*)." - ] - }, - { - "cell_type": "markdown", + "The visualization comparing geographic sampling bias before preprocessing (in blue) and after preprocessing (in red) is shown below. To facilitate comparison, the map has been centered on the area with a high concentration of Fairy pitta occurrence coordinates in Hallasan National Park." + ], "metadata": { - "id": "U7i55vr_aKCB" - }, - "source": [ - "### Run me first\n", - "\n", - "Run the following cell to initialize the API. The output will contain instructions on how to grant this notebook access to Earth Engine using your account." - ] + "id": "Rhu4b4BxuMHE" + } }, { "cell_type": "code", - "metadata": { - "id": "XeFsiSp2aDL6" - }, "source": [ - "import ee\n", + "# Visualization of geographic sampling bias before (blue) and after (red) preprocessing\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", "\n", - "# Trigger the authentication flow.\n", - "ee.Authenticate()\n", + "# Add the random raster layer\n", + "random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", + "Map.addLayer(random_raster, {'min': 0, 'max': 1, 'palette': ['black', 'white'], 'opacity': 0.5}, 'Random Raster')\n", "\n", - "# Initialize the library.\n", - "# ee.Initialize(project='my-project')\n", - "ee.Initialize(project='ee-foss4g')" + "# Add the original data layer in blue\n", + "Map.addLayer(data_raw, {'color': 'blue'}, 'Original data')\n", + "\n", + "# Add the final data layer in red\n", + "Map.addLayer(Data, {'color': 'red'}, 'Final data')\n", + "\n", + "# Set the center of the map to the coordinates\n", + "Map.setCenter(126.712, 33.516, 15)\n", + "Map" ], - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "markdown", "metadata": { - "id": "VOf_UnIcZKBJ" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 421, + "referenced_widgets": [ + "efab02307cff46e3ad2b1cf108b25aa7", + "5fdf7951a18a4dad874e612b06afd99f", + "f81b89aa37fc4957afcaa7c9516a89f2", + "c9dd146878df458c9a2d5e2e2896ffe5", + "e8454329e19546c491b79019dc9028a4", + "84e3257c97214d6e96d6a06ff71f232e", + "16dda2a123a348da98ef01457d510c03", + "d838762362394200a14e6c4b11a99038", + "57304b09f95a4568b3e2ef27cd57b889", + "377111f79365487b80b229301d7d2cac", + "b2f31b6c8b1a453d9aa6a54b6fdd41b5", + "3f35fcb1b3344b898d5d2abf6c01ac70", + "7974287029b74bab93714f247bfe610a", + "357da287cd30446cbdde93021cfed110", + "cc24bb56a5ac4d039f40bbfdd2c6b1ae", + "3477ccc2ddd44453910981689fe08dbf", + "f18475bb22ac4d358708033c04225e1a", + "7c182f4aedcf43e5b81f1e29b16431a1", + "fa4824b4fe794ec39fb8d6943df059df", + "efd9c41cd1c14ec794795ac01b3650b9", + "c854c5f0c07543caa9a38e99dc86e400", + "478a0bd3dd9e4de69aa9a762a66b6f37", + "22b0b5eb8a0744e5850187ed868e6126", + "d83d9508820043e8b5cdbebc6cfbf72b", + "40dde1d58af5411bbd31e7608956c137", + "b432796c003a42b39d7903ce203bf0d7", + "4d15c7b8e49841f5abda675f0c436a2a", + "79cbf087f6c04b9cb8a660c732ffe162" + ] + }, + "id": "d9pFgpUztsB-", + "outputId": "69cb5466-17a0-4dae-fda5-f6e823aaaf47" }, - "source": [ - "## A brief overview of Species Distribution Modeling\n", - "\n", - "Let's explore what species distribution models are, the advantages of using Google Earth Engine for their processing, the required data for the models, and how the workflow is structured.\n", - "\n", - "### What is Species Distribution Modeling?\n", - "\n", - "Species Distribution Modeling (SDM below) is the most common methodology used to estimate the actual or potential geographic distribution of a species. It involves characterizing the environmental conditions suitable for a particular species and then identifying where these suitable conditions are distributed geographically.\n", - "\n", - "SDM has emerged as a crucial component of conservation planning in recent years, and various modeling techniques have been developed for this purpose. Implementing SDM in Google Earth Engine (GEE below) provides easy access to large-scale environmental data, along with powerful computing capabilities and support for machine learning algorithms, allowing for rapid modeling.\n", - "\n", - " > Note: Conservation biologist Dr. Ramiro D. Crego implemented SDM using the GEE JavaScript Code Editor and published his research findings [(Crego et al, 2022)](https://onlinelibrary.wiley.com/doi/10.1111/ddi.13491). The methodology of SDM introduced here has been translated and modified from the [JavaScript source code](https://smithsonian.github.io/SDMinGEE/) he shared into the Python language.\n", - "\n", - "### Data Required for SDM\n", - "\n", - "SDM typically utilizes the relationship between known species occurrence records and environmental variables to identify the conditions under which a population can sustain. In other words, two types of model input data are required:\n", - "\n", - "1. Occurrence records of known species\n", - "1. Various environmental variables\n", - "\n", - "These data are input into algorithms to identify environmental conditions associated with the presence of species.\n", - "\n", - "### Workflow of SDM using GEE\n", - "\n", - "The workflow for SDM using GEE is as follows:\n", - "\n", - "1. Collection and preprocessing of species occurrence data\n", - "1. Definition of the area of interest\n", - "1. Addition of GEE environmental variables\n", - "1. Generation of pseudo-absence data\n", - "1. Model fitting and prediction\n", - "1. Variable importance and accuracy assessment" + "execution_count": 24, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(center=[33.516, 126.712], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDat…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "efab02307cff46e3ad2b1cf108b25aa7" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } + } ] }, { "cell_type": "markdown", "source": [ - "## Habitat Prediction and Analysis Using GEE\n", - "\n", - "The [Fairy pitta (*Pitta nympha*)](https://datazone.birdlife.org/species/factsheet/22698684) will be used as a case study to demonstrate the application of GEE-based SDM. While this specific species has been selected for one example, researchers can apply the methodology to any target species of interest with slight modifications to the provided source code.\n", + "### Definition of the Area of Interest\n", "\n", - "The Fairy pitta is a rare summer migrant and passage migrant in South Korea, whose distribution area is expanding due to recent climate warming on the Korean Peninsula. It is classified as a rare species, endangered wildlife of class II, Natural Monument No. 204, evaluated as Regionally Extinct (RE) in the National Red List, and Vulnerable (VU) according to the IUCN categories.\n", + "Defining the Area of Interest (AOI below) refers to the term used by researchers to denote the geographical area they want to analyze. It has a similar meaning to the term Study Area.\n", "\n", - "Conducting SDM for the conservation planning of the Fairy pitta appears to be quite valuable. Now, let's proceed with habitat prediction and analysis through GEE." - ], - "metadata": { - "id": "tjomxWfVcTmN" - } - }, - { - "cell_type": "markdown", - "source": [ - "First, the Python libraries are imported.The `import` statement brings in the entire contents of a module, while the `from import` statement allows for the importation of specific objects from a module." + "In this context, we obtained the bounding box of the occurrence point layer geometry and created a 50-kilometer buffer around it (with a maximum tolerance of 1,000 meters) to define the AOI." ], "metadata": { - "id": "pViK9PM-gLjh" + "id": "3oPtTj_O6d3H" } }, { "cell_type": "code", "source": [ - "# Import libraries\n", - "import geemap\n", + "# Define the AOI\n", + "AOI = Data.geometry().bounds().buffer(distance=50000, maxError=1000)\n", "\n", - "import geemap.colormaps as cm\n", - "import pandas as pd, geopandas as gpd\n", - "import numpy as np, matplotlib.pyplot as plt\n", - "import os, requests, math, random\n", + "# Add the AOI to the map\n", + "outline = ee.Image().byte().paint(\n", + " featureCollection=AOI, color=1, width=3)\n", "\n", - "from ipyleaflet import TileLayer\n", - "from statsmodels.stats.outliers_influence import variance_inflation_factor" + "Map.remove_layer(\"Random Raster\")\n", + "Map.addLayer(outline, {'palette': 'FF0000'}, \"AOI\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { - "id": "4jbM03uIrjST", "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 421, + "referenced_widgets": [ + "efab02307cff46e3ad2b1cf108b25aa7", + "5fdf7951a18a4dad874e612b06afd99f", + "f81b89aa37fc4957afcaa7c9516a89f2", + "c9dd146878df458c9a2d5e2e2896ffe5", + "e8454329e19546c491b79019dc9028a4", + "84e3257c97214d6e96d6a06ff71f232e", + "16dda2a123a348da98ef01457d510c03", + "d838762362394200a14e6c4b11a99038", + "57304b09f95a4568b3e2ef27cd57b889", + "377111f79365487b80b229301d7d2cac", + "b2f31b6c8b1a453d9aa6a54b6fdd41b5", + "3f35fcb1b3344b898d5d2abf6c01ac70", + "357da287cd30446cbdde93021cfed110", + "cc24bb56a5ac4d039f40bbfdd2c6b1ae", + "79cbf087f6c04b9cb8a660c732ffe162", + "3477ccc2ddd44453910981689fe08dbf", + "7c182f4aedcf43e5b81f1e29b16431a1", + "fa4824b4fe794ec39fb8d6943df059df", + "efd9c41cd1c14ec794795ac01b3650b9", + "c854c5f0c07543caa9a38e99dc86e400", + "478a0bd3dd9e4de69aa9a762a66b6f37", + "22b0b5eb8a0744e5850187ed868e6126", + "d83d9508820043e8b5cdbebc6cfbf72b", + "40dde1d58af5411bbd31e7608956c137", + "b432796c003a42b39d7903ce203bf0d7", + "4d15c7b8e49841f5abda675f0c436a2a" + ] }, - "outputId": "baa5449b-af0f-4c39-f086-535c984f0509" + "id": "XIyhhdzUvyZw", + "outputId": "044a343b-1c78-4792-dada-f3bf0948b71a" }, - "execution_count": 3, + "execution_count": 25, "outputs": [ { "output_type": "display_data", @@ -1202,76 +6613,61 @@ ] }, "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(bottom=3364750.0, center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['positi…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "efab02307cff46e3ad2b1cf108b25aa7" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, { "cell_type": "markdown", "source": [ - "### Collection and Preprocessing of Species Occurrence Data\n", + "### Addition of GEE environmental variables\n", "\n", - "Now, let's collect occurrence data for the Fairy pitta. Even if you don't currently have access to occurrence data for the species of interest, you can obtain observational data about specific species through the GBIF API. The [GBIF API](https://techdocs.gbif.org/en/openapi/) is an interface that allows access to the species distribution data provided by GBIF, enabling users to search, filter, and download data, as well as acquire various information related to species.\n", + "Now, let's add environmental variables to the analysis. GEE provides a wide range of datasets for environmental variables such as temperature, precipitation, elevation, land cover, and terrain. These datasets enable us to comprehensively analyze various factors that may influence the habitat preferences of the Fairy pitta.\n", "\n", - "In the code below, the `species_name` variable is assigned the scientific name of the species (e.g. *Pitta nympha* for Fairy pitta), and the `country_code` variable is assigned the country code (e.g. KR for South Korea). The `base_url` variable stores the address of the GBIF API. `params` is a dictionary containing parameters to be used in the API request:\n", + "The selection of GEE environmental variables in SDM should reflect the habitat preference characteristics of the species. To do this, prior research and literature review on the Fairy pitta's habitat preferences should be conducted. This tutorial primarily focuses on the workflow of SDM using GEE, so some in-depth details are omitted.\n", "\n", - "* `scientificName`: Sets the scientific name of the species to be searched.\n", - "* `country`: Limits the search to a specific country.\n", - "* `hasCoordinate`: Ensures only data with coordinates (true) are searched.\n", - "* `basisOfRecord`: Chooses only records of human observation (`HUMAN_OBSERVATION`).\n", - "* `limit`: Sets the maximum number of results returned to 10000." + "[**WorldClim V1 Bioclim**](https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO): This dataset provides 19 bioclimatic variables derived from monthly temperature and precipitation data. It covers the period from 1960 to 1991 and has a resolution of 927.67 meters." ], "metadata": { - "id": "SRrwa4ROghr9" + "id": "fWsplwHD8ZFd" } }, { "cell_type": "code", "source": [ - "def get_gbif_species_data(species_name, country_code):\n", - " \"\"\"\n", - " Retrieves observational data for a specific species using the GBIF API and returns it as a pandas DataFrame.\n", - "\n", - " Parameters:\n", - " species_name (str): The scientific name of the species to query.\n", - " country_code (str): The country code of the where the observation data will be queried.\n", - "\n", - " Returns:\n", - " pd.DataFrame: A pandas DataFrame containing the observational data.\n", - " \"\"\"\n", - " base_url = \"https://api.gbif.org/v1/occurrence/search\"\n", - " params = {\n", - " \"scientificName\": species_name,\n", - " \"country\": country_code,\n", - " \"hasCoordinate\": \"true\",\n", - " \"basisOfRecord\": \"HUMAN_OBSERVATION\",\n", - " \"limit\": 10000,\n", - " }\n", - "\n", - " try:\n", - " response = requests.get(base_url, params=params)\n", - " response.raise_for_status() # Raises an exception for a response error.\n", - " data = response.json()\n", - " occurrences = data.get(\"results\", [])\n", - "\n", - " if occurrences: # If data is present\n", - " df = pd.json_normalize(occurrences)\n", - " return df\n", - " else:\n", - " print(\"No data found for the given species and country code.\")\n", - " return pd.DataFrame() # Returns an empty DataFrame\n", - " except requests.RequestException as e:\n", - " print(f\"Request failed: {e}\")\n", - " return pd.DataFrame() # Returns an empty DataFrame in case of an exception" + "# WorldClim V1 Bioclim\n", + "BIO = ee.Image(\"WORLDCLIM/V1/BIO\")" ], "metadata": { + "id": "TNW23qpX7shy", + "outputId": "d886b9b3-cb4e-4925-c569-567260043609", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - }, - "id": "oHtKaH0FgXTz", - "outputId": "35aaf721-029e-4c34-b35c-06608f1b5250" + } }, - "execution_count": 6, + "execution_count": 26, "outputs": [ { "output_type": "display_data", @@ -1312,33 +6708,27 @@ { "cell_type": "markdown", "source": [ - "Using the parameters set previously, we query the GBIF API for observational records of the Fairy pitta (*Pitta nympha*), and load the results into a DataFrame to check the first row. A DataFrame is a data structure for handling table-formatted data, consisting of rows and columns. If necessary, the DataFrame can be saved as a CSV file and read back in." + "[**NASA SRTM Digital Elevation 30m**](https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003): This dataset contains digital elevation data from the Shuttle Radar Topography Mission (SRTM). The data was primarily collected around the year 2000 and is provided at a resolution of approximately 30 meters (1 arc-second). The following code calculates elevation, slope, aspect, and hillshade layers from the SRTM data." ], "metadata": { - "id": "Zs5ZUfZUnjZ2" + "id": "AzrCwWu39iLw" } }, { "cell_type": "code", "source": [ - "# Retrieve Fairy Pitta data\n", - "df = get_gbif_species_data(\"Pitta nympha\", \"KR\")\n", - "\"\"\"\n", - "# Save DataFrame to CSV and read back in.\n", - "df.to_csv(\"pitta_nympha_data.csv\", index=False)\n", - "df = pd.read_csv(\"pitta_nympha_data.csv\")\n", - "\"\"\"\n", - "df.head(1) # Display the first row of the DataFrame" + "# NASA SRTM Digital Elevation 30m\n", + "Terrain = ee.Algorithms.Terrain(ee.Image(\"USGS/SRTMGL1_003\"))" ], "metadata": { + "id": "O8lPyhWv9hHn", + "outputId": "5c72fdbc-a3d1-4315-93f9-d8afedffb25a", "colab": { "base_uri": "https://localhost:8080/", - "height": 182 - }, - "id": "Mx-DjtGNnUXk", - "outputId": "95a3b0ee-25cb-4547-cb3b-8da17eed9fff" + "height": 17 + } }, - "execution_count": 8, + "execution_count": 27, "outputs": [ { "output_type": "display_data", @@ -1373,232 +6763,230 @@ ] }, "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " key datasetKey \\\n", - "0 4126765284 50c9509d-22c7-4a22-a47d-8c48425ef4a7 \n", - "\n", - " publishingOrgKey installationKey \\\n", - "0 28eb1a3f-1c15-4a95-931a-4af90ecb574d 997448a8-f762-11e1-a439-00145eb45e9a \n", - "\n", - " hostingOrganizationKey publishingCountry protocol \\\n", - "0 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE \n", - "\n", - " lastCrawled lastParsed crawlId ... \\\n", - "0 2024-01-23T16:28:21.693+00:00 2024-01-25T09:13:47.069+00:00 431 ... \n", - "\n", - " nomenclaturalCode fieldNotes behavior verbatimElevation \\\n", - "0 NaN NaN NaN NaN \n", - "\n", - " higherClassification extensions.http://rs.tdwg.org/ac/terms/Multimedia \\\n", - "0 NaN NaN \n", - "\n", - " distanceFromCentroidInMeters associatedTaxa lifeStage occurrenceRemarks \n", - "0 NaN NaN NaN NaN \n", - "\n", - "[1 rows x 110 columns]" - ], - "text/html": [ - "\n", - "
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\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "`BIO` (Bioclimatic variables), `Terrain` (topography), and `MedianTCC` (tree canopy cover) are combined into a single multiband image. The `elevation` band is selected from `Terrain`, and a `watermask` is created for locations where `elevation` is greater than `0`. This masks regions below sea level (e.g. the ocean) and prepares the researcher to analyze various environmental factors for the AOI comprehensively." + ], + "metadata": { + "id": "CMrmqQ5X-uOB" + } + }, + { + "cell_type": "code", + "source": [ + "# Combine bands into a multi-band image\n", + "predictors = BIO.addBands(Terrain).addBands(MedianTCC)\n", + "\n", + "# Create a water mask\n", + "watermask = Terrain.select('elevation').gt(0)\n", + "\n", + "# Mask out ocean pixels and clip to the area of interest\n", + "predictors = predictors.updateMask(watermask).clip(AOI)" + ], + "metadata": { + "id": "gSbuqe6k-k6B", + "outputId": "43e1a04f-cd1c-45f0-a30b-b564a9b21dab", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 29, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "When highly correlated predictor variables are included together in a model, multicollinearity issues can arise. Multicollinearity is a phenomenon that occurs when there are strong linear relationships among independent variables in a model, leading to instability in the estimation of the model's coefficients (weights). This instability can reduce the model's reliability and make predictions or interpretations for new data challenging. Therefore, we will consider multicollinearity and proceed with the process of selecting predictor variables.\n", + "\n", + "First, we will generate 5,000 random points and then extract the predictor variable values of the single multiband image at those points." + ], + "metadata": { + "id": "WmuXiScSAlxX" + } + }, + { + "cell_type": "code", + "source": [ + "# Generate 5,000 random points\n", + "DataCor = predictors.sample(scale=GrainSize, numPixels=5000, geometries=True)\n", + "\n", + "# Extract predictor variable values\n", + "PixelVals = predictors.sampleRegions(collection=DataCor, scale=GrainSize, tileScale=16)" + ], + "metadata": { + "id": "vYLvzPDwAeyk", + "outputId": "b833588d-d4db-438c-fd7b-377bf0d9a4fb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ "\n", - "
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\n" + " \n", + " " ] }, - "metadata": {}, - "execution_count": 8 + "metadata": {} } ] }, { "cell_type": "markdown", "source": [ - "Next, we convert the DataFrame into a GeoDataFrame that includes a column for geographic information (`geometry`) and check the first row. A GeoDataFrame can be saved as a GeoPackage file (*.gpkg) and read back in." + "We will convert the extracted predictor values for each point into a DataFrame and then check the first row." ], "metadata": { - "id": "TEjSEmK3pfe0" + "id": "oHWmXtTxCmfV" } }, { "cell_type": "code", "source": [ - "# Convert DataFrame to GeoDataFrame\n", - "gdf = gpd.GeoDataFrame(\n", - " df,\n", - " geometry=gpd.points_from_xy(df.decimalLongitude,\n", - " df.decimalLatitude),\n", - " crs=\"EPSG:4326\"\n", - ")[[\"species\", \"year\", \"month\", \"geometry\"]]\n", - "\"\"\"\n", - "# Convert GeoDataFrame to GeoPackage (requires pycrs module)\n", - "%pip install -U -q pycrs\n", - "gdf.to_file(\"pitta_nympha_data.gpkg\", driver=\"GPKG\")\n", - "gdf = gpd.read_file(\"pitta_nympha_data.gpkg\")\n", - "\"\"\"\n", - "gdf.head(1) # Display the first row of the GeoDataFrame" + "# Converting predictor values from Earth Engine to a DataFrame\n", + "PixelVals_df = geemap.ee_to_df(PixelVals)\n", + "PixelVals_df.head(1)" ], "metadata": { + "id": "x1Bv0knrBbWn", + "outputId": "ceb8b28f-6570-4d5f-dc12-5f704251fea1", "colab": { "base_uri": "https://localhost:8080/", - "height": 81 - }, - "id": "qjt0jgJCpALg", - "outputId": "f8850f22-66b4-4cd9-a082-71b919df2ee0" + "height": 110 + } }, - "execution_count": 9, + "execution_count": 32, "outputs": [ { "output_type": "display_data", @@ -1638,12 +7026,20 @@ "output_type": "execute_result", "data": { "text/plain": [ - " species year month geometry\n", - "0 Pitta nympha 2023 5 POINT (126.72514 33.20314)" + " TCC aspect bio01 bio02 bio03 bio04 bio05 bio06 bio07 bio08 ... \\\n", + "0 9.0 288 140 89 26 8572 304 -26 330 246 ... \n", + "\n", + " bio13 bio14 bio15 bio16 bio17 bio18 bio19 elevation hillshade \\\n", + "0 215 32 63 561 111 539 111 28 187 \n", + "\n", + " slope \n", + "0 2 \n", + "\n", + "[1 rows x 24 columns]" ], "text/html": [ "\n", - "
\n", + "
\n", "
\n", "\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Index(['TCC', 'aspect', 'bio01', 'bio02', 'bio03', 'bio04', 'bio05', 'bio06',\n", + " 'bio07', 'bio08', 'bio09', 'bio10', 'bio11', 'bio12', 'bio13', 'bio14',\n", + " 'bio15', 'bio16', 'bio17', 'bio18', 'bio19', 'elevation', 'hillshade',\n", + " 'slope'],\n", + " dtype='object')\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Calculating Spearman correlation coefficients between the given predictor variables and visualizing them in a heatmap." + ], + "metadata": { + "id": "f3YiwTb8DOzb" + } + }, + { + "cell_type": "code", + "source": [ + "def plot_correlation_heatmap(dataframe, h_size=10):\n", + " # Calculate Spearman correlation coefficients\n", + " correlation_matrix = dataframe.corr(method=\"spearman\")\n", + "\n", + " # Create a heatmap\n", + " plt.figure(figsize=(h_size, h_size-2))\n", + " plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='nearest')\n", + "\n", + " # Display values on the heatmap\n", + " for i in range(correlation_matrix.shape[0]):\n", + " for j in range(correlation_matrix.shape[1]):\n", + " plt.text(j, i, f\"{correlation_matrix.iloc[i, j]:.2f}\",\n", + " ha='center', va='center', color='white', fontsize=8)\n", + "\n", + " columns = dataframe.columns.tolist()\n", + " plt.xticks(range(len(columns)), columns, rotation=90)\n", + " plt.yticks(range(len(columns)), columns)\n", + " plt.title(\"Variables correlation matrix\")\n", + " plt.colorbar(label=\"Spearman Correlation\")\n", + " plt.savefig('correlation_heatmap_plot.png')\n", + " plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "i9YelZ5qDG_t", + "outputId": "531e7f2b-c554-4e5b-dc78-43af1810a2c9" + }, + "execution_count": 34, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Plot the correlation heatmap of variables\n", + "plot_correlation_heatmap(PixelVals_df)" + ], + "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 108 - } + "height": 749 + }, + "id": "DP5ect8cDb7C", + "outputId": "176118ff-4374-4fb1-c154-bcab119d89ca" }, - "execution_count": 33, + "execution_count": 35, "outputs": [ { "output_type": "display_data", @@ -7251,61 +11246,119 @@ "metadata": {} }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Index(['TCC', 'aspect', 'bio01', 'bio02', 'bio03', 'bio04', 'bio05', 'bio06',\n", - " 'bio07', 'bio08', 'bio09', 'bio10', 'bio11', 'bio12', 'bio13', 'bio14',\n", - " 'bio15', 'bio16', 'bio17', 'bio18', 'bio19', 'elevation', 'hillshade',\n", - " 'slope'],\n", - " dtype='object')\n" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} } ] }, { "cell_type": "markdown", "source": [ - "Calculating Spearman correlation coefficients between the given predictor variables and visualizing them in a heatmap." + "Spearman correlation coefficient is useful for understanding the general associations among predictor variables but does not directly assess how multiple variables interact, specifically detecting multicollinearity.\n", + "\n", + "The **Variance Inflation Factor (VIF below)** is a statistical metric used to evaluate multicollinearity and guide variable selection. It indicates the degree of linear relationship of each independent variable with the other independent variables, and high VIF values can be evidence of multicollinearity.\n", + "\n", + "Typically, when VIF values exceed 5 or 10, it suggests that the variable has a strong correlation with other variables, potentially compromising the stability and interpretability of the model. In this tutorial, a criterion of VIF values less than 10 was used for variable selection. The following 6 variables were selected based on VIF." ], "metadata": { - "id": "f3YiwTb8DOzb" + "id": "07B62CNfDyGz" } }, { "cell_type": "code", "source": [ - "def plot_correlation_heatmap(dataframe, h_size=10):\n", - " # Calculate Spearman correlation coefficients\n", - " correlation_matrix = dataframe.corr(method=\"spearman\")\n", + "# Filter variables based on Variance Inflation Factor (VIF)\n", + "def filter_variables_by_vif(dataframe, threshold=10):\n", "\n", - " # Create a heatmap\n", - " plt.figure(figsize=(h_size, h_size-2))\n", - " plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='nearest')\n", + " original_columns = dataframe.columns.tolist()\n", + " remaining_columns = original_columns[:]\n", "\n", - " # Display values on the heatmap\n", - " for i in range(correlation_matrix.shape[0]):\n", - " for j in range(correlation_matrix.shape[1]):\n", - " plt.text(j, i, f\"{correlation_matrix.iloc[i, j]:.2f}\",\n", - " ha='center', va='center', color='white', fontsize=8)\n", + " while True:\n", + " vif_data = dataframe[remaining_columns]\n", + " vif_values = [variance_inflation_factor(vif_data.values, i) for i in range(vif_data.shape[1])]\n", "\n", - " columns = dataframe.columns.tolist()\n", - " plt.xticks(range(len(columns)), columns, rotation=90)\n", - " plt.yticks(range(len(columns)), columns)\n", - " plt.title(\"Variables correlation matrix\")\n", - " plt.colorbar(label=\"Spearman Correlation\")\n", - " plt.savefig('correlation_heatmap_plot.png')\n", - " plt.show()" + " max_vif_index = vif_values.index(max(vif_values))\n", + " max_vif = max(vif_values)\n", + "\n", + " if max_vif < threshold:\n", + " break\n", + "\n", + " print(f\"Removing '{remaining_columns[max_vif_index]}' with VIF {max_vif:.2f}\")\n", + "\n", + " del remaining_columns[max_vif_index]\n", + "\n", + " filtered_data = dataframe[remaining_columns]\n", + " bands = filtered_data.columns.tolist()\n", + " print('Bands:', bands)\n", + "\n", + " return filtered_data, bands" ], "metadata": { - "id": "i9YelZ5qDG_t", - "outputId": "531e7f2b-c554-4e5b-dc78-43af1810a2c9", "colab": { "base_uri": "https://localhost:8080/", "height": 17 + }, + "id": "TJdGzd3SDisO", + "outputId": "09a82a63-62ba-4488-c9bd-6eaaa73d2ca8" + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} } + ] + }, + { + "cell_type": "code", + "source": [ + "filtered_PixelVals_df, bands = filter_variables_by_vif(PixelVals_df)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "e3iiKaK5Eg0q", + "outputId": "bf150d7c-6a38-4a86-b70f-69259c8cf528" }, - "execution_count": 34, + "execution_count": 37, "outputs": [ { "output_type": "display_data", @@ -7340,24 +11393,52 @@ ] }, "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Removing 'bio05' with VIF inf\n", + "Removing 'bio04' with VIF 183937.54\n", + "Removing 'bio10' with VIF 64460.60\n", + "Removing 'bio07' with VIF 47244.59\n", + "Removing 'bio17' with VIF 26253.01\n", + "Removing 'bio01' with VIF 9428.16\n", + "Removing 'bio16' with VIF 3700.80\n", + "Removing 'bio03' with VIF 2468.04\n", + "Removing 'bio18' with VIF 1716.89\n", + "Removing 'bio08' with VIF 1247.20\n", + "Removing 'bio06' with VIF 959.97\n", + "Removing 'bio12' with VIF 606.08\n", + "Removing 'bio19' with VIF 400.13\n", + "Removing 'bio15' with VIF 348.96\n", + "Removing 'hillshade' with VIF 129.83\n", + "Removing 'bio13' with VIF 47.33\n", + "Removing 'bio11' with VIF 31.44\n", + "Removing 'bio02' with VIF 13.75\n", + "Bands: ['TCC', 'aspect', 'bio09', 'bio14', 'elevation', 'slope']\n" + ] } ] }, { "cell_type": "code", "source": [ + "# Variable Selection Based on VIF\n", + "predictors = predictors.select(bands)\n", + "\n", "# Plot the correlation heatmap of variables\n", - "plot_correlation_heatmap(PixelVals_df)" + "plot_correlation_heatmap(filtered_PixelVals_df, h_size=6)" ], "metadata": { - "id": "DP5ect8cDb7C", - "outputId": "176118ff-4374-4fb1-c154-bcab119d89ca", "colab": { "base_uri": "https://localhost:8080/", - "height": 749 - } + "height": 441 + }, + "id": "TEFmil6WErkl", + "outputId": "c62cc9fd-72f1-4b5a-9793-222fe537f8fd" }, - "execution_count": 35, + "execution_count": 38, "outputs": [ { "output_type": "display_data", @@ -7397,9 +11478,9 @@ "output_type": "display_data", "data": { "text/plain": [ - "
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\n" + "image/png": 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\n" }, "metadata": {} } @@ -7408,54 +11489,130 @@ { "cell_type": "markdown", "source": [ - "Spearman correlation coefficient is useful for understanding the general associations among predictor variables but does not directly assess how multiple variables interact, specifically detecting multicollinearity.\n", - "\n", - "The **Variance Inflation Factor (VIF below)** is a statistical metric used to evaluate multicollinearity and guide variable selection. It indicates the degree of linear relationship of each independent variable with the other independent variables, and high VIF values can be evidence of multicollinearity.\n", + "Next, let's visualize the 6 selected predictor variables on the map.\n", + "![Predictor Variables for Analysis](https://github.com/osgeokr/earthengine-community/blob/master/tutorials/species-distribution-modeling/predictor_variables.png?raw=1)\n", "\n", - "Typically, when VIF values exceed 5 or 10, it suggests that the variable has a strong correlation with other variables, potentially compromising the stability and interpretability of the model. In this tutorial, a criterion of VIF values less than 10 was used for variable selection. The following 6 variables were selected based on VIF." + "You can explore the available palettes for map visualization using the following code. For example, the `terrain` palette looks like this.\n", + "```\n", + "cm.plot_colormaps(width=8.0, height=0.2)\n", + "```" ], "metadata": { - "id": "07B62CNfDyGz" + "id": "j0dc6evyFFhw" } }, { "cell_type": "code", "source": [ - "# Filter variables based on Variance Inflation Factor (VIF)\n", - "def filter_variables_by_vif(dataframe, threshold=10):\n", - "\n", - " original_columns = dataframe.columns.tolist()\n", - " remaining_columns = original_columns[:]\n", - "\n", - " while True:\n", - " vif_data = dataframe[remaining_columns]\n", - " vif_values = [variance_inflation_factor(vif_data.values, i) for i in range(vif_data.shape[1])]\n", - "\n", - " max_vif_index = vif_values.index(max(vif_values))\n", - " max_vif = max(vif_values)\n", - "\n", - " if max_vif < threshold:\n", - " break\n", - "\n", - " print(f\"Removing '{remaining_columns[max_vif_index]}' with VIF {max_vif:.2f}\")\n", - "\n", - " del remaining_columns[max_vif_index]\n", - "\n", - " filtered_data = dataframe[remaining_columns]\n", - " bands = filtered_data.columns.tolist()\n", - " print('Bands:', bands)\n", + "cm.plot_colormap('terrain', width=8.0, height=0.2, orientation='horizontal')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "id": "H8uHX-83E8x7", + "outputId": "9a232171-e69c-4f53-e1a8-bbfb81e57e97" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Elevation layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", "\n", - " return filtered_data, bands" + "vis_params = {'bands':['elevation'], 'min': 0, 'max': 1800, 'palette': cm.palettes.terrain}\n", + "Map.addLayer(predictors, vis_params, 'elevation')\n", + "Map.add_colorbar(vis_params, label=\"Elevation (m)\", orientation=\"vertical\", layer_name=\"elevation\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { - "id": "TJdGzd3SDisO", - "outputId": "09a82a63-62ba-4488-c9bd-6eaaa73d2ca8", "colab": { "base_uri": "https://localhost:8080/", - "height": 17 - } + "height": 421, + "referenced_widgets": [ + "0ac7d26aa1814d8f8543b1e51934fe6e", + "7b6fe79343ff4e0ba26e3d6e15efb025", + "bc3830f5a2fb40aa959b59e420d707f8", + "5523004c1fe240439047f5179327ac59", + "08b3b839623e48d98cabad14b768473d", + "61b6639d4538439f9221a21eea1db29e", + "41ff2a139e7a42bc8543914b96d906bb", + "c0cbbed906f344648bc4cff3adcbbf75", + "02dc65fdd68040ea908713060cbc5d1a", + "a55df16b967d47df8d47c7ffc55e462a", + "8123d2220a574ad0aa40be4afd6759aa", + "f87ea7550b9148bc9ec2cd984ffddaea", + "ec5842a999604555aa95c5ed9b82dc37", + "571ddde0c22841d68a7868ac66109281", + "055394cd168c497ab3d2fd16d40d6a5d", + "b145a01c3d5945fb8ed2472518cb1ba7", + "f0eaba4f88bc4a56aee79e689a62fdeb", + "77534dc0bec14c2888cc9217abbf8035", + "6023eac322f247d0808755ba38f8a808", + "0943fd4685a54b70b341bd000cdbc91f", + "f2315c32563e452a9632e16c6e1f8796", + "f0a072b0d2174bd88c2bf8dedea9fa4b", + "8f5295fa75d145a89f760e300aa91493", + "dc7a0a178b8d49109d8d5d4fe7615858", + "08e382e7a20b4eee90eb087d8225e7c1", + "d2e8ba57951749ab955048105ddcad11", + "ee6822f7630e4e7f8d2b41138d55bf61", + "5e67d047f0ca4265aed15af47375d653" + ] + }, + "id": "RswVTmLFFUb3", + "outputId": "c8fc2323-0b78-4960-aa67-1fc203c51186" }, - "execution_count": 36, + "execution_count": 54, "outputs": [ { "output_type": "display_data", @@ -7490,23 +11647,86 @@ ] }, "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "0ac7d26aa1814d8f8543b1e51934fe6e" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, { "cell_type": "code", "source": [ - "filtered_PixelVals_df, bands = filter_variables_by_vif(PixelVals_df)" + "# Calculate the minimum and maximum values for bio09\n", + "min_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", + "max_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", + "\n", + "# bio09 (Mean temperature of driest quarter) layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "\n", + "vis_params = {'min': math.floor(min_val['bio09']), 'max': math.ceil(max_val['bio09']), 'palette': cm.palettes.hot}\n", + "Map.addLayer(predictors.select(\"bio09\").multiply(0.1), vis_params, 'bio09')\n", + "Map.add_colorbar(vis_params, label=\"Mean temperature of driest quarter (℃)\", orientation=\"vertical\", layer_name=\"bio09\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { - "id": "e3iiKaK5Eg0q", - "outputId": "bf150d7c-6a38-4a86-b70f-69259c8cf528", "colab": { "base_uri": "https://localhost:8080/", - "height": 363 - } + "height": 421, + "referenced_widgets": [ + "bd778ad418634776993304e38cd1ad02", + "89ba6396ab264cb1827aa7084280ab72", + "bf6555c5b2814169a35ff3b3b6bc8095", + "6a171d05b22b4dde8f13ecfde310d022", + "4eb8738933d8458c905fe9c1cb02aeec", + "33111afdc5b7461ba0b58c3b5f87d1e8", + "c61c10dbef514e878086a63de0f84b44", + "fd727297110d4fada437c5e998becbe3", + "448af660fa9c4243a5d0ea80c1adc0b2", + "2b00969d96e34bc39e1acdb7b23cc7ba", + "e6aa5ecde8be4747827bb8d42d726390", + "afd0660ef7f248b4be6d6b5fdaeb1fe1", + "3faf6fa1bdce4906a7b253f15f873577", + "414d832f4e044cc1934714a7ed8fb21b", + "2b3e3fb6d8124dd49cfe7bdefc7ba96c", + "8e9e786751bf4a1fb2bc0989e61c6be3", + "8c868f718f804df095927f5b21318bf5", + "00cacd66691647cd80d41f88208aed1c", + "20d8e4f3dd624250bb28d2da9d112fcc", + "5ad8210daf1448b88a415b7c5b43f703", + "4f733436546340baadaaf3cab7d11716", + "a8f5ca16930848c68e378969e327babb", + "4858f5dc633d411a9c4594d0a232587a", + "9af3c9a565f346a2872c8e7c1fe0a36b", + "4ab5dfa79618456f97b6ed532c9829d4", + "50b552f96b464adea9b15273a1596e91", + "33bd735ab4e54f47b79fe6804b2c409e", + "cbda0586f2a34f8ca4032b9a713318d2" + ] + }, + "id": "DOSMZpbsF8sH", + "outputId": "8adb3542-4d87-4004-bf6d-48a9d92c5cb9" }, - "execution_count": 37, + "execution_count": 57, "outputs": [ { "output_type": "display_data", @@ -7543,50 +11763,80 @@ "metadata": {} }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Removing 'bio05' with VIF inf\n", - "Removing 'bio04' with VIF 183937.54\n", - "Removing 'bio10' with VIF 64460.60\n", - "Removing 'bio07' with VIF 47244.59\n", - "Removing 'bio17' with VIF 26253.01\n", - "Removing 'bio01' with VIF 9428.16\n", - "Removing 'bio16' with VIF 3700.80\n", - "Removing 'bio03' with VIF 2468.04\n", - "Removing 'bio18' with VIF 1716.89\n", - "Removing 'bio08' with VIF 1247.20\n", - "Removing 'bio06' with VIF 959.97\n", - "Removing 'bio12' with VIF 606.08\n", - "Removing 'bio19' with VIF 400.13\n", - "Removing 'bio15' with VIF 348.96\n", - "Removing 'hillshade' with VIF 129.83\n", - "Removing 'bio13' with VIF 47.33\n", - "Removing 'bio11' with VIF 31.44\n", - "Removing 'bio02' with VIF 13.75\n", - "Bands: ['TCC', 'aspect', 'bio09', 'bio14', 'elevation', 'slope']\n" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "bd778ad418634776993304e38cd1ad02" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, { "cell_type": "code", "source": [ - "# Variable Selection Based on VIF\n", - "predictors = predictors.select(bands)\n", + "# Slope layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", "\n", - "# Plot the correlation heatmap of variables\n", - "plot_correlation_heatmap(filtered_PixelVals_df, h_size=6)" + "vis_params = {'bands':['slope'], 'min': 0, 'max': 25, 'palette': cm.palettes.RdYlGn_r}\n", + "Map.addLayer(predictors, vis_params, 'slope')\n", + "Map.add_colorbar(vis_params, label=\"Slope\", orientation=\"vertical\", layer_name=\"slope\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { - "id": "TEFmil6WErkl", - "outputId": "c62cc9fd-72f1-4b5a-9793-222fe537f8fd", "colab": { "base_uri": "https://localhost:8080/", - "height": 441 - } + "height": 421, + "referenced_widgets": [ + "eeed0724c6774ca4ae71f9414d7d3d36", + "d7add71f9a694c1eab1a68d22844182f", + "a7f87d5b06044d57b272eb3df58c27ed", + "e6e6b787daa04ca68a4591406c7995f7", + "e7b4368dad5448a79254daf75160eec2", + "f50ee59d00bd4c5f82b18ce212a20609", + "117743a21cf44085a07f630c0b3b861a", + "0c63f792b3114394bd82e137f6392512", + "d2f84366501d48eca89e772ec96e0d81", + "9762a337a7bc40848087c346ba0032e0", + "d4faaee6a05e41cd91940435b39b9364", + "2f57730680724aa6800e00a81897560d", + "d0354527efe24848864f4cb06adb6195", + "553cd97ec41c47ce999e1db0a46787c5", + "669a619518424e6ba7a434d920814d8a", + "510af8d670614271a20ccb4c2868a71d", + "1c78454c17ad4a32a1737be1dc8ca79c", + "3742a1693d0a4ec38689ef4abd3b445f", + "7141995bd812493ba260e61947727b52", + "d7df4e6709ba4292bd71c0d6d4a06b33", + "c58b354684e2468dbdd7d8477d33b5a5", + "703de6e1dede42afae7090e28942afb1", + "ec512f45d5304791b2a1e05a2feed243", + "df9e63a9968a415bb6331ed06676b201", + "2747267b006447ed890a7b8d6715085a", + "04cfd6bdc7c34d6885ba9149d9f14158", + "b228661b472c4012a07a9f8cc75ff29e", + "a5189495d93f4c499c20626066f527e3" + ] + }, + "id": "cQpG8qRzIQcw", + "outputId": "32fb0083-4d81-49fe-9ed0-34273b0383d2" }, - "execution_count": 38, + "execution_count": 59, "outputs": [ { "output_type": "display_data", @@ -7626,40 +11876,77 @@ "output_type": "display_data", "data": { "text/plain": [ - "
" + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" ], - "image/png": 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\n" + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "eeed0724c6774ca4ae71f9414d7d3d36" + } }, - "metadata": {} + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, - { - "cell_type": "markdown", - "source": [ - "Next, let's visualize the 6 selected predictor variables on the map. You can explore the available palettes for map visualization using the following code. For example, the `terrain` palette looks like this.\n", - "```\n", - "cm.plot_colormaps(width=8.0, height=0.2)\n", - "```" - ], - "metadata": { - "id": "j0dc6evyFFhw" - } - }, { "cell_type": "code", "source": [ - "cm.plot_colormap('terrain', width=8.0, height=0.2, orientation='horizontal')" + "# Aspect layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "\n", + "vis_params = {'bands':['aspect'], 'min': 0, 'max': 360, 'palette': cm.palettes.rainbow}\n", + "Map.addLayer(predictors, vis_params, 'aspect')\n", + "Map.add_colorbar(vis_params, label=\"Aspect\", orientation=\"vertical\", layer_name=\"aspect\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { - "id": "H8uHX-83E8x7", - "outputId": "9a232171-e69c-4f53-e1a8-bbfb81e57e97", + "id": "u1hW_wAXIvvG", + "outputId": "f20aa1e4-4b8a-4b2e-96bd-4dd1debaf505", "colab": { "base_uri": "https://localhost:8080/", - "height": 52 + "height": 421, + "referenced_widgets": [ + "ef4fd7dfae2e452c8f4190884fbccb42", + "4aa9d7e81cd44ab2b9fe5860c33057e9", + "06ce1b5cba0a4ba19b17bd566bd45f65", + "f07bebea44b64c76a56d1ab0a7d7f5e7", + "94e01dc8e4234d4791d103169905a6ae", + "65478ba4e1d349d3b9d54f056d54e6a9", + "a2570adcb9704dfe80d4aefe9a9911ab", + "f3a2ac8a04d449a1b0c57b0c573a019f", + "0b1e4d1e0d13456ba9e976251e955207", + "291da14bcdff4172a9dafcdb9015a6a8", + "812ce2194c6a45e7accd47717e3706d6", + "87a59ec985624f938acee2d5a65b337f", + "f4c1ce3e881c4b5780d9f074c2013b2c", + "be97d93287fa491ca8d6b097c9d373ba", + "f441b5da4e4f444eb1282070aac5398e", + "f3fb193232284f45becafbd6a3a24b46", + "fd9b803ae3f34cb4bda622545423554d", + "a9479b474ddc43f0b1ea377f5d72781b", + "9547a5384a064ae4b65b3e2c341ff873", + "120ecccbdce240c4bb2e6fe548fa452e", + "520892cebe1d466c8aafeca7a98f22b3", + "fbc54ef0969849aebe2cdce27cae4c64", + "62513b235c2242e1a6fd416c5d255ab8", + "8978ae99e798438ab7ec3be6c75a75b1", + "eb836cc637c44cc99499e5d5fa143c06", + "376eaa60c64c44adb7842d4457478935", + "dce21eaa742d4801a11d006d3486cc10", + "e2527a8c99034a60853950a96330d203" + ] } }, - "execution_count": 39, + "execution_count": 61, "outputs": [ { "output_type": "display_data", @@ -7699,65 +11986,145 @@ "output_type": "display_data", "data": { "text/plain": [ - "
" + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" ], - "image/png": "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\n" + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "ef4fd7dfae2e452c8f4190884fbccb42" + } }, - "metadata": {} + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, { "cell_type": "code", "source": [ - "# Elevation layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "# Calculate the minimum and maximum values for bio14\n", + "min_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", + "max_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", "\n", - "vis_params = {'bands':['elevation'], 'min': 0, 'max': 1800, 'palette': cm.palettes.terrain}\n", - "Map.addLayer(predictors, vis_params, 'elevation')\n", - "Map.add_colorbar(vis_params, label=\"Elevation (m)\", orientation=\"vertical\", layer_name=\"elevation\")\n", + "# bio14 (Precipitation of driest month) layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'400px'})\n", + "\n", + "vis_params = {'bands':['bio14'], 'min': math.floor(min_val['bio14']), 'max': math.ceil(max_val['bio14']), 'palette': cm.palettes.Blues}\n", + "Map.addLayer(predictors, vis_params, 'bio14')\n", + "Map.add_colorbar(vis_params, label=\"Precipitation of driest month (mm)\", orientation=\"vertical\", layer_name=\"bio14\")\n", "Map.centerObject(AOI, 6)\n", "Map" ], "metadata": { - "id": "RswVTmLFFUb3", - "outputId": "c8fc2323-0b78-4960-aa67-1fc203c51186", + "id": "C49-BnnxJGUD", + "outputId": "637c976f-f657-4e25-c226-c15d45b08f28", "colab": { "base_uri": "https://localhost:8080/", "height": 421, "referenced_widgets": [ - "0ac7d26aa1814d8f8543b1e51934fe6e", - "7b6fe79343ff4e0ba26e3d6e15efb025", - "bc3830f5a2fb40aa959b59e420d707f8", - "5523004c1fe240439047f5179327ac59", - "08b3b839623e48d98cabad14b768473d", - "61b6639d4538439f9221a21eea1db29e", - "41ff2a139e7a42bc8543914b96d906bb", - "c0cbbed906f344648bc4cff3adcbbf75", - "02dc65fdd68040ea908713060cbc5d1a", - "a55df16b967d47df8d47c7ffc55e462a", - "8123d2220a574ad0aa40be4afd6759aa", - "f87ea7550b9148bc9ec2cd984ffddaea", - "ec5842a999604555aa95c5ed9b82dc37", - "571ddde0c22841d68a7868ac66109281", - "055394cd168c497ab3d2fd16d40d6a5d", - "b145a01c3d5945fb8ed2472518cb1ba7", - "f0eaba4f88bc4a56aee79e689a62fdeb", - "77534dc0bec14c2888cc9217abbf8035", - "6023eac322f247d0808755ba38f8a808", - "0943fd4685a54b70b341bd000cdbc91f", - "f2315c32563e452a9632e16c6e1f8796", - "f0a072b0d2174bd88c2bf8dedea9fa4b", - "8f5295fa75d145a89f760e300aa91493", - "dc7a0a178b8d49109d8d5d4fe7615858", - "08e382e7a20b4eee90eb087d8225e7c1", - "d2e8ba57951749ab955048105ddcad11", - "ee6822f7630e4e7f8d2b41138d55bf61", - "5e67d047f0ca4265aed15af47375d653" + "98332e9033d047a3afa4de4fd757086a", + "abe17e68e7ef426abb7b89f791f32504", + "6d5d097a0c194c6987fbc53e76fd01cd", + "5aae35f7526946a781313a5249ca2f19", + "2f48734e6d584f2c81535541e3503b4e", + "5e4d3d92942943789d1065d0f77dd065", + "9decebe145a94349aaf272792defc090", + "4993728ff0e34736a89106d047d724aa", + "1ea67a5f22724e3baeccfbe36f40e27f", + "046a0505d54e4de49cc7264c4af0b511", + "70519ab7d376412e8435c8a1ced12860", + "82740b69f1ab474a8c4d61ddb91c9160", + "820cf782485c49d88a3e4c07406f53fb", + "1f2e891f97554ec8a3e0dc234097debd", + "2de5589c92c140e0b778d059ad73a089", + "cd532df7e3bd4e269fb0a8cd0ed32a3c", + "2fccea87350c4a47919fea5de23328aa", + "b219fe25fc704a33a7cad9cc27419e87", + "f79d1e8b1c734baaa44d7b143a235534", + "8e057fc2964c4489b51cd9b9655bba20", + "42ce404638d64ae5931a86d22cf04b4a", + "921ae15287514023ab0fb4efd252e113", + "21be6278798149e493909fee08e0d18f", + "f87a886e7fdf46c59f3c84283eaff00f", + "bea8c926dfee495ba610548774e2af0e", + "61efcccee8d1443c8c23cf88bc7c0acb", + "63c581aaf2f04ca3a8fdb688fa6c8ac3", + "0c580330deeb4aecb527346e9f8cc768", + "5714e727760f47adb021adc2c8cf80ca", + "843854894e124c03b9e4708f39ce832b", + "85c9834cb00d4d2eb069391f9a7f9e3e", + "5628d73ac5254e79addedea774843062", + "d6930533499a4265b804963c8eda3901", + "5f987a46dc184be182d63ce8c38e4f14", + "3938ad0042fc48ee88ea11d96a03dac6", + "0ff738bb42ad4e6c95b9b909e331f96f", + "c094c31becb44438946a8df0b31879a5", + "30bd8e6388e944a2879c578a07d0ba21", + "368bdfb440c14dc9ac9bbd24d2d8c70b", + "4a6e0bdfbdfe463b8ef27542a3027fe1", + "83ef5c0c20e445b0bf3d2c480e927d51", + "21f8c5f449564d01a4eea5e6c9370dbe", + "dea38ce7cc994618b74ec611f9b22246", + "8ddd4fb6d87c4bcc8eaae1abef388f9f", + "4d6a1053a9ab4c4692b38f4cb1aafc19", + "daef43c060044cd98a35c6387174f430", + "7e7623872e02419ca16f406dc1a72428", + "9ec45ac75185440ead53ebf6ec33848f", + "6c6e4e68a5cb4e83b5b98b59052ee21c", + "62143941002b431e9b2db2c91486a2b5", + "98e780348c2a4bf58e55dc8f37162348", + "07e28a62d9bd4e8c91e3ddb4c5a6c8e6", + "05a8ad47f0be4e4b82541d97e8d08dd0", + "2f6e5861ad03461dabe245d8e2d1d882", + "1335a5f2bf06491a8473eefe56f09359", + "e45909ab9c0a4a49955a7bbcda741b10", + "5a797220a1cc495c803e3c5fafd3ac89", + "1ae96a66581a45bab0cf7ef4b00728b3", + "dcd3e84b18634b25a059e4c0454451c8", + "ea6bdbf4e90546239009318663a96315", + "975c260d0dbf47e6a8136fb5193a66a3", + "02bcebc94a054d3fb9461461039ac915", + "c4ff3969ae7c482b9e7471cce6185ae5", + "2b817e971d5e4a16acea4cc4155b6774", + "664528d4f076437cbd5bda39fe090253", + "d6af373559c1418db3aee473c6e0bbd7", + "6be2901ada634887802061ec27d247e9", + "d4e8f05f06eb4e9eb22f0acd99c3209c", + "ba3a6880c0e64ff09881dd0b791a523b", + "b01f8c8ed8e34077b0c591b06d4a8a0e", + "963b3660f6fc4ed994a65ed27a618763", + "119f26d6657f49bba17c5b780a3c81c6", + "6bd3ec1f82bf4cea84bab3766bbde62c", + "087593701161437296a0eb27cef9b716", + "94aada827ee64e9dab783a4b73963f61", + "f27564fed87641f88c7048fa84b25aca", + "49bd7f7f66e14bd3929477f147202f0c", + "48acec616784491dbaebab3c5ba024a1", + "176978bf174b497bbb15768cdd775e34", + "1eab6cd41f8248e9a5cdc7c7cffca9d3", + "3b9b9842aaaf4e65a292c9f598872052", + "e45dbde4b1ec47feb8fd6ed88c2835a9", + "28fca67f9b69480fbf8a580bfda8d3c7", + "992a59ea35974a5881da757a54f91cde", + "66f8f07d34604a74b2915e4384eb99be", + "8d634952468e4d0e80aacf0653475e0b", + "2b834689e65342dfa3193e00123b2ea2", + "915e6ac6407c499cb91eaaab804dffa0", + "9aa6d2e08f4a404fb0a5fa576b38f33c", + "3227ac581f2f49618104f02bfdcf181d", + "d0ef4b7456054651af8d0cfdf300c053", + "8f0df688c3d146118ddf81caecc857d8" ] } }, - "execution_count": 54, + "execution_count": 64, "outputs": [ { "output_type": "display_data", @@ -7802,7 +12169,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "0ac7d26aa1814d8f8543b1e51934fe6e" + "model_id": "98332e9033d047a3afa4de4fd757086a" } }, "metadata": { @@ -7820,122 +12187,54 @@ { "cell_type": "code", "source": [ - "# Calculate the minimum and maximum values for bio09\n", - "min_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", - "max_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", - "\n", - "# bio09 (Mean temperature of driest quarter) layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'400px'})\n", + "# TCC (Tree Canopy Cover) layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", "\n", - "vis_params = {'min': math.floor(min_val['bio09']), 'max': math.ceil(max_val['bio09']), 'palette': cm.palettes.hot}\n", - "Map.addLayer(predictors.select(\"bio09\").multiply(0.1), vis_params, 'bio09')\n", - "Map.add_colorbar(vis_params, label=\"Mean temperature of driest quarter (℃)\", orientation=\"vertical\", layer_name=\"bio09\")\n", + "vis_params = {'bands':['TCC'], 'min': 0, 'max': 100, 'palette': ['ffffff', 'afce56', '5f9c00', '0e6a00', '003800']}\n", + "Map.addLayer(predictors, vis_params, 'TCC')\n", + "Map.add_colorbar(vis_params, label=\"Tree Canopy Cover (%)\", orientation=\"vertical\", layer_name=\"TCC\")\n", "Map.centerObject(AOI, 6)\n", "Map" ], "metadata": { - "id": "DOSMZpbsF8sH", - "outputId": "00d1243e-8c7e-4109-b1b7-5dfd21e96dfd", + "id": "zKVnKhLYJicg", + "outputId": "8c7cc3a5-466d-45ce-b980-26a7a686308e", "colab": { "base_uri": "https://localhost:8080/", "height": 421, "referenced_widgets": [ - "fe884b10e3a34714ac137b547d69327a", - "1c75c26c3eb34b959c047eb0c3276915", - "ac735c74a55c415699f1b6ccbe8fc32e", - "68e0102c2df84c1e8853e6b42c3e5292", - "523194a619ab4ba3a91acce3330f63e4", - "8b7f398490eb42d99fb74bb928b483e5", - 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"execution_count": 56, + "execution_count": 67, "outputs": [ { "output_type": "display_data", @@ -7980,7 +12279,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "fe884b10e3a34714ac137b547d69327a" + "model_id": "047835b9364440e9bd37f023534440ba" } }, "metadata": { @@ -7999,7 +12298,7 @@ "cell_type": "code", "source": [], "metadata": { - "id": "cQpG8qRzIQcw" + "id": "E-aa4QcqKd1t" }, "execution_count": null, "outputs": [] From a1a4348bbd98fbb70c7989710169fd50e816547b Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 21:25:28 +0900 Subject: [PATCH 10/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 2486 ++--------------- 1 file changed, 191 insertions(+), 2295 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 38f36cd96..c0740b959 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -4878,7 +4878,7 @@ "description_width": "" } }, - "98332e9033d047a3afa4de4fd757086a": { + "82ebad0576144aaaa0773dd83736b54b": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -4900,25 +4900,25 @@ ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_abe17e68e7ef426abb7b89f791f32504", - "IPY_MODEL_6d5d097a0c194c6987fbc53e76fd01cd", - "IPY_MODEL_5aae35f7526946a781313a5249ca2f19", - "IPY_MODEL_2f48734e6d584f2c81535541e3503b4e", - "IPY_MODEL_5e4d3d92942943789d1065d0f77dd065", - "IPY_MODEL_9decebe145a94349aaf272792defc090", - "IPY_MODEL_4993728ff0e34736a89106d047d724aa", - "IPY_MODEL_1ea67a5f22724e3baeccfbe36f40e27f", - "IPY_MODEL_046a0505d54e4de49cc7264c4af0b511" + "IPY_MODEL_852876ffcabd4b5b8e4d3b8efb8329b8", + "IPY_MODEL_8cd9ebc2b0b241a79bd79590ef875026", + "IPY_MODEL_ad9990fa49cc49d3a0055d7832f47e5b", + "IPY_MODEL_be76f04c454c404abf577ab9f46c0ad6", + "IPY_MODEL_4a6f43cf340849bbaa86c3842f7a43a2", + "IPY_MODEL_cf03dbe7b9c247d9a6abe1d2a1888038", + "IPY_MODEL_cd436ad9c9824776b07815c16954081f", + "IPY_MODEL_f875b1c5bd824eb58e8c284245a73c2c", + "IPY_MODEL_b207ea8fb81b4232b68a544c6831f3ba" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_70519ab7d376412e8435c8a1ced12860", + "default_style": "IPY_MODEL_7f7e0df3325e484ab3e57d1f967bb895", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_82740b69f1ab474a8c4d61ddb91c9160", - "east": 131.28662109375003, + "dragging_style": "IPY_MODEL_0f20ef8a9ef04072a8f7d3872d0308c9", + "east": 135.68115234375003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -4928,11 +4928,11 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_820cf782485c49d88a3e4c07406f53fb", - "IPY_MODEL_1f2e891f97554ec8a3e0dc234097debd" + "IPY_MODEL_cc1159482781459995a853ebcd38d2ce", + "IPY_MODEL_29eb066536f4418c8697ac915846e675" ], - "layout": "IPY_MODEL_2de5589c92c140e0b778d059ad73a089", - "left": 13767, + "layout": "IPY_MODEL_09f31594c1b3489ab41574a269c5cc06", + "left": 13567, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", @@ -4967,15 +4967,15 @@ ], "panes": {}, "prefer_canvas": false, - "right": 14167, + "right": 14367, "scroll_wheel_zoom": true, "south": 31.93351676190369, - "style": "IPY_MODEL_cd532df7e3bd4e269fb0a8cd0ed32a3c", + "style": "IPY_MODEL_db28c0e617e14d40869d2ee580f22880", "tap": true, "tap_tolerance": 15, "top": 6257, "touch_zoom": true, - "west": 122.49755859375001, + "west": 118.10302734375001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, "zoom": 6, @@ -4984,7 +4984,7 @@ "zoom_snap": 1 } }, - "abe17e68e7ef426abb7b89f791f32504": { + "852876ffcabd4b5b8e4d3b8efb8329b8": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -5006,10 +5006,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_2fccea87350c4a47919fea5de23328aa" + "widget": "IPY_MODEL_c7bc94df64504017938f5d2e51555b45" } }, - "6d5d097a0c194c6987fbc53e76fd01cd": { + "8cd9ebc2b0b241a79bd79590ef875026": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -5035,7 +5035,7 @@ "zoom_out_title": "Zoom out" } }, - "5aae35f7526946a781313a5249ca2f19": { + "ad9990fa49cc49d3a0055d7832f47e5b": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -5053,7 +5053,7 @@ "position": "topleft" } }, - "2f48734e6d584f2c81535541e3503b4e": { + "be76f04c454c404abf577ab9f46c0ad6": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -5092,7 +5092,7 @@ "remove": true } }, - "5e4d3d92942943789d1065d0f77dd065": { + "4a6f43cf340849bbaa86c3842f7a43a2": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -5118,7 +5118,7 @@ "update_when_idle": false } }, - "9decebe145a94349aaf272792defc090": { + "cf03dbe7b9c247d9a6abe1d2a1888038": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -5159,7 +5159,7 @@ "secondary_length_unit": null } }, - "4993728ff0e34736a89106d047d724aa": { + "cd436ad9c9824776b07815c16954081f": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -5181,10 +5181,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_b219fe25fc704a33a7cad9cc27419e87" + "widget": "IPY_MODEL_f3cb106fec554cb48e630d347e6fc2f4" } }, - "1ea67a5f22724e3baeccfbe36f40e27f": { + "f875b1c5bd824eb58e8c284245a73c2c": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -5204,7 +5204,7 @@ "prefix": "ipyleaflet" } }, - "046a0505d54e4de49cc7264c4af0b511": { + "b207ea8fb81b4232b68a544c6831f3ba": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -5226,10 +5226,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_f79d1e8b1c734baaa44d7b143a235534" + "widget": "IPY_MODEL_122d7ea1db1243f4b829b9df49aa86e7" } }, - "70519ab7d376412e8435c8a1ced12860": { + "7f7e0df3325e484ab3e57d1f967bb895": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -5244,7 +5244,7 @@ "cursor": "grab" } }, - "82740b69f1ab474a8c4d61ddb91c9160": { + "0f20ef8a9ef04072a8f7d3872d0308c9": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -5259,7 +5259,7 @@ "cursor": "move" } }, - "820cf782485c49d88a3e4c07406f53fb": { + "cc1159482781459995a853ebcd38d2ce": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -5307,11 +5307,11 @@ "tile_size": 256, "tms": false, "url": "https://tile.openstreetmap.org/{z}/{x}/{y}.png", - "visible": false, + "visible": true, "zoom_offset": 0 } }, - "1f2e891f97554ec8a3e0dc234097debd": { + "29eb066536f4418c8697ac915846e675": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -5358,12 +5358,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/ce787d2ffbbec1a207cdc4ee5025ce27-8539ed8673068e554117a0ce0abeead2/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/ce787d2ffbbec1a207cdc4ee5025ce27-9bc880dcefc880250c6e737a458e8270/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "2de5589c92c140e0b778d059ad73a089": { + "09f31594c1b3489ab41574a269c5cc06": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5412,10 +5412,10 @@ "right": null, "top": null, "visibility": null, - "width": "400px" + "width": "800px" } }, - "cd532df7e3bd4e269fb0a8cd0ed32a3c": { + "db28c0e617e14d40869d2ee580f22880": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -5430,7 +5430,7 @@ "cursor": "grab" } }, - "2fccea87350c4a47919fea5de23328aa": { + "c7bc94df64504017938f5d2e51555b45": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -5447,12 +5447,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_8e057fc2964c4489b51cd9b9655bba20" + "IPY_MODEL_da4e2a7f1bd14d398ffee2388fbaf5ef" ], - "layout": "IPY_MODEL_42ce404638d64ae5931a86d22cf04b4a" + "layout": "IPY_MODEL_e2adb45640f2478fa7f1fc93ea1c061f" } }, - "b219fe25fc704a33a7cad9cc27419e87": { + "f3cb106fec554cb48e630d347e6fc2f4": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -5469,12 +5469,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - 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x6luvW/6ePn0afn5+sLa2xrVr17Bjxw4sW7aMft+UWo5WVlY4ffo0fvrpJ5SWlpp8grLXLSIOHDiAJUuWoLW1Fa6uroiLi8Py5cvp902p5ajDyckJTk5ORF/TFUQqx+iP/x8kltIXfZyI5kY1Poqa2mMqx9/85jfIyckBAEycOLHbXzbnjzLoi4kb/YJsPfrcXz7EXLYJDw8HAHrxsHXrVsalrjrCm0VETzNgwAAcO3aMXpmxAdk2j0jESeMLISEhOHHiBGv2iMMC21tePPItHj58iLS0NOzduxfDhg0zmENz/rC5vqpb0NHDD5vr+Xkul7D9sLl+P3K/+eYboz7329/+lth2v5+KzZ8/3+D/arUabW1tGDhwIJqammBmZgZra2vua5b3xUWEvnzp4MGDuHr1Knbs2IHBgwejuroaa9euxaRJkxjZJlr+vptxDQNZXv42NaqxJ8ibF4f85HI5iouLDRYRz549g4eHB63EIUHQ5+rR2tqKhw8fGlx7+PCh0RucHSHbiRBxsBPBI+/GxMRg9uzZUCgUcHd3R2lpKVJSUjpVJDGWfj9b0CcpKQkjRozAyZMn8ejRI7i4uGD9+vVYtGgRI3sMVmhsz3NZNWcSIpEIixYtYuzMjvT7qRiXCGGBQ4QDJxzS67RibPO3v/2Nfs2kov7z6PfO/eCDD+jXCxYsYNV2v8+KjRkzBjExMRg/fjyam5vxj3/8o8vPcV4SwEysbWxiir1nz55h/PjxqKmpwZMnTwBol+qxsbE4c+YMXUBow4YN3do4efIkdu7ciaysLLS1teG///1vp8+8lHoLfEvcbNy4Ee7u7qipqaGvkRbK9PDwQGpqKgBgypQprNZc4E3MJZWQXrt2DefPn8eqVavoa42NjTh16hS2bdsGOzs7jB49GgqFwmgJ6eXLlwFoa5ZfvXrV5Gf1ED/InvWkzc/OJZGQtra2IiYmBikpKQa7taYWyqytre3BmuUQcdIAbfZJqVTS7Xk1a5OTkzFx4sROuwNqtdqkQpnx8fF9s2a5sY8DLy4uxuHDh/H99993ek8qldKFMnUOJimUyXbNct7EXGPJyclBVVUVRo8eDUdHR8yfPx8qlQqOjo5QqVQmFcrU1SzX56XVLOeDPjc4OBjFxcXIz89Hfn4+0tLSYG1tjfz8fEybNs2kQpk9XLO857NilpaWBnUWnZycIBKJ6IN4phTK7NGa5fu/LMQgqXHxy1ieqRugmPUqL/bQ2IZwm0fb2IRte3yi3+cWuEQ4WM0hvTq3wHfIirahb480QPscirt373Za1QlaMRM5ffo0Fi9ejObmZoOK+yKRiHutWF+/oa1cuRIpKSl0rQVTIYu5IE2jGWeTLyiVSkRGRrJmTzhwokdkZCQjeX53CLMFPUpLSxEREYHdu3fD2dnZ4D3uz0Sgb4cFb29veHt7s2ZPuKHp0aNnIvrDCu27777Dxx9/TGfFoqKiDGrokCCIn/U4deoU5syZA4qiMGPGDIhEIvj5+SE9PZ2RPcKY+8ueF1uwbc8Utm3bhrNnzxqsxsLDwxEbG4uwsDBie8IKTY+Kigq6CIYOHx8fPHr0iJE9who33PzjC+PGjcORI0cMrqWmpmLs2LGM7AkjV499+/bB398fKSkpGD58OH788UdUV1fj3LlzjOwRLyLYPiDCp0XE5MmTUVxcjLNnz6K8vBwRERGYO3euQSktEoSR2wFbW1u6wIWp9EpluUKhQGZmJi34CAoKwq5duyCRSIhVjmvWrKGlUx0r7evDpCh8rxOFANq6ND/88ANUKhVu3LiBGzduYNeuXQAMVY7ffvstUlNTcfz48W5t6aSngPaoaneNCbwZuR2Lrj/vceC6slYAQFEUxGIx7t27R6scc3NzYWdnBzs7O1rl2J124dChQ/RrXdV9tuDNCo1E5QgAO3bsgFQqxeDBg3Hjxg0oFAqTVY7d6SaY1nJkkFtge+RqMbZQpo7Vq1dj9erVuHPnDk6cOAFnZ2eUlJSYpHLsSh/D9GnVAI/OoRmrcuyIp6cnXnvtNURHRyM5OZmRyjEwMBCAtjCm7rWO0tJS+vnCpPAm5ppCS0sL7t27hzFjxtAqR11e1hiVo855Z8+eNXCkWCyGn58fgoKCGPWLNyPXWNRqNTIyMrBw4ULY2tqisLAQ27Ztg6+vLywtLWmVY3p6Oqqrq7F//35s3br1uTZ1edwJEyZ0qhxiCuRZMbZXaIQjVyQS4eTJk1ixYgWampowePBgvPXWW9iyZQsA01SOOsc+fvwYNTU1BjHYy8uLqJ8Aocox+3oprKzZVSI+bVBh9iR3XqgcCwoKEBwcjLt370IkEoGiKHqnhMmNjfjACSeNJygUCvj7+6Ourg42Njaor6/H0qVLn7sIeR5EI/fL78s4GbmzJg7jxciVyWSoqqqCRCKBnZ0dnjx5ArVaDS8vLzx48IDYHm+OSvEBiURCV+6XyWR4/PgxxGLxyzkq1cejAqZNm4bPP/8cAODv74/AwEDMmzcPv/71rxnZI340bV/e/j1x4gQ9cvfs2YPdu3dDpVIZlO0mQdjm0SMrK4tezVlYWGDdunXYuXMn4zoMvEnc8IGlS5d2eV2/9DYJwk4Efkl3UhSFhoYGg8XD/fv3YW5uzshun8gtmIqdnR29WOi4XyYWixnLnISRC6CkpAQURWHKlCnIy8ujr4vFYjg5OTEuEC9oxQC4u7sDgMn1FTrS76diXG5Q9vuY23GDkk2IcguXbldAynJuQd2ggs9YV17kFthGiLkdKCkpQXp6OioqKuDq6orQ0NCXU8yirycXMjMzMXbsWOTk5KC9vR25ubl49dVXuyyHZQzCgRM9Vq1ahYyMDLz55pv0tbNnzyIxMZHR0/yErJgelZWVmDt3rsE1Pz8/xlM0ISzoERQUhGPHjhlc++ijjxAcHMzIXr+fiulTVVWFuLg47Nu3j65ZfvfuXfj5+RnoGYw9kyYsf/Xo+GwepklyHb1uKtbU1IT4+HhkZ2ejpqYGrq6uWLlyJV1nnFRCqk+PnkPjg3dbW1sxdOhQZGdnY+TIkbh8+TL8/f0hl8sxZ84c4kKZt27dwrhx4wDguYI9znUL1+9VcrJCmzTKuUshnrHF0gIDA/Hqq69i9erVkMlkyM3Npf+8k5OTcebMGVy8eLHLr7W2tqaFevrPP9NHJBK9HN0CV7u/pBJSHRqNBnl5efDy8mIkIdVXQLa3t3fZmCodeXNDI5WQAtqdg8WLF2PUqFEIDAxEbm6uSRLSpqYmiMVig52HlpYWtLe3Myo5yJsNSmOfta6DoijExcWhqKgImZmZEIvFBoUydZAUyvT19cWVK1cMruXl5cHf35/ETTS9coOSoigsW7YMly9fxhdffEE/R11fQqqDpFBmQUEBpk6danBt6tSpBs9uJ6FXLn/j4+ORm5uLCxcuQCaT0df1JaRMCmVaWFhArVYbXFOr1YwfV9vrlr+lpaU4ePAgioqK4O7uDqlUCqlUitjYWABaCamtrS3kcjmmT5+OP//5z0ZLSGfNmoXExEQ0NzcDAJqbm7F8+XK88cYbZJ38GaKp2M2SalizPBVraFBh/IjBvEiWV1VV4c0330RRURFcXV1RUVGBMWPG4PPPP+9UlsUYyFdobM8W2DVnEkOGDEFeXh7y8vJQVlYGd3d3vP7662BazaTXLX+5RleLl6Iok3MLvXK2wBWPHz/GzJkz4e7ujtmzZwPQVsmLiYlhZE+oK6bHO++8gylTpkCtVtMLiVmzZuHLL79kZE8IC3rk5ubi008/xYABA+hfuoODA2praxnZE8KCHra2tp0KYpaXl2PIkCGM7PFm+csHwsPDERERgZs3b4KiKNy9exexsbGIjo5mZK/XLSK4ZOPGjZg4cSJ8fHygVCrh7e0NT09PrFy5kpE9Ieb+TGtrK44cOYKkpCTs2rULNTU1cHBwMOmGK8TcnxkwYADWrVtHy0UdHR1NnskIMVcPX19fZGdns2av1+2hcYm1tTUWLFiAOXPmYNiwYQbbPi9BQtqnfYu2tjb6+L9SqTTZHnGJV/YfB84f9+7Zs8cgP2wqwg0NwPXr1+Hm5gZHR0e88soruHXrFit2+VR4ucdYsWIFgoKCUFBQgICAAIMHLZsCUbK8tLKO9YS2SqWCu7N9jybLHR0d8ejRI0gkEjQ2NsLDw4Nx5VF9BCEetNs5un0yS0tLaDQaVuzyRrfQk3R8UrVGo+n05GomD1UmCgvlVfWchAX5EFmPhoXf/e53z521iEQiZoerKSNQKpUUAKq8up5SadpYbeXV9RQASqlUGtMVav/+/ZS3tzclkUio+fPnd+pnWFgYZW1tTQ0ePJhKSkoyyiZX9LqY6+LigvXr1yM7Oxvl5eUG75EqHLmGN3XFjC2UqVN45+fnGziXSZFMruFNPpepylGHqUUyuYA3YYGJylEfUx8FzgVEzm1oULE+dWpo0IYDpoUydZj6KHAuMMq5EokEzs7OGDXCjZNOODs7Mxa76WBaJJNLjHKuhYUFSkpKaIEa20gkEqMLRrS2ttKtvb0dGo0GYrGYcZFMTunRiSADNm3aRAEwaDNnzqQoSjvPDQ0NpaRSKeXk5ERt2bKlR/tq1ApNgBlCypFDBOdyiOBcDhGcyyGCczlEcC6HCM7lEMG5HCI4l0ME53KI4FwO+f8jcN45qn0rkAAAAABJRU5ErkJggg==\n" 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+10084,6 @@ "Map" ], "metadata": { - "id": "zKVnKhLYJicg", - "outputId": "8c7cc3a5-466d-45ce-b980-26a7a686308e", "colab": { "base_uri": "https://localhost:8080/", "height": 421, @@ -12232,7 +10117,9 @@ "80a240efbce743cda485bbd86eafc979", "ce0e6089614c43e3a180388a5bd3bfec" ] - } + }, + "id": "zKVnKhLYJicg", + "outputId": "8c7cc3a5-466d-45ce-b980-26a7a686308e" }, "execution_count": 67, "outputs": [ @@ -12294,6 +10181,15 @@ } ] }, + { + "cell_type": "markdown", + "source": [ + "### Generation of pseudo-absence data" + ], + "metadata": { + "id": "xIFAutp6M16q" + } + }, { "cell_type": "code", "source": [], From f3c5e9c0fdc76ab2985b3e24d20a35e0e2c13347 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 21:59:00 +0900 Subject: [PATCH 11/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 42 +++++++++++++------ 1 file changed, 29 insertions(+), 13 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index c0740b959..ed30d04f0 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -2002,12 +2002,12 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 13146, + "bottom": 1984, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 35.29943548054545, - 131.23168945312503 + 22.63429269379353, + 107.00683593750001 ], "close_popup_on_click": true, "controls": [ @@ -2029,7 +2029,7 @@ "double_click_zoom": true, "dragging": true, "dragging_style": "IPY_MODEL_afd0660ef7f248b4be6d6b5fdaeb1fe1", - "east": 135.62622070312503, + "east": 142.20703125000003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -2043,11 +2043,11 @@ "IPY_MODEL_414d832f4e044cc1934714a7ed8fb21b" ], "layout": "IPY_MODEL_2b3e3fb6d8124dd49cfe7bdefc7ba96c", - "left": 27929, + "left": 2866, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 37.07271048132946, + "north": 37.71859032558816, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -2078,18 +2078,18 @@ ], "panes": {}, "prefer_canvas": false, - "right": 28729, + "right": 3666, "scroll_wheel_zoom": true, - "south": 33.486435450999885, + "south": 5.61598581915534, "style": "IPY_MODEL_e6aa5ecde8be4747827bb8d42d726390", "tap": true, "tap_tolerance": 15, - "top": 12746, + "top": 1584, "touch_zoom": true, - "west": 126.83715820312501, + "west": 71.89453125000001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 7, + "zoom": 4, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 @@ -9304,7 +9304,7 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 363 + "height": 362 }, "id": "e3iiKaK5Eg0q", "outputId": "bf150d7c-6a38-4a86-b70f-69259c8cf528" @@ -10184,7 +10184,23 @@ { "cell_type": "markdown", "source": [ - "### Generation of pseudo-absence data" + "### Generation of pseudo-absence data\n", + "\n", + "In the process of SDM, the selection of input data for a species is mainly approached using two methods:\n", + "\n", + "1. **Presence-Background Method**: This method compares the locations where a particular species has been observed (presence) with other locations where the species has not been observed (background). Here, the background data does not necessarily mean areas where the species does not exist but rather is set up to reflect the overall environmental conditions of the study area. It is used to distinguish suitable environments where the species could exist from less suitable ones.\n", + "\n", + "2. **Presence-Absence Method**: This method compares locations where the species has been observed (presence) with locations where it has definitively not been observed (absence). Here, absence data represents specific locations where the species is known not to exist. It does not reflect the overall environmental conditions of the study area but rather points to locations where the species is estimated not to exist.\n", + "\n", + "In practice, it is often difficult to collect true absence data, so pseudo-absence data generated artificially is frequently used. However, it's important to acknowledge the limitations and potential errors of this method, as artificially generated pseudo-absence points may not accurately reflect true absence areas.\n", + "\n", + "The choice between these two methods depends on data availability, research objectives, model accuracy and reliability, as well as time and resources. Here, we will use occurrence data collected from GBIF and artificially generated pseudo-absence data to model using the \"Presence-Absence\" method.\n", + "\n", + "The generation of pseudo-absence data will be done through the \"environmental profiling approach\", and the specific steps are as follows:\n", + "\n", + "Environmental Classification Using k-means Clustering: The k-means clustering algorithm, based on Euclidean distance, will be used to divide the pixels within the study area into two clusters. One cluster will represent areas with similar environmental characteristics to randomly selected 100 presence locations, while the other cluster will represent areas with different characteristics.\n", + "\n", + "Generation of Pseudo-Absence Data within Dissimilar Clusters: Within the second cluster identified in the first step (which has different environmental characteristics from the presence data), randomly generated pseudo-absence points will be created. These pseudo-absence points will represent locations where the species is not expected to exist.\"" ], "metadata": { "id": "xIFAutp6M16q" From fe4612a5aab0fe24d686148e67d71ae657b4fd6a Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 23:07:28 +0900 Subject: [PATCH 12/23] Add files via upload --- .../sdm_results.png | Bin 0 -> 1324393 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 tutorials/species-distribution-modeling/sdm_results.png diff --git a/tutorials/species-distribution-modeling/sdm_results.png b/tutorials/species-distribution-modeling/sdm_results.png new file mode 100644 index 0000000000000000000000000000000000000000..813224593f38bf99e3297aabf2b5f2035fbe94bb GIT binary patch literal 1324393 zcmV)LK)Jt(P)Px#1ZP1_K>z@;j|==^1poj532;bRa{vGr5dZ)e5dq33^FIIp|D{PpK~#8N%>4&= zBiDI74pUvSq}6J7*?aH3cM>27kRU*U1?(gU5+Fc=z4x+>TUBhi%5s&{oH&l1IF9YO z_iq1U$Gsq|55uq))$OG)JDSkyY)0dt7LB8Nlnpc^$J&C(+A>7x 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a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -5841,7 +5841,7 @@ "description_width": "" } }, - "047835b9364440e9bd37f023534440ba": { + "6bf79345b0bc455284a79101c04bfe6c": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -5854,34 +5854,34 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 6690, + "bottom": 6657, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 35.003003395276714, - 132.80273437500003 + 35.587733558095216, + 126.8959721004684 ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_a98a603b92b744a8b36544312491e8c8", - "IPY_MODEL_5213cdbb556d45ca885e925302671180", - "IPY_MODEL_8372014946204e0ba5d7d5b3598c2ac3", - "IPY_MODEL_d05dbda652784523833a9d8f4798d82a", - "IPY_MODEL_33224841869a49b381c6135673b605b7", - "IPY_MODEL_91e285a75166493ca709933f5a0caeb5", - "IPY_MODEL_78835da23f734709b78301fc7f4c0db9", - "IPY_MODEL_06316e1c7f314c28ba12c2a4d058771c", - "IPY_MODEL_870f85e2aa38449892ff3fe84a25a877" + "IPY_MODEL_a9778b13aefc4fffbba2416b920619b2", + "IPY_MODEL_9ac7f474f466419383918545e30d84a6", + "IPY_MODEL_2218fda1b3fc464c8b3770f05da5d563", + "IPY_MODEL_1409d3b265bf4cd5a9af029d15f44044", + "IPY_MODEL_d07cec1d500c4bf2b1ecb18568e2ff78", + "IPY_MODEL_5cdb634f5d494fd1b447d22c04019742", + "IPY_MODEL_8c3b03d535b1442c90a12993279b1352", + "IPY_MODEL_da75b5ad3fd74726ad5038f2f192f23a", + "IPY_MODEL_131e70c768004603a5207b118bbf3719" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_b436a62599784890947e7bea7b9577b4", + "default_style": "IPY_MODEL_f899333c85e4435db472ddaa1d963f7e", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_551b671c63e445a58430d1d05f1aeb90", - "east": 141.59179687500003, + "dragging_style": "IPY_MODEL_844e37f9de8040f5ab30a791b58b418d", + "east": 135.68115234375003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -5891,15 +5891,15 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_00bd7007511c423584b73efbd9f117f3", - "IPY_MODEL_da69650f86654e4ba9ee37ae28cdf058" + "IPY_MODEL_82d9b5d4364e4ce9879e22b9052205d5", + "IPY_MODEL_e45ef406bd9d494a87607d105a261aba" ], - "layout": "IPY_MODEL_c23bdd929db845e38a4eeca8a5abe8e9", - "left": 13836, + "layout": "IPY_MODEL_da98d8045da44afd94730bf81a214584", + "left": 13567, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 38.51378825951165, + "north": 39.07890809706475, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -5930,15 +5930,15 @@ ], "panes": {}, "prefer_canvas": false, - "right": 14636, + "right": 14367, "scroll_wheel_zoom": true, - "south": 31.31610138349565, - "style": "IPY_MODEL_b436a62599784890947e7bea7b9577b4", + "south": 31.93351676190369, + "style": "IPY_MODEL_b5270d8c6e8c4d31a42d7301f73e3e4f", "tap": true, "tap_tolerance": 15, - "top": 6290, + "top": 6257, "touch_zoom": true, - "west": 124.01367187500001, + "west": 118.10302734375001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, "zoom": 6, @@ -5947,7 +5947,7 @@ "zoom_snap": 1 } }, - "a98a603b92b744a8b36544312491e8c8": { + "a9778b13aefc4fffbba2416b920619b2": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -5969,10 +5969,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_0724736d314047ada03a5418f341d80e" + "widget": "IPY_MODEL_402adee293e64a098ec269db39fdbf25" } }, - "5213cdbb556d45ca885e925302671180": { + "9ac7f474f466419383918545e30d84a6": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -5998,7 +5998,7 @@ "zoom_out_title": "Zoom out" } }, - "8372014946204e0ba5d7d5b3598c2ac3": { + "2218fda1b3fc464c8b3770f05da5d563": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -6016,7 +6016,7 @@ "position": "topleft" } }, - "d05dbda652784523833a9d8f4798d82a": { + "1409d3b265bf4cd5a9af029d15f44044": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -6055,7 +6055,7 @@ "remove": true } }, - "33224841869a49b381c6135673b605b7": { + "d07cec1d500c4bf2b1ecb18568e2ff78": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -6081,7 +6081,7 @@ "update_when_idle": false } }, - "91e285a75166493ca709933f5a0caeb5": { + "5cdb634f5d494fd1b447d22c04019742": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -6122,7 +6122,7 @@ "secondary_length_unit": null } }, - "78835da23f734709b78301fc7f4c0db9": { + "8c3b03d535b1442c90a12993279b1352": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -6144,10 +6144,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_d5533fd93bbc4a83b77db92694f2a4db" + "widget": "IPY_MODEL_cd1c6151a73844a5a8f31b5b66905d1a" } }, - "06316e1c7f314c28ba12c2a4d058771c": { + "da75b5ad3fd74726ad5038f2f192f23a": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -6167,7 +6167,7 @@ "prefix": "ipyleaflet" } }, - "870f85e2aa38449892ff3fe84a25a877": { + "131e70c768004603a5207b118bbf3719": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -6189,10 +6189,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_1761803ef2f643c8b188a759ee987305" + "widget": "IPY_MODEL_17fd729332064fb8b5d6858d3bb203aa" } }, - "b436a62599784890947e7bea7b9577b4": { + "f899333c85e4435db472ddaa1d963f7e": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -6207,7 +6207,7 @@ "cursor": "grab" } }, - "551b671c63e445a58430d1d05f1aeb90": { + "844e37f9de8040f5ab30a791b58b418d": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -6222,7 +6222,7 @@ "cursor": "move" } }, - "00bd7007511c423584b73efbd9f117f3": { + "82d9b5d4364e4ce9879e22b9052205d5": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -6274,7 +6274,7 @@ "zoom_offset": 0 } }, - "da69650f86654e4ba9ee37ae28cdf058": { + "e45ef406bd9d494a87607d105a261aba": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -6321,12 +6321,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/50d93db5b042e0a9ef5f323f310e6c20-8ac31e844cd5018b7585f62a2b8df9f9/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/50d93db5b042e0a9ef5f323f310e6c20-298f9499451ae70efc5c71d5f434933d/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "c23bdd929db845e38a4eeca8a5abe8e9": { + "da98d8045da44afd94730bf81a214584": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -6378,7 +6378,7 @@ "width": "800px" } }, - "57d78b44141549aa8b06197c22ccb244": { + "b5270d8c6e8c4d31a42d7301f73e3e4f": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -6393,7 +6393,7 @@ "cursor": "grab" } }, - "0724736d314047ada03a5418f341d80e": { + "402adee293e64a098ec269db39fdbf25": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -6410,12 +6410,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_becc47305be546fcb2a2cd80bfd0d238" + "IPY_MODEL_0e41e337484e470083d54c219c6680eb" ], - "layout": "IPY_MODEL_d30bfd3ba3b04fbdb0a107b0c110b276" + "layout": "IPY_MODEL_7c304115b3034e2bac307e9587aa6ac4" } }, - "d5533fd93bbc4a83b77db92694f2a4db": { + "cd1c6151a73844a5a8f31b5b66905d1a": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -6432,12 +6432,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_787e50fb090049178a4f6ad43ce3655d" + "IPY_MODEL_5b23dd0eace74c37b121bc25da52ad2b" ], - "layout": "IPY_MODEL_1362e614de274758b7a0319c8f6294e5" + "layout": "IPY_MODEL_1f877208eb8540d189b0ba8e39622c7c" } }, - "1761803ef2f643c8b188a759ee987305": { + "17fd729332064fb8b5d6858d3bb203aa": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ 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"1.5.0", @@ -6803,305 +6803,5012 @@ "_view_name": "StyleView", "description_width": "" } - } - } - } - }, - "cells": [ - { - "cell_type": "code", - "metadata": { - "id": "8kdsGkYJXXKc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 }, - "outputId": "ea49ceb7-2fdf-4929-a5bf-a3bd2536fe8f" - }, - "source": [ - "#@title Copyright 2023 The Earth Engine Community Authors { display-mode: \"form\" }\n", - "#\n", - "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ], - "execution_count": 4, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" + "c8390c8a357e494ca0388a061faf9add": { + "model_module": "jupyter-leaflet", + "model_name": "LeafletMapModel", + "model_module_version": "^0.18", + "state": { + "_dom_classes": [], + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletMapModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletMapView", + "bottom": 6657, + "bounce_at_zoom_limits": true, + "box_zoom": true, + "center": [ + 35.587733558095216, + 126.8959721004684 ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "l18M9_r5XmAQ" - }, - "source": [ - "# Species Distribution Modeling\n", - "Author: Byeong-Hyeok Yu\n", - "\n", - "In this tutorial, the methodology of Species Distribution Modeling using Google Earth Engine will be introduced. A brief overview of Species Distribution Modeling will be provided, followed by the process of predicting and analyzing the habitat of an endangered bird species known as the Fairy pitta (scientific name: *Pitta nympha*)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "U7i55vr_aKCB" - }, - "source": [ - "### Run me first\n", - "\n", - "Run the following cell to initialize the API. The output will contain instructions on how to grant this notebook access to Earth Engine using your account." - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "XeFsiSp2aDL6" - }, - "source": [ - "import ee\n", - "\n", - "# Trigger the authentication flow.\n", - "ee.Authenticate()\n", - "\n", - "# Initialize the library.\n", - "# ee.Initialize(project='my-project')\n", - "ee.Initialize(project='ee-foss4g')" - ], - "execution_count": 1, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VOf_UnIcZKBJ" - }, - "source": [ - "## A brief overview of Species Distribution Modeling\n", - "\n", - "Let's explore what species distribution models are, the advantages of using Google Earth Engine for their processing, the required data for the models, and how the workflow is structured.\n", - "\n", - "### What is Species Distribution Modeling?\n", - "\n", - "Species Distribution Modeling (SDM below) is the most common methodology used to estimate the actual or potential geographic distribution of a species. It involves characterizing the environmental conditions suitable for a particular species and then identifying where these suitable conditions are distributed geographically.\n", - "\n", - "SDM has emerged as a crucial component of conservation planning in recent years, and various modeling techniques have been developed for this purpose. Implementing SDM in Google Earth Engine (GEE below) provides easy access to large-scale environmental data, along with powerful computing capabilities and support for machine learning algorithms, allowing for rapid modeling.\n", - "\n", - " > Note: Conservation biologist Dr. Ramiro D. Crego implemented SDM using the GEE JavaScript Code Editor and published his research findings [(Crego et al, 2022)](https://onlinelibrary.wiley.com/doi/10.1111/ddi.13491). The methodology of SDM introduced here has been translated and modified from the [JavaScript source code](https://smithsonian.github.io/SDMinGEE/) he shared into the Python language.\n", - "\n", - "### Data Required for SDM\n", - "\n", - "SDM typically utilizes the relationship between known species occurrence records and environmental variables to identify the conditions under which a population can sustain. In other words, two types of model input data are required:\n", - "\n", - "1. Occurrence records of known species\n", - "1. Various environmental variables\n", - "\n", - "These data are input into algorithms to identify environmental conditions associated with the presence of species.\n", - "\n", - "### Workflow of SDM using GEE\n", - "\n", - "The workflow for SDM using GEE is as follows:\n", - "\n", - "1. Collection and preprocessing of species occurrence data\n", - "1. Definition of the Area of Interest\n", - "1. Addition of GEE environmental variables\n", - "1. Generation of pseudo-absence data\n", - "1. Model fitting and prediction\n", - "1. Variable importance and accuracy assessment" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Habitat Prediction and Analysis Using GEE\n", - "\n", - "The [Fairy pitta (*Pitta nympha*)](https://datazone.birdlife.org/species/factsheet/22698684) will be used as a case study to demonstrate the application of GEE-based SDM. While this specific species has been selected for one example, researchers can apply the methodology to any target species of interest with slight modifications to the provided source code.\n", - "\n", - "The Fairy pitta is a rare summer migrant and passage migrant in South Korea, whose distribution area is expanding due to recent climate warming on the Korean Peninsula. It is classified as a rare species, endangered wildlife of class II, Natural Monument No. 204, evaluated as Regionally Extinct (RE) in the National Red List, and Vulnerable (VU) according to the IUCN categories.\n", - "\n", - "Conducting SDM for the conservation planning of the Fairy pitta appears to be quite valuable. Now, let's proceed with habitat prediction and analysis through GEE." - ], - "metadata": { - "id": "tjomxWfVcTmN" - } - }, - { - "cell_type": "markdown", - "source": [ - "First, the Python libraries are imported.The `import` statement brings in the entire contents of a module, while the `from import` statement allows for the importation of specific objects from a module." - ], - "metadata": { - "id": "pViK9PM-gLjh" - } - }, - { - "cell_type": "code", - "source": [ - "# Import libraries\n", - "import geemap\n", - "\n", - "import geemap.colormaps as cm\n", - "import pandas as pd, geopandas as gpd\n", - "import numpy as np, matplotlib.pyplot as plt\n", - "import os, requests, math, random\n", - "\n", - "from ipyleaflet import TileLayer\n", - "from statsmodels.stats.outliers_influence import variance_inflation_factor" - ], - "metadata": { - "id": "4jbM03uIrjST", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "outputId": "baa5449b-af0f-4c39-f086-535c984f0509" - 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"source": [ + "#@title Copyright 2023 The Earth Engine Community Authors { display-mode: \"form\" }\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l18M9_r5XmAQ" + }, + "source": [ + "# Species Distribution Modeling\n", + "Author: Byeong-Hyeok Yu\n", + "\n", + "In this tutorial, the methodology of Species Distribution Modeling using Google Earth Engine will be introduced. A brief overview of Species Distribution Modeling will be provided, followed by the process of predicting and analyzing the habitat of an endangered bird species known as the Fairy pitta (scientific name: *Pitta nympha*)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U7i55vr_aKCB" + }, + "source": [ + "### Run me first\n", + "\n", + "Run the following cell to initialize the API. The output will contain instructions on how to grant this notebook access to Earth Engine using your account." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XeFsiSp2aDL6" + }, + "source": [ + "import ee\n", + "\n", + "# Trigger the authentication flow.\n", + "ee.Authenticate()\n", + "\n", + "# Initialize the library.\n", + "# ee.Initialize(project='my-project')\n", + "ee.Initialize(project='ee-foss4g')" + ], + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VOf_UnIcZKBJ" + }, + "source": [ + "## A brief overview of Species Distribution Modeling\n", + "\n", + "Let's explore what species distribution models are, the advantages of using Google Earth Engine for their processing, the required data for the models, and how the workflow is structured.\n", + "\n", + "### What is Species Distribution Modeling?\n", + "\n", + "Species Distribution Modeling (SDM below) is the most common methodology used to estimate the actual or potential geographic distribution of a species. It involves characterizing the environmental conditions suitable for a particular species and then identifying where these suitable conditions are distributed geographically.\n", + "\n", + "SDM has emerged as a crucial component of conservation planning in recent years, and various modeling techniques have been developed for this purpose. Implementing SDM in Google Earth Engine (GEE below) provides easy access to large-scale environmental data, along with powerful computing capabilities and support for machine learning algorithms, allowing for rapid modeling.\n", + "\n", + " > Note: Conservation biologist Dr. Ramiro D. Crego implemented SDM using the GEE JavaScript Code Editor and published his research findings [(Crego et al, 2022)](https://onlinelibrary.wiley.com/doi/10.1111/ddi.13491). The methodology of SDM introduced here has been translated and modified from the [JavaScript source code](https://smithsonian.github.io/SDMinGEE/) he shared into the Python language.\n", + "\n", + "### Data Required for SDM\n", + "\n", + "SDM typically utilizes the relationship between known species occurrence records and environmental variables to identify the conditions under which a population can sustain. In other words, two types of model input data are required:\n", + "\n", + "1. Occurrence records of known species\n", + "1. Various environmental variables\n", + "\n", + "These data are input into algorithms to identify environmental conditions associated with the presence of species.\n", + "\n", + "### Workflow of SDM using GEE\n", + "\n", + "The workflow for SDM using GEE is as follows:\n", + "\n", + "1. Collection and preprocessing of species occurrence data\n", + "1. Definition of the Area of Interest\n", + "1. Addition of GEE environmental variables\n", + "1. Generation of pseudo-absence data\n", + "1. Model fitting and prediction\n", + "1. Variable importance and accuracy assessment" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Habitat Prediction and Analysis Using GEE\n", + "\n", + "The [Fairy pitta (*Pitta nympha*)](https://datazone.birdlife.org/species/factsheet/22698684) will be used as a case study to demonstrate the application of GEE-based SDM. While this specific species has been selected for one example, researchers can apply the methodology to any target species of interest with slight modifications to the provided source code.\n", + "\n", + "The Fairy pitta is a rare summer migrant and passage migrant in South Korea, whose distribution area is expanding due to recent climate warming on the Korean Peninsula. It is classified as a rare species, endangered wildlife of class II, Natural Monument No. 204, evaluated as Regionally Extinct (RE) in the National Red List, and Vulnerable (VU) according to the IUCN categories.\n", + "\n", + "Conducting SDM for the conservation planning of the Fairy pitta appears to be quite valuable. Now, let's proceed with habitat prediction and analysis through GEE." + ], + "metadata": { + "id": "tjomxWfVcTmN" + } + }, + { + "cell_type": "markdown", + "source": [ + "First, the Python libraries are imported.The `import` statement brings in the entire contents of a module, while the `from import` statement allows for the importation of specific objects from a module." + ], + "metadata": { + "id": "pViK9PM-gLjh" + } + }, + { + "cell_type": "code", + "source": [ + "# Import libraries\n", + "import geemap\n", + "\n", + "import geemap.colormaps as cm\n", + "import pandas as pd, geopandas as gpd\n", + "import numpy as np, matplotlib.pyplot as plt\n", + "import os, requests, math, random\n", + "\n", + "from ipyleaflet import TileLayer\n", + "from statsmodels.stats.outliers_influence import variance_inflation_factor" + ], + "metadata": { + "id": "4jbM03uIrjST", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "baa5449b-af0f-4c39-f086-535c984f0509" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Collection and Preprocessing of Species Occurrence Data\n", + "\n", + "Now, let's collect occurrence data for the Fairy pitta. Even if you don't currently have access to occurrence data for the species of interest, you can obtain observational data about specific species through the GBIF API. The [GBIF API](https://techdocs.gbif.org/en/openapi/) is an interface that allows access to the species distribution data provided by GBIF, enabling users to search, filter, and download data, as well as acquire various information related to species.\n", + "\n", + "In the code below, the `species_name` variable is assigned the scientific name of the species (e.g., *Pitta nympha* for Fairy pitta), and the `country_code` variable is assigned the country code (e.g., KR for South Korea). The `base_url` variable stores the address of the GBIF API. `params` is a dictionary containing parameters to be used in the API request:\n", + "\n", + "* `scientificName`: Sets the scientific name of the species to be searched.\n", + "* `country`: Limits the search to a specific country.\n", + "* `hasCoordinate`: Ensures only data with coordinates (true) are searched.\n", + "* `basisOfRecord`: Chooses only records of human observation (`HUMAN_OBSERVATION`).\n", + "* `limit`: Sets the maximum number of results returned to 10000." + ], + "metadata": { + "id": "SRrwa4ROghr9" + } + }, + { + "cell_type": "code", + "source": [ + "def get_gbif_species_data(species_name, country_code):\n", + " \"\"\"\n", + " Retrieves observational data for a specific species using the GBIF API and returns it as a pandas DataFrame.\n", + "\n", + " Parameters:\n", + " species_name (str): The scientific name of the species to query.\n", + " country_code (str): The country code of the where the observation data will be queried.\n", + "\n", + " Returns:\n", + " pd.DataFrame: A pandas DataFrame containing the observational data.\n", + " \"\"\"\n", + " base_url = \"https://api.gbif.org/v1/occurrence/search\"\n", + " params = {\n", + " \"scientificName\": species_name,\n", + " \"country\": country_code,\n", + " \"hasCoordinate\": \"true\",\n", + " \"basisOfRecord\": \"HUMAN_OBSERVATION\",\n", + " \"limit\": 10000,\n", + " }\n", + "\n", + " try:\n", + " response = requests.get(base_url, params=params)\n", + " response.raise_for_status() # Raises an exception for a response error.\n", + " data = response.json()\n", + " occurrences = data.get(\"results\", [])\n", + "\n", + " if occurrences: # If data is present\n", + " df = pd.json_normalize(occurrences)\n", + " return df\n", + " else:\n", + " print(\"No data found for the given species and country code.\")\n", + " return pd.DataFrame() # Returns an empty DataFrame\n", + " except requests.RequestException as e:\n", + " print(f\"Request failed: {e}\")\n", + " return pd.DataFrame() # Returns an empty DataFrame in case of an exception" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "oHtKaH0FgXTz", + "outputId": "35aaf721-029e-4c34-b35c-06608f1b5250" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Using the parameters set previously, we query the GBIF API for observational records of the Fairy pitta (*Pitta nympha*), and load the results into a DataFrame to check the first row. A DataFrame is a data structure for handling table-formatted data, consisting of rows and columns. If necessary, the DataFrame can be saved as a CSV file and read back in." + ], + "metadata": { + "id": "Zs5ZUfZUnjZ2" + } + }, + { + "cell_type": "code", + "source": [ + "# Retrieve Fairy Pitta data\n", + "df = get_gbif_species_data(\"Pitta nympha\", \"KR\")\n", + "\"\"\"\n", + "# Save DataFrame to CSV and read back in.\n", + "df.to_csv(\"pitta_nympha_data.csv\", index=False)\n", + "df = pd.read_csv(\"pitta_nympha_data.csv\")\n", + "\"\"\"\n", + "df.head(1) # Display the first row of the DataFrame" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 182 + }, + "id": "Mx-DjtGNnUXk", + "outputId": "95a3b0ee-25cb-4547-cb3b-8da17eed9fff" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " key datasetKey \\\n", + "0 4126765284 50c9509d-22c7-4a22-a47d-8c48425ef4a7 \n", + "\n", + " publishingOrgKey installationKey \\\n", + "0 28eb1a3f-1c15-4a95-931a-4af90ecb574d 997448a8-f762-11e1-a439-00145eb45e9a \n", + "\n", + " hostingOrganizationKey publishingCountry protocol \\\n", + "0 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE \n", + "\n", + " lastCrawled lastParsed crawlId ... \\\n", + "0 2024-01-23T16:28:21.693+00:00 2024-01-25T09:13:47.069+00:00 431 ... \n", + "\n", + " nomenclaturalCode fieldNotes behavior verbatimElevation \\\n", + "0 NaN NaN NaN NaN \n", + "\n", + " higherClassification extensions.http://rs.tdwg.org/ac/terms/Multimedia \\\n", + "0 NaN NaN \n", + "\n", + " distanceFromCentroidInMeters associatedTaxa lifeStage occurrenceRemarks \n", + "0 NaN NaN NaN NaN \n", + "\n", + "[1 rows x 110 columns]" + ], + "text/html": [ + "\n", + "
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Oz8/P119//aW///5bbdq0sekY66efftKBAweK/TyRkZGXnQFelH9eLVBwyWdubq6+/vrrYsdwNXl5efrqq6/Us2dP1a1b11xes2ZNPfLII9q0aVOhpTeGDBlicTngnXfeqby8PP36668lFidgDSSlgFKk4Na4gYGB8vPz08GDB2UYhsaMGSNfX1+LrWBa96lTpyRdGhxMmTJFDRo0kIuLi6pXry5fX1/9+OOPFtfmF7jWwU6fPn3k4uKihQsXSpIyMzO1cuVK9evXz+KLrzjOnj0rSapataq57OjRoxo4cKC8vb3l7u4uX19fc/KrqOMoysqVK3XHHXfI1dVV3t7e8vX1VVxc3DXvf70x/9sLL7wgd3d33X777WrQoIGio6P13XffXdfzXO9gtEGDBhaP69WrJwcHh0JrXljbr7/+WuS6F40bNzbX/1NQUJDF42rVqkm6lDwEAKAovr6+Cg0NVUJCgpYuXaq8vDw9+OCDRbb99ddfFRAQUOh7+lq/l6RL303X871U3D4GDBigCxcuaNmyZZIu3WkuOTlZ/fv3v+bnvpyixis//fSTHnjgAXl6esrDw0O+vr7mRcevdYw0f/58NW/e3Lyek6+vr1atWmWzMdb48eOVkZGhm2++WbfccotGjRqlH3/88bqe53rGWA4ODhZJIUnmyyFLcoz1+++/6/z585cdY+Xn5+vYsWMW5YyxUFaRlAJKsYLr25977jmtWbOmyK1+/fqSpDfeeEMxMTHq2LGjFixYoKSkJK1Zs0ZNmzYt8jr5a5klJV36QuvWrZs5KfXZZ58pJyen0J1TimPv3r2SZD6GvLw83XPPPVq1apVeeOEFLV++XGvWrDEvEHm56/3/6dtvv9X9998vV1dXzZgxQ19++aXWrFmjRx55pNACp9aIuSiNGzdWSkqKFi1apA4dOmjJkiXq0KHDda0Pca2vz+X8O2F4uQTiPxc3tQVHR8ciy63x2gAAyq9HHnlEq1ev1syZMxUeHi4vLy+r9GuN76Xi9tGkSRO1bt1aCxYskCQtWLBAzs7O6t279zU/9+X8e7ySkZGhu+66Sz/88IPGjx+vL774QmvWrDHf6e5axlgLFizQwIEDVa9ePc2dO1eJiYlas2aNunTpck37X2/MRenYsaMOHTqkDz74QM2aNdN///tftWrVSv/973+v+XludIz1b4yxgBvDQudAKVbwy4yTk5NCQ0Ov2Pazzz5T586dNXfuXIvyjIwM86KcxTVgwAD16NFD27dv18KFC9WyZUs1bdr0hvqUZF7ksWvXrpKkPXv26JdfftH8+fM1YMAAc7t/3nGnwOUGAEuWLJGrq6uSkpIsbiMcHx9/w/EWxGwymcwLs19OlSpV1KdPH/Xp00e5ubnq1auXJkyYoNGjR8vV1fWGZ5n924EDByx++Tt48KDy8/PNC6QX/FqWkZFhsV9RU7qvJ7bg4GClpKQUKi+4DCA4OPia+wIA4HIeeOABPfnkk9q6desVF24ODg7W119/rTNnzljMuLmR7yVrf2f/04ABAxQTE6OTJ08qISFBERER5u/sG/Hv8cr69ev1559/aunSperYsaO5XWpqaqF9L3e8n332merWraulS5datLnRRdmlSwmchIQEVa5c2WJJgKJ4e3vrscce02OPPaazZ8+qY8eOGjdunB5//PErxl8c+fn5Onz4sMXNbQruaFiSYyxfX19Vrlz5smMsBwcHBQYGXlNfQGnHTCmgFKtRo4Y6deqkWbNm6eTJk4Xq/3mbV0dHx0K/hCxevNi85tSNCA8PV/Xq1TV58mRt2LDBKrOkEhIS9N///lchISG6++67Jf3vF55/HodhGHrvvfcK7V+lShVJhQcAjo6OMplMFr9OHTlyRMuXL7/hmCdNmqSvvvpKffr0KXS53D/9+eefFo+dnZ3VpEkTGYZhvvXz5eIvroJbDBeYNm2aJJnvVOTh4aHq1atr48aNFu1mzJhRqK/rie2+++7T999/ry1btpjLzp07p9mzZ6t27drXtS4WAACX4+7urri4OI0bN07du3e/bLv77rtPeXl5mj59ukX5lClTZDKZzN+L18Pa39n/1LdvX5lMJj3zzDM6fPiwVcZYRY1Xihpj5ebmXnYcUNTleEX1sW3bNosxQHHk5eVp+PDh2r9/v4YPHy4PD4/Ltv33GMvd3V3169dXTk6ORfyS9V6vf76XDMPQ9OnT5eTkZB6/BgcHy9HR0apjLEdHR4WFhenzzz+3uEwwPT1dCQkJ6tChwxXPE1CWMFMKKOViY2PVoUMH3XLLLXriiSdUt25dpaena8uWLfrtt9/0ww8/SJK6deum8ePH67HHHlO7du20Z88eLVy4sNB18MXh5OSkhx9+WNOnTzffsvd6fPbZZ3J3d1dubq6OHz+upKQkfffdd2rRooUWL15sbteoUSPVq1dPzz33nI4fPy4PDw8tWbKkyGvhW7duLUkaPny4unbtKkdHRz388MOKiIjQu+++q3vvvVePPPKITp06pdjYWNWvX/+a1xz4+++/zVPps7Oz9euvv2rFihX68ccf1blzZ82ePfuK+4eFhcnf31/t27eXn5+f9u/fr+nTpysiIsL8q21B/C+99JIefvhhOTk5qXv37ubByvVKTU3V/fffr3vvvVdbtmzRggUL9Mgjj6hFixbmNo8//rgmTZqkxx9/XG3atNHGjRvNv/b90/XE9uKLL5pv0z18+HB5e3tr/vz5Sk1N1ZIlS8yL7wMAcKOioqKu2qZ79+7q3LmzXnrpJR05ckQtWrTQV199pc8//1wjRoywWJD8Wln7O/uffH19de+992rx4sXy8vJSRETENe97PeOVdu3aqVq1aoqKitLw4cNlMpn00UcfFXlpV+vWrfXJJ58oJiZGt912m9zd3dW9e3d169ZNS5cu1QMPPKCIiAilpqZq5syZatKkiXk9qKvJzMw0x3z+/HkdPHhQS5cu1aFDh/Twww/rtddeu+L+TZo0UadOndS6dWt5e3trx44d+uyzzywWI7/cGLE4XF1dlZiYqKioKLVt21arV6/WqlWr9H//93/mxdI9PT310EMPadq0aTKZTKpXr55WrlxpXvf1n64nttdff11r1qxRhw4d9PTTT6tSpUqaNWuWcnJy9OabbxbreIBSyfY3/ANwJUXdLvfQoUPGgAEDDH9/f8PJycm46aabjG7duhmfffaZuU12drbx7LPPGjVr1jTc3NyM9u3bG1u2bDHuuusu46677jK3K7ht7eLFiws99+VuaWsYhvH9998bkoywsLBrPpaxY8caksybq6urUatWLaNbt27GBx98YHH74QL79u0zQkNDDXd3d6N69erGE088Yb618j9vqfv3338bw4YNM3x9fQ2TyWRxe925c+caDRo0MFxcXIxGjRoZ8fHx5liupuD2vwVb5cqVjdq1axuRkZHGZ599ZuTl5RXa59/neNasWUbHjh0NHx8fw8XFxahXr54xatQoIzMz02K/1157zbjpppsMBwcHi9tESzKio6OLjE//ul1wwXHt27fPePDBB42qVasa1apVM4YOHWpcuHDBYt/z588bgwcPNjw9PY2qVasavXv3Nk6dOlWozyvF9u9bMxvGpffngw8+aHh5eRmurq7G7bffbqxcudKizeXed0XdLhkAgILb3G/fvv2K7YKDg42IiAiLsjNnzhgjR440AgICDCcnJ6NBgwbGW2+9ZeTn51u0u9z3bVHfddf7nf3vPgqOp2C/f/r0008NScaQIUOueKz/VJzxynfffWfccccdhpubmxEQEGA8//zzRlJSUqGx39mzZ41HHnnE8PLyMiQZwcHBhmEYRn5+vvHGG28YwcHBhouLi9GyZUtj5cqVRlRUlLnNldx1110WMbu7uxsNGjQwHn30UeOrr74qcp9/n8fXX3/duP322w0vLy/Dzc3NaNSokTFhwgQjNzfX3OZyY8SCMcdbb71V6HmKGo9ERUUZVapUMQ4dOmSEhYUZlStXNvz8/IyxY8cWOr+///67ERkZaVSuXNmoVq2a8eSTTxp79+69rvFrUeOxnTt3Gl27djXc3d2NypUrG507dzY2b95s0eZyfytXGtcDpYnJMFj5DMDV/fDDD7r11lv14YcfWuWuMAAAAJA+//xz9ezZUxs3btSdd95p73AAwKa4rgLANZkzZ47c3d3Vq1cve4cCAABQbsyZM0d169a96gLfAFAesaYUgCv64osvtG/fPs2ePVtDhw61yvoJAAAAFd2iRYv0448/atWqVXrvvfdK9C5/AFBacfkegCuqXbu20tPT1bVrV3300UcWt1cGAABA8ZhMJrm7u6tPnz6aOXOmKlVivgCAioekFAAAAAAAAGzOrmtK1a5dWyaTqdAWHR0t6dKtTaOjo+Xj4yN3d3dFRkYqPT3dniEDAAAAAADACuw6U+r3339XXl6e+fHevXt1zz33aN26derUqZOeeuoprVq1SvPmzZOnp6eGDh0qBwcHfffdd/YKGQAAAAAAAFZQqi7fGzFihFauXKkDBw4oKytLvr6+SkhI0IMPPihJ+vnnn9W4cWNt2bJFd9xxxzX1mZ+frxMnTqhq1aosHggAAK6LYRg6c+aMqlatKg8PjwozlmD8BAAAbkTBGCogIEAODpe/SK/UrKaXm5urBQsWKCYmRiaTScnJybp48aJCQ0PNbRo1aqSgoKDrSkqdOHFCgYGBJRU2AACoIDIzM+Xh4WHvMGyC8RMAALCGY8eOqVatWpetLzVJqeXLlysjI0MDBw6UJKWlpcnZ2VleXl4W7fz8/JSWlnbZfnJycpSTk2N+XDAR7NixYxVmIAkAAKwjKytLgYGBOnbsWIW6+2jBsTJ+AgAAxVEwhrra+KnUJKXmzp2r8PBwBQQE3FA/EydO1Kuvvlqo3MPDg0EVAACXcfz4cb3wwgtavXq1zp8/r/r16ys+Pl5t2rSRpMtewvXmm29q1KhRtgzVLirSpXvS/15vxk+wtTNnzmjMmDFatmy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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The data from 1995 is very sparse, with significant gaps compared to other years, and the months of August and September also have limited samples and exhibit different seasonal characteristics compared to other periods. Excluding this data could contribute to improving the stability and predictive power of the model.\n", + "\n", + "However, it's important to note that excluding data may enhance the model's generalization ability, but it could also lead to the loss of valuable information relevant to the research objectives. Therefore, such decisions should be made with careful consideration." + ], + "metadata": { + "id": "vIj9CO1RrCSs" + } + }, + { + "cell_type": "code", + "source": [ + "# Filtering data by year and month\n", + "filtered_gdf = gdf[\n", + " (~gdf['year'].eq(1995)) &\n", + " (~gdf['month'].between(8, 9))\n", + "]\n", + "\n", + "# Visualize the filtered data distribution\n", + "plot_data_distribution(filtered_gdf)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 407 + }, + "id": "k1PpbNsGq9qh", + "outputId": "1c90e189-5c7d-4a60-cef6-6096603f7e97" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} } ] }, { "cell_type": "markdown", "source": [ - "### Collection and Preprocessing of Species Occurrence Data\n", - "\n", - "Now, let's collect occurrence data for the Fairy pitta. Even if you don't currently have access to occurrence data for the species of interest, you can obtain observational data about specific species through the GBIF API. The [GBIF API](https://techdocs.gbif.org/en/openapi/) is an interface that allows access to the species distribution data provided by GBIF, enabling users to search, filter, and download data, as well as acquire various information related to species.\n", - "\n", - "In the code below, the `species_name` variable is assigned the scientific name of the species (e.g. *Pitta nympha* for Fairy pitta), and the `country_code` variable is assigned the country code (e.g. KR for South Korea). The `base_url` variable stores the address of the GBIF API. `params` is a dictionary containing parameters to be used in the API request:\n", - "\n", - "* `scientificName`: Sets the scientific name of the species to be searched.\n", - "* `country`: Limits the search to a specific country.\n", - "* `hasCoordinate`: Ensures only data with coordinates (true) are searched.\n", - "* `basisOfRecord`: Chooses only records of human observation (`HUMAN_OBSERVATION`).\n", - "* `limit`: Sets the maximum number of results returned to 10000." + "Additionally, the use of a heatmap allows us to quickly grasp the frequency of species occurrence by year and month, providing an intuitive visualization of the temporal changes and patterns within the data." ], "metadata": { - "id": "SRrwa4ROghr9" + "id": "-r2fND5nr0dU" } }, { "cell_type": "code", "source": [ - "def get_gbif_species_data(species_name, country_code):\n", - " \"\"\"\n", - " Retrieves observational data for a specific species using the GBIF API and returns it as a pandas DataFrame.\n", + "# Yearly and monthly data distribution heatmap\n", + "def plot_heatmap(gdf, h_size=8):\n", "\n", - " Parameters:\n", - " species_name (str): The scientific name of the species to query.\n", - " country_code (str): The country code of the where the observation data will be queried.\n", + " statistics = gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)\n", "\n", - " Returns:\n", - " pd.DataFrame: A pandas DataFrame containing the observational data.\n", - " \"\"\"\n", - " base_url = \"https://api.gbif.org/v1/occurrence/search\"\n", - " params = {\n", - " \"scientificName\": species_name,\n", - " \"country\": country_code,\n", - " \"hasCoordinate\": \"true\",\n", - " \"basisOfRecord\": \"HUMAN_OBSERVATION\",\n", - " \"limit\": 10000,\n", - " }\n", + " # Heatmap\n", + " plt.figure(figsize=(h_size, h_size-6))\n", + " heatmap = plt.imshow(statistics.values, cmap=\"YlOrBr\", origin=\"upper\", aspect=\"auto\")\n", "\n", - " try:\n", - " response = requests.get(base_url, params=params)\n", - " response.raise_for_status() # Raises an exception for a response error.\n", - " data = response.json()\n", - " occurrences = data.get(\"results\", [])\n", + " # Display values above each pixel\n", + " for i in range(len(statistics.index)):\n", + " for j in range(len(statistics.columns)):\n", + " plt.text(j, i, statistics.values[i, j], ha=\"center\", va=\"center\", color=\"black\")\n", "\n", - " if occurrences: # If data is present\n", - " df = pd.json_normalize(occurrences)\n", - " return df\n", - " else:\n", - " print(\"No data found for the given species and country code.\")\n", - " return pd.DataFrame() # Returns an empty DataFrame\n", - " except requests.RequestException as e:\n", - " print(f\"Request failed: {e}\")\n", - " return pd.DataFrame() # Returns an empty DataFrame in case of an exception" + " plt.colorbar(heatmap, label=\"Count\")\n", + " plt.title(\"Monthly Species Count by Year\")\n", + " plt.xlabel(\"Year\")\n", + " plt.ylabel(\"Month\")\n", + " plt.xticks(range(len(statistics.columns)), statistics.columns)\n", + " plt.yticks(range(len(statistics.index)), statistics.index)\n", + " plt.tight_layout()\n", + " plt.savefig('heatmap_plot.png')\n", + " plt.show()\n", + " print(gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)) # Display the data statistics" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "Iju-dNZErzkJ", + "outputId": "2d39d407-8d38-4e88-87f2-10124086e104" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plot_heatmap(filtered_gdf)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 298 + }, + "id": "dlW1nIQZrjx3", + "outputId": "e1b92df6-edab-4d3b-d5d6-d7eccc985aaf" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "year 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023\n", + "month \n", + "5 0 0 5 0 2 1 5 4 5 22 8 1\n", + "6 0 2 6 1 1 6 0 6 13 35 20 3\n", + "7 1 0 0 1 0 1 1 7 1 10 8 0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now, the filtered GeoDataFrame is converted into a Google Earth Engine object." + ], + "metadata": { + "id": "jy0BBd-6sdLz" + } + }, + { + "cell_type": "code", + "source": [ + "# Convert GeoDataFrame to Earth Engine object\n", + "data_raw = geemap.geopandas_to_ee(filtered_gdf)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, - "id": "oHtKaH0FgXTz", - "outputId": "35aaf721-029e-4c34-b35c-06608f1b5250" + "id": "Fxu0OsBksMKM", + "outputId": "5e7004a8-e0ee-4b27-93ec-3a7a359b0a5d" }, - "execution_count": 6, + "execution_count": 15, "outputs": [ { "output_type": "display_data", @@ -7142,33 +11849,27 @@ { "cell_type": "markdown", "source": [ - "Using the parameters set previously, we query the GBIF API for observational records of the Fairy pitta (*Pitta nympha*), and load the results into a DataFrame to check the first row. A DataFrame is a data structure for handling table-formatted data, consisting of rows and columns. If necessary, the DataFrame can be saved as a CSV file and read back in." + "Next, we will define the raster pixel size of the SDM results as 1km resolution." ], "metadata": { - "id": "Zs5ZUfZUnjZ2" + "id": "55p_GfB6sv3U" } }, { "cell_type": "code", "source": [ - "# Retrieve Fairy Pitta data\n", - "df = get_gbif_species_data(\"Pitta nympha\", \"KR\")\n", - "\"\"\"\n", - "# Save DataFrame to CSV and read back in.\n", - "df.to_csv(\"pitta_nympha_data.csv\", index=False)\n", - "df = pd.read_csv(\"pitta_nympha_data.csv\")\n", - "\"\"\"\n", - "df.head(1) # Display the first row of the DataFrame" + "# Spatial resolution setting (meters)\n", + "GrainSize = 1000" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 182 + "height": 17 }, - "id": "Mx-DjtGNnUXk", - "outputId": "95a3b0ee-25cb-4547-cb3b-8da17eed9fff" + "id": "FsTbNQ17s1l-", + "outputId": "b18c9435-8539-4ff0-8b50-fa8201baf69d" }, - "execution_count": 8, + "execution_count": 16, "outputs": [ { "output_type": "display_data", @@ -7203,232 +11904,157 @@ ] }, "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "When multiple occurrence points are present within the same 1km resolution raster pixel, there is a high likelihood that they share the same environmental conditions at the same geographic location. Using such data directly in the analysis can introduce bias into the results.\n", + "\n", + "In other words, we need to limit the potential impact of geographic sampling bias. To achieve this, we will retain only one location within each 1km pixel and remove all others, allowing the model to more objectively reflect the environmental conditions." + ], + "metadata": { + "id": "A20gAGUZtN6S" + } + }, + { + "cell_type": "code", + "source": [ + "def remove_duplicates(data, GrainSize):\n", + " # Select one occurrence record per pixel at the chosen spatial resolution (1km)\n", + " random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", + " rand_point_vals = random_raster.sampleRegions(collection=ee.FeatureCollection(data), scale=10, geometries=True)\n", + " return rand_point_vals.distinct('random')\n", + "\n", + "Data = remove_duplicates(data_raw, GrainSize)\n", + "\n", + "# Before selection and after selection\n", + "print('Original data size:', data_raw.size().getInfo())\n", + "print('Final data size:', Data.size().getInfo())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " key datasetKey \\\n", - "0 4126765284 50c9509d-22c7-4a22-a47d-8c48425ef4a7 \n", - "\n", - " publishingOrgKey installationKey \\\n", - "0 28eb1a3f-1c15-4a95-931a-4af90ecb574d 997448a8-f762-11e1-a439-00145eb45e9a \n", - "\n", - " hostingOrganizationKey publishingCountry protocol \\\n", - "0 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE \n", - "\n", - " lastCrawled lastParsed crawlId ... \\\n", - "0 2024-01-23T16:28:21.693+00:00 2024-01-25T09:13:47.069+00:00 431 ... \n", - "\n", - " nomenclaturalCode fieldNotes behavior verbatimElevation \\\n", - "0 NaN NaN NaN NaN \n", - "\n", - " higherClassification extensions.http://rs.tdwg.org/ac/terms/Multimedia \\\n", - "0 NaN NaN \n", - "\n", - " distanceFromCentroidInMeters associatedTaxa lifeStage occurrenceRemarks \n", - "0 NaN NaN NaN NaN \n", - "\n", - "[1 rows x 110 columns]" - ], - "text/html": [ - "\n", - "
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\n" + " \n", + " " ] }, - "metadata": {}, - "execution_count": 8 + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Original data size: 176\n", + "Final data size: 111\n" + ] } ] }, { "cell_type": "markdown", "source": [ - "Next, we convert the DataFrame into a GeoDataFrame that includes a column for geographic information (`geometry`) and check the first row. A GeoDataFrame can be saved as a GeoPackage file (*.gpkg) and read back in." + "The visualization comparing geographic sampling bias before preprocessing (in blue) and after preprocessing (in red) is shown below. To facilitate comparison, the map has been centered on the area with a high concentration of Fairy pitta occurrence coordinates in Hallasan National Park." ], "metadata": { - "id": "TEjSEmK3pfe0" + "id": "Rhu4b4BxuMHE" } }, { "cell_type": "code", "source": [ - "# Convert DataFrame to GeoDataFrame\n", - "gdf = gpd.GeoDataFrame(\n", - " df,\n", - " geometry=gpd.points_from_xy(df.decimalLongitude,\n", - " df.decimalLatitude),\n", - " crs=\"EPSG:4326\"\n", - ")[[\"species\", \"year\", \"month\", \"geometry\"]]\n", - "\"\"\"\n", - "# Convert GeoDataFrame to GeoPackage (requires pycrs module)\n", - "%pip install -U -q pycrs\n", - "gdf.to_file(\"pitta_nympha_data.gpkg\", driver=\"GPKG\")\n", - "gdf = gpd.read_file(\"pitta_nympha_data.gpkg\")\n", - "\"\"\"\n", - "gdf.head(1) # Display the first row of the GeoDataFrame" + "# Visualization of geographic sampling bias before (blue) and after (red) preprocessing\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "\n", + "# Add the random raster layer\n", + "random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", + "Map.addLayer(random_raster, {'min': 0, 'max': 1, 'palette': ['black', 'white'], 'opacity': 0.5}, 'Random Raster')\n", + "\n", + "# Add the original data layer in blue\n", + "Map.addLayer(data_raw, {'color': 'blue'}, 'Original data')\n", + "\n", + "# Add the final data layer in red\n", + "Map.addLayer(Data, {'color': 'red'}, 'Final data')\n", + "\n", + "# Set the center of the map to the coordinates\n", + "Map.setCenter(126.712, 33.516, 15)\n", + "Map" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 81 + "height": 421, + "referenced_widgets": [ + "efab02307cff46e3ad2b1cf108b25aa7", + "5fdf7951a18a4dad874e612b06afd99f", + "f81b89aa37fc4957afcaa7c9516a89f2", + "c9dd146878df458c9a2d5e2e2896ffe5", + "e8454329e19546c491b79019dc9028a4", + "84e3257c97214d6e96d6a06ff71f232e", + "16dda2a123a348da98ef01457d510c03", + "d838762362394200a14e6c4b11a99038", + "57304b09f95a4568b3e2ef27cd57b889", + "377111f79365487b80b229301d7d2cac", + "b2f31b6c8b1a453d9aa6a54b6fdd41b5", + "3f35fcb1b3344b898d5d2abf6c01ac70", + "7974287029b74bab93714f247bfe610a", + "357da287cd30446cbdde93021cfed110", + "cc24bb56a5ac4d039f40bbfdd2c6b1ae", + "3477ccc2ddd44453910981689fe08dbf", + "f18475bb22ac4d358708033c04225e1a", + "7c182f4aedcf43e5b81f1e29b16431a1", + "fa4824b4fe794ec39fb8d6943df059df", + "efd9c41cd1c14ec794795ac01b3650b9", + "c854c5f0c07543caa9a38e99dc86e400", + "478a0bd3dd9e4de69aa9a762a66b6f37", + "22b0b5eb8a0744e5850187ed868e6126", + "d83d9508820043e8b5cdbebc6cfbf72b", + "40dde1d58af5411bbd31e7608956c137", + "b432796c003a42b39d7903ce203bf0d7", + "4d15c7b8e49841f5abda675f0c436a2a", + "79cbf087f6c04b9cb8a660c732ffe162" + ] }, - "id": "qjt0jgJCpALg", - "outputId": "f8850f22-66b4-4cd9-a082-71b919df2ee0" + "id": "d9pFgpUztsB-", + "outputId": "69cb5466-17a0-4dae-fda5-f6e823aaaf47" }, - "execution_count": 9, + "execution_count": 24, "outputs": [ { "output_type": "display_data", @@ -7465,197 +12091,94 @@ "metadata": {} }, { - "output_type": "execute_result", + "output_type": "display_data", "data": { "text/plain": [ - " species year month geometry\n", - "0 Pitta nympha 2023 5 POINT (126.72514 33.20314)" + "Map(center=[33.516, 126.712], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDat…" ], - "text/html": [ - "\n", - "
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speciesyearmonthgeometry
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This allows for the identification of temporal patterns and seasonal variations in species occurrence data, as well as the rapid detection of outliers or quality issues within the data." + "### Definition of the Area of Interest\n", + "\n", + "Defining the Area of Interest (AOI below) refers to the term used by researchers to denote the geographical area they want to analyze. It has a similar meaning to the term Study Area.\n", + "\n", + "In this context, we obtained the bounding box of the occurrence point layer geometry and created a 50-kilometer buffer around it (with a maximum tolerance of 1,000 meters) to define the AOI." ], "metadata": { - "id": "5Lj919AaqmUq" + "id": "3oPtTj_O6d3H" } }, { "cell_type": "code", "source": [ - "# Visualize the distribution of data by year and month\n", - "def plot_data_distribution(gdf, h_size=12):\n", - "\n", - " plt.figure(figsize=(h_size, h_size-8))\n", - "\n", - " # Yearly data distribution graph (left)\n", - " plt.subplot(1, 2, 1)\n", - " year_counts = gdf['year'].value_counts().sort_index()\n", - " plt.bar(year_counts.index, year_counts.values)\n", - " plt.xlabel('Year')\n", - " plt.ylabel('Count')\n", - " plt.title('Yearly Data Distribution')\n", - "\n", - " # Display data counts above the bars\n", - " for i, count in enumerate(year_counts.values):\n", - " plt.text(year_counts.index[i], count, str(count), ha='center', va='bottom')\n", - "\n", - " # Monthly data distribution graph (right)\n", - " plt.subplot(1, 2, 2)\n", - " month_counts = gdf['month'].value_counts().sort_index()\n", - " plt.bar(month_counts.index, month_counts.values)\n", - " plt.xlabel('Month')\n", - " plt.ylabel('Count')\n", - " plt.title('Monthly Data Distribution')\n", - "\n", - " # Display data counts above the bars\n", - " for i, count in enumerate(month_counts.values):\n", - " plt.text(month_counts.index[i], count, str(count), ha='center', va='bottom')\n", + "# Define the AOI\n", + "AOI = Data.geometry().bounds().buffer(distance=50000, maxError=1000)\n", "\n", - " # Set x-axis ticks as integers\n", - " plt.xticks(month_counts.index, map(int, month_counts.index))\n", + "# Add the AOI to the map\n", + "outline = ee.Image().byte().paint(\n", + " featureCollection=AOI, color=1, width=3)\n", "\n", - " # Output the graph\n", - " plt.tight_layout()\n", - " plt.savefig('data_distribution_plot.png')\n", - " plt.show()" + "Map.remove_layer(\"Random Raster\")\n", + "Map.addLayer(outline, {'palette': 'FF0000'}, \"AOI\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 421, + "referenced_widgets": [ + "efab02307cff46e3ad2b1cf108b25aa7", + "5fdf7951a18a4dad874e612b06afd99f", + "f81b89aa37fc4957afcaa7c9516a89f2", + "c9dd146878df458c9a2d5e2e2896ffe5", + "e8454329e19546c491b79019dc9028a4", + "84e3257c97214d6e96d6a06ff71f232e", + "16dda2a123a348da98ef01457d510c03", + "d838762362394200a14e6c4b11a99038", + "57304b09f95a4568b3e2ef27cd57b889", + "377111f79365487b80b229301d7d2cac", + "b2f31b6c8b1a453d9aa6a54b6fdd41b5", + "3f35fcb1b3344b898d5d2abf6c01ac70", + "357da287cd30446cbdde93021cfed110", + "cc24bb56a5ac4d039f40bbfdd2c6b1ae", + "79cbf087f6c04b9cb8a660c732ffe162", + "3477ccc2ddd44453910981689fe08dbf", + "7c182f4aedcf43e5b81f1e29b16431a1", + "fa4824b4fe794ec39fb8d6943df059df", + "efd9c41cd1c14ec794795ac01b3650b9", + "c854c5f0c07543caa9a38e99dc86e400", + "478a0bd3dd9e4de69aa9a762a66b6f37", + "22b0b5eb8a0744e5850187ed868e6126", + "d83d9508820043e8b5cdbebc6cfbf72b", + "40dde1d58af5411bbd31e7608956c137", + "b432796c003a42b39d7903ce203bf0d7", + "4d15c7b8e49841f5abda675f0c436a2a" + ] }, - "id": "N1fS3YuOqOQQ", - "outputId": "9764ac5e-06f6-4547-ccce-cd4e0f6c8c21" + "id": "XIyhhdzUvyZw", + "outputId": "044a343b-1c78-4792-dada-f3bf0948b71a" }, - "execution_count": 10, + "execution_count": 25, "outputs": [ { "output_type": "display_data", @@ -7690,23 +12213,61 @@ ] }, "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(bottom=3364750.0, center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['positi…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "efab02307cff46e3ad2b1cf108b25aa7" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, + { + "cell_type": "markdown", + "source": [ + "### Addition of GEE environmental variables\n", + "\n", + "Now, let's add environmental variables to the analysis. GEE provides a wide range of datasets for environmental variables such as temperature, precipitation, elevation, land cover, and terrain. These datasets enable us to comprehensively analyze various factors that may influence the habitat preferences of the Fairy pitta.\n", + "\n", + "The selection of GEE environmental variables in SDM should reflect the habitat preference characteristics of the species. To do this, prior research and literature review on the Fairy pitta's habitat preferences should be conducted. This tutorial primarily focuses on the workflow of SDM using GEE, so some in-depth details are omitted.\n", + "\n", + "[**WorldClim V1 Bioclim**](https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO): This dataset provides 19 bioclimatic variables derived from monthly temperature and precipitation data. It covers the period from 1960 to 1991 and has a resolution of 927.67 meters." + ], + "metadata": { + "id": "fWsplwHD8ZFd" + } + }, { "cell_type": "code", "source": [ - "plot_data_distribution(gdf)" + "# WorldClim V1 Bioclim\n", + "BIO = ee.Image(\"WORLDCLIM/V1/BIO\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 407 + "height": 17 }, - "id": "SWirf5m2q9Mx", - "outputId": "6092a17c-eb71-4710-a967-9412ca8e85ee" + "id": "TNW23qpX7shy", + "outputId": "d886b9b3-cb4e-4925-c569-567260043609" }, - "execution_count": 11, + "execution_count": 26, "outputs": [ { "output_type": "display_data", @@ -7741,51 +12302,33 @@ ] }, "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": {} } ] }, { "cell_type": "markdown", "source": [ - "The data from 1995 is very sparse, with significant gaps compared to other years, and the months of August and September also have limited samples and exhibit different seasonal characteristics compared to other periods. Excluding this data could contribute to improving the stability and predictive power of the model.\n", - "\n", - "However, it's important to note that excluding data may enhance the model's generalization ability, but it could also lead to the loss of valuable information relevant to the research objectives. Therefore, such decisions should be made with careful consideration." + "[**NASA SRTM Digital Elevation 30m**](https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003): This dataset contains digital elevation data from the Shuttle Radar Topography Mission (SRTM). The data was primarily collected around the year 2000 and is provided at a resolution of approximately 30 meters (1 arc-second). The following code calculates elevation, slope, aspect, and hillshade layers from the SRTM data." ], "metadata": { - "id": "vIj9CO1RrCSs" + "id": "AzrCwWu39iLw" } }, { "cell_type": "code", "source": [ - "# Filtering data by year and month\n", - "filtered_gdf = gdf[\n", - " (~gdf['year'].eq(1995)) &\n", - " (~gdf['month'].between(8, 9))\n", - "]\n", - "\n", - "# Visualize the filtered data distribution\n", - "plot_data_distribution(filtered_gdf)" + "# NASA SRTM Digital Elevation 30m\n", + "Terrain = ee.Algorithms.Terrain(ee.Image(\"USGS/SRTMGL1_003\"))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 407 + "height": 17 }, - "id": "k1PpbNsGq9qh", - "outputId": "1c90e189-5c7d-4a60-cef6-6096603f7e97" + "id": "O8lPyhWv9hHn", + "outputId": "5c72fdbc-a3d1-4315-93f9-d8afedffb25a" }, - "execution_count": 12, + "execution_count": 27, "outputs": [ { "output_type": "display_data", @@ -7820,14 +12363,67 @@ ] }, "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "[**Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m**](https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3): The Vegetation Continuous Fields (VCF) dataset from Landsat estimates the proportion of vertically projected vegetation cover when the vegetation height is greater than 5 meters. This dataset is provided for four time periods centered around the years 2000, 2005, 2010, and 2015, with a resolution of 30 meters. Here, the median values from these four time periods are used." + ], + "metadata": { + "id": "H-kFHdWP9_3N" + } + }, + { + "cell_type": "code", + "source": [ + "# Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m\n", + "TCC = ee.ImageCollection(\"NASA/MEASURES/GFCC/TC/v3\")\n", + "MedianTCC = TCC.filterDate('2000-01-01', '2015-12-31')\n", + "MedianTCC = MedianTCC.select(['tree_canopy_cover'], ['TCC']).median()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 }, + "id": "-ymsifA098H6", + "outputId": "3c32f234-0cbb-48db-e31b-d974054415ea" + }, + "execution_count": 28, + "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ - "
" + "" ], - "image/png": 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+ "text/html": [ + "\n", + " \n", + " " + ] }, "metadata": {} } @@ -7836,49 +12432,33 @@ { "cell_type": "markdown", "source": [ - "Additionally, the use of a heatmap allows us to quickly grasp the frequency of species occurrence by year and month, providing an intuitive visualization of the temporal changes and patterns within the data." + "`BIO` (Bioclimatic variables), `Terrain` (topography), and `MedianTCC` (tree canopy cover) are combined into a single multiband image. The `elevation` band is selected from `Terrain`, and a `watermask` is created for locations where `elevation` is greater than `0`. This masks regions below sea level (e.g. the ocean) and prepares the researcher to analyze various environmental factors for the AOI comprehensively." ], "metadata": { - "id": "-r2fND5nr0dU" + "id": "CMrmqQ5X-uOB" } }, { "cell_type": "code", "source": [ - "# Yearly and monthly data distribution heatmap\n", - "def plot_heatmap(gdf, h_size=8):\n", - "\n", - " statistics = gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)\n", - "\n", - " # Heatmap\n", - " plt.figure(figsize=(h_size, h_size-6))\n", - " heatmap = plt.imshow(statistics.values, cmap=\"YlOrBr\", origin=\"upper\", aspect=\"auto\")\n", - "\n", - " # Display values above each pixel\n", - " for i in range(len(statistics.index)):\n", - " for j in range(len(statistics.columns)):\n", - " plt.text(j, i, statistics.values[i, j], ha=\"center\", va=\"center\", color=\"black\")\n", + "# Combine bands into a multi-band image\n", + "predictors = BIO.addBands(Terrain).addBands(MedianTCC)\n", "\n", - " plt.colorbar(heatmap, label=\"Count\")\n", - " plt.title(\"Monthly Species Count by Year\")\n", - " plt.xlabel(\"Year\")\n", - " plt.ylabel(\"Month\")\n", - " plt.xticks(range(len(statistics.columns)), statistics.columns)\n", - " plt.yticks(range(len(statistics.index)), statistics.index)\n", - " plt.tight_layout()\n", - " plt.savefig('heatmap_plot.png')\n", - " plt.show()\n", - " print(gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)) # Display the data statistics" + "# Create a water mask\n", + "watermask = Terrain.select('elevation').gt(0)\n", + "\n", + "# Mask out ocean pixels and clip to the area of interest\n", + "predictors = predictors.updateMask(watermask).clip(AOI)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, - "id": "Iju-dNZErzkJ", - "outputId": "2d39d407-8d38-4e88-87f2-10124086e104" + "id": "gSbuqe6k-k6B", + "outputId": "43e1a04f-cd1c-45f0-a30b-b564a9b21dab" }, - "execution_count": 13, + "execution_count": 29, "outputs": [ { "output_type": "display_data", @@ -7916,20 +12496,35 @@ } ] }, + { + "cell_type": "markdown", + "source": [ + "When highly correlated predictor variables are included together in a model, multicollinearity issues can arise. Multicollinearity is a phenomenon that occurs when there are strong linear relationships among independent variables in a model, leading to instability in the estimation of the model's coefficients (weights). This instability can reduce the model's reliability and make predictions or interpretations for new data challenging. Therefore, we will consider multicollinearity and proceed with the process of selecting predictor variables.\n", + "\n", + "First, we will generate 5,000 random points and then extract the predictor variable values of the single multiband image at those points." + ], + "metadata": { + "id": "WmuXiScSAlxX" + } + }, { "cell_type": "code", "source": [ - "plot_heatmap(filtered_gdf)" + "# Generate 5,000 random points\n", + "DataCor = predictors.sample(scale=GrainSize, numPixels=5000, geometries=True)\n", + "\n", + "# Extract predictor variable values\n", + "PixelVals = predictors.sampleRegions(collection=DataCor, scale=GrainSize, tileScale=16)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 298 + "height": 17 }, - "id": "dlW1nIQZrjx3", - "outputId": "e1b92df6-edab-4d3b-d5d6-d7eccc985aaf" + "id": "vYLvzPDwAeyk", + "outputId": "b833588d-d4db-438c-fd7b-377bf0d9a4fb" }, - "execution_count": 14, + "execution_count": 31, "outputs": [ { "output_type": "display_data", @@ -7964,54 +12559,34 @@ ] }, "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "year 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023\n", - "month \n", - "5 0 0 5 0 2 1 5 4 5 22 8 1\n", - "6 0 2 6 1 1 6 0 6 13 35 20 3\n", - "7 1 0 0 1 0 1 1 7 1 10 8 0\n" - ] } ] }, { "cell_type": "markdown", "source": [ - "Now, the filtered GeoDataFrame is converted into a Google Earth Engine object." + "We will convert the extracted predictor values for each point into a DataFrame and then check the first row." ], "metadata": { - "id": "jy0BBd-6sdLz" + "id": "oHWmXtTxCmfV" } }, { "cell_type": "code", "source": [ - "# Convert GeoDataFrame to Earth Engine object\n", - "data_raw = geemap.geopandas_to_ee(filtered_gdf)" + "# Converting predictor values from Earth Engine to a DataFrame\n", + "PixelVals_df = geemap.ee_to_df(PixelVals)\n", + "PixelVals_df.head(1)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 110 }, - "id": "Fxu0OsBksMKM", - "outputId": "5e7004a8-e0ee-4b27-93ec-3a7a359b0a5d" + "id": "x1Bv0knrBbWn", + "outputId": "ceb8b28f-6570-4d5f-dc12-5f704251fea1" }, - "execution_count": 15, + "execution_count": 32, "outputs": [ { "output_type": "display_data", @@ -8046,105 +12621,200 @@ ] }, "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Next, we will define the raster pixel size of the SDM results as 1km resolution." - ], - "metadata": { - "id": "55p_GfB6sv3U" - } - }, - { - "cell_type": "code", - "source": [ - "# Spatial resolution setting (meters)\n", - "GrainSize = 1000" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 }, - "id": "FsTbNQ17s1l-", - "outputId": "b18c9435-8539-4ff0-8b50-fa8201baf69d" - }, - "execution_count": 16, - "outputs": [ { - "output_type": "display_data", + "output_type": "execute_result", "data": { "text/plain": [ - "" + " TCC aspect bio01 bio02 bio03 bio04 bio05 bio06 bio07 bio08 ... \\\n", + "0 9.0 288 140 89 26 8572 304 -26 330 246 ... \n", + "\n", + " bio13 bio14 bio15 bio16 bio17 bio18 bio19 elevation hillshade \\\n", + "0 215 32 63 561 111 539 111 28 187 \n", + "\n", + " slope \n", + "0 2 \n", + "\n", + "[1 rows x 24 columns]" ], "text/html": [ "\n", - " \n", - " " + "
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\n" ] }, - "metadata": {} + "metadata": {}, + "execution_count": 32 } ] }, - { - "cell_type": "markdown", - "source": [ - "When multiple occurrence points are present within the same 1km resolution raster pixel, there is a high likelihood that they share the same environmental conditions at the same geographic location. Using such data directly in the analysis can introduce bias into the results.\n", - "\n", - "In other words, we need to limit the potential impact of geographic sampling bias. To achieve this, we will retain only one location within each 1km pixel and remove all others, allowing the model to more objectively reflect the environmental conditions." - ], - "metadata": { - "id": "A20gAGUZtN6S" - } - }, { "cell_type": "code", "source": [ - "def remove_duplicates(data, GrainSize):\n", - " # Select one occurrence record per pixel at the chosen spatial resolution (1km)\n", - " random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", - " rand_point_vals = random_raster.sampleRegions(collection=ee.FeatureCollection(data), scale=10, geometries=True)\n", - " return rand_point_vals.distinct('random')\n", - "\n", - "Data = remove_duplicates(data_raw, GrainSize)\n", - "\n", - "# Before selection and after selection\n", - "print('Original data size:', data_raw.size().getInfo())\n", - "print('Final data size:', Data.size().getInfo())" + "# Displaying the columns\n", + "columns = PixelVals_df.columns\n", + "print(columns)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 53 + "height": 108 }, - "id": "dHtyQyMQs82v", - "outputId": "a3cb25be-98e7-4c7d-9515-2c96226f1cf9" + "id": "H3K-knJnC65R", + "outputId": "792b0bb6-d26c-40dd-98fa-bfbfd4d9edb9" }, - "execution_count": 17, + "execution_count": 33, "outputs": [ { "output_type": "display_data", @@ -8184,8 +12854,11 @@ "output_type": "stream", "name": "stdout", "text": [ - "Original data size: 176\n", - "Final data size: 111\n" + "Index(['TCC', 'aspect', 'bio01', 'bio02', 'bio03', 'bio04', 'bio05', 'bio06',\n", + " 'bio07', 'bio08', 'bio09', 'bio10', 'bio11', 'bio12', 'bio13', 'bio14',\n", + " 'bio15', 'bio16', 'bio17', 'bio18', 'bio19', 'elevation', 'hillshade',\n", + " 'slope'],\n", + " dtype='object')\n" ] } ] @@ -8193,71 +12866,46 @@ { "cell_type": "markdown", "source": [ - "The visualization comparing geographic sampling bias before preprocessing (in blue) and after preprocessing (in red) is shown below. To facilitate comparison, the map has been centered on the area with a high concentration of Fairy pitta occurrence coordinates in Hallasan National Park." + "Calculating Spearman correlation coefficients between the given predictor variables and visualizing them in a heatmap." ], "metadata": { - "id": "Rhu4b4BxuMHE" + "id": "f3YiwTb8DOzb" } }, { "cell_type": "code", "source": [ - "# Visualization of geographic sampling bias before (blue) and after (red) preprocessing\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "\n", - "# Add the random raster layer\n", - "random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", - "Map.addLayer(random_raster, {'min': 0, 'max': 1, 'palette': ['black', 'white'], 'opacity': 0.5}, 'Random Raster')\n", + "def plot_correlation_heatmap(dataframe, h_size=10):\n", + " # Calculate Spearman correlation coefficients\n", + " correlation_matrix = dataframe.corr(method=\"spearman\")\n", "\n", - "# Add the original data layer in blue\n", - "Map.addLayer(data_raw, {'color': 'blue'}, 'Original data')\n", + " # Create a heatmap\n", + " plt.figure(figsize=(h_size, h_size-2))\n", + " plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='nearest')\n", "\n", - "# Add the final data layer in red\n", - "Map.addLayer(Data, {'color': 'red'}, 'Final data')\n", + " # Display values on the heatmap\n", + " for i in range(correlation_matrix.shape[0]):\n", + " for j in range(correlation_matrix.shape[1]):\n", + " plt.text(j, i, f\"{correlation_matrix.iloc[i, j]:.2f}\",\n", + " ha='center', va='center', color='white', fontsize=8)\n", "\n", - "# Set the center of the map to the coordinates\n", - "Map.setCenter(126.712, 33.516, 15)\n", - "Map" + " columns = dataframe.columns.tolist()\n", + " plt.xticks(range(len(columns)), columns, rotation=90)\n", + " plt.yticks(range(len(columns)), columns)\n", + " plt.title(\"Variables correlation matrix\")\n", + " plt.colorbar(label=\"Spearman Correlation\")\n", + " plt.savefig('correlation_heatmap_plot.png')\n", + " plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "efab02307cff46e3ad2b1cf108b25aa7", - "5fdf7951a18a4dad874e612b06afd99f", - "f81b89aa37fc4957afcaa7c9516a89f2", - "c9dd146878df458c9a2d5e2e2896ffe5", - "e8454329e19546c491b79019dc9028a4", - "84e3257c97214d6e96d6a06ff71f232e", - "16dda2a123a348da98ef01457d510c03", - "d838762362394200a14e6c4b11a99038", - "57304b09f95a4568b3e2ef27cd57b889", - "377111f79365487b80b229301d7d2cac", - "b2f31b6c8b1a453d9aa6a54b6fdd41b5", - "3f35fcb1b3344b898d5d2abf6c01ac70", - "7974287029b74bab93714f247bfe610a", - "357da287cd30446cbdde93021cfed110", - "cc24bb56a5ac4d039f40bbfdd2c6b1ae", - "3477ccc2ddd44453910981689fe08dbf", - "f18475bb22ac4d358708033c04225e1a", - "7c182f4aedcf43e5b81f1e29b16431a1", - "fa4824b4fe794ec39fb8d6943df059df", - "efd9c41cd1c14ec794795ac01b3650b9", - "c854c5f0c07543caa9a38e99dc86e400", - "478a0bd3dd9e4de69aa9a762a66b6f37", - "22b0b5eb8a0744e5850187ed868e6126", - "d83d9508820043e8b5cdbebc6cfbf72b", - "40dde1d58af5411bbd31e7608956c137", - "b432796c003a42b39d7903ce203bf0d7", - "4d15c7b8e49841f5abda675f0c436a2a", - "79cbf087f6c04b9cb8a660c732ffe162" - ] + "height": 17 }, - "id": "d9pFgpUztsB-", - "outputId": "69cb5466-17a0-4dae-fda5-f6e823aaaf47" + "id": "i9YelZ5qDG_t", + "outputId": "531e7f2b-c554-4e5b-dc78-43af1810a2c9" }, - "execution_count": 24, + "execution_count": 34, "outputs": [ { "output_type": "display_data", @@ -8292,96 +12940,24 @@ ] }, "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[33.516, 126.712], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDat…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "efab02307cff46e3ad2b1cf108b25aa7" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } } ] }, - { - "cell_type": "markdown", - "source": [ - "### Definition of the Area of Interest\n", - "\n", - "Defining the Area of Interest (AOI below) refers to the term used by researchers to denote the geographical area they want to analyze. It has a similar meaning to the term Study Area.\n", - "\n", - "In this context, we obtained the bounding box of the occurrence point layer geometry and created a 50-kilometer buffer around it (with a maximum tolerance of 1,000 meters) to define the AOI." - ], - "metadata": { - "id": "3oPtTj_O6d3H" - } - }, { "cell_type": "code", "source": [ - "# Define the AOI\n", - "AOI = Data.geometry().bounds().buffer(distance=50000, maxError=1000)\n", - "\n", - "# Add the AOI to the map\n", - "outline = ee.Image().byte().paint(\n", - " featureCollection=AOI, color=1, width=3)\n", - "\n", - "Map.remove_layer(\"Random Raster\")\n", - "Map.addLayer(outline, {'palette': 'FF0000'}, \"AOI\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" + "# Plot the correlation heatmap of variables\n", + "plot_correlation_heatmap(PixelVals_df)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "efab02307cff46e3ad2b1cf108b25aa7", - "5fdf7951a18a4dad874e612b06afd99f", - "f81b89aa37fc4957afcaa7c9516a89f2", - "c9dd146878df458c9a2d5e2e2896ffe5", - "e8454329e19546c491b79019dc9028a4", - "84e3257c97214d6e96d6a06ff71f232e", - "16dda2a123a348da98ef01457d510c03", - "d838762362394200a14e6c4b11a99038", - "57304b09f95a4568b3e2ef27cd57b889", - "377111f79365487b80b229301d7d2cac", - "b2f31b6c8b1a453d9aa6a54b6fdd41b5", - "3f35fcb1b3344b898d5d2abf6c01ac70", - "357da287cd30446cbdde93021cfed110", - "cc24bb56a5ac4d039f40bbfdd2c6b1ae", - "79cbf087f6c04b9cb8a660c732ffe162", - "3477ccc2ddd44453910981689fe08dbf", - "7c182f4aedcf43e5b81f1e29b16431a1", - "fa4824b4fe794ec39fb8d6943df059df", - "efd9c41cd1c14ec794795ac01b3650b9", - "c854c5f0c07543caa9a38e99dc86e400", - "478a0bd3dd9e4de69aa9a762a66b6f37", - "22b0b5eb8a0744e5850187ed868e6126", - "d83d9508820043e8b5cdbebc6cfbf72b", - "40dde1d58af5411bbd31e7608956c137", - "b432796c003a42b39d7903ce203bf0d7", - "4d15c7b8e49841f5abda675f0c436a2a" - ] + "height": 749 }, - "id": "XIyhhdzUvyZw", - "outputId": "044a343b-1c78-4792-dada-f3bf0948b71a" + "id": "DP5ect8cDb7C", + "outputId": "176118ff-4374-4fb1-c154-bcab119d89ca" }, - "execution_count": 25, + "execution_count": 35, "outputs": [ { "output_type": "display_data", @@ -8421,56 +12997,65 @@ "output_type": "display_data", "data": { "text/plain": [ - "Map(bottom=3364750.0, center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['positi…" + "
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\n" }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } + "metadata": {} } ] }, { "cell_type": "markdown", "source": [ - "### Addition of GEE environmental variables\n", - "\n", - "Now, let's add environmental variables to the analysis. GEE provides a wide range of datasets for environmental variables such as temperature, precipitation, elevation, land cover, and terrain. These datasets enable us to comprehensively analyze various factors that may influence the habitat preferences of the Fairy pitta.\n", + "Spearman correlation coefficient is useful for understanding the general associations among predictor variables but does not directly assess how multiple variables interact, specifically detecting multicollinearity.\n", "\n", - "The selection of GEE environmental variables in SDM should reflect the habitat preference characteristics of the species. To do this, prior research and literature review on the Fairy pitta's habitat preferences should be conducted. This tutorial primarily focuses on the workflow of SDM using GEE, so some in-depth details are omitted.\n", + "The **Variance Inflation Factor (VIF below)** is a statistical metric used to evaluate multicollinearity and guide variable selection. It indicates the degree of linear relationship of each independent variable with the other independent variables, and high VIF values can be evidence of multicollinearity.\n", "\n", - "[**WorldClim V1 Bioclim**](https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO): This dataset provides 19 bioclimatic variables derived from monthly temperature and precipitation data. It covers the period from 1960 to 1991 and has a resolution of 927.67 meters." + "Typically, when VIF values exceed 5 or 10, it suggests that the variable has a strong correlation with other variables, potentially compromising the stability and interpretability of the model. In this tutorial, a criterion of VIF values less than 10 was used for variable selection. The following 6 variables were selected based on VIF." ], "metadata": { - "id": "fWsplwHD8ZFd" + "id": "07B62CNfDyGz" } }, { "cell_type": "code", "source": [ - "# WorldClim V1 Bioclim\n", - "BIO = ee.Image(\"WORLDCLIM/V1/BIO\")" + "# Filter variables based on Variance Inflation Factor (VIF)\n", + "def filter_variables_by_vif(dataframe, threshold=10):\n", + "\n", + " original_columns = dataframe.columns.tolist()\n", + " remaining_columns = original_columns[:]\n", + "\n", + " while True:\n", + " vif_data = dataframe[remaining_columns]\n", + " vif_values = [variance_inflation_factor(vif_data.values, i) for i in range(vif_data.shape[1])]\n", + "\n", + " max_vif_index = vif_values.index(max(vif_values))\n", + " max_vif = max(vif_values)\n", + "\n", + " if max_vif < threshold:\n", + " break\n", + "\n", + " print(f\"Removing '{remaining_columns[max_vif_index]}' with VIF {max_vif:.2f}\")\n", + "\n", + " del remaining_columns[max_vif_index]\n", + "\n", + " filtered_data = dataframe[remaining_columns]\n", + " bands = filtered_data.columns.tolist()\n", + " print('Bands:', bands)\n", + "\n", + " return filtered_data, bands" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, - "id": "TNW23qpX7shy", - "outputId": "d886b9b3-cb4e-4925-c569-567260043609" + "id": "TJdGzd3SDisO", + "outputId": "09a82a63-62ba-4488-c9bd-6eaaa73d2ca8" }, - "execution_count": 26, + "execution_count": 36, "outputs": [ { "output_type": "display_data", @@ -8508,30 +13093,20 @@ } ] }, - { - "cell_type": "markdown", - "source": [ - "[**NASA SRTM Digital Elevation 30m**](https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003): This dataset contains digital elevation data from the Shuttle Radar Topography Mission (SRTM). The data was primarily collected around the year 2000 and is provided at a resolution of approximately 30 meters (1 arc-second). The following code calculates elevation, slope, aspect, and hillshade layers from the SRTM data." - ], - "metadata": { - "id": "AzrCwWu39iLw" - } - }, { "cell_type": "code", "source": [ - "# NASA SRTM Digital Elevation 30m\n", - "Terrain = ee.Algorithms.Terrain(ee.Image(\"USGS/SRTMGL1_003\"))" + "filtered_PixelVals_df, bands = filter_variables_by_vif(PixelVals_df)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 362 }, - "id": "O8lPyhWv9hHn", - "outputId": "5c72fdbc-a3d1-4315-93f9-d8afedffb25a" + "id": "e3iiKaK5Eg0q", + "outputId": "bf150d7c-6a38-4a86-b70f-69259c8cf528" }, - "execution_count": 27, + "execution_count": 37, "outputs": [ { "output_type": "display_data", @@ -8566,35 +13141,52 @@ ] }, "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Removing 'bio05' with VIF inf\n", + "Removing 'bio04' with VIF 183937.54\n", + "Removing 'bio10' with VIF 64460.60\n", + "Removing 'bio07' with VIF 47244.59\n", + "Removing 'bio17' with VIF 26253.01\n", + "Removing 'bio01' with VIF 9428.16\n", + "Removing 'bio16' with VIF 3700.80\n", + "Removing 'bio03' with VIF 2468.04\n", + "Removing 'bio18' with VIF 1716.89\n", + "Removing 'bio08' with VIF 1247.20\n", + "Removing 'bio06' with VIF 959.97\n", + "Removing 'bio12' with VIF 606.08\n", + "Removing 'bio19' with VIF 400.13\n", + "Removing 'bio15' with VIF 348.96\n", + "Removing 'hillshade' with VIF 129.83\n", + "Removing 'bio13' with VIF 47.33\n", + "Removing 'bio11' with VIF 31.44\n", + "Removing 'bio02' with VIF 13.75\n", + "Bands: ['TCC', 'aspect', 'bio09', 'bio14', 'elevation', 'slope']\n" + ] } ] }, - { - "cell_type": "markdown", - "source": [ - "[**Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m**](https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3): The Vegetation Continuous Fields (VCF) dataset from Landsat estimates the proportion of vertically projected vegetation cover when the vegetation height is greater than 5 meters. This dataset is provided for four time periods centered around the years 2000, 2005, 2010, and 2015, with a resolution of 30 meters. Here, the median values from these four time periods are used." - ], - "metadata": { - "id": "H-kFHdWP9_3N" - } - }, { "cell_type": "code", "source": [ - "# Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m\n", - "TCC = ee.ImageCollection(\"NASA/MEASURES/GFCC/TC/v3\")\n", - "MedianTCC = TCC.filterDate('2000-01-01', '2015-12-31')\n", - "MedianTCC = MedianTCC.select(['tree_canopy_cover'], ['TCC']).median()" + "# Variable Selection Based on VIF\n", + "predictors = predictors.select(bands)\n", + "\n", + "# Plot the correlation heatmap of variables\n", + "plot_correlation_heatmap(filtered_PixelVals_df, h_size=6)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 441 }, - "id": "-ymsifA098H6", - "outputId": "3c32f234-0cbb-48db-e31b-d974054415ea" + "id": "TEFmil6WErkl", + "outputId": "c62cc9fd-72f1-4b5a-9793-222fe537f8fd" }, - "execution_count": 28, + "execution_count": 38, "outputs": [ { "output_type": "display_data", @@ -8629,39 +13221,48 @@ ] }, "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} } ] }, { "cell_type": "markdown", "source": [ - "`BIO` (Bioclimatic variables), `Terrain` (topography), and `MedianTCC` (tree canopy cover) are combined into a single multiband image. The `elevation` band is selected from `Terrain`, and a `watermask` is created for locations where `elevation` is greater than `0`. This masks regions below sea level (e.g. the ocean) and prepares the researcher to analyze various environmental factors for the AOI comprehensively." + "Next, let's visualize the 6 selected predictor variables on the map.\n", + "![Predictor Variables for Analysis](https://github.com/osgeokr/earthengine-community/blob/master/tutorials/species-distribution-modeling/predictor_variables.png?raw=1)\n", + "\n", + "You can explore the available palettes for map visualization using the following code. For example, the `terrain` palette looks like this.\n", + "```\n", + "cm.plot_colormaps(width=8.0, height=0.2)\n", + "```" ], "metadata": { - "id": "CMrmqQ5X-uOB" + "id": "j0dc6evyFFhw" } }, { "cell_type": "code", "source": [ - "# Combine bands into a multi-band image\n", - "predictors = BIO.addBands(Terrain).addBands(MedianTCC)\n", - "\n", - "# Create a water mask\n", - "watermask = Terrain.select('elevation').gt(0)\n", - "\n", - "# Mask out ocean pixels and clip to the area of interest\n", - "predictors = predictors.updateMask(watermask).clip(AOI)" + "cm.plot_colormap('terrain', width=8.0, height=0.2, orientation='horizontal')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 52 }, - "id": "gSbuqe6k-k6B", - "outputId": "43e1a04f-cd1c-45f0-a30b-b564a9b21dab" + "id": "H8uHX-83E8x7", + "outputId": "9a232171-e69c-4f53-e1a8-bbfb81e57e97" }, - "execution_count": 29, + "execution_count": 39, "outputs": [ { "output_type": "display_data", @@ -8696,38 +13297,70 @@ ] }, "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAAjCAYAAAAE2rtKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAABwUlEQVR4nO3dy27CMBAFUDvhk/jafioVcRclD6ibOBGPxZyzIRoPY0MkuGJDLqWUBABAGN2nDwAAwHsJgAAAwQiAAADBCIAAAMEIgAAAwQiAAADBCIAAAMEIgAAAwQiAAADBnFobz+evlFJKpe+n2tB1f2rj9bi2rLWs1eZ+Ys/ac/fu+ZTX0l+ntZQvv4/dZa71ldp4Pfb3lbVa/1otN+55dP7iOufvlFJKp5ynpfH6rvawVut7df9d7WD/5jka+qszXty/fM7R/uo53njP8u2PkIbr/IdIw1Cp3a7HtVptc8ZUG3b2/1+rnmdzxv3+x2Y0nKM6d9jX3/p+r/YPT5hx7H1ZvuZ8+0zvuvnzvrt93neL75GptvheGNfn/uWM7bl3s9b2XJm7dp7Wuc+ZsVhrmLu5Z8PcvffnGTP23uPHeS38AggAEIwACAAQjAAIABCMAAgAEIwACAAQjAAIABCMAAgAEIwACAAQjAAIABCMAAgAEIwACAAQjAAIABCMAAgAEIwACAAQTC6llE8fAgCA9/ELIABAMAIgAEAwAiAAQDACIABAMAIgAEAwAiAAQDACIABAMAIgAEAwAiAAQDA/8I8KTmYbO2UAAAAASUVORK5CYII=\n" + }, + "metadata": {} } ] }, - { - "cell_type": "markdown", - "source": [ - "When highly correlated predictor variables are included together in a model, multicollinearity issues can arise. Multicollinearity is a phenomenon that occurs when there are strong linear relationships among independent variables in a model, leading to instability in the estimation of the model's coefficients (weights). This instability can reduce the model's reliability and make predictions or interpretations for new data challenging. Therefore, we will consider multicollinearity and proceed with the process of selecting predictor variables.\n", - "\n", - "First, we will generate 5,000 random points and then extract the predictor variable values of the single multiband image at those points." - ], - "metadata": { - "id": "WmuXiScSAlxX" - } - }, { "cell_type": "code", "source": [ - "# Generate 5,000 random points\n", - "DataCor = predictors.sample(scale=GrainSize, numPixels=5000, geometries=True)\n", + "# Elevation layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", "\n", - "# Extract predictor variable values\n", - "PixelVals = predictors.sampleRegions(collection=DataCor, scale=GrainSize, tileScale=16)" + "vis_params = {'bands':['elevation'], 'min': 0, 'max': 1800, 'palette': cm.palettes.terrain}\n", + "Map.addLayer(predictors, vis_params, 'elevation')\n", + "Map.add_colorbar(vis_params, label=\"Elevation (m)\", orientation=\"vertical\", layer_name=\"elevation\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 421, + "referenced_widgets": [ + "0ac7d26aa1814d8f8543b1e51934fe6e", + "7b6fe79343ff4e0ba26e3d6e15efb025", + "bc3830f5a2fb40aa959b59e420d707f8", + "5523004c1fe240439047f5179327ac59", + "08b3b839623e48d98cabad14b768473d", + "61b6639d4538439f9221a21eea1db29e", + "41ff2a139e7a42bc8543914b96d906bb", + "c0cbbed906f344648bc4cff3adcbbf75", + "02dc65fdd68040ea908713060cbc5d1a", + "a55df16b967d47df8d47c7ffc55e462a", + "8123d2220a574ad0aa40be4afd6759aa", + "f87ea7550b9148bc9ec2cd984ffddaea", + "ec5842a999604555aa95c5ed9b82dc37", + "571ddde0c22841d68a7868ac66109281", + "055394cd168c497ab3d2fd16d40d6a5d", + "b145a01c3d5945fb8ed2472518cb1ba7", + "f0eaba4f88bc4a56aee79e689a62fdeb", + "77534dc0bec14c2888cc9217abbf8035", + "6023eac322f247d0808755ba38f8a808", + "0943fd4685a54b70b341bd000cdbc91f", + "f2315c32563e452a9632e16c6e1f8796", + "f0a072b0d2174bd88c2bf8dedea9fa4b", + "8f5295fa75d145a89f760e300aa91493", + "dc7a0a178b8d49109d8d5d4fe7615858", + "08e382e7a20b4eee90eb087d8225e7c1", + "d2e8ba57951749ab955048105ddcad11", + "ee6822f7630e4e7f8d2b41138d55bf61", + "5e67d047f0ca4265aed15af47375d653" + ] }, - "id": "vYLvzPDwAeyk", - "outputId": "b833588d-d4db-438c-fd7b-377bf0d9a4fb" + "id": "RswVTmLFFUb3", + "outputId": "c8fc2323-0b78-4960-aa67-1fc203c51186" }, - "execution_count": 31, + "execution_count": 54, "outputs": [ { "output_type": "display_data", @@ -8762,34 +13395,86 @@ ] }, "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "0ac7d26aa1814d8f8543b1e51934fe6e" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, - { - "cell_type": "markdown", - "source": [ - "We will convert the extracted predictor values for each point into a DataFrame and then check the first row." - ], - "metadata": { - "id": "oHWmXtTxCmfV" - } - }, { "cell_type": "code", "source": [ - "# Converting predictor values from Earth Engine to a DataFrame\n", - "PixelVals_df = geemap.ee_to_df(PixelVals)\n", - "PixelVals_df.head(1)" + "# Calculate the minimum and maximum values for bio09\n", + "min_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", + "max_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", + "\n", + "# bio09 (Mean temperature of driest quarter) layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "\n", + "vis_params = {'min': math.floor(min_val['bio09']), 'max': math.ceil(max_val['bio09']), 'palette': cm.palettes.hot}\n", + "Map.addLayer(predictors.select(\"bio09\").multiply(0.1), vis_params, 'bio09')\n", + "Map.add_colorbar(vis_params, label=\"Mean temperature of driest quarter (℃)\", orientation=\"vertical\", layer_name=\"bio09\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 110 + "height": 421, + "referenced_widgets": [ + "bd778ad418634776993304e38cd1ad02", + "89ba6396ab264cb1827aa7084280ab72", + "bf6555c5b2814169a35ff3b3b6bc8095", + "6a171d05b22b4dde8f13ecfde310d022", + "4eb8738933d8458c905fe9c1cb02aeec", + "33111afdc5b7461ba0b58c3b5f87d1e8", + "c61c10dbef514e878086a63de0f84b44", + "fd727297110d4fada437c5e998becbe3", + "448af660fa9c4243a5d0ea80c1adc0b2", + "2b00969d96e34bc39e1acdb7b23cc7ba", + "e6aa5ecde8be4747827bb8d42d726390", + "afd0660ef7f248b4be6d6b5fdaeb1fe1", + "3faf6fa1bdce4906a7b253f15f873577", + "414d832f4e044cc1934714a7ed8fb21b", + "2b3e3fb6d8124dd49cfe7bdefc7ba96c", + "8e9e786751bf4a1fb2bc0989e61c6be3", + "8c868f718f804df095927f5b21318bf5", + "00cacd66691647cd80d41f88208aed1c", + "20d8e4f3dd624250bb28d2da9d112fcc", + "5ad8210daf1448b88a415b7c5b43f703", + "4f733436546340baadaaf3cab7d11716", + "a8f5ca16930848c68e378969e327babb", + "4858f5dc633d411a9c4594d0a232587a", + "9af3c9a565f346a2872c8e7c1fe0a36b", + "4ab5dfa79618456f97b6ed532c9829d4", + "50b552f96b464adea9b15273a1596e91", + "33bd735ab4e54f47b79fe6804b2c409e", + "cbda0586f2a34f8ca4032b9a713318d2" + ] }, - 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"height": 108 + "height": 421, + "referenced_widgets": [ + "eeed0724c6774ca4ae71f9414d7d3d36", + "d7add71f9a694c1eab1a68d22844182f", + "a7f87d5b06044d57b272eb3df58c27ed", + "e6e6b787daa04ca68a4591406c7995f7", + "e7b4368dad5448a79254daf75160eec2", + "f50ee59d00bd4c5f82b18ce212a20609", + "117743a21cf44085a07f630c0b3b861a", + "0c63f792b3114394bd82e137f6392512", + "d2f84366501d48eca89e772ec96e0d81", + "9762a337a7bc40848087c346ba0032e0", + "d4faaee6a05e41cd91940435b39b9364", + "2f57730680724aa6800e00a81897560d", + "d0354527efe24848864f4cb06adb6195", + "553cd97ec41c47ce999e1db0a46787c5", + "669a619518424e6ba7a434d920814d8a", + "510af8d670614271a20ccb4c2868a71d", + "1c78454c17ad4a32a1737be1dc8ca79c", + "3742a1693d0a4ec38689ef4abd3b445f", + "7141995bd812493ba260e61947727b52", + "d7df4e6709ba4292bd71c0d6d4a06b33", + "c58b354684e2468dbdd7d8477d33b5a5", + "703de6e1dede42afae7090e28942afb1", + "ec512f45d5304791b2a1e05a2feed243", + "df9e63a9968a415bb6331ed06676b201", + "2747267b006447ed890a7b8d6715085a", + "04cfd6bdc7c34d6885ba9149d9f14158", + "b228661b472c4012a07a9f8cc75ff29e", + "a5189495d93f4c499c20626066f527e3" + ] }, - "id": "H3K-knJnC65R", - "outputId": "792b0bb6-d26c-40dd-98fa-bfbfd4d9edb9" + "id": "cQpG8qRzIQcw", + "outputId": "32fb0083-4d81-49fe-9ed0-34273b0383d2" }, - "execution_count": 33, + "execution_count": 59, "outputs": [ { "output_type": "display_data", @@ -9054,61 +13621,80 @@ "metadata": {} }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Index(['TCC', 'aspect', 'bio01', 'bio02', 'bio03', 'bio04', 'bio05', 'bio06',\n", - " 'bio07', 'bio08', 'bio09', 'bio10', 'bio11', 'bio12', 'bio13', 'bio14',\n", - " 'bio15', 'bio16', 'bio17', 'bio18', 'bio19', 'elevation', 'hillshade',\n", - " 'slope'],\n", - " dtype='object')\n" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "eeed0724c6774ca4ae71f9414d7d3d36" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, - { - "cell_type": "markdown", - "source": [ - "Calculating Spearman correlation coefficients between the given predictor variables and visualizing them in a heatmap." - ], - "metadata": { - "id": "f3YiwTb8DOzb" - } - }, { "cell_type": "code", "source": [ - "def plot_correlation_heatmap(dataframe, h_size=10):\n", - " # Calculate Spearman correlation coefficients\n", - " correlation_matrix = dataframe.corr(method=\"spearman\")\n", - "\n", - " # Create a heatmap\n", - " plt.figure(figsize=(h_size, h_size-2))\n", - " plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='nearest')\n", - "\n", - " # Display values on the heatmap\n", - " for i in range(correlation_matrix.shape[0]):\n", - " for j in range(correlation_matrix.shape[1]):\n", - " plt.text(j, i, f\"{correlation_matrix.iloc[i, j]:.2f}\",\n", - " ha='center', va='center', color='white', fontsize=8)\n", + "# Aspect layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", "\n", - " columns = dataframe.columns.tolist()\n", - " plt.xticks(range(len(columns)), columns, rotation=90)\n", - " plt.yticks(range(len(columns)), columns)\n", - " plt.title(\"Variables correlation matrix\")\n", - " plt.colorbar(label=\"Spearman Correlation\")\n", - " plt.savefig('correlation_heatmap_plot.png')\n", - " plt.show()" + "vis_params = {'bands':['aspect'], 'min': 0, 'max': 360, 'palette': cm.palettes.rainbow}\n", + "Map.addLayer(predictors, vis_params, 'aspect')\n", + "Map.add_colorbar(vis_params, label=\"Aspect\", orientation=\"vertical\", layer_name=\"aspect\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 421, + "referenced_widgets": [ + "ef4fd7dfae2e452c8f4190884fbccb42", + "4aa9d7e81cd44ab2b9fe5860c33057e9", + "06ce1b5cba0a4ba19b17bd566bd45f65", + "f07bebea44b64c76a56d1ab0a7d7f5e7", + "94e01dc8e4234d4791d103169905a6ae", + "65478ba4e1d349d3b9d54f056d54e6a9", + "a2570adcb9704dfe80d4aefe9a9911ab", + "f3a2ac8a04d449a1b0c57b0c573a019f", + "0b1e4d1e0d13456ba9e976251e955207", + "291da14bcdff4172a9dafcdb9015a6a8", + "812ce2194c6a45e7accd47717e3706d6", + "87a59ec985624f938acee2d5a65b337f", + "f4c1ce3e881c4b5780d9f074c2013b2c", + "be97d93287fa491ca8d6b097c9d373ba", + "f441b5da4e4f444eb1282070aac5398e", + "f3fb193232284f45becafbd6a3a24b46", + "fd9b803ae3f34cb4bda622545423554d", + "a9479b474ddc43f0b1ea377f5d72781b", + "9547a5384a064ae4b65b3e2c341ff873", + "120ecccbdce240c4bb2e6fe548fa452e", + "520892cebe1d466c8aafeca7a98f22b3", + "fbc54ef0969849aebe2cdce27cae4c64", + "62513b235c2242e1a6fd416c5d255ab8", + "8978ae99e798438ab7ec3be6c75a75b1", + "eb836cc637c44cc99499e5d5fa143c06", + "376eaa60c64c44adb7842d4457478935", + "dce21eaa742d4801a11d006d3486cc10", + "e2527a8c99034a60853950a96330d203" + ] }, - "id": "i9YelZ5qDG_t", - "outputId": "531e7f2b-c554-4e5b-dc78-43af1810a2c9" + "id": "u1hW_wAXIvvG", + "outputId": "f20aa1e4-4b8a-4b2e-96bd-4dd1debaf505" }, - "execution_count": 34, + "execution_count": 61, "outputs": [ { "output_type": "display_data", @@ -9143,24 +13729,86 @@ ] }, "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "ef4fd7dfae2e452c8f4190884fbccb42" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, { "cell_type": "code", "source": [ - "# Plot the correlation heatmap of variables\n", - "plot_correlation_heatmap(PixelVals_df)" + "# Calculate the minimum and maximum values for bio14\n", + "min_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", + "max_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", + "\n", + "# bio14 (Precipitation of driest month) layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "\n", + "vis_params = {'bands':['bio14'], 'min': math.floor(min_val['bio14']), 'max': math.ceil(max_val['bio14']), 'palette': cm.palettes.Blues}\n", + "Map.addLayer(predictors, vis_params, 'bio14')\n", + "Map.add_colorbar(vis_params, label=\"Precipitation of driest month (mm)\", orientation=\"vertical\", layer_name=\"bio14\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 749 + "height": 421, + "referenced_widgets": [ + "82ebad0576144aaaa0773dd83736b54b", + "852876ffcabd4b5b8e4d3b8efb8329b8", + "8cd9ebc2b0b241a79bd79590ef875026", + "ad9990fa49cc49d3a0055d7832f47e5b", + "be76f04c454c404abf577ab9f46c0ad6", + "4a6f43cf340849bbaa86c3842f7a43a2", + "cf03dbe7b9c247d9a6abe1d2a1888038", + "cd436ad9c9824776b07815c16954081f", + "f875b1c5bd824eb58e8c284245a73c2c", + "b207ea8fb81b4232b68a544c6831f3ba", + "7f7e0df3325e484ab3e57d1f967bb895", + "0f20ef8a9ef04072a8f7d3872d0308c9", + "cc1159482781459995a853ebcd38d2ce", + "29eb066536f4418c8697ac915846e675", + "09f31594c1b3489ab41574a269c5cc06", + "db28c0e617e14d40869d2ee580f22880", + "c7bc94df64504017938f5d2e51555b45", + "f3cb106fec554cb48e630d347e6fc2f4", + "122d7ea1db1243f4b829b9df49aa86e7", + "da4e2a7f1bd14d398ffee2388fbaf5ef", + "e2adb45640f2478fa7f1fc93ea1c061f", + "9dc5d86b463a476b86d330edb7f3793f", + "de6e23ac962243ff881a2c312d672438", + "14e0c6c57f7c4d3e8f72e274e20ae4fa", + "4ec85e571fad4e3ba322e7abca055262", + "dada2c4572a14dd2838915ffc3baadc5", + "d8a97ae03b724e15a6adbef9a1dc9989", + "6e1a18cd4e834875aad9ab7212b5150d" + ] }, - "id": "DP5ect8cDb7C", - "outputId": "176118ff-4374-4fb1-c154-bcab119d89ca" + "id": "C49-BnnxJGUD", + "outputId": "905bcf30-c827-4987-f4ac-0396893e250b" }, - "execution_count": 35, + "execution_count": 68, "outputs": [ { "output_type": "display_data", @@ -9200,65 +13848,77 @@ "output_type": "display_data", "data": { "text/plain": [ - "
" + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" ], - "image/png": 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\n" + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "82ebad0576144aaaa0773dd83736b54b" + } }, - "metadata": {} + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, - { - "cell_type": "markdown", - "source": [ - "Spearman correlation coefficient is useful for understanding the general associations among predictor variables but does not directly assess how multiple variables interact, specifically detecting multicollinearity.\n", - "\n", - "The **Variance Inflation Factor (VIF below)** is a statistical metric used to evaluate multicollinearity and guide variable selection. It indicates the degree of linear relationship of each independent variable with the other independent variables, and high VIF values can be evidence of multicollinearity.\n", - "\n", - "Typically, when VIF values exceed 5 or 10, it suggests that the variable has a strong correlation with other variables, potentially compromising the stability and interpretability of the model. In this tutorial, a criterion of VIF values less than 10 was used for variable selection. The following 6 variables were selected based on VIF." - ], - "metadata": { - "id": "07B62CNfDyGz" - } - }, { "cell_type": "code", "source": [ - "# Filter variables based on Variance Inflation Factor (VIF)\n", - "def filter_variables_by_vif(dataframe, threshold=10):\n", - "\n", - " original_columns = dataframe.columns.tolist()\n", - " remaining_columns = original_columns[:]\n", - "\n", - " while True:\n", - " vif_data = dataframe[remaining_columns]\n", - " vif_values = [variance_inflation_factor(vif_data.values, i) for i in range(vif_data.shape[1])]\n", - "\n", - " max_vif_index = vif_values.index(max(vif_values))\n", - " max_vif = max(vif_values)\n", - "\n", - " if max_vif < threshold:\n", - " break\n", - "\n", - " print(f\"Removing '{remaining_columns[max_vif_index]}' with VIF {max_vif:.2f}\")\n", - "\n", - " del remaining_columns[max_vif_index]\n", - "\n", - " filtered_data = dataframe[remaining_columns]\n", - " bands = filtered_data.columns.tolist()\n", - " print('Bands:', bands)\n", + "# TCC (Tree Canopy Cover) layer\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", "\n", - " return filtered_data, bands" + "vis_params = {'bands':['TCC'], 'min': 0, 'max': 100, 'palette': ['ffffff', 'afce56', '5f9c00', '0e6a00', '003800']}\n", + "Map.addLayer(predictors, vis_params, 'TCC')\n", + "Map.add_colorbar(vis_params, label=\"Tree Canopy Cover (%)\", orientation=\"vertical\", layer_name=\"TCC\")\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 17 + "height": 421, + "referenced_widgets": [ + "6bf79345b0bc455284a79101c04bfe6c", + "a9778b13aefc4fffbba2416b920619b2", + "9ac7f474f466419383918545e30d84a6", + "2218fda1b3fc464c8b3770f05da5d563", + "1409d3b265bf4cd5a9af029d15f44044", + "d07cec1d500c4bf2b1ecb18568e2ff78", + "5cdb634f5d494fd1b447d22c04019742", + "8c3b03d535b1442c90a12993279b1352", + "da75b5ad3fd74726ad5038f2f192f23a", + "131e70c768004603a5207b118bbf3719", + "f899333c85e4435db472ddaa1d963f7e", + "844e37f9de8040f5ab30a791b58b418d", + "82d9b5d4364e4ce9879e22b9052205d5", + "e45ef406bd9d494a87607d105a261aba", + "da98d8045da44afd94730bf81a214584", + "b5270d8c6e8c4d31a42d7301f73e3e4f", + "402adee293e64a098ec269db39fdbf25", + "cd1c6151a73844a5a8f31b5b66905d1a", + "17fd729332064fb8b5d6858d3bb203aa", + "0e41e337484e470083d54c219c6680eb", + "7c304115b3034e2bac307e9587aa6ac4", + "5b23dd0eace74c37b121bc25da52ad2b", + "1f877208eb8540d189b0ba8e39622c7c", + "099ed50edf6f4efba91d1b29a5c5afd2", + "e70da2c319f3407f85177e06e7c7b06d", + "d685862ffafe4598951487c94eacf8fb", + "2b44cc0739a64bd7aef172fe53ed2850", + "e22f39836af04531b9215fb431728700" + ] }, - "id": "TJdGzd3SDisO", - "outputId": "09a82a63-62ba-4488-c9bd-6eaaa73d2ca8" + "id": "zKVnKhLYJicg", + "outputId": "df08a7a1-9fdc-4def-c778-40dd2c0c3e56" }, - "execution_count": 36, + "execution_count": 70, "outputs": [ { "output_type": "display_data", @@ -9293,23 +13953,97 @@ ] }, "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "6bf79345b0bc455284a79101c04bfe6c" + } + }, + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, + { + "cell_type": "markdown", + "source": [ + "### Generation of pseudo-absence data\n", + "\n", + "In the process of SDM, the selection of input data for a species is mainly approached using two methods:\n", + "\n", + "1. **Presence-Background Method**: This method compares the locations where a particular species has been observed (presence) with other locations where the species has not been observed (background). Here, the background data does not necessarily mean areas where the species does not exist but rather is set up to reflect the overall environmental conditions of the study area. It is used to distinguish suitable environments where the species could exist from less suitable ones.\n", + "\n", + "2. **Presence-Absence Method**: This method compares locations where the species has been observed (presence) with locations where it has definitively not been observed (absence). Here, absence data represents specific locations where the species is known not to exist. It does not reflect the overall environmental conditions of the study area but rather points to locations where the species is estimated not to exist.\n", + "\n", + "In practice, it is often difficult to collect true absence data, so pseudo-absence data generated artificially is frequently used. However, it's important to acknowledge the limitations and potential errors of this method, as artificially generated pseudo-absence points may not accurately reflect true absence areas.\n", + "\n", + "The choice between these two methods depends on data availability, research objectives, model accuracy and reliability, as well as time and resources. Here, we will use occurrence data collected from GBIF and artificially generated pseudo-absence data to model using the \"Presence-Absence\" method.\n", + "\n", + "The generation of pseudo-absence data will be done through the \"environmental profiling approach\", and the specific steps are as follows:\n", + "\n", + "1. Environmental Classification Using k-means Clustering: The k-means clustering algorithm, based on Euclidean distance, will be used to divide the pixels within the study area into two clusters. One cluster will represent areas with similar environmental characteristics to randomly selected 100 presence locations, while the other cluster will represent areas with different characteristics.\n", + "\n", + "2. Generation of Pseudo-Absence Data within Dissimilar Clusters: Within the second cluster identified in the first step (which has different environmental characteristics from the presence data), randomly generated pseudo-absence points will be created. These pseudo-absence points will represent locations where the species is not expected to exist." + ], + "metadata": { + "id": "xIFAutp6M16q" + } + }, { "cell_type": "code", "source": [ - "filtered_PixelVals_df, bands = filter_variables_by_vif(PixelVals_df)" + "# Randomly select 100 locations for occurrence\n", + "PixelVals = predictors.sampleRegions(\n", + " collection=Data.randomColumn().sort('random').limit(100),\n", + " properties=[],\n", + " tileScale=16,\n", + " scale=GrainSize\n", + ")\n", + "\n", + "# Perform k-means clustering\n", + "clusterer = ee.Clusterer.wekaKMeans(\n", + " nClusters=2,\n", + " distanceFunction=\"Euclidean\"\n", + ").train(PixelVals)\n", + "\n", + "Clresult = predictors.cluster(clusterer)\n", + "\n", + "# Get cluster ID for locations similar to occurrence\n", + "clustID = Clresult.sampleRegions(\n", + " collection=Data.randomColumn().sort('random').limit(200),\n", + " properties=[],\n", + " tileScale=16,\n", + " scale=GrainSize\n", + ")\n", + "\n", + "# Define non-occurrence areas in dissimilar clusters\n", + "clustID = ee.FeatureCollection(clustID).reduceColumns(ee.Reducer.mode(),['cluster'])\n", + "clustID = ee.Number(clustID.get('mode')).subtract(1).abs()\n", + "cl_mask = Clresult.select(['cluster']).eq(clustID)" ], "metadata": { + "id": "hMnMjWpOYG_B", + "outputId": "c24939a5-a52d-4362-a85e-066d6cb5379c", "colab": { "base_uri": "https://localhost:8080/", - "height": 362 - }, - "id": "e3iiKaK5Eg0q", - "outputId": "bf150d7c-6a38-4a86-b70f-69259c8cf528" + "height": 17 + } }, - "execution_count": 37, + "execution_count": 72, "outputs": [ { "output_type": "display_data", @@ -9344,52 +14078,63 @@ ] }, "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Removing 'bio05' with VIF inf\n", - "Removing 'bio04' with VIF 183937.54\n", - "Removing 'bio10' with VIF 64460.60\n", - "Removing 'bio07' with VIF 47244.59\n", - "Removing 'bio17' with VIF 26253.01\n", - "Removing 'bio01' with VIF 9428.16\n", - "Removing 'bio16' with VIF 3700.80\n", - "Removing 'bio03' with VIF 2468.04\n", - "Removing 'bio18' with VIF 1716.89\n", - "Removing 'bio08' with VIF 1247.20\n", - "Removing 'bio06' with VIF 959.97\n", - "Removing 'bio12' with VIF 606.08\n", - "Removing 'bio19' with VIF 400.13\n", - "Removing 'bio15' with VIF 348.96\n", - "Removing 'hillshade' with VIF 129.83\n", - "Removing 'bio13' with VIF 47.33\n", - "Removing 'bio11' with VIF 31.44\n", - "Removing 'bio02' with VIF 13.75\n", - "Bands: ['TCC', 'aspect', 'bio09', 'bio14', 'elevation', 'slope']\n" - ] } ] }, { "cell_type": "code", "source": [ - "# Variable Selection Based on VIF\n", - "predictors = predictors.select(bands)\n", + "# Presence location mask\n", + "presence_mask = Data.reduceToImage(properties=['random'],\n", + "reducer=ee.Reducer.first()\n", + ").reproject('EPSG:4326', None,\n", + " ee.Number(GrainSize)).mask().neq(1).selfMask()\n", "\n", - "# Plot the correlation heatmap of variables\n", - "plot_correlation_heatmap(filtered_PixelVals_df, h_size=6)" + "# Masking presence locations in non-occurrence areas and clipping to AOI\n", + "AreaForPA = presence_mask.updateMask(cl_mask).clip(AOI)\n", + "\n", + "# Area for Pseudo-absence\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "Map.addLayer(AreaForPA, {'palette': 'black'}, 'AreaForPA')\n", + "Map.centerObject(AOI, 6)\n", + "Map" ], "metadata": { + "id": "Z6sGrkHGYWat", + "outputId": "2bf344f8-28c2-4abb-fa11-dd7267495bba", "colab": { "base_uri": "https://localhost:8080/", - "height": 441 - }, - "id": "TEFmil6WErkl", - "outputId": "c62cc9fd-72f1-4b5a-9793-222fe537f8fd" + "height": 421, + "referenced_widgets": [ + "c8390c8a357e494ca0388a061faf9add", + "2a837dc65930427497ce755a7570c647", + "cfe27c7f82b049649998dca11c8b9ba2", + "86b55571c64148c489458847fa3b614c", + "dcc8b35490a14d5e90e5068a1d93ed8f", + "c59df9e346364b469a1c9b0ad652579d", + "54079158de52461a9838082d7c9d2c83", + "aa149595ca44424788fe9ef2465b362d", + "5d3f9b95b2504b3688f69426bc2d9935", + "2121cc09f58043f7934c1e209ae4bb4d", + "24f7eacbb86444e3ae9a2dfdbf4b09ea", + "37237b8830b441b6832b22d011bfd82c", + "fb1e45a11fcb46dcb3a99fba2d90cd80", + "0f07f0f199d94affac1ab0a3aba7edaa", + "a1565349fe944d2d95cbfde0d5afe06a", + "2833b9da12bd4ca9acc491bc98f0d697", + "27869fe8b8a44e129b2b1b1e84b841d9", + "4f6dc23d7fdb4b8fae9e94593d4d20a5", + "2ae5540a04a74ea0bab6768badb25a07", + "4503893df33943388214bc55369bdb48", + "8aadf9d4530f4ffab1fcbe2dcba26671", + "7394468aff2a4556bc9ad0f93c5e34ea", + "a6132a6ed8c44a62af43b4bb33a52fa8", + "6fe813343d89446998d20b027fb563c5", + "4ddf2568c7bd4df99672e0f0352f287f" + ] + } }, - "execution_count": 38, + "execution_count": 74, "outputs": [ { "output_type": "display_data", @@ -9429,43 +14174,74 @@ "output_type": "display_data", "data": { "text/plain": [ - "
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\n" + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "c8390c8a357e494ca0388a061faf9add" + } }, - "metadata": {} + "metadata": { + "application/vnd.jupyter.widget-view+json": { + "colab": { + "custom_widget_manager": { + "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" + } + } + } + } } ] }, { "cell_type": "markdown", "source": [ - "Next, let's visualize the 6 selected predictor variables on the map.\n", - "![Predictor Variables for Analysis](https://github.com/osgeokr/earthengine-community/blob/master/tutorials/species-distribution-modeling/predictor_variables.png?raw=1)\n", + "### Model fitting and prediction\n", "\n", - "You can explore the available palettes for map visualization using the following code. For example, the `terrain` palette looks like this.\n", - "```\n", - "cm.plot_colormaps(width=8.0, height=0.2)\n", - "```" + "We will now divide the data into training data and test data. The training data will be used to find the optimal parameters by training the model, while the test data will be used to evaluate the model trained beforehand. An important concept to consider in this context is spatial autocorrelation.\n", + "\n", + "**Spatial autocorrelation** is an essential element in SDM, associated with Tobler's law. It embodies the concept that \"everything is related to everything else, but near things are more related than distant things\". Spatial autocorrelation represents the significant relationship between the location of species and environmental variables. However, if spatial autocorrelation exists between the training and test data, the independence between the two data sets can be compromised. This significantly impacts the evaluation of the model's generalization ability.\n", + "\n", + "One method to address this issue is the spatial block cross-validation technique, which involves dividing the data into training and testing datasets. This technique involves dividing the data into multiple blocks, using each block independently as training and test datasets to reduce the impact of spatial autocorrelation. This enhances the independence between datasets, allowing for a more accurate evaluation of the model's generalization ability.\n", + "\n", + "The specific procedure is as follows:\n", + "1. Creation of spatial blocks: Divide the entire dataset into spatial blocks of equal size (e.g., 50x50 km).\n", + "2. Assignment of training and testing sets: Each spatial block is randomly assigned to either the training set (70%) or the test set (30%). This prevents the model from overfitting to data from specific areas and aims to achieve more generalized results.\n", + "3. Iterative cross-validation: The entire process is repeated n times (e.g., 10 times). In each iteration, the blocks are randomly divided into training and test sets again, which is intended to improve the model's stability and reliability.\n", + "4. Generation of pseudo-absence data: In each iteration, pseudo-absence data are randomly generated to evaluate the model's performance." ], "metadata": { - "id": "j0dc6evyFFhw" + "id": "iTWaV5fQZPgA" } }, { "cell_type": "code", "source": [ - "cm.plot_colormap('terrain', width=8.0, height=0.2, orientation='horizontal')" + "# Generate a grid for spatial block cross-validation\n", + "def makeGrid(geometry, scale):\n", + " # Create an image with longitude & latitude in degrees\n", + " lonLat = ee.Image.pixelLonLat()\n", + " # Convert longitude & latitude images to integers\n", + " lonGrid = lonLat.select('longitude').multiply(100000).toInt()\n", + " latGrid = lonLat.select('latitude').multiply(100000).toInt()\n", + "\n", + " return lonGrid.multiply(latGrid).reduceToVectors(\n", + " # Create a grid that includes the boundaries of the geometry\n", + " geometry = geometry.buffer(distance=20000, maxError=1000),\n", + " scale = scale,\n", + " geometryType = 'polygon'\n", + " )" ], "metadata": { + "id": "Zt3sZ8-TZDNr", + "outputId": "c856ae55-7ede-43cd-bdd9-9f1f6294898d", "colab": { "base_uri": "https://localhost:8080/", - "height": 52 - }, - "id": "H8uHX-83E8x7", - "outputId": "9a232171-e69c-4f53-e1a8-bbfb81e57e97" + "height": 17 + } }, - "execution_count": 39, + "execution_count": 75, "outputs": [ { "output_type": "display_data", @@ -9500,70 +14276,61 @@ ] }, "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} } ] }, { "cell_type": "code", "source": [ - "# Elevation layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "Scale = 50000\n", + "grid = makeGrid(AOI, Scale)\n", + "Grid = watermask.reduceRegions(\n", + " collection=grid,\n", + " reducer=ee.Reducer.mean()).filter(ee.Filter.neq('mean', None))\n", "\n", - "vis_params = {'bands':['elevation'], 'min': 0, 'max': 1800, 'palette': cm.palettes.terrain}\n", - "Map.addLayer(predictors, vis_params, 'elevation')\n", - "Map.add_colorbar(vis_params, label=\"Elevation (m)\", orientation=\"vertical\", layer_name=\"elevation\")\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "Map.addLayer(Grid, {}, \"Grid for spatial block cross validation\")\n", + "Map.addLayer(outline, {'palette': 'FF0000'}, \"Study Area\")\n", "Map.centerObject(AOI, 6)\n", "Map" ], "metadata": { + "id": "HASln4gfb1pZ", + "outputId": "c75fd067-00a3-4f56-fa87-608231a09c78", "colab": { "base_uri": "https://localhost:8080/", "height": 421, "referenced_widgets": [ - "0ac7d26aa1814d8f8543b1e51934fe6e", - "7b6fe79343ff4e0ba26e3d6e15efb025", - "bc3830f5a2fb40aa959b59e420d707f8", - "5523004c1fe240439047f5179327ac59", - "08b3b839623e48d98cabad14b768473d", - "61b6639d4538439f9221a21eea1db29e", - "41ff2a139e7a42bc8543914b96d906bb", - "c0cbbed906f344648bc4cff3adcbbf75", - "02dc65fdd68040ea908713060cbc5d1a", - "a55df16b967d47df8d47c7ffc55e462a", - "8123d2220a574ad0aa40be4afd6759aa", - "f87ea7550b9148bc9ec2cd984ffddaea", - "ec5842a999604555aa95c5ed9b82dc37", - "571ddde0c22841d68a7868ac66109281", - "055394cd168c497ab3d2fd16d40d6a5d", - "b145a01c3d5945fb8ed2472518cb1ba7", - "f0eaba4f88bc4a56aee79e689a62fdeb", - "77534dc0bec14c2888cc9217abbf8035", - "6023eac322f247d0808755ba38f8a808", - "0943fd4685a54b70b341bd000cdbc91f", - "f2315c32563e452a9632e16c6e1f8796", - "f0a072b0d2174bd88c2bf8dedea9fa4b", - "8f5295fa75d145a89f760e300aa91493", - "dc7a0a178b8d49109d8d5d4fe7615858", - "08e382e7a20b4eee90eb087d8225e7c1", - "d2e8ba57951749ab955048105ddcad11", - "ee6822f7630e4e7f8d2b41138d55bf61", - "5e67d047f0ca4265aed15af47375d653" + "abaa5b5611554316a11951a8a1e4017d", + "c8d8dfadeade4ea9949163d44e1a8e64", + "d737a2812366419684b7a3ca9381761b", + "d3bc6e4739f84b4cbb878f5ab0684e32", + "b3ad7898b48a44cf814358671eaa511c", + "85f2edc0a908484fbb1def9f30fa931f", + "0287f469129248a0bfd51aac2e80f412", + "300bf65c5dcf4ed3ab887baf03cf0f9f", + "6851da7f282346c0904ecf577d2941b6", + "e866bfbf8b9346d78aba88b2d5e98cf3", + "c643413fe487499399af76c469c28562", + "7c5d51803870404089278fd0d7ed4a42", + "404edb6d1a974ac0a471328610a4ec17", + "fe12c542e3fd41b3839accfac33e18f0", + "04d39a39f365408a894d5602f5584daa", + "ea0d8d84d5994efab3f63ba4b97efdff", + "ca216e28581f4b00b2b6878e4ce4cb0c", + "d80d8a9cd4244b87b16c0f8e63788801", + "be58465b29e94ac6b801ddcd6684dde8", + "c039d261e9b84c20af1f66e9b297135b", + "41a23e03ce0d406daf40573f269139a1", + "e7475d2c40c44271a895b3f273816bc5", + "34cf6a2d8443456e81bba3a09a9939bd", + "9c36f1f12ec444e6a9b1370a84dd7b24", + "62d5b3f2bdad483d8638105a5af6182b", + "54f26606e2bf4e5b9d91e013c0691d3e" ] - }, - "id": "RswVTmLFFUb3", - "outputId": "c8fc2323-0b78-4960-aa67-1fc203c51186" + } }, - "execution_count": 54, + "execution_count": 76, "outputs": [ { "output_type": "display_data", @@ -9608,7 +14375,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "0ac7d26aa1814d8f8543b1e51934fe6e" + "model_id": "abaa5b5611554316a11951a8a1e4017d" } }, "metadata": { @@ -9623,61 +14390,92 @@ } ] }, + { + "cell_type": "markdown", + "source": [ + "Now we can fit the model. Fitting a model involves understanding the patterns in the data and adjusting the model's parameters (weights and biases) accordingly. This process enables the model to make more accurate predictions when presented with new data. For this purpose, we have defined a function called SDM() to fit the model.\n", + "\n", + "We will use the **Random Forest** algorithm." + ], + "metadata": { + "id": "EsPbyMZ-cJg2" + } + }, { "cell_type": "code", "source": [ - "# Calculate the minimum and maximum values for bio09\n", - "min_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", - "max_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", + "def SDM(x):\n", + " Seed = ee.Number(x)\n", "\n", - "# bio09 (Mean temperature of driest quarter) layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + " # Random block division for training and validation\n", + " GRID = ee.FeatureCollection(Grid).randomColumn(seed=Seed).sort('random')\n", + " TrainingGrid = GRID.filter(ee.Filter.lt('random', split)) # Grid for training\n", + " TestingGrid = GRID.filter(ee.Filter.gte('random', split)) # Grid for testing\n", "\n", - "vis_params = {'min': math.floor(min_val['bio09']), 'max': math.ceil(max_val['bio09']), 'palette': cm.palettes.hot}\n", - "Map.addLayer(predictors.select(\"bio09\").multiply(0.1), vis_params, 'bio09')\n", - "Map.add_colorbar(vis_params, label=\"Mean temperature of driest quarter (℃)\", orientation=\"vertical\", layer_name=\"bio09\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" + " # Presence points\n", + " PresencePoints = ee.FeatureCollection(Data)\n", + " PresencePoints = PresencePoints.map(lambda feature: feature.set('PresAbs', 1))\n", + " TrPresencePoints = PresencePoints.filter(ee.Filter.bounds(TrainingGrid)) # Presence points for training\n", + " TePresencePoints = PresencePoints.filter(ee.Filter.bounds(TestingGrid)) # Presence points for testing\n", + "\n", + " # Pseudo-absence points for training\n", + " TrPseudoAbsPoints = AreaForPA.sample(region=TrainingGrid,\n", + " scale=GrainSize,\n", + " numPixels=TrPresencePoints.size().add(300),\n", + " seed=Seed,\n", + " geometries=True)\n", + " # Same number of pseudo-absence points as presence points for training\n", + " TrPseudoAbsPoints = TrPseudoAbsPoints.randomColumn().sort('random').limit(ee.Number(TrPresencePoints.size()))\n", + " TrPseudoAbsPoints = TrPseudoAbsPoints.map(lambda feature: feature.set('PresAbs', 0))\n", + "\n", + " TePseudoAbsPoints = AreaForPA.sample(region=TestingGrid,\n", + " scale=GrainSize,\n", + " numPixels=TePresencePoints.size().add(100),\n", + " seed=Seed,\n", + " geometries=True)\n", + " # Same number of pseudo-absence points as presence points for testing\n", + " TePseudoAbsPoints = TePseudoAbsPoints.randomColumn().sort('random').limit(ee.Number(TePresencePoints.size()))\n", + " TePseudoAbsPoints = TePseudoAbsPoints.map(lambda feature: feature.set('PresAbs', 0))\n", + "\n", + " # Merge training and pseudo-absence points\n", + " trainingPartition = TrPresencePoints.merge(TrPseudoAbsPoints)\n", + " testingPartition = TePresencePoints.merge(TePseudoAbsPoints)\n", + "\n", + " # Extract predictor variable values at training points\n", + " trainPixelVals = predictors.sampleRegions(collection=trainingPartition,\n", + " properties=['PresAbs'],\n", + " scale=GrainSize,\n", + " tileScale=16,\n", + " geometries=True)\n", + "\n", + " # Random Forest classifier\n", + " Classifier = ee.Classifier.smileRandomForest(\n", + " numberOfTrees=500,\n", + " variablesPerSplit=None,\n", + " minLeafPopulation=10,\n", + " bagFraction=0.5,\n", + " maxNodes=None,\n", + " seed=Seed\n", + " )\n", + " # Presence probability: Habitat suitability map\n", + " ClassifierPr = Classifier.setOutputMode('PROBABILITY').train(trainPixelVals, 'PresAbs', bands)\n", + " ClassifiedImgPr = predictors.select(bands).classify(ClassifierPr)\n", + "\n", + " # Binary presence/absence map: Potential distribution map\n", + " ClassifierBin = Classifier.setOutputMode('CLASSIFICATION').train(trainPixelVals, 'PresAbs', bands)\n", + " ClassifiedImgBin = predictors.select(bands).classify(ClassifierBin)\n", + "\n", + " return [ClassifiedImgPr, ClassifiedImgBin, trainingPartition, testingPartition], ClassifierPr" ], "metadata": { + "id": "89R79JvGb6c8", + "outputId": "de01dee1-9565-497d-e067-f7e1fcc4bd77", "colab": { "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "bd778ad418634776993304e38cd1ad02", - "89ba6396ab264cb1827aa7084280ab72", - "bf6555c5b2814169a35ff3b3b6bc8095", - "6a171d05b22b4dde8f13ecfde310d022", - "4eb8738933d8458c905fe9c1cb02aeec", - "33111afdc5b7461ba0b58c3b5f87d1e8", - "c61c10dbef514e878086a63de0f84b44", - "fd727297110d4fada437c5e998becbe3", - "448af660fa9c4243a5d0ea80c1adc0b2", - "2b00969d96e34bc39e1acdb7b23cc7ba", - "e6aa5ecde8be4747827bb8d42d726390", - "afd0660ef7f248b4be6d6b5fdaeb1fe1", - "3faf6fa1bdce4906a7b253f15f873577", - "414d832f4e044cc1934714a7ed8fb21b", - "2b3e3fb6d8124dd49cfe7bdefc7ba96c", - "8e9e786751bf4a1fb2bc0989e61c6be3", - "8c868f718f804df095927f5b21318bf5", - "00cacd66691647cd80d41f88208aed1c", - "20d8e4f3dd624250bb28d2da9d112fcc", - "5ad8210daf1448b88a415b7c5b43f703", - "4f733436546340baadaaf3cab7d11716", - "a8f5ca16930848c68e378969e327babb", - "4858f5dc633d411a9c4594d0a232587a", - "9af3c9a565f346a2872c8e7c1fe0a36b", - "4ab5dfa79618456f97b6ed532c9829d4", - "50b552f96b464adea9b15273a1596e91", - "33bd735ab4e54f47b79fe6804b2c409e", - "cbda0586f2a34f8ca4032b9a713318d2" - ] - }, - "id": "DOSMZpbsF8sH", - "outputId": "8adb3542-4d87-4004-bf6d-48a9d92c5cb9" + "height": 17 + } }, - "execution_count": 57, + "execution_count": 78, "outputs": [ { "output_type": "display_data", @@ -9712,82 +14510,40 @@ ] }, "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "bd778ad418634776993304e38cd1ad02" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } } ] }, + { + "cell_type": "markdown", + "source": [ + "Spatial blocks are divided into 70% for model training and 30% for model testing, respectively. Pseudo-absence data are randomly generated within each training and testing set in every iteration. As a result, each execution yields different sets of presence and pseudo-absence data for model training and testing." + ], + "metadata": { + "id": "yCGB0Y6meW2B" + } + }, { "cell_type": "code", "source": [ - "# Slope layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "split = 0.7\n", + "numiter = 10\n", "\n", - "vis_params = {'bands':['slope'], 'min': 0, 'max': 25, 'palette': cm.palettes.RdYlGn_r}\n", - "Map.addLayer(predictors, vis_params, 'slope')\n", - "Map.add_colorbar(vis_params, label=\"Slope\", orientation=\"vertical\", layer_name=\"slope\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" + "# Random Seed\n", + "runif = lambda length: [random.randint(1, 1000) for _ in range(length)]\n", + "items = runif(numiter)\n", + "\n", + "# Fixed seed\n", + "# items = [287, 288, 553, 226, 151, 255, 902, 267, 419, 538]" ], "metadata": { + "id": "KV6Jg50AeA7B", + "outputId": "2130e3c5-2f9e-4ec7-de26-9596ca991a7d", "colab": { "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "eeed0724c6774ca4ae71f9414d7d3d36", - "d7add71f9a694c1eab1a68d22844182f", - "a7f87d5b06044d57b272eb3df58c27ed", - "e6e6b787daa04ca68a4591406c7995f7", - "e7b4368dad5448a79254daf75160eec2", - "f50ee59d00bd4c5f82b18ce212a20609", - "117743a21cf44085a07f630c0b3b861a", - "0c63f792b3114394bd82e137f6392512", - "d2f84366501d48eca89e772ec96e0d81", - "9762a337a7bc40848087c346ba0032e0", - "d4faaee6a05e41cd91940435b39b9364", - "2f57730680724aa6800e00a81897560d", - "d0354527efe24848864f4cb06adb6195", - "553cd97ec41c47ce999e1db0a46787c5", - "669a619518424e6ba7a434d920814d8a", - "510af8d670614271a20ccb4c2868a71d", - "1c78454c17ad4a32a1737be1dc8ca79c", - "3742a1693d0a4ec38689ef4abd3b445f", - "7141995bd812493ba260e61947727b52", - "d7df4e6709ba4292bd71c0d6d4a06b33", - "c58b354684e2468dbdd7d8477d33b5a5", - "703de6e1dede42afae7090e28942afb1", - "ec512f45d5304791b2a1e05a2feed243", - "df9e63a9968a415bb6331ed06676b201", - "2747267b006447ed890a7b8d6715085a", - "04cfd6bdc7c34d6885ba9149d9f14158", - "b228661b472c4012a07a9f8cc75ff29e", - "a5189495d93f4c499c20626066f527e3" - ] - }, - "id": "cQpG8qRzIQcw", - "outputId": "32fb0083-4d81-49fe-9ed0-34273b0383d2" + "height": 17 + } }, - "execution_count": 59, + "execution_count": 80, "outputs": [ { "output_type": "display_data", @@ -9822,82 +14578,36 @@ ] }, "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "eeed0724c6774ca4ae71f9414d7d3d36" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } } ] }, { "cell_type": "code", "source": [ - "# Aspect layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "results_list = [] # Initialize SDM results list\n", + "importances_list = [] # Initialize variable importance list\n", "\n", - "vis_params = {'bands':['aspect'], 'min': 0, 'max': 360, 'palette': cm.palettes.rainbow}\n", - "Map.addLayer(predictors, vis_params, 'aspect')\n", - "Map.add_colorbar(vis_params, label=\"Aspect\", orientation=\"vertical\", layer_name=\"aspect\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" + "for item in items:\n", + " result, trained = SDM(item)\n", + " # Accumulate SDM results into the list\n", + " results_list.extend(result)\n", + "\n", + " # Accumulate variable importance into the list\n", + " importance = ee.Dictionary(trained.explain()).get('importance')\n", + " importances_list.extend(importance.getInfo().items())\n", + "\n", + "# Flatten the SDM results list\n", + "results = ee.List(results_list).flatten()" ], "metadata": { + "id": "BHCnkZvveq_Q", + "outputId": "282df7ed-54c7-4438-9a00-095bd3dee3db", "colab": { "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "ef4fd7dfae2e452c8f4190884fbccb42", - "4aa9d7e81cd44ab2b9fe5860c33057e9", - "06ce1b5cba0a4ba19b17bd566bd45f65", - "f07bebea44b64c76a56d1ab0a7d7f5e7", - "94e01dc8e4234d4791d103169905a6ae", - "65478ba4e1d349d3b9d54f056d54e6a9", - "a2570adcb9704dfe80d4aefe9a9911ab", - "f3a2ac8a04d449a1b0c57b0c573a019f", - "0b1e4d1e0d13456ba9e976251e955207", - "291da14bcdff4172a9dafcdb9015a6a8", - "812ce2194c6a45e7accd47717e3706d6", - "87a59ec985624f938acee2d5a65b337f", - "f4c1ce3e881c4b5780d9f074c2013b2c", - "be97d93287fa491ca8d6b097c9d373ba", - "f441b5da4e4f444eb1282070aac5398e", - "f3fb193232284f45becafbd6a3a24b46", - "fd9b803ae3f34cb4bda622545423554d", - "a9479b474ddc43f0b1ea377f5d72781b", - "9547a5384a064ae4b65b3e2c341ff873", - "120ecccbdce240c4bb2e6fe548fa452e", - "520892cebe1d466c8aafeca7a98f22b3", - "fbc54ef0969849aebe2cdce27cae4c64", - "62513b235c2242e1a6fd416c5d255ab8", - "8978ae99e798438ab7ec3be6c75a75b1", - "eb836cc637c44cc99499e5d5fa143c06", - "376eaa60c64c44adb7842d4457478935", - "dce21eaa742d4801a11d006d3486cc10", - "e2527a8c99034a60853950a96330d203" - ] - }, - "id": "u1hW_wAXIvvG", - "outputId": "f20aa1e4-4b8a-4b2e-96bd-4dd1debaf505" + "height": 17 + } }, - "execution_count": 61, + "execution_count": 81, "outputs": [ { "output_type": "display_data", @@ -9932,86 +14642,83 @@ ] }, "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "ef4fd7dfae2e452c8f4190884fbccb42" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } } ] }, + { + "cell_type": "markdown", + "source": [ + "Now we can visualize the **habitat suitability map** and **potential distribution map** for the Fairy pitta. In this case, the habitat suitability map is created by using the `mean()` function to calculate the average for each pixel location across all images, and the potential distribution map is generated by using the `mode()` function to determine the most frequently occurring value at each pixel location across all images.\n", + "\n", + "![SDM results](https://github.com/osgeokr/earthengine-community/blob/master/tutorials/species-distribution-modeling/sdm_results.png?raw=1)" + ], + "metadata": { + "id": "UHQ6Hx07gG2m" + } + }, { "cell_type": "code", "source": [ - "# Calculate the minimum and maximum values for bio14\n", - "min_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", - "max_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", + "# Habitat suitability map\n", + "images = ee.List.sequence(\n", + " 0, ee.Number(numiter).multiply(4).subtract(1), 4).\\\n", + " map(lambda x: results.get(x))\n", + "ModelAverage = ee.ImageCollection.fromImages(images).mean()\n", "\n", - "# bio14 (Precipitation of driest month) layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'}, basemap='Esri.WorldImagery')\n", "\n", - "vis_params = {'bands':['bio14'], 'min': math.floor(min_val['bio14']), 'max': math.ceil(max_val['bio14']), 'palette': cm.palettes.Blues}\n", - "Map.addLayer(predictors, vis_params, 'bio14')\n", - "Map.add_colorbar(vis_params, label=\"Precipitation of driest month (mm)\", orientation=\"vertical\", layer_name=\"bio14\")\n", + "vis_params = {\n", + " 'min': 0,\n", + " 'max': 1,\n", + " 'palette': cm.palettes.viridis_r}\n", + "Map.addLayer(ModelAverage, vis_params, 'Habitat suitability')\n", + "Map.add_colorbar(vis_params, label=\"Habitat suitability\",\n", + " orientation=\"horizontal\",\n", + " layer_name=\"Habitat suitability\")\n", + "Map.addLayer(Data, {'color':'red'}, 'Presence')\n", "Map.centerObject(AOI, 6)\n", "Map" ], "metadata": { + "id": "J_QUmonhfuSM", + "outputId": "76cdb481-f7df-4d9d-adf4-69374a6d37f4", "colab": { "base_uri": "https://localhost:8080/", "height": 421, "referenced_widgets": [ - 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}, - "id": "C49-BnnxJGUD", - "outputId": "905bcf30-c827-4987-f4ac-0396893e250b" + } }, - "execution_count": 68, + "execution_count": 89, "outputs": [ { "output_type": "display_data", @@ -10056,7 +14763,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "82ebad0576144aaaa0773dd83736b54b" + "model_id": "d8d52cc8abfc412f9babec3a2cdc14f4" } }, "metadata": { @@ -10074,54 +14781,65 @@ { "cell_type": "code", "source": [ - "# TCC (Tree Canopy Cover) layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", + "# Potential distribution map\n", + "images2 = ee.List.sequence(1, ee.Number(numiter).multiply(4).\\\n", + " subtract(1), 4).map(lambda x: results.get(x))\n", + "DistributionMap = ee.ImageCollection.fromImages(images2).mode()\n", "\n", - "vis_params = {'bands':['TCC'], 'min': 0, 'max': 100, 'palette': ['ffffff', 'afce56', '5f9c00', '0e6a00', '003800']}\n", - "Map.addLayer(predictors, vis_params, 'TCC')\n", - "Map.add_colorbar(vis_params, label=\"Tree Canopy Cover (%)\", orientation=\"vertical\", layer_name=\"TCC\")\n", - "Map.centerObject(AOI, 6)\n", + "Map = geemap.Map(layout={'height':'400px', 'width':'800px'}, basemap='Esri.WorldImagery')\n", + "\n", + "vis_params = {\n", + " 'min': 0,\n", + " 'max': 1,\n", + " 'palette': ['white', 'green']}\n", + "Map.addLayer(DistributionMap, vis_params, 'Potential distribution')\n", + "Map.addLayer(Data, {'color':'red'}, 'Presence')\n", + "Map.add_colorbar(vis_params, label=\"Potential distribution\",\n", + " discrete=True, orientation=\"horizontal\",\n", + " layer_name=\"Potential distribution\")\n", + "Map.centerObject(Data.geometry(), 6)\n", "Map" ], "metadata": { + "id": "cBZa-J43jAL2", + "outputId": "fc6c0816-6828-484e-f830-9711ce3fec01", "colab": { "base_uri": "https://localhost:8080/", "height": 421, "referenced_widgets": [ - "047835b9364440e9bd37f023534440ba", - "a98a603b92b744a8b36544312491e8c8", - "5213cdbb556d45ca885e925302671180", - "8372014946204e0ba5d7d5b3598c2ac3", - "d05dbda652784523833a9d8f4798d82a", - "33224841869a49b381c6135673b605b7", - "91e285a75166493ca709933f5a0caeb5", - 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}, - "id": "zKVnKhLYJicg", - "outputId": "8c7cc3a5-466d-45ce-b980-26a7a686308e" + } }, - "execution_count": 67, + "execution_count": 90, "outputs": [ { "output_type": "display_data", @@ -10161,12 +14879,12 @@ "output_type": "display_data", "data": { "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" + "Map(center=[35.533064630393035, 126.8858638222748], controls=(WidgetControl(options=['position', 'transparent_…" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "047835b9364440e9bd37f023534440ba" + "model_id": "c86e1a74c965421194fe6cee9b4909b6" } }, "metadata": { @@ -10180,40 +14898,6 @@ } } ] - }, - { - "cell_type": "markdown", - "source": [ - "### Generation of pseudo-absence data\n", - "\n", - "In the process of SDM, the selection of input data for a species is mainly approached using two methods:\n", - "\n", - "1. **Presence-Background Method**: This method compares the locations where a particular species has been observed (presence) with other locations where the species has not been observed (background). Here, the background data does not necessarily mean areas where the species does not exist but rather is set up to reflect the overall environmental conditions of the study area. It is used to distinguish suitable environments where the species could exist from less suitable ones.\n", - "\n", - "2. **Presence-Absence Method**: This method compares locations where the species has been observed (presence) with locations where it has definitively not been observed (absence). Here, absence data represents specific locations where the species is known not to exist. It does not reflect the overall environmental conditions of the study area but rather points to locations where the species is estimated not to exist.\n", - "\n", - "In practice, it is often difficult to collect true absence data, so pseudo-absence data generated artificially is frequently used. However, it's important to acknowledge the limitations and potential errors of this method, as artificially generated pseudo-absence points may not accurately reflect true absence areas.\n", - "\n", - "The choice between these two methods depends on data availability, research objectives, model accuracy and reliability, as well as time and resources. Here, we will use occurrence data collected from GBIF and artificially generated pseudo-absence data to model using the \"Presence-Absence\" method.\n", - "\n", - "The generation of pseudo-absence data will be done through the \"environmental profiling approach\", and the specific steps are as follows:\n", - "\n", - "Environmental Classification Using k-means Clustering: The k-means clustering algorithm, based on Euclidean distance, will be used to divide the pixels within the study area into two clusters. One cluster will represent areas with similar environmental characteristics to randomly selected 100 presence locations, while the other cluster will represent areas with different characteristics.\n", - "\n", - "Generation of Pseudo-Absence Data within Dissimilar Clusters: Within the second cluster identified in the first step (which has different environmental characteristics from the presence data), randomly generated pseudo-absence points will be created. These pseudo-absence points will represent locations where the species is not expected to exist.\"" - ], - "metadata": { - "id": "xIFAutp6M16q" - } - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "E-aa4QcqKd1t" - }, - "execution_count": null, - "outputs": [] } ] } \ No newline at end of file From dbd6490fb6bf6387d0376ee5b61cefa7a7d8d8b6 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Mon, 5 Feb 2024 23:23:27 +0900 Subject: [PATCH 14/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 100 +++++++++++------- 1 file changed, 61 insertions(+), 39 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 08babc576..998bc36ad 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -9598,12 +9598,12 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 13169, + "bottom": 3497, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 35.092945313732635, - 129.18823242187503 + 33.100745405144245, + 117.65258789062501 ], "close_popup_on_click": true, "controls": [ @@ -9625,7 +9625,7 @@ "double_click_zoom": true, "dragging": true, "dragging_style": "IPY_MODEL_64618ee8d9fb4ec6ada73fe6a03b9a71", - "east": 133.58276367187503, + "east": 135.21972656250003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -9640,11 +9640,11 @@ "IPY_MODEL_1501f246f07a4d91a19667e1090b1f73" ], "layout": "IPY_MODEL_fd25e9570f374653ae9468209fe3a54b", - "left": 27743, + "left": 6373, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 36.870832155646326, + "north": 40.1452892956766, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -9675,18 +9675,18 @@ ], "panes": {}, "prefer_canvas": false, - "right": 28543, + "right": 7173, "scroll_wheel_zoom": true, - "south": 33.27543541298162, + "south": 25.443274612305746, "style": "IPY_MODEL_dee5114c449f458a889ede5f5447d693", "tap": true, "tap_tolerance": 15, - "top": 12769, + "top": 3097, "touch_zoom": true, - "west": 124.79370117187501, + "west": 100.06347656250001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 7, + "zoom": 5, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 @@ -10036,7 +10036,7 @@ "bottom": true, "bounds": null, "detect_retina": false, - "loading": true, + "loading": false, "max_native_zoom": null, "max_zoom": 24, "min_native_zoom": null, @@ -14036,12 +14036,12 @@ "cl_mask = Clresult.select(['cluster']).eq(clustID)" ], "metadata": { - "id": "hMnMjWpOYG_B", - "outputId": "c24939a5-a52d-4362-a85e-066d6cb5379c", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "hMnMjWpOYG_B", + "outputId": "c24939a5-a52d-4362-a85e-066d6cb5379c" }, "execution_count": 72, "outputs": [ @@ -14100,8 +14100,6 @@ "Map" ], "metadata": { - "id": "Z6sGrkHGYWat", - "outputId": "2bf344f8-28c2-4abb-fa11-dd7267495bba", "colab": { "base_uri": "https://localhost:8080/", "height": 421, @@ -14132,7 +14130,9 @@ "6fe813343d89446998d20b027fb563c5", "4ddf2568c7bd4df99672e0f0352f287f" ] - } + }, + "id": "Z6sGrkHGYWat", + "outputId": "2bf344f8-28c2-4abb-fa11-dd7267495bba" }, "execution_count": 74, "outputs": [ @@ -14234,12 +14234,12 @@ " )" ], "metadata": { - "id": "Zt3sZ8-TZDNr", - "outputId": "c856ae55-7ede-43cd-bdd9-9f1f6294898d", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "Zt3sZ8-TZDNr", + "outputId": "c856ae55-7ede-43cd-bdd9-9f1f6294898d" }, "execution_count": 75, "outputs": [ @@ -14295,8 +14295,6 @@ "Map" ], "metadata": { - "id": "HASln4gfb1pZ", - "outputId": "c75fd067-00a3-4f56-fa87-608231a09c78", "colab": { "base_uri": "https://localhost:8080/", "height": 421, @@ -14328,7 +14326,9 @@ "62d5b3f2bdad483d8638105a5af6182b", "54f26606e2bf4e5b9d91e013c0691d3e" ] - } + }, + "id": "HASln4gfb1pZ", + "outputId": "c75fd067-00a3-4f56-fa87-608231a09c78" }, "execution_count": 76, "outputs": [ @@ -14468,12 +14468,12 @@ " return [ClassifiedImgPr, ClassifiedImgBin, trainingPartition, testingPartition], ClassifierPr" ], "metadata": { - "id": "89R79JvGb6c8", - "outputId": "de01dee1-9565-497d-e067-f7e1fcc4bd77", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "89R79JvGb6c8", + "outputId": "de01dee1-9565-497d-e067-f7e1fcc4bd77" }, "execution_count": 78, "outputs": [ @@ -14536,12 +14536,12 @@ "# items = [287, 288, 553, 226, 151, 255, 902, 267, 419, 538]" ], "metadata": { - "id": "KV6Jg50AeA7B", - "outputId": "2130e3c5-2f9e-4ec7-de26-9596ca991a7d", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "KV6Jg50AeA7B", + "outputId": "2130e3c5-2f9e-4ec7-de26-9596ca991a7d" }, "execution_count": 80, "outputs": [ @@ -14600,12 +14600,12 @@ "results = ee.List(results_list).flatten()" ], "metadata": { - "id": "BHCnkZvveq_Q", - "outputId": "282df7ed-54c7-4438-9a00-095bd3dee3db", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "BHCnkZvveq_Q", + "outputId": "282df7ed-54c7-4438-9a00-095bd3dee3db" }, "execution_count": 81, "outputs": [ @@ -14680,8 +14680,6 @@ "Map" ], "metadata": { - "id": "J_QUmonhfuSM", - "outputId": "76cdb481-f7df-4d9d-adf4-69374a6d37f4", "colab": { "base_uri": "https://localhost:8080/", "height": 421, @@ -14716,7 +14714,9 @@ "d5a4e42464f04fd6a06619c90ade6880", "7cba088702d942f6ab480723e7cef639" ] - } + }, + "id": "J_QUmonhfuSM", + "outputId": "76cdb481-f7df-4d9d-adf4-69374a6d37f4" }, "execution_count": 89, "outputs": [ @@ -14801,8 +14801,6 @@ "Map" ], "metadata": { - "id": "cBZa-J43jAL2", - "outputId": "fc6c0816-6828-484e-f830-9711ce3fec01", "colab": { "base_uri": "https://localhost:8080/", "height": 421, @@ -14837,7 +14835,9 @@ "a9a30c1d7ce2494ea74f1cfbb01c355f", "f3684f852fe34e2fa56e21e365ab70eb" ] - } + }, + "id": "cBZa-J43jAL2", + "outputId": "fc6c0816-6828-484e-f830-9711ce3fec01" }, "execution_count": 90, "outputs": [ @@ -14898,6 +14898,28 @@ } } ] + }, + { + "cell_type": "markdown", + "source": [ + "### Variable importance and accuracy assessment\n", + "\n", + "Random Forest (`ee.Classifier.smileRandomForest`) is one of the ensemble learning methods, which operates by constructing multiple decision trees to make predictions. Each decision tree independently learns from different subsets of the data, and their results are aggregated to enable more accurate and stable predictions.\n", + "\n", + "Variable importance is a measure that evaluates the impact of each variable on the predictions within the Random Forest model. We will use the previously defined `importances_list` to calculate and print the average variable importance." + ], + "metadata": { + "id": "B5cjWyzAmd4Z" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "uoJ0mBeRme-E" + }, + "execution_count": null, + "outputs": [] } ] } \ No newline at end of file From 9a72d488f025fbf146f6d28034a71fbf465ce388 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Tue, 6 Feb 2024 00:19:29 +0900 Subject: [PATCH 15/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 611 +++++++++++++++++- 1 file changed, 599 insertions(+), 12 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 998bc36ad..27a2ee396 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -8582,12 +8582,12 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 12942, + "bottom": 25876, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 37.10776507118514, - 130.22094726562503 + 36.26199220445664, + 130.13854980468753 ], "close_popup_on_click": true, "controls": [ @@ -8609,7 +8609,7 @@ "double_click_zoom": true, "dragging": true, "dragging_style": "IPY_MODEL_d82f07921c02492c98bd4f8ad9cd1ae0", - "east": 134.61547851562503, + "east": 132.33581542968753, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -8624,11 +8624,11 @@ "IPY_MODEL_933c5b3c2934476bb1634bbf8f6220bc" ], "layout": "IPY_MODEL_683d083367064c8dbffc9e52d3c860ad", - "left": 27837, + "left": 56059, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 38.839707613545144, + "north": 37.142803443716836, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -8659,18 +8659,18 @@ ], "panes": {}, "prefer_canvas": false, - "right": 28637, + "right": 56859, "scroll_wheel_zoom": true, - "south": 35.33529320309331, + "south": 35.37113502280101, "style": "IPY_MODEL_e3bc92404ac44b64a66a9351444aaa22", "tap": true, "tap_tolerance": 15, - "top": 12542, + "top": 25476, "touch_zoom": true, - "west": 125.826416015625, + "west": 127.94128417968751, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 7, + "zoom": 8, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 @@ -14912,11 +14912,598 @@ "id": "B5cjWyzAmd4Z" } }, + { + "cell_type": "code", + "source": [ + "def plot_variable_importance(importances_list):\n", + " # Extract each variable importance value into a list\n", + " variables = [item[0] for item in importances_list]\n", + " importances = [item[1] for item in importances_list]\n", + "\n", + " # Calculate the average importance for each variable\n", + " average_importances = {}\n", + " for variable in set(variables):\n", + " indices = [i for i, var in enumerate(variables) if var == variable]\n", + " average_importance = np.mean([importances[i] for i in indices])\n", + " average_importances[variable] = average_importance\n", + "\n", + " # Print the average variable importance\n", + " for variable, avg_importance in average_importances.items():\n", + " print(f\"{variable}: {avg_importance}\")\n", + "\n", + " # Sort the importances in descending order of importance\n", + " sorted_importances = sorted(average_importances.items(),\n", + " key=lambda x: x[1], reverse=False)\n", + " variables = [item[0] for item in sorted_importances]\n", + " avg_importances = [item[1] for item in sorted_importances]\n", + "\n", + " # Adjust the graph size\n", + " plt.figure(figsize=(8, 4))\n", + "\n", + " # Plot the average importance as a horizontal bar chart\n", + " plt.barh(variables, avg_importances)\n", + " plt.xlabel('Importance')\n", + " plt.ylabel('Variables')\n", + " plt.title('Average Variable Importance')\n", + "\n", + " # Display values above the bars\n", + " for i, v in enumerate(avg_importances):\n", + " plt.text(v + 0.02, i, f\"{v:.2f}\", va='center')\n", + "\n", + " # Adjust the x-axis range\n", + " plt.xlim(0, max(avg_importances) + 5) # Adjust to the desired range\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig('variable_importance.png')\n", + " plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "uoJ0mBeRme-E", + "outputId": "54593455-00a4-4219-efde-127a01f86642" + }, + "execution_count": 92, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plot_variable_importance(importances_list)" + ], + "metadata": { + "id": "2nhhAUydurwm", + "outputId": "4594b1bd-5407-4f0d-f88b-de830ff9e3a8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 516 + } + }, + "execution_count": 93, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "slope: 8.815585915421398\n", + "bio14: 11.305832336890663\n", + "TCC: 24.970013507247337\n", + "bio09: 28.510260305453976\n", + "aspect: 3.489922562389851\n", + "elevation: 59.08189101408956\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Using the Testing Datasets, we calculate AUC-ROC and AUC-PR for each run. Then, we compute the average AUC-ROC and AUC-PR over n iterations.\n", + "\n", + "**AUC-ROC** represents the area under the curve of the 'Sensitivity (Recall) vs. 1-Specificity' graph, illustrating the relationship between sensitivity and specificity as the threshold changes. Specificity is based on all observed non-occurrences. Therefore, AUC-ROC encompasses all quadrants of the confusion matrix.\n", + "\n", + "**AUC-PR** represents the area under the curve of the 'Precision vs. Recall (Sensitivity)' graph, showing the relationship between precision and recall as the threshold varies. Precision is based on all predicted occurrences. Hence, AUC-PR does not include the true negatives (TN).\n", + "\n", + "> Note: It's important to ensure that each run has a sufficient number of points for model validation. The final number of points may vary due to the random partitioning of spatial blocks, so it's crucial to verify if there are enough presence and pseudo-absence points for model validation. In the case of endangered or rare species, there might be a shortage of occurrence data, leading to an insufficient test dataset. In such cases, alternatives may include additional data collection based on expert knowledge and experience or utilizing relevant alternative data sources." + ], + "metadata": { + "id": "m889_YCzwNhR" + } + }, + { + "cell_type": "code", + "source": [ + "def print_pres_abs_sizes(TestingDatasets, numiter):\n", + " # Check and print the sizes of presence and pseudo-absence coordinates\n", + " def get_pres_abs_size(x):\n", + " fc = ee.FeatureCollection(TestingDatasets.get(x))\n", + " presence_size = fc.filter(ee.Filter.eq('PresAbs', 1)).size()\n", + " pseudo_absence_size = fc.filter(ee.Filter.eq('PresAbs', 0)).size()\n", + " return ee.List([presence_size, pseudo_absence_size])\n", + "\n", + " sizes_info = ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(get_pres_abs_size).getInfo()\n", + "\n", + " for i, sizes in enumerate(sizes_info):\n", + " presence_size = sizes[0]\n", + " pseudo_absence_size = sizes[1]\n", + " print(f'Iteration {i + 1}: Presence Size = {presence_size}, Pseudo-absence Size = {pseudo_absence_size}')" + ], + "metadata": { + "id": "sdyvrG7swBUp", + "outputId": "2f1dc8dd-ceee-44bf-abca-2ea272fc5208", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 95, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Extracting the Testing Datasets\n", + "TestingDatasets = (ee.List.sequence(3, ee.Number(numiter).multiply(4).subtract(1), 4).map(lambda x: results.get(x)))\n", + "\n", + "print_pres_abs_sizes(TestingDatasets, numiter)" + ], + "metadata": { + "id": "MccWAsdTxjUS", + "outputId": "b150fc57-4086-44ac-9fa4-d28cfadca49a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 199 + } + }, + "execution_count": 97, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Iteration 1: Presence Size = 27, Pseudo-absence Size = 27\n", + "Iteration 2: Presence Size = 35, Pseudo-absence Size = 35\n", + "Iteration 3: Presence Size = 48, Pseudo-absence Size = 25\n", + "Iteration 4: Presence Size = 43, Pseudo-absence Size = 18\n", + "Iteration 5: Presence Size = 36, Pseudo-absence Size = 36\n", + "Iteration 6: Presence Size = 25, Pseudo-absence Size = 25\n", + "Iteration 7: Presence Size = 44, Pseudo-absence Size = 23\n", + "Iteration 8: Presence Size = 21, Pseudo-absence Size = 21\n", + "Iteration 9: Presence Size = 32, Pseudo-absence Size = 32\n", + "Iteration 10: Presence Size = 29, Pseudo-absence Size = 29\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def getAcc(HSM, TData, GrainSize):\n", + " Pr_Prob_Vals = HSM.sampleRegions(collection=TData, properties=['PresAbs'], scale=GrainSize, tileScale=16)\n", + " seq = ee.List.sequence(start=0, end=1, count=25) # Divide 0 to 1 into 25 intervals\n", + " def calculate_metrics(cutoff):\n", + " # Each element of the seq list is passed as cutoff(threshold value)\n", + "\n", + " # Observed present = TP + FN\n", + " Pres = Pr_Prob_Vals.filterMetadata('PresAbs', 'equals', 1)\n", + "\n", + " # TP (True Positive)\n", + " TP = ee.Number(Pres.filterMetadata('classification', 'greater_than', cutoff).size())\n", + "\n", + " # TPR (True Positive Rate) = Recall = Sensitivity = TP / (TP + FN) = TP / Observed present\n", + " TPR = TP.divide(Pres.size())\n", + "\n", + " # Observed absent = FP + TN\n", + " Abs = Pr_Prob_Vals.filterMetadata('PresAbs', 'equals', 0)\n", + "\n", + " # FN (False Negative)\n", + " FN = ee.Number(Pres.filterMetadata('classification', 'less_than', cutoff).size())\n", + "\n", + " # TNR (True Negative Rate) = Specificity = TN / (FP + TN) = TN / Observed absent\n", + " TN = ee.Number(Abs.filterMetadata('classification', 'less_than', cutoff).size())\n", + " TNR = TN.divide(Abs.size())\n", + "\n", + " # FP (False Positive)\n", + " FP = ee.Number(Abs.filterMetadata('classification', 'greater_than', cutoff).size())\n", + "\n", + " # FPR (False Positive Rate) = FP / (FP + TN) = FP / Observed absent\n", + " FPR = FP.divide(Abs.size())\n", + "\n", + " # Precision = TP / (TP + FP) = TP / Predicted present\n", + " Precision = TP.divide(TP.add(FP))\n", + "\n", + " # SUMSS = SUM of Sensitivity and Specificity\n", + " SUMSS = TPR.add(TNR)\n", + "\n", + " return ee.Feature(\n", + " None,\n", + " {\n", + " 'cutoff': cutoff,\n", + " 'TP': TP,\n", + " 'TN': TN,\n", + " 'FP': FP,\n", + " 'FN': FN,\n", + " 'TPR': TPR,\n", + " 'TNR': TNR,\n", + " 'FPR': FPR,\n", + " 'Precision': Precision,\n", + " 'SUMSS': SUMSS\n", + " }\n", + " )\n", + "\n", + " return ee.FeatureCollection(seq.map(calculate_metrics))" + ], + "metadata": { + "id": "GvEH7C9SzI0M", + "outputId": "a5e4a205-648e-4aae-b4c9-bb0cc283d04b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 98, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "def calculate_and_print_auc_metrics(images, TestingDatasets, GrainSize, numiter):\n", + " # Calculate AUC-ROC and AUC-PR\n", + " def calculate_auc_metrics(x):\n", + " HSM = ee.Image(images.get(x))\n", + " TData = ee.FeatureCollection(TestingDatasets.get(x))\n", + " Acc = getAcc(HSM, TData, GrainSize)\n", + "\n", + " # Calculate AUC-ROC\n", + " X = ee.Array(Acc.aggregate_array('FPR'))\n", + " Y = ee.Array(Acc.aggregate_array('TPR'))\n", + " X1 = X.slice(0, 1).subtract(X.slice(0, 0, -1))\n", + " Y1 = Y.slice(0, 1).add(Y.slice(0, 0, -1))\n", + " auc_roc = X1.multiply(Y1).multiply(0.5).reduce('sum', [0]).abs().toList().get(0)\n", + "\n", + " # Calculate AUC-PR\n", + " X = ee.Array(Acc.aggregate_array('TPR'))\n", + " Y = ee.Array(Acc.aggregate_array('Precision'))\n", + " X1 = X.slice(0, 1).subtract(X.slice(0, 0, -1))\n", + " Y1 = Y.slice(0, 1).add(Y.slice(0, 0, -1))\n", + " auc_pr = X1.multiply(Y1).multiply(0.5).reduce('sum', [0]).abs().toList().get(0)\n", + "\n", + " return (auc_roc, auc_pr)\n", + "\n", + " auc_metrics = ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(calculate_auc_metrics).getInfo()\n", + "\n", + " # Print AUC-ROC and AUC-PR for each iteration\n", + " df = pd.DataFrame(auc_metrics, columns=['AUC-ROC', 'AUC-PR'])\n", + " df.index = [f'Iteration {i + 1}' for i in range(len(df))]\n", + " df.to_csv('auc_metrics.csv', index_label='Iteration')\n", + " print(df)\n", + "\n", + " # Calculate mean and standard deviation of AUC-ROC and AUC-PR\n", + " mean_auc_roc, std_auc_roc = df['AUC-ROC'].mean(), df['AUC-ROC'].std()\n", + " mean_auc_pr, std_auc_pr = df['AUC-PR'].mean(), df['AUC-PR'].std()\n", + " print(f'Mean AUC-ROC = {mean_auc_roc:.4f} ± {std_auc_roc:.4f}')\n", + " print(f'Mean AUC-PR = {mean_auc_pr:.4f} ± {std_auc_pr:.4f}')" + ], + "metadata": { + "id": "O6JACvJbzrn7", + "outputId": "a63108a3-6fbe-4cc4-aec1-d1ce31f99fac", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": 99, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "\n", + "# Calculate AUC-ROC and AUC-PR\n", + "calculate_and_print_auc_metrics(images, TestingDatasets, GrainSize, numiter)" + ], + "metadata": { + "id": "WVM4DTduz41j", + "outputId": "ee875256-29b8-4bf7-c579-abdad03cb901", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + } + }, + "execution_count": 100, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " AUC-ROC AUC-PR\n", + "Iteration 1 0.740028 0.667844\n", + "Iteration 2 0.794089 0.710423\n", + "Iteration 3 0.793023 0.796493\n", + "Iteration 4 0.855413 0.919148\n", + "Iteration 5 0.804330 0.777548\n", + "Iteration 6 0.752727 0.798367\n", + "Iteration 7 0.885093 0.762420\n", + "Iteration 8 0.796296 0.698985\n", + "Iteration 9 0.821759 0.763688\n", + "Iteration 10 0.892414 0.883947\n", + "Mean AUC-ROC = 0.8135 ± 0.0510\n", + "Mean AUC-PR = 0.7779 ± 0.0784\n", + "CPU times: user 1.9 s, sys: 199 ms, total: 2.1 s\n", + "Wall time: 4min 31s\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "This tutorial has provided a practical example of using Google Earth Engine (GEE) for Species Distribution Modeling (SDM). An important takeaway is the versatility and flexibility of GEE in the field of SDM. Leveraging Earth Engine's powerful geospatial data processing capabilities opens up endless possibilities for researchers and conservationists to understand and preserve biodiversity on our planet. By applying the knowledge and skills gained from this tutorial, individuals can explore and contribute to this fascinating field of ecological research." + ], + "metadata": { + "id": "QDlaa47s1WjC" + } + }, { "cell_type": "code", "source": [], "metadata": { - "id": "uoJ0mBeRme-E" + "id": "mlyD_GNF2MzK" }, "execution_count": null, "outputs": [] From 8dae819f49b0d4a38ccbda633c6be570dc7626c1 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Tue, 6 Feb 2024 00:20:43 +0900 Subject: [PATCH 16/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 36 +++++++++---------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 27a2ee396..a29c7cdf4 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -15009,12 +15009,12 @@ "plot_variable_importance(importances_list)" ], "metadata": { - "id": "2nhhAUydurwm", - "outputId": "4594b1bd-5407-4f0d-f88b-de830ff9e3a8", "colab": { "base_uri": "https://localhost:8080/", "height": 516 - } + }, + "id": "2nhhAUydurwm", + "outputId": "4594b1bd-5407-4f0d-f88b-de830ff9e3a8" }, "execution_count": 93, "outputs": [ @@ -15110,12 +15110,12 @@ " print(f'Iteration {i + 1}: Presence Size = {presence_size}, Pseudo-absence Size = {pseudo_absence_size}')" ], "metadata": { - "id": "sdyvrG7swBUp", - "outputId": "2f1dc8dd-ceee-44bf-abca-2ea272fc5208", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "sdyvrG7swBUp", + "outputId": "2f1dc8dd-ceee-44bf-abca-2ea272fc5208" }, "execution_count": 95, "outputs": [ @@ -15164,12 +15164,12 @@ "print_pres_abs_sizes(TestingDatasets, numiter)" ], "metadata": { - "id": "MccWAsdTxjUS", - "outputId": "b150fc57-4086-44ac-9fa4-d28cfadca49a", "colab": { "base_uri": "https://localhost:8080/", "height": 199 - } + }, + "id": "MccWAsdTxjUS", + "outputId": "b150fc57-4086-44ac-9fa4-d28cfadca49a" }, "execution_count": 97, "outputs": [ @@ -15284,12 +15284,12 @@ " return ee.FeatureCollection(seq.map(calculate_metrics))" ], "metadata": { - "id": "GvEH7C9SzI0M", - "outputId": "a5e4a205-648e-4aae-b4c9-bb0cc283d04b", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "GvEH7C9SzI0M", + "outputId": "a5e4a205-648e-4aae-b4c9-bb0cc283d04b" }, "execution_count": 98, "outputs": [ @@ -15370,12 +15370,12 @@ " print(f'Mean AUC-PR = {mean_auc_pr:.4f} ± {std_auc_pr:.4f}')" ], "metadata": { - "id": "O6JACvJbzrn7", - "outputId": "a63108a3-6fbe-4cc4-aec1-d1ce31f99fac", "colab": { "base_uri": "https://localhost:8080/", "height": 17 - } + }, + "id": "O6JACvJbzrn7", + "outputId": "a63108a3-6fbe-4cc4-aec1-d1ce31f99fac" }, "execution_count": 99, "outputs": [ @@ -15424,12 +15424,12 @@ "calculate_and_print_auc_metrics(images, TestingDatasets, GrainSize, numiter)" ], "metadata": { - "id": "WVM4DTduz41j", - "outputId": "ee875256-29b8-4bf7-c579-abdad03cb901", "colab": { "base_uri": "https://localhost:8080/", "height": 289 - } + }, + "id": "WVM4DTduz41j", + "outputId": "ee875256-29b8-4bf7-c579-abdad03cb901" }, "execution_count": 100, "outputs": [ From 3456aedd0fdbd1c9b037f8217ee7cab641930d23 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Tue, 6 Feb 2024 00:27:26 +0900 Subject: [PATCH 17/23] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5?= =?UTF-8?q?=ED=95=B4=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../species-distribution-modeling.ipynb | 2487 ++++++++--------- 1 file changed, 1173 insertions(+), 1314 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index a29c7cdf4..f13aca137 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -12,7 +12,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "efab02307cff46e3ad2b1cf108b25aa7": { + "ec18a01a256745efaed76a622519ac4f": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -25,33 +25,33 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 1786, + "bottom": 6657, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 37.54457732085584, - 108.17138671875001 + 35.587733558095216, + 126.8959721004684 ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_5fdf7951a18a4dad874e612b06afd99f", - "IPY_MODEL_f81b89aa37fc4957afcaa7c9516a89f2", - "IPY_MODEL_c9dd146878df458c9a2d5e2e2896ffe5", - "IPY_MODEL_e8454329e19546c491b79019dc9028a4", - "IPY_MODEL_84e3257c97214d6e96d6a06ff71f232e", - "IPY_MODEL_16dda2a123a348da98ef01457d510c03", - "IPY_MODEL_d838762362394200a14e6c4b11a99038", - "IPY_MODEL_57304b09f95a4568b3e2ef27cd57b889" + "IPY_MODEL_40081735809a4ac19c4d84acbde91938", + "IPY_MODEL_914f049607f6472a85e3afe6a97c81b1", + "IPY_MODEL_419ff70fd8e34667b276467f756b618d", + "IPY_MODEL_ac7bde123bae43dc934c9a253f2273c7", + "IPY_MODEL_b174597906d04261a024cf014aa3c842", + "IPY_MODEL_5bc2e90959fb4fbbb1a1864085f12954", + "IPY_MODEL_6f1447ee68bc42778df44e95bb36862d", + "IPY_MODEL_8293fa80884e45449ac9b7950ee4cc70" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_377111f79365487b80b229301d7d2cac", + "default_style": "IPY_MODEL_2d37126201cc4f8f85d88ca8e9f8affc", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_b2f31b6c8b1a453d9aa6a54b6fdd41b5", - "east": 143.34960937500003, + "dragging_style": "IPY_MODEL_6d5ccb249d9146a2b1c5dd9bb84e9f8b", + "east": 135.68115234375003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -61,17 +61,17 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_3f35fcb1b3344b898d5d2abf6c01ac70", - "IPY_MODEL_357da287cd30446cbdde93021cfed110", - "IPY_MODEL_cc24bb56a5ac4d039f40bbfdd2c6b1ae", - "IPY_MODEL_79cbf087f6c04b9cb8a660c732ffe162" + "IPY_MODEL_907d89b6d73a47f08cc4680f1811123a", + "IPY_MODEL_07d6287890bd4f939df3a65145789715", + "IPY_MODEL_d8466aad5f6245d6b077cb2dc4ac8496", + "IPY_MODEL_1d19a7648bf44be39dfd7a8ce8bafc78" ], - "layout": "IPY_MODEL_3477ccc2ddd44453910981689fe08dbf", - "left": 2879, + "layout": "IPY_MODEL_3e8823008c094bc5823e1f7d7a698edc", + "left": 13567, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 50.17689812200107, + "north": 39.07890809706475, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -102,24 +102,24 @@ ], "panes": {}, "prefer_canvas": false, - "right": 3679, + "right": 14367, "scroll_wheel_zoom": true, - "south": 22.43134015636062, - "style": "IPY_MODEL_377111f79365487b80b229301d7d2cac", + "south": 31.93351676190369, + "style": "IPY_MODEL_6d5ccb249d9146a2b1c5dd9bb84e9f8b", "tap": true, "tap_tolerance": 15, - "top": 1386, + "top": 6257, "touch_zoom": true, - "west": 73.03710937500001, + "west": 118.10302734375001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 4, + "zoom": 6, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 } }, - "5fdf7951a18a4dad874e612b06afd99f": { + "40081735809a4ac19c4d84acbde91938": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -141,10 +141,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_7c182f4aedcf43e5b81f1e29b16431a1" + "widget": "IPY_MODEL_8e4af62156be4d8b8104821691ad682b" } }, - "f81b89aa37fc4957afcaa7c9516a89f2": { + "914f049607f6472a85e3afe6a97c81b1": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -170,7 +170,7 @@ "zoom_out_title": "Zoom out" } }, - "c9dd146878df458c9a2d5e2e2896ffe5": { + "419ff70fd8e34667b276467f756b618d": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -188,7 +188,7 @@ "position": "topleft" } }, - "e8454329e19546c491b79019dc9028a4": { + "ac7bde123bae43dc934c9a253f2273c7": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -227,7 +227,7 @@ "remove": true } }, - "84e3257c97214d6e96d6a06ff71f232e": { + "b174597906d04261a024cf014aa3c842": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -253,7 +253,7 @@ "update_when_idle": false } }, - "16dda2a123a348da98ef01457d510c03": { + "5bc2e90959fb4fbbb1a1864085f12954": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -294,7 +294,7 @@ "secondary_length_unit": null } }, - "d838762362394200a14e6c4b11a99038": { + "6f1447ee68bc42778df44e95bb36862d": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -316,10 +316,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_fa4824b4fe794ec39fb8d6943df059df" + "widget": "IPY_MODEL_42a9668a07dd4497980f0c25277e6d18" } }, - "57304b09f95a4568b3e2ef27cd57b889": { + "8293fa80884e45449ac9b7950ee4cc70": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -339,7 +339,7 @@ "prefix": "ipyleaflet" } }, - "377111f79365487b80b229301d7d2cac": { + "2d37126201cc4f8f85d88ca8e9f8affc": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -354,7 +354,7 @@ "cursor": "grab" } }, - "b2f31b6c8b1a453d9aa6a54b6fdd41b5": { + "6d5ccb249d9146a2b1c5dd9bb84e9f8b": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -369,7 +369,7 @@ "cursor": "move" } }, - "3f35fcb1b3344b898d5d2abf6c01ac70": { + "907d89b6d73a47f08cc4680f1811123a": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -421,59 +421,7 @@ "zoom_offset": 0 } }, - "7974287029b74bab93714f247bfe610a": { - "model_module": "jupyter-leaflet", - "model_name": "LeafletTileLayerModel", - "model_module_version": "^0.18", - "state": { - "_model_module": "jupyter-leaflet", - "_model_module_version": "^0.18", - "_model_name": "LeafletTileLayerModel", - "_view_count": null, - "_view_module": "jupyter-leaflet", - "_view_module_version": "^0.18", - "_view_name": "LeafletTileLayerView", - "attribution": "Google Earth Engine", - "base": false, - "bottom": true, - "bounds": null, - "detect_retina": false, - "loading": false, - "max_native_zoom": null, - "max_zoom": 24, - "min_native_zoom": null, - "min_zoom": 0, - "name": "Random Raster", - "no_wrap": false, - "opacity": 1, - "options": [ - "attribution", - "bounds", - "detect_retina", - "max_native_zoom", - "max_zoom", - "min_native_zoom", - "min_zoom", - "no_wrap", - "tile_size", - "tms", - "zoom_offset" - ], - "pane": "", - "popup": null, - "popup_max_height": null, - "popup_max_width": 300, - "popup_min_width": 50, - "show_loading": false, - "subitems": [], - "tile_size": 256, - "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/470074cb26268596f91e594d8e05b884-f977ec3887dbb19c285859c9b463d997/tiles/{z}/{x}/{y}", - "visible": true, - "zoom_offset": 0 - } - }, - "357da287cd30446cbdde93021cfed110": { + "07d6287890bd4f939df3a65145789715": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -520,12 +468,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/b8c6626abab342d31a0a20518f122c13-3bbcc2576d8528bc9ea5eb455e21f7dd/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/b8c6626abab342d31a0a20518f122c13-e77ddd8d5be12988c4e4985ee0a0dfb9/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "cc24bb56a5ac4d039f40bbfdd2c6b1ae": { + "d8466aad5f6245d6b077cb2dc4ac8496": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -572,12 +520,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-26f5c527e702a52f1fb2bd7d79439548/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-b3950a3da89598201ad633ce137ab376/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "3477ccc2ddd44453910981689fe08dbf": { + "3e8823008c094bc5823e1f7d7a698edc": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -629,7 +577,7 @@ "width": "800px" } }, - "f18475bb22ac4d358708033c04225e1a": { + "87b94cfce4fa4be1a814b11c6e92ffc4": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -644,7 +592,7 @@ "cursor": "grab" } }, - "7c182f4aedcf43e5b81f1e29b16431a1": { + "8e4af62156be4d8b8104821691ad682b": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -661,12 +609,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_efd9c41cd1c14ec794795ac01b3650b9" + "IPY_MODEL_e150b8ea0e9e4e6ca4d7fdcac9537b88" ], - "layout": "IPY_MODEL_c854c5f0c07543caa9a38e99dc86e400" + "layout": "IPY_MODEL_144e1da326c249c288e4878a1e526679" } }, - "fa4824b4fe794ec39fb8d6943df059df": { + "42a9668a07dd4497980f0c25277e6d18": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -683,12 +631,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_478a0bd3dd9e4de69aa9a762a66b6f37" + "IPY_MODEL_ddad8da26065420695ff8deea1af347d" ], - "layout": "IPY_MODEL_22b0b5eb8a0744e5850187ed868e6126" + "layout": "IPY_MODEL_649c76ab02b04c8dad91b7be4d01d89e" } }, - "efd9c41cd1c14ec794795ac01b3650b9": { + "e150b8ea0e9e4e6ca4d7fdcac9537b88": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -706,13 +654,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_d83d9508820043e8b5cdbebc6cfbf72b", - "style": "IPY_MODEL_40dde1d58af5411bbd31e7608956c137", + "layout": "IPY_MODEL_379f53be6bc048638c518ca28b0d52d2", + "style": "IPY_MODEL_a1f00c3c17bf482498d4ee29b0670db7", "tooltip": "Search location/data", "value": false } }, - "c854c5f0c07543caa9a38e99dc86e400": { + "144e1da326c249c288e4878a1e526679": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -764,7 +712,7 @@ "width": null } }, - "478a0bd3dd9e4de69aa9a762a66b6f37": { + "ddad8da26065420695ff8deea1af347d": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -782,13 +730,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_b432796c003a42b39d7903ce203bf0d7", - "style": "IPY_MODEL_4d15c7b8e49841f5abda675f0c436a2a", + "layout": "IPY_MODEL_78a60a4cb4ae4304bb0650947b557db5", + "style": "IPY_MODEL_5d10998ac151453cb1ac262a0ee6f8f6", "tooltip": "Toolbar", "value": false } }, - "22b0b5eb8a0744e5850187ed868e6126": { + "649c76ab02b04c8dad91b7be4d01d89e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -840,7 +788,7 @@ "width": null } }, - "d83d9508820043e8b5cdbebc6cfbf72b": { + "379f53be6bc048638c518ca28b0d52d2": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -892,7 +840,7 @@ "width": "28px" } }, - "40dde1d58af5411bbd31e7608956c137": { + "a1f00c3c17bf482498d4ee29b0670db7": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -907,7 +855,7 @@ "description_width": "" } }, - "b432796c003a42b39d7903ce203bf0d7": { + "78a60a4cb4ae4304bb0650947b557db5": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -959,7 +907,7 @@ "width": "28px" } }, - "4d15c7b8e49841f5abda675f0c436a2a": { + "5d10998ac151453cb1ac262a0ee6f8f6": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -974,7 +922,7 @@ "description_width": "" } }, - "79cbf087f6c04b9cb8a660c732ffe162": { + "1d19a7648bf44be39dfd7a8ce8bafc78": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -1021,12 +969,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/15b20caa5748ff2b7410a140758befb4-37911fbfa2a1030265d607abea4544c9/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/15b20caa5748ff2b7410a140758befb4-1f41cf5c7bf54fa1cb733cbff8b05cee/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "0ac7d26aa1814d8f8543b1e51934fe6e": { + "ad4b6510fe6441b8b18d640fefa6ed02": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -1039,34 +987,34 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 3496, + "bottom": 6657, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 33.15594830078649, - 119.55322265625001 + 35.587733558095216, + 126.8959721004684 ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_7b6fe79343ff4e0ba26e3d6e15efb025", - "IPY_MODEL_bc3830f5a2fb40aa959b59e420d707f8", - "IPY_MODEL_5523004c1fe240439047f5179327ac59", - "IPY_MODEL_08b3b839623e48d98cabad14b768473d", - "IPY_MODEL_61b6639d4538439f9221a21eea1db29e", - "IPY_MODEL_41ff2a139e7a42bc8543914b96d906bb", - "IPY_MODEL_c0cbbed906f344648bc4cff3adcbbf75", - "IPY_MODEL_02dc65fdd68040ea908713060cbc5d1a", - "IPY_MODEL_a55df16b967d47df8d47c7ffc55e462a" + "IPY_MODEL_68c6f333036a49c7aaa82f12467c6a44", + "IPY_MODEL_3fe18c8e85b944108c5556b544d9b619", + "IPY_MODEL_8f4046ce7dd24e189ee183b50c7ea3c9", + "IPY_MODEL_70365465ac10473695da7052bc4ce132", + "IPY_MODEL_03b3ff0f74c74be9af7b14868935c379", + "IPY_MODEL_82b74bc6e67e461cbac3436c24405bf7", + "IPY_MODEL_6c8fa4067706438f91a06200897cd914", + "IPY_MODEL_0d425d905d0c4407b6a1b6015d2ba33a", + "IPY_MODEL_2fd7087133154808aea0c7fdb9ae1094" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_8123d2220a574ad0aa40be4afd6759aa", + "default_style": "IPY_MODEL_1bb5e66353b940e0be30b69c46eb0ef8", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_f87ea7550b9148bc9ec2cd984ffddaea", - "east": 137.15332031250003, + "dragging_style": "IPY_MODEL_55397a8b887140f2a476238786ac95b1", + "east": 135.68115234375003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -1076,15 +1024,15 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_ec5842a999604555aa95c5ed9b82dc37", - "IPY_MODEL_571ddde0c22841d68a7868ac66109281" + "IPY_MODEL_81274fed697e4a6e89a380e8c83476ad", + "IPY_MODEL_8e1091fe3999443987a2a393e11b67e5" ], - "layout": "IPY_MODEL_055394cd168c497ab3d2fd16d40d6a5d", - "left": 6417, + "layout": "IPY_MODEL_265f960413c44ef1b35c3a11f75eec3d", + "left": 13567, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 40.17887331434698, + "north": 39.07890809706475, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -1115,24 +1063,24 @@ ], "panes": {}, "prefer_canvas": false, - "right": 7217, + "right": 14367, "scroll_wheel_zoom": true, - "south": 25.48295117535531, - "style": "IPY_MODEL_8123d2220a574ad0aa40be4afd6759aa", + "south": 31.93351676190369, + "style": "IPY_MODEL_28cb9df4b67841b78c63362605940046", "tap": true, "tap_tolerance": 15, - "top": 3096, + "top": 6257, "touch_zoom": true, - "west": 101.99707031250001, + "west": 118.10302734375001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 5, + "zoom": 6, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 } }, - "7b6fe79343ff4e0ba26e3d6e15efb025": { + "68c6f333036a49c7aaa82f12467c6a44": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -1154,10 +1102,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_f0eaba4f88bc4a56aee79e689a62fdeb" + "widget": "IPY_MODEL_c3aba1a59cb044af9e66498026e66893" } }, - "bc3830f5a2fb40aa959b59e420d707f8": { + "3fe18c8e85b944108c5556b544d9b619": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -1183,7 +1131,7 @@ "zoom_out_title": "Zoom out" } }, - "5523004c1fe240439047f5179327ac59": { + "8f4046ce7dd24e189ee183b50c7ea3c9": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -1201,7 +1149,7 @@ "position": "topleft" } }, - "08b3b839623e48d98cabad14b768473d": { + "70365465ac10473695da7052bc4ce132": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -1240,7 +1188,7 @@ "remove": true } }, - "61b6639d4538439f9221a21eea1db29e": { + "03b3ff0f74c74be9af7b14868935c379": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -1266,7 +1214,7 @@ "update_when_idle": false } }, - "41ff2a139e7a42bc8543914b96d906bb": { + "82b74bc6e67e461cbac3436c24405bf7": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -1307,7 +1255,7 @@ "secondary_length_unit": null } }, - "c0cbbed906f344648bc4cff3adcbbf75": { + "6c8fa4067706438f91a06200897cd914": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -1329,10 +1277,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_77534dc0bec14c2888cc9217abbf8035" + "widget": "IPY_MODEL_0ec2004e54a74f8d9012e1377dd24704" } }, - "02dc65fdd68040ea908713060cbc5d1a": { + "0d425d905d0c4407b6a1b6015d2ba33a": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -1352,7 +1300,7 @@ "prefix": "ipyleaflet" } }, - "a55df16b967d47df8d47c7ffc55e462a": { + "2fd7087133154808aea0c7fdb9ae1094": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -1374,10 +1322,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_6023eac322f247d0808755ba38f8a808" + "widget": "IPY_MODEL_c6fb58a471354b7fbd2139e7177907fd" } }, - "8123d2220a574ad0aa40be4afd6759aa": { + "1bb5e66353b940e0be30b69c46eb0ef8": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -1392,7 +1340,7 @@ "cursor": "grab" } }, - "f87ea7550b9148bc9ec2cd984ffddaea": { + "55397a8b887140f2a476238786ac95b1": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -1407,7 +1355,7 @@ "cursor": "move" } }, - "ec5842a999604555aa95c5ed9b82dc37": { + "81274fed697e4a6e89a380e8c83476ad": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -1459,7 +1407,7 @@ "zoom_offset": 0 } }, - "571ddde0c22841d68a7868ac66109281": { + "8e1091fe3999443987a2a393e11b67e5": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -1506,12 +1454,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/89b579926d9cc89296c0bffc92f2dd2f-09f70b2aefb5a30807e9f71cdfac2dff/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/89b579926d9cc89296c0bffc92f2dd2f-2daf94178f185a00ac300cecd475b176/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "055394cd168c497ab3d2fd16d40d6a5d": { + "265f960413c44ef1b35c3a11f75eec3d": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1563,7 +1511,7 @@ "width": "800px" } }, - "b145a01c3d5945fb8ed2472518cb1ba7": { + "28cb9df4b67841b78c63362605940046": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -1578,7 +1526,7 @@ "cursor": "grab" } }, - "f0eaba4f88bc4a56aee79e689a62fdeb": { + "c3aba1a59cb044af9e66498026e66893": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -1595,12 +1543,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0943fd4685a54b70b341bd000cdbc91f" + "IPY_MODEL_05cda8397b1d4c1ab0d6ec8b8782292f" ], - "layout": "IPY_MODEL_f2315c32563e452a9632e16c6e1f8796" + "layout": "IPY_MODEL_1122e9578ed24015860360c3fb9bd3c0" } }, - "77534dc0bec14c2888cc9217abbf8035": { + "0ec2004e54a74f8d9012e1377dd24704": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -1617,12 +1565,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_f0a072b0d2174bd88c2bf8dedea9fa4b" + "IPY_MODEL_7dff1f5368e040ce8aa0a2e0d3593051" ], - "layout": "IPY_MODEL_8f5295fa75d145a89f760e300aa91493" + "layout": "IPY_MODEL_3fb8469a03be4d8d80ec96b95d3a7be3" } }, - "6023eac322f247d0808755ba38f8a808": { + "c6fb58a471354b7fbd2139e7177907fd": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ -1637,7 +1585,7 @@ "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", - "layout": "IPY_MODEL_dc7a0a178b8d49109d8d5d4fe7615858", + "layout": "IPY_MODEL_6017d2ba81cf47059b53473b9b02a613", "msg_id": "", "outputs": [ { @@ -1651,7 +1599,7 @@ ] } }, - "0943fd4685a54b70b341bd000cdbc91f": { + "05cda8397b1d4c1ab0d6ec8b8782292f": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -1669,13 +1617,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_08e382e7a20b4eee90eb087d8225e7c1", - "style": "IPY_MODEL_d2e8ba57951749ab955048105ddcad11", + "layout": "IPY_MODEL_6df98ca6cbf741e293b86cb9b21491d3", + "style": "IPY_MODEL_65bea7d4f8a542fdb4db941c30e7e2f6", "tooltip": "Search location/data", "value": false } }, - "f2315c32563e452a9632e16c6e1f8796": { + "1122e9578ed24015860360c3fb9bd3c0": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1727,7 +1675,7 @@ "width": null } }, - "f0a072b0d2174bd88c2bf8dedea9fa4b": { + "7dff1f5368e040ce8aa0a2e0d3593051": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -1745,13 +1693,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_ee6822f7630e4e7f8d2b41138d55bf61", - "style": "IPY_MODEL_5e67d047f0ca4265aed15af47375d653", + "layout": "IPY_MODEL_dc3b7752f47a4d16b96525983f17e626", + "style": "IPY_MODEL_37806e7c513c48e1b524cc90e0228cd5", "tooltip": "Toolbar", "value": false } }, - "8f5295fa75d145a89f760e300aa91493": { + "3fb8469a03be4d8d80ec96b95d3a7be3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1803,7 +1751,7 @@ "width": null } }, - "dc7a0a178b8d49109d8d5d4fe7615858": { + "6017d2ba81cf47059b53473b9b02a613": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1855,7 +1803,7 @@ "width": "100px" } }, - "08e382e7a20b4eee90eb087d8225e7c1": { + "6df98ca6cbf741e293b86cb9b21491d3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1907,7 +1855,7 @@ "width": "28px" } }, - "d2e8ba57951749ab955048105ddcad11": { + "65bea7d4f8a542fdb4db941c30e7e2f6": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -1922,7 +1870,7 @@ "description_width": "" } }, - "ee6822f7630e4e7f8d2b41138d55bf61": { + "dc3b7752f47a4d16b96525983f17e626": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1974,7 +1922,7 @@ "width": "28px" } }, - "5e67d047f0ca4265aed15af47375d653": { + "37806e7c513c48e1b524cc90e0228cd5": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -1989,7 +1937,7 @@ "description_width": "" } }, - "bd778ad418634776993304e38cd1ad02": { + "6edda6ba5bb648bd9dc55dd3328fd82a": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -2002,34 +1950,34 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 1984, + "bottom": 6657, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 22.63429269379353, - 107.00683593750001 + 35.587733558095216, + 126.8959721004684 ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_89ba6396ab264cb1827aa7084280ab72", - "IPY_MODEL_bf6555c5b2814169a35ff3b3b6bc8095", - "IPY_MODEL_6a171d05b22b4dde8f13ecfde310d022", - "IPY_MODEL_4eb8738933d8458c905fe9c1cb02aeec", - "IPY_MODEL_33111afdc5b7461ba0b58c3b5f87d1e8", - "IPY_MODEL_c61c10dbef514e878086a63de0f84b44", - "IPY_MODEL_fd727297110d4fada437c5e998becbe3", - "IPY_MODEL_448af660fa9c4243a5d0ea80c1adc0b2", - "IPY_MODEL_2b00969d96e34bc39e1acdb7b23cc7ba" + "IPY_MODEL_5dfe200a57c847e396f711de43ec719d", + "IPY_MODEL_f39d82350d1f47bcb63d2817c70c587b", + "IPY_MODEL_29d110e4bbd44afa85f1db5db4748f90", + "IPY_MODEL_780acf40c0274d4089592719b43b3f43", + "IPY_MODEL_7baefba25b77424a99027681c99c34cb", + "IPY_MODEL_f47a03bc228649eb92cc412445d64c68", + "IPY_MODEL_945b1b623cdf444c909fd2a1da927dff", + "IPY_MODEL_f6efdfd64e614ac8813e9e911a1fce80", + "IPY_MODEL_7e330ef004474efda8b3eced32178003" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_e6aa5ecde8be4747827bb8d42d726390", + "default_style": "IPY_MODEL_2069e091220c42b6ba1d79c063f3e9e6", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_afd0660ef7f248b4be6d6b5fdaeb1fe1", - "east": 142.20703125000003, + "dragging_style": "IPY_MODEL_0bd23be2e86e4e8b9776cac7e2c47ed8", + "east": 135.68115234375003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -2039,15 +1987,15 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_3faf6fa1bdce4906a7b253f15f873577", - "IPY_MODEL_414d832f4e044cc1934714a7ed8fb21b" + "IPY_MODEL_4920efe9f2684b91b5a02a7d7cb0440b", + "IPY_MODEL_8eea4e8291c0497b81257afdb11651c1" ], - "layout": "IPY_MODEL_2b3e3fb6d8124dd49cfe7bdefc7ba96c", - "left": 2866, + "layout": "IPY_MODEL_cef9eeda18df40a4a7e2fb4ad7d63240", + "left": 13567, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 37.71859032558816, + "north": 39.07890809706475, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -2078,24 +2026,24 @@ ], "panes": {}, "prefer_canvas": false, - "right": 3666, + "right": 14367, "scroll_wheel_zoom": true, - "south": 5.61598581915534, - "style": "IPY_MODEL_e6aa5ecde8be4747827bb8d42d726390", + "south": 31.93351676190369, + "style": "IPY_MODEL_b90d0d82f3d944f99dd83e103b57fa60", "tap": true, "tap_tolerance": 15, - "top": 1584, + "top": 6257, "touch_zoom": true, - "west": 71.89453125000001, + "west": 118.10302734375001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 4, + "zoom": 6, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 } }, - "89ba6396ab264cb1827aa7084280ab72": { + "5dfe200a57c847e396f711de43ec719d": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -2117,10 +2065,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_8c868f718f804df095927f5b21318bf5" + "widget": "IPY_MODEL_8169818152d9407798f5df01cc0addc9" } }, - "bf6555c5b2814169a35ff3b3b6bc8095": { + "f39d82350d1f47bcb63d2817c70c587b": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -2146,7 +2094,7 @@ "zoom_out_title": "Zoom out" } }, - "6a171d05b22b4dde8f13ecfde310d022": { + "29d110e4bbd44afa85f1db5db4748f90": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -2164,7 +2112,7 @@ "position": "topleft" } }, - "4eb8738933d8458c905fe9c1cb02aeec": { + "780acf40c0274d4089592719b43b3f43": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -2203,7 +2151,7 @@ "remove": true } }, - "33111afdc5b7461ba0b58c3b5f87d1e8": { + "7baefba25b77424a99027681c99c34cb": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -2229,7 +2177,7 @@ "update_when_idle": false } }, - "c61c10dbef514e878086a63de0f84b44": { + "f47a03bc228649eb92cc412445d64c68": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -2270,7 +2218,7 @@ "secondary_length_unit": null } }, - "fd727297110d4fada437c5e998becbe3": { + "945b1b623cdf444c909fd2a1da927dff": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -2292,10 +2240,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_00cacd66691647cd80d41f88208aed1c" + "widget": "IPY_MODEL_63420d977a5f4c8495d23c02b320581e" } }, - "448af660fa9c4243a5d0ea80c1adc0b2": { + "f6efdfd64e614ac8813e9e911a1fce80": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -2315,7 +2263,7 @@ "prefix": "ipyleaflet" } }, - "2b00969d96e34bc39e1acdb7b23cc7ba": { + "7e330ef004474efda8b3eced32178003": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -2337,10 +2285,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_20d8e4f3dd624250bb28d2da9d112fcc" + "widget": "IPY_MODEL_a2aedccb9e66493e8669508559d05668" } }, - "e6aa5ecde8be4747827bb8d42d726390": { + "2069e091220c42b6ba1d79c063f3e9e6": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -2355,7 +2303,7 @@ "cursor": "grab" } }, - "afd0660ef7f248b4be6d6b5fdaeb1fe1": { + "0bd23be2e86e4e8b9776cac7e2c47ed8": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -2370,7 +2318,7 @@ "cursor": "move" } }, - "3faf6fa1bdce4906a7b253f15f873577": { + "4920efe9f2684b91b5a02a7d7cb0440b": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -2422,7 +2370,7 @@ "zoom_offset": 0 } }, - "414d832f4e044cc1934714a7ed8fb21b": { + "8eea4e8291c0497b81257afdb11651c1": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -2469,12 +2417,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/c9f31fa05f620e58d015a416e54d1c3b-7842e5f3f92a63f565998695384a2eca/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/c9f31fa05f620e58d015a416e54d1c3b-58585a831eac1c7848000caae7b91d28/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "2b3e3fb6d8124dd49cfe7bdefc7ba96c": { + "cef9eeda18df40a4a7e2fb4ad7d63240": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2526,7 +2474,7 @@ "width": "800px" } }, - "8e9e786751bf4a1fb2bc0989e61c6be3": { + "b90d0d82f3d944f99dd83e103b57fa60": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -2541,7 +2489,7 @@ "cursor": "grab" } }, - "8c868f718f804df095927f5b21318bf5": { + "8169818152d9407798f5df01cc0addc9": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -2558,12 +2506,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_5ad8210daf1448b88a415b7c5b43f703" + "IPY_MODEL_9999e11fbf834f869596f2db90ea9c37" ], - "layout": "IPY_MODEL_4f733436546340baadaaf3cab7d11716" + "layout": "IPY_MODEL_8fae16cef3104206861b1450faf0e412" } }, - "00cacd66691647cd80d41f88208aed1c": { + "63420d977a5f4c8495d23c02b320581e": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -2580,12 +2528,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_a8f5ca16930848c68e378969e327babb" + "IPY_MODEL_7877ecf0cb624571a8926cb6dc78c0a2" ], - "layout": "IPY_MODEL_4858f5dc633d411a9c4594d0a232587a" + "layout": "IPY_MODEL_3ba3e0736a404fb3bb9cf8ad68609f50" } }, - "20d8e4f3dd624250bb28d2da9d112fcc": { + "a2aedccb9e66493e8669508559d05668": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ -2600,7 +2548,7 @@ "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", - "layout": "IPY_MODEL_9af3c9a565f346a2872c8e7c1fe0a36b", + "layout": "IPY_MODEL_5cd12bc944d04c1ca1caf58d92b7b5ae", "msg_id": "", "outputs": [ { @@ -2614,7 +2562,7 @@ ] } }, - "5ad8210daf1448b88a415b7c5b43f703": { + "9999e11fbf834f869596f2db90ea9c37": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -2632,13 +2580,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_4ab5dfa79618456f97b6ed532c9829d4", - "style": "IPY_MODEL_50b552f96b464adea9b15273a1596e91", + "layout": "IPY_MODEL_fdef9d8a239141ba9498aa9f99dbbea3", + "style": "IPY_MODEL_03c7e1346a2c413d8463905a96ceaad0", "tooltip": "Search location/data", "value": false } }, - "4f733436546340baadaaf3cab7d11716": { + "8fae16cef3104206861b1450faf0e412": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2690,7 +2638,7 @@ "width": null } }, - "a8f5ca16930848c68e378969e327babb": { + "7877ecf0cb624571a8926cb6dc78c0a2": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -2708,13 +2656,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_33bd735ab4e54f47b79fe6804b2c409e", - "style": "IPY_MODEL_cbda0586f2a34f8ca4032b9a713318d2", + "layout": "IPY_MODEL_72e131ab901d49c6af32a4a37fcb54ff", + "style": "IPY_MODEL_384a473fe1124fcd98888b8692807b59", "tooltip": "Toolbar", "value": false } }, - "4858f5dc633d411a9c4594d0a232587a": { + "3ba3e0736a404fb3bb9cf8ad68609f50": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2766,7 +2714,7 @@ "width": null } }, - "9af3c9a565f346a2872c8e7c1fe0a36b": { + "5cd12bc944d04c1ca1caf58d92b7b5ae": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2818,7 +2766,7 @@ "width": "100px" } }, - "4ab5dfa79618456f97b6ed532c9829d4": { + "fdef9d8a239141ba9498aa9f99dbbea3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2870,7 +2818,7 @@ "width": "28px" } }, - "50b552f96b464adea9b15273a1596e91": { + "03c7e1346a2c413d8463905a96ceaad0": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -2885,7 +2833,7 @@ "description_width": "" } }, - "33bd735ab4e54f47b79fe6804b2c409e": { + "72e131ab901d49c6af32a4a37fcb54ff": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2937,7 +2885,7 @@ "width": "28px" } }, - "cbda0586f2a34f8ca4032b9a713318d2": { + "384a473fe1124fcd98888b8692807b59": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -2952,7 +2900,7 @@ "description_width": "" } }, - "eeed0724c6774ca4ae71f9414d7d3d36": { + "7a7383eaa7d644fca2613b964ab02df3": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -2974,24 +2922,24 @@ ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_d7add71f9a694c1eab1a68d22844182f", - "IPY_MODEL_a7f87d5b06044d57b272eb3df58c27ed", - "IPY_MODEL_e6e6b787daa04ca68a4591406c7995f7", - "IPY_MODEL_e7b4368dad5448a79254daf75160eec2", - "IPY_MODEL_f50ee59d00bd4c5f82b18ce212a20609", - "IPY_MODEL_117743a21cf44085a07f630c0b3b861a", - "IPY_MODEL_0c63f792b3114394bd82e137f6392512", - "IPY_MODEL_d2f84366501d48eca89e772ec96e0d81", - "IPY_MODEL_9762a337a7bc40848087c346ba0032e0" + "IPY_MODEL_431b70f7b67e4c8c86bf93e37cdb66fc", + "IPY_MODEL_e134170fd7b44730875e7e1b207ebbe9", + "IPY_MODEL_5d95e044822c4965963e7af30fb36dea", + "IPY_MODEL_ec202420cbab485481e6a5ea9583155f", + "IPY_MODEL_04323a5dcf5044b28bafab055e34bba7", + "IPY_MODEL_0cc1944758a44781905ffb19ffe5f88e", + "IPY_MODEL_b9f365a7574542168f0b1cb9557ce503", + "IPY_MODEL_c29a2bde31134939b51486c39b9fccdb", + "IPY_MODEL_1a0b7bfa32124363b0912f80ddb76cce" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_d4faaee6a05e41cd91940435b39b9364", + "default_style": "IPY_MODEL_81c963e64c134ffaa6e81fd95eb6daad", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_2f57730680724aa6800e00a81897560d", + "dragging_style": "IPY_MODEL_dd1783b57f1145a1bb5980b66db7a985", "east": 135.68115234375003, "fullscreen": false, "inertia": true, @@ -3002,10 +2950,10 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_d0354527efe24848864f4cb06adb6195", - "IPY_MODEL_553cd97ec41c47ce999e1db0a46787c5" + "IPY_MODEL_4c7788f05efc4416ac2eaf5fad1fe479", + "IPY_MODEL_454132b50c3f47c0a857f830d4b74447" ], - "layout": "IPY_MODEL_669a619518424e6ba7a434d920814d8a", + "layout": "IPY_MODEL_f27e44ff08864d7fbc22379b92f572f6", "left": 13567, "max_zoom": 24, "min_zoom": null, @@ -3044,7 +2992,7 @@ "right": 14367, "scroll_wheel_zoom": true, "south": 31.93351676190369, - "style": "IPY_MODEL_510af8d670614271a20ccb4c2868a71d", + "style": "IPY_MODEL_f0fd3c53404945cb8f024216ff4cb5cf", "tap": true, "tap_tolerance": 15, "top": 6257, @@ -3058,7 +3006,7 @@ "zoom_snap": 1 } }, - "d7add71f9a694c1eab1a68d22844182f": { + "431b70f7b67e4c8c86bf93e37cdb66fc": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -3080,10 +3028,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_1c78454c17ad4a32a1737be1dc8ca79c" + "widget": "IPY_MODEL_b2bb93f6400a48b4afd487a1353048a8" } }, - "a7f87d5b06044d57b272eb3df58c27ed": { + "e134170fd7b44730875e7e1b207ebbe9": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -3109,7 +3057,7 @@ "zoom_out_title": "Zoom out" } }, - "e6e6b787daa04ca68a4591406c7995f7": { + "5d95e044822c4965963e7af30fb36dea": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -3127,7 +3075,7 @@ "position": "topleft" } }, - "e7b4368dad5448a79254daf75160eec2": { + "ec202420cbab485481e6a5ea9583155f": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -3166,7 +3114,7 @@ "remove": true } }, - "f50ee59d00bd4c5f82b18ce212a20609": { + "04323a5dcf5044b28bafab055e34bba7": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -3192,7 +3140,7 @@ "update_when_idle": false } }, - "117743a21cf44085a07f630c0b3b861a": { + "0cc1944758a44781905ffb19ffe5f88e": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -3233,7 +3181,7 @@ "secondary_length_unit": null } }, - "0c63f792b3114394bd82e137f6392512": { + "b9f365a7574542168f0b1cb9557ce503": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -3255,10 +3203,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_3742a1693d0a4ec38689ef4abd3b445f" + "widget": "IPY_MODEL_591b3f89b5404fcea06c3b688cb8424a" } }, - "d2f84366501d48eca89e772ec96e0d81": { + "c29a2bde31134939b51486c39b9fccdb": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -3278,7 +3226,7 @@ "prefix": "ipyleaflet" } }, - "9762a337a7bc40848087c346ba0032e0": { + "1a0b7bfa32124363b0912f80ddb76cce": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -3300,10 +3248,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_7141995bd812493ba260e61947727b52" + "widget": "IPY_MODEL_8d17f8cf005f40bb8febfbeaff4d511c" } }, - "d4faaee6a05e41cd91940435b39b9364": { + "81c963e64c134ffaa6e81fd95eb6daad": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -3318,7 +3266,7 @@ "cursor": "grab" } }, - "2f57730680724aa6800e00a81897560d": { + "dd1783b57f1145a1bb5980b66db7a985": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -3333,7 +3281,7 @@ "cursor": "move" } }, - "d0354527efe24848864f4cb06adb6195": { + "4c7788f05efc4416ac2eaf5fad1fe479": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -3385,7 +3333,7 @@ "zoom_offset": 0 } }, - "553cd97ec41c47ce999e1db0a46787c5": { + "454132b50c3f47c0a857f830d4b74447": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -3432,12 +3380,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/e8d569f7a238d148217e868ced4f87a2-69cb382dc8ea09f105c4b82d8f594303/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/e8d569f7a238d148217e868ced4f87a2-bc60d67d748c81a77a0d1f62719686ab/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "669a619518424e6ba7a434d920814d8a": { + "f27e44ff08864d7fbc22379b92f572f6": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3489,7 +3437,7 @@ "width": "800px" } }, - "510af8d670614271a20ccb4c2868a71d": { + "f0fd3c53404945cb8f024216ff4cb5cf": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -3504,7 +3452,7 @@ "cursor": "grab" } }, - "1c78454c17ad4a32a1737be1dc8ca79c": { + "b2bb93f6400a48b4afd487a1353048a8": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -3521,12 +3469,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_d7df4e6709ba4292bd71c0d6d4a06b33" + "IPY_MODEL_cb650a05204047079dd426bb7b0eff4d" ], - "layout": "IPY_MODEL_c58b354684e2468dbdd7d8477d33b5a5" + "layout": "IPY_MODEL_9dd59080201c42cf900756e44000215a" } }, - "3742a1693d0a4ec38689ef4abd3b445f": { + "591b3f89b5404fcea06c3b688cb8424a": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -3543,12 +3491,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_703de6e1dede42afae7090e28942afb1" + "IPY_MODEL_34ff26975acf4a8d80318b099346d1ce" ], - "layout": "IPY_MODEL_ec512f45d5304791b2a1e05a2feed243" + "layout": "IPY_MODEL_1af4287c8c17433a8f7b6c0862107cf7" } }, - "7141995bd812493ba260e61947727b52": { + "8d17f8cf005f40bb8febfbeaff4d511c": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ -3563,7 +3511,7 @@ "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", - "layout": "IPY_MODEL_df9e63a9968a415bb6331ed06676b201", + "layout": "IPY_MODEL_5ebdf59b3c5a4e59aca4c129f902c333", "msg_id": "", "outputs": [ { @@ -3577,7 +3525,7 @@ ] } }, - "d7df4e6709ba4292bd71c0d6d4a06b33": { + "cb650a05204047079dd426bb7b0eff4d": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -3595,13 +3543,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_2747267b006447ed890a7b8d6715085a", - "style": "IPY_MODEL_04cfd6bdc7c34d6885ba9149d9f14158", + "layout": "IPY_MODEL_aa06322df74e46eea3851794cfdf7002", + "style": "IPY_MODEL_77fcffd404a84de0ac12b00b7336b1fe", "tooltip": "Search location/data", "value": false } }, - "c58b354684e2468dbdd7d8477d33b5a5": { + "9dd59080201c42cf900756e44000215a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3653,7 +3601,7 @@ "width": null } }, - "703de6e1dede42afae7090e28942afb1": { + "34ff26975acf4a8d80318b099346d1ce": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -3671,13 +3619,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_b228661b472c4012a07a9f8cc75ff29e", - "style": "IPY_MODEL_a5189495d93f4c499c20626066f527e3", + "layout": "IPY_MODEL_e1c614cfbe7548758e2d4d78255ea3ae", + "style": "IPY_MODEL_78a44e68b9274b5a9dd41dfda518fcbc", "tooltip": "Toolbar", "value": false } }, - "ec512f45d5304791b2a1e05a2feed243": { + "1af4287c8c17433a8f7b6c0862107cf7": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3729,7 +3677,7 @@ "width": null } }, - "df9e63a9968a415bb6331ed06676b201": { + "5ebdf59b3c5a4e59aca4c129f902c333": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3781,7 +3729,7 @@ "width": "100px" } }, - "2747267b006447ed890a7b8d6715085a": { + "aa06322df74e46eea3851794cfdf7002": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3833,7 +3781,7 @@ "width": "28px" } }, - "04cfd6bdc7c34d6885ba9149d9f14158": { + "77fcffd404a84de0ac12b00b7336b1fe": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -3848,7 +3796,7 @@ "description_width": "" } }, - "b228661b472c4012a07a9f8cc75ff29e": { + "e1c614cfbe7548758e2d4d78255ea3ae": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -3900,7 +3848,7 @@ "width": "28px" } }, - "a5189495d93f4c499c20626066f527e3": { + "78a44e68b9274b5a9dd41dfda518fcbc": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -3915,7 +3863,7 @@ "description_width": "" } }, - "ef4fd7dfae2e452c8f4190884fbccb42": { + "83423ce2b9e54f68a49b38481fd8e469": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -3928,34 +3876,34 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 50974, + "bottom": 6657, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 37.53259896192619, - 126.73278808593751 + 35.587733558095216, + 126.8959721004684 ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_4aa9d7e81cd44ab2b9fe5860c33057e9", - "IPY_MODEL_06ce1b5cba0a4ba19b17bd566bd45f65", - "IPY_MODEL_f07bebea44b64c76a56d1ab0a7d7f5e7", - "IPY_MODEL_94e01dc8e4234d4791d103169905a6ae", - "IPY_MODEL_65478ba4e1d349d3b9d54f056d54e6a9", - "IPY_MODEL_a2570adcb9704dfe80d4aefe9a9911ab", - "IPY_MODEL_f3a2ac8a04d449a1b0c57b0c573a019f", - "IPY_MODEL_0b1e4d1e0d13456ba9e976251e955207", - "IPY_MODEL_291da14bcdff4172a9dafcdb9015a6a8" + "IPY_MODEL_6f35054727ee43959a5630683d50a622", + "IPY_MODEL_ae588b174ef64f779ee364e27592e993", + "IPY_MODEL_0d7218a539fa4c02b327103fdb1cfe76", + "IPY_MODEL_01a0753c1da24e0eb084dc40409f8f28", + "IPY_MODEL_461c16efc802454096aa5cf192e94253", + "IPY_MODEL_6d260d1c16674ea0b0dd42403135d029", + "IPY_MODEL_0212e33ecb234182b364e1bcee104515", + "IPY_MODEL_a1f5c446dd5543e09f3dad56f59f251b", + "IPY_MODEL_ebba29a794a947bcac0668a1f30da1ad" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_812ce2194c6a45e7accd47717e3706d6", + "default_style": "IPY_MODEL_a8995359c9324eb5a3d7d1b65c2a320f", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_87a59ec985624f938acee2d5a65b337f", - "east": 127.83142089843751, + "dragging_style": "IPY_MODEL_9ed8d5a1d90a47bf8f916cd1500a0653", + "east": 135.68115234375003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -3965,15 +3913,15 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_f4c1ce3e881c4b5780d9f074c2013b2c", - "IPY_MODEL_be97d93287fa491ca8d6b097c9d373ba" + "IPY_MODEL_d53d118dde584cb497411f4c8dafcaaa", + "IPY_MODEL_351c6179212e47c48cd9965a9d27de90" ], - "layout": "IPY_MODEL_f441b5da4e4f444eb1282070aac5398e", - "left": 111278, + "layout": "IPY_MODEL_61b5c2520f524bb58ddb26e16a607953", + "left": 13567, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 37.965854128749434, + "north": 39.07890809706475, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -4004,24 +3952,24 @@ ], "panes": {}, "prefer_canvas": false, - "right": 112078, + "right": 14367, "scroll_wheel_zoom": true, - "south": 37.09462150015557, - "style": "IPY_MODEL_812ce2194c6a45e7accd47717e3706d6", + "south": 31.93351676190369, + "style": "IPY_MODEL_01a5f1504f5d44e6ace27c2cf33e6314", "tap": true, "tap_tolerance": 15, - "top": 50574, + "top": 6257, "touch_zoom": true, - "west": 125.63415527343751, + "west": 118.10302734375001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 9, + "zoom": 6, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 } }, - "4aa9d7e81cd44ab2b9fe5860c33057e9": { + "6f35054727ee43959a5630683d50a622": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -4043,10 +3991,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_fd9b803ae3f34cb4bda622545423554d" + "widget": "IPY_MODEL_e6501a8d76e442bd9f5de2f86f9ce179" } }, - "06ce1b5cba0a4ba19b17bd566bd45f65": { + "ae588b174ef64f779ee364e27592e993": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -4072,7 +4020,7 @@ "zoom_out_title": "Zoom out" } }, - "f07bebea44b64c76a56d1ab0a7d7f5e7": { + "0d7218a539fa4c02b327103fdb1cfe76": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -4090,7 +4038,7 @@ "position": "topleft" } }, - "94e01dc8e4234d4791d103169905a6ae": { + "01a0753c1da24e0eb084dc40409f8f28": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -4129,7 +4077,7 @@ "remove": true } }, - "65478ba4e1d349d3b9d54f056d54e6a9": { + "461c16efc802454096aa5cf192e94253": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -4155,7 +4103,7 @@ "update_when_idle": false } }, - "a2570adcb9704dfe80d4aefe9a9911ab": { + "6d260d1c16674ea0b0dd42403135d029": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -4196,7 +4144,7 @@ "secondary_length_unit": null } }, - "f3a2ac8a04d449a1b0c57b0c573a019f": { + "0212e33ecb234182b364e1bcee104515": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -4218,10 +4166,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_a9479b474ddc43f0b1ea377f5d72781b" + "widget": "IPY_MODEL_b5655ceed92a43d5992a5fde031110c8" } }, - "0b1e4d1e0d13456ba9e976251e955207": { + "a1f5c446dd5543e09f3dad56f59f251b": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -4241,7 +4189,7 @@ "prefix": "ipyleaflet" } }, - "291da14bcdff4172a9dafcdb9015a6a8": { + "ebba29a794a947bcac0668a1f30da1ad": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -4263,10 +4211,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_9547a5384a064ae4b65b3e2c341ff873" + "widget": "IPY_MODEL_6067658655b6493c9dde8c753f2e72aa" } }, - "812ce2194c6a45e7accd47717e3706d6": { + "a8995359c9324eb5a3d7d1b65c2a320f": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -4281,7 +4229,7 @@ "cursor": "grab" } }, - "87a59ec985624f938acee2d5a65b337f": { + "9ed8d5a1d90a47bf8f916cd1500a0653": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -4296,7 +4244,7 @@ "cursor": "move" } }, - "f4c1ce3e881c4b5780d9f074c2013b2c": { + "d53d118dde584cb497411f4c8dafcaaa": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -4348,7 +4296,7 @@ "zoom_offset": 0 } }, - "be97d93287fa491ca8d6b097c9d373ba": { + "351c6179212e47c48cd9965a9d27de90": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -4395,12 +4343,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/9120f791e7bf382318ef66a7e858537f-f6385155d9c8f9a4fa4d4016a7af96d7/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/9120f791e7bf382318ef66a7e858537f-987e53f3f984fb3709435166009f75f1/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "f441b5da4e4f444eb1282070aac5398e": { + "61b5c2520f524bb58ddb26e16a607953": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4452,7 +4400,7 @@ "width": "800px" } }, - "f3fb193232284f45becafbd6a3a24b46": { + "01a5f1504f5d44e6ace27c2cf33e6314": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -4467,7 +4415,7 @@ "cursor": "grab" } }, - "fd9b803ae3f34cb4bda622545423554d": { + "e6501a8d76e442bd9f5de2f86f9ce179": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -4484,12 +4432,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_120ecccbdce240c4bb2e6fe548fa452e" + "IPY_MODEL_a076579ca63c4f0f9267af8af6820b3e" ], - "layout": "IPY_MODEL_520892cebe1d466c8aafeca7a98f22b3" + "layout": "IPY_MODEL_3b00a188691d445d897d5fb4f5d3b285" } }, - "a9479b474ddc43f0b1ea377f5d72781b": { + "b5655ceed92a43d5992a5fde031110c8": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -4506,12 +4454,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_fbc54ef0969849aebe2cdce27cae4c64" + "IPY_MODEL_f2824092392e430a9492dbf3a1421216" ], - "layout": "IPY_MODEL_62513b235c2242e1a6fd416c5d255ab8" + "layout": "IPY_MODEL_e80b2be5cc1a45ab9ca1aae28292866c" } }, - "9547a5384a064ae4b65b3e2c341ff873": { + "6067658655b6493c9dde8c753f2e72aa": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ -4526,7 +4474,7 @@ "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", - "layout": "IPY_MODEL_8978ae99e798438ab7ec3be6c75a75b1", + "layout": "IPY_MODEL_60d7cd77522f49cda5044c8bb65ecb0b", "msg_id": "", "outputs": [ { @@ -4540,7 +4488,7 @@ ] } }, - "120ecccbdce240c4bb2e6fe548fa452e": { + "a076579ca63c4f0f9267af8af6820b3e": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -4558,13 +4506,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_eb836cc637c44cc99499e5d5fa143c06", - "style": "IPY_MODEL_376eaa60c64c44adb7842d4457478935", + "layout": "IPY_MODEL_a5cb1f53b8da4786ac22661f04a7abac", + "style": "IPY_MODEL_dddb90bcda1b4104bf55b54c4db4e45a", "tooltip": "Search location/data", "value": false } }, - "520892cebe1d466c8aafeca7a98f22b3": { + "3b00a188691d445d897d5fb4f5d3b285": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4616,7 +4564,7 @@ "width": null } }, - "fbc54ef0969849aebe2cdce27cae4c64": { + "f2824092392e430a9492dbf3a1421216": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -4634,13 +4582,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_dce21eaa742d4801a11d006d3486cc10", - "style": "IPY_MODEL_e2527a8c99034a60853950a96330d203", + "layout": "IPY_MODEL_4a26e72a97014097b4d0209ededd9b98", + "style": "IPY_MODEL_11d294929130402e8603112f50e7e51b", "tooltip": "Toolbar", "value": false } }, - "62513b235c2242e1a6fd416c5d255ab8": { + "e80b2be5cc1a45ab9ca1aae28292866c": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4692,7 +4640,7 @@ "width": null } }, - "8978ae99e798438ab7ec3be6c75a75b1": { + "60d7cd77522f49cda5044c8bb65ecb0b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4744,7 +4692,7 @@ "width": "100px" } }, - "eb836cc637c44cc99499e5d5fa143c06": { + "a5cb1f53b8da4786ac22661f04a7abac": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4796,7 +4744,7 @@ "width": "28px" } }, - "376eaa60c64c44adb7842d4457478935": { + "dddb90bcda1b4104bf55b54c4db4e45a": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -4811,7 +4759,7 @@ "description_width": "" } }, - "dce21eaa742d4801a11d006d3486cc10": { + "4a26e72a97014097b4d0209ededd9b98": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4863,7 +4811,7 @@ "width": "28px" } }, - "e2527a8c99034a60853950a96330d203": { + "11d294929130402e8603112f50e7e51b": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -4878,7 +4826,7 @@ "description_width": "" } }, - "82ebad0576144aaaa0773dd83736b54b": { + "54eec2b7d1be430499568ac5041c78e3": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -4900,24 +4848,24 @@ ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_852876ffcabd4b5b8e4d3b8efb8329b8", - "IPY_MODEL_8cd9ebc2b0b241a79bd79590ef875026", - "IPY_MODEL_ad9990fa49cc49d3a0055d7832f47e5b", - "IPY_MODEL_be76f04c454c404abf577ab9f46c0ad6", - "IPY_MODEL_4a6f43cf340849bbaa86c3842f7a43a2", - "IPY_MODEL_cf03dbe7b9c247d9a6abe1d2a1888038", - "IPY_MODEL_cd436ad9c9824776b07815c16954081f", - "IPY_MODEL_f875b1c5bd824eb58e8c284245a73c2c", - "IPY_MODEL_b207ea8fb81b4232b68a544c6831f3ba" + "IPY_MODEL_1f22e72cc0754b4bb765671ec2889356", + "IPY_MODEL_d111a3c4ec774f37823a3b7ae9b5d880", + "IPY_MODEL_c855762df07c42bfa53b6e371877b21d", + "IPY_MODEL_a9ca43e4eb3d47e09ab0e2e6f5d3bcf9", + "IPY_MODEL_684be00132ba4d39989acee0ff30655f", + "IPY_MODEL_b88430e7ccc84746aed0a0b2b7ab17c8", + "IPY_MODEL_7b95708b4e75491bb38c42be6cd57671", + "IPY_MODEL_11a8a57de8fe4c9992a33c714cb79563", + "IPY_MODEL_d506549484404f8eb8a7f3123d9e2392" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_7f7e0df3325e484ab3e57d1f967bb895", + "default_style": "IPY_MODEL_31dadff85a954834beb59ebe4de364f8", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_0f20ef8a9ef04072a8f7d3872d0308c9", + "dragging_style": "IPY_MODEL_12348a8d1e95445ea50ebdefb30be589", "east": 135.68115234375003, "fullscreen": false, "inertia": true, @@ -4928,10 +4876,10 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_cc1159482781459995a853ebcd38d2ce", - "IPY_MODEL_29eb066536f4418c8697ac915846e675" + "IPY_MODEL_c5fa4e97a62b4f66a7d6dfe59f4822cb", + "IPY_MODEL_625609fa98c84412a4879561cc233296" ], - "layout": "IPY_MODEL_09f31594c1b3489ab41574a269c5cc06", + "layout": "IPY_MODEL_c4b3537f37ab4ff28844f50f32c1ef88", "left": 13567, "max_zoom": 24, "min_zoom": null, @@ -4970,7 +4918,7 @@ "right": 14367, "scroll_wheel_zoom": true, "south": 31.93351676190369, - "style": "IPY_MODEL_db28c0e617e14d40869d2ee580f22880", + "style": "IPY_MODEL_4bb06d6bbc184111be4efcf34686548f", "tap": true, "tap_tolerance": 15, "top": 6257, @@ -4984,7 +4932,7 @@ "zoom_snap": 1 } }, - "852876ffcabd4b5b8e4d3b8efb8329b8": { + "1f22e72cc0754b4bb765671ec2889356": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -5006,10 +4954,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_c7bc94df64504017938f5d2e51555b45" + "widget": "IPY_MODEL_d985d8f5249441e6a1d622cf20a5c13e" } }, - "8cd9ebc2b0b241a79bd79590ef875026": { + "d111a3c4ec774f37823a3b7ae9b5d880": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -5035,7 +4983,7 @@ "zoom_out_title": "Zoom out" } }, - "ad9990fa49cc49d3a0055d7832f47e5b": { + "c855762df07c42bfa53b6e371877b21d": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -5053,7 +5001,7 @@ "position": "topleft" } }, - "be76f04c454c404abf577ab9f46c0ad6": { + "a9ca43e4eb3d47e09ab0e2e6f5d3bcf9": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -5092,7 +5040,7 @@ "remove": true } }, - "4a6f43cf340849bbaa86c3842f7a43a2": { + "684be00132ba4d39989acee0ff30655f": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -5118,7 +5066,7 @@ "update_when_idle": false } }, - "cf03dbe7b9c247d9a6abe1d2a1888038": { + "b88430e7ccc84746aed0a0b2b7ab17c8": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -5159,7 +5107,7 @@ "secondary_length_unit": null } }, - "cd436ad9c9824776b07815c16954081f": { + "7b95708b4e75491bb38c42be6cd57671": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -5181,10 +5129,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_f3cb106fec554cb48e630d347e6fc2f4" + "widget": "IPY_MODEL_23d1e0fdce1b476689d77ca18298940b" } }, - "f875b1c5bd824eb58e8c284245a73c2c": { + "11a8a57de8fe4c9992a33c714cb79563": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -5204,7 +5152,7 @@ "prefix": "ipyleaflet" } }, - "b207ea8fb81b4232b68a544c6831f3ba": { + "d506549484404f8eb8a7f3123d9e2392": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -5226,10 +5174,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_122d7ea1db1243f4b829b9df49aa86e7" + "widget": "IPY_MODEL_b709e83f3ce44afabfd6578198259bf0" } }, - "7f7e0df3325e484ab3e57d1f967bb895": { + "31dadff85a954834beb59ebe4de364f8": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -5244,7 +5192,7 @@ "cursor": "grab" } }, - "0f20ef8a9ef04072a8f7d3872d0308c9": { + "12348a8d1e95445ea50ebdefb30be589": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -5259,7 +5207,7 @@ "cursor": "move" } }, - "cc1159482781459995a853ebcd38d2ce": { + "c5fa4e97a62b4f66a7d6dfe59f4822cb": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -5311,7 +5259,7 @@ "zoom_offset": 0 } }, - "29eb066536f4418c8697ac915846e675": { + "625609fa98c84412a4879561cc233296": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -5358,12 +5306,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/ce787d2ffbbec1a207cdc4ee5025ce27-9bc880dcefc880250c6e737a458e8270/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/ce787d2ffbbec1a207cdc4ee5025ce27-07f351c8a6017c499caf2c7a614fede6/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "09f31594c1b3489ab41574a269c5cc06": { + "c4b3537f37ab4ff28844f50f32c1ef88": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5415,7 +5363,7 @@ "width": "800px" } }, - "db28c0e617e14d40869d2ee580f22880": { + "4bb06d6bbc184111be4efcf34686548f": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -5430,7 +5378,7 @@ "cursor": "grab" } }, - "c7bc94df64504017938f5d2e51555b45": { + "d985d8f5249441e6a1d622cf20a5c13e": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -5447,12 +5395,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_da4e2a7f1bd14d398ffee2388fbaf5ef" + "IPY_MODEL_2fdc6d38d4f54c2baee42039c7220d31" ], - "layout": "IPY_MODEL_e2adb45640f2478fa7f1fc93ea1c061f" + "layout": "IPY_MODEL_b25923dab23a4654b7c48c69dcf19bda" } }, - "f3cb106fec554cb48e630d347e6fc2f4": { + "23d1e0fdce1b476689d77ca18298940b": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -5469,12 +5417,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_9dc5d86b463a476b86d330edb7f3793f" + "IPY_MODEL_de2cbdb673684ae594d5ece8df028f02" ], - "layout": "IPY_MODEL_de6e23ac962243ff881a2c312d672438" + "layout": "IPY_MODEL_1ecbb63948914fa69d92aed5ab12869a" } }, - "122d7ea1db1243f4b829b9df49aa86e7": { + "b709e83f3ce44afabfd6578198259bf0": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ -5489,7 +5437,7 @@ "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", - "layout": "IPY_MODEL_14e0c6c57f7c4d3e8f72e274e20ae4fa", + "layout": "IPY_MODEL_fe8b7fe1e78a44ec9cf03ef304272a2a", "msg_id": "", "outputs": [ { @@ -5503,7 +5451,7 @@ ] } }, - "da4e2a7f1bd14d398ffee2388fbaf5ef": { + "2fdc6d38d4f54c2baee42039c7220d31": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -5521,13 +5469,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_4ec85e571fad4e3ba322e7abca055262", - "style": "IPY_MODEL_dada2c4572a14dd2838915ffc3baadc5", + "layout": "IPY_MODEL_8606e706ad8748d8897eb5273831b5a0", + "style": "IPY_MODEL_acd579dda58241e6a0abef8edc15fd9c", "tooltip": "Search location/data", "value": false } }, - "e2adb45640f2478fa7f1fc93ea1c061f": { + "b25923dab23a4654b7c48c69dcf19bda": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5579,7 +5527,7 @@ "width": null } }, - "9dc5d86b463a476b86d330edb7f3793f": { + "de2cbdb673684ae594d5ece8df028f02": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -5597,13 +5545,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_d8a97ae03b724e15a6adbef9a1dc9989", - "style": "IPY_MODEL_6e1a18cd4e834875aad9ab7212b5150d", + "layout": "IPY_MODEL_31c640e9eca4431c99c2b9e0c2f16924", + "style": "IPY_MODEL_ed7dad206f72455f8c40df4ae8422040", "tooltip": "Toolbar", "value": false } }, - "de6e23ac962243ff881a2c312d672438": { + "1ecbb63948914fa69d92aed5ab12869a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5655,7 +5603,7 @@ "width": null } }, - "14e0c6c57f7c4d3e8f72e274e20ae4fa": { + "fe8b7fe1e78a44ec9cf03ef304272a2a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5707,7 +5655,7 @@ "width": "100px" } }, - "4ec85e571fad4e3ba322e7abca055262": { + "8606e706ad8748d8897eb5273831b5a0": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5759,7 +5707,7 @@ "width": "28px" } }, - "dada2c4572a14dd2838915ffc3baadc5": { + "acd579dda58241e6a0abef8edc15fd9c": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -5774,7 +5722,7 @@ "description_width": "" } }, - "d8a97ae03b724e15a6adbef9a1dc9989": { + "31c640e9eca4431c99c2b9e0c2f16924": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -5826,7 +5774,7 @@ "width": "28px" } }, - "6e1a18cd4e834875aad9ab7212b5150d": { + "ed7dad206f72455f8c40df4ae8422040": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -5841,7 +5789,7 @@ "description_width": "" } }, - "6bf79345b0bc455284a79101c04bfe6c": { + "d91aa8cd659840cdabcf2518460efba7": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -5863,24 +5811,24 @@ ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_a9778b13aefc4fffbba2416b920619b2", - "IPY_MODEL_9ac7f474f466419383918545e30d84a6", - "IPY_MODEL_2218fda1b3fc464c8b3770f05da5d563", - "IPY_MODEL_1409d3b265bf4cd5a9af029d15f44044", - "IPY_MODEL_d07cec1d500c4bf2b1ecb18568e2ff78", - "IPY_MODEL_5cdb634f5d494fd1b447d22c04019742", - "IPY_MODEL_8c3b03d535b1442c90a12993279b1352", - "IPY_MODEL_da75b5ad3fd74726ad5038f2f192f23a", - "IPY_MODEL_131e70c768004603a5207b118bbf3719" + "IPY_MODEL_8230dc94bcd24606b22acf8b9f4ed27e", + "IPY_MODEL_0693b1bf45c141d1876a86164986ffad", + "IPY_MODEL_1c6459455f954bc69756117f39bfba25", + "IPY_MODEL_e1ab1f5595a0422c99289d5505c7dc8e", + "IPY_MODEL_8dc288349f6e4650b8a6214b73b031a2", + "IPY_MODEL_56abbd8c5596414d91b49b2d3f6a05d5", + "IPY_MODEL_a3b8d82bf185431fade1f3f243c2b886", + "IPY_MODEL_30dacdcac5d94b87844c93ed3dc3651a", + "IPY_MODEL_6c06712f6a884b12a435465030f4227e" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_f899333c85e4435db472ddaa1d963f7e", + "default_style": "IPY_MODEL_10652514728e425487aefe34110c6e0e", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_844e37f9de8040f5ab30a791b58b418d", + "dragging_style": "IPY_MODEL_64ed9df1deb548088457b9f854a1507b", "east": 135.68115234375003, "fullscreen": false, "inertia": true, @@ -5891,10 +5839,10 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_82d9b5d4364e4ce9879e22b9052205d5", - "IPY_MODEL_e45ef406bd9d494a87607d105a261aba" + "IPY_MODEL_f763ae3b8a744295ab233aa604447ddc", + "IPY_MODEL_19776eb17c694b40bdde2af20173539a" ], - "layout": "IPY_MODEL_da98d8045da44afd94730bf81a214584", + "layout": "IPY_MODEL_30aa580f0c7b483e914e4570eec34cb6", "left": 13567, "max_zoom": 24, "min_zoom": null, @@ -5933,7 +5881,7 @@ "right": 14367, "scroll_wheel_zoom": true, "south": 31.93351676190369, - "style": "IPY_MODEL_b5270d8c6e8c4d31a42d7301f73e3e4f", + "style": "IPY_MODEL_2792863938ba402c82e6c1f6b4eb00fc", "tap": true, "tap_tolerance": 15, "top": 6257, @@ -5947,7 +5895,7 @@ "zoom_snap": 1 } }, - "a9778b13aefc4fffbba2416b920619b2": { + "8230dc94bcd24606b22acf8b9f4ed27e": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -5969,10 +5917,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_402adee293e64a098ec269db39fdbf25" + "widget": "IPY_MODEL_5cec03584256403f8c4b603648055a73" } }, - "9ac7f474f466419383918545e30d84a6": { + "0693b1bf45c141d1876a86164986ffad": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -5998,7 +5946,7 @@ "zoom_out_title": "Zoom out" } }, - "2218fda1b3fc464c8b3770f05da5d563": { + "1c6459455f954bc69756117f39bfba25": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -6016,7 +5964,7 @@ "position": "topleft" } }, - "1409d3b265bf4cd5a9af029d15f44044": { + "e1ab1f5595a0422c99289d5505c7dc8e": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -6055,7 +6003,7 @@ "remove": true } }, - "d07cec1d500c4bf2b1ecb18568e2ff78": { + "8dc288349f6e4650b8a6214b73b031a2": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -6081,7 +6029,7 @@ "update_when_idle": false } }, - "5cdb634f5d494fd1b447d22c04019742": { + "56abbd8c5596414d91b49b2d3f6a05d5": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -6122,7 +6070,7 @@ "secondary_length_unit": null } }, - "8c3b03d535b1442c90a12993279b1352": { + "a3b8d82bf185431fade1f3f243c2b886": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -6144,10 +6092,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_cd1c6151a73844a5a8f31b5b66905d1a" + "widget": "IPY_MODEL_574c2a716b8b428cac93470484d49df8" } }, - "da75b5ad3fd74726ad5038f2f192f23a": { + "30dacdcac5d94b87844c93ed3dc3651a": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -6167,7 +6115,7 @@ "prefix": "ipyleaflet" } }, - "131e70c768004603a5207b118bbf3719": { + "6c06712f6a884b12a435465030f4227e": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -6189,10 +6137,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_17fd729332064fb8b5d6858d3bb203aa" + "widget": "IPY_MODEL_5504dd271fc0436e8bcf279dcf189fd2" } }, - "f899333c85e4435db472ddaa1d963f7e": { + "10652514728e425487aefe34110c6e0e": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -6207,7 +6155,7 @@ "cursor": "grab" } }, - "844e37f9de8040f5ab30a791b58b418d": { + "64ed9df1deb548088457b9f854a1507b": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -6222,7 +6170,7 @@ "cursor": "move" } }, - "82d9b5d4364e4ce9879e22b9052205d5": { + "f763ae3b8a744295ab233aa604447ddc": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -6274,7 +6222,7 @@ "zoom_offset": 0 } }, - "e45ef406bd9d494a87607d105a261aba": { + "19776eb17c694b40bdde2af20173539a": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -6321,12 +6269,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/50d93db5b042e0a9ef5f323f310e6c20-298f9499451ae70efc5c71d5f434933d/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/50d93db5b042e0a9ef5f323f310e6c20-743c42a3a477edb0d8b8d3f1ad19ad1e/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "da98d8045da44afd94730bf81a214584": { + "30aa580f0c7b483e914e4570eec34cb6": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -6378,7 +6326,7 @@ "width": "800px" } }, - "b5270d8c6e8c4d31a42d7301f73e3e4f": { + "2792863938ba402c82e6c1f6b4eb00fc": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -6393,7 +6341,7 @@ "cursor": "grab" } }, - "402adee293e64a098ec269db39fdbf25": { + "5cec03584256403f8c4b603648055a73": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -6410,12 +6358,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_0e41e337484e470083d54c219c6680eb" + "IPY_MODEL_3e331e7013a64c5cbfa10b161b96d1c6" ], - "layout": "IPY_MODEL_7c304115b3034e2bac307e9587aa6ac4" + "layout": "IPY_MODEL_815b65421d00426f9f06a358fb36c13e" } }, - "cd1c6151a73844a5a8f31b5b66905d1a": { + "574c2a716b8b428cac93470484d49df8": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -6432,12 +6380,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_5b23dd0eace74c37b121bc25da52ad2b" + "IPY_MODEL_ac8ae8cce0d44274abfdf0cd88ca0c4e" ], - "layout": "IPY_MODEL_1f877208eb8540d189b0ba8e39622c7c" + "layout": "IPY_MODEL_ebead44b051c47378d0cf8a6bf7e9868" } }, - "17fd729332064fb8b5d6858d3bb203aa": { + "5504dd271fc0436e8bcf279dcf189fd2": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ -6452,7 +6400,7 @@ "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", - "layout": "IPY_MODEL_099ed50edf6f4efba91d1b29a5c5afd2", + "layout": "IPY_MODEL_40bbbfa39ea84cb29288339d066aaf7f", "msg_id": "", "outputs": [ { @@ -6466,7 +6414,7 @@ ] } }, - "0e41e337484e470083d54c219c6680eb": { + "3e331e7013a64c5cbfa10b161b96d1c6": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -6484,13 +6432,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_e70da2c319f3407f85177e06e7c7b06d", - "style": "IPY_MODEL_d685862ffafe4598951487c94eacf8fb", + "layout": "IPY_MODEL_60e61276073c49db8dfd42390e5a0483", + "style": "IPY_MODEL_cd39756371f74bd6933c2adfd505d1c7", "tooltip": "Search location/data", "value": false } }, - "7c304115b3034e2bac307e9587aa6ac4": { + "815b65421d00426f9f06a358fb36c13e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -6542,7 +6490,7 @@ "width": null } }, - "5b23dd0eace74c37b121bc25da52ad2b": { + "ac8ae8cce0d44274abfdf0cd88ca0c4e": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -6560,13 +6508,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_2b44cc0739a64bd7aef172fe53ed2850", - "style": "IPY_MODEL_e22f39836af04531b9215fb431728700", + "layout": "IPY_MODEL_29d82b51d9274521850724825433b6fb", + "style": "IPY_MODEL_740025d9d9934a688b4006910e56ba64", "tooltip": "Toolbar", "value": false } }, - "1f877208eb8540d189b0ba8e39622c7c": { + "ebead44b051c47378d0cf8a6bf7e9868": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -6618,7 +6566,7 @@ "width": null } }, - "099ed50edf6f4efba91d1b29a5c5afd2": { + "40bbbfa39ea84cb29288339d066aaf7f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -6670,7 +6618,7 @@ "width": "100px" } }, - "e70da2c319f3407f85177e06e7c7b06d": { + "60e61276073c49db8dfd42390e5a0483": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -6722,7 +6670,7 @@ "width": "28px" } }, - "d685862ffafe4598951487c94eacf8fb": { + "cd39756371f74bd6933c2adfd505d1c7": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -6737,7 +6685,7 @@ "description_width": "" } }, - "2b44cc0739a64bd7aef172fe53ed2850": { + "29d82b51d9274521850724825433b6fb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -6789,7 +6737,7 @@ "width": "28px" } }, - "e22f39836af04531b9215fb431728700": { + "740025d9d9934a688b4006910e56ba64": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -6804,7 +6752,7 @@ "description_width": "" } }, - "c8390c8a357e494ca0388a061faf9add": { + "117f21ad7cab4ca99ecf8880734b51d9": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -6826,23 +6774,23 @@ ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_2a837dc65930427497ce755a7570c647", - "IPY_MODEL_cfe27c7f82b049649998dca11c8b9ba2", - "IPY_MODEL_86b55571c64148c489458847fa3b614c", - "IPY_MODEL_dcc8b35490a14d5e90e5068a1d93ed8f", - "IPY_MODEL_c59df9e346364b469a1c9b0ad652579d", - "IPY_MODEL_54079158de52461a9838082d7c9d2c83", - "IPY_MODEL_aa149595ca44424788fe9ef2465b362d", - "IPY_MODEL_5d3f9b95b2504b3688f69426bc2d9935" + "IPY_MODEL_107a1dc5987d486d8568d96fb57bee20", + "IPY_MODEL_6c2089bcae8b496797d64e6ff6d3338c", + "IPY_MODEL_24db6baf09cc4e5485a3bd668bfd81dd", + "IPY_MODEL_10af6004221e4603aa54e1a335b1aeb4", + "IPY_MODEL_f512c4a4fa324d83bb6f68eff59aa045", + "IPY_MODEL_821f359ef5eb4351a7b6b185067aa37a", + "IPY_MODEL_69e97d3534564c1ca0ad5da1fc83eb7c", + "IPY_MODEL_64eb6395a1434a54ad5bdc6967545172" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_2121cc09f58043f7934c1e209ae4bb4d", + "default_style": "IPY_MODEL_8c6f7649bceb41e290c32a86cd7795d0", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_24f7eacbb86444e3ae9a2dfdbf4b09ea", + "dragging_style": "IPY_MODEL_dc79172c32bc4b488dcdffcd3a7c58f1", "east": 135.68115234375003, "fullscreen": false, "inertia": true, @@ -6853,10 +6801,10 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_37237b8830b441b6832b22d011bfd82c", - "IPY_MODEL_fb1e45a11fcb46dcb3a99fba2d90cd80" + "IPY_MODEL_74cba67a7a354421a03d98e2c5787b57", + "IPY_MODEL_1aa0307594e94839809c23b848b906f8" ], - "layout": "IPY_MODEL_0f07f0f199d94affac1ab0a3aba7edaa", + "layout": "IPY_MODEL_53dca60d10c7403fbe6bb3851e289fc0", "left": 13567, "max_zoom": 24, "min_zoom": null, @@ -6895,7 +6843,7 @@ "right": 14367, "scroll_wheel_zoom": true, "south": 31.93351676190369, - "style": "IPY_MODEL_a1565349fe944d2d95cbfde0d5afe06a", + "style": "IPY_MODEL_d451ccb15b6e4d18a98c0575ee801a57", "tap": true, "tap_tolerance": 15, "top": 6257, @@ -6909,7 +6857,7 @@ "zoom_snap": 1 } }, - "2a837dc65930427497ce755a7570c647": { + "107a1dc5987d486d8568d96fb57bee20": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -6931,10 +6879,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_2833b9da12bd4ca9acc491bc98f0d697" + "widget": "IPY_MODEL_04a4e8af739c4d5ab0dc652b2d7c45d3" } }, - "cfe27c7f82b049649998dca11c8b9ba2": { + "6c2089bcae8b496797d64e6ff6d3338c": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -6960,7 +6908,7 @@ "zoom_out_title": "Zoom out" } }, - "86b55571c64148c489458847fa3b614c": { + "24db6baf09cc4e5485a3bd668bfd81dd": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -6978,7 +6926,7 @@ "position": "topleft" } }, - "dcc8b35490a14d5e90e5068a1d93ed8f": { + "10af6004221e4603aa54e1a335b1aeb4": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -7017,7 +6965,7 @@ "remove": true } }, - "c59df9e346364b469a1c9b0ad652579d": { + "f512c4a4fa324d83bb6f68eff59aa045": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -7043,7 +6991,7 @@ "update_when_idle": false } }, - "54079158de52461a9838082d7c9d2c83": { + "821f359ef5eb4351a7b6b185067aa37a": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -7084,7 +7032,7 @@ "secondary_length_unit": null } }, - "aa149595ca44424788fe9ef2465b362d": { + "69e97d3534564c1ca0ad5da1fc83eb7c": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -7106,10 +7054,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_27869fe8b8a44e129b2b1b1e84b841d9" + "widget": "IPY_MODEL_92b18c10d1d94dc78db0676829a0a8fb" } }, - "5d3f9b95b2504b3688f69426bc2d9935": { + "64eb6395a1434a54ad5bdc6967545172": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -7129,7 +7077,7 @@ "prefix": "ipyleaflet" } }, - "2121cc09f58043f7934c1e209ae4bb4d": { + "8c6f7649bceb41e290c32a86cd7795d0": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -7144,7 +7092,7 @@ "cursor": "grab" } }, - "24f7eacbb86444e3ae9a2dfdbf4b09ea": { + "dc79172c32bc4b488dcdffcd3a7c58f1": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -7159,7 +7107,7 @@ "cursor": "move" } }, - "37237b8830b441b6832b22d011bfd82c": { + "74cba67a7a354421a03d98e2c5787b57": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -7211,7 +7159,7 @@ "zoom_offset": 0 } }, - "fb1e45a11fcb46dcb3a99fba2d90cd80": { + "1aa0307594e94839809c23b848b906f8": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -7258,12 +7206,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/7b9cc1796acf2b66d9c807055961786e-697c06bc9373942c7fb633ec5a3ea8f7/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/7b9cc1796acf2b66d9c807055961786e-8940715aa576f5e15ed38083079a3581/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "0f07f0f199d94affac1ab0a3aba7edaa": { + "53dca60d10c7403fbe6bb3851e289fc0": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -7315,7 +7263,7 @@ "width": "800px" } }, - "a1565349fe944d2d95cbfde0d5afe06a": { + "d451ccb15b6e4d18a98c0575ee801a57": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -7330,7 +7278,7 @@ "cursor": "grab" } }, - "2833b9da12bd4ca9acc491bc98f0d697": { + "04a4e8af739c4d5ab0dc652b2d7c45d3": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -7347,12 +7295,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_4f6dc23d7fdb4b8fae9e94593d4d20a5" + "IPY_MODEL_e0e2e80c861c490e9f440079263a66db" ], - "layout": "IPY_MODEL_2ae5540a04a74ea0bab6768badb25a07" + "layout": "IPY_MODEL_ee67d082365c47e480136d2911fc682b" } }, - "27869fe8b8a44e129b2b1b1e84b841d9": { + "92b18c10d1d94dc78db0676829a0a8fb": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -7369,12 +7317,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_4503893df33943388214bc55369bdb48" + "IPY_MODEL_f4099b820b0e4d7691e5176e52b23a76" ], - "layout": "IPY_MODEL_8aadf9d4530f4ffab1fcbe2dcba26671" + "layout": "IPY_MODEL_d8d7a59ffd1e4c90b71cdb2cf7c799ac" } }, - "4f6dc23d7fdb4b8fae9e94593d4d20a5": { + "e0e2e80c861c490e9f440079263a66db": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -7392,13 +7340,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_7394468aff2a4556bc9ad0f93c5e34ea", - "style": "IPY_MODEL_a6132a6ed8c44a62af43b4bb33a52fa8", + "layout": "IPY_MODEL_c9dbe3c4a93740e8806d406c10a11000", + "style": "IPY_MODEL_fd67dca06419491aac8a0696a112c2a4", "tooltip": "Search location/data", "value": false } }, - "2ae5540a04a74ea0bab6768badb25a07": { + "ee67d082365c47e480136d2911fc682b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -7450,7 +7398,7 @@ "width": null } }, - "4503893df33943388214bc55369bdb48": { + "f4099b820b0e4d7691e5176e52b23a76": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -7468,13 +7416,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_6fe813343d89446998d20b027fb563c5", - "style": "IPY_MODEL_4ddf2568c7bd4df99672e0f0352f287f", + "layout": "IPY_MODEL_eafad36928ba41c9a93129048cf16244", + "style": "IPY_MODEL_cca7597070bb4bf6a55a239d23823628", "tooltip": "Toolbar", "value": false } }, - "8aadf9d4530f4ffab1fcbe2dcba26671": { + "d8d7a59ffd1e4c90b71cdb2cf7c799ac": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -7526,7 +7474,7 @@ "width": null } }, - "7394468aff2a4556bc9ad0f93c5e34ea": { + "c9dbe3c4a93740e8806d406c10a11000": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -7578,7 +7526,7 @@ "width": "28px" } }, - "a6132a6ed8c44a62af43b4bb33a52fa8": { + "fd67dca06419491aac8a0696a112c2a4": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -7593,7 +7541,7 @@ "description_width": "" } }, - "6fe813343d89446998d20b027fb563c5": { + "eafad36928ba41c9a93129048cf16244": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -7645,7 +7593,7 @@ "width": "28px" } }, - "4ddf2568c7bd4df99672e0f0352f287f": { + "cca7597070bb4bf6a55a239d23823628": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -7660,7 +7608,7 @@ "description_width": "" } }, - "abaa5b5611554316a11951a8a1e4017d": { + "1a333b4994a542e494a892204be492e6": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -7682,23 +7630,23 @@ ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_c8d8dfadeade4ea9949163d44e1a8e64", - "IPY_MODEL_d737a2812366419684b7a3ca9381761b", - "IPY_MODEL_d3bc6e4739f84b4cbb878f5ab0684e32", - "IPY_MODEL_b3ad7898b48a44cf814358671eaa511c", - "IPY_MODEL_85f2edc0a908484fbb1def9f30fa931f", - "IPY_MODEL_0287f469129248a0bfd51aac2e80f412", - "IPY_MODEL_300bf65c5dcf4ed3ab887baf03cf0f9f", - "IPY_MODEL_6851da7f282346c0904ecf577d2941b6" + "IPY_MODEL_e06969cdab0840aeb111c3d1360c458c", + "IPY_MODEL_e9eb5df4dce54168a6568b5cd3d07382", + "IPY_MODEL_d1f8d2f2dbfc4caf841b4a7b1064375e", + "IPY_MODEL_c882a936a93f4919bd17579085857fb1", + "IPY_MODEL_b7a1e09928ce4984baf55cf8f726fd68", + "IPY_MODEL_3a2a202212e3416c8f1539df5e186f01", + "IPY_MODEL_52bddeb3302446e8ad5eb9f5fcac1124", + "IPY_MODEL_0b88c043fdf64c898d5582da7fbf9dc1" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_e866bfbf8b9346d78aba88b2d5e98cf3", + "default_style": "IPY_MODEL_a3af014c95ef475b8a264f95b2968c44", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_c643413fe487499399af76c469c28562", + "dragging_style": "IPY_MODEL_92116fbaa377464ca6a106a145d2238a", "east": 135.68115234375003, "fullscreen": false, "inertia": true, @@ -7709,11 +7657,11 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_7c5d51803870404089278fd0d7ed4a42", - "IPY_MODEL_404edb6d1a974ac0a471328610a4ec17", - "IPY_MODEL_fe12c542e3fd41b3839accfac33e18f0" + "IPY_MODEL_68bb9a45e7a744bdb3c371f57e5141ae", + "IPY_MODEL_0f0e8df6f5e547d8992952829c50d540", + "IPY_MODEL_646d2ab47a4941e2ba0eeb5d5dc44136" ], - "layout": "IPY_MODEL_04d39a39f365408a894d5602f5584daa", + "layout": "IPY_MODEL_c371b1008d7b41a4b91fcd110fbd8f46", "left": 13567, "max_zoom": 24, "min_zoom": null, @@ -7752,7 +7700,7 @@ "right": 14367, "scroll_wheel_zoom": true, "south": 31.93351676190369, - "style": "IPY_MODEL_ea0d8d84d5994efab3f63ba4b97efdff", + "style": "IPY_MODEL_fe5196970a8f437080d46951c730aa71", "tap": true, "tap_tolerance": 15, "top": 6257, @@ -7766,7 +7714,7 @@ "zoom_snap": 1 } }, - "c8d8dfadeade4ea9949163d44e1a8e64": { + "e06969cdab0840aeb111c3d1360c458c": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -7788,10 +7736,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_ca216e28581f4b00b2b6878e4ce4cb0c" + "widget": "IPY_MODEL_6c5d7e69966c4bb08fb33437d2a1b1a3" } }, - "d737a2812366419684b7a3ca9381761b": { + "e9eb5df4dce54168a6568b5cd3d07382": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -7817,7 +7765,7 @@ "zoom_out_title": "Zoom out" } }, - "d3bc6e4739f84b4cbb878f5ab0684e32": { + "d1f8d2f2dbfc4caf841b4a7b1064375e": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -7835,7 +7783,7 @@ "position": "topleft" } }, - "b3ad7898b48a44cf814358671eaa511c": { + "c882a936a93f4919bd17579085857fb1": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -7874,7 +7822,7 @@ "remove": true } }, - "85f2edc0a908484fbb1def9f30fa931f": { + "b7a1e09928ce4984baf55cf8f726fd68": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -7900,7 +7848,7 @@ "update_when_idle": false } }, - "0287f469129248a0bfd51aac2e80f412": { + "3a2a202212e3416c8f1539df5e186f01": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -7941,7 +7889,7 @@ "secondary_length_unit": null } }, - "300bf65c5dcf4ed3ab887baf03cf0f9f": { + "52bddeb3302446e8ad5eb9f5fcac1124": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -7963,10 +7911,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_d80d8a9cd4244b87b16c0f8e63788801" + "widget": "IPY_MODEL_79a38e05b0a54bf69193ec1390f32983" } }, - "6851da7f282346c0904ecf577d2941b6": { + "0b88c043fdf64c898d5582da7fbf9dc1": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -7986,7 +7934,7 @@ "prefix": "ipyleaflet" } }, - "e866bfbf8b9346d78aba88b2d5e98cf3": { + "a3af014c95ef475b8a264f95b2968c44": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -8001,7 +7949,7 @@ "cursor": "grab" } }, - "c643413fe487499399af76c469c28562": { + "92116fbaa377464ca6a106a145d2238a": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -8016,7 +7964,7 @@ "cursor": "move" } }, - "7c5d51803870404089278fd0d7ed4a42": { + "68bb9a45e7a744bdb3c371f57e5141ae": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -8068,7 +8016,7 @@ "zoom_offset": 0 } }, - "404edb6d1a974ac0a471328610a4ec17": { + "0f0e8df6f5e547d8992952829c50d540": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -8115,12 +8063,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/feee601563317a15bcbc1af6c7d7e4d7-0285dc42a5a3f8e0a8c1906ed87b4997/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/feee601563317a15bcbc1af6c7d7e4d7-46dc39acfd7ae366d0731c24eab765ee/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "fe12c542e3fd41b3839accfac33e18f0": { + "646d2ab47a4941e2ba0eeb5d5dc44136": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -8167,12 +8115,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/15b20caa5748ff2b7410a140758befb4-3cda3a8ecca0f6990a4b241a6900733d/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/15b20caa5748ff2b7410a140758befb4-22001db1b9d7ca153900c22c108563a5/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "04d39a39f365408a894d5602f5584daa": { + "c371b1008d7b41a4b91fcd110fbd8f46": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -8224,7 +8172,7 @@ "width": "800px" } }, - "ea0d8d84d5994efab3f63ba4b97efdff": { + "fe5196970a8f437080d46951c730aa71": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -8239,7 +8187,7 @@ "cursor": "grab" } }, - "ca216e28581f4b00b2b6878e4ce4cb0c": { + "6c5d7e69966c4bb08fb33437d2a1b1a3": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -8256,12 +8204,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_be58465b29e94ac6b801ddcd6684dde8" + "IPY_MODEL_7656b5b7dfaa477daf258dc06adc790e" ], - "layout": "IPY_MODEL_c039d261e9b84c20af1f66e9b297135b" + "layout": "IPY_MODEL_f04155a5772a4b088855fd6e40b39f68" } }, - "d80d8a9cd4244b87b16c0f8e63788801": { + "79a38e05b0a54bf69193ec1390f32983": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -8278,12 +8226,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_41a23e03ce0d406daf40573f269139a1" + "IPY_MODEL_680e1aa403094658b558a4b201fd29c2" ], - "layout": "IPY_MODEL_e7475d2c40c44271a895b3f273816bc5" + "layout": "IPY_MODEL_433fd7c4f3f94d85917544c05eaaa9f8" } }, - "be58465b29e94ac6b801ddcd6684dde8": { + "7656b5b7dfaa477daf258dc06adc790e": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -8301,13 +8249,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_34cf6a2d8443456e81bba3a09a9939bd", - "style": "IPY_MODEL_9c36f1f12ec444e6a9b1370a84dd7b24", + "layout": "IPY_MODEL_538bf7fa416e4ba28443af6329a37736", + "style": "IPY_MODEL_1ff27de545024bc7ae6eab65de8b2e6f", "tooltip": "Search location/data", "value": false } }, - "c039d261e9b84c20af1f66e9b297135b": { + "f04155a5772a4b088855fd6e40b39f68": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -8359,7 +8307,7 @@ "width": null } }, - "41a23e03ce0d406daf40573f269139a1": { + "680e1aa403094658b558a4b201fd29c2": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -8377,13 +8325,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_62d5b3f2bdad483d8638105a5af6182b", - "style": "IPY_MODEL_54f26606e2bf4e5b9d91e013c0691d3e", + "layout": "IPY_MODEL_b26a8880932346ea8da2f34bc634859a", + "style": "IPY_MODEL_9dfce8841e644e0e90a7b4037028fb93", "tooltip": "Toolbar", "value": false } }, - "e7475d2c40c44271a895b3f273816bc5": { + "433fd7c4f3f94d85917544c05eaaa9f8": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -8435,7 +8383,7 @@ "width": null } }, - "34cf6a2d8443456e81bba3a09a9939bd": { + "538bf7fa416e4ba28443af6329a37736": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -8487,7 +8435,7 @@ "width": "28px" } }, - "9c36f1f12ec444e6a9b1370a84dd7b24": { + "1ff27de545024bc7ae6eab65de8b2e6f": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -8502,7 +8450,7 @@ "description_width": "" } }, - "62d5b3f2bdad483d8638105a5af6182b": { + "b26a8880932346ea8da2f34bc634859a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -8554,7 +8502,7 @@ "width": "28px" } }, - "54f26606e2bf4e5b9d91e013c0691d3e": { + "9dfce8841e644e0e90a7b4037028fb93": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -8569,7 +8517,7 @@ "description_width": "" } }, - "d8d52cc8abfc412f9babec3a2cdc14f4": { + "f73fe0f1f63349d3a3e3d1903cc07645": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -8582,34 +8530,34 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 25876, + "bottom": 6657, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 36.26199220445664, - 130.13854980468753 + 35.587733558095216, + 126.8959721004684 ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_040bdca435ff4040a00664745e1a637d", - "IPY_MODEL_e75a3655279a4cf3bb44b96b1ea79186", - "IPY_MODEL_aa61b91c7f4e42ca80f9a948303bdd46", - "IPY_MODEL_1b8df6b943814954a4cc8e81548db3be", - "IPY_MODEL_77b74c4841d94a90adbe952a9b885918", - "IPY_MODEL_7ed530a7f9e940879af351246fde854e", - "IPY_MODEL_1c5c7b6279d24984b2a2a2b573f2ca89", - "IPY_MODEL_121ec75328a34cf9a8d919602cd46948", - "IPY_MODEL_7ae86c3b877c47ee9e916341f1c83525" + "IPY_MODEL_174423090a424922a7ce2823fb3ffed3", + "IPY_MODEL_ca01b3b073b24a33b33c3519f2ceb8a6", + "IPY_MODEL_dd24387aff7141418cbd645a418b97b3", + "IPY_MODEL_e64f35fa89944b6290f597d4f7182b1c", + "IPY_MODEL_3d1948d8a58a444aa6e9ee4de6127292", + "IPY_MODEL_e10f6046737d43ad931387a38bee8442", + "IPY_MODEL_ab81e584619a44dbb3c6be7a2453e718", + "IPY_MODEL_0de06343152d47d686e6611d5bcedaaa", + "IPY_MODEL_64a396f1b27048a0b54e0f35d8f7372d" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_e3bc92404ac44b64a66a9351444aaa22", + "default_style": "IPY_MODEL_d3f22884fdb545268360fa752a52fac5", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_d82f07921c02492c98bd4f8ad9cd1ae0", - "east": 132.33581542968753, + "dragging_style": "IPY_MODEL_0f802a5279f249dcafebb3ce0fae5a5b", + "east": 135.68115234375003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -8619,16 +8567,16 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_19e8722e51b6444c923292f4929d7ce7", - "IPY_MODEL_3463923609f74502b12beada037ffd95", - "IPY_MODEL_933c5b3c2934476bb1634bbf8f6220bc" + "IPY_MODEL_2750440ca498413ea267bac448595fe3", + "IPY_MODEL_0fdd64fd02e8442ba622ad006742441f", + "IPY_MODEL_2d99e9a9b4f343419ad4af3fe397efeb" ], - "layout": "IPY_MODEL_683d083367064c8dbffc9e52d3c860ad", - "left": 56059, + "layout": "IPY_MODEL_3cba4731c9a64a458e342a0c63a97c03", + "left": 13567, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 37.142803443716836, + "north": 39.07890809706475, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -8659,24 +8607,24 @@ ], "panes": {}, "prefer_canvas": false, - "right": 56859, + "right": 14367, "scroll_wheel_zoom": true, - "south": 35.37113502280101, - "style": "IPY_MODEL_e3bc92404ac44b64a66a9351444aaa22", + "south": 31.93351676190369, + "style": "IPY_MODEL_60cd78daa2204b1a9f5b6aeedd018b58", "tap": true, "tap_tolerance": 15, - "top": 25476, + "top": 6257, "touch_zoom": true, - "west": 127.94128417968751, + "west": 118.10302734375001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 8, + "zoom": 6, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 } }, - "040bdca435ff4040a00664745e1a637d": { + "174423090a424922a7ce2823fb3ffed3": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -8698,10 +8646,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_729e3c3def8b434b832581f876facd86" + "widget": "IPY_MODEL_2585e9ef9fda479ba19e2102cee44d12" } }, - "e75a3655279a4cf3bb44b96b1ea79186": { + "ca01b3b073b24a33b33c3519f2ceb8a6": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -8727,7 +8675,7 @@ "zoom_out_title": "Zoom out" } }, - "aa61b91c7f4e42ca80f9a948303bdd46": { + "dd24387aff7141418cbd645a418b97b3": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -8745,7 +8693,7 @@ "position": "topleft" } }, - "1b8df6b943814954a4cc8e81548db3be": { + "e64f35fa89944b6290f597d4f7182b1c": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -8784,7 +8732,7 @@ "remove": true } }, - "77b74c4841d94a90adbe952a9b885918": { + "3d1948d8a58a444aa6e9ee4de6127292": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -8810,7 +8758,7 @@ "update_when_idle": false } }, - "7ed530a7f9e940879af351246fde854e": { + "e10f6046737d43ad931387a38bee8442": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -8851,7 +8799,7 @@ "secondary_length_unit": null } }, - "1c5c7b6279d24984b2a2a2b573f2ca89": { + "ab81e584619a44dbb3c6be7a2453e718": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -8873,10 +8821,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_6cc01807d6cb4ba1be2cfb9ca1daeed8" + "widget": "IPY_MODEL_c6472040655249f4a40bd732d1555343" } }, - "121ec75328a34cf9a8d919602cd46948": { + "0de06343152d47d686e6611d5bcedaaa": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -8896,7 +8844,7 @@ "prefix": "ipyleaflet" } }, - "7ae86c3b877c47ee9e916341f1c83525": { + "64a396f1b27048a0b54e0f35d8f7372d": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -8918,10 +8866,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_7dc77d4c84424ddc925c963d870fe1ea" + "widget": "IPY_MODEL_4e6ca208a3c440f0941dd02746c2cd9a" } }, - "e3bc92404ac44b64a66a9351444aaa22": { + "d3f22884fdb545268360fa752a52fac5": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -8936,7 +8884,7 @@ "cursor": "grab" } }, - "d82f07921c02492c98bd4f8ad9cd1ae0": { + "0f802a5279f249dcafebb3ce0fae5a5b": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -8951,7 +8899,7 @@ "cursor": "move" } }, - "19e8722e51b6444c923292f4929d7ce7": { + "2750440ca498413ea267bac448595fe3": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -9003,7 +8951,7 @@ "zoom_offset": 0 } }, - "3463923609f74502b12beada037ffd95": { + "0fdd64fd02e8442ba622ad006742441f": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -9050,12 +8998,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/7d5de4b3591b897fb412102e98b2f3bd-1c6ea5ddda9c860688907d5abdd17812/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/e9b9c8fa3d04ada4064c84b4ccdd83e9-c1b09ea459afc70ddf3d1db70a8fd6b5/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "933c5b3c2934476bb1634bbf8f6220bc": { + "2d99e9a9b4f343419ad4af3fe397efeb": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -9102,12 +9050,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-98cb5db8e1683ef5648e01e6b4121efe/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-e56a9eca297fd2e39dce7d811227b7ca/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "683d083367064c8dbffc9e52d3c860ad": { + "3cba4731c9a64a458e342a0c63a97c03": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -9159,7 +9107,7 @@ "width": "800px" } }, - "b901188c0c5e4ec6b2e5150f887dd599": { + "60cd78daa2204b1a9f5b6aeedd018b58": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -9174,7 +9122,7 @@ "cursor": "grab" } }, - "729e3c3def8b434b832581f876facd86": { + "2585e9ef9fda479ba19e2102cee44d12": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -9191,12 +9139,12 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_57cc8245ab7344b8b0402859140e47ce" + "IPY_MODEL_2d1da4da1fad47ffb8ae33c74a2f29cd" ], - "layout": "IPY_MODEL_c79191350f834731a3b352f683d74441" + "layout": "IPY_MODEL_d1710e178fda454aa01652832eca9bd1" } }, - "6cc01807d6cb4ba1be2cfb9ca1daeed8": { + "c6472040655249f4a40bd732d1555343": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", @@ -9213,12 +9161,12 @@ "_view_name": "VBoxView", "box_style": "", "children": [ - "IPY_MODEL_446a3ca30c1444d4b559505dabb3becd" + "IPY_MODEL_ab016f1ef6744faa989d2ee70f1ddcf8" ], - "layout": "IPY_MODEL_fa7eaeddb6824a4db8e95f4b9964c9ab" + "layout": "IPY_MODEL_29255578a88e4506a7c91dea0ab7fe1b" } }, - "7dc77d4c84424ddc925c963d870fe1ea": { + "4e6ca208a3c440f0941dd02746c2cd9a": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ -9233,7 +9181,7 @@ "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", - "layout": "IPY_MODEL_22d254760f864e048cd5c1182a1cf02f", + "layout": "IPY_MODEL_29d4e118ec954d48b49753449f14fca7", "msg_id": "", "outputs": [ { @@ -9247,7 +9195,7 @@ ] } }, - "57cc8245ab7344b8b0402859140e47ce": { + "2d1da4da1fad47ffb8ae33c74a2f29cd": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -9265,13 +9213,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_f287e274ed334175965bf8fef217bfd5", - "style": "IPY_MODEL_2b56a943fb78495596f1fd98592671ad", + "layout": "IPY_MODEL_89827292cac042cd971c5cac3f7dba57", + "style": "IPY_MODEL_233f08d42af647a78a084f6e99aead56", "tooltip": "Search location/data", "value": false } }, - "c79191350f834731a3b352f683d74441": { + "d1710e178fda454aa01652832eca9bd1": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -9323,7 +9271,7 @@ "width": null } }, - "446a3ca30c1444d4b559505dabb3becd": { + "ab016f1ef6744faa989d2ee70f1ddcf8": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -9341,13 +9289,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_d5a4e42464f04fd6a06619c90ade6880", - "style": "IPY_MODEL_7cba088702d942f6ab480723e7cef639", + "layout": "IPY_MODEL_e834223685554121abd1be7ad61abcf9", + "style": "IPY_MODEL_97979ca2bed04a0093e468d7c2e47691", "tooltip": "Toolbar", "value": false } }, - "fa7eaeddb6824a4db8e95f4b9964c9ab": { + "29255578a88e4506a7c91dea0ab7fe1b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -9399,7 +9347,7 @@ "width": null } }, - "22d254760f864e048cd5c1182a1cf02f": { + "29d4e118ec954d48b49753449f14fca7": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -9451,7 +9399,7 @@ "width": "270px" } }, - "f287e274ed334175965bf8fef217bfd5": { + "89827292cac042cd971c5cac3f7dba57": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -9503,7 +9451,7 @@ "width": "28px" } }, - "2b56a943fb78495596f1fd98592671ad": { + "233f08d42af647a78a084f6e99aead56": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -9518,7 +9466,7 @@ "description_width": "" } }, - "d5a4e42464f04fd6a06619c90ade6880": { + "e834223685554121abd1be7ad61abcf9": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -9570,7 +9518,7 @@ "width": "28px" } }, - "7cba088702d942f6ab480723e7cef639": { + "97979ca2bed04a0093e468d7c2e47691": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -9585,7 +9533,7 @@ "description_width": "" } }, - "c86e1a74c965421194fe6cee9b4909b6": { + "3e90aff8906e456eb60e2c5cc946f8b1": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapModel", "model_module_version": "^0.18", @@ -9598,34 +9546,34 @@ "_view_module": "jupyter-leaflet", "_view_module_version": "^0.18", "_view_name": "LeafletMapView", - "bottom": 3497, + "bottom": 6660, "bounce_at_zoom_limits": true, "box_zoom": true, "center": [ - 33.100745405144245, - 117.65258789062501 + 35.533064630393035, + 126.8858638222748 ], "close_popup_on_click": true, "controls": [ - "IPY_MODEL_1136a62f368c488dbd989bbe7aafe6e1", - "IPY_MODEL_0120edb48cec4e768fb2e7a0590f2c8b", - "IPY_MODEL_3b95748faa7a4f5c9b97d0533e14f609", - "IPY_MODEL_e6069552718849fd8098062260ac9dfb", - "IPY_MODEL_a4762f33f5674375a05813cbcff93403", - "IPY_MODEL_40be0705f15f431e89c083e01eab94f9", - "IPY_MODEL_d00f8c81b4754052850cd5ae3b67973f", - "IPY_MODEL_08355bb00ea045fc945ee47ae4adfa7a", - "IPY_MODEL_da37950eddb042d9bb43f495da888d89" + "IPY_MODEL_813d63ffdac340fa82f3654ddf84b22c", + "IPY_MODEL_e6ffba1da6054cb28715a9864b14989d", + "IPY_MODEL_464f1099f31e4078bbd426d5bc1b8abe", + "IPY_MODEL_5fe98f52135641a1893b2077510220fb", + "IPY_MODEL_1d3607906336463aa025c93dbeaf3bde", + "IPY_MODEL_9c171813f8a54b15ab647658d1a093f2", + "IPY_MODEL_b8d325de674a42ca85486a1a6de25b2f", + "IPY_MODEL_4dfdff3c33d44d958c2a2277aa3a99a8", + "IPY_MODEL_aeaedfd96af54c82a1620ea70730bf65" ], "crs": { "name": "EPSG3857", "custom": false }, - "default_style": "IPY_MODEL_dee5114c449f458a889ede5f5447d693", + "default_style": "IPY_MODEL_068a892df4784761971df6b52b81b657", "double_click_zoom": true, "dragging": true, - "dragging_style": "IPY_MODEL_64618ee8d9fb4ec6ada73fe6a03b9a71", - "east": 135.21972656250003, + "dragging_style": "IPY_MODEL_e940f18961b64bc2b95ec8b35a5c8ece", + "east": 135.68115234375003, "fullscreen": false, "inertia": true, "inertia_deceleration": 3000, @@ -9635,16 +9583,16 @@ "keyboard_pan_offset": 80, "keyboard_zoom_offset": 1, "layers": [ - "IPY_MODEL_053f3088b4ad436da16f8269e5d07b2e", - "IPY_MODEL_e5b7afbfdbc34e02b6d6908696a431b4", - "IPY_MODEL_1501f246f07a4d91a19667e1090b1f73" + "IPY_MODEL_ff13f896a17041cda9d863aa9c9b7a85", + "IPY_MODEL_36088b2ad89d403997bdba095418da88", + "IPY_MODEL_ad7a8631b42142c2aaab712451dbbf21" ], - "layout": "IPY_MODEL_fd25e9570f374653ae9468209fe3a54b", - "left": 6373, + "layout": "IPY_MODEL_33ba445201c8466092fe1bfaa03d66ce", + "left": 13567, "max_zoom": 24, "min_zoom": null, "modisdate": "2024-02-04", - "north": 40.1452892956766, + "north": 39.027718840211605, "options": [ "bounce_at_zoom_limits", "box_zoom", @@ -9675,24 +9623,24 @@ ], "panes": {}, "prefer_canvas": false, - "right": 7173, + "right": 14367, "scroll_wheel_zoom": true, - "south": 25.443274612305746, - "style": "IPY_MODEL_dee5114c449f458a889ede5f5447d693", + "south": 31.87755764334002, + "style": "IPY_MODEL_0bfcff1d63ab4e39b5d4807d9054dd70", "tap": true, "tap_tolerance": 15, - "top": 3097, + "top": 6260, "touch_zoom": true, - "west": 100.06347656250001, + "west": 118.10302734375001, "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", "world_copy_jump": false, - "zoom": 5, + "zoom": 6, "zoom_animation_threshold": 4, "zoom_delta": 1, "zoom_snap": 1 } }, - "1136a62f368c488dbd989bbe7aafe6e1": { + "813d63ffdac340fa82f3654ddf84b22c": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -9714,10 +9662,10 @@ ], "position": "topleft", "transparent_bg": false, - "widget": "IPY_MODEL_1760d31416424e489022ad2f9d2358e7" + "widget": "IPY_MODEL_b27fb9823e194bdab0197c64c24d8270" } }, - "0120edb48cec4e768fb2e7a0590f2c8b": { + "e6ffba1da6054cb28715a9864b14989d": { "model_module": "jupyter-leaflet", "model_name": "LeafletZoomControlModel", "model_module_version": "^0.18", @@ -9743,7 +9691,7 @@ "zoom_out_title": "Zoom out" } }, - "3b95748faa7a4f5c9b97d0533e14f609": { + "464f1099f31e4078bbd426d5bc1b8abe": { "model_module": "jupyter-leaflet", "model_name": "LeafletFullScreenControlModel", "model_module_version": "^0.18", @@ -9761,7 +9709,7 @@ "position": "topleft" } }, - "e6069552718849fd8098062260ac9dfb": { + "5fe98f52135641a1893b2077510220fb": { "model_module": "jupyter-leaflet", "model_name": "LeafletDrawControlModel", "model_module_version": "^0.18", @@ -9800,7 +9748,7 @@ "remove": true } }, - "a4762f33f5674375a05813cbcff93403": { + "1d3607906336463aa025c93dbeaf3bde": { "model_module": "jupyter-leaflet", "model_name": "LeafletScaleControlModel", "model_module_version": "^0.18", @@ -9826,7 +9774,7 @@ "update_when_idle": false } }, - "40be0705f15f431e89c083e01eab94f9": { + "9c171813f8a54b15ab647658d1a093f2": { "model_module": "jupyter-leaflet", "model_name": "LeafletMeasureControlModel", "model_module_version": "^0.18", @@ -9867,7 +9815,7 @@ "secondary_length_unit": null } }, - "d00f8c81b4754052850cd5ae3b67973f": { + "b8d325de674a42ca85486a1a6de25b2f": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -9889,10 +9837,10 @@ ], "position": "topright", "transparent_bg": false, - "widget": "IPY_MODEL_137d487ae6d64157b96becf8e2934a84" + "widget": "IPY_MODEL_b7fe85d1519349a5837c0e353cd68771" } }, - "08355bb00ea045fc945ee47ae4adfa7a": { + "4dfdff3c33d44d958c2a2277aa3a99a8": { "model_module": "jupyter-leaflet", "model_name": "LeafletAttributionControlModel", "model_module_version": "^0.18", @@ -9912,7 +9860,7 @@ "prefix": "ipyleaflet" } }, - "da37950eddb042d9bb43f495da888d89": { + "aeaedfd96af54c82a1620ea70730bf65": { "model_module": "jupyter-leaflet", "model_name": "LeafletWidgetControlModel", "model_module_version": "^0.18", @@ -9934,10 +9882,10 @@ ], "position": "bottomright", "transparent_bg": false, - "widget": "IPY_MODEL_ce79d4180d314deaac48f63581a328c9" + "widget": "IPY_MODEL_3983d97bef154958b9bed5df4760d133" } }, - "dee5114c449f458a889ede5f5447d693": { + "068a892df4784761971df6b52b81b657": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -9952,7 +9900,7 @@ "cursor": "grab" } }, - "64618ee8d9fb4ec6ada73fe6a03b9a71": { + "e940f18961b64bc2b95ec8b35a5c8ece": { "model_module": "jupyter-leaflet", "model_name": "LeafletMapStyleModel", "model_module_version": "^0.18", @@ -9967,7 +9915,7 @@ "cursor": "move" } }, - "053f3088b4ad436da16f8269e5d07b2e": { + "ff13f896a17041cda9d863aa9c9b7a85": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -10019,7 +9967,7 @@ "zoom_offset": 0 } }, - "e5b7afbfdbc34e02b6d6908696a431b4": { + "36088b2ad89d403997bdba095418da88": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -10066,12 +10014,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/fc71a429733131108c53b6fe14eccb20-00cbcfbeabfea8c4cb7c78562e5671d3/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/cb5a0accf78958fe8ee42b834ff97a51-b2ddae629cf5a4c67fb633b331beadc9/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - "1501f246f07a4d91a19667e1090b1f73": { + "ad7a8631b42142c2aaab712451dbbf21": { "model_module": "jupyter-leaflet", "model_name": "LeafletTileLayerModel", "model_module_version": "^0.18", @@ -10118,12 +10066,12 @@ "subitems": [], "tile_size": 256, "tms": false, - "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-cf30347e31a8547d2a571efa6d03d7f3/tiles/{z}/{x}/{y}", + "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-73296bd0dc24a5fb2e3fb65a5a03f65a/tiles/{z}/{x}/{y}", "visible": true, "zoom_offset": 0 } }, - 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"IPY_MODEL_5bdea2b1052e4e3899b3cc5ccb05ebda" + "IPY_MODEL_467b9855bc8c4aa28f6349957b77d984" ], - "layout": "IPY_MODEL_9f5f2770e9e145dca7d403b8e3d2deaf" + "layout": "IPY_MODEL_8e55852b68324cd9981639250e21677e" } }, - "ce79d4180d314deaac48f63581a328c9": { + "3983d97bef154958b9bed5df4760d133": { "model_module": "@jupyter-widgets/output", "model_name": "OutputModel", "model_module_version": "1.0.0", @@ -10249,7 +10197,7 @@ "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", - "layout": "IPY_MODEL_1c15810c3b664092bab387df504d9211", + "layout": "IPY_MODEL_9a5769371ae94e74aafbae4296637b79", "msg_id": "", "outputs": [ { @@ -10263,7 +10211,7 @@ ] } }, - "d1bcf6455dfe466098bae22d88db7a40": { + "84de4feaa968477f80e33d73dc99bce6": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -10281,13 +10229,13 @@ "description_tooltip": null, "disabled": false, "icon": "globe", - "layout": "IPY_MODEL_53c2802cbe654c00bdefc54c1828299d", - "style": "IPY_MODEL_013047cffc06425e912d3623696c2e65", + "layout": "IPY_MODEL_a507514f7c8c4c2993cc6df6b8b2b98c", + "style": "IPY_MODEL_76a573d962fb426abc03edba4717ee8f", "tooltip": "Search location/data", "value": false } }, - "d1f00923fa374f21b0d0f85337e48433": { + "90c1dbad942044b8a5ca4ef8d81bd099": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -10339,7 +10287,7 @@ "width": null } }, - "5bdea2b1052e4e3899b3cc5ccb05ebda": { + "467b9855bc8c4aa28f6349957b77d984": { "model_module": "@jupyter-widgets/controls", "model_name": "ToggleButtonModel", "model_module_version": "1.5.0", @@ -10357,13 +10305,13 @@ "description_tooltip": null, "disabled": false, "icon": "wrench", - "layout": "IPY_MODEL_a9a30c1d7ce2494ea74f1cfbb01c355f", - "style": "IPY_MODEL_f3684f852fe34e2fa56e21e365ab70eb", + "layout": "IPY_MODEL_624a8bb1365341ceaf7f3dc3a02c2bcd", + "style": "IPY_MODEL_1a430d25cfac48d3b6e37c886c2503d6", "tooltip": "Toolbar", "value": false } }, - 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"f3684f852fe34e2fa56e21e365ab70eb": { + "1a430d25cfac48d3b6e37c886c2503d6": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -10608,12 +10556,7 @@ { "cell_type": "code", "metadata": { - "id": "8kdsGkYJXXKc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "outputId": "ea49ceb7-2fdf-4929-a5bf-a3bd2536fe8f" + "id": "8kdsGkYJXXKc" }, "source": [ "#@title Copyright 2023 The Earth Engine Community Authors { display-mode: \"form\" }\n", @@ -10630,43 +10573,8 @@ "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], - "execution_count": 4, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "execution_count": 1, + "outputs": [] }, { "cell_type": "markdown", @@ -10703,10 +10611,9 @@ "ee.Authenticate()\n", "\n", "# Initialize the library.\n", - "# ee.Initialize(project='my-project')\n", - "ee.Initialize(project='ee-foss4g')" + "ee.Initialize(project='my-project')" ], - "execution_count": 1, + "execution_count": 2, "outputs": [] }, { @@ -10787,50 +10694,10 @@ "from statsmodels.stats.outliers_influence import variance_inflation_factor" ], "metadata": { - "id": "4jbM03uIrjST", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "outputId": "baa5449b-af0f-4c39-f086-535c984f0509" + "id": "4jbM03uIrjST" }, "execution_count": 3, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "outputs": [] }, { "cell_type": "markdown", @@ -10896,9 +10763,9 @@ "height": 17 }, "id": "oHtKaH0FgXTz", - "outputId": "35aaf721-029e-4c34-b35c-06608f1b5250" + "outputId": "cbbb4c25-24cc-4427-cc22-8093989428ca" }, - "execution_count": 6, + "execution_count": 4, "outputs": [ { "output_type": "display_data", @@ -10963,9 +10830,9 @@ "height": 182 }, "id": "Mx-DjtGNnUXk", - "outputId": "95a3b0ee-25cb-4547-cb3b-8da17eed9fff" + "outputId": "63f1438f-7513-4f05-b827-e7da084e42e0" }, - "execution_count": 8, + "execution_count": 5, "outputs": [ { "output_type": "display_data", @@ -11030,7 +10897,7 @@ ], "text/html": [ "\n", - "
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This allows for the identification of temporal patterns and seasonal variations in species occurrence data, as well as the rapid detection of outliers or quality issues within the data." - ], - "metadata": { - "id": "5Lj919AaqmUq" - } - }, - { - "cell_type": "code", - "source": [ - "# Visualize the distribution of data by year and month\n", - "def plot_data_distribution(gdf, h_size=12):\n", - "\n", - " plt.figure(figsize=(h_size, h_size-8))\n", - "\n", - " # Yearly data distribution graph (left)\n", - " plt.subplot(1, 2, 1)\n", - " year_counts = gdf['year'].value_counts().sort_index()\n", - " plt.bar(year_counts.index, year_counts.values)\n", - " plt.xlabel('Year')\n", - " plt.ylabel('Count')\n", - " plt.title('Yearly Data Distribution')\n", - "\n", - " # Display data counts above the bars\n", - " for i, count in enumerate(year_counts.values):\n", - " plt.text(year_counts.index[i], count, str(count), ha='center', va='bottom')\n", - "\n", - " # Monthly data distribution graph (right)\n", - 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- }, - "metadata": {} - } - ] + "position": "topleft" + } }, - { - "cell_type": "markdown", - "source": [ - "The data from 1995 is very sparse, with significant gaps compared to other years, and the months of August and September also have limited samples and exhibit different seasonal characteristics compared to other periods. Excluding this data could contribute to improving the stability and predictive power of the model.\n", - "\n", - "However, it's important to note that excluding data may enhance the model's generalization ability, but it could also lead to the loss of valuable information relevant to the research objectives. Therefore, such decisions should be made with careful consideration." - ], - "metadata": { - "id": "vIj9CO1RrCSs" - } - }, - { - "cell_type": "code", - "source": [ - "# Filtering data by year and month\n", - "filtered_gdf = gdf[\n", - " (~gdf['year'].eq(1995)) &\n", - " (~gdf['month'].between(8, 9))\n", - "]\n", - "\n", - "# Visualize the filtered data distribution\n", - "plot_data_distribution(filtered_gdf)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 407 - }, - "id": "k1PpbNsGq9qh", - "outputId": "19e2ffd5-ae56-444d-ae63-d955a07c1b8b" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {} - } - ] + "ddad8da26065420695ff8deea1af347d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ToggleButtonModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ToggleButtonModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ToggleButtonView", + "button_style": "", + "description": "", + "description_tooltip": null, + "disabled": false, + "icon": "wrench", + "layout": "IPY_MODEL_78a60a4cb4ae4304bb0650947b557db5", + "style": "IPY_MODEL_5d10998ac151453cb1ac262a0ee6f8f6", + "tooltip": "Toolbar", + "value": false + } }, - { - "cell_type": "markdown", - "source": [ - "Additionally, the use of a heatmap allows us to quickly grasp the frequency of species occurrence by year and month, providing an intuitive visualization of the temporal changes and patterns within the data." - ], - "metadata": { - "id": "-r2fND5nr0dU" - } - }, - { - "cell_type": "code", - "source": [ - "# Yearly and monthly data distribution heatmap\n", - "def plot_heatmap(gdf, h_size=8):\n", - "\n", - " statistics = gdf.groupby([\"month\", \"year\"]).size().unstack(fill_value=0)\n", - "\n", - " # Heatmap\n", - " plt.figure(figsize=(h_size, h_size-6))\n", - " heatmap = plt.imshow(statistics.values, cmap=\"YlOrBr\", origin=\"upper\", aspect=\"auto\")\n", - "\n", - " # Display values above each pixel\n", - " for i in range(len(statistics.index)):\n", - " for j in range(len(statistics.columns)):\n", - " plt.text(j, i, statistics.values[i, j], ha=\"center\", va=\"center\", color=\"black\")\n", - "\n", - " plt.colorbar(heatmap, label=\"Count\")\n", - " plt.title(\"Monthly Species Count by Year\")\n", - " plt.xlabel(\"Year\")\n", - " plt.ylabel(\"Month\")\n", - " plt.xticks(range(len(statistics.columns)), statistics.columns)\n", - " 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\n" - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "year 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023\n", - "month \n", - "5 0 0 5 0 2 1 5 4 5 22 8 1\n", - "6 0 2 6 1 1 6 0 6 13 35 20 3\n", - "7 1 0 0 1 0 1 1 7 1 10 8 0\n" - ] - } - ] + "position": "topleft", + "transparent_bg": false, + "widget": "IPY_MODEL_6c5d7e69966c4bb08fb33437d2a1b1a3" + } }, - { - "cell_type": "markdown", - "source": [ - "Now, the filtered GeoDataFrame is converted into a Google Earth Engine object." - ], - "metadata": { - "id": "jy0BBd-6sdLz" - } - }, - { - "cell_type": "code", - "source": [ - "# Convert GeoDataFrame to Earth Engine object\n", - "data_raw = geemap.geopandas_to_ee(filtered_gdf)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "Fxu0OsBksMKM", - "outputId": "48a02436-ea36-45f0-f0e8-f20badf9c9e4" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "e0e2e80c861c490e9f440079263a66db": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ToggleButtonModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ToggleButtonModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ToggleButtonView", + "button_style": "", + "description": "", + "description_tooltip": null, + "disabled": false, + "icon": "globe", + "layout": "IPY_MODEL_c9dbe3c4a93740e8806d406c10a11000", + "style": "IPY_MODEL_fd67dca06419491aac8a0696a112c2a4", + "tooltip": "Search location/data", + "value": false + } }, - { - "cell_type": "markdown", - "source": [ - "Next, we will define the raster pixel size of the SDM results as 1km resolution." - ], - "metadata": { - "id": "55p_GfB6sv3U" - } - }, - { - "cell_type": "code", - "source": [ - "# Spatial resolution setting (meters)\n", - "GrainSize = 1000" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "FsTbNQ17s1l-", - "outputId": "7590e4de-a8f9-4c5a-e3ea-1fee26d4fe08" + "e10f6046737d43ad931387a38bee8442": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletMeasureControlModel", + "state": { + "_custom_units": {}, + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletMeasureControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletMeasureControlView", + "active_color": "orange", + "capture_z_index": 10000, + "completed_color": "#C8F2BE", + "options": [ + "active_color", + "capture_z_index", + "completed_color", + "popup_options", + "position", + "primary_area_unit", + "primary_length_unit", + "secondary_area_unit", + "secondary_length_unit" + ], + "popup_options": { + "autoPanPadding": [ + 10, + 10 + ], + "className": "leaflet-measure-resultpopup" }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "position": "bottomleft", + "primary_area_unit": "acres", + "primary_length_unit": "kilometers", + "secondary_area_unit": null, + "secondary_length_unit": null + } }, - { - "cell_type": "markdown", - "source": [ - "When multiple occurrence points are present within the same 1km resolution raster pixel, there is a high likelihood that they share the same environmental conditions at the same geographic location. Using such data directly in the analysis can introduce bias into the results.\n", - "\n", - "In other words, we need to limit the potential impact of geographic sampling bias. To achieve this, we will retain only one location within each 1km pixel and remove all others, allowing the model to more objectively reflect the environmental conditions." - ], - "metadata": { - "id": "A20gAGUZtN6S" - } - }, - { - "cell_type": "code", - "source": [ - "def remove_duplicates(data, GrainSize):\n", - " # Select one occurrence record per pixel at the chosen spatial resolution (1km)\n", - " random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", - " rand_point_vals = random_raster.sampleRegions(collection=ee.FeatureCollection(data), scale=10, geometries=True)\n", - " return rand_point_vals.distinct('random')\n", - "\n", - "Data = remove_duplicates(data_raw, GrainSize)\n", - "\n", - "# Before selection and after selection\n", - "print('Original data size:', data_raw.size().getInfo())\n", - "print('Final data size:', Data.size().getInfo())" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 53 - }, - "id": "dHtyQyMQs82v", - "outputId": "6143f4c1-31cb-4189-ada4-7d165b229794" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Original data size: 176\n", - "Final data size: 111\n" - ] - } - ] + "e134170fd7b44730875e7e1b207ebbe9": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletZoomControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletZoomControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletZoomControlView", + "options": [ + "position", + "zoom_in_text", + "zoom_in_title", + "zoom_out_text", + "zoom_out_title" + ], + "position": "topleft", + "zoom_in_text": "+", + "zoom_in_title": "Zoom in", + "zoom_out_text": "-", + "zoom_out_title": "Zoom out" + } }, - { - "cell_type": "markdown", - "source": [ - "The visualization comparing geographic sampling bias before preprocessing (in blue) and after preprocessing (in red) is shown below. To facilitate comparison, the map has been centered on the area with a high concentration of Fairy pitta occurrence coordinates in Hallasan National Park." - ], - "metadata": { - "id": "Rhu4b4BxuMHE" - } - }, - { - "cell_type": "code", - "source": [ - "# Visualization of geographic sampling bias before (blue) and after (red) preprocessing\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "\n", - "# Add the random raster layer\n", - "random_raster = ee.Image.random().reproject('EPSG:4326', None, GrainSize)\n", - "Map.addLayer(random_raster, {'min': 0, 'max': 1, 'palette': ['black', 'white'], 'opacity': 0.5}, 'Random Raster')\n", - "\n", - "# Add the original data layer in blue\n", - "Map.addLayer(data_raw, {'color': 'blue'}, 'Original data')\n", - "\n", - "# Add the final data layer in red\n", - "Map.addLayer(Data, {'color': 'red'}, 'Final data')\n", - "\n", - "# Set the center of the map to the coordinates\n", - "Map.setCenter(126.712, 33.516, 15)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "ec18a01a256745efaed76a622519ac4f", - "40081735809a4ac19c4d84acbde91938", - "914f049607f6472a85e3afe6a97c81b1", - "419ff70fd8e34667b276467f756b618d", - "ac7bde123bae43dc934c9a253f2273c7", - "b174597906d04261a024cf014aa3c842", - "5bc2e90959fb4fbbb1a1864085f12954", - "6f1447ee68bc42778df44e95bb36862d", - "8293fa80884e45449ac9b7950ee4cc70", - "2d37126201cc4f8f85d88ca8e9f8affc", - "6d5ccb249d9146a2b1c5dd9bb84e9f8b", - "907d89b6d73a47f08cc4680f1811123a", - "07d6287890bd4f939df3a65145789715", - "d8466aad5f6245d6b077cb2dc4ac8496", - "3e8823008c094bc5823e1f7d7a698edc", - "87b94cfce4fa4be1a814b11c6e92ffc4", - "8e4af62156be4d8b8104821691ad682b", - "42a9668a07dd4497980f0c25277e6d18", - "e150b8ea0e9e4e6ca4d7fdcac9537b88", - "144e1da326c249c288e4878a1e526679", - "ddad8da26065420695ff8deea1af347d", - "649c76ab02b04c8dad91b7be4d01d89e", - "379f53be6bc048638c518ca28b0d52d2", - "a1f00c3c17bf482498d4ee29b0670db7", - "78a60a4cb4ae4304bb0650947b557db5", - "5d10998ac151453cb1ac262a0ee6f8f6", - "1d19a7648bf44be39dfd7a8ce8bafc78" - ] - }, - "id": "d9pFgpUztsB-", - "outputId": "784ea56e-d600-4ee6-9497-038966a46896" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[33.516, 126.712], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=SearchDat…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "ec18a01a256745efaed76a622519ac4f" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ] + "e150b8ea0e9e4e6ca4d7fdcac9537b88": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ToggleButtonModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ToggleButtonModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ToggleButtonView", + "button_style": "", + "description": "", + "description_tooltip": null, + "disabled": false, + "icon": "globe", + "layout": "IPY_MODEL_379f53be6bc048638c518ca28b0d52d2", + "style": "IPY_MODEL_a1f00c3c17bf482498d4ee29b0670db7", + "tooltip": "Search location/data", + "value": false + } }, - { - "cell_type": "markdown", - "source": [ - "### Definition of the Area of Interest\n", - "\n", - "Defining the Area of Interest (AOI below) refers to the term used by researchers to denote the geographical area they want to analyze. It has a similar meaning to the term Study Area.\n", - "\n", - "In this context, we obtained the bounding box of the occurrence point layer geometry and created a 50-kilometer buffer around it (with a maximum tolerance of 1,000 meters) to define the AOI." - ], - "metadata": { - "id": "3oPtTj_O6d3H" - } - }, - { - "cell_type": "code", - "source": [ - "# Define the AOI\n", - "AOI = Data.geometry().bounds().buffer(distance=50000, maxError=1000)\n", - "\n", - "# Add the AOI to the map\n", - "outline = ee.Image().byte().paint(\n", - " featureCollection=AOI, color=1, width=3)\n", - "\n", - "Map.remove_layer(\"Random Raster\")\n", - "Map.addLayer(outline, {'palette': 'FF0000'}, \"AOI\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "ec18a01a256745efaed76a622519ac4f", - "40081735809a4ac19c4d84acbde91938", - "914f049607f6472a85e3afe6a97c81b1", - "419ff70fd8e34667b276467f756b618d", - "ac7bde123bae43dc934c9a253f2273c7", - "b174597906d04261a024cf014aa3c842", - "5bc2e90959fb4fbbb1a1864085f12954", - "6f1447ee68bc42778df44e95bb36862d", - "8293fa80884e45449ac9b7950ee4cc70", - "2d37126201cc4f8f85d88ca8e9f8affc", - "6d5ccb249d9146a2b1c5dd9bb84e9f8b", - "907d89b6d73a47f08cc4680f1811123a", - "07d6287890bd4f939df3a65145789715", - "d8466aad5f6245d6b077cb2dc4ac8496", - "1d19a7648bf44be39dfd7a8ce8bafc78", - "3e8823008c094bc5823e1f7d7a698edc", - "87b94cfce4fa4be1a814b11c6e92ffc4", - "8e4af62156be4d8b8104821691ad682b", - "42a9668a07dd4497980f0c25277e6d18", - "e150b8ea0e9e4e6ca4d7fdcac9537b88", - "144e1da326c249c288e4878a1e526679", - "ddad8da26065420695ff8deea1af347d", - "649c76ab02b04c8dad91b7be4d01d89e", - "379f53be6bc048638c518ca28b0d52d2", - "a1f00c3c17bf482498d4ee29b0670db7", - "78a60a4cb4ae4304bb0650947b557db5", - "5d10998ac151453cb1ac262a0ee6f8f6" - ] - }, - "id": "XIyhhdzUvyZw", - "outputId": "f33d1856-6944-4e3a-e755-b2b2653166c7" + "e1ab1f5595a0422c99289d5505c7dc8e": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletDrawControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletDrawControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletDrawControlView", + "circle": {}, + "circlemarker": {}, + "data": [], + "edit": true, + "marker": { + "shapeOptions": { + "color": "#3388ff" + } }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "ec18a01a256745efaed76a622519ac4f" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Addition of GEE environmental variables\n", - "\n", - "Now, let's add environmental variables to the analysis. GEE provides a wide range of datasets for environmental variables such as temperature, precipitation, elevation, land cover, and terrain. These datasets enable us to comprehensively analyze various factors that may influence the habitat preferences of the Fairy pitta.\n", - "\n", - "The selection of GEE environmental variables in SDM should reflect the habitat preference characteristics of the species. To do this, prior research and literature review on the Fairy pitta's habitat preferences should be conducted. This tutorial primarily focuses on the workflow of SDM using GEE, so some in-depth details are omitted.\n", - "\n", - "[**WorldClim V1 Bioclim**](https://developers.google.com/earth-engine/datasets/catalog/WORLDCLIM_V1_BIO): This dataset provides 19 bioclimatic variables derived from monthly temperature and precipitation data. It covers the period from 1960 to 1991 and has a resolution of 927.67 meters." - ], - "metadata": { - "id": "fWsplwHD8ZFd" - } - }, - { - "cell_type": "code", - "source": [ - "# WorldClim V1 Bioclim\n", - "BIO = ee.Image(\"WORLDCLIM/V1/BIO\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "TNW23qpX7shy", - "outputId": "bb047528-fbbc-474a-dafb-36ac8b260101" + "options": [ + "position" + ], + "polygon": { + "shapeOptions": {} }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "[**NASA SRTM Digital Elevation 30m**](https://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003): This dataset contains digital elevation data from the Shuttle Radar Topography Mission (SRTM). The data was primarily collected around the year 2000 and is provided at a resolution of approximately 30 meters (1 arc-second). The following code calculates elevation, slope, aspect, and hillshade layers from the SRTM data." - ], - "metadata": { - "id": "AzrCwWu39iLw" - } - }, - { - "cell_type": "code", - "source": [ - "# NASA SRTM Digital Elevation 30m\n", - "Terrain = ee.Algorithms.Terrain(ee.Image(\"USGS/SRTMGL1_003\"))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "O8lPyhWv9hHn", - "outputId": "ccd4ebfd-e70f-4c27-8159-4d6d7f861972" + "polyline": { + "shapeOptions": {} }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "[**Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m**](https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3): The Vegetation Continuous Fields (VCF) dataset from Landsat estimates the proportion of vertically projected vegetation cover when the vegetation height is greater than 5 meters. This dataset is provided for four time periods centered around the years 2000, 2005, 2010, and 2015, with a resolution of 30 meters. Here, the median values from these four time periods are used." - ], - "metadata": { - "id": "H-kFHdWP9_3N" - } - }, - { - "cell_type": "code", - "source": [ - "# Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m\n", - "TCC = ee.ImageCollection(\"NASA/MEASURES/GFCC/TC/v3\")\n", - "MedianTCC = TCC.filterDate('2000-01-01', '2015-12-31')\n", - "MedianTCC = MedianTCC.select(['tree_canopy_cover'], ['TCC']).median()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "-ymsifA098H6", - "outputId": "6316ead5-71ca-431a-a82a-f25d7a14e75b" + "position": "topleft", + "rectangle": { + "shapeOptions": { + "color": "#3388ff" + } }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "remove": true + } }, - { - "cell_type": "markdown", - "source": [ - "`BIO` (Bioclimatic variables), `Terrain` (topography), and `MedianTCC` (tree canopy cover) are combined into a single multiband image. The `elevation` band is selected from `Terrain`, and a `watermask` is created for locations where `elevation` is greater than `0`. This masks regions below sea level (e.g. the ocean) and prepares the researcher to analyze various environmental factors for the AOI comprehensively." - ], - "metadata": { - "id": "CMrmqQ5X-uOB" - } - }, - { - "cell_type": "code", - "source": [ - "# Combine bands into a multi-band image\n", - "predictors = BIO.addBands(Terrain).addBands(MedianTCC)\n", - "\n", - "# Create a water mask\n", - "watermask = Terrain.select('elevation').gt(0)\n", - "\n", - "# Mask out ocean pixels and clip to the area of interest\n", - "predictors = predictors.updateMask(watermask).clip(AOI)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "gSbuqe6k-k6B", - "outputId": "53122bee-0eef-426e-d94b-1a54745cf955" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "e1c614cfbe7548758e2d4d78255ea3ae": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": "28px", + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": "0px 0px 0px 4px", + "right": null, + "top": null, + "visibility": null, + "width": "28px" + } }, - { - "cell_type": "markdown", - "source": [ - "When highly correlated predictor variables are included together in a model, multicollinearity issues can arise. Multicollinearity is a phenomenon that occurs when there are strong linear relationships among independent variables in a model, leading to instability in the estimation of the model's coefficients (weights). This instability can reduce the model's reliability and make predictions or interpretations for new data challenging. Therefore, we will consider multicollinearity and proceed with the process of selecting predictor variables.\n", - "\n", - "First, we will generate 5,000 random points and then extract the predictor variable values of the single multiband image at those points." - ], - "metadata": { - "id": "WmuXiScSAlxX" - } - }, - { - "cell_type": "code", - "source": [ - "# Generate 5,000 random points\n", - "DataCor = predictors.sample(scale=GrainSize, numPixels=5000, geometries=True)\n", - "\n", - "# Extract predictor variable values\n", - "PixelVals = predictors.sampleRegions(collection=DataCor, scale=GrainSize, tileScale=16)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "vYLvzPDwAeyk", - "outputId": "265e6828-38fb-4da3-def0-681fdbfbe1e7" + "e64f35fa89944b6290f597d4f7182b1c": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletDrawControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletDrawControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletDrawControlView", + "circle": {}, + "circlemarker": {}, + "data": [], + "edit": true, + "marker": { + "shapeOptions": { + "color": "#3388ff" + } }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "We will convert the extracted predictor values for each point into a DataFrame and then check the first row." - ], - "metadata": { - "id": "oHWmXtTxCmfV" - } - }, - { - "cell_type": "code", - "source": [ - "# Converting predictor values from Earth Engine to a DataFrame\n", - "PixelVals_df = geemap.ee_to_df(PixelVals)\n", - "PixelVals_df.head(1)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 110 - }, - "id": "x1Bv0knrBbWn", - "outputId": "9442a776-eed2-415f-fd29-7758161de37c" + "options": [ + "position" + ], + "polygon": { + "shapeOptions": {} }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " TCC aspect bio01 bio02 bio03 bio04 bio05 bio06 bio07 bio08 ... \\\n", - "0 9.0 288 140 89 26 8572 304 -26 330 246 ... \n", - "\n", - " bio13 bio14 bio15 bio16 bio17 bio18 bio19 elevation hillshade \\\n", - "0 215 32 63 561 111 539 111 28 187 \n", - "\n", - " slope \n", - "0 2 \n", - "\n", - "[1 rows x 24 columns]" - ], - "text/html": [ - "\n", - "
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\n" - ] - }, - "metadata": {}, - "execution_count": 22 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Displaying the columns\n", - "columns = PixelVals_df.columns\n", - "print(columns)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 108 - }, - "id": "H3K-knJnC65R", - "outputId": "9a4743ad-6c95-44be-d4b3-b6656be31ea0" + "polyline": { + "shapeOptions": {} }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Index(['TCC', 'aspect', 'bio01', 'bio02', 'bio03', 'bio04', 'bio05', 'bio06',\n", - " 'bio07', 'bio08', 'bio09', 'bio10', 'bio11', 'bio12', 'bio13', 'bio14',\n", - " 'bio15', 'bio16', 'bio17', 'bio18', 'bio19', 'elevation', 'hillshade',\n", - " 'slope'],\n", - " dtype='object')\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Calculating Spearman correlation coefficients between the given predictor variables and visualizing them in a heatmap." - ], - "metadata": { - "id": "f3YiwTb8DOzb" - } - }, - { - "cell_type": "code", - "source": [ - "def plot_correlation_heatmap(dataframe, h_size=10):\n", - " # Calculate Spearman correlation coefficients\n", - " correlation_matrix = dataframe.corr(method=\"spearman\")\n", - "\n", - " # Create a heatmap\n", - " plt.figure(figsize=(h_size, h_size-2))\n", - " plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='nearest')\n", - "\n", - " # Display values on the heatmap\n", - " for i in range(correlation_matrix.shape[0]):\n", - " for j in range(correlation_matrix.shape[1]):\n", - " plt.text(j, i, f\"{correlation_matrix.iloc[i, j]:.2f}\",\n", - " ha='center', va='center', color='white', fontsize=8)\n", - "\n", - " columns = dataframe.columns.tolist()\n", - " plt.xticks(range(len(columns)), columns, rotation=90)\n", - " plt.yticks(range(len(columns)), columns)\n", - " plt.title(\"Variables correlation matrix\")\n", - " plt.colorbar(label=\"Spearman Correlation\")\n", - " plt.savefig('correlation_heatmap_plot.png')\n", - " plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "i9YelZ5qDG_t", - "outputId": "2652c5ad-f00a-4c16-c179-44673bd0eb79" + "position": "topleft", + "rectangle": { + "shapeOptions": { + "color": "#3388ff" + } }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "remove": true + } }, - { - "cell_type": "code", - "source": [ - "# Plot the correlation heatmap of variables\n", - "plot_correlation_heatmap(PixelVals_df)" + "e6501a8d76e442bd9f5de2f86f9ce179": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [ + "geemap-colab" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 749 - }, - "id": "DP5ect8cDb7C", - "outputId": "e0ffef35-c3ee-4799-e076-24f3ae4136d5" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ] + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a076579ca63c4f0f9267af8af6820b3e" + ], + "layout": "IPY_MODEL_3b00a188691d445d897d5fb4f5d3b285" + } }, - { - "cell_type": "markdown", - "source": [ - "Spearman correlation coefficient is useful for understanding the general associations among predictor variables but does not directly assess how multiple variables interact, specifically detecting multicollinearity.\n", - "\n", - "The **Variance Inflation Factor (VIF below)** is a statistical metric used to evaluate multicollinearity and guide variable selection. It indicates the degree of linear relationship of each independent variable with the other independent variables, and high VIF values can be evidence of multicollinearity.\n", - "\n", - "Typically, when VIF values exceed 5 or 10, it suggests that the variable has a strong correlation with other variables, potentially compromising the stability and interpretability of the model. In this tutorial, a criterion of VIF values less than 10 was used for variable selection. The following 6 variables were selected based on VIF." - ], - "metadata": { - "id": "07B62CNfDyGz" - } - }, - { - "cell_type": "code", - "source": [ - "# Filter variables based on Variance Inflation Factor (VIF)\n", - "def filter_variables_by_vif(dataframe, threshold=10):\n", - "\n", - " original_columns = dataframe.columns.tolist()\n", - " remaining_columns = original_columns[:]\n", - "\n", - " while True:\n", - " vif_data = dataframe[remaining_columns]\n", - " vif_values = [variance_inflation_factor(vif_data.values, i) for i in range(vif_data.shape[1])]\n", - "\n", - " max_vif_index = vif_values.index(max(vif_values))\n", - " max_vif = max(vif_values)\n", - "\n", - " if max_vif < threshold:\n", - " break\n", - "\n", - " print(f\"Removing '{remaining_columns[max_vif_index]}' with VIF {max_vif:.2f}\")\n", - "\n", - " del remaining_columns[max_vif_index]\n", - "\n", - " filtered_data = dataframe[remaining_columns]\n", - " bands = filtered_data.columns.tolist()\n", - " print('Bands:', bands)\n", - "\n", - " return filtered_data, bands" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "TJdGzd3SDisO", - "outputId": "74452672-8591-4da0-982c-945b4dd529b9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "e6ffba1da6054cb28715a9864b14989d": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletZoomControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletZoomControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletZoomControlView", + "options": [ + "position", + "zoom_in_text", + "zoom_in_title", + "zoom_out_text", + "zoom_out_title" + ], + "position": "topleft", + "zoom_in_text": "+", + "zoom_in_title": "Zoom in", + "zoom_out_text": "-", + "zoom_out_title": "Zoom out" + } + }, + "e80b2be5cc1a45ab9ca1aae28292866c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "e834223685554121abd1be7ad61abcf9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": "28px", + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": "0px 0px 0px 4px", + "right": null, + "top": null, + "visibility": null, + "width": "28px" + } + }, + "e940f18961b64bc2b95ec8b35a5c8ece": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletMapStyleModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletMapStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "cursor": "move" + } }, - { - "cell_type": "code", - "source": [ - "filtered_PixelVals_df, bands = filter_variables_by_vif(PixelVals_df)" + "e9eb5df4dce54168a6568b5cd3d07382": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletZoomControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletZoomControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletZoomControlView", + "options": [ + "position", + "zoom_in_text", + "zoom_in_title", + "zoom_out_text", + "zoom_out_title" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 362 - }, - "id": "e3iiKaK5Eg0q", - "outputId": "c03e43d7-821f-44ff-cbb0-226aa989c41c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Removing 'bio05' with VIF inf\n", - "Removing 'bio04' with VIF 183937.54\n", - "Removing 'bio10' with VIF 64460.60\n", - "Removing 'bio07' with VIF 47244.59\n", - "Removing 'bio17' with VIF 26253.01\n", - "Removing 'bio01' with VIF 9428.16\n", - "Removing 'bio16' with VIF 3700.80\n", - "Removing 'bio03' with VIF 2468.04\n", - "Removing 'bio18' with VIF 1716.89\n", - "Removing 'bio08' with VIF 1247.20\n", - "Removing 'bio06' with VIF 959.97\n", - "Removing 'bio12' with VIF 606.08\n", - "Removing 'bio19' with VIF 400.13\n", - "Removing 'bio15' with VIF 348.96\n", - "Removing 'hillshade' with VIF 129.83\n", - "Removing 'bio13' with VIF 47.33\n", - "Removing 'bio11' with VIF 31.44\n", - "Removing 'bio02' with VIF 13.75\n", - "Bands: ['TCC', 'aspect', 'bio09', 'bio14', 'elevation', 'slope']\n" - ] - } - ] + "position": "topleft", + "zoom_in_text": "+", + "zoom_in_title": "Zoom in", + "zoom_out_text": "-", + "zoom_out_title": "Zoom out" + } }, - { - "cell_type": "code", - "source": [ - "# Variable Selection Based on VIF\n", - "predictors = predictors.select(bands)\n", - "\n", - "# Plot the correlation heatmap of variables\n", - "plot_correlation_heatmap(filtered_PixelVals_df, h_size=6)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 441 - }, - "id": "TEFmil6WErkl", - "outputId": "c2cbd221-d66d-41da-b297-587b7009afc2" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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For example, the `terrain` palette looks like this.\n", - "```\n", - "cm.plot_colormaps(width=8.0, height=0.2)\n", - "```" - ], - "metadata": { - "id": "j0dc6evyFFhw" - } - }, - { - "cell_type": "code", - "source": [ - "cm.plot_colormap('terrain', width=8.0, height=0.2, orientation='horizontal')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 52 - }, - "id": "H8uHX-83E8x7", - "outputId": "d0095867-c502-487a-dd98-9a40fce553b7" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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{ - "cell_type": "code", - "source": [ - "# Elevation layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "\n", - "vis_params = {'bands':['elevation'], 'min': 0, 'max': 1800, 'palette': cm.palettes.terrain}\n", - "Map.addLayer(predictors, vis_params, 'elevation')\n", - "Map.add_colorbar(vis_params, label=\"Elevation (m)\", orientation=\"vertical\", layer_name=\"elevation\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "ad4b6510fe6441b8b18d640fefa6ed02", - "68c6f333036a49c7aaa82f12467c6a44", - "3fe18c8e85b944108c5556b544d9b619", - "8f4046ce7dd24e189ee183b50c7ea3c9", - "70365465ac10473695da7052bc4ce132", - "03b3ff0f74c74be9af7b14868935c379", - "82b74bc6e67e461cbac3436c24405bf7", - "6c8fa4067706438f91a06200897cd914", - "0d425d905d0c4407b6a1b6015d2ba33a", - "2fd7087133154808aea0c7fdb9ae1094", - "1bb5e66353b940e0be30b69c46eb0ef8", - "55397a8b887140f2a476238786ac95b1", - "81274fed697e4a6e89a380e8c83476ad", - "8e1091fe3999443987a2a393e11b67e5", - "265f960413c44ef1b35c3a11f75eec3d", - "28cb9df4b67841b78c63362605940046", - "c3aba1a59cb044af9e66498026e66893", - "0ec2004e54a74f8d9012e1377dd24704", - "c6fb58a471354b7fbd2139e7177907fd", - "05cda8397b1d4c1ab0d6ec8b8782292f", - "1122e9578ed24015860360c3fb9bd3c0", - "7dff1f5368e040ce8aa0a2e0d3593051", - "3fb8469a03be4d8d80ec96b95d3a7be3", - "6017d2ba81cf47059b53473b9b02a613", - "6df98ca6cbf741e293b86cb9b21491d3", - "65bea7d4f8a542fdb4db941c30e7e2f6", - "dc3b7752f47a4d16b96525983f17e626", - "37806e7c513c48e1b524cc90e0228cd5" - ] - }, - "id": "RswVTmLFFUb3", - "outputId": "6c6d5f0a-9da5-4feb-e6f1-9a355e85798d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - 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{ - "cell_type": "code", - "source": [ - "# Calculate the minimum and maximum values for bio09\n", - "min_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", - "max_val = predictors.select(\"bio09\").multiply(0.1).reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", - "\n", - "# bio09 (Mean temperature of driest quarter) layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "\n", - "vis_params = {'min': math.floor(min_val['bio09']), 'max': math.ceil(max_val['bio09']), 'palette': cm.palettes.hot}\n", - "Map.addLayer(predictors.select(\"bio09\").multiply(0.1), vis_params, 'bio09')\n", - "Map.add_colorbar(vis_params, label=\"Mean temperature of driest quarter (℃)\", orientation=\"vertical\", layer_name=\"bio09\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "6edda6ba5bb648bd9dc55dd3328fd82a", - "5dfe200a57c847e396f711de43ec719d", - "f39d82350d1f47bcb63d2817c70c587b", - "29d110e4bbd44afa85f1db5db4748f90", - "780acf40c0274d4089592719b43b3f43", - "7baefba25b77424a99027681c99c34cb", - "f47a03bc228649eb92cc412445d64c68", - "945b1b623cdf444c909fd2a1da927dff", - "f6efdfd64e614ac8813e9e911a1fce80", - "7e330ef004474efda8b3eced32178003", - "2069e091220c42b6ba1d79c063f3e9e6", - "0bd23be2e86e4e8b9776cac7e2c47ed8", - "4920efe9f2684b91b5a02a7d7cb0440b", - "8eea4e8291c0497b81257afdb11651c1", - "cef9eeda18df40a4a7e2fb4ad7d63240", - "b90d0d82f3d944f99dd83e103b57fa60", - "8169818152d9407798f5df01cc0addc9", - "63420d977a5f4c8495d23c02b320581e", - "a2aedccb9e66493e8669508559d05668", - "9999e11fbf834f869596f2db90ea9c37", - "8fae16cef3104206861b1450faf0e412", - "7877ecf0cb624571a8926cb6dc78c0a2", - "3ba3e0736a404fb3bb9cf8ad68609f50", - "5cd12bc944d04c1ca1caf58d92b7b5ae", - "fdef9d8a239141ba9498aa9f99dbbea3", - "03c7e1346a2c413d8463905a96ceaad0", - "72e131ab901d49c6af32a4a37fcb54ff", - "384a473fe1124fcd98888b8692807b59" - ] - }, - "id": "DOSMZpbsF8sH", - "outputId": "6d375fd2-5fa9-414e-efb5-48c7ca30e506" + "ec18a01a256745efaed76a622519ac4f": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletMapModel", + "state": { + "_dom_classes": [], + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletMapModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletMapView", + "bottom": 6657, + "bounce_at_zoom_limits": true, + "box_zoom": true, + "center": [ + 35.587733558095216, + 126.8959721004684 + ], + "close_popup_on_click": true, + "controls": [ + "IPY_MODEL_40081735809a4ac19c4d84acbde91938", + "IPY_MODEL_914f049607f6472a85e3afe6a97c81b1", + "IPY_MODEL_419ff70fd8e34667b276467f756b618d", + "IPY_MODEL_ac7bde123bae43dc934c9a253f2273c7", + "IPY_MODEL_b174597906d04261a024cf014aa3c842", + "IPY_MODEL_5bc2e90959fb4fbbb1a1864085f12954", + "IPY_MODEL_6f1447ee68bc42778df44e95bb36862d", + "IPY_MODEL_8293fa80884e45449ac9b7950ee4cc70" + ], + "crs": { + "custom": false, + "name": "EPSG3857" }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "6edda6ba5bb648bd9dc55dd3328fd82a" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ] + "default_style": "IPY_MODEL_2d37126201cc4f8f85d88ca8e9f8affc", + "double_click_zoom": true, + "dragging": true, + "dragging_style": "IPY_MODEL_6d5ccb249d9146a2b1c5dd9bb84e9f8b", + "east": 135.68115234375003, + "fullscreen": false, + "inertia": true, + "inertia_deceleration": 3000, + "inertia_max_speed": 1500, + "interpolation": "bilinear", + "keyboard": true, + "keyboard_pan_offset": 80, + "keyboard_zoom_offset": 1, + "layers": [ + "IPY_MODEL_907d89b6d73a47f08cc4680f1811123a", + "IPY_MODEL_07d6287890bd4f939df3a65145789715", + "IPY_MODEL_d8466aad5f6245d6b077cb2dc4ac8496", + "IPY_MODEL_1d19a7648bf44be39dfd7a8ce8bafc78" + ], + "layout": "IPY_MODEL_3e8823008c094bc5823e1f7d7a698edc", + "left": 13567, + "max_zoom": 24, + "min_zoom": null, + "modisdate": "2024-02-04", + "north": 39.07890809706475, + "options": [ + "bounce_at_zoom_limits", + "box_zoom", + "center", + "close_popup_on_click", + "double_click_zoom", + "dragging", + "fullscreen", + "inertia", + "inertia_deceleration", + "inertia_max_speed", + "interpolation", + "keyboard", + "keyboard_pan_offset", + "keyboard_zoom_offset", + "max_zoom", + "min_zoom", + "prefer_canvas", + "scroll_wheel_zoom", + "tap", + "tap_tolerance", + "touch_zoom", + "world_copy_jump", + "zoom", + "zoom_animation_threshold", + "zoom_delta", + "zoom_snap" + ], + "panes": {}, + "prefer_canvas": false, + "right": 14367, + "scroll_wheel_zoom": true, + "south": 31.93351676190369, + "style": "IPY_MODEL_6d5ccb249d9146a2b1c5dd9bb84e9f8b", + "tap": true, + "tap_tolerance": 15, + "top": 6257, + "touch_zoom": true, + "west": 118.10302734375001, + "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229", + "world_copy_jump": false, + "zoom": 6, + "zoom_animation_threshold": 4, + "zoom_delta": 1, + "zoom_snap": 1 + } }, - { - "cell_type": "code", - "source": [ - "# Slope layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "\n", - "vis_params = {'bands':['slope'], 'min': 0, 'max': 25, 'palette': cm.palettes.RdYlGn_r}\n", - "Map.addLayer(predictors, vis_params, 'slope')\n", - "Map.add_colorbar(vis_params, label=\"Slope\", orientation=\"vertical\", layer_name=\"slope\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "7a7383eaa7d644fca2613b964ab02df3", - "431b70f7b67e4c8c86bf93e37cdb66fc", - "e134170fd7b44730875e7e1b207ebbe9", - "5d95e044822c4965963e7af30fb36dea", - "ec202420cbab485481e6a5ea9583155f", - "04323a5dcf5044b28bafab055e34bba7", - "0cc1944758a44781905ffb19ffe5f88e", - "b9f365a7574542168f0b1cb9557ce503", - "c29a2bde31134939b51486c39b9fccdb", - "1a0b7bfa32124363b0912f80ddb76cce", - "81c963e64c134ffaa6e81fd95eb6daad", - "dd1783b57f1145a1bb5980b66db7a985", - "4c7788f05efc4416ac2eaf5fad1fe479", - "454132b50c3f47c0a857f830d4b74447", - "f27e44ff08864d7fbc22379b92f572f6", - "f0fd3c53404945cb8f024216ff4cb5cf", - "b2bb93f6400a48b4afd487a1353048a8", - "591b3f89b5404fcea06c3b688cb8424a", - "8d17f8cf005f40bb8febfbeaff4d511c", - "cb650a05204047079dd426bb7b0eff4d", - "9dd59080201c42cf900756e44000215a", - "34ff26975acf4a8d80318b099346d1ce", - "1af4287c8c17433a8f7b6c0862107cf7", - "5ebdf59b3c5a4e59aca4c129f902c333", - "aa06322df74e46eea3851794cfdf7002", - "77fcffd404a84de0ac12b00b7336b1fe", - "e1c614cfbe7548758e2d4d78255ea3ae", - "78a44e68b9274b5a9dd41dfda518fcbc" - ] - }, - "id": "cQpG8qRzIQcw", - "outputId": "742daf59-48e7-4f74-cbca-69b15e5b651f" + "ec202420cbab485481e6a5ea9583155f": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletDrawControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletDrawControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletDrawControlView", + "circle": {}, + "circlemarker": {}, + "data": [], + "edit": true, + "marker": { + "shapeOptions": { + "color": "#3388ff" + } }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "7a7383eaa7d644fca2613b964ab02df3" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Aspect layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "\n", - "vis_params = {'bands':['aspect'], 'min': 0, 'max': 360, 'palette': cm.palettes.rainbow}\n", - "Map.addLayer(predictors, vis_params, 'aspect')\n", - "Map.add_colorbar(vis_params, label=\"Aspect\", orientation=\"vertical\", layer_name=\"aspect\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "83423ce2b9e54f68a49b38481fd8e469", - "6f35054727ee43959a5630683d50a622", - "ae588b174ef64f779ee364e27592e993", - "0d7218a539fa4c02b327103fdb1cfe76", - "01a0753c1da24e0eb084dc40409f8f28", - "461c16efc802454096aa5cf192e94253", - "6d260d1c16674ea0b0dd42403135d029", - "0212e33ecb234182b364e1bcee104515", - "a1f5c446dd5543e09f3dad56f59f251b", - "ebba29a794a947bcac0668a1f30da1ad", - "a8995359c9324eb5a3d7d1b65c2a320f", - "9ed8d5a1d90a47bf8f916cd1500a0653", - "d53d118dde584cb497411f4c8dafcaaa", - "351c6179212e47c48cd9965a9d27de90", - "61b5c2520f524bb58ddb26e16a607953", - "01a5f1504f5d44e6ace27c2cf33e6314", - "e6501a8d76e442bd9f5de2f86f9ce179", - "b5655ceed92a43d5992a5fde031110c8", - "6067658655b6493c9dde8c753f2e72aa", - "a076579ca63c4f0f9267af8af6820b3e", - "3b00a188691d445d897d5fb4f5d3b285", - "f2824092392e430a9492dbf3a1421216", - "e80b2be5cc1a45ab9ca1aae28292866c", - "60d7cd77522f49cda5044c8bb65ecb0b", - "a5cb1f53b8da4786ac22661f04a7abac", - "dddb90bcda1b4104bf55b54c4db4e45a", - "4a26e72a97014097b4d0209ededd9b98", - "11d294929130402e8603112f50e7e51b" - ] - }, - "id": "u1hW_wAXIvvG", - "outputId": "51e012b6-9948-4ca9-93c4-67a7ffd0bae2" + "options": [ + "position" + ], + "polygon": { + "shapeOptions": {} }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "83423ce2b9e54f68a49b38481fd8e469" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Calculate the minimum and maximum values for bio14\n", - "min_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.min(), scale=1000).getInfo()\n", - "max_val = predictors.select(\"bio14\").reduceRegion(reducer=ee.Reducer.max(), scale=1000).getInfo()\n", - "\n", - "# bio14 (Precipitation of driest month) layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "\n", - "vis_params = {'bands':['bio14'], 'min': math.floor(min_val['bio14']), 'max': math.ceil(max_val['bio14']), 'palette': cm.palettes.Blues}\n", - "Map.addLayer(predictors, vis_params, 'bio14')\n", - "Map.add_colorbar(vis_params, label=\"Precipitation of driest month (mm)\", orientation=\"vertical\", layer_name=\"bio14\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "54eec2b7d1be430499568ac5041c78e3", - "1f22e72cc0754b4bb765671ec2889356", - "d111a3c4ec774f37823a3b7ae9b5d880", - "c855762df07c42bfa53b6e371877b21d", - "a9ca43e4eb3d47e09ab0e2e6f5d3bcf9", - "684be00132ba4d39989acee0ff30655f", - "b88430e7ccc84746aed0a0b2b7ab17c8", - "7b95708b4e75491bb38c42be6cd57671", - "11a8a57de8fe4c9992a33c714cb79563", - "d506549484404f8eb8a7f3123d9e2392", - "31dadff85a954834beb59ebe4de364f8", - "12348a8d1e95445ea50ebdefb30be589", - "c5fa4e97a62b4f66a7d6dfe59f4822cb", - "625609fa98c84412a4879561cc233296", - "c4b3537f37ab4ff28844f50f32c1ef88", - "4bb06d6bbc184111be4efcf34686548f", - "d985d8f5249441e6a1d622cf20a5c13e", - "23d1e0fdce1b476689d77ca18298940b", - "b709e83f3ce44afabfd6578198259bf0", - "2fdc6d38d4f54c2baee42039c7220d31", - "b25923dab23a4654b7c48c69dcf19bda", - "de2cbdb673684ae594d5ece8df028f02", - "1ecbb63948914fa69d92aed5ab12869a", - 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} - } - } - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "# TCC (Tree Canopy Cover) layer\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "\n", - "vis_params = {'bands':['TCC'], 'min': 0, 'max': 100, 'palette': ['ffffff', 'afce56', '5f9c00', '0e6a00', '003800']}\n", - "Map.addLayer(predictors, vis_params, 'TCC')\n", - "Map.add_colorbar(vis_params, label=\"Tree Canopy Cover (%)\", orientation=\"vertical\", layer_name=\"TCC\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "d91aa8cd659840cdabcf2518460efba7", - "8230dc94bcd24606b22acf8b9f4ed27e", - "0693b1bf45c141d1876a86164986ffad", - "1c6459455f954bc69756117f39bfba25", - "e1ab1f5595a0422c99289d5505c7dc8e", - "8dc288349f6e4650b8a6214b73b031a2", - "56abbd8c5596414d91b49b2d3f6a05d5", - "a3b8d82bf185431fade1f3f243c2b886", - "30dacdcac5d94b87844c93ed3dc3651a", - "6c06712f6a884b12a435465030f4227e", - 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" \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.587733558095216, 126.8959721004684], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "d91aa8cd659840cdabcf2518460efba7" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ] + "remove": true + } }, - { - "cell_type": "markdown", - "source": [ - "### Generation of pseudo-absence data\n", - "\n", - "In the process of SDM, the selection of input data for a species is mainly approached using two methods:\n", - "\n", - "1. **Presence-Background Method**: This method compares the locations where a particular species has been observed (presence) with other locations where the species has not been observed (background). Here, the background data does not necessarily mean areas where the species does not exist but rather is set up to reflect the overall environmental conditions of the study area. It is used to distinguish suitable environments where the species could exist from less suitable ones.\n", - "\n", - "2. **Presence-Absence Method**: This method compares locations where the species has been observed (presence) with locations where it has definitively not been observed (absence). Here, absence data represents specific locations where the species is known not to exist. It does not reflect the overall environmental conditions of the study area but rather points to locations where the species is estimated not to exist.\n", - "\n", - "In practice, it is often difficult to collect true absence data, so pseudo-absence data generated artificially is frequently used. However, it's important to acknowledge the limitations and potential errors of this method, as artificially generated pseudo-absence points may not accurately reflect true absence areas.\n", - "\n", - "The choice between these two methods depends on data availability, research objectives, model accuracy and reliability, as well as time and resources. Here, we will use occurrence data collected from GBIF and artificially generated pseudo-absence data to model using the \"Presence-Absence\" method.\n", - "\n", - "The generation of pseudo-absence data will be done through the \"environmental profiling approach\", and the specific steps are as follows:\n", - "\n", - "1. Environmental Classification Using k-means Clustering: The k-means clustering algorithm, based on Euclidean distance, will be used to divide the pixels within the study area into two clusters. One cluster will represent areas with similar environmental characteristics to randomly selected 100 presence locations, while the other cluster will represent areas with different characteristics.\n", - "\n", - "2. Generation of Pseudo-Absence Data within Dissimilar Clusters: Within the second cluster identified in the first step (which has different environmental characteristics from the presence data), randomly generated pseudo-absence points will be created. These pseudo-absence points will represent locations where the species is not expected to exist." - ], - "metadata": { - "id": "xIFAutp6M16q" - } - }, - { - "cell_type": "code", - "source": [ - "# Randomly select 100 locations for occurrence\n", - "PixelVals = predictors.sampleRegions(\n", - " collection=Data.randomColumn().sort('random').limit(100),\n", - " properties=[],\n", - " tileScale=16,\n", - " scale=GrainSize\n", - ")\n", - "\n", - "# Perform k-means clustering\n", - "clusterer = ee.Clusterer.wekaKMeans(\n", - " nClusters=2,\n", - " distanceFunction=\"Euclidean\"\n", - ").train(PixelVals)\n", - "\n", - "Clresult = predictors.cluster(clusterer)\n", - "\n", - "# Get cluster ID for locations similar to occurrence\n", - "clustID = Clresult.sampleRegions(\n", - " collection=Data.randomColumn().sort('random').limit(200),\n", - " properties=[],\n", - " tileScale=16,\n", - " scale=GrainSize\n", - ")\n", - "\n", - "# Define non-occurrence areas in dissimilar clusters\n", - "clustID = ee.FeatureCollection(clustID).reduceColumns(ee.Reducer.mode(),['cluster'])\n", - "clustID = ee.Number(clustID.get('mode')).subtract(1).abs()\n", - "cl_mask = Clresult.select(['cluster']).eq(clustID)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "hMnMjWpOYG_B", - "outputId": "a12fbd51-eb22-4abc-d3bf-06d018200d2c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "ed7dad206f72455f8c40df4ae8422040": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } }, - { - "cell_type": "code", - "source": [ - "# Presence location mask\n", - "presence_mask = Data.reduceToImage(properties=['random'],\n", - "reducer=ee.Reducer.first()\n", - ").reproject('EPSG:4326', None,\n", - " ee.Number(GrainSize)).mask().neq(1).selfMask()\n", - "\n", - "# Masking presence locations in non-occurrence areas and clipping to AOI\n", - "AreaForPA = presence_mask.updateMask(cl_mask).clip(AOI)\n", - "\n", - "# Area for Pseudo-absence\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "Map.addLayer(AreaForPA, {'palette': 'black'}, 'AreaForPA')\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "117f21ad7cab4ca99ecf8880734b51d9", - "107a1dc5987d486d8568d96fb57bee20", - "6c2089bcae8b496797d64e6ff6d3338c", - "24db6baf09cc4e5485a3bd668bfd81dd", - "10af6004221e4603aa54e1a335b1aeb4", - "f512c4a4fa324d83bb6f68eff59aa045", - "821f359ef5eb4351a7b6b185067aa37a", - "69e97d3534564c1ca0ad5da1fc83eb7c", - "64eb6395a1434a54ad5bdc6967545172", - 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{ - "cell_type": "markdown", - "source": [ - "### Model fitting and prediction\n", - "\n", - "We will now divide the data into training data and test data. The training data will be used to find the optimal parameters by training the model, while the test data will be used to evaluate the model trained beforehand. An important concept to consider in this context is spatial autocorrelation.\n", - "\n", - "**Spatial autocorrelation** is an essential element in SDM, associated with Tobler's law. It embodies the concept that \"everything is related to everything else, but near things are more related than distant things\". Spatial autocorrelation represents the significant relationship between the location of species and environmental variables. However, if spatial autocorrelation exists between the training and test data, the independence between the two data sets can be compromised. This significantly impacts the evaluation of the model's generalization ability.\n", - "\n", - "One method to address this issue is the spatial block cross-validation technique, which involves dividing the data into training and testing datasets. This technique involves dividing the data into multiple blocks, using each block independently as training and test datasets to reduce the impact of spatial autocorrelation. This enhances the independence between datasets, allowing for a more accurate evaluation of the model's generalization ability.\n", - "\n", - "The specific procedure is as follows:\n", - "1. Creation of spatial blocks: Divide the entire dataset into spatial blocks of equal size (e.g., 50x50 km).\n", - "2. Assignment of training and testing sets: Each spatial block is randomly assigned to either the training set (70%) or the test set (30%). This prevents the model from overfitting to data from specific areas and aims to achieve more generalized results.\n", - "3. Iterative cross-validation: The entire process is repeated n times (e.g., 10 times). In each iteration, the blocks are randomly divided into training and test sets again, which is intended to improve the model's stability and reliability.\n", - "4. Generation of pseudo-absence data: In each iteration, pseudo-absence data are randomly generated to evaluate the model's performance." - ], - "metadata": { - "id": "iTWaV5fQZPgA" - } - }, - { - "cell_type": "code", - "source": [ - "# Generate a grid for spatial block cross-validation\n", - "def makeGrid(geometry, scale):\n", - " # Create an image with longitude & latitude in degrees\n", - " lonLat = ee.Image.pixelLonLat()\n", - " # Convert longitude & latitude images to integers\n", - " lonGrid = lonLat.select('longitude').multiply(100000).toInt()\n", - " latGrid = lonLat.select('latitude').multiply(100000).toInt()\n", - "\n", - " return lonGrid.multiply(latGrid).reduceToVectors(\n", - " # Create a grid that includes the boundaries of the geometry\n", - " geometry = geometry.buffer(distance=20000, maxError=1000),\n", - " scale = scale,\n", - " geometryType = 'polygon'\n", - " )" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "Zt3sZ8-TZDNr", - 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{ - "cell_type": "code", - "source": [ - "Scale = 50000\n", - "grid = makeGrid(AOI, Scale)\n", - "Grid = watermask.reduceRegions(\n", - " collection=grid,\n", - " reducer=ee.Reducer.mean()).filter(ee.Filter.neq('mean', None))\n", - "\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'})\n", - "Map.addLayer(Grid, {}, \"Grid for spatial block cross validation\")\n", - "Map.addLayer(outline, {'palette': 'FF0000'}, \"Study Area\")\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "1a333b4994a542e494a892204be492e6", - "e06969cdab0840aeb111c3d1360c458c", - "e9eb5df4dce54168a6568b5cd3d07382", - "d1f8d2f2dbfc4caf841b4a7b1064375e", - "c882a936a93f4919bd17579085857fb1", - "b7a1e09928ce4984baf55cf8f726fd68", - "3a2a202212e3416c8f1539df5e186f01", - "52bddeb3302446e8ad5eb9f5fcac1124", - "0b88c043fdf64c898d5582da7fbf9dc1", - "a3af014c95ef475b8a264f95b2968c44", - "92116fbaa377464ca6a106a145d2238a", - 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], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "1a333b4994a542e494a892204be492e6" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ] + "f0fd3c53404945cb8f024216ff4cb5cf": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletMapStyleModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletMapStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "cursor": "grab" + } }, - { - "cell_type": "markdown", - "source": [ - "Now we can fit the model. Fitting a model involves understanding the patterns in the data and adjusting the model's parameters (weights and biases) accordingly. This process enables the model to make more accurate predictions when presented with new data. For this purpose, we have defined a function called SDM() to fit the model.\n", - "\n", - "We will use the **Random Forest** algorithm." - ], - "metadata": { - "id": "EsPbyMZ-cJg2" - } - }, - { - "cell_type": "code", - "source": [ - "def SDM(x):\n", - " Seed = ee.Number(x)\n", - "\n", - " # Random block division for training and validation\n", - " GRID = ee.FeatureCollection(Grid).randomColumn(seed=Seed).sort('random')\n", - " TrainingGrid = GRID.filter(ee.Filter.lt('random', split)) # Grid for training\n", - " TestingGrid = GRID.filter(ee.Filter.gte('random', split)) # Grid for testing\n", - "\n", - " # Presence points\n", - " PresencePoints = ee.FeatureCollection(Data)\n", - " PresencePoints = PresencePoints.map(lambda feature: feature.set('PresAbs', 1))\n", - " TrPresencePoints = PresencePoints.filter(ee.Filter.bounds(TrainingGrid)) # Presence points for training\n", - " TePresencePoints = PresencePoints.filter(ee.Filter.bounds(TestingGrid)) # Presence points for testing\n", - "\n", - " # Pseudo-absence points for training\n", - " TrPseudoAbsPoints = AreaForPA.sample(region=TrainingGrid,\n", - " scale=GrainSize,\n", - " numPixels=TrPresencePoints.size().add(300),\n", - " seed=Seed,\n", - " geometries=True)\n", - " # Same number of pseudo-absence points as presence points for training\n", - " TrPseudoAbsPoints = TrPseudoAbsPoints.randomColumn().sort('random').limit(ee.Number(TrPresencePoints.size()))\n", - " TrPseudoAbsPoints = TrPseudoAbsPoints.map(lambda feature: feature.set('PresAbs', 0))\n", - "\n", - " TePseudoAbsPoints = AreaForPA.sample(region=TestingGrid,\n", - " scale=GrainSize,\n", - " numPixels=TePresencePoints.size().add(100),\n", - " seed=Seed,\n", - " geometries=True)\n", - " # Same number of pseudo-absence points as presence points for testing\n", - " TePseudoAbsPoints = TePseudoAbsPoints.randomColumn().sort('random').limit(ee.Number(TePresencePoints.size()))\n", - " TePseudoAbsPoints = TePseudoAbsPoints.map(lambda feature: feature.set('PresAbs', 0))\n", - "\n", - " # Merge training and pseudo-absence points\n", - " trainingPartition = TrPresencePoints.merge(TrPseudoAbsPoints)\n", - " testingPartition = TePresencePoints.merge(TePseudoAbsPoints)\n", - "\n", - " # Extract predictor variable values at training points\n", - " trainPixelVals = predictors.sampleRegions(collection=trainingPartition,\n", - " properties=['PresAbs'],\n", - " scale=GrainSize,\n", - " tileScale=16,\n", - " geometries=True)\n", - "\n", - " # Random Forest classifier\n", - " Classifier = ee.Classifier.smileRandomForest(\n", - " numberOfTrees=500,\n", - " variablesPerSplit=None,\n", - " minLeafPopulation=10,\n", - " bagFraction=0.5,\n", - " maxNodes=None,\n", - " seed=Seed\n", - " )\n", - " # Presence probability: Habitat suitability map\n", - " ClassifierPr = Classifier.setOutputMode('PROBABILITY').train(trainPixelVals, 'PresAbs', bands)\n", - " ClassifiedImgPr = predictors.select(bands).classify(ClassifierPr)\n", - "\n", - " # Binary presence/absence map: Potential distribution map\n", - " ClassifierBin = Classifier.setOutputMode('CLASSIFICATION').train(trainPixelVals, 'PresAbs', bands)\n", - " ClassifiedImgBin = predictors.select(bands).classify(ClassifierBin)\n", - "\n", - " return [ClassifiedImgPr, ClassifiedImgBin, trainingPartition, testingPartition], ClassifierPr" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "89R79JvGb6c8", - "outputId": "1acce52f-5891-4782-9dbd-ec1dabac2638" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "f27e44ff08864d7fbc22379b92f572f6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": "400px", + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "800px" + } }, - { - "cell_type": "markdown", - "source": [ - "Spatial blocks are divided into 70% for model training and 30% for model testing, respectively. Pseudo-absence data are randomly generated within each training and testing set in every iteration. As a result, each execution yields different sets of presence and pseudo-absence data for model training and testing." - ], - "metadata": { - "id": "yCGB0Y6meW2B" - } - }, - { - "cell_type": "code", - "source": [ - "split = 0.7\n", - "numiter = 10\n", - "\n", - "# Random Seed\n", - "runif = lambda length: [random.randint(1, 1000) for _ in range(length)]\n", - "items = runif(numiter)\n", - "\n", - "# Fixed seed\n", - "# items = [287, 288, 553, 226, 151, 255, 902, 267, 419, 538]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "KV6Jg50AeA7B", - "outputId": "ea67aff9-49bf-4769-a53c-d2b515055ed1" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "f2824092392e430a9492dbf3a1421216": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ToggleButtonModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ToggleButtonModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ToggleButtonView", + "button_style": "", + "description": "", + "description_tooltip": null, + "disabled": false, + "icon": "wrench", + "layout": "IPY_MODEL_4a26e72a97014097b4d0209ededd9b98", + "style": "IPY_MODEL_11d294929130402e8603112f50e7e51b", + "tooltip": "Toolbar", + "value": false + } }, - { - "cell_type": "code", - "source": [ - "results_list = [] # Initialize SDM results list\n", - "importances_list = [] # Initialize variable importance list\n", - "\n", - "for item in items:\n", - " result, trained = SDM(item)\n", - " # Accumulate SDM results into the list\n", - " results_list.extend(result)\n", - "\n", - " # Accumulate variable importance into the list\n", - " importance = ee.Dictionary(trained.explain()).get('importance')\n", - " importances_list.extend(importance.getInfo().items())\n", - "\n", - "# Flatten the SDM results list\n", - "results = ee.List(results_list).flatten()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "BHCnkZvveq_Q", - "outputId": "448b9200-c9b6-44d2-e97d-c78e49c247d4" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "f39d82350d1f47bcb63d2817c70c587b": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletZoomControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletZoomControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletZoomControlView", + "options": [ + "position", + "zoom_in_text", + "zoom_in_title", + "zoom_out_text", + "zoom_out_title" + ], + "position": "topleft", + "zoom_in_text": "+", + "zoom_in_title": "Zoom in", + "zoom_out_text": "-", + "zoom_out_title": "Zoom out" + } }, - { - "cell_type": "markdown", - "source": [ - "Now we can visualize the **habitat suitability map** and **potential distribution map** for the Fairy pitta. In this case, the habitat suitability map is created by using the `mean()` function to calculate the average for each pixel location across all images, and the potential distribution map is generated by using the `mode()` function to determine the most frequently occurring value at each pixel location across all images.\n", - "\n", - "![SDM results](sdm_results.png)" - ], - "metadata": { - "id": "UHQ6Hx07gG2m" - } - }, - { - "cell_type": "code", - "source": [ - "# Habitat suitability map\n", - "images = ee.List.sequence(\n", - " 0, ee.Number(numiter).multiply(4).subtract(1), 4).\\\n", - " map(lambda x: results.get(x))\n", - "ModelAverage = ee.ImageCollection.fromImages(images).mean()\n", - "\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'}, basemap='Esri.WorldImagery')\n", - "\n", - "vis_params = {\n", - " 'min': 0,\n", - " 'max': 1,\n", - " 'palette': cm.palettes.viridis_r}\n", - "Map.addLayer(ModelAverage, vis_params, 'Habitat suitability')\n", - "Map.add_colorbar(vis_params, label=\"Habitat suitability\",\n", - " orientation=\"horizontal\",\n", - " layer_name=\"Habitat suitability\")\n", - "Map.addLayer(Data, {'color':'red'}, 'Presence')\n", - "Map.centerObject(AOI, 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "f73fe0f1f63349d3a3e3d1903cc07645", - "174423090a424922a7ce2823fb3ffed3", - "ca01b3b073b24a33b33c3519f2ceb8a6", - "dd24387aff7141418cbd645a418b97b3", - "e64f35fa89944b6290f597d4f7182b1c", - "3d1948d8a58a444aa6e9ee4de6127292", - "e10f6046737d43ad931387a38bee8442", - "ab81e584619a44dbb3c6be7a2453e718", - "0de06343152d47d686e6611d5bcedaaa", - "64a396f1b27048a0b54e0f35d8f7372d", - "d3f22884fdb545268360fa752a52fac5", - "0f802a5279f249dcafebb3ce0fae5a5b", - "2750440ca498413ea267bac448595fe3", - "0fdd64fd02e8442ba622ad006742441f", - "2d99e9a9b4f343419ad4af3fe397efeb", - "3cba4731c9a64a458e342a0c63a97c03", - "60cd78daa2204b1a9f5b6aeedd018b58", - "2585e9ef9fda479ba19e2102cee44d12", - "c6472040655249f4a40bd732d1555343", - 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"# Potential distribution map\n", - "images2 = ee.List.sequence(1, ee.Number(numiter).multiply(4).\\\n", - " subtract(1), 4).map(lambda x: results.get(x))\n", - "DistributionMap = ee.ImageCollection.fromImages(images2).mode()\n", - "\n", - "Map = geemap.Map(layout={'height':'400px', 'width':'800px'}, basemap='Esri.WorldImagery')\n", - "\n", - "vis_params = {\n", - " 'min': 0,\n", - " 'max': 1,\n", - " 'palette': ['white', 'green']}\n", - "Map.addLayer(DistributionMap, vis_params, 'Potential distribution')\n", - "Map.addLayer(Data, {'color':'red'}, 'Presence')\n", - "Map.add_colorbar(vis_params, label=\"Potential distribution\",\n", - " discrete=True, orientation=\"horizontal\",\n", - " layer_name=\"Potential distribution\")\n", - "Map.centerObject(Data.geometry(), 6)\n", - "Map" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 421, - "referenced_widgets": [ - "3e90aff8906e456eb60e2c5cc946f8b1", - "813d63ffdac340fa82f3654ddf84b22c", - "e6ffba1da6054cb28715a9864b14989d", - 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"text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map(center=[35.533064630393035, 126.8858638222748], controls=(WidgetControl(options=['position', 'transparent_…" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "3e90aff8906e456eb60e2c5cc946f8b1" - } - }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js" - } - } - } - } - } - ] + "position": "bottomleft", + "primary_area_unit": "acres", + "primary_length_unit": "kilometers", + "secondary_area_unit": null, + "secondary_length_unit": null + } }, - { - "cell_type": "markdown", - "source": [ - "### Variable importance and accuracy assessment\n", - "\n", - "Random Forest (`ee.Classifier.smileRandomForest`) is one of the ensemble learning methods, which operates by constructing multiple decision trees to make predictions. Each decision tree independently learns from different subsets of the data, and their results are aggregated to enable more accurate and stable predictions.\n", - "\n", - "Variable importance is a measure that evaluates the impact of each variable on the predictions within the Random Forest model. We will use the previously defined `importances_list` to calculate and print the average variable importance." - ], - "metadata": { - "id": "B5cjWyzAmd4Z" - } - }, - { - "cell_type": "code", - "source": [ - "def plot_variable_importance(importances_list):\n", - " # Extract each variable importance value into a list\n", - " variables = [item[0] for item in importances_list]\n", - " importances = [item[1] for item in importances_list]\n", - "\n", - " # Calculate the average importance for each variable\n", - " average_importances = {}\n", - " for variable in set(variables):\n", - " indices = [i for i, var in enumerate(variables) if var == variable]\n", - " average_importance = np.mean([importances[i] for i in indices])\n", - " average_importances[variable] = average_importance\n", - "\n", - " # Print the average variable importance\n", - " for variable, avg_importance in average_importances.items():\n", - " print(f\"{variable}: {avg_importance}\")\n", - "\n", - " # Sort the importances in descending order of importance\n", - " sorted_importances = sorted(average_importances.items(),\n", - " key=lambda x: x[1], reverse=False)\n", - " variables = [item[0] for item in sorted_importances]\n", - " avg_importances = [item[1] for item in sorted_importances]\n", - "\n", - " # Adjust the graph size\n", - " plt.figure(figsize=(8, 4))\n", - "\n", - " # Plot the average importance as a horizontal bar chart\n", - " plt.barh(variables, avg_importances)\n", - " plt.xlabel('Importance')\n", - " plt.ylabel('Variables')\n", - " plt.title('Average Variable Importance')\n", - "\n", - " # Display values above the bars\n", - " for i, v in enumerate(avg_importances):\n", - " plt.text(v + 0.02, i, f\"{v:.2f}\", va='center')\n", - "\n", - " # Adjust the x-axis range\n", - " plt.xlim(0, max(avg_importances) + 5) # Adjust to the desired range\n", - "\n", - " plt.tight_layout()\n", - " plt.savefig('variable_importance.png')\n", - " plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "uoJ0mBeRme-E", - "outputId": "57e15247-b416-4590-b046-0553d9a49885" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ] + "f512c4a4fa324d83bb6f68eff59aa045": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletScaleControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletScaleControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletScaleControlView", + "imperial": true, + "max_width": 100, + "metric": true, + "options": [ + "imperial", + "max_width", + "metric", + "position", + "update_when_idle" + ], + "position": "bottomleft", + "update_when_idle": false + } }, - { - "cell_type": "code", - "source": [ - "plot_variable_importance(importances_list)" + "f6efdfd64e614ac8813e9e911a1fce80": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletAttributionControlModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletAttributionControlModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletAttributionControlView", + "options": [ + "position", + "prefix" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 516 - }, - "id": "2nhhAUydurwm", - "outputId": "6319dc90-b167-48dc-f487-cfe57047e4bb" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "bio09: 27.06617668282514\n", - "bio14: 11.56570503744132\n", - "elevation: 51.812563029059774\n", - "slope: 13.908311353185116\n", - "aspect: 3.8891469966922387\n", - "TCC: 24.78555268223786\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ] + "position": "bottomright", + "prefix": "ipyleaflet" + } }, - { - "cell_type": "markdown", - "source": [ - "Using the Testing Datasets, we calculate AUC-ROC and AUC-PR for each run. Then, we compute the average AUC-ROC and AUC-PR over n iterations.\n", - "\n", - "**AUC-ROC** represents the area under the curve of the 'Sensitivity (Recall) vs. 1-Specificity' graph, illustrating the relationship between sensitivity and specificity as the threshold changes. Specificity is based on all observed non-occurrences. Therefore, AUC-ROC encompasses all quadrants of the confusion matrix.\n", - "\n", - "**AUC-PR** represents the area under the curve of the 'Precision vs. Recall (Sensitivity)' graph, showing the relationship between precision and recall as the threshold varies. Precision is based on all predicted occurrences. Hence, AUC-PR does not include the true negatives (TN).\n", - "\n", - "> Note: It's important to ensure that each run has a sufficient number of points for model validation. The final number of points may vary due to the random partitioning of spatial blocks, so it's crucial to verify if there are enough presence and pseudo-absence points for model validation. In the case of endangered or rare species, there might be a shortage of occurrence data, leading to an insufficient test dataset. In such cases, alternatives may include additional data collection based on expert knowledge and experience or utilizing relevant alternative data sources." - ], - "metadata": { - "id": "m889_YCzwNhR" - } - }, - { - "cell_type": "code", - "source": [ - "def print_pres_abs_sizes(TestingDatasets, numiter):\n", - " # Check and print the sizes of presence and pseudo-absence coordinates\n", - " def get_pres_abs_size(x):\n", - " fc = ee.FeatureCollection(TestingDatasets.get(x))\n", - " presence_size = fc.filter(ee.Filter.eq('PresAbs', 1)).size()\n", - " pseudo_absence_size = fc.filter(ee.Filter.eq('PresAbs', 0)).size()\n", - " return ee.List([presence_size, pseudo_absence_size])\n", - "\n", - " sizes_info = ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(get_pres_abs_size).getInfo()\n", - "\n", - " for i, sizes in enumerate(sizes_info):\n", - " presence_size = sizes[0]\n", - " pseudo_absence_size = sizes[1]\n", - " print(f'Iteration {i + 1}: Presence Size = {presence_size}, Pseudo-absence Size = {pseudo_absence_size}')" - 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{ - "cell_type": "code", - "source": [ - "# Extracting the Testing Datasets\n", - "TestingDatasets = (ee.List.sequence(3, ee.Number(numiter).multiply(4).subtract(1), 4).map(lambda x: results.get(x)))\n", - "\n", - "print_pres_abs_sizes(TestingDatasets, numiter)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 199 - }, - "id": "MccWAsdTxjUS", - "outputId": "9946610e-220f-4916-aab1-8a4d4abc8b8a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Iteration 1: Presence Size = 22, Pseudo-absence Size = 18\n", - "Iteration 2: Presence Size = 36, Pseudo-absence Size = 29\n", - "Iteration 3: Presence Size = 19, Pseudo-absence Size = 18\n", - "Iteration 4: Presence Size = 27, Pseudo-absence Size = 20\n", - "Iteration 5: Presence Size = 26, Pseudo-absence Size = 26\n", - "Iteration 6: Presence Size = 43, Pseudo-absence Size = 21\n", - "Iteration 7: Presence Size = 24, Pseudo-absence Size = 20\n", - "Iteration 8: Presence Size = 43, Pseudo-absence Size = 37\n", - "Iteration 9: Presence Size = 22, Pseudo-absence Size = 22\n", - "Iteration 10: Presence Size = 41, Pseudo-absence Size = 21\n" - ] - } - ] + "f763ae3b8a744295ab233aa604447ddc": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletTileLayerModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletTileLayerModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletTileLayerView", + "attribution": "© OpenStreetMap contributors", + "base": true, + "bottom": true, + "bounds": null, + "detect_retina": false, + "loading": false, + "max_native_zoom": null, + "max_zoom": 19, + "min_native_zoom": null, + "min_zoom": 1, + "name": "OpenStreetMap.Mapnik", + "no_wrap": false, + "opacity": 1, + "options": [ + "attribution", + "bounds", + "detect_retina", + "max_native_zoom", + "max_zoom", + "min_native_zoom", + "min_zoom", + "no_wrap", + "tile_size", + "tms", + "zoom_offset" + ], + "pane": "", + "popup": null, + "popup_max_height": null, + "popup_max_width": 300, + "popup_min_width": 50, + "show_loading": false, + "subitems": [], + "tile_size": 256, + "tms": false, + "url": "https://tile.openstreetmap.org/{z}/{x}/{y}.png", + "visible": true, + "zoom_offset": 0 + } }, - { - "cell_type": "code", - "source": [ - "def getAcc(HSM, TData, GrainSize):\n", - " Pr_Prob_Vals = HSM.sampleRegions(collection=TData, properties=['PresAbs'], scale=GrainSize, tileScale=16)\n", - " seq = ee.List.sequence(start=0, end=1, count=25) # Divide 0 to 1 into 25 intervals\n", - " def calculate_metrics(cutoff):\n", - " # Each element of the seq list is passed as cutoff(threshold value)\n", - "\n", - " # Observed present = TP + FN\n", - " Pres = Pr_Prob_Vals.filterMetadata('PresAbs', 'equals', 1)\n", - "\n", - " # TP (True Positive)\n", - " TP = ee.Number(Pres.filterMetadata('classification', 'greater_than', cutoff).size())\n", - "\n", - " # TPR (True Positive Rate) = Recall = Sensitivity = TP / (TP + FN) = TP / Observed present\n", - " TPR = TP.divide(Pres.size())\n", - "\n", - " # Observed absent = FP + TN\n", - " Abs = Pr_Prob_Vals.filterMetadata('PresAbs', 'equals', 0)\n", - "\n", - " # FN (False Negative)\n", - " FN = ee.Number(Pres.filterMetadata('classification', 'less_than', cutoff).size())\n", - "\n", - " # TNR (True Negative Rate) = Specificity = TN / (FP + TN) = TN / Observed absent\n", - " TN = ee.Number(Abs.filterMetadata('classification', 'less_than', cutoff).size())\n", - " TNR = TN.divide(Abs.size())\n", - "\n", - " # FP (False Positive)\n", - " FP = ee.Number(Abs.filterMetadata('classification', 'greater_than', cutoff).size())\n", - "\n", - " # FPR (False Positive Rate) = FP / (FP + TN) = FP / Observed absent\n", - " FPR = FP.divide(Abs.size())\n", - "\n", - " # Precision = TP / (TP + FP) = TP / Predicted present\n", - " Precision = TP.divide(TP.add(FP))\n", - "\n", - " # SUMSS = SUM of Sensitivity and Specificity\n", - " SUMSS = TPR.add(TNR)\n", - "\n", - " return ee.Feature(\n", - " None,\n", - " {\n", - " 'cutoff': cutoff,\n", - " 'TP': TP,\n", - " 'TN': TN,\n", - " 'FP': FP,\n", - " 'FN': FN,\n", - " 'TPR': TPR,\n", - " 'TNR': TNR,\n", - " 'FPR': FPR,\n", - " 'Precision': Precision,\n", - " 'SUMSS': SUMSS\n", - " }\n", - " )\n", - "\n", - " return ee.FeatureCollection(seq.map(calculate_metrics))" - 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" def calculate_auc_metrics(x):\n", - " HSM = ee.Image(images.get(x))\n", - " TData = ee.FeatureCollection(TestingDatasets.get(x))\n", - " Acc = getAcc(HSM, TData, GrainSize)\n", - "\n", - " # Calculate AUC-ROC\n", - " X = ee.Array(Acc.aggregate_array('FPR'))\n", - " Y = ee.Array(Acc.aggregate_array('TPR'))\n", - " X1 = X.slice(0, 1).subtract(X.slice(0, 0, -1))\n", - " Y1 = Y.slice(0, 1).add(Y.slice(0, 0, -1))\n", - " auc_roc = X1.multiply(Y1).multiply(0.5).reduce('sum', [0]).abs().toList().get(0)\n", - "\n", - " # Calculate AUC-PR\n", - " X = ee.Array(Acc.aggregate_array('TPR'))\n", - " Y = ee.Array(Acc.aggregate_array('Precision'))\n", - " X1 = X.slice(0, 1).subtract(X.slice(0, 0, -1))\n", - " Y1 = Y.slice(0, 1).add(Y.slice(0, 0, -1))\n", - " auc_pr = X1.multiply(Y1).multiply(0.5).reduce('sum', [0]).abs().toList().get(0)\n", - "\n", - " return (auc_roc, auc_pr)\n", - "\n", - " auc_metrics = ee.List.sequence(0, ee.Number(numiter).subtract(1), 1).map(calculate_auc_metrics).getInfo()\n", - "\n", - " # Print AUC-ROC and AUC-PR for each iteration\n", - " df = pd.DataFrame(auc_metrics, columns=['AUC-ROC', 'AUC-PR'])\n", - " df.index = [f'Iteration {i + 1}' for i in range(len(df))]\n", - " df.to_csv('auc_metrics.csv', index_label='Iteration')\n", - " print(df)\n", - "\n", - " # Calculate mean and standard deviation of AUC-ROC and AUC-PR\n", - " mean_auc_roc, std_auc_roc = df['AUC-ROC'].mean(), df['AUC-ROC'].std()\n", - " mean_auc_pr, std_auc_pr = df['AUC-PR'].mean(), df['AUC-PR'].std()\n", - " print(f'Mean AUC-ROC = {mean_auc_roc:.4f} ± {std_auc_roc:.4f}')\n", - " print(f'Mean AUC-PR = {mean_auc_pr:.4f} ± {std_auc_pr:.4f}')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17 - }, - "id": "O6JACvJbzrn7", - "outputId": "62e334aa-8fa2-49b5-a72a-3f0c6a4ef30a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - 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{ - "cell_type": "code", - "source": [ - "%%time\n", - "\n", - "# Calculate AUC-ROC and AUC-PR\n", - "calculate_and_print_auc_metrics(images, TestingDatasets, GrainSize, numiter)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 289 - }, - "id": "WVM4DTduz41j", - "outputId": "717561c3-77f4-4d3d-9924-4773ad536235" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - " AUC-ROC AUC-PR\n", - "Iteration 1 0.802778 0.758025\n", - "Iteration 2 0.683673 0.763472\n", - "Iteration 3 0.823529 0.842339\n", - "Iteration 4 0.706731 0.717820\n", - "Iteration 5 0.848077 0.764575\n", - "Iteration 6 0.762566 0.720878\n", - "Iteration 7 0.946591 0.833802\n", - "Iteration 8 0.891517 0.817098\n", - "Iteration 9 0.675000 0.622018\n", - "Iteration 10 0.876871 0.884682\n", - "Mean AUC-ROC = 0.8017 ± 0.0930\n", - "Mean AUC-PR = 0.7725 ± 0.0759\n", - "CPU times: user 1.16 s, sys: 123 ms, total: 1.29 s\n", - "Wall time: 3min 5s\n" - ] - } - ] + "fe5196970a8f437080d46951c730aa71": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletMapStyleModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletMapStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "cursor": "grab" + } + }, + "fe8b7fe1e78a44ec9cf03ef304272a2a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100px" + } }, - { - "cell_type": "markdown", - "source": [ - "This tutorial has provided a practical example of using Google Earth Engine (GEE) for Species Distribution Modeling (SDM). An important takeaway is the versatility and flexibility of GEE in the field of SDM. Leveraging Earth Engine's powerful geospatial data processing capabilities opens up endless possibilities for researchers and conservationists to understand and preserve biodiversity on our planet. By applying the knowledge and skills gained from this tutorial, individuals can explore and contribute to this fascinating field of ecological research." + "ff13f896a17041cda9d863aa9c9b7a85": { + "model_module": "jupyter-leaflet", + "model_module_version": "^0.18", + "model_name": "LeafletTileLayerModel", + "state": { + "_model_module": "jupyter-leaflet", + "_model_module_version": "^0.18", + "_model_name": "LeafletTileLayerModel", + "_view_count": null, + "_view_module": "jupyter-leaflet", + "_view_module_version": "^0.18", + "_view_name": "LeafletTileLayerView", + "attribution": "Esri", + "base": true, + "bottom": true, + "bounds": null, + "detect_retina": false, + "loading": false, + "max_native_zoom": null, + "max_zoom": 24, + "min_native_zoom": null, + "min_zoom": 0, + "name": "Esri.WorldImagery", + "no_wrap": false, + "opacity": 1, + "options": [ + "attribution", + "bounds", + "detect_retina", + "max_native_zoom", + "max_zoom", + "min_native_zoom", + "min_zoom", + "no_wrap", + "tile_size", + "tms", + "zoom_offset" ], - "metadata": { - "id": "QDlaa47s1WjC" - } + "pane": "", + "popup": null, + "popup_max_height": null, + "popup_max_width": 300, + "popup_min_width": 50, + "show_loading": false, + "subitems": [], + "tile_size": 256, + "tms": false, + "url": "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}", + "visible": true, + "zoom_offset": 0 + } } - ] + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } From f2a03710db1e66b974a66032687d1108bf493bc9 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Thu, 29 Feb 2024 13:28:55 +0900 Subject: [PATCH 21/23] Update species-distribution-modeling.ipynb --- .../species-distribution-modeling.ipynb | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 7e128147b..2264b1800 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -460,7 +460,7 @@ "outputs": [], "source": [ "def remove_duplicates(data, grain_size):\n", - " # Select one occurrence record per pixel at the chosen spatial resolution (1km)\n", + " # Select one occurrence record per pixel at the chosen spatial resolution\n", " random_raster = ee.Image.random().reproject(\"EPSG:4326\", None, grain_size)\n", " rand_point_vals = random_raster.sampleRegions(\n", " collection=ee.FeatureCollection(data), geometries=True\n", @@ -1629,7 +1629,6 @@ " collection=training_partition,\n", " properties=[\"PresAbs\"],\n", " scale=grain_size,\n", - " tileScale=16,\n", " geometries=True,\n", " )\n", "\n", @@ -2035,7 +2034,7 @@ "source": [ "def get_acc(hsm, t_data, grain_size):\n", " pr_prob_vals = hsm.sampleRegions(\n", - " collection=t_data, properties=[\"PresAbs\"], scale=grain_size, tileScale=16\n", + " collection=t_data, properties=[\"PresAbs\"], scale=grain_size, " )\n", " seq = ee.List.sequence(start=0, end=1, count=25) # Divide 0 to 1 into 25 intervals\n", "\n", From 5310aebc6d17a7a6a096802e0dbee942ad5d619f Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Thu, 29 Feb 2024 13:32:24 +0900 Subject: [PATCH 22/23] Add files via upload --- .../species-distribution-modeling.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 2264b1800..6d35a6c61 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -460,7 +460,7 @@ "outputs": [], "source": [ "def remove_duplicates(data, grain_size):\n", - " # Select one occurrence record per pixel at the chosen spatial resolution\n", + " # Select one occurrence record per pixel at the chosen spatial resolution (1km)\n", " random_raster = ee.Image.random().reproject(\"EPSG:4326\", None, grain_size)\n", " rand_point_vals = random_raster.sampleRegions(\n", " collection=ee.FeatureCollection(data), geometries=True\n", @@ -2034,7 +2034,7 @@ "source": [ "def get_acc(hsm, t_data, grain_size):\n", " pr_prob_vals = hsm.sampleRegions(\n", - " collection=t_data, properties=[\"PresAbs\"], scale=grain_size, + " collection=t_data, properties=[\"PresAbs\"], scale=grain_size\n", " )\n", " seq = ee.List.sequence(start=0, end=1, count=25) # Divide 0 to 1 into 25 intervals\n", "\n", From 5c1583fd78c1beeba245e2c68a9b8b29877c2ad3 Mon Sep 17 00:00:00 2001 From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com> Date: Thu, 29 Feb 2024 13:40:35 +0900 Subject: [PATCH 23/23] Add files via upload --- .../species-distribution-modeling.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb index 6d35a6c61..fc12cd33a 100644 --- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb +++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb @@ -460,7 +460,7 @@ "outputs": [], "source": [ "def remove_duplicates(data, grain_size):\n", - " # Select one occurrence record per pixel at the chosen spatial resolution (1km)\n", + " # Select one occurrence record per pixel at the chosen spatial resolution\n", " random_raster = ee.Image.random().reproject(\"EPSG:4326\", None, grain_size)\n", " rand_point_vals = random_raster.sampleRegions(\n", " collection=ee.FeatureCollection(data), geometries=True\n",