LandTrendr is a set of spectral-temporal segmentation algorithms that focuses on removing the natural spectral variations in a time series of Landsat Images. Stabilizing the natural variation in a time series emphasizes how a landscape evolves with time. This is useful in many areas as it gives information on the state of a landscape. This includes many different natural and anthropogenic processes including: growing seasons, phenology, stable landscapes, senesence, clearcut etc. LandTrendr is mostly used in Google Earth Engine (GEE), an online image processing console, where it is readily available for use.
One impediment to running LT over large geographic domains is selecting the best paramater set for a given landscape. The LandTrendr GEE function uses 9 arguments: 8 parameters that control how spectral-temporal segmentation is executed, and an annual image collection on which to assess and remove the natural variations. The original LandTrendr article (Kennedy et al., 2010) illustrates some of the effects and sensitivity of changing some of these values. The default parameters for the LandTrendr GEE algorithm do a satisfactory job in many circumstances, but extensive testing and time is needed to hone the parameter selection to get the best segmentation out of the LandTrendr algorithm for a given region. Thus, augmenting the LandTrendr parameter selection process would save time and standardize a method to choose parameters, but we also aim to take this augmentation a step further.
Traditionally, LandTrendr is run over an image collection with a single LandTrendr parameter configuration and is able to remove natural variation for every pixel time series in an image. But no individual LandTrendr parameter configuration is best for all surface conditions. For example, one paramater set might be best for forest cover change while another might be preferred for agricultural phenology or reservoir flooding. To address this shortcoming, we developed a method that delineates patches of spectrally similar pixels from input imagery and then finds the best LandTrendr parameters group. We then run LandTrendr on each patch group location with a number of different paramater sets and assign scores to decide on the best parameter configuration.
This repository is organized into two primary directories:
-Documentation
-Scripts
Documentation holds markdown files for different optimized versions and things that can be done with the outputs.
Scripts contains all the associated scripts referenced in the documentation.