🛰️Monitoring Soil Degradation with Remote Sensing

🌍Overview

A comprehensive analysis of soil degradation in Karnataka, India (2015-2019) using remote sensing techniques and Google Earth Engine. The project implements three different change detection methods to assess and visualize land degradation patterns, achieving up to 76.3% accuracy.

📊Methods

1. Index Differencing

   • NDVI (Normalized Difference Vegetation Index)
   • BI (Bare Soil Index)

2. Change Vector Analysis

   • Using NIR, SWIR, and Red bands
   • Analysis of magnitude (rho) and direction (alpha) of change

3. RUSLE with Ancillary Data

   • Rainfall erosivity factor (R)
   • Soil erodibility factor (K)
   • Slope length and steepness factor (LS)
   • Cover management factor (C)
   • Conservation practice factor (P)

📈Results

• Index Differencing: 60.3% accuracy with BI
• Change Vector Analysis: 50.05% accuracy
• RUSLE with Ancillary Data: 76.3% accuracy

🛠️Tech Stack

• Google Earth Engine
• Remote Sensing
• RUSLE (Revised Universal Soil Loss Equation)
• Change Detection Analysis

📚Data Sources

• USGS Landsat 8 Level 2
• MODIS Land Cover
• CHIRPS Dataset
• OpenLandMap
• WWF HydroSHEDS