Forest Aboveground Biomass Estimation Using GEDI Observations Over Indian Forest

Above-ground biomass density (AGBD) quantification is crucial for understanding carbon dynamics, climate change, and sustainable forest management. This study integrates Global Ecosystem Dynamics Investigation (GEDI) satellite data with multi-spectral, Synthetic Aperture Radar (SAR) based earth observations and soil data for continuous estimation of forest AGBD. Study was focused on the Indian forest. GEDI’s AGBD data from 10,000 points serves as the dependent variable and independent variables are derived from Sentinel-1, Sentinel-2, Digital Elevation Model (DEM), SoilGrids, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). We evaluated Support Vector Regression, Random Forest Regression, and Light Gradient Boosting Machine algorithms for various feature-set scenarios. Hyperparameter tuning employed grid-search-based cross-validation. Results shows that, LightGBM performed well, being computationally efficient and delivering lower RMSE. For the selected LightGBM model, with Sentinel-1, Sentinel-2, DEM, and forest attributes, an RMSE of 68.02 Mg/ha and R
²
of 0.57 were achieved. Model-generated AGB maps were compared with openly available National Remote Sensing Centre AGBD data at 100m and existing forest AGBD work. Comparison between model predicted AGBD and literature based maps and studies shows that, our model was able to capture the AGBD variations across multiple forest sub-regions from India.