Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel-1 and Sentinel-2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India

The present study aims to map forest canopy height by integrating ICESat-2 and Sentinel-1 data and investigate the effect of integrating forest canopy height information with Sentinel-2 data-derived spectral variables on the prediction of spatial distribution of forest aboveground biomass (AGB). Random forest (RF) algorithm was used to develop forest canopy height and AGB models. It was observed that ICESat-2 and Sentinel-1 based model was able to predict forest canopy height with R-2 = 0.84 and %RMSE = 4.48%. Two forest AGB models were developed, with only spectral variables and by incorporating forest height information with spectral variables. The results reflected that incorporation of forest canopy height in the forest AGB model improved the accuracy of the AGB predictions (R-2 = 0.83, %RMSE = 4.64%). The study presents a comprehensive methodology for mapping forest canopy height and AGB.

Automated summary

This study successfully mapped forest canopy height by integrating different satellite data and investigated the impact of incorporating canopy height information into AGB models on prediction accuracy. The results demonstrated that incorporating canopy height information significantly improved the accuracy of AGB predictions.