Integration of ICESat/GLAS data and random forest to estimate canopy height and biomass in Central Indian Forest

Satellite lidar systems, such as ICESat, GEDI, and ICESat-2, have revolutionized global above-ground biomass (AGB) estimation by providing precise forest height data. These missions highlight the transformative potential of spaceborne lidar in advancing biomass assessment and forest monitoring. The present research effectively utilized spaceborne ICESat -1 LiDAR to measure forest canopy height and estimate above-ground biomass (AGB) in Madhya Pradesh's Central Indian Forest. Data from LiDAR, radar, optical, and digital elevation models were integrated with ancillary climate variables and field-based forest inventory during 2009-10. Further, this approach was validated against new data obtained from ICESAT-2 (October 2018 onwards), GEDI (March 2019 onwards), and other spaceborne LiDAR sensors by numerous researchers globally. Lorey's height method established a relationship between GLAS-derived AGB and other variables, creating a spatial–height map using a K-Nearest Neighbour-based random forest approach. Estimated forest canopy height ranged from 2.16 m to 17.63 m with an RMSE of ± 2.57 m. Spatial AGB was estimated for prominent forest types, with R2 values ranging from 0.62 to 0.71 (p < 0.01). The total AGB over Madhya Pradesh's forests were estimated at 315.77 Mt with an RMSE of ±19.22 t ha^-1. Relative errors ranged from 33% to 45% over different forest types, suggesting newer missions like ICESat/GLAS-2 are needed for more precise estimates in future research.