Forest Above Ground Biomass Estimation Using Multi-Sensor Geostatistical Approach

This study analyses the potential of integrating spaceborne radar and LiDAR remote sensing products for above ground biomass estimation. Vegetation height product derived from ICESat-2 was interpolated into a 2-D surface using ordinary kriging. Interpolated forest height was validated with in situ data which resulted in determination coefficient of 0.6221 and RMSE of 3.17 m. Interpolated forest surface height and terrain corrected backscatter from L-band ALOS-2/PALSAR-2 data was used to develop linear regression models for above ground biomass estimation. A combination of HV backscatter and ICESat-2 interpolated height-based regression model performed better with a relative RMSE of 30.67%.