APPLICATION OF REMOTE SENSING TO ESTIMATE ABOVE GROUND BIOMASS IN TROPICAL FORESTS OF INDONESIA

Abstract

This work aims to estimate Above Ground biomass (AGB) of a tropical rainforest in East Kalimantan, Indonesia using equation derived from the stand volume prediction and to study the spatial distribution of AGB over aforest area. The potential of remote sensing and field measurement data to predict stand volume and AGB were studied Landsat ElM data were atmospherically corrected using Dark Object Subtraction (DOS) technique, and topographic corrections were conducted using C-correction method Stand volume was estimated using field data and remote sensing data using Levenberg-Marquardt neural networks. Stand volume data was converted into the above ground biomass using available volume - AGB equations. Spatial distribution of the AGB and the error estimate were then interpolated using kriging. Validated with observation data, the stand volume estimate showed integration of field measurement and remote sensing data has better prediction than the solitary uses of those data. The AGB estimate showed good correlations with stand volume, number of stems, and basal area.