* Corresponding Author
APPLICATION OF REMOTE SENSING TO ESTIMATE ABOVE GROUND BIOMASS IN TROPICAL FORESTS OF INDONESIA
https://doi.org/10.22146/ijg.2254 Arief Wijaya(1*), Richard Gloaguen(2), Hermann Heilmeier(3)
(1)
(2)
(3)
(*) Corresponding Author
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.
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.
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PDF DOI: https://doi.org/10.22146/ijg.2254
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