Estimation of above ground biomass using support vector machines and ALOS/PALSAR data

Abstract

L-band Synthetic aperture radar (SAR) data has been extensively used for forest aboveground biomass (AGB) estimation due to its higher saturation level. However, SAR backscatter is highly influenced by the topography characteristics along with the bio-geophysical properties of vegetation and underneath soil characteristics. This has limited the accuracy of directly relating the SAR backscatter with above ground biomass in highly undulated terrain. In this study, it has been observed that terrain degree of slope and aspect plays a vital role in influencing the SAR backscatter in addition with AGB. Because of this, the degree of slope and aspect along with SAR backscatter in HH (transmit and receive polarizations are horizontal) and HV (transmit horizontal and receive vertical) polarizations have been considered as inputs for Support Vector Machine (SVM) to improve the biomass retrieval accuracy. Our results demonstrate that the accuracy of AGB estimation over hilly terrain can be significantly improved by considering topographical characteristics in addition to L-band backscatter.