Forest Biomass Estimation Using Texture Measurements of High-Resolution Dual-Polarization C-Band SAR Data

Abstract

Recent synthetic aperture radar (SAR) sensors with a capability of providing data with varying spatial resolutions, polarizations, and incidence angles have attracted greater interest for forest biomass and carbon storage estimation. This study investigates the capability of RADARSAT-2 fine-beam dual-polarization (C-HV and C-HH) data for forest biomass estimation in complex subtropical forest, with different types of processing: 1) raw intensity data (both polarizations separately and as polarization ratio) and 2) texture parameters of both polarizations (separately, jointly, and as polarization ratio). Field data (diameter at breast height and height) were collected from 53 field plots and converted to biomass (dry weight) using a newly developed allometric model. Finally, biomass estimation models were developed between SAR signatures from different processing steps and field plot biomass using stepwise multiple regression. All biomass estimation models using radar intensity data (C-HV, C-HH, and ratio of C-HV and C-HH) proved ineffective, but texture parameters derived from intensity data showed potential. We were able to estimate forest biomass amounts up to 360 t/ha with a goodness of fit of 0.78 (adjusted r2) and an rmse of 28.68 t/ha using the combination of texture parameters of both polarizations (C-HV and C-HH). However, goodness of fit could be improved to 0.91 (adjusted r2) and an rmse of 26.95 t/ha for biomass levels up to 532 t/ha using the ratio of texture parameters of C-HV/C-HH. The result is very encouraging and indicates that the dual-polarization C-band SAR sensor has a potential for the estimation of forest biomass, particularly using the polarization ratio of texture measurements, and biomass estimation can be improved substantially beyond the previously stated saturation level for C-band SAR.