Potential of Multiscale Texture Polarization Ratio of C-band SAR for Forest Biomass Estimation

Abstract

Estimation of forest biomass is still a challenging task over large areas because of the saturation problem of remote sensing data as well as the environmental, topographic, and biophysical complexity of forest ecosystems. However, Synthetic Aperture Radar (SAR) is still one of the most attractive choices for the estimation of biomass or carbon storage capacity of vegetation due to its sensitivity to plant canopy structure, and new SAR sensors have attracted greater interest as they are able to provide data with varying spatial resolutions, polarizations, and incidence angles. This research investigates the potential of C-band dual polarization (HH and HV) SAR (Radarsat-2) imagery for forest biomass estimation using different combinations of raw backscattering (intensity), polarization texture parameters and texture polarization ratios. Several models have been developed between field biomass and SAR signatures using stepwise multiple regression. Results indicate that SAR intensity images (C-HV and C-HH) and the ratio of intensity data (C-HV/C-HH) have relatively low potential (r2 = 0.20) for biomass estimation. However, the SAR polarization (C-HV and C-HH) texture parameters were found to be effective and about 82 % (r2 = 0.82 and RMSE = 28.68 t/ha) of the variability in the field data (forest biomass up to 360 t/ha) was explained by the model. Further improvement of the estimation was achieved (r2 = 0.90 and RMSE = 21.55 t/ha) using the texture polarization ratio (C-HV/C-HH). The outcomes suggest that a clear improvement in forest biomass estimation can be obtained using the texture parameters of dual polarization C-band SAR and more improvement can be achieved using the ratio of texture parameters, as this combines the advantages of both texture and ratio.