International Journal of Engineering and Technology

A reliable assessment of above ground forest biomass spatial distribution is needed for various applications ranging from carbon and bioenergy policies to sustainable forest management. Remotely sensed images have become one of the primary sources for rapidly assessing the above ground biomass (AGB) from local to global scales. Among various remote sensing techniques, Radar remote sensing has shown promising results for forest ecosystem studies due to its unique sensitivity towards dielectric, geometrical and structural properties of vegetation cover. In this study, we have investigated the use of multi-frequency multi-polarized synthetic aperture radar (SAR) data from different sensors i.e., ALOS-2/PALSAR-2 and Sentinel-1/SAR for AGB estimation. Field inventory data from about 53 plots of 1 ha size each, collected from Nongkhyllem wildlife sanctuary and reserve forest located in the state of Meghalaya, India have been utilized for analysis. As anticipated from previous studies, the L-band backscatter has shown higher sensitivity towards AGB than C-band. Moreover, cross polarization has shown higher sensitivity towards AGB than like polarization. An attempt has been made of utilizing the multi-polarized backscatter for AGB retrieval using Support Vector Machine (SVM). An attempt has also been made to train and validate the SVM using C-band VH backscatter and L-band HV backscatter for AGB estimation. This model observed R and RMSE of 0.96 and 21.30 ton/ha respectively. The study results indicate that the multi-frequency cross polarized backscatter can significantly improve the AGB retrieval accuracy than single-band multi-polarized backscatter.