ABSTRACT
Chapter 10 involves the fundamental principles and applications of different techniques of synthetic aperture radar (SAR), such as polarimetric SAR (PolSAR) and polarimetric SAR interferometry (PolInSAR), for the estimation of aboveground biomass (AGB) of sub-tropical forests. This estimation is vital as AGB is an important parameter for assessing forest health and quality, and from AGB the amount of carbon sequestration can be easily evaluated. This chapter mainly focuses on describing regression-based methods such as random forest regression (RFR) and multiple linear regression (MLR), used for the estimation of AGB of the Malhan Forest Range of Dehradun Forest Division. A pair of fully polarimetric Radarsat-2 C-band SAR datasets were used for the analysis along with field inventory data. The chapter also illustrates the selection of different PolSAR and PolInSAR image parameters and the methodological development implemented for modeling purposes, followed by a validation and accuracy assessment that was performed in order to determine the most efficient method for the prediction of AGB.
