Improvement of Biomass Estimation in Forest Areas based on Polarimetric Parameters Optimization of SETHI airborne Data using Particle Swarm Optimization Method

Estimation of forest biomass has received much attention in recent decades. Airborne and spaceborne (SAR) have a great potential to quantify biomass and structural diversity because of its penetration capability. Polarizations are important elements in SAR systems due to sensitivity of them to backscattering mechanisms and can be useful to estimate biomass. Full Polarimetric Synthetic Aperture Radar (SAR) data used in this research was acquired by SETHI over Remningstorp, a boreal forest in south of Sweden. A new method based on Polarimetric indicators from covariance and coherency matrixes by changing the polarization basis using transformation matrix in the boreal forests at L and P-band is presented. The presented method showed its capability to improve forest biomass estimation. The correlation between biomass and extracted Polarimetric indicators is investigated before and after changing polarization basis. Particle swarm optimization in binary version is used to select optimum Polarimetric indicators and afterward biomass is estimated based on these optimum parameters. Results indicated that maximum correlation between biomass and Polarimetric indicators was in HV and HH-VV polarizations before changing polarization basis. After changing the polarization bases, the results show significantly higher correlation of biomass with the extracted polarization variables. The results have been improved approximately about 6% and 2% in L and P band respectively, after extraction of optimum parameters by particle swarm optimization and using linear regression model for estimation of forest biomass.