Polarimetric classification of C-band SAR data for forest density characterization

Polarimetric classification is one of the most significant applications of synthetic aperture radar (SAR) remote sensing. Sensitivity of C-band SAR in discerning the variation in canopy roughness and limited penetration capability through forest canopy have been well studied at a given frequency, polarization and incidence angle. However, the scope of C-band SAR in characterizing and monitoring forest density has not been adequately understood with polarimetric techniques. The objectives of the present study were to understand the scattering behaviour of different land-cover classes and evaluate the feasibility of polarimetric SAR data classification methods in forest canopy density slicing using C-band SAR data. The RADARSAT-2 image with fine quad-pol obtained on 27 October 2011 over Madhav National Park, Madhya Pradesh, India and its surroundings was used for the analysis. Forest patches exhibit a-angle around 45, which means the dominant scattering mechanism is volume; entropy of one or a value close to it denotes distributed targets and low anisotropy values than all other land units, which shows a dominant first scattering mechanism. This study comparatively analysed Wishart supervized classifier and Support Vector Machine (SVM) classifier for classification of the forest canopy density along with other associated land-cover classes for a better understanding of the class separability. All forest density classes showed comparatively good separability in Wishart supervized classification (73.8-84.7%) and in SVM classifier (82.3-84.8%). The results demonstrate the effectiveness of SVM classifier (88.7%) over Wishart supervized classifier (87.8%) with kappa coefficient of 0.86 and 0.85 respectively. The experimental results obtained with polarimetric C-band SAR data over dry deciduous forest area imply that SAR data have a significant potential for estimating stand density in operational forestry.