Land Cover Classification Using Multi-source Data Fusion of Envisat-asar and Irs P6 Liss-iii Satellite Data – a Case Study over Tropical Moist Deciduous Forested Regions of Karnataka, India

The present study addresses the potential of Synthetic Aperture Radar (SAR) data for land cover classification in parts of Dandeli forested regions, Karnataka, India. a FCC has been generated from coherence and backscattering co-efficient images of ENVISATASAR data (HH polarizations) of 25 Sep 2006 and 30 Oct 2006. Similarly, ENVISAT-ASAR data (HH polarization) of 25 Sep 2006 along with IRS –P6 LISS-III of 11 Jan 2005 were subjected to data fusion to generate a False Colored Composite (FCC) using multi-source Intensity Hue Saturation (IHS) fusion technique. The two FCCs were subjected to maximum-likelihood classification technique separately and classification accuracy from the two methods is computed. Results suggested that SAR data is capable of discriminating major land cover types viz., forests, agriculture, water bodies, barren/fallow, urban settlements. Composition of coherence information given by the ASAR along with backscatter images enhanced the delineation capabilities of SAR data. The over all classification accuracy and kappa coefficient of the False Colored Composite (FCC) were observed to be 78% and 0.75 respectively. Further, an attempt has been made to discriminate different forest types by merging the optical LISS-III data with HH polarized ASAR data. The merged output has been found to better delineate the forest types apart from other land-cover classes and minimize the shadow effect. The overall classification accuracy and kappa coefficient of merged data was observed to be 82% and 0.80 respectively. Results of the study suggest the significance of SAR data towards better classification of the land cover classes, when used in conjunction with optical RS data.