Land Cover Classification Using IRS LISS III Image and DEM in a Rugged Terrain: A Case Study in Himalayas

Abstract

Digital image classification is generally performed to produce land cover maps from remote sensing data, particularly for large areas. The performance of image classifiers that utilize only the remote sensing data may deteriorate, especially in mountainous regions, due to the presence of shadows of high peaks. In this study, a multisource classification approach to map land cover in Himalayan region with high mountain peaks having elevations up to 4785 m above mean sea level has been adopted. Remote sensing data from IRS LISS III image along with NDVI and DEM data layers have been used to perform multi‐source classification using maximum likelihood classifier. The results show a substantial improvement in accuracy of classification on incorporation of NDVI and DEM as ancillary data over the classification performed solely on the basis of remote sensing data.