Assessment of digital image classification algorithms for forest and land-use classification in the eastern Himalayas using the IRS LISS III sensor

Abstract

The article describes the assessment and discusses the potential of different methods of classifying Linear Imaging Self-Scanning Sensor (LISS) III sensor data for vegetation-cover type and land-use mapping in the hilly terrain of the eastern Arunachal Pradesh, in the northeastern part of India. The forest cover types and their distribution in the study area are governed by climatic conditions and topographical features, which change along the gradient from tropical in the south to alpine in the Greater Himalayas in the north. Arunachal Pradesh is part of the Himalayan Biodiversity Hotspot and is at the tri-junction of Indian, Sino-Japanese, and Indo-Malayan floristic regions. The evergreen forests in the area represent climatic climaxes, which are partly original virgin and partly affected by anthropogenic pressures. Due to phenological and physiognomic similarities in ecotone regions, the differences in the spectral reflectance between adjacent forest types are not well pronounced. The age of the forests, terrain characteristics, and the nature of the vegetation itself could be other reasons for the near-similar reflectance. It is for these reasons that conventional classification algorithms for supervised and unsupervised classification do not perform well. Therefore, there is a need to find a suitable method for vegetation and land-use mapping with high mapping accuracy in this region, which is a biodiversity hotspot. A suitable classification technique is important to characterize vegetation-cover type in this complex terrain. We tested unsupervised, supervised, hybrid, object-oriented, and expert classification techniques for vegetation and land-use mapping on Indian Remote Sensing (IRS)-1C LISS III data. The expert classifier produced the highest accuracy (93.39%) followed by object-oriented, hybrid, and supervised and unsupervised classification techniques.