Arun B Inamdar
Indian Institute of Technology Bombay, India
Title: GIS based inter-class change analysis of classified multi-temporal landsat satellite images at a drought–prone semi-arid river basin in India
Biography
Biography: Arun B Inamdar
Abstract
Shivna river basin is a drought prone region comprising six talukas and three districts in Western Maharashtra State, India. The major livelihood of the region is agricultural practices, but the area is facing a severe water scarcity problem. Landsat satellite images from 1972-2016 are classified (Level I, hybrid classification) to estimate the change detection analysis especially for agricultural land and forest cover shrinkage and barren-lands expansions at the study area. Each image was separately classified into Agricultural land, Forest, Barren-lands, Water and Built-up area. Though the trend analysis is carried out earlier, the key objectives of the current research are to estimate the class which converted to increase the barren-lands and, to quantify the agricultural land and forest cover decrease. A GIS based pixel-to-pixel conversion estimation technique in ArcMap 10.3 environment in spatial analyst tool is applied. The change detection analysis are performed for the duration of 1972-1980, 1980-1991, 1991-2001, 2001-2011, 2011-2016 and 1972-2016. The built-up area expansions occurred at the cost of agricultural land and barren-lands. The addition in the barren-lands occurred at the cost of agricultural land, forest cover and temporary/permanent shrinkage of water bodies and/or dams. The water content (in dams and reservoirs) fluctuates randomly depending upon the recent rainfall. There is a connection between the rainfall pattern change and the inter-class conversion. The recent decrease in overall rainfall is the major factor for barren-lands increase from other three classes. These inter-class fluctuations are because of the climate change at the area over the period.