Assessment of Land Use And Land Cover Change Detection And Prediction Using Remote Sensing And CA Markov In The Northern Coastal Districts of Tamil Nadu, India

Abstract

Abstract Land use and land cover (LULC) change analysis and forecasting aids the upcoming generation in research and evaluate the global climate change for managing and controlling environmental sustainability. This research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA- Markov model to forecast future LULC change. The CA-Markov’s Land change modeler (LCM) was set up to create future LULC. It was carried out in four steps: (1) Change analysis, (2) Transition potential, (3) Change prediction, and (4) Model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are under stress and developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all three years. This region is highly susceptible to inland floods, coastal floods, and other natural disasters; thus, this study’s results support future development plans and decision-making.