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

• Published in: Environmental science and pollution research international Vol. 29; no. 57; pp. 86055 - 86067
• Main Authors: Abijith, Devanantham, Saravanan, Subbarayan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Springer Nature B.V
• Subjects: Agricultural production; Anthropogenic factors; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Barren lands; Change detection; Coastal zone; Coasts; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Flooding; Floods; Geographic information systems; Industrial development; Land cover; Land use; Landsat; Markov chains; Rain; Remote sensing; Research on Sustainable Developments for Environment Management; Rivers; Satellite imagery; Satellites; Software; Statistical analysis; Trends; Urban development; Urbanization; Waste Water Technology; Water bodies; Water Management; Water Pollution Control; Wind; Tamil Nadu India; India; Land change modeler; Geographical Information System; Google Earth Engine; Land use and land cover; CA-Markov; Remote sensing; Random Forest
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-021-15782-6

The study on land use and land cover (LULC) changes assists in analyzing the change and regulates environment sustainability. Hence, 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. 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 found to be under stress, developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all 3 years. This study can provide suggestions and a basis for urban development planning as it is highly susceptible to coastal flooding.