Land use land cover classification and two decade change detection using remote sensing and GIS of Mungeli district
Er Aryak Shori, Bharatdwaj Verma and Shubham Raj
Mungeli, a relatively newly formed district in Chhattisgarh, India, is undergoing rapid socio-economic and infrastructural development. As such, understanding the spatio-temporal dynamics of its land use and land cover (LULC) is essential for informed planning and sustainable growth. This study employs multi-temporal satellite data from Landsat (2000 and 2020) and GIS-based supervised classification using the Maximum Likelihood Classifier (MLC) to evaluate LULC changes over a 20-year period. Five key LULC classes were identified: agricultural land, forest, barren land, built-up area, and waterbodies.
The results reveal a significant decline in agricultural land (−4.56%) and forest cover (−2.61%), accompanied by substantial increases in built-up areas (+186.6%) and waterbodies (+255.5%). These transformations point to urban expansion and changing land use priorities, potentially at the cost of ecological balance.
Given Mungeli's status as a developing administrative region, these findings are crucial for resource allocation, environmental conservation, and infrastructure development. The study demonstrates the effectiveness of remote sensing and GIS in generating timely, accurate, and actionable insights for regional planning and supports the integration of geospatial tools into policy-making frameworks for newly formed districts in India.
Er Aryak Shori, Bharatdwaj Verma, Shubham Raj. Land use land cover classification and two decade change detection using remote sensing and GIS of Mungeli district. Int J Res Agron 2025;8(5S):115-118. DOI: 10.33545/2618060X.2025.v8.i5Sb.2928
