Asian Journal of Geographical Research

Land use and land cover (LULC) change is a fundamental indicator of environmental transformation, resource consumption and anthropogenic pressure on natural systems. This study presents a temporal analysis of LULC dynamics in Rewa District, Madhya Pradesh for the years 2020 and 2025, using Sentinel-2 multispectral imagery processed through the Google Earth Engine (GEE) cloud platform. A supervised Random Forest classification approach was adopted, integrating a comprehensive feature set comprising spectral bands, vegetation indices (NDVI, EVI, SAVI) water and built-up indices (MNDWI, NDBI, BSI) Grey Level Co-occurrence Matrix (GLCM) texture metrics and terrain derivatives (DEM, slope, aspect) from the SRTM dataset. A year (January–December) cloud-free median composites were used to minimise seasonal bias. Five LULC classes were mapped: Agriculture Land, Vegetation Land, Water Body, Built-up Land, and Waste Land. The classification achieved an Overall Accuracy of 94.59% and a Kappa coefficient of 0.9308 for 2020, and 95.45% and 0.9387 respectively for 2025. Post-classification change analysis reveals a landscape undergoing active transformation driven by agricultural intensification, rapid built-up expansion and large-scale wasteland reclamation. Agriculture Land remained dominant, expanding from 3,441 km² (54.5%) in 2020 to 3,548 km² (56.2%) in 2025. The Built-up Land class recorded the most dramatic proportional change, expanding by 113 km² (+44.66%), driven by highway corridor development along NH-30 and NH-27 and the creation of Mauganj as a new administrative district. Waste land declined most substantially by 239 km² (-13.87%), as ravines and degraded surfaces were reclaimed for cultivation and solar energy infrastructure. Forest cover recorded a marginal increase of 17 km² (+2.16%) attributable to open forest regeneration and social forestry on the Kaimur Range escarpment. The study demonstrates the utility of cloud-based geospatial analysis for regional LULC monitoring and provides a spatial evidence base for land management and development planning in Rewa District.