Multi-temporal remote sensing and GIS-based geospatial assessment of iron ore mining dynamics and LULC impacts in Bailadila Range, Bastar Craton, Central India

The Bailadila iron ore range located in the Bastar Craton of southern Chhattisgarh represents one of India’s most important high-grade hematite mining provinces. Rapid expansion of open-cast mining activities in this region has significantly altered land cover, terrain morphology, and environmental conditions. This study applies an integrated Remote Sensing and Geographic Information System (RS–GIS) approach to evaluate spatial patterns of mining expansion, land use/land cover (LULC) dynamics, and environmental disturbances in the Bailadila range. Multi-temporal Sentinel-2B satellite imagery and ALOS PALSAR Digital Elevation Model (DEM) data were processed to generate vegetation, thermal, and terrain indicators including the normalized difference vegetation index (NDVI), land surface temperature (LST), and mining disturbance index (MDI). Supervised classification using machine learning techniques produced detailed thematic maps representing forest cover, mining areas, exposed rock, and other land cover classes. The results indicate that dense forest remains the dominant land cover type, occupying approximately 55.7% (184 km²) of the total mapped area, while moderately dense forest accounts for 29.66% (97.96 km²). Active mining and exposed rock surfaces cover around 7.05 km² (2.13%), reflecting localized but intensive landscape modification. NDVI analysis revealed values ranging from −0.29 to 0.87 in 2020 and −0.37 to 0.88 in 2025, indicating vegetation degradation near mining zones. Land Surface Temperature analysis showed maximum temperatures of 44.09°C in 2020 and 38.01°C in 2025, with higher thermal anomalies concentrated in disturbed mining areas. The Mining Disturbance Index increased from 0.06–2.41 in 2020 to 0.05–2.68 in 2025, highlighting progressive environmental stress associated with mining expansion. Accuracy assessment using 350 ground reference samples produced an overall classification accuracy of about 94% with a Kappa coefficient of 0.92, confirming the reliability of the classification results. This study advances the application of integrated multi-temporal RS-GIS techniques for assessing mining-induced landscape transformation and provides a reliable decision-support framework for environmental monitoring, sustainable mine management, and policy planning in mineral-rich mountainous regions.