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<p>Rapid economic growth and accelerated industrialisation have significantly impacted the environment, particularly in resource-extractive regions such as the Jharia Coalfield in Dhanbad, Jharkhand, India. This study employs a multi-method approach integrating remote sensing, GIS, and geospatial artificial intelligence (GeoAI) to analyse and simulate Land Use/Land Cover (LULC) changes over a 22-year period (1992–2014). The approach employs supervised classification, utilising the Maximum Likelihood algorithm to enhance spatial accuracy. Results and analyses are comprehensively focused on LULC modelling and the dynamics of mining-induced land cover transformation. Remarkable, significant spatiotemporal LULC shifts between 1992 and 2014. The analysis reveals consistent reductions in coal location areas, dense forest, water bodies, and barren land categories (Barren Land_1 and Barren Land_2). Conversely, there has been a substantial increase in vegetation types (Vegetation_1 and Vegetation_2) and residential areas, indicating urban encroachment and secondary vegetation regrowth in disturbed zones. This change analysis is crucial for identifying and monitoring high-risk vegetation zones and degraded landscapes due to unregulated mining activities. The transition matrix and classified maps highlight a net decline of over 38% in dense forest and water bodies, while coal extraction zones expanded considerably, correlating with ecological deterioration and unplanned settlement growth. The application of remote sensing and GeoAI proves effective in generating accurate spatiotemporal statistics and delineating patterns of land degradation. The study’s findings provide essential insights for policy formulation and environmental planning. The observed LULC dynamics underscore the urgent need for sustainable land management strategies and rigorous regulatory oversight. This research contributes to the development of a comprehensive management plan for the Jharia Coalfield region, emphasising long-term land-use planning to safeguard natural resources and rehabilitate degraded landscapes.</p>

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Leveraging GeoAI for spatiotemporal analysis of land use changes and mining impacts in Jharia Coalfield, India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Ajay Kumar,
  • Rachan Daimary

摘要

Rapid economic growth and accelerated industrialisation have significantly impacted the environment, particularly in resource-extractive regions such as the Jharia Coalfield in Dhanbad, Jharkhand, India. This study employs a multi-method approach integrating remote sensing, GIS, and geospatial artificial intelligence (GeoAI) to analyse and simulate Land Use/Land Cover (LULC) changes over a 22-year period (1992–2014). The approach employs supervised classification, utilising the Maximum Likelihood algorithm to enhance spatial accuracy. Results and analyses are comprehensively focused on LULC modelling and the dynamics of mining-induced land cover transformation. Remarkable, significant spatiotemporal LULC shifts between 1992 and 2014. The analysis reveals consistent reductions in coal location areas, dense forest, water bodies, and barren land categories (Barren Land_1 and Barren Land_2). Conversely, there has been a substantial increase in vegetation types (Vegetation_1 and Vegetation_2) and residential areas, indicating urban encroachment and secondary vegetation regrowth in disturbed zones. This change analysis is crucial for identifying and monitoring high-risk vegetation zones and degraded landscapes due to unregulated mining activities. The transition matrix and classified maps highlight a net decline of over 38% in dense forest and water bodies, while coal extraction zones expanded considerably, correlating with ecological deterioration and unplanned settlement growth. The application of remote sensing and GeoAI proves effective in generating accurate spatiotemporal statistics and delineating patterns of land degradation. The study’s findings provide essential insights for policy formulation and environmental planning. The observed LULC dynamics underscore the urgent need for sustainable land management strategies and rigorous regulatory oversight. This research contributes to the development of a comprehensive management plan for the Jharia Coalfield region, emphasising long-term land-use planning to safeguard natural resources and rehabilitate degraded landscapes.