Monitoring Historical Waste Coal Piles Using Image Classification and Change Detection Algorithms on Satellite Images

Abandoned coal mine lands, particularly waste coal piles that predate the Surface Mining Control and Reclamation Act (SMCRA) of 1977, pose significant environmental and safety risks. Unlike sites mined after SMCRA—where operators are legally mandated to conduct reclamation—there is no legal obligation for companies or individuals to restore lands disturbed before the law’s enactment. As a result, these historical sites remain largely unmanaged and understudied. This study develops a satellite imagery-based analytical workflow to identify and monitor such historical waste coal piles. Using supervised classification of Sentinel-2 imagery with four machine learning models, we identified waste coal piles in both active mining areas and regions disturbed prior to SMCRA. Among the models tested, Random Forest achieved the highest accuracy for classifying waste coal, with a precision of 86% and a recall of 77%. A subsequent time-series analysis revealed that historical waste coal piles have undergone gradual but consistent vegetation recovery since 1986, indicating a natural reclamation process. These areas showed minimal changes in disturbance magnitude, suggesting the absence of significant disturbing events. In contrast, active mining regions showed substantial disturbance consistent with ongoing operations. The combined classification and change detection approach successfully distinguished historical waste coal piles from those in active mining regions, with a precision of 78% and recall of 100%. These findings highlight the potential of remote sensing and temporal analysis to support the identification and assessment of historical waste coal piles. The proposed approach can help prioritize reclamation efforts and inform policy decisions addressing the long-term environmental impacts of historical coal mining.