Coal-grain overlap areas (CGOA) with high groundwater levels are vulnerable to subsidence and water logging during a series of mining activities, which have adverse impacts on crop yields. Such damage requires full reports of disturbed boundaries for the agricultural reimbursement and ongoing reclamation. Since direct measurements are difficult in such a case because of vast, unreachable areas, so it is necessary to be able to identify out-of-production boundary (OB) and reduced-production boundary (RB) in the corresponding region. In this study, OB was extracted by setting thresholds through characteristics of the cultivated land elevation based on UAV-generated digital surface model (DSM) and digital orthophoto map (DOM).Meanwhile, aboveground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI), were used to select the appropriate vegetation indexes (VI) to perform a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR) and random forest (RF) algorithms. Finally, an improved OTSU segmentation algorithm was applied to extract mild RB and severe RB. The results show elevation threshold segmentation method and the improved OTSU segmentation method can accurately recognize and extract disturbed boundaries, which are consistent with the tonal difference after crop damage in the image. This study provides reference methods and theoretical supports for disturbed boundaries determination in CGOA with high groundwater levels for further agricultural compensation and reclamation processes.

Coal-grain overlap areas (CGOA) with high groundwater levels are vulnerable to subsidence and water logging during a series of mining activities, which have adverse impacts on crop yields. Such damage requires full reports of disturbed boundaries for the agricultural reimbursement and ongoing reclamation. Since direct measurements are difficult in such a case because of vast, unreachable areas, so it is necessary to be able to identify out-of-production boundary (OB) and reduced-production boundary (RB) in the corresponding region. In this study, OB was extracted by setting thresholds through characteristics of the cultivated land elevation based on UAV-generated digital surface model (DSM) and digital orthophoto map (DOM). Meanwhile, aboveground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI), were used to select the appropriate vegetation indexes (VI) to perform a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR) and random forest (RF) algorithms. Finally, an improved OTSU segmentation algorithm was applied to extract mild RB and severe RB. The results show elevation threshold segmentation method and the improved OTSU segmentation method can accurately recognize and extract disturbed boundaries, which are consistent with the tonal difference after crop damage in the image. This study provides reference methods and theoretical supports for disturbed boundaries determination in CGOA with high groundwater levels for further agricultural compensation and reclamation processes.