Disturbed boundaries extraction in coal-grain overlap areas with high groundwater levels using UAV-based visible and multispectral imagery.

Abstract

With high groundwater levels, coal-grain overlap areas (CGOAs) are vulnerable to subsidence and water logging during mining activities, thereby impacting crop yields adversely. Such damage requires full reports of disturbed boundaries for agricultural reimbursement and ongoing reclamation, but because direct measurements are difficult in such cases because of vast unreachable areas, it is necessary to be able to identify out-of-production boundaries (OBs) and reduced-production boundaries (RBs) in the corresponding region. In this study, an OB was extracted by setting a threshold via the characteristics of the cultivated-land elevation based on a digital surface model and a digital orthophoto map generated using an unmanned aerial vehicle (UAV). Meanwhile, the above-ground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI) were used to select the appropriate vegetation indices (VIs) to produce 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 used to extract mild and severe RBs. The results showed the following. (1) Crop growth heights in a typical ponding basin of the CGOA rendered a fast and efficient approach to distinguishing the OB. (2) In subsequent sample modeling, the red-edge microwave VI (MVIredge), the normalized difference VI (NDVI), and the red-edge modified simple ratio index (MSRredge) combined with RF were shown to be optimal estimators for AGB (R2 = 0.83, RMSE = 0.114kg·m-2); the red-edge NDVI (NDVIredge), the green NDVI (GNDVI), and the red-edge chlorophyll index (CIredge) acted as strong tools in SPAD prediction using RF (R2 = 0.83, RMSE = 0.152 SPAD); the red-edge modified simple ratio index (MSRredge), the GNDVI, and the green chlorophyll index (CIgreen) via MR were more accurate when conducting the inversion of LAI (R2 = 0.88, RMSE = 1.070). (3) With the improved Otsu algorithm, multiple degrees of RB extraction can be achieved in RM. This study provides reference methods and theoretical support for determining disturbed boundaries in CGOAs with high groundwater levels for further agricultural compensation and reclamation processes.