Institute of Land Reclamation and Ecological Restoration, China University of Mining and Technology (Beijing), Beijing, 100083, China.
Environ Sci Pollut Res Int. 2022 Aug;29(39):58892-58905. doi: 10.1007/s11356-022-19966-6. Epub 2022 Apr 4.
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 (MVI), the normalized difference VI (NDVI), and the red-edge modified simple ratio index (MSR) combined with RF were shown to be optimal estimators for AGB (R = 0.83, RMSE = 0.114 kg·m); the red-edge NDVI (NDVI), the green NDVI (GNDVI), and the red-edge chlorophyll index (CI) acted as strong tools in SPAD prediction using RF (R = 0.83, RMSE = 0.152 SPAD); the red-edge modified simple ratio index (MSR), the GNDVI, and the green chlorophyll index (CI) via MR were more accurate when conducting the inversion of LAI (R = 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.
在高地下水位的情况下，煤粮重叠区（CGOAs）在采矿活动中容易发生沉降和水涝，从而对作物产量产生不利影响。这种破坏需要对受干扰边界进行全面报告，以便进行农业补偿和持续复垦，但由于大面积的不可到达区域，直接测量在这种情况下很困难，因此需要能够识别相应区域的停产边界（OB）和减产边界（RB）。在这项研究中，通过基于数字表面模型和使用无人机生成的数字正射影像图的耕地高程特征设置阈值，提取了 OB。同时，利用地上生物量（AGB）、叶绿素含量的土壤植物分析开发（SPAD）值和叶面积指数（LAI），选择合适的植被指数（VI），基于幂回归（PR）、指数回归（ER）、多元线性回归（MR）和随机森林（RF）算法生成减产图（RM）。最后，采用改进的 Otsu 分割算法提取轻度和重度 RB。结果表明：（1）CGOA 典型积水盆地中的作物生长高度为区分 OB 提供了快速高效的方法；（2）在后续的样本建模中，红边微波 VI（MVI）、归一化差值 VI（NDVI）和红边修正简单比指数（MSR）与 RF 相结合，被证明是 AGB 的最佳估计值（R=0.83，RMSE=0.114kg·m）；红边 NDVI（NDVI）、绿边 NDVI（GNDVI）和红边叶绿素指数（CI）在使用 RF 进行 SPAD 预测时是强有力的工具（R=0.83，RMSE=0.152SPAD）；红边修正简单比指数（MSR）、GNDVI 和绿边叶绿素指数（CI）通过 MR 进行 LAI 反演时更为准确（R=0.88，RMSE=1.070）；（3）通过改进的 Otsu 算法，可以在 RM 中实现 RB 的多梯度提取。本研究为确定高地下水位 CGOAs 的受干扰边界提供了方法和理论支持，以便进一步进行农业补偿和复垦过程。
