Institute of Land Reclamation and Ecological Restoration, China University of Mining and Technology (Beijing), Beijing, 100083, China.
Department of Land Management, Zhejiang University, Hangzhou, 310058, China.
J Environ Manage. 2022 Sep 1;317:115502. doi: 10.1016/j.jenvman.2022.115502. Epub 2022 Jun 11.
Spontaneous combustion of coal waste dumps is a serious threat to the ecological environment and the safety of mining areas. Even after land reclamation and ecological restoration, such spontaneous combustion activities are still active. Achieving early warning of spontaneous combustion is necessary to protect the reclaimed ecosystem and reduce environmental pollution, but it has not yet been well studied. To this end, this study proposed a spatial analysis method to achieve early warning spontaneous combustion of coal waste dump after reclamation by integrating unmanned aerial vehicle (UAV) and vegetation (Medicago sativa/alfalfa) growth status. The experiment was implemented in two slope areas (Areas I and II) of a coal waste dump after reclamation in Shanxi province, China, which were under threat of spontaneous combustion. Three alfalfa growth parameters, aboveground biomass (AGB), plant water content (PWC), and plant height (PH) of the study area, were estimated from UAV imagery features and used to assess the spontaneous combustion risk. Then, soil deep temperature points (25 cm depth) distributed evenly in the study area were collected to determine the underground temperature situation. It was found that the UAV-derived rededge Chlorophyll index (CIrededge), canopy temperature depression (CTD), and canopy height model (CHM) achieved a better estimation of alfalfa AGB (R = 0.81, RMSE = 99.2 g/m, and MAE = 74.9 g/m), PWC (R = 0.68, RMSE = 3.9%, and MAE = 3.2%), and PH (R = 0.77, RMSE = 9.79 cm, and MAE = 7.68 cm) of the study area, respectively. We also observed that three alfalfa parameters were highly correlated with the soil deep temperature, but differed in degree (R = 0.46-0.81). Furthermore, they were consistent with the soil deep temperature in spatial distribution and could reveal the change direction of underground temperature, which will help us to detect those potential spontaneous combustion areas. These results indicated that vegetation is a prior indicator to the changes in underground temperature of coal waste dump. We believed that UAV can be an effective environmental management tool for the initial assessment of spontaneous combustion risk of coal waste dump after reclamation.
煤矸石山自燃是对矿区生态环境和安全的严重威胁。即使在土地复垦和生态恢复后，这种自燃活动仍然很活跃。实现自燃的早期预警对于保护复垦生态系统和减少环境污染是必要的，但尚未得到很好的研究。为此，本研究提出了一种空间分析方法，通过整合无人机 (UAV) 和植被 (紫花苜蓿/苜蓿) 生长状况，实现复垦后煤矸石山自燃的早期预警。该实验在中国山西省的一个复垦后的煤矸石山两个坡面区域 (区域 I 和 II) 进行，这些区域受到自燃的威胁。利用无人机图像特征估算了研究区三个紫花苜蓿生长参数，即地上生物量 (AGB)、植物水分含量 (PWC) 和植物高度 (PH)，并用于评估自燃风险。然后，采集了均匀分布在研究区域的土壤深层温度点 (25 cm 深度)，以确定地下温度情况。结果表明，UAV 衍生的红边叶绿素指数 (CIrededge)、冠层温度降低 (CTD) 和冠层高度模型 (CHM) 对苜蓿 AGB (R = 0.81, RMSE = 99.2 g/m, MAE = 74.9 g/m)、PWC (R = 0.68, RMSE = 3.9%, MAE = 3.2%) 和 PH (R = 0.77, RMSE = 9.79 cm, MAE = 7.68 cm) 的估算效果较好。此外，我们还观察到三个紫花苜蓿参数与土壤深层温度高度相关，但程度不同 (R = 0.46-0.81)。此外，它们与土壤深层温度的空间分布一致，可以揭示地下温度的变化方向，有助于检测潜在的自燃区域。这些结果表明，植被是煤矸石山地下温度变化的先行指标。我们相信，无人机可以成为复垦后煤矸石山自燃风险初步评估的有效环境管理工具。
