College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 102209, China.
Int J Environ Res Public Health. 2022 Apr 24;19(9):5176. doi: 10.3390/ijerph19095176.
Identifying the spatial range of mining disturbance on vegetation is of significant importance for the plan of environmental rehabilitation in mining areas. This paper proposes a method to identify the spatial range of mining disturbance (SRMD). First, a non-linear and quantitative relationship between driving factors and fractional vegetation cover (FVC) was constructed by geographically weighted artificial neural network (GWANN). The driving factors include precipitation, temperature, topography, urban activities, and mining activities. Second, the contribution of mining activities () to FVC was quantified using the differential method. Third, the virtual contribution of mining activities () to FVC during the period without mining activity was calculated, which was taken as the noise in the contribution of mining activities. Finally, the SRMD in 2020 was identified by the significance test based on the and noise. The results show that: (1) the mean RMSE and MRE for the 11 years of the GWANN in the whole study area are 0.0526 and 0.1029, which illustrates the successful construction of the relationship between driving factors and FVC; (2) the noise in the contribution of mining activities obeys normal distribution, and the critical value is 0.085 for the significance test; (3) most of the SRMD are inside the 3 km buffer with an average disturbance distance of 2.25 km for the whole SRMD, and significant directional heterogeneity is possessed by the SRMD. In conclusion, the usability of the proposed method for identifying SRMD has been demonstrated, with the advantages of elimination of coupling impact, spatial continuity, and threshold stability. This study can serve as an early environmental warning by identifying SRMD and also provide scientific data for developing plans of environmental rehabilitation in mining areas.
识别采矿干扰的空间范围对矿区环境恢复计划具有重要意义。本文提出了一种识别采矿干扰空间范围（SRMD）的方法。首先，通过地理加权人工神经网络（GWANN）构建了驱动因素与植被覆盖分数（FVC）之间的非线性定量关系。驱动因素包括降水、温度、地形、城市活动和采矿活动。其次，采用微分法量化采矿活动对 FVC 的贡献。第三，计算了采矿活动期间无采矿活动时采矿活动对 FVC 的虚拟贡献，作为采矿活动贡献的噪声。最后，基于显著性检验，利用 和噪声识别 2020 年的 SRMD。结果表明：（1）整个研究区 GWANN 11 年的平均 RMSE 和 MRE 分别为 0.0526 和 0.1029，表明成功构建了驱动因素与 FVC 之间的关系；（2）采矿活动贡献的噪声服从正态分布，显著性检验的临界值为 0.085；（3）SRMD 大部分位于 3km 缓冲区以内，整体平均干扰距离为 2.25km，具有显著的方向性异质性。总之，所提出的方法在识别 SRMD 方面具有实用性，具有消除耦合影响、空间连续性和阈值稳定性的优点。本研究可以通过识别 SRMD 作为早期环境预警，并为矿区环境恢复计划的制定提供科学数据。
