Tingting Qin

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 (W_mine) to FVC was quantified using the differential method. Third, the virtual contribution of mining activities (V-W_mine) 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 W_mine 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.

Mining has caused considerable damage to vegetation coverage, especially in grasslands. It is of great significance to investigate the specific contributions of various factors to vegetation cover change. In this study, fractional vegetation coverage (FVC) is used as a proxy indicator for vegetation coverage. We constructed 50 sets of geographically weighted artificial neural network models for FVC and its driving factors in the Shengli Coalfield. Based on the idea of differentiation, we proposed the geographically weighted differential factors-artificial neural network (GWDF-ANN) to quantify the contributions of different driving factors on FVC changes in mining areas. The highlights of the study are as follows: (1) For the 50 models, the average RMSE was 0.052. The lowest RMSE was 0.007, and the highest was 0.112. For the MRE, the average value was 0.007, the lowest was 0.001, and the highest was 0.023. The GWDF-ANN model is suitable for quantifying FVC changes in mining areas. (2) Precipitation and temperature were the main driving factors for FVC change. The contributions were 32.45% for precipitation, 24.80% for temperature, 22.44% for mining, 14.44% for urban expansion, and 5.87% for topography. (3) Over time, the contributions of precipitation and temperature exhibited downward trends, while mining and urban expansion showed positive trajectories. For topography, its contribution remains generally unchanged. (4) As the distance from the mining area increases, the contribution of mining gradually decreases. At 200 m away, the contribution of mining was 26.69%; at 2000 m away, the value drops to 17.8%. (5) Mining has a cumulative effect on vegetation coverage both interannually and spatially. This study provides important support for understanding the mechanism of vegetation coverage change in mining areas.