In order to study the influence of fissure morphology on soil moisture-content changes under different fissure types, this study established HYDRUS 2.0 numerical models of stepped fissures and planar fissures with different fissure widths and depths based on the experimental condition parameters obtained from physical simulation tests. Then, we simulated the spatial and temporal variation rules of soil moisture around the fissures. The results showed a high level of agreement between the HYDRUS numerical simulations and actual measurements, indicating that the model accurately reflects the movement of soil moisture near fissures. The study found that ground fissures affected the spatial distribution of soil moisture, leading to an increased rate of moisture loss in the deep soil near the fissure walls. Moreover, larger fissures had greater horizontal and vertical effects on soil moisture. The soil moisture content is lower closer to the fissure walls. As the soil depth increased, the influence of the fissures gradually diminished. For planar fissure with a depth of 50 cm, the soil moisture content was 30.6%, 17.8%, and 8.4% lower at depths of 10, 30, and 50 cm, respectively, compared to a fissure with a depth of 10 cm. For a stepped fissure with a depth of 50 cm, the soil moisture content was 29.2%, 20.9%, and 13.9% lower at depths of 10, 30, and 50 cm, respectively, compared to a fissure with a depth of 10 cm. Under the same conditions of fissure width and depth, stepped fissures exhibit faster moisture loss, and the larger the fissure, the more significant the additional moisture loss compared to planar fissures.

The coal mining might cause the disturbance to the vegetation and the disturbance impacts might exist the differences for different areas, and few literatures compared and analyzed different disturbed areas based on the location of the mining face, and paid attention to the post mining self-healing effects of vegetation. Here, this paper selected the GaoFen multispectral images during 2017–2021 to study different areas of Shangwan Mine which includes the old mining area more than 5 years after mining, the new working face underground mined in 2018 and 2019, the natural growth control area and the open-pit mining affected area. The spatiotemporal changes of the surface fraction vegetation coverage (FVC) were analyzed in each area and the correlation between vegetation coverage and climatic factors was studied. The results showed that: (1) The overall vegetation coverage showed a moderate decrease trend in fluctuation from 2017 to 2021. The Open-pit mining affected areas showed the largest decline, reaching 68.3%. The FVC in the underground mining areas had a downward trend, but self-healing effect after mining was also observed. (2) The overall FVC in the study area was positively correlated with the number of precipitation days. (3) There were differences in the sensitivity to mining disturbance for different landform in the underground mining areas. (4) Although the FVC in the Old mining areas had recovered to the level of Natural growth control area, but the annual fluctuation was larger, which might mean lower ecological stability.

Accurate identification of vegetation in mining areas is crucial for conducting pre-mining ecological assessments and post-mining ecological monitoring. However, the vegetation in the mining area is always highly heterogeneous including both field crops and naturally scattered growing vegetation, which brings great challenges for fine vegetation mapping. Feature combinations are an important factor to influence the vegetation mapping. Thus, to effectively identify the vegetation, this study utilized an unmanned aerial vehicle (UAV) RGB image to extract vegetation indexes and textures, and then selected features based on standard deviation and difference coefficient. By integrating selected optimal features with RGB images, different combinations were constructed and classified using Support Vector Machine (SVM). The results demonstrated that the combination of RGB and all selected features yielded the highest accuracy, followed by the combination of RGB and a single type of texture, and then the combination of RGB and VIs, which indicated that texture features were more important than VIs for vegetation identification. The OA and Kappa for the best combination were 87.76% and 0.8351 for study area A, and 88.74% and 0.8505 for study area B, indicating the effectiveness of the adopted method. Besides, compared with the commonly used random forest (RF) feature selection method, the adopted method avoided complex parameter settings and constructed a superior optimal combination, which further proved the simplicity and effectiveness of difference coefficient-based feature selection methods for vegetation classification in highly heterogeneous environments, contributing to more accurate ecological assessments and monitoring.