This study conducted land cover classification and inversion analysis to estimate land surface temperature, soil moisture, specific humidity, atmospheric water vapor density, and relative humidity using remote sensing and multi-source mining data. Using 1990–2020 data from the Shendong mining area in Inner Mongolia, China, the eco-environmental evolution and the ecological cumulative effects (ECE) of mining operations were characterized and analyzed at a long-term scale. The results show that while the eco-environment was generally stable, mining activities affected the eco-environment at the initial stage (1990–2000) to a certain degree. During the rapid development stage of coal mining, the eco-environment was severely damaged, and the ECE were significant at the temporal scale. The absolute value of the change rate of ecological parameters was increasing. Due to an increased focus on ecological restoration, starting in 2010, the environmental indicators gradually stabilized and the eco-environment improved considerably, ushering in a period of stability for coal mining activities. The absolute value of the change rate of ecological parameters became stable. Analysis of the change in eco-environmental indicators with distance and comparison to the contrast area showed the ECE characteristics from mining disturbance at the spatial scale. This study shows that remote sensing technology can be used to characterize the ECE from mining operations and analyze eco-environmental indicators, providing crucial information in support of ecological protection and restoration, particularly in coal mining areas.

Overstocked dumping sites associated with open-pit coal mining occupy original vegetation areas and cause damage to the environment. The monitoring of vegetation disturbance and restoration at dumping sites is important for the accurate planning of ecological restoration in mining areas. This paper aimed to monitor and assess vegetation disturbance and restoration in the dumping sites of the Baorixile open-pit mine using the LandTrendr algorithm and remote sensing images. Firstly, based on the temporal datasets of Landsat from 1990 to 2021, the boundaries of the dumping sites in the Baorixile open-pit mine in Hulunbuir city were extracted. Secondly, the LandTrendr algorithm was used to identify the initial time and duration of vegetation disturbance and restoration, while the Normalized Difference Vegetation Index (NDVI) was used as the input parameter for the LandTrendr algorithm. Thirdly, the vegetation restoration effect at the dumping sites was monitored and analyzed from both temporal and spatial perspectives. The results showed that the dumping sites of the Baorixile open-pit mine were disturbed sharply by the mining activities. The North dumping site, the South dumping site, and the East dumping site (hereinafter referred to as the North site, the South site, and the East site) were established in 1999, 2006, and 2010, respectively. The restored areas were mainly concentrated in the South site, the East site, and the northwest of the North site. The average restoration intensity in the North site, South site, and East site was 0.515, 0.489, and 0.451, respectively, and the average disturbance intensity was 0.371, 0.398, and 0.320, respectively. The average restoration intensity in the three dumping sites was greater than the average disturbance intensity. This study demonstrates that the combination of temporal remote sensing images and the LandTrendr algorithm can follow the vegetation restoration process of an open-pit mine clearly and can be used to monitor the progress and quality of ecological restoration projects such as vegetation restoration in mining areas. It provides important data and support for accurate ecological restoration in mining areas.

The surface discontinuous deformation caused by coal mining has great damage to the ecological environment and threatens the safety of human lives. Focusing on the problem of discontinuous deformation (ground fissures and collapsed pits) in mining areas with a thick loess and thin bedrock, this paper uses a coal panel in southern Shanxi in China as research background, and uses field investigation, theoretical analysis and the particle flow code 2D (PFC2D) numerical simulation method to study the movement of overburden and discontinuous ground deformation of mining areas with a thick loess layer and a thin bedrock. The results show that with the continual advance of the working face, the failure of the overlying rock, the changing of force chain shape and the development of cracks under this geological and mining condition have their unique rules. This study analyzes the law of movement of overburden in coal seam mining, explains why discontinuous deformation of the surface occurs in case of a thick loess layer and thin bedrock, and provides reference for the prediction of fracture development under the same geological conditions and the application of the PFC2D in coal seam mining in different geological conditions.

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.

Ground deformation measurements in mining areas play a key role in revealing the surface subsidence law, retrieving the subsidence parameters, warning of geological disasters and restoring the surface ecology. With the development of science and technology, there have emerged a great number of monitoring techniques and buildings of diverse protection levels. The diversity of monitoring techniques and the multiplicity of monitoring objects have brought challenges for surface deformation monitoring in the coal industry. Based on the existing deformation monitoring techniques, this paper established a framework of “space-sky-ground” collaborative monitoring system in mining area. We also constructed an AHP-TOPSIS (Analytic Hierarchy Process method- Technique for Order Preference by Similarity to an Ideal Solution) preference model of “space-sky-ground” collaborative monitoring of surface deformation in mining area, and carried out engineering application. Our study shows that the framework of the “space-sky-ground” collaborative monitoring system for surface subsidence in mining areas established in this paper, combined with the AHP-TOPSIS monitoring preference model, which can fully combine the advantages of each monitoring technique, overcome the limitations of a single monitoring technique, comprehensively obtain the surface subsidence data and work out the surface deformation subsidence pattern. This information provides a data and technical support for surface environment management.