Open-pit coal mine dumps in semi-arid areas in northern China are affected by serious soil erosion problems. The conventional field investigation method cannot ensure a fine spatial analysis of gully erosion. With recent technological and algorithmic developments in high-resolution terrain measurement, Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) technology have become powerful tools to capture high-resolution terrain data. In this study, two UAV Photogrammetry surveys and modeling were performed at one opencast coal mine dump gully before and after a freeze-thaw cycle. Finally, a three-dimensional digital model of the slope of the drainage field was established, and a centimeter-level-resolution Digital Orthophoto Map (DOM) and a Digital Elevation Model (DEM) were created. Moreover, the development process of the erosion zone of the open-pit mine dump during a freeze-thaw cycle was studied by UAVs. The results show that there are clear soil erosion phenomena in the erosion gully of the dump during a freeze-thaw cycle. The erosion degree was different across regions, with the highest erosion occurring in high-slope areas at the upper edge of the bank. Moreover, the phenomenon of flake erosion and “crumble” was recorded. At the same time, the NE-E-SE slope and the high-sunshine radiation zone were seriously eroded. Finally, the relationship between the development process of the erosion gully and micro-topography factors was analyzed, providing managers with a sound scientific basis to implement land restoration.

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Increased attention has been paid to the influence of coal mining subsidence on ecological environment. Restoration of ecosystem in damaged mining area is critical for restoring disturbed environment. The comparing of plant communities and microbial communities in the artificial restoration and natural restoration areas provides an effective method for evaluating the restoration effects. However, such studies are limited in coal mining subsidence restoration areas. Subsidence area in Shendong mining area, located in the semi-arid region of Western China, was restored from 2003 with 5 ecological restoration plant species. In July 2017, the comparison and analysis of plant and microbial communities were conducted at the artificial restoration areas (AR) and the natural remediation areas (NR). The results showed that the artificial ecological restoration in Shendong mining area has achieved some success, but it has not recovered to a similar ecosystem before the destruction. A higher plant species, coverage and bacterial community diversity were observed in AR. However, these features have lower similarity compared with those in NR sites. Potential soil factors, such as pH, moisture content, total carbon content, organic matter, nitrogen and bulk density, have a greater impact on soil bacterial community structure and diversity. In the ecological restoration of the mining area, attention should be paid to the restoration of soil properties in the mining area. This study can provide theoretical guidance for more scientific ecological restoration in the damaged mining area.

Accurate monitoring of plant dust retention can provide a basis for dust pollution control and environmental protection. The aims of this study were to analyze the spectral response features of grassland plants to mining dust and to predict the spatial distribution of dust retention using hyperspectral data. The dust retention content was determined by an electronic analytical balance and a leaf area meter. The leaf reflectance spectrum was measured by a handheld hyperspectral camera, and the airborne hyperspectral data were obtained using an imaging spectrometer. We analyzed the difference between the leaf spectral before and after dust removal. The sensitive spectra of dust retention on the leaf- and the canopy-scale were determined through two-dimensional correlation spectroscopy (2DCOS). The competitive adaptive reweighted sampling (CARS) algorithm was applied to select the feature bands of canopy dust retention. The estimation model of canopy dust retention was built through random forest regression (RFR), and the dust distribution map was obtained based on the airborne hyperspectral image. The results showed that dust retention enhanced the spectral reflectance of leaves in the visible wavelength but weakened the reflectance in the near-infrared wavelength. Caused by the canopy structure and multiple scattering, a slight difference in the sensitive spectra on dust retention existed between the canopy and leaves. Similarly, the sensitive spectra of leaves and the canopy were closely related to dust and plant physiological parameters. The estimation model constructed through 2DCOS-CARS-RFR showed higher precision, compared with genetic algorithm-random forest regression (GA-RFR) and simulated annealing algorithm-random forest regression (SAA-RFR). Spatially, the amount of canopy dust increased and then decreased with increasing distance from the mining area, reaching a maximum within 300–500 m. This study not only demonstrated the importance of extracting feature bands based on the response of plant physical and chemical parameters to dust, but also laid a foundation for the rapid and non-destructive monitoring of grassland plant dust retention.

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Open-pit coal mining plays an important role in supporting national economic development; however, it has caused ecological problems and even seriously impacted regional ecological stability. Given the importance of maintaining ecological stability in semi-arid coal mining areas, this study used a coupling coordination degree approach based on the structural and functional state transition model (SFSTM) to evaluate the spatio–temporal variation of ecological stability from 2002 to 2017 by using MODIS and Landsat datasets in the semi-arid open-pit coal mining area. Besides, random points were created for different ecological stability levels (containing natural areas, coal mining areas, and reclamation areas) and segment linear regression was conducted to determine the structural change threshold for negative state transitions based on mining and positive state transitions based on reclamation. Furthermore, the impact factors of ecological stability were analyzed. Results showed that ecological stability fluctuated significantly over 16 years, showing a trend of first increasing and then decreasing. It was found that precipitation and temperature were the key natural factors affecting ecological stability, and mining activities constituted the dominant factor. The average perturbation distances to ecological stability from mining activities in the west, southwest, and east mining groups were 7500, 5500, and 8000 m, respectively. SFSTM is appliable to the coal mining ecosystem. Quantitative models of ecological stability response can help resolve ambiguity about management efficacy and the ecological stability results facilitate iterative updating of knowledge by using monitoring data from coal mining areas. Moreover, the proposed ecological structural threshold provides a useful early warning tool, which can aid in the reduction of ecosystem uncertainty and avoid reverse transformations of the positive state in the coal mining areas.

The open-pit coal mine dump in the study area contains many low-concentration heavy metal pollutants, which may cause pollution to the soil interface. Firstly, statistical analysis and geostatistical spatial interpolation methods described heavy metal pollution’s spatial distribution. The mine dump heavy metal pollution distribution is strongly random due to disorderly piles, but it is closely related to slope soil erosion. Furthermore, the soil deposition area is where pollutants accumulate. For example, all heavy metal elements converge at the bottom of the dump. Usually, the pollution in the lower part is higher than that in the upper part; the pollution in the lower step is higher than the upper step; the pollution in the soil deposition locations such as flat plate and slope bottom is higher than the soil erosion locations such as slope tip and middle slope. Finally, the hyperspectral remote sensing method described heavy metals pollution’s migration characteristics, that the pollutants could affect the soil interface by at least 1 km. This study provides a basis for preventing and controlling critical parts of mine dump heavy metal pollution and pollution path control.

Coal mining and grazing cause land degradation. Plant diversity loss is one of the characteristics. Ground surveys cannot analyze the effects of mining and grazing on plant diversity in large areas, and remote sensing offers a novel solution. The aims of this study were as follows: (1) to analyze the impacts of mining and grazing on plant diversity composition by considering α‐, β‐, and γ‐ diversity comprehensively; (2) to determine the impacts of mining and grazing on the spatial distribution of α‐diversity; (3) to identify priority areas for ecological restoration. We applied a novel remote sensing method to obtain plant α‐, β‐, and γ‐ diversity maps and performed spatial analysis. The results were as follows: (1) mining reduced the proportion of α‐diversity in the total diversity (γ‐diversity) but increased that of β‐diversity. Grazing increased the proportion of α‐diversity but decreased that of β‐diversity. Mining increased the heterogeneity among plant communities, whereas grazing increased the homogeneity. (2) Both mining and grazing reduced α‐diversity. With increasing distance from the mine, α‐diversity increased logarithmically. (3) The severely affected range of mining on plant diversity is approximately 325 m. When there is grazing around the mine, the severely affected range is approximately 400 m. Severely affected areas are priority areas for ecological restoration. Improving the soil quality can be an effective measure. This study gains new insights into the effects of coal mining and grazing on plant diversity. These findings can provide some guidance for governments to formulate ecological restoration measures in mining areas.