Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas.

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The ecological rehabilitation of potential toxic metal-contaminated soils in sites disturbed by mining has been a great challenge in recent decades. Phytoremediation is one of the most widely promoted renovation methods due to its environmental friendliness and low cost. However, there is a lack of in situ investigation on the influence of vegetation pattern and spontaneous succession on the rehabilitation of potential toxic metal-polluted soil. To clarify how the vegetation pattern in the early stage of restoration and the spontaneous succession influence the remediation of the soil, we investigated a metal mining dump in Sichuan, China, by field investigation and laboratory analysis. We determined the plant growth, soil fertility, and the capacity of potential toxic metals (PTMs) in metal mining soil under different initial vegetation patterns for different years to understand the role of vegetation pattern and spontaneous succession in PTM pollution phytoremediation projects. The results show that: (1) Phytoremediation with a simple initial vegetation pattern (RP rehabilitative plant pattern) which involves two rehabilitation plants, Agave sisalana and Neyraudia reynaudiana, achieves a PTM pollution index that is 9.28% lower than that obtained with the complex vegetation pattern (RP&LP rehabilitation plants mixed with local plants pattern), 21.86% lower in the soil fertility index, and 73.69% lower in the biodiversity index; (2) The phytoremediation with the 10-year RP&LP pattern was associated with a PTM pollution index that was 4.04% higher than that for the 17-year RP&LP pattern, a soil fertility index that was 4.48% lower, and a biodiversity index that was 12.49% lower. During the process of vegetation succession, if accumulator plants face inhibition of growth or retreat, the reclamation rate will decrease. The vegetation patterns influence the effect of phytoremediation. Spontaneous vegetation succession will cause the phytoremediation process to deviate from the intended target. Therefore, according to the goal of vegetation restoration, choosing a suitable vegetation pattern is the main premise to ensure the effect of phytoremediation. The indispensable manipulation of succession is significant during the succession series, and more attention should be paid to the rehabilitative plants to ensure the stable effect of reclamation. The results obtained in this study could provide a guideline for the in situ remediation of PTM-polluted soil in China. is widely employed around the world since it is a low-cost method of environmental protection. Although phytoremediation has many advantages, there are also many limitations to its effective implementation [8][9][10][11]. Rehabilitation plant species play the key role in reclamation projects, particularly the high accumulation plants which still have different definitions [12]. In the future, genetics may be used to produce new high accumulation plants [13]. These plants can absorb potential toxic metals in soil and thus reduce the negative effects on ecosyste...

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Underground mining (as opposed to open‐cast) often causes large‐scale subsidence, leading to various types of disturbances to surface vegetation. Adequate quantitative assessment of the long‐term effects of underground mining on the growth of different plant communities is important and still lacking. To address these issues, a vegetation growth contract model (VGCM) was proposed, and six indicators including the growth trend (GT), annual growth (AG), normalized spectrum entropy (Hsn), as well as the average value of annual‐average normalized difference vegetation index (NDVI; ANDVIave), annual‐maximum NDVI (ANDVImax), and annual‐minimum NDVI (ANDVImin) were selected. The long‐term effects of underground mining (EM) on the herb, shrub, and tree communities in the Nanjiao mining area, China, from 1987 to 2017 were evaluated. The results show that the plant communities, which maintained the same type in the areas influenced and not influenced by mining, accounting for 48.07% and 46% of the total area, respectively. As for these plant communities, underground mining had a significant negative effect on the AG, ANDVIave, and ANDVImax of both the herb and tree communities, while it had a positive effect on the GT and Hsn of the shrub community. Overall, underground mining had a negative effect on these three types of plant communities, and the EMs of the herb, tree, and shrub communities were −15.10, −6.79, and −4.03%, respectively. This research could provide a reference for evaluating the long‐term effects of mining activities on vegetation, and also give more insights into the effects of underground mining on different plant communities.

Mining-induced ground fissures are the main type of geological disasters found on the Loess Plateau, China, and cause great impacts on the soil properties around ground fissures. However, little research has been conducted on the quantitative relationship between ground fissures and changes in soil properties. To address this, 40 ground fissures in the Yungang mining area, Datong City, Shanxi Province, China, were investigated, and changes in soil properties (soil organic matter, soil moisture, field capacity, bulk density, soil porosity, and grain compositions) were revealed by the difference in soil properties between the edge and contrast points around ground fissures. Redundancy analyses were used to illustrate the relationships between the value (Si_DV) and percentage (Si_DP) of the difference in soil properties between the edge and contrast points, as well as the ground fissures. The characteristics of ground fissures that had a significant correlation according to Pearson correlation analysis with Si_DP were selected and analyzed via multivariate linear fitting model, random forest model, and Back Propagation (BP) neural network model, respectively. Results show that soil organic matter, soil moisture content, bulk density, field capacity, and the content of clay at the edge points were significantly less than those at the contrast points; conversely, soil porosity at the edge points was significantly greater. The average percentage of the difference between the edge points and contrast points of ground fissures in these six properties was 15.27%, while soil moisture content showed the greatest change (20.65%). The Si_DP was significantly correlated with the width, slope, and vegetation coverage of ground fissures; however, the vegetation coverage was the determining factor. BP neural network model had the greatest performance in revealing the relationships between ground fissures and changes in soil properties. The model for soil organic matter had the highest accuracy (R2 = 0.89), and all others were above 0.5. This research provides insights into the quantitative relationship between ground fissures and their impacts on soil physical properties, which can be used in conjunction with remote sensing images to rapidly assess soil erosion risks caused by mining on a large scale, given that soil physical properties are closely related to topsoil stability.

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