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The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labor‐intensive manual methods, which are difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify α‐diversity index on a sub‐meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semiautomate large‐scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry‐standard supervised classification algorithms (support vector machine [SVM], maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (normalized difference vegetation index [NDVI], difference vegetation index [DVI], visible‐band difference vegetation index, soil‐adjusted vegetation index, modified soil‐adjusted vegetation index, and excess green–excess red) were used to assess vegetation diversity obtained from on‐ground survey plot data (the Margalef, Pielou, Simpson, and Shannon–Wiener indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy of 84%). Significant relationships were observed between vegetation indices and diversity indices, with the DVI performing significantly better than many more commonly used indices such as the NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine‐scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools. [ABSTRACT FROM AUTHOR]

Ground deformation is one of the most common geological disasters arising in underground mining areas, and mining-induced environmental impacts have resulted in numerous concerns, especially the impacts on the surface vegetation. The evaluation of mining-induced impacts on vegetation is beneficial to revegetation in mining areas; however, the impacts of ground deformation have seldom been systematically evaluated and explained on long time scales despite the long-term existence of ground deformation in underground mining areas. To address this, in this study a vegetation-soil-climate (VSC) model was developed to evaluate the long-term impacts of ground deformation on vegetation, and to reveal its mechanism. The results revealed that the long-term impacts of ground deformation on vegetation result from the degradation of the vegetation and soil when ground deformation occurs, which thereby limits the growth and succession of plants after the ground deformation has occurred. The intensity of the long-term impacts is determined by the severity of the ground deformation, but the duration, climate factors, the substrate conditions of the vegetation and soil before the deformation, and the natural change coefficient of the vegetation and soil are also relevant. Furthermore, the characteristics of the long-term impacts on vegetation were analyzed, and a framework for implementing revegetation and suggestions for the supervision of revegetation in underground mining areas are presented based on the characteristics. The results of this study provide insights into the impacts of mining-induced ground deformation on vegetation on long time scales, considering the comprehensive interactions between the vegetation and other environmental factors, and provide theoretical support for revegetation in underground mining areas. [ABSTRACT FROM AUTHOR]

The overlapped areas of cropland and coal resources play a fundamental role in promoting economic and social progress. However, intensive mining operations in high water-level areas have brought significant spatial–temporal heterogeneity and ecological problems. From the dual dimensions of the ecosystem service value (ESV) and ecological risk (ER), it is of great significance to explore the influence characteristics of underground mining on the landscape, such as above-ground cultivated land, which is valuable to achieving regional governance and coordinated development. In this study, taking Peixian as the research area, a multiple-dimensional correlation framework was constructed based on the revised ESV and ER, integrating the grey relational degree, spatial–temporal heterogeneity, disequilibrium, and inconsistency index to explore the ESV and ER assessment and correlation characteristics from 2010 to 2020. The results show that (1) the ESV showed a high agglomerated distribution pattern in the east, with a net decrease of 13.61%. (2) The ER decreased by 78.18 and was concentrated in the western and southern regions, with overall contiguous and local agglomeration characteristics. This indicates that the ecological security of the region has improved. (3) The comprehensive grey correlation between the cultural service value and the ecological risk index was the highest. Furthermore, the spatial–temporal heterogeneity of the ESV and ER weakened, and the disequilibrium rose and then fell, indicating that the ecosystem gradually tended to be stable. The study is crucial for overlapped cropland and coal resource areas to maintain stability and sustainable development. The multivariate correlation framework provides practical value for ecosystem management and risk control. [ABSTRACT FROM AUTHOR]