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Aggregated mining development has direct and indirect impacts on vegetation changes. This impact shows spatial differences due to the complex influence of multiple mines, which is a common issue in resource regions. To estimate the spatial heterogeneity of vegetation response to mining activities, we coupled vegetation changes and mining development through a geographically weighted regression (GWR) model for three cumulative periods between 1999 and 2018 in integrated resource regions of northwestern China. Vegetation changes were monitored by Sen’s slope and the Mann–Kendall test according to a total of 72 Landsat images. Spatial distribution of mining development was quantified, due to four land-use maps in 2000, 2005, 2010, and 2017. The results showed that 80% of vegetation in the study area experienced different degrees of degradation, more serious in the overlapping areas of multiple mines and mining areas. The scope of influence for single mines on vegetation shrunk by about 48%, and the mean coefficients increased by 20%, closer to mining areas. The scope of influence for multiple mines on vegetation gradually expanded to 86% from the outer edge to the inner overlapping areas of mining areas, where the mean coefficients increased by 92%. The correlation between elevation and vegetation changes varied according to the average elevation of the total mining areas. Ultimately, the available ecological remediation should be systematically considered for local conditions and mining consequences.

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The restoration of surface mining (open‐cast) sites is key to meeting global ecosystem restoration targets. With the improving of data availabilities and technologies, it has become possible to expand restoration monitoring from single to multiple mine sites on a large scale. Based on the MODIS global disturbance index (MGDI), this study proposes a mine landscape restoration index (MLRI), by coupling the LST and EVI to simultaneously monitor the restoration of multiple mine sites. Restoration areas were identified by MLRI time‐series analysis and classified into significant consistent increase (SCI) and significant anti‐consistent increase (SAI) areas. The restoration effects of 46 surface coal mine sites located in the ecologically fragile northwestern region of China from 2000 to 2019 were assessed based on 3,675 LANDSAT images from the Google Earth Engine. Results show that the MLRI was effective at identifying restoration areas and processes, and this effectiveness was validated by high‐resolution images and field investigations of mine samples. The overall percentage of restored area for mines that started mining before 2000 was 55.01% ; for mines that starting mining after 2000, 33.68%. According to the differences of SCI and SAI area percentages, 46 mine sites were classified into three clusters, with 13, 11, and 22 mine sites, respectively. The mine sites with high restoration percentage are located mainly in Hailar and Shanbeimengnan regions. This study provides a new approach for monitoring the restoration effects of multiple mine sites and informs government managers about developing mine restoration programs and sustainable mining development plans.

Mining sites are areas where mining and restoration coexist and are constantly changing. The vegetation condition can reflect the process of surface mining and restoration, while quantifying the impacts of different mining patterns and surrounding environments on vegetation is the key to balancing mining activities and ecological restoration. In this study, long-term monitoring from 1986 to 2020 was implemented by the LandTrendr algorithm to reveal the ecological impacts of two concentrated and contiguous surface mining sites with different mining patterns (scattered and aggregated mining) and surrounding environments in Inner Mongolia, China. The results show that it is reasonable to use the LandTrendr algorithm for long-term monitoring of surface mining sites, and that the ecological impacts of different surface mining sites in ecologically fragile areas have the same regularity. As the duration increases, the magnitude of disturbance decreases, and the magnitude of recovery first decreases and then reaches a natural fluctuation state after 20 years of recovery. Different mining patterns and surrounding environments bring different ecological impacts. Scattered mining areas are more likely to produce natural recovery while the restored ecosystem is more stable. The performance of mining development disturbance is more obvious in places with better ecological environment, while the effect of ecological restoration is also more significant. This study can provide guidance for the rational planning of mining and restoration activities in ecologically fragile areas.