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The development of coal resources is necessary, but it has a huge negative impact on land, ecology, and the environment. With the increasing awareness of environmental protection and the requirements of related regulations, the design and practice of reclamation projects run through the mining life cycle and continue for a long time after the coal production. High-precision monitoring of mining disturbance and reclamation, quantifying the degree and time of vegetation disturbance and restoration, is of great significance to minimize the environmental effect of mining. Remote sensing, widely used as efficient monitoring tool, but there is not enough research on disturbance and reclamation monitoring taking into account large-scale areas and high temporal and spatial accuracy. Especially when mining sites remain unknown, how to distinguish the disturbance of coal mining and other human activities affecting the surface land cover has become a challenge. Therefore, this paper proposed a method to reconstruct the time series of mining disturbance and reclamation in a large area by using the POI (point of interest) and Landsat time series images using multiple buffer analysis methods. The process includes: (1) Retrieval of POI in the study area based on the public mining list using Python crawler, and buffering 100 km for preliminary extraction of potential mining areas; (2) Using spectral index mask and random forest algorithm to accurately extract the exposed coal on the Google Earth Engine (GEE) platform; (3) Buffering 10 km to identify the occurrence of disturbance and reclamation, using pixel-based temporal trajectory identification of LandTrendr algorithm under GEE. The method successful detect the change points of surface coal mining disturbance and reclamation in eastern Inner Mongolia of China. The results show that: (1) The method can effectively identify the extent of surface coal mining disturbance and reclamation, and the overall extraction accuracy is 81%. (2) Surface coal mining disturbance in eastern Inner Mongolia was concentrated in 2006–2011. By 2020, the total disturbed area is 627.8 km2, with an average annual disturbance of 18.5 km2, and the annual maximum disturbance to the ground reached 64.6 km2 in 2008. With the total reclaimed area being 236.3 km2, the reclamation rate is about 37.6%. This study provides a systematic solution and process for monitoring the disturbance and reclamation of surface coal mining in a large range with little known about the mines' location. It can effectively identify the mining disturbance and reclamation process which can also be extended to other areas, providing a quantitative assessment of mining disturbance and reclamation, which can support further ecological restoration decision-making. [Display omitted] • A remote sensing procedure of surface coal mine classification, disturbance and reclamation monitoring is developed. • Landsat, random forest, web scraping, and Google Earth Engine has been used to detect 73 surface coal mining sites. • 0.94% of the total study area has been disturbed by surface mining. • The developed procedure is able to reveal the surface coal mine reclamation pattern. [ABSTRACT FROM AUTHOR]

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km2 of land from grassland, water, etc., to barren earth, and only 23.54 km2 was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas. [ABSTRACT FROM AUTHOR]

• Ecological vulnerability index was used to evaluate ecological condition in intensive coal mining area. • The driving factors affecting EV was calculated by stepwise regression. • Coal mining activities didn't show significant negative impact on EV in study area. Ecological integrity of coal mining areas is subject to intense disruption due to the large scale and intensity of operations and the intensification of associated human activity. Negative impacts of coal mining include soil erosion, decline in soil fertility, water pollution, and structural changes to land use. Impacts of coal mining are acute in western China which has been defined as an ecologically fragile zone on account of the arid soils and low rainfall climate that render ecosystem restoration problematic. Here, we studied drivers of ecological vulnerability (EV) in an ecologically fragile area of Shaanxi province and Inner Mongolia that is one of the most globally active and intensive coal mining regions. An evaluation model, using eight indicators of EV such as slope (SL), relief amplitude (RA), rainfall erosion factor (RE), average temperature (AT), land use type (LUT), soil erosion factor (SE), net primary productivity of vegetation (NPP) and coverage of vegetation (CV), was constructed based on a Pressure-State-Response model. An Ecological Vulnerablity Index (EVI) was calculated within the region for 2005, 2010, and 2015 by using the model of EVI and spatio-temporal changes in EV were analyzed. Mechanisms and drivers of EV were assessed using the stepwise regression. The results showed that the area of industrial and mining land increased by 912.96% between 2005 and 2015, where the greatest increase occurred between 2010 and 2015 by 620.00%. The EVI fluctuated insignificantly from 2.69 to 2.78 during the study period and was little related to changes in somewhat areas of urban land use and coal mining intensity. We concluded that the EV of the region slightly increased, despite the extent and intensity of coal mining actives. The main drivers of EV were temperature, precipitation, and land use change. This research will help inform environmental protection policies and environmental planning for coal mining operations in the study area. [ABSTRACT FROM AUTHOR]