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Surface mining has been subject to increased criticism and pressure due to its potential to cause environmental damage. China accounts for more than half of world coal consumption, and this continued demand for coal, combined with low costs, has accelerated surface coal mining worldwide. However, it isn't easy to understand the impact of surface mining in China. Therefore, combined with POI, the Exposed Coal Frequency Index (ECFI) was constructed without any mining information in China. A LandTrendr algorithm was used to identify past mining disturbances and reclamation events (1986–2021). While examining these events, surface destruction due to coal mining in China was revealed. Based on the results, (1) the detection accuracy of statistical surface mining sites by province reaches 91.8%, while the detection accuracy of disturbance and reclamation events exceeds 72.59 %. Also, by comparing the mine data, our results provide more accurate information regarding both time and spatial consistency. (2) 253.61 km2 of surface mining sites were identified in 14 provinces or autonomous regions during 1986–2021. Of these, Inner Mongolia, Ningxia, Shanxi and Xinjiang ranked in the top four occupying 89.0% of the total mining site. (3) China has cumulatively disturbed 545.84 km2 and reclaimed 169.41 km2 including more comprehensive human activities. In the last 12 years, large-scale mining and reclamation have accounted for 51.63% and 75.17% of the cumulative area, respectively. (4) Generally, reclamation in China can be completed within four years. In contrast, reclamation time intervals and the proportion of reclamation varied widely between mining areas. Clearly, this study provides the most accurate data on surface coal mining disturbances in China, which will be beneficial for future studies in spatial geography related to mining. It also includes more policies and management creation for mines. [Display omitted] [ABSTRACT FROM AUTHOR]

• Multi-source data are integrated to effectively construct RSEI in successive years. • The characteristics of ecological quality variation have been revealed. • The weak positive trend delayed by mining has been proved. • The extent and area of the better or worse trend have been identified. Coal-based industries are critical to the contemporary world's growth, while the environmental degradation associated with the industry is also increasing. In this study, we develop the Remote Sensing Ecological Index (RSEI) and map its distribution from 2000 to 2020 using the Muli coalfield near Qinghai Lake as the research object. This index is obtained by fitting four components that are critically related to the quality of the ecological environment, including greenness, humidity, heat and dryness, to finally obtain a desirable evaluation. The results illustrate that: (1) The RSEI displayed an overall increasing trend on the time scale, but it also went through three lengthier phases of trend changes. In space view, the RSEI of the unaltered areas has greatly improved, while the damaged areas are clearly concentrated in the study area's northwestern region, where mining is common. (2) In the trend test, the overall variation of RSEI was found to be around 3%. RSEI significantly increased in 69.57% of the study region, whereas RSEI decreased dramatically in 30.43% of the study area. (3) In opencast mining disturbed areas and undisturbed areas, a different quality structure can be discovered, with Moderate and Good levels predominating in disturbed areas and Good and Excellent levels predominating in undisturbed areas. The ecological decline in damaged areas significantly hampered the overall upward trend of RSEI. (4) The Globalmoran's I value was 0.558, 0.546, 0.409, 0.574, and 0.680 in 2000, 2005, 2010, 2015, and 2020, respectively, showing a positive association between regional distribution of ecological environment quality and that opencast mining does really degrade that quality. It has been demonstrated that our evaluation accurately classify the local environment as affected by opencast mining, making it crucial for studying the relationship between coal mining and ecological quality. This index can also be utilized in the future when conducting surveys of other regions of intense human disturbance. [ABSTRACT FROM AUTHOR]

Assessing urban environments is crucial for promoting sustainable urban development. However, current studies often lack a dynamic processes-oriented perspective and are geographically limited. This study utilizes the Comprehensive Evaluation Index (CEI), derived from multi-source data, to evaluate the environmental conditions of the 36 Chinese metropolitan areas from 2000 to 2021 and captures spatiotemporal non-stationarity through trend analysis and the Dynamic Time Warping algorithm. The results indicate a tendency of deterioration followed by improvement in the urban environment, with an inflection point in 2013. Initially, the environmental conditions in the core areas lagged behind those in the expanded areas, but eventually, the core areas surpassed the expanded areas. The trajectory of environmental change displays regional variations and spatial clustering. The contribution of surface fine particulate matter began to decline around 2013, while land surface temperature and vegetation coverage emerged as the primary contributors. Our object-oriented management approach can provide directional guidance for future urban environmental management. This study contributes to promoting sustainable development. [ABSTRACT FROM AUTHOR]

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]