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Mineral resource exploitation and farming are the main human production activities, which meet the two basic needs of industrial development and food security. However, mining and farming co-occurred in some specific spatial sites across the globe, making significant negative impacts on land and the environment. Here, we presented a remote sensing and spatial gradient model to quantitatively evaluate grain production caused by underground coal mining in eastern China. The results show that more than 80103.15 ha of land has been impacted by coal mining subsidence by 2020, with 28417.86 ha (35.48%) directly turned into water or wetland, and 51685.29 ha (64.52%) indirectly impacted by subsidence basin. The accumulated grain losses reached 537.12 million tons for the past 20 years, and only 14% of the subsided water area was reclaimed. According to the reclamation scenario analysis, a one-time investment of approximate 3.11 billion US dollars (USD) for land reclamation, will recover 60% of the water logging area, and this investment could bring total agricultural profits of 171.25 million USD per year, and the supplemented grain could feed 0.9 million population. Our proposed framework for evaluating the grain loss from mining quantitively will help to weigh the grain and coal conflicts, and is expected to be applied in similar regions globally. [ABSTRACT FROM AUTHOR]

High-strength mining of super-large coalfaces in the Shendong coalfield causes significant damage to the ground surface. To study the key parameters of undermined coalfaces that affect ground damage, 25 numerical simulation models were designed using an orthogonal experimental method based on the geological and mining conditions of the Bulianta Mine. In the orthogonal design, four factors (the lengths in both the dip and strike directions, the thickness and the mining speed of the coalface) were considered, with five levels designed for each factor. The subsidence displacements and deformations caused by the excavation were then simulated and verified using field surveying data. A damage extent index (DEI) was introduced and used to assess the extent of global ground damage caused by each simulative excavation. Analysis of variance (ANOVA) method was then employed to determine the key parameters of the coalface that significantly influence the ground damage. It was found that the coalface dip length and thickness and the coalface thickness can be regarded as the key parameters for ground objects of building and timberland, respectively. This research provides theoretical and technical support for the coordinated exploitation of resources and environments in Shendong and other similar, ecologically fragile coalfields. [ABSTRACT FROM AUTHOR]

Coal mining has contributed to a solid and stable global economy. Surface mining and underground mining are the principal methods for extraction of coal worldwide and given the developments in mining technology and equipment, these methods are increasingly generating more and more coal. Surface mining is also a subject that cannot be overlooked in the context of sustainable development because amongst human activities it causes comparatively strong land disturbance. Much of the damage and assessment of land and ecosystems from surface mining occurs at the site, and at the regional scale; hence access to the mining data is limited. Moreover, it is difficult for remote sensing to extract information on larger-scale areas with high precision. As a consequence, a frequency index of open coal mining can be created by merging the vast high-resolution images acquired by Landsat sensors with the spectral properties of open coal scraped from the overburden layer. Such indices and morphological principles can be used to develop strategies for quickly identifying the open coal boundaries in a vast area without mine-related information. In addition, LandTrendr may be used to reconstruct the spatial and temporal processes of surface disturbance and remediation caused by regional surface coal mining to identify surface mining disturbance and reclamation. Seven typical mining locations worldwide were selected to test and confirm the proposed method's practicality and robustness, including delivering accurate findings based on randomly testing sample points and comparing the mature data outputs. The results showed that: (1) the overall accuracy of the open coal areas identified by the OCFI (Open Coal Frequency Index) was over 85 % (range 0.85–0.96). In all study areas, the accuracy of recognizing disturbance and reclamation events was greater than 76 %. (2) The surface mining disturbed 3046.42 km2 of land and 1487.39 km2 was reclaimed in the seven mining areas investigated between 1986 and 2018, resulting in a reclamation rate of 48.82 %. The proposed method is more accurate and faster in identifying open coal locations than the current commonly used land cover datasets based on comparisons with other data products. The approach can extract surface coal mining in the absence of mining data and offer precise boundary data for mining research, which can be extremely useful for extending mining operations. [ABSTRACT FROM AUTHOR]

Eco-city construction is a powerful method which can advance a city from traditional industrial civilisation to ecological civilisation. The city of Huaibei, with 50 years of coal mining history, has been listed as a national resource-exhausted city. The city's sustainable development and ecological restoration work face severe challenges. This study presents a time-space evolution analysis of mining subsidence to show the evolution and the distribution of the subsidence area in Huaibei. Intensive use was made of land evaluation to formulate land use measures. According to this analysis, and based on the popular eco-reconstruction movement, suggestions for land reclamation and eco-reconstruction are proposed for the city of Huaibei. The paper aims to make strategic recommendations to help Huaibei city transform from a resource-exhausted city to an eco-city. [ABSTRACT FROM AUTHOR]

[Display omitted] • The framework coupling mining intensity and socioecological resilience was constructed. • The resilience and mining intensity were analyzed for 547 counties in China. • 12 socioecological indicators were used to evaluate resilience in spatial scale. • The zoning results enhanced our understanding of sustainable development in mining areas. Coal is an important global energy resource. However, the negative impacts of mining are important factors restricting the sustainable development of mining areas. Under the superimposed influences of different mining intensities, different natural ground conditions, and social-economic development, there are large differences in the restoration characteristics and response strategies for mining. To guide the development and utilization of coal resources and ecological restoration, the spatial distribution should be appropriately analyzed at the macroscale. Based on the web-crawler technology and multi-source remote sensing data, this paper attempted to estimate the mining intensity and the social-ecological resilience respectively at the county level, to divide the mining areas into nine regions in China. Based on the zoning results, differentiated strategies were proposed for ecological restoration and resource development and utilization in the various coal mining regions. The valuation results suggested that coal mines are widely distributed and abundant in China, mainly concentrated in the central, southwest and northwest regions. However, nearly half of the coal mining regions are with low resilience. Especially, high mining intensity-low resilience areas are concentrated in north China which account for 10.83%. These areas should receive special attention to prevent possible ecological risks and minimize economic effects. Social-ecological resilience measures system adaptation to coal mining disturbances, which can be regarded as the standard of mining intensity adjustment. In the future, local governments can try to combine ecological restoration and resource development strategies to provide support for the sustainable development of coal mining areas in China. [ABSTRACT FROM AUTHOR]

Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mines and mining management. Thus, it is the basement of formulating policies related to mining management and implementing reclamation that detection of mining disturbance rapidly and accurately. This research carries on an empirical study in the Dexing copper mine, Jiangxi, China, aiming at exploring the process of distance and reclamation. Based on the dense time-series stack derived from the Landsat archive on Google Earth Engine (GEE), the disturbance of surface mining in the 1986–2020 period has been detected using the continuous change detection and classification (CCDC) algorithm. The results are that: (1) the overall accuracy of damage and recovery is 92% and 88%, respectively, and the Kappa coefficient is 85% and 84% respectively. This means that we obtained an ideal detection effect; (2) the surface-mining area was increasing from 1986–2020 in the Dexing copper mine, and the accumulation of mining damage is approximately 2865.96 ha with an annual area of 81.88 ha. We also found that the area was fluctuating with the increase. The detected natural restoration was appraised at a total of 544.95 ha in the 1988–2020 period with an average restoration of 16.03 ha. This means that it just restores less in general; (3) it has always been the case that the Dexing mine is damaged by mining and reclamation in the whole year (it is most frequently damaged month is July). All imageries in the mine are detected by the CCDC algorithm, and they are classified as four types by disturbing number in pixel scale (i.e., 0, 1, 2, more than 2 times). Based on that, we found that the only once disturbed pixels account for 64.75% of the whole disturbed pixels, which is the majority in the four classes; (4) this method provides an innovative perspective for obtaining the mining disturbed dynamic information timely and accurately and ensures that the time and number of surface mining disturbed areas are identified accurately. This method is also valuable in other applications including the detection of other similar regions. [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]

• A new index was proposed to monitor surface mining disturbance. • The new index enhance the sensitivity to improve the detection accuracy. • Compared with five products, the detection accuracy is superior. • Mine Landscape Damage/Reclamation Index can be widely used in the detection of open-pit mining. The exploitation and utilization of mineral resources have significantly contributed to economic and social development; however, the procession has also negatively impacted the ecological environment. Thus, it is necessary to effectively monitor coalfields, an important basis for reasonable development and utilization of coalfields and following ecological reclamation. Based on MODIS Global Damage Index, We couple the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), and proposal the Mine Landscape Damage Index (MLDI) and Mine Landscape Reclamation Index (MLRI) to supervise the disturbed vegetation of mines. Huolinhe coalfield, one of the largest mines in China, is monitored in this research. 3675 Landsat imageries from 1990 to 2020 are analyzed using the LandTrendr algorithm with MLDI and MLRI time series data on Google Earth Engine. The results demonstrate that this method could distinguish the region and disturbing process of damage and reclamation due to its effectiveness proved by visual interpretation of high-resolution imageries and verification of field investigation. The total detected area of vegetation damage was 16505.10 ha, and the vegetation damage in the mining was active from 2006 to 2011, with average annual damage of more than 1000 ha. The total area of vegetation restoration in the mine is 8640.81 ha, accounting for 52.35% of the vegetation damage. Vegetation restoration has increased gradually since 2010. In the past three years, vegetation restoration accounted for 47.61% of the total reclamation area. A novel index for monitoring the effects of surface damage in multi-open-pit mining areas is provided to us, and the detection is reliable. It can give the government an effective practical data reference in the sustainable development of utilization management and ecological reclamation in coalfield. [ABSTRACT FROM AUTHOR]