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Conservation management usually carried out for a period of time to maintain the vegetation restoration of coal waste dumps after reclamation. However, the natural restoration of vegetation is faced with great challenges in semi-arid mining areas without management, due to the fragile ecological environment. Therefore, it is necessary to determine a reasonable management plan so that vegetation restoration can reach a stable state although the abandonment of the management. The objective was to explore the difference of vegetation restoration under different management modes in a typical semi-arid mining area. Two reclaimed coal waste dumps under different management measures, the north waste dump (ND) and the south waste dump (SD), were examined in the ShengLi coalfield in Inner Mongolia, China. The normalized difference vegetation index (NDVI) dataset based on Landsat series imagery was obtained using the Google Earth Engine (GEE) platform, and the landscape metrics were also calculated based on different vegetation coverage. The results proved that 3 years of management was not enough to stabilize vegetation restoration. A serious vegetation degradation occurred at the ND after the management stopped, with 40.1% of the pixels recorded a significant decrease (ρ = 0.05). The vegetation coverage became fragmented, and there was a tendency of succession to lower coverage. On the contrary, the vegetation restoration of SD was better under continuous management, and no significant degradation trend was observed. Furthermore, the results indicated that rainfall is the main influencing factor on vegetation restoration in semi-arid mining areas. The coal waste dump was more susceptible to weather change in natural restoration. By contrast, continuous management measures will resist such climate disturbances, even in dry years. This research will provide support for the formulation of the reclamation management plan of coal waste dumps in semi-arid mining areas. [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]

• 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]

Underground coal mining with high groundwater levels causes many environmental problems, one of the main ones being subsidence waterlogging. After the subsidence waterlogging, the land faces many problems, such as insufficient land development and difficult and costly land restoration. Therefore, it is necessary to monitor the spatial range and temporal trajectory pattern of surface subsidence in a timely manner. When information about underground mining is lacking, it is difficult by using remote sensing alone to identify and distinguish (i) natural water, (ii) engineering water, and (iii) subsidence waterlogging. In this study, we used the Google Earth Engine platform to develop a method to detect subsidence waterlogging area and the disturbance year. The method includes pixel-based trajectory extraction and object-based water type recognition. First, LandTrendr is used to extract the change water (engineering water and subsidence waterlogging) pixels and its disturbance year. Then, the morphological method to further eliminate engineering water patches, so as to extract subsidence waterlogging. We selected the Panxie mining area in Huainan, China as the study area. Using 33 years of Landsat time-series data to generate values of the annual water frequency index, maps of the year of water accumulation caused by underground coal mining and the year of restoration during 1989–2016 are drawn with accuracies of 86.5% and 80.7%, respectively. The results show that from 1989 to 2016, the accumulated area of subsidence waterlogging was 7715.25 ha, accounting for 14.5% of the total area of the study area, of which 75.8% occurred from 2008 to 2016. Furthermore, the accumulated area of restoration was 207.18 ha, which occurred after 2007 and accounts for 2.7% of the total area of subsidence waterlogging. Based on the analysis results of the changes of waterlogging types, the best time window for restoration of the waterlogging area is 3y after water accumulation. The main innovation of this paper is to make use of the temporal heterogeneity (Number of year in patches, Y_num) and morphological index (Landscape shape index in patches, LSI) of the change water to distinguish engineering water and subsidence waterlogging. • Map of subsidence waterlogging caused by mining coal using Landsat time-series. • The annual water frequency index is capable to capture the water variability. • A spatial-temporal algorithm identifies the timing of subsidence waterlogging • The algorithm identifies the timing of backfill after subsidence waterlogging. • The algorithm is applicable to other mining areas too. [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 resilience assessment model was built integrating Over-Underground features. • Entropy and linear weighting method are used to quantify ecological resilience in 10 m grid scale. • Influencing factors of each partition are analyzed to provide reference for decision-making. The exploitation of coal resources will bring about a series of environmental problems, which will not only affect the surface ecological environment of the region, seriously threatening underground resources, but will also affect the normal production and living activities of human beings. The impacted ecological system is combined by both overground and underground features. In this paper, Shendong coal field, one of the high-strength coal mining areas in western China is used as a research area. An O-U (Overground-Underground) analysis framework and model was established to quantitatively assess the degree of mining impact on the eco-system and its resilience. The entropy method is used to calculate the weight of each index, and the comprehensive value of each grid is calculated by the linear weighted method. The comprehensive value of each grid's indicators is divided into four ecological resilience (ER) partitions by natural breakpoint method. Finally, Pearson coefficients is used to analyze the main influencing factors of the four sub-areas, and reasonable suggestions are given. By zoning the ER of the study area, it can provide reference for the mining method selection and land reclamation measures, and also provide direction for the ecological environment management in 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]