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Environmental problems caused by mines have been increasing. As one of the most serious types of mining damage caused to the eco-environment, open pits have been the focus of monitoring and management. Previous studies have obtained effective results when evaluating the ecological quality of a mining area by using the remote sensing ecological index (RSEI). However, the calculation of RSEI does not consider that the ecological environmental impact is limited under natural conditions. To overcome this shortcoming, this paper proposes an improved RSEI based on a moving window model, namely the moving window-based remote sensing ecological index (MW-RSEI). This improved index is more in agreement with the First Law of Geography than RSEI. This study uses Landsat ETM/OLI/TIRS images to extract MW-RSEI information of a case area in Zhengzhou City, Henan Province, central China, in 2009 and 2018. The results revealed that the average value of MW-RSEI declined from 0.668 to 0.611 from 2009 to 2018, and the main drivers of the deterioration of the eco-environment were land use/cover (LUCC) changes, most of which were derived from urban expansion and mining. The serious impact of open pits on the eco-environment in mining areas is mainly due to their low vegetation cover; therefore, some effectively managed open pits can have a positive impact on the mining environment. The use of MW-RSEI provides valuable information on the eco-environment surrounding the open pit, which can be used for the rapid and effective monitoring of the eco-environment in mining areas.

Open-pit mining area extraction is crucial for environmental monitoring and sustainable resource management. To address the limitations of low accuracy and weak generalization in existing methods, this study proposes a detection-segmentation framework that integrates Grounding DINO and Segment Anything Model (SAM). The method first employs Grounding DINO to locate potential mining regions, and then utilizes SAM for fine-grained boundary segmentation. By combining the strong detection capability of Grounding DINO with SAM’s fine segmentation performance, the framework effectively captures complex mining boundaries and reduces false positives. Comparative experiments with U-Net, DeepLab, and U-Net+DeepLab show that the proposed model achieves an IoU of 84.20% and an F1-score of 91.42%, demonstrating superior accuracy. This approach provides a scalable and reliable solution for remote sensing-based monitoring of mining activities.

Blasting is widely employed as an accepted mechanism for rock breakage in mining and civil activities. As an environmental side effect of blasting, flyrock should be investigated precisely in open-pit mining operations. This paper proposes a novel integration of artificial neural network and fuzzy cognitive map (FCM) with Z-number reliability information to predict flyrock distance in open-pit mine blasting. The developed model is called the artificial causality-weighted neural networks, based on reliability (ACWNNsR). The reliability information of Z-numbers is used to eliminate uncertainty in expert opinions required for the initial matrix of FCM, which is one of the main advantages of this method. FCM calculates weights of input neurons using the integration of nonlinear Hebbian and differential evolution algorithms. Burden, stemming, spacing, powder factor, and charge per delay are used as the input parameters, and flyrock distance is the output parameter. Four hundred sixteen recorded basting rounds are used from a real large-scale lead–zinc mine to design the architecture of the models. The performance of the proposed ACWNNsR model is compared with the Bayesian regularized neural network and multilayer perceptron neural network and is proven to result in more accurate prediction in estimating blast-induced flyrock distance. In addition, the results of a sensitivity analysis conducted on effective parameters determined the spacing as the most significant parameter in controlling flyrock distance. Based on the type of datasets used in this study, the presented model is recommended for flyrock distance prediction in surface mines where buildings are close to the blasting site.Highlights An expert-based ANN is developed to predict blast-induced flyrock distance.A Fuzzy cognitive map and expert knowledge are used to simulate the weight of neurons.Z-number theory is employed to overcome the uncertainty of opinions.

A destructive landslide occurred in Himmetoğlu village in Göynük District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between − 12 and 39 mm year −1 and between − 24 and 6 mm year −1 , respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between − 16 and 31 mm year −1 and between − 18 and 20 mm year −1 , respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes.

Global demands for nickel—a key energy transition metal—have notably risen in recent years, positioning Indonesia as the world’s major supplier. Nickel in Indonesia occurs in laterite ore deposits, and is extracted with open-pit mining, a process which induces land transformation of high carbon stock biomass sources such as forests. This study investigates the spatial and temporal impacts of nickel mining on land cover and biomass carbon stock by combining satellite imagery assessment with a national dataset of land cover classes and biomass carbon stock data from the national Forest Reference Emission Level (FREL) dataset. Focusing on the concession area between 2013 and 2022, 217 nickel mining concession areas within Indonesia were analyzed. Our results revealed that nickel mining activities induced 53% of the forest land transformation within the concession area. By 2022, approximately 6.9 million tCO2 were emitted from the land use induced by nickel mining activities, more than double the amount in 2013. If continued unabated, the trend could reach 7%–12% of the allowable Nationally Determined Contribution target in Indonesia for the land use sector in 2030. Moreover, we observed delays in the official recognition of mining areas in the national land cover map, which relies on manual visual interpretation and potentially leads to misclassification. Additionally, several instances of mining outside officially designated concession zones were detected, indicating governance gaps and regulatory compliance issues. These findings highlight the need for disclosure by mining companies and stronger governance in monitoring and regulating mining extraction activities to address the environmental cost of the energy transition.

Tropical forests are among the most diverse ecosystems in the world, completed by huge biodiversity. An expansion in natural resource extraction through open-pit mining activities leads to increasing land and tropical forest degradation. Proper science-based practices are needed as an effort to reclaim their function. This paper summarizes the existing practice of coal mining, covering the regulatory aspects and their reclamation obligations, the practices of coal mining from various sites with different land characteristics, and the reclamation efforts of the post-mining landscapes in Indonesia. The regulations issued accommodate the difference between mining land inside the forest area and outside the forest area, especially in the aspect of the permit authority and in evaluating the success rate of reclamation. In coal-mining practices, this paper describes starting from land clearing activities and followed by storing soil layers and overburden materials. In this step, proper handling of potentially acid-forming materials is crucial to prevent acid mine drainage. At the reclamation stage, this paper sequentially presents research results and the field applications in rearranging the overburden and soil materials, controlling acid mine drainage and erosion, and managing the drainage system, settling ponds, and pit lakes. Many efforts to reclaim post-coal-mining lands and their success rate have been reported and highlighted. Several success stories describe that post-coal-mining lands can be returned to forests that provide ecosystem services and goods. A set of science-based best management practices for post-coal-mine reforestation is needed to develop to promote the success of forest reclamation and restoration in post-coal-mining lands through the planting of high-value hardwood trees, increasing trees’ survival rates and growth, and accelerating the establishment of forest habitat through the application of proper tree planting technique. The monitoring and evaluation aspect is also crucial, as corrective action may be taken considering the different success rates for different site characteristics.

Similar to the mixed-integer programming library (MIPLIB), we present a library of publicly available test problem instances for three classical types of open pit mining problems: the ultimate pit limit problem and two variants of open pit production scheduling problems. The ultimate pit limit problem determines a set of notional three-dimensional blocks containing ore and/or waste material to extract to maximize value subject to geospatial precedence constraints. Open pit production scheduling problems seek to determine when, if ever, a block is extracted from an open pit mine. A typical objective is to maximize the net present value of the extracted ore; constraints include precedence and upper bounds on operational resource usage. Extensions of this problem can include ( i ) lower bounds on operational resource usage, ( ii ) the determination of whether a block is sent to a waste dump, i.e., discarded, or to a processing plant, i.e., to a facility that derives salable mineral from the block, ( iii ) average grade constraints at the processing plant, and ( iv ) inventories of extracted but unprocessed material. Although open pit mining problems have appeared in academic literature dating back to the 1960s, no standard representations exist, and there are no commonly available corresponding data sets. We describe some representative open pit mining problems, briefly mention related literature, and provide a library consisting of mathematical models and sets of instances, available on the Internet. We conclude with directions for use of this newly established mining library. The library serves not only as a suggestion of standard expressions of and available data for open pit mining problems, but also as encouragement for the development of increasingly sophisticated algorithms.

Numerous open-pit mines are scattered within the southern mountainous areas of China. Due to the complex mountainous terrain and abundant rainfall, surface disturbances caused by open-pit mining activities pose a serious risk of landslides. To identify potential landslide hazards in mountainous open-pit mining areas in advance, this study proposes a quantitative method that utilizes ascending and descending orbit Interferometric Synthetic Aperture Radar (InSAR) technology to accurately identify landslide hazards. We select the Xiaolongtan coal mining area in Yunnan, China, as a case study. Small Baseline Subset InSAR (SBAS-InSAR) technology was employed to obtain the Line of Sight (LOS) deformation of ascending and descending orbits from November 2019 to November 2021. Following this, two-dimensional deformations were calculated based on the obtained LOS deformations. Multiple remote sensing data sources, including Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Landsat 8 Operational Land Imager (OLI), were utilized to extract potential landslide points based on vertical deformation. A combined subjective and objective weighting method was then used to assess the landslide hazard in the study area, and an information quantity model was constructed for landslide hazards in the mining area. Finally, based on high-resolution remote sensing images from the study period, potential landslide hazards were identified in the study area. The results reveal that the vertical deformation rate in the mining area ranges from − 231.73 to 81.42 mm/year, indicating significant subsidence and uplift tendencies. A total of 2353 potential landslide points were identified, primarily located near the slopes of two open-pit mines and in areas with low vegetation coverage. The Xiaolongtan and Buzhaoba open-pit mines, along with the surrounding regions in the study area, were identified to exhibit relatively high landslide hazards. Among the three coal mine waste dumps, the Beipingba waste dump presents a higher landslide hazard. This study provides a scientific basis and practical reference for identifying landslide hazards in mountainous open-pit mining areas.

Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.

Two deadly rockslides, triggered by heavy precipitation and open-pit mining, were reported in Emei County, Sichuan Province, China, from 2011 to 2015. About 6.0 million m3 of rock detached from the upper slopes, pushed the pre-sliding deposits, and hit the opposite mountains at average velocity of 18 to 36 km/h. Detailed field investigation, geological mapping, and UAV aerial photographic interpretation are presented to analyze the failure mechanisms of the events. The results suggest that the high-speed consequent bedding rockslides were triggered by the failure of rock mass, which were influenced by the engineering activities and climate change. Key contributive factors were weathered and fragmented basalts that were affected by open-pit mining and frequent blasting, as well as the weak underlying tuffs with swell-shrink potential. Persistent rainfall was the direct trigger in initiating and reactivating the landslide. Water affected the slope stability by increasing the slope material’s unit weight and penetrating into joints and cracks to make the tuffs degrade and causing a reduction in effective stress. The mechanisms for the two landslide events are a high-speed regressive consequent bedding (RCB) rockslide in 2011 and a reactivated high-speed advancing consequent bedding (ACB) rockslide in 2015. This paper can provide an insight into large-scale consequent bedding rockslides associated with the interaction between the rainfall and open-pit mining slopes instabilities.