Open Pit Mining Area Research Articles

A lightweight convolutional neural network based on dense connection for open-pit coal mine service identification using the edge-cloud architecture

Remote sensing is an important technical tool for rapid detection of illegal mining behavior. Due to the complex features of open-pit coal mines, there are few studies about automatic extraction of open-pit coal mines. Based on Convolutional Neural Network and Dense Block, we propose a lightweight densely connected network-AD-Net for the extraction of open-pit coal mining areas from Sentinel-2 remote sensing images, and construct three sample libraries of open-pit coal mining areas in north-central Xinzhou City, Shanxi Province. The AD-Net model consists of two convolutional layers, two pooling layers, a channel attention module, and a Dense Block. The two convolutional layers greatly reduce the complexity of the model, and the Dense Block enhances the feature propagation while reducing the parameter computation. The application is designed in different modules that runs independently on different machines and communicate with each other. Furthermore, we create and build a unique remote sensing image service system that connects a remote datacentre and its associated edge networks, employing the edge-cloud architecture. While the datacentre acts as the cloud platform and is in charge of storing and processing the original remote sensing images, the edge network is largely utilised for caching, predicting, and disseminating the processed images. First, we find out the optimal optimizer and the optimal size of the input image by extensive experiments, and then we compare the extraction effect of AD-Net with AlexNet, VGG-16, GoogLeNet, Xception, ResNet50, and DenseNet121 models in the study area. The experimental results show that the combination of NIR, red, green, and blue band synthesis is more suitable for the extraction of the open-pit coal mine, and the OA and Kappa of AD-Net reach 0.959 and 0.918 respectively, which is better than other models and well balances the classification accuracy and running speed. With this design of edge-cloud, the proposed system not only evenly distributes the strain of processing activities across the edges but also achieves data efficiency among them, reducing the cost of data transmission and improving the latency.

Metabarcoding of Soil Fungal Communities in Rupestrian Grassland Areas Preserved and Degraded by Mining: Implications for Restoration.

Rupestrian grasslands are vegetation complexes of the Cerrado biome (Brazilian savanna), exhibiting simultaneously great biodiversity and important open-pit mining areas. There is a strong demand for the conservation of remaining areas and restoration of degraded. This study evaluated, using next-generation sequencing, the diversity and ecological aspects of soil fungal communities in ferruginous rupestrian grassland areas preserved and degraded by bauxite mining in Brazil. In the preserved and degraded area, respectively, 565 and 478 amplicon sequence variants (ASVs) were detected. Basidiomycota and Ascomycota comprised nearly 72% of the DNA, but Ascomycota showed greater abundance than Basidiomycota in the degraded area (64% and 10%, respectively). In the preserved area, taxa of different hierarchical levels (Agaromycetes, Agaricales, Mortierelaceae, and Mortierella) associated with symbiosis and decomposition were predominant. However, taxa that colonize environments under extreme conditions and pathogens (Dothideomycetes, Pleoporales, Pleosporaceae, and Curvularia) prevailed in the degraded area. The degradation reduced the diversity, and modified the composition of taxa and predominant ecological functions in the community. The lack of fungi that facilitate plant establishment and development in the degraded area suggests the importance of seeking the restoration of this community to ensure the success of the ecological restoration of the environment. The topsoil of preserved area can be a source of inocula of several groups of fungi important for the restoration process but which occur in low abundance or are absent in the degraded area.

Ecological Restoration and Transformation of Maoming Oil Shale Mining Area: Experience and Inspirations

Oil shale is a kind of unconventional energy resource with abundant reserves, but its exploitation has a continuous negative impact on the environment, which has hindered the research and exploitation of oil shale under the international environmental consensus on issues such as climate change. Therefore, more attention should be paid to environmental problems as the side effect of oil shale exploitation. With the combination of field research, literature collection, and tracking survey, the oil shale open-pit exploitation and management process in Maoming, Guangdong, China, has been investigated, and its development and transformation model has been subsequently refined and summarized. The research results show that Maoming oil shale open-pit mine area has gone through four main stages: pre-exploitation stage, large-scale utilization stage, restoration stage, and green development stage. Through the management of mine pit treatment, vegetation restoration, ecological park construction, and tourism resource development, the abandoned open-pit mine has been transformed into an ecological park combining ecosystem, tourism, and cultural resources. In this process, this area has achieved the transformation from rough resource extraction to environment-friendly sustainable growth in its development mode. As a successful case of open-pit mine management in the world, the ecological restoration experience in Maoming can function as a reference for the smooth development and transformation of other oil shale mines in developing countries.

A Real-Time Negative Obstacle Detection Method for Autonomous Trucks in Open-Pit Mines

Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the characteristics of road negative obstacles in open-pit mines, a real-time target detection model based on the Yolov4 network was built. It uses RepVGG as the backbone feature extraction network, applying SimAM space and a channel attention mechanism to negative obstacle multiscale feature fusion. In addition, the classification and prediction modules of the network are optimized to improve the accuracy with which it detects negative obstacle targets. A non-maximum suppression optimization algorithm (CIoU Soft Non-Maximum Suppression, CS-NMS) is proposed in the post-processing stage of negative obstacle detection. The CS-NMS calculates the confidence of each detection frame with weighted optimization to solve the problems of encountering obscure negative obstacles or poor positioning accuracy of the detection boxes. The experimental results show that this research method achieves 96.35% mAP for detecting negative obstacles on mining roads with a real-time detection speed of 69.3 fps, and that it can effectively identify negative obstacles on unstructured roads in open-pit mines with complex backgrounds.

Extraction and spatiotemporal changes of open-pit mines during 1985-2020 using Google Earth Engine: A case study of Qingzhou City, Shandong Province, China.

The global use of mineral resources has increased exponentially for decades and will continue to grow for the foreseeable future, resulting in increasingly negative impacts on the surrounding environment. However, to date, there are a lack of historical and current spatial extent datasets with high accuracy for mining areas in many parts of the world, which has hindered a more comprehensive understanding of the environmental impacts of mining. Using the Google Earth Engine cloud platform and the Landsat normalized difference vegetation index (NDVI) datasets, the spatial extent data of open-pit mining areas for eight years (1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020) was extracted by the Otsu algorithm. The limestone mining areas in Qingzhou, Shandong Province, China, was selected as a case study. The annual maximum NDVI was first derived from the Landsat NDVI datasets, and then the Otsu algorithm was used to segment the annual maximum NDVI images to obtain the extent of the mining areas. Finally, the spatiotemporal characteristics of the mining areas in the study region were analyzed in reference to previous survey data. The results showed that the mining areas were primarily located in Shaozhuang Town, Wangfu Street and the northern part of Miaozi Town, and the proportion of mining areas within these three administrative areas has increased annually from 88% in 1985 to more than 98% in 2010. Moreover, the open-pit mining areas in ​​Qingzhou gradually expanded from a scattered, point-like distribution to a large, contiguous distribution. From 1985 to 2020, the open-pit mining area expanded to more than 10 times its original size at a rate of 0.5 km2/year. In 2015, this area reached its maximum size of 19.7 km2 and slightly decreased in 2020. Furthermore, the expansion of the mining areas in Qingzhou went through three stages: a slow growth period before 1995, a rapid expansion period from 1995 to 2005, and a shutdown and remediation period after 2005. A quantitative accuracy assessment was performed by calculating the Intersection over Union (IoU) of the extraction results and the visual interpretation results from Gaofen-2 images with 1-m spatial resolution. The IoU reached 72%. The results showed that it was feasible to threshold the Landsat annual maximum NDVI data by the Otsu algorithm to extract the annual spatial extent of the open-pit mining areas. Our method will be easily transferable to other regions worldwide, enabling the monitoring of mine environments.

Anthropogenic influence of open pit mining on river floods, an example of the Blessem flood 2021

Large scale anthropogenic relief changes result in a non-equilibrium landscape and such landscapes are prone to catastrophic erosion events during river floods. An example is the major flood event in western German in July 2021. During this event, the Erft River flooded an open gravel pit near the village of Erftstadt-Blessem, resulting in a massive erosional event, including the catastrophic destruction of parts of the settlement. Three different geomorphological periods occurred during the event: I) rapid backward erosion caused the destruction of the overlaying soils and floodplain sediments. Consequently, the gravel pit was filled with water and sediments. Three different pathways of backward erosion were activated during this period. II) Sedimentation in a temporary lake and the formation of alluvial fans. III) The Erft River moved towards the gravel pit due to the erosional processes followed by a second flood. Understanding of such complex erosional and depositional processes in open pit mining areas might serve as a blueprint for processes and hazards in anthropogenic influenced landscapes.

Impact of Land Cover Change on a Typical Mining Region and Its Ecological Environment Quality Evaluation Using Remote Sensing Based Ecological Index (RSEI)

Ecological environment in mining cities has become an important part of ecological construction. This paper takes Tongling, a mining city, as the research area, and uses Landsat series remote sensing images from 2000 to 2020 as data sources. Using the principal component analysis method and the Remote Sensing Ecological Index (RSEI) integrated with four indexes of greenness, humidity, dryness, and heat, the ecological disturbance of the mining area was evaluated and studied. Meanwhile, the land cover spatiotemporal classification of Tongling city was extracted by the maximum likelihood method. Furthermore, landscape metrics were used, based on the information on open-pit mining areas, to quantitatively analyze the ecological environment quality and its change characteristics in the study area. The results show that (1) RSEI can better characterize the ecological quality of Tongling city, greenness and humidity are positively correlated with it, dryness and heat are negatively correlated with it, and dryness and RSEI have the highest correlation coefficient, indicating that urban expansion will cause ecological environment deterioration to a certain extent. (2) The ecological environment quality of the research area showed a “decline-rising” trend, and the mean value of RSEI decreased from 0.706 to 0.644. Spatially, the areas with poor RSEI are mainly distributed in the central urban area and the open-pit mining area in the south. (3) Land cover change leads to changes in landscape metrics, and most landscape-level metrics are positively or negatively correlated with RSEI. The more concentrated the land cover type distribution is, the smaller the change is, and the more regional RSEI can be improved. (4) The mean value of RESI of the ten open-pit mining areas in Tongling city decreased significantly, with a maximum decrease of 52.73%. Among them, the RESI decline rate in the area around the no.1 open pit mine is 0.034/year. The ecological degradation in Tongling city is attributed to the rapid expansion of built-up areas and the development of the mining industry. The research results can provide a scientific basis for protecting the ecological environment of mining cities.

Intelligent classification of land cover types in open-pit mine area using object-oriented method and multitask learning

Although the exploitation of mineral areas brings wealth to society, it inevitably leads to the degradation of the surrounding natural environment. To understand and assess the influences of mining activities on the geological and ecological environment, land cover classification in open-pit mine areas (LCCMA) is of great significance. This research proposes an intelligent classification framework for LCCMA based on an object-oriented method and multitask learning (MTL), named the MTL Classification Framework (MTLCF). With the help of MTL, each land cover type in open-pit mine areas obtains its exclusive and receivable object-oriented feature sets using the model-agnostic method. After that, the feature sets are fused with the original images. EfficientNet, a spatial pyramid pooling module, and a global attention upsample module are assembled as the segmentation models with the structure of the encoder and decoder to classify intelligently each land cover type in open-pit mine areas. Finally, the models were trained, and ablation experiments were performed. The experimental results show that our proposed framework -MTLCF was effective for classification in LCCMA, and the overall accuracy and the mean of F1 score for the MTLCF in LCCMA were 85.6% and 86.06%, respectively.

Influence of revegetation on soil microbial community and its assembly process in the open-pit mining area of the Loess Plateau, China.

Vegetation recovery is an important marker of ecosystem health in the mining area. Clarifying the influence of vegetation recovery on the characteristics of soil microbial community and its assembly process can improve our understanding of the ecological resilience and self-maintaining mechanism in the open-pit mining area. For this purpose, we employed MiSeq high-throughput sequencing coupled with null model analysis to determine the composition, molecular ecological network characteristics, key bacterial and fungal clusters, and the assembly mechanism of the soil microbial communities in shrubs (BL), coniferous forest (CF), broad-leaved forests (BF), mixed forest (MF), and the control plot (CK, the poplar plantation nearby that had been continuously grown for over 30 a without disturbance). The results showed that the vegetation restoration model had a significant influence on the α-diversity of the microbial community (p < 0.05). Compared with CK, Sobs and Shannon index of MF and CF have increased by 35.29, 3.50, and 25.18%, 1.05%, respectively, whereas there was no significant difference in the α-diversity of fungal community among different vegetation restoration types, Actinobacteria, Chloroflexi, Proteobacteria, and Acidobacteria were the dominant phyla. The diversity of the first two phyla was significantly higher than those of CK. However, the diversity of the last two phyla was dramatically lower than those of CK (p < 0.05). Ascomycota and Basidiomycota were dominant phyla in the fungal community. The abundance and diversity of Ascomycota were significantly higher than those of CK, while the abundance and diversity of the latter were considerably lower than those of CK (p < 0.05). The stochastic process governed the assembly of the soil microbial community, and the contribution rate to the bacterial community construction of CK, CF, BF, and MF was 100.0%. Except for MF, where the soil fungal community assembly was governed by the deterministic process, all other fungal communities were governed by the stochastic process. Proteobacteria and Acidobacteria are key taxa of the bacterial network, while Mortierellales, Thelebolales, Chaetothyriales, and Hypocreales are the key taxa of the fungal network. All these results might provide the theoretical foundation for restoring the fragile ecosystem in the global mining region.

Ecosystem Service Assessment and Sensitivity Analysis of a Typical Mine–Agriculture–Urban Compound Area in North Shanxi, China

The production–life–ecology balance in mine–agriculture–urban compound areas is receiving increasing attention in the context of urbanization and industrialization. This study aims to explore the coordinated development modes of ecosystem services and resident well-being in the Pingshuo open-pit mining area and the surrounding mine–agriculture–urban compound area in Pinglu District, Shuozhou City. Relevant models were used to evaluate the ecosystem service value of water and soil conservation, as well as ecological sensitivity. Additionally, using the hierarchical multiple regression method, we analyzed the responses of soil and water conservation services and ecological sensitivity to different land use patterns. The results showed the following. (1) The water conservation function (WCF) and soil conservation function (SCF) were greatly affected by land use and supplied mostly by the natural habitat, followed by the open-pit coal mining area. (2) Ecological sensitivity was greatly affected by land use patterns, with obvious differences in the same land use types in different spatial locations. (3) In order to enhance the WCF and SCF of the study area and reduce ecological sensitivity, the area and diversity of forest and grassland should be increased, and the area of open-pit mining, cultivated land, and urban land, as well as the land use compound degree, should be reasonably controlled. This study will help guide the regional land use layout and provide countermeasures and suggestions for the management of ecosystems in the mine–agriculture–urban compound area.

Land Use Dynamic Evolution and Driving Factors of Typical Open-Pit Coal Mines in Inner Mongolia.

Although coal is difficult to replace in the short term, the large-scale production and consumption of coal have significant impacts on the ecological environment. The severe disturbances, such as land excavation and occupation, that accompany the mining of mineral resources have caused dramatic changes in land cover and a significant pressure on the sensitive and fragile ecological environment. To analyze the temporal and spatial evolution trends and the differences in land use in different typical mining areas in Inner Mongolia, as well as the evaluation system and driving mechanisms of land use evolution, this study takes the typical open-pit coal mines in Inner Mongolia as the research objects and, based on the Google Earth Engine (GEE) platform, analyzes the dynamic evolution characteristics and driving factors of land use in typical open-pit coal mines in Inner Mongolia from 2001 to 2020. The change trend of land use in typical open-pit mining areas in Inner Mongolia for the past 20 years is obvious, with the highest fluctuations for grassland, mining land, cropland, and residential/industrial land. Land use in the open-pit coal mining area is greatly affected by mining factors. From the perspective of spatial variation, the most important driving factor is the distance from national roads and railways, followed by the annual average temperature and annual average precipitation and topographical conditions, such as elevation. In terms of policy, land reclamation and ecological restoration in mining areas have a positive impact on land use change. Improving the mechanism for environmental compensation in mining areas can promote the efficient and rational use of mining areas and the protection of ecosystems.

Geomorphic responses due to the second-largest global producer of ceramic tiles in the State of São Paulo, Brazil

Land use/land cover changes (LULCC) have modified the Earth’s surface. In especial, mining activities have been responsible for landscape changes over a range of temporal and spatial scales. The production of mineral commodities in Brazil represents almost 2 % of the country’s gross domestic product with non-metallic mining activities spread throughout the territory. The Santa Gertrudes Ceramic Polo (SGCP), located in the State of São Paulo, is the second-largest global producer of ceramic tiles and it is responsible for 70 % of the production of non-metallic mineral resources in Brazil. Thus, the aim of this study was to assess geomorphic responses in the SGCP, using land use changes and geomorphological mapping in two study areas, in different scenarios (1962, 1988, 2010). The sedimentation rates were measured to estimate changes in the depositional dynamic due to expansion of the SGCP. The expansion of SGCP generated several geomorphic responses, such as topographic changes, hydrologic alteration, construction of landfill with roads, and erosive/depositional processes adjustments. The expansion of open pit mining areas occurred in Riparian Forestry and pasture areas, creating slope breaks with a groundwater withdrawal and decreasing the length of the river channels. The landfill with roads increased the surface runoff and, consequently, the linear erosive processes and the mass wasting in the slopes. The land displacement within the open pit mining areas, and in the quarry dumps, promoted an increase in the alluvial plains from 1962 to 2010, changing the average sedimentation rates from 26.5 mm yr−1 to 33.0 mm yr−1. The geomorphic responses allowed to comprehend the influence of human-landscape system in the SGCP. Finally, this study highlights the importance of studies involving geomorphic responses in areas with non-metallic mining activities, which could be used in different areas in Brazil or elsewhere.

The Landscape Pattern Evolution of Typical Open-Pit Coal Mines Based on Land Use in Inner Mongolia of China during 20 Years

As the province most abundant in mineral resources in China, Inner Mongolia has more than 200 open-pit coal mines. The coal mining activities seriously hinder the sustainable use of regional land and the improvement of residents’ wellbeing. Taking 13 typical open-pit coal mines of Inner Mongolia as the study area, combining remote sensing images and the Google Earth Engine (GEE) platform, the trend features of spatial and temporal evolution of land use and landscape patterns from 2001 to 2020 were analyzed by transfer matrix and landscape pattern index methods. According to the mining plan and reclamation measures of the “Land Reclamation Plan”, the impacts of ecological restoration projects on land-use structure and landscape patterns were evaluated. The results showed that the following: (1) The landscape types of typical open-pit coal mining areas were mainly grassland, cropland, and industrial landscapes. The change trend of landscape pattern was obvious over the past 20 years, and the changes in grassland and mining land were relatively large, which decreased by 56.51 km2 and increased by 60.42 km2, respectively. (2) Land reclamation and ecological restoration projects had positive impacts on landscape pattern changes. With the transformation from mining activities to land reclamation activities beginning in 2007, nearly 70% of the study area showed a decreasing trend in landscape fragmentation, indicating a better landscape pattern of mining area. (3) Positive policy orientation of mine ecological restoration promoted more reasonable landscape patterns.

Identitätsfindungsprozesse in einer Tagebau-folgelandschaft – vom Ausnutzen, Wandeln,Aneignen und Wertschätzen

In many places, Lusatia is viewed with negative connotations as a dying open-pit lignite mining area. This applies to outside perspectives in particular, but also corresponds to the self-image of many people in Lusatia. The cultural landscape resulting from open-cast mining is not perceived as such. One of the goals of the Land-Innovation-Lusatia project is to learn to read, understand and appreciate this landscape and to design and develop it based on this knowledge. Awareness of history and optimism about the future are mutually dependent. In this landscape construction site in Lusatia, attempts have been made for more than a century to recultivate, renature and redesign the landscapes that follow the depletion of the mining area, and thus to reshape the landscapes in line with the ideals and models of the prevailing zeitgeist. The mosaic of post-open pit landscape designs formed in this process over the last twelve decades constitutes a globally unique representation of human restoration of former open-cast mining landscapes through the ages. Lusatia can be understood as a globally unique showcase of the changing approaches to opencast mining recultivation over time. The goal of a World Heritage nomination of this landscape supports the social and cultural integration of this research in the region by enhancing the construction of an identity in Lusatia that proudly looks back on more than a century of innovative land use. The combination of unique, innovative land-use traditions with innovative and contemporary land-use research is intended to bring the Land-Innovation-Lusatia (LIL) research initiatives closer to the tradition-focused segments of the population and also increase the appreciation of them. But where and how do you start? This article aims to address these questions in more detail, explain the background to them and illuminate the identification process. Identity-finding processes in a former open-cast mining landscape – on exploiting, changing, assimilating and appreciating

Genetic Algorithm in Multimedia Dynamic Prediction of Groundwater in Open-Pit Mine.

This study is aiming at the nonlinear mapping relationship between the groundwater level and its influencing factors. Through the design and calculation process of matlab7 platform, taking the monitoring wells distributed in an open-pit mining area as an example, the short-term prediction of groundwater dynamics in the study area is carried out by using BP neural network model and BP neural network model based on genetic algorithm. Root mean squared error (RMSE), Mean absolute percent-age error (MAPE) and Nash–Sutcliffe efficiency (NSE) are used coefficients,, and the results were compared with BP neural network and stepwise regression model. From the results of the comparative analysis, the genetic algorithm optimized the BP neural network model in the training phase and the test phase, the RMSE was 0.25 and 0.36, the MAPE was 6.7 and 8.13%, and the NSE was 0.87 and 0.72, respectively. The BP neural network model optimized by genetic algorithm is obviously superior to the BP neural network model, which is an ideal prediction model for short-term groundwater level. This model can provide a prediction method for groundwater dynamic prediction and has a good application prospect.

Assessing spatio-temporal dynamics of large airport’s surface stability

Istanbul Airport will be one of the biggest airports in the world when the construction in old open-pit mining areas which turned into wetlands is completed. To investigate the structural health monitoring of the Istanbul Airport, firstly the temporal evaluation of land use/land cover (LULC) in the region was generated for determining reclaimed lands using Random Forest classifier with Landsat images between 1984 and 2020. The outcomes were then examined with results of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) methods, namely Small Baseline Subset (SBAS) for general displacement overview and Persistent Scatterer Interferometry (PSI) for detailed analysis of the settlement rate in the airport and runways which were applied to Sentinel-1 images. Both of the methods showed similar results for the same regionsand it is determined that there are movements up to 10 mm in Runway A and Runway B and more than 20 mm subsidence in Runway C.

The Impact of Human Activities on Net Primary Productivity in a Grassland Open-Pit Mine: The Case Study of the Shengli Mining Area in Inner Mongolia, China

In grassland open-pit mining areas, net primary productivity (NPP) is mainly affected by climate conditions and human activities. The identification and assessment of the influence of human activities on NPP is important for mining production and the implementation of ecological restoration. In this study, we explored the influence of human activities on the NPP in the Shengli mining area in Inner Mongolia, China by using the Carnegie–Ames–Stanford Approach (CASA) model and the Chikugo model, in which a calibration method was applied. An analysis of four representative years showed that the proportion of NPP induced by human activities reached 56.2%, that the percentage of pixels with an inhibitory effect on NPP was 99% in 2011 with the highest intensity of mining activity, and that these two values decreased to 11.9% and 69% in 2020, respectively, with the steady implementation of ecological restoration. Moreover, from the analysis of global and local spatial correlation, mining activities and ecological restoration aggravated and weakened the aggregation of NPP induced by human activities, respectively.

Analysis on the Difference of Reconstructed Soil Moisture Content in a Grassland Open-Pit Mining Area of China

To reveal the variability of different reconstructed soil ratios and different vegetation growth grades on the water holding capacity of recon-structed soil, the most suitable ratio of reconstructed soil materials was explored. Taking the inner dump of Shengli mining area in Inner Mongolia of China as the research area, the reconstructed soil of reclaimed land was investigated and sampled. One-way analysis of variance was used to analyze the difference of the moisture content of the reconstructed soil, and the reasons for the difference were discussed. The results showed that: (1) Among the different soil reconstructions, soil moisture content was higher when soil reconstructions were rock and soil stripping material: coal gangue: fly ash = 3:4:3. The soil moisture content of un-reclaimed land was mostly at a high level when the soil reconstruction method was rock and soil stripping material; and the soil moisture content was not at the highest level when the vegetation growth grade was higher. This indicates that it was not the case that the better the vegetation growth condition was, the higher the soil moisture content was. (2) In the case where the soil reconstruction method was rock and soil stripping material: coal gangue = 2:3, the soil moisture content of the reclaimed land decreased with the reduction of tillage frequency when the vegetation growth condition was optimal (vegetation growth grade was 4). (3) In the case where the soil reconstruction method was rock and soil stripping material: coal gangue: fly ash = 3:4:3, when the vegetation growth condition was better (vegetation growth grade was 3 and 4), the soil moisture content of the re-claimed land was highest when it was tilled once every 15 days. It was also found in combination with other soil reconstruction methods that it was not always the case that the higher the frequency of tillage, the higher the soil moisture content. The study of coal gangue, fly ash and rocky soil stripping as topsoil substitute materials with their different ratios on reconstructed soil provides support for the reclamation work in topsoil scarce mines and provides technical reference for the ecological restoration project of grassland open-pit mines in the same climate zone.

Soil Quality Investigation of an Abandoned Mine Area Using Geochemical and Geospatial Approach in Jantang Village

The physical and chemical environmental impact in a mining area is inevitable, particularly for open pit mining areas. The impact could affect soil and water quality where mining activities, such as land clearing, blasting and hauling, occur. Thus, environmental monitoring in mining areas should be taken to measure the impact of mining activity for reclamation purposes. The objective of this research focuses on the measure of environmental impact on soil quality in terms of the nutrient content in an abandoned mine area at Jantang village, Lhoong, Aceh Besar. The research was conducted by collecting 15 soil samples, followed by laboratory analysis using atomic absorption spectrophotometry to investigate sampled 'soil's nutrients which are pH, Carbon (C-organic), Nitrogen (N-total), Phosphor (P-availability), and Ferro substance (Fe-concentration). In addition, to estimate the soil properties at locations outside the sampling area, a spatial interpolation method called inverse distance weight with an optimum power was used. The result shows that the soil is acidic, with low C-organic in the range of 0.02%–1.84%, N-total 0.02%–0.16%, and P-availability 0.55%–3.75%. In contrast, the Fe-concentration is very high, at 3000–3400 ppm.

Deformation monitoring and analysis of Kunyang phosphate mine fusion with InSAR and GPS measurements

In order to prevent landslide and collapse in the mining area, it is essential not only to gain a full understanding of the surface deformation information about the open-pit mining area and surrounding dumps, but also to closely monitor the development of surface deformation. Small baseline set (SBAS) technology can be applied to extract the high-resolution spatial-temporal deformation information from the mining areas. Due to decoherence, however, it is difficult to obtain the effective information on deformation from some areas where the results are monitored. This paper proposes a kriging interpolation method that performs better in filling the missing values in the InSAR monitored results by integrating GPS measured data with InSAR monitored data. In the experimental process, 69 Sentinel-1 image data were collected from July 2018 to November 2020. The SBAS-InSAR technology was applied to monitor the surface deformation caused to the Kunyang phosphate mining area and surrounding dumping sites. It was found out that all the mining areas across the Kunyang Phosphorus Mine and the surrounding dumps suffered severe surface deformation, with the maximum line of sight (LOS) deformation rate of −132 mm/yr located in the dump in the north of the second mining area. The verification result shows that InSAR measurement results are consistent with the deformation estimated from GPS measurements. The root mean square error of most points is maintained within ±10 mm. The Kriging interpolation results of fusion GPS data are in good agreement with the GPS results. The root mean square errors at the 4 verification points are ±6.2 mm, ±4.6 mm, ±6.4 mm, ±7.8 mm. The method proposed in this paper is effective in filling in the missing values in the incoherent area, and can be applied to mine surface deformation monitoring with high reliability. The results of this paper serve as a scientific basis for the prevention and control of landslides, collapses and other hidden risks of geological disasters posed by the mining operations conducted in the Kunyang Phosphorus Mine.