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Sustainable Application of Remote Sensing in Environmental Monitoring of Mining Area

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 9535

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Special Issue Editors

Dr. Hao Sun

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Guest Editor

College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: optical and thermal remote sensing; remote sensing of soil moisture, agricultural and ecological drought; remote sensing of ecological environment; remote sensing of mining area
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jinbao Jiang

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Guest Editor

College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: hyperspectral remote sensing; remote sensing of natural resources and environment

Dr. Peng Hou

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Guest Editor

Center for Satellite Application on Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China
Interests: ecosystem monitoring and assessment; ecology and environment of remote sensing; ecosystem protection; nature reserves and national parks
Special Issues, Collections and Topics in MDPI journals

Dr. Yuebin Wang

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Guest Editor

School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Interests: remote sensing; machine learning; deep learning

Special Issue Information

Dear Colleagues,

Mineral and energy resources are required for the maintenance of normal social and economic activities. However, mining activities cause some severe ecological and environmental problems, including land excavation, land occupation, ground settlement, vegetation deterioration, and environmental pollution, etc., which are detrimental to human well-being. In order to achieve sustainable development, mining activity should maintain a harmonious coexistence with environment conservation. As we move towards greater harmony, the monitoring and assessment of mining area environments are indispensable. As an advanced observation technique, remote sensing can play significant roles, such as providing necessary support data for mining land reclamation or ecological restoration, as well as providing a management tool for related departments. Motivated by these requirements, this Special Issue aims to collect papers on the cutting-edge progress in remote sensing for the monitoring of mining area environments. The scope of this Special Issue includes, but not limited to, the following topics:

(1) Quantitative remote sensing inversion of land surface parameters over mining areas;

(2) Advanced machine learning algorithms and their application for mining areas;

(3) Methods and applications of multi-source remote sensing (optical, thermal, microwave, LIDAR, SAR, hyperspectral remote sensing, fluorescence remote sensing, gravity satellite remote sensing, etc.) for mining areas;

(4) Remote sensing of ecosystem composition, structure, and service function for mining areas;

(5) Models or indexes of ecosystem quality driven by remote sensing data for mining areas;

(6) Remote sensing of ecosystem disturbance, recovery, and evolution for mining areas;

(7) Monitoring, forecasting, and assessing the impacts of mining area surface subsidence with remote sensing technology;

(8) Remote sensing of geological disasters in mining areas, such as landslides, land collapse and ground fissures.

Dr. Hao Sun
Prof. Dr. Jinbao Jiang
Dr. Peng Hou
Dr. Yuebin Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

mining area
quantitative remote sensing
machine learning
multi-source remote sensing
ecosystem
environment
geological disaster

Published Papers (5 papers)

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Research

18 pages, 13289 KiB

Open AccessArticle

Analysis of Storage Capacity Change and Dam Failure Risk for Tailings Ponds Using WebGIS-Based UAV 3D Image

by Meihong Zhi, Yun Zhu, Ji-Cheng Jang, Shuxiao Wang, Pen-Chi Chiang, Chuang Su, Shenglun Liang, Ying Li and Yingzhi Yuan

Sustainability 2023, 15(19), 14062; https://doi.org/10.3390/su151914062 - 22 Sep 2023

Viewed by 885

Abstract

Tailings ponds, essential components of mining operations worldwide, present considerable potential hazards downstream in the event of tailings dam failures. In recent years, instances of tailings dam failures, carrying potential environmental safety hazards, have occasionally occurred on a global scale due to the limited technical approaches available for safety supervision of tailings ponds. In this study, an innovative WebGIS-based unmanned aerial vehicle oblique photography (UAVOP) method was developed to analyze the storage capacity change and dam failure risk of tailings ponds. Its applicability was then validated by deploying it at a tailings pond in Yunfu City, Guangdong Province, China. The results showed that the outcomes of two phases of real-scene 3D images met the specified accuracy requirements with an RSME of 0.147–0.188 m in the plane and 0.198–0.201 m along the elevation. The storage capacities of phase I and phase II tailings ponds were measured at 204,798.63 m³ and 148,291.27 m³, respectively, with a storage capacity change of 56,031.51 m³. Moreover, the minimum flood control dam widths, minimum free heights, and dam slope ratios of the tailings pond were determined to comply with the flood control requirements, indicating a low risk of dam failure of the tailings pond. This pilot case study demonstrated the performance of the UAVOP in evaluating storage capacity change and dam failure risk for tailings ponds. It not only enhanced the efficiency of dynamic safety supervision of tailings ponds but also offered valuable references for globally analogous research endeavors. Full article

(This article belongs to the Special Issue Sustainable Application of Remote Sensing in Environmental Monitoring of Mining Area)

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26 pages, 26686 KiB

Open AccessArticle

Tracing and Determining the Duration of Illegal Sand Mining in Specific River Channels in the Limpopo Province

by Maropene Tebello Dinah Rapholo, Isaac Tebogo Rampedi and Fhatuwani Sengani

Sustainability 2023, 15(18), 13299; https://doi.org/10.3390/su151813299 - 05 Sep 2023

Viewed by 960

Abstract

Artisanal and Small-scale river sand mining is one of the upcoming activities associated with the environmental crisis concerning the water ecosystem. However, the determination of the duration in which illegal sand mining has occurred, and the future prediction on the extent of river sand mining is not well-established in most of the world. This study aimed to assess the extent of river sand mining activities across some of the catchments in Limpopo province, South Africa and understand the sustainable extraction of sand resources. This was followed by the determination of when sand mining activities commenced in each of the individual catchments. Thus, remote sensing was applied to predict the extent of river sand mining from the year 1992 to 2022, and statistical prediction models were utilised to predict the extent of sand mining for the next 10 years. The results of the study suggest that most of the catchments started to experience illegal sand mining activities from the year 1992, though the extraction was relatively low. Equally, a decrease in vegetation coverage across the river system has been evidenced, which also suggests that the extraction of sand and gravel has been elevated from the year 2010. In terms of the prediction model, the Turfloop River system was predicted to experience a large extraction ratio in the coming 10 years, with about 92.415 ha of land expected to be affected. Meanwhile, the Molototsi River system was denoted to be the least affected river system, with a reduced extraction ratio of about 6.57 ha expected in the next 10 years’ time. Full article

(This article belongs to the Special Issue Sustainable Application of Remote Sensing in Environmental Monitoring of Mining Area)

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32 pages, 30987 KiB

Open AccessArticle

Spatial and Temporal Study of Supernatant Process Water Pond in Tailings Storage Facilities: Use of Remote Sensing Techniques for Preventing Mine Tailings Dam Failures

by Carlos Cacciuttolo and Deyvis Cano

Sustainability 2023, 15(6), 4984; https://doi.org/10.3390/su15064984 - 10 Mar 2023

Cited by 10 | Viewed by 3945

Abstract

Considering the global impact on society due to tailings storage facilities (TSFs) accidents, this article describes a study to monitor mine tailings management and prevent mining tailings dam failures, considering the analysis of different TSFs real cases. The spatial and temporal dynamic behavior of the supernatant process water pond of the TSFs is studied as a critical issue, using remote sensing techniques based on multispectral satellite imagery. To understand the current state of the art, a brief description of engineering studies for the control and management of the supernatant process water pond in TSFs is presented. This research considers the main method of the study of practical cases with the use of techniques of multispectral interpretation of satellite images from the Sentinel 2 remote sensor. In addition, the management of tools such as Geographical Information System (GIS) and Google Earth Engine (GEE) is implemented, as well as the application of some spectral indices such as NDWI and the joint use of (i) NDVI, (ii) mNDWI, and (iii) EVI. Real TSF cases are analyzed, including the dam failures of Jagersfontain TSF in South Africa and Williamson TSF in Tanzania. Finally, this article concludes that the size, location, and temporal variability of the supernatant process water pond within a TSF has a direct impact on safety and the possible potential risk of the physical instability of tailings dams. Full article

(This article belongs to the Special Issue Sustainable Application of Remote Sensing in Environmental Monitoring of Mining Area)

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25 pages, 40400 KiB

Open AccessArticle

The Description and Application of BRDF Based on Shape Vectors for Typical Landcovers

by Jian Yang, Jiapeng Huang, Hongdong Fan, Junbo Duan and Xianwei Ma

Sustainability 2022, 14(19), 11883; https://doi.org/10.3390/su141911883 - 21 Sep 2022

Viewed by 1234

Abstract

As the inherent attribute of land cover, anisotropy leads to the heterogeneity of directional reflection; meanwhile, it creates the opportunity for retrieving characteristics of land surface based on multi-angle observations. BRDF (Bidirectional Reflectance Distribution Function) is the theoretical expression of anisotropy and describes the reflectance in terms of incident-view geometry. Prior BRDF knowledge is used to achieve the multi-angle retrieval for earth observation systems with a narrow FOV (Field of View). Shape indicators are a feasible way to capture the characteristics of BRDF or to build an a priori database of BRDF. However, existing shape indicators based on the ratio of reflectance or the weight of scattering effects are too rough to describe the BRDF’s shape. Thus, it is necessary to propose new shape vectors to satisfy the demand. We selected six typical land covers from MODIS-MCD12 on the homogeneous underlayers as the study sites in North America. The daily BRDF is retrieved by MODIS-BRDF parameters and the RossThick-LiSparseR model. When the SZA (Solar Zenith Angle) is set at 45°, seven directions (−70°, −45°, −20°, 0°, 20°, 45°, and 70°) including edge spot, zenith spot, hot spot and approximate dark spot of the BRDF principal plane were selected to construct two vectors by the change rate of reflectance and angle formulation: Partial Anisotropic Vector (PAV) and Angular Effect Vector (AEV). Then, we assessed the effectiveness of PAV and AEV compared with ANIX (Anisotropic Index), ANIF (Anisotropic Factor) and AFX (Anisotropic Flat Index) by two typical BRDF shapes. The representativeness of PAV and AEV for the original BRDF was also assessed by cosine similarity and error transfer function. Lastly, the application of hot spot components in AEV for land cover classification, the monitoring of land cover in mining areas and the adjustment effect by NDVI (Normalized Difference Vegetation Index) were investigated. The results show that (1) the shape vectors have good representativeness compared with original BRDF. The representativeness of PAV assessed by cosine similarity is 0.980, 0.979 and 0.969, and the representativeness of AEV assessed by error transfer function is 0.987, 0.991 and 0.994 in the three MODIS broadbands of Near Infrared (NIR, 0.7–5.0 µm), Short Wave (SW, 0.3–5.0 µm) and Visible (VIS, 0.3–0.7 µm). (2) Some components of shape vectors have high correlation with AFX. The correlation coefficient between hot spot components in AEV and AFX is 0.936, 0.945 and 0.863, respectively, in NIR, SW and VIS bands. (3) The shape vectors show potentiality for land cover classification and the monitoring of land cover in mining areas. The correlation coefficients of hot spot components in AEV for MODIS-pixels with the same types (0.557, 0.561, 0.527) are significantly higher than MODIS-pixels with various types (0.069, 0.055, 0.051) in NIR, SW and VIS bands. The coefficients of variation for hot spot components are significantly higher after land reclamation (0.0071, 0.0099) than before land reclamation (0.0020, 0.0028). (4) The correlation between NDVI and the BRDF shapes is poor in three MODIS broad bands. The correlation coefficients between NDVI and the BRDF shapes in three temporal scales of annual, seasonal and monthly phases are only 0.134, 0.063 and 0.038 (NIR), 0.199, 0.185 and 0.165 (SW), and 0.323, 0.320 and 0.337 (VIS), on average. Full article

(This article belongs to the Special Issue Sustainable Application of Remote Sensing in Environmental Monitoring of Mining Area)

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27 pages, 9238 KiB

Open AccessArticle

Spatiotemporal Differentiation Characteristics of Land Ecological Quality and Its Obstacle Factors in the Typical Compound Area of Mine Agriculture Urban

by Xinchuang Wang, Wenkai Liu, Hebing Zhang and Fenglian Lu

Sustainability 2022, 14(16), 10427; https://doi.org/10.3390/su141610427 - 22 Aug 2022

Cited by 3 | Viewed by 1543

Abstract

Mining activity combines industrialization, urbanization, and urban-rural integration in the compound area of mine agriculture urban. The land ecological environment has become a major hidden problem, restricting the sustainable development and ecological security of the region. It is imminent to understand the spatiotemporal differentiation characteristics of land ecological quality and its obstacle factors to scientifically carry out land ecological restoration. Here, the Macun coal area in Jiaozuo City, Central China, was selected for the case study, and an evaluation index system including four criteria layers of ecological foundation, structure, benefit, and stress was established. The multiperiod evaluation index data were acquired by utilizing remote sensing and geographic information system (GIS) technology. Based on the multi-objective comprehensive evaluation method, a comprehensive evaluation of land ecological quality was conducted, and the spatiotemporal differentiation characteristics of land ecological quality were explored. Moreover, an obstacle factor diagnosis model was constructed to confirm the spatiotemporal differentiation characteristics of obstacle factors affecting the change of the land ecological quality in the study zone. The results showed the following: (1) From 1980 to 2020, the land ecological quality index in the study zone showed a downward trend, and the proportion of the regional area with general and poor land ecological quality increased from 6.55% to 35.02%. (2) The areas with lower land ecological quality in each period of the study zone were mainly distributed in the mining areas with long mining history in the west and the areas with continuous urbanization and industrialization in the south. In contrast, the compound area of mine agriculture urban with short mining history in the east and southeast had higher land ecological quality. The aggregation of the land ecological quality index also showed similar spatial distribution characteristics. (3) The diagnosis results of obstacle factors showed that, due to the poor land ecological foundation and interference of mining activities, the land ecological quality of the mountain area in the north and west of the study area has been low. It is suggested that the land ecological quality of the area should be improved through measures such as terrain regulation, soil reconstruction, afforestation, and forest land conservation. Under the influence of mining activities and the continuous promotion of urbanization in the south of the study area, the regional ecological quality has been reduced. It is suggested that the regional land ecological quality should be improved by building ecological agriculture and ecological communities. The northeast of the study area is still in the mining area, and the ecological quality of the land tends to deteriorate. The ecological restoration in this area should be conducted by the combination of pre-mining planning, while-mining control, and post-mining restoration. The methodology of this study can provide reference for the identification and restoration of land ecological problems in the compound area of mine agriculture urban. Full article

(This article belongs to the Special Issue Sustainable Application of Remote Sensing in Environmental Monitoring of Mining Area)

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