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Quan L, Jin S, Zhang J, Chen J, He J. Subsidence Characteristics in North Anhui Coal Mining Areas Using Space-Air-Ground Collaborative Observations. SENSORS (BASEL, SWITZERLAND) 2024; 24:3869. [PMID: 38931653 PMCID: PMC11207287 DOI: 10.3390/s24123869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/12/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024]
Abstract
To fully comprehend the patterns of land and ecological damage caused by coal mining subsidence, and to scientifically carry out ecological mine restoration and management, it is urgent to accurately grasp the information of coal mining, particularly in complex coaling areas, such as North Anhui, China. In this paper, a space-air-ground collaborative monitoring system was constructed for coal mining areas based on multi-source remote sensing data and subsidence characteristics of coaling areas were investigated in North Anhui. It was found that from 2019 to 2022, 16 new coal mining subsidence areas were found in northern Anhui, with the total area increasing by 8.1%. In terms of land use, water areas were increased by 101.9 km2 from 2012 to 2022, cultivated land was decreased by 99.3 km2, and residence land was decreased by 11.8 km2. The depth of land subsidence in the subsidence areas is divided into 307.9 km2 of light subsidence areas with a subsidence depth of less than 500 mm; 161.8 km2 of medium subsidence areas with a subsidence depth between 500 mm and 1500 mm; and 281.2 km2 of heavy subsidence areas with a subsidence depth greater than 1500 mm. The total subsidence governance area is 191.2 km2, accounting for 26.5% of the total subsidence area. From the perspective of prefecture-level cities, the governance rate reaches 51.3% in Huaibei, 10.1% in Huainan, and 13.6% in Fuyang. The total reclamation area is 68.8 km2, accounting for 34.5% of the subsidence governance area. At present, 276.1 km2 within the subsidence area has reached stable subsidence conditions, mainly distributed in the Huaibei mining area, which accounts for about 60% of the total stable subsidence area.
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Affiliation(s)
- Li’ao Quan
- Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China;
- School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
- The Fourth Surveying and Mapping Institute of Anhui Province, Hefei 230031, China
| | - Shuanggen Jin
- Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China;
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
| | - Jianxin Zhang
- The Fourth Surveying and Mapping Institute of Anhui Province, Hefei 230031, China
| | - Junyun Chen
- The Fourth Surveying and Mapping Institute of Anhui Province, Hefei 230031, China
| | - Junjun He
- The Fourth Surveying and Mapping Institute of Anhui Province, Hefei 230031, China
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Zhu M, Yu X, Tan H, Yuan J. Integrated high-precision monitoring method for surface subsidence in mining areas using D-InSAR, SBAS, and UAV technologies. Sci Rep 2024; 14:12445. [PMID: 38822112 PMCID: PMC11143354 DOI: 10.1038/s41598-024-63400-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/28/2024] [Indexed: 06/02/2024] Open
Abstract
The use of unmanned operations to monitor mining induced land subsidence is increasing. This study conducts a detailed comparative analysis of accuracy of measured ground deformation provided by Differential Interferometric Synthetic Aperture Radar (D-InSAR), Small Baseline Subset (SBAS), and Unmanned Aerial Vehicle (UAV) tilt photogrammetry with respect to levelling measurements. Based on such analysis we propose an integrated approach that combines multiple remote sensing methods to achieve a better global accuracy in the land subsidence monitoring in mining areas. Conducted at the Banji Coal Mine, this study collected subsidence data from April 10, 2021, to June 28, 2022, through D-InSAR, SBAS, and UAV techniques. After segmenting the subsidence basin into distinct zones, we qualitatively assessed each area with UAV-derived 3D models and quantitatively evaluated the precision of all applied techniques, benchmarking against leveling data. Our findings indicate that integrating D-InSAR, SBAS, and UAV technologies significantly enhances monitoring accuracy over any single method, demonstrating their combined effectiveness in different subsidence areas. Consequently, the synergistic integration of D-InSAR, SBAS, and UAV technologies, capitalizing on their complementary strengths, enables the achievement of intuitive, comprehensive, and high-precision monitoring of subsidence basins in mining areas.
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Affiliation(s)
- Mingfei Zhu
- School of Earth and Environment, Anhui University of Science and Technology, Huainan, 232001, China
- School of Geomatics, Anhui University of Science and Technology, Huainan, 232001, China
| | - Xuexiang Yu
- Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, 232001, China.
- School of Geomatics, Anhui University of Science and Technology, Huainan, 232001, China.
| | - Hao Tan
- Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, 232001, China
- School of Geomatics, Anhui University of Science and Technology, Huainan, 232001, China
| | - Jiajia Yuan
- Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, 232001, China
- School of Geomatics, Anhui University of Science and Technology, Huainan, 232001, China
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Chen HS, Lan L, Nie YP, Wang YG, Wang XK. Assessment of sediment transport in Luxiapuqu watershed using RUSLE-TLSD and InSAR techniques: Yarlung Tsangpo River, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:8082-8098. [PMID: 38175517 DOI: 10.1007/s11356-023-31636-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 12/16/2023] [Indexed: 01/05/2024]
Abstract
The Yarlung Tsangpo River Basin is characterized by its intricate topography and a significant presence of erosive materials. These often coincide with heavy localized precipitation, resulting in pronounced hydraulic erosion and geological hazards in mountainous regions. To tackle this challenge, we integrated the RUSLE-TLSD (Revised Universal Soil Loss Equation-Transportation-limited sediment delivery) model with InSAR (Interferometric Synthetic Aperture Radar) data, aiming to explore the sediment transport process and pinpoint hazard-prone sites within mountainous small watershed. The RUSLE-TLSD model aids in evaluating multi-year sediment transport dynamics in mountainous zones. And, the InSAR data precisely delineates changes in sediment scouring and siltation at sites vulnerable to hazards. Our research estimates that the potential average soil erosion within the watershed stands at 52.33 t/(hm2 a), with a net soil erosion of 0.69 t/(hm2 a), the sediment transport pathways manifest within the watershed's gullies and channels. Around 4.32% of the watershed area undergoes sedimentation, predominantly at the base of slopes and within channels. Notably, areas (d) and (e) emerge as the most susceptible to disasters within the watershed. Further analysis of the InSAR data highlighted four regions in the typical area (e) from 2017 that are either sedimentation- or erosion-prone, referred to as "hotspots." Among them, R1 exhibits a strong interplay between water and sediment, rendering it highly sensitive to environmental factors. In contrast, R4, characterized by a sharp bend in siltation, remains relatively impervious to external elements. The NDVI (normalized difference vegetation index) stands out as the pivotal determinant influencing sediment transport within the watershed, exerting a pronounced impact on the outlet section, especially in spring. By employing this approach, we gained a deeper understanding of sediment transport mechanisms and potential hazards in small watershed in uninformative mountainous areas. This study furnishes a robust scientific framework beneficial for erosion mitigation and disaster surveillance in mountainous watersheds.
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Affiliation(s)
- Hao-Shuang Chen
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China
| | - Ling Lan
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China
| | - Yi-Pin Nie
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China
| | - Yu-Ge Wang
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China
| | - Xie-Kang Wang
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, China.
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Xie W, Wu J, Gao H, Chen J, He Y. SBAS-InSAR Based Deformation Monitoring of Tailings Dam: The Case Study of the Dexing Copper Mine No.4 Tailings Dam. SENSORS (BASEL, SWITZERLAND) 2023; 23:9707. [PMID: 38139553 PMCID: PMC10747512 DOI: 10.3390/s23249707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/26/2023] [Accepted: 11/29/2023] [Indexed: 12/24/2023]
Abstract
The No.4 tailings pond of the Dexing Copper Mine is the second largest in Asia. The tailing pond is a dangerous source of man-made debris flow with high potential energy. In view of the lack of effective and low-cost global safety monitoring means in this region, in this paper, the time-series InSAR technology is innovatively introduced to monitor the deformation of tailings dam and significant key findings are obtained. First, the surface deformation information of the tailings pond and its surrounding areas was extracted by using SBAS-InSAR technology and Sentinel-1A data. Second, the cause of deformation is explored by analyzing the deformation rate, deformation accumulation, and three typical deformation rate profiles of the representative observation points on the dam body. Finally, the power function model is used to predict the typical deformation observation points. The results of this paper indicated that: (1) the surface deformation of the tailings dam can be categorized into two directions: the upper portion of the dam moving away from the satellite along the Line of Sight (LOS) at a rate of -40 mm/yr, whereas the bottom portion approaching the satellite along the LOS at a rate of 8 mm/yr; (2) the deformation of the dam body is mainly affected by the inventory deposits and the construction materials of the dam body; (3) according to the current trend, deformation of two typical observation points in the LOS direction will reach the cumulative deformation of 80 mm and -360 mm respectively. The research results can provide data support for safety management of No.4 tailings dam in the Dexing Copper Mine, and provide a method reference for monitoring other similar tailings dams.
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Affiliation(s)
- Weiguo Xie
- Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; (W.X.); (J.C.); (Y.H.)
- Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China
| | - Jianhua Wu
- Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; (W.X.); (J.C.); (Y.H.)
- Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China
| | - Hua Gao
- Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; (W.X.); (J.C.); (Y.H.)
- Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China
| | - Jiehong Chen
- Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; (W.X.); (J.C.); (Y.H.)
- Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China
| | - Yufeng He
- Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; (W.X.); (J.C.); (Y.H.)
- Key Laboratory of Natural Disaster Monitoring, Early Warning and Assessment of Jiangxi Province, Jiangxi Normal University, Nanchang 330022, China
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Yang F, An Y, Ren C, Xu J, Li J, Li D, Peng Z. Monitoring and analysis of surface deformation in alpine valley areas based on multidimensional InSAR technology. Sci Rep 2023; 13:12896. [PMID: 37558719 PMCID: PMC10412562 DOI: 10.1038/s41598-023-39677-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 07/28/2023] [Indexed: 08/11/2023] Open
Abstract
Joshimath has received much attention for its massive ground subsidence at the beginning of the year. Rapid urbanization and its unique geographical location may have been one of the factors contributing to the occurrence of this geological disaster. In high mountain valley areas, the complex occurrence mechanism and diverse disaster patterns of geological hazards highlight the inadequacy of manual monitoring. To address this problem, the inversion of deformation of the Joshimath surface in multiple directions can be achieved by multidimensional InSAR techniques. Therefore, in this paper, the multidimensional SBAS-InSAR technique was used to process the lift-track Sentinel-1 data from 2020 to 2023 to obtain the two-dimensional vertical and horizontal deformation rates and time series characteristics of the Joshimath ground surface. To discover the causes of deformation and its correlation with anthropogenic activities and natural disasters by analyzing the spatial and temporal evolution of surface deformation. The results show that the area with the largest cumulative deformation is located in the northeastern part of the town, with a maximum cumulative subsidence of 271.2 mm and a cumulative horizontal movement of 336.5 mm. The spatial distribution of surface deformation is based on the lower part of the hill and develops towards the upper part of the hill, showing a trend of expansion from the bottom to the top. The temporal evolution is divided into two phases: gentle to rapid, and it is tentatively concluded that the decisive factor that caused the significant change in the rate of surface deformation and the early onset of the geological subsidence hazard was triggered by the 4.7 magnitude earthquake that struck near the town on 11 September 2021.
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Affiliation(s)
- Fan Yang
- Institute of Science and Technology, Liaoning Technical University, Fuxin, 123000, China
- School of Geomatics, Liaoning Technical University, Fuxin, 123000, China
| | - Yan An
- School of Geomatics, Liaoning Technical University, Fuxin, 123000, China.
| | - Chuang Ren
- School of Geomatics, Liaoning Technical University, Fuxin, 123000, China
| | - Jia Xu
- School of Geomatics, Liaoning Technical University, Fuxin, 123000, China
| | - Jinbo Li
- Shanxi Changping Coal Industry Co., LTD., Jincheng, 046700, China
| | - Dongliang Li
- Jinneng Holding Equipment Manufacturing Group, Zhaozhuang Coal Industry Co., LTD., Changzhi, 046600, China
| | - Zhiwei Peng
- Jinneng Holding Equipment Manufacturing Group, Zhaozhuang Coal Industry Co., LTD., Changzhi, 046600, China
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Liu Q, Lv X, Huang P, Xu W. An Enhanced Offset Tracking Method: Providing Auxiliary Information for DInSAR Phase Filtering in Urban Areas. SENSORS (BASEL, SWITZERLAND) 2023; 23:3802. [PMID: 37112143 PMCID: PMC10140909 DOI: 10.3390/s23083802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 06/19/2023]
Abstract
In the application of synthetic aperture radar differential interferometry in urban environments, it is easy to regard the phase change in the deformation band of buildings under construction as noise that requires filtering. This introduces an error into the surrounding area while over-filtering, resulting in an error in the magnitude of the deformation measurement results for the entire region and the loss of deformation details in the surrounding area. Based on the traditional DInSAR workflow, this study added a deformation magnitude identification step, determined the deformation magnitude by using enhanced offset tracking technology, supplemented the filtering quality map and removed the construction areas that affect the interferometry in the filtering stage. The enhanced offset tracking technique adjusted the ratio of contrast saliency and coherence via the contrast consistency peak in the radar intensity image, which was used as the basis for adjusting the adaptive window size. The method proposed in this paper was evaluated in an experiment on a stable region using simulated data and in an experiment on a large deformation region using Sentinel-1 data. The experimental results show that the enhanced method has a better anti-noise ability than the traditional method, and the accuracy rate is improved by about 12%. The supplemented quality map can effectively remove the large deformation area to prevent over-filtering while ensuring the filtering quality, and it can achieve better filtering results.
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Affiliation(s)
- Qingyu Liu
- Correspondence: ; Tel.: +86-182-0235-8261
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Zhu X, Huang P, Xu W, Tan W, Qi Y. A Modified Keystone-Based Forward-Looking Arc Array Synthetic Aperture Radar 3D Imaging Method. SENSORS (BASEL, SWITZERLAND) 2023; 23:2674. [PMID: 36904878 PMCID: PMC10006887 DOI: 10.3390/s23052674] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/20/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
An arc array synthetic aperture radar (AA-SAR) is a new type of omnidirectional observation and imaging system. Based on linear array 3D imaging, this paper introduces a keystone algorithm combined with the arc array SAR 2D imaging method and proposes a modified 3D imaging algorithm based on keystone transformation. The first step is to discuss the target azimuth angle, retain the far-field approximation method of the first-order term, analyze the influence of the forward motion of the platform on the along-track position, and realize the two-dimensional focusing of the target slant range-azimuth direction. The second step is to redefine a new azimuth angle variable in the slant-range along-track imaging and use the keystone-based processing algorithm in the range frequency domain to eliminate the coupling term generated by the array angle and the slant-range time. The corrected data are used to perform along-track pulse compression to obtain the focused image of the target and realize the three-dimensional imaging of the target. Finally, in this article, the spatial resolution of the AA-SAR system in the forward-looking state is analyzed in detail, and the change in the spatial resolution of the system and the effectiveness of the algorithm are verified through simulation.
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Affiliation(s)
- Xiaofan Zhu
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
- Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China
| | - Pingping Huang
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
- Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China
| | - Wei Xu
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
- Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China
| | - Weixian Tan
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
- Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China
| | - Yaolong Qi
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
- Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China
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