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Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 5864

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Special Issue Editors

Dr. Sen Du

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Guest Editor

Instituto de Geociencias (IGEO), Spanish National Research Council, 28040 Madrid, Spain
Interests: InSAR processing and its application; offset tracking; PloSAR data processing

Dr. Jordi J. Mallorquí

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Guest Editor

School of Signal Theory and Communications, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Interests: differential interferometric (InSAR); synthetic aperture radar (SAR); remote sensing

Dr. Feng Zhao

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Guest Editor

School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Interests: advanced pixel selection and optimization algorithms for multi-temporal (Pol)DInSAR techniques and its application on terrain deformation detection and monitoring

Dr. Joaquín Escayo Menéndez

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Guest Editor

Instituto de Geociencias (IGEO), CSIC-UCM, c/ Doctor Severo Ochoa, nº 7, Ciudad Universitaria, 28040 Madrid, Spain
Interests: InSAR processing and its application; data fusion of GNSS and InSAR

Special Issue Information

Dear Colleagues,

By utilizing remote sensing techniques, such as Synthetic Aperture Radar Interferometry (InSAR) and GNSS (Global Navigation Satellite System), precise measurements of land surface deformation can be obtained, revolutionizing our ability to monitor and understand surface deformation, and providing valuable insights into various geophysical processes. On the technical side, more advanced algorithms are proposed based on the advantage of multiple polarization, combination of different sensors and dataset, etc. On the application side, the importance of monitoring surface deformation lies in its wide range of applications and implications. The study and monitoring of surface deformation using remote sensing techniques have significant scientific and practical implications. It enables us to better understand the Earth's dynamic processes, assess natural hazards, manage resources, and ensure the safety of infrastructure. The latest developments in remote sensing technologies and applications continue to expand our capabilities in monitoring and analyzing surface deformation, leading to advancements in various fields of study and practical applications.

This Special Issue aims to include studies introducing new algorithms or new applications of remote sensing data, including the processing the data from airborne or spaceborne sensors, such as SAR, GNSS, optical images and Lidar. Therefore, studies concerned with remote sensing techniques for surface deformation monitoring, applications of surface deformation monitoring, data processing and analysis and case studies are welcome. Articles may address, but are not limited, to the following topics:

Environmental assessment;
Natural hazards mission area;
Landslide monitoring;
Sentinel-1 PSI and SBAS InSAR;
Sinkhole early warning;
Coseismic deformation monitoring;
Geological environment;
Volcano deformation monitoring;
Earthquake early warning;
Mining subsidence monitoring;
Coastal erosion monitoring;
Urban subsidence monitoring.

Dr. Sen Du
Dr. Jordi J. Mallorquí
Dr. Feng Zhao
Dr. Joaquín Escayo Menéndez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Remote Sensing 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 2700 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

synthetic aperture radar (SAR)
interferometric synthetic aperture radar (InSAR)
persistent scatterer interferometry (PSI)
global navigation satellite system (GNSS)
data fusion
land subsidence analysis
geophysics and geology
structural health monitoring

Published Papers (7 papers)

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Research

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27 pages, 13389 KiB

Open AccessArticle

Quantitative Assessment and Impact Analysis of Land Surface Deformation in Wuxi Based on PS-InSAR and GARCH Model

by Shengyi Zhang, Lichang Xu, Rujian Long, Le Chen, Shenghan Wang, Shaowei Ning, Fan Song and Linlin Zhang

Remote Sens. 2024, 16(9), 1568; https://doi.org/10.3390/rs16091568 - 28 Apr 2024

Viewed by 417

Abstract

Land surface deformation, including subsidence and uplift, has significant impacts on human life and the natural environment. In recent years, the city of Wuxi, China has experienced large-scale surface deformation following the implementation of a groundwater abstraction ban policy in 2005. To accurately measure the regional impacts and understand the underlying mechanisms, we investigated the spatiotemporal characteristics of surface deformation in Wuxi from 2015 to 2023 using 100 Sentinel-1A SAR images and the Persistent Scatterer InSAR (PS-InSAR) technique. The results revealed that surface deformation in Wuxi exhibited significant spatial and temporal variations, with some areas experiencing alternating trends of subsidence and uplift rather than consistent unidirectional change. To uncover the factors influencing this volatility, we conducted a comprehensive analysis focusing on groundwater, precipitation, and soil geology. This study found strong correlations between the groundwater level changes and surface deformation, with the soft soil geology of the area, characterized by alternating layers of sand and clay, further increasing the surface volatility. Moreover, we innovatively applied the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, typically used in financial analyses, to analyze the subsidence displacement time series in Wuxi. Based on this model, we propose a new “Amplitude Factor” index to evaluate overall surface deformation volatility in the city. Our qualitative assessment of surface stability based on the Amplitude Factor was consistent with research findings, demonstrating the accuracy and effectiveness of the proposed model. These results provide valuable insights for urban planning, construction, and safety control, highlighting the importance of continuous monitoring and analysis of surface deformation volatility for the city’s future development and safety. Full article

(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)

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13 pages, 28286 KiB

Open AccessArticle

Research on Collapse Detection in Old Coal Mine Goafs Based on Space–Sky–Earth Remote Sensing Survey

by Jiayi Yao, Keming Han, Wu Zhu and Yanbo Cao

Remote Sens. 2024, 16(7), 1164; https://doi.org/10.3390/rs16071164 - 27 Mar 2024

Viewed by 496

Abstract

A considerable number of coal mines employed room and pillar mining in the last century in northern China, where the goaf remained stable for a period of time; however, with the increased exposure of coal pillars, their collapse may gradually increase. The stability assessment of these old rooms and pillar goafs is challenging due to their concealment, irregular mining patterns, and the long passage of time. The methodology developed in this study, based on “space-sky-earth” remote sensing such as InSAR to trace historical deformation, the UAV observation of current surface damage, and comparison of mining spaces, can rapidly detect on a large scale the collapse of old goafs and the trend of damage. This study is conducted with an example of a coal mine in Yulin, Northern China, where obtained quantitative surface deformation values were integrated with qualitative surface damage interpretation results, followed by a yearly analysis of the overlying rock movement in accordance with the underground coal mining process. The results show that from 2007 to 2021, corresponding surface deformation and damage occurred following mining progress. However, the room and pillar goaf areas had not undergone any surface deformation, nor had there been incidents of landslides or ground fissures; therefore, it was speculated that no roof collapse had occurred in this region. The surface deformation and damage associated with underground coal mining are complex and influenced by the coal seam occurrence, mining methods, strata lithology, terrain slope, temporal evolution, and anthropogenic modifications. These phenomena are representative of the coal mining area, and this methodology can provide a reference for similar endeavors. Full article

(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)

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24 pages, 28577 KiB

Open AccessArticle

Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques

by Tao Tao, Keming Han, Xin Yao, Ximing Chen, Zuoqi Wu, Chuangchuang Yao, Xuwen Tian, Zhenkai Zhou and Kaiyu Ren

Remote Sens. 2024, 16(6), 1046; https://doi.org/10.3390/rs16061046 - 15 Mar 2024

Viewed by 671

Abstract

The occurrence of surface strata movement in underground coal mining leads to the generation of numerous ground fissures, which not only damage the ecological environment but also disrupt building facilities, lead to airflow and easily trigger coal spontaneous combustion, induce geological disasters, posing a serious threat to people’s lives, property, and mining production. Therefore, it is particularly important to quickly and accurately obtain the information of ground fissures and then study their distribution patterns and the law of spatial-temporal evolution. The traditional field investigation methods for identifying fissures have low efficiency. The rapid development of UAVs has brought an opportunity to address this issue. However, it also poses new questions, such as how to interpret numerous fissures and the distribution law of fissures with underground mining. Taking a mine in the Shenfu coalfield on the semi-desert aeolian sand surface as the research area, this paper studies the fissure recognition from UAV images by deep learning, fissure development law, as well as the mutual feed of surface condition corresponding to the under-ground mining progress. The results show that the DRs-UNet deep learning method can identify more than 85% of the fissures; however, due to the influence of seasonal vegetation changes and different fissure development stages, the continuity and integrity of fissure recognition methods need to be improved. Four fissure distribution patterns were found. In open-cut areas, arc-shaped fissures are frequently observed, displaying significant dimensions in terms of depth, length, and width. Within subsidence basins, central collapse areas exhibit fissures that form perpendicular to the direction of the working face. Along roadways, parallel or oblique fissures tend to develop at specific angles. In regions characterized by weak roof strata and depressed basins, abnormal reverse-“C”-shaped fissures emerge along the mining direction. The research results comprehensively demonstrate the process of automatically identifying ground fissures from UAV images as well as the spatial distribution patterns of fissures, which can provide technical support for the prediction of ground fissures, monitoring of geological hazards in mining areas, control of land environmental damage, and land ecological restoration. In the future, it is suggested that this method be applied to different mining areas and geotechnical contexts to enhance its applicability and effectiveness. Full article

(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)

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18 pages, 3405 KiB

Open AccessArticle

Monitoring and Analysis of the Collapse at Xinjing Open-Pit Mine, Inner Mongolia, China, Using Multi-Source Remote Sensing

by Nianbin Zhang, Yunjia Wang, Feng Zhao, Teng Wang, Kewei Zhang, Hongdong Fan, Dawei Zhou, Leixin Zhang, Shiyong Yan, Xinpeng Diao and Rui Song

Remote Sens. 2024, 16(6), 993; https://doi.org/10.3390/rs16060993 - 12 Mar 2024

Viewed by 733

Abstract

The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue. Full article

(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)

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22 pages, 5323 KiB

Open AccessArticle

Active–Passive Remote Sensing Evaluation of Ecological Environment Quality in Juye Mining Area, China

by Yu Chen, Zhihui Suo, Hui Lu, Huibin Cheng and Qian Li

Remote Sens. 2023, 15(24), 5750; https://doi.org/10.3390/rs15245750 - 15 Dec 2023

Viewed by 739

Abstract

The coal industry is a crucial component of China’s energy sector. However, the persistent exploitation of coal resources has gravely impacted the ecological environment. While the Remote Sensing Ecology Index (RSEI) is predominantly used for assessing ecological quality, its primary focus has been urban or aquatic environments. There is limited research focused on the evaluation of the ecological environment quality in mining areas. Moreover, the information regarding surface deformation caused by coal mining extraction is an essential factor in the ecological monitoring of mining areas. Therefore, this study proposed the Modified Remote Sensing Ecology Index (MRSEI). This enhanced model merges active and passive remote sensing techniques and incorporates a deformation factor (Surface Deformation Index, SDI) to provide a holistic evaluation of mining area ecologies. Furthermore, for comparative verification, we developed the Eco-environmental Quality Index (EQI) model by selecting 12 ecological parameters and employing a hierarchical analysis. The Juye mining area in Shandong Province was selected as the region of study. MRSEI results from 2015 to 2021 indicate a decline in the ecological quality of the Juye mining area, with MRSEI values registering at 0.691, 0.644, and 0.617. The EQI model mirrors this decreasing trend over the same period. Despite MRSEI using fewer indicators, its assessments align closely with the multi-indicator EQI method. This validates the accuracy of the MRSEI method, providing reliable technical support for the monitoring and evaluation of ecological environment quality in mining areas. Full article

(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)

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27 pages, 93361 KiB

Open AccessArticle

A New Strategy for Extracting 3D Deformation of Mining Areas from a Single-Geometry Synthetic Aperture Radar Dataset

by Ruonan Zhao, Zhabko Andrey Viktorovich, Junfeng Li, Chuang Chen and Meinan Zheng

Remote Sens. 2023, 15(21), 5244; https://doi.org/10.3390/rs15215244 - 4 Nov 2023

Viewed by 1063

Abstract

This paper presents a strategy for extracting three-dimensional (3D) mining deformation from a single-geometry synthetic aperture radar (SAR) dataset. In light of the directionality of horizontal displacement caused by underground mining, we first re-model the proportional relationship between horizontal displacement and horizontal gradient of subsidence. Afterward, to improve the stability of the re-model, a solution strategy is proposed by setting different solution starting points and directions. The proposed method allows hiring of arbitrary single-geometry SAR data (e.g., air-borne, space-borne, and ground-borne SAR data) to reconstruct 3D displacements of mining areas. The proposed method has been validated through simulation and in-site data. The simulation data monitoring results indicate that the root mean square errors (RMSE) of the 3D displacements extracted by the proposed strategy are 0.45, 0.5, and 2.98 mm for the vertical subsidence, east–west, and north–south horizontal displacements, respectively. The in-site data monitoring results indicate that the RMSE of vertical subsidence compared with the leveling data are 7.3 mm. Furthermore, the MSBAS method was employed to further validate the reliability of the proposed method, the results show that the proposed method is effective to obtain the 3D deformation of the mining area, which greatly improves the applicability of SAR interferometry in the 3D deformation monitoring of the mining areas. Full article

(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)

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Other

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16 pages, 13759 KiB

Open AccessTechnical Note

Optimal Pair Selection Applied to Sentinel-2 Images for Mapping Ground Deformation Using Pixel Offset Tracking: A Case Study of the 2022 Menyuan Earthquake (Mw 6.9), China

by Xiaowen Wang, Siqi Wu, Jiaxin Cai and Guoxiang Liu

Remote Sens. 2023, 15(19), 4735; https://doi.org/10.3390/rs15194735 - 27 Sep 2023

Viewed by 940

Abstract

Pixel Offset Tracking (POT) for optical imagery is a widely used method for extracting large-scale ground deformation. However, the influence of imaging parameters on the measurement accuracy of POT is still unclear. In this study, based on 16 pairs of Sentinel-2 images covering the period before and after the Ms6.9 Menyuan earthquake in 2022, we quantitatively assessed the effects of imaging bands, time intervals between image pairs, and differences in solar zenith angles on the measurement accuracy of optical POT. The results showed that the quality of ground deformation extracted using the near-infrared band was superior to other bands. The accuracy of optical POT measurements exhibited a negative correlation with both the time interval between image pairs and the differences in solar zenith angles. The maximum difference in optical POT measurement accuracy for the near-infrared band between image pairs with different time intervals (5/10/15 days) reached 30.3%, while the maximum difference in deformation measurement accuracy for pairs with different solar zenith angle differences was 30.56%. Utilizing the optimal POT image pair, the accuracy of co-seismic deformation measurement for the Menyuan earthquake improved by 48.3% compared to the worst image pair. The maximum co-seismic horizontal displacement caused by the earthquake was estimated to be 3.00 ± 0.51 m. Full article

(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data)

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