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Advances in Remote Sensing for Land Subsidence Monitoring
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Advances in Remote Sensing for Land Subsidence Monitoring

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 8592

Special Issue Editors

Dr. Xiaoqiong Qin

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Interests: time-series InSAR; land subsidence monitoring; structural health monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Tang

E-Mail Website
Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: InSAR and GNSS; land subsidence monitoring; geophysical modeling and parameter inversion
Dr. Xuguo Shi

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: multi-source data remote sensing for landslide deformation monitoring; geological hazard monitoring; radar interferometry
Special Issues, Collections and Topics in MDPI journals
Dr. Cheng Zhong

E-Mail Website
Guest Editor
Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing of geological disasters; remote sensing of the environment; data mining in GIS applications; machine leaning and data mining in multi-platform remote sensing; InSAR technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous development of global urbanization, land subsidence is widely present in large cities and plain areas with high population density. This land subsidence may cause problems such as displacement, cracks, and collapse of the land surface, damage buildings and facilities, affect urban development and utilization, and even threaten the safety of people’s lives and property. Therefore, the contradiction between urbanization development and land subsidence issues on a global scale is becoming increasingly prominent, and efficient monitoring of land subsidence has become an urgent need. Compared to traditional monitoring methods, remote sensing technology usually has advantages such as high precision, all-weather, all-day, and large-scale, and is widely used in the field of surface deformation monitoring. The use of remote sensing technology to continuously monitor the intensity, rate, time, and spatial changes of land subsidence can provide people with an understanding of the causes of land subsidence and allow them to take effective measures to reduce its harm in order to reduce the impact of land subsidence on people's lives and property safety.

Benefiting from the rapid development of remote sensing techniques (higher resolution, shorter revisit time, multiple bands and platforms, etc.), research on these techniques has been very active in the past few decades. In this context, the present Special Issue of “Advance of Remote Sensing in Land Subsidence Monitoring” aims to be a state-of-the-art collection of studies on remote sensing techniques available for land subsidence monitoring, damage mapping, mechanism exploration, and risk assessment, showing the most relevant research currently underway, highlighting future challenges, and including representative case studies.

 Topics may cover anything from the detailed analysis of land subsidence at the local subway station level to more comprehensive aims and global scales. Hence, multisource data integration (e.g., multispectral, hyperspectral, and thermal) and multiscale approaches or studies focused on land subsidence monitoring and analysis, among other issues, are welcome. Articles may address, but are not limited to, the following topics:

  • Land subsidence monitoring and analysis by InSAR;
  • Land subsidence monitoring and analysis by GNSS;
  • Land surface damage mapping; 
  • Land subsidence analysis and modeling;
  • Land subsidence mechanism exploration;
  • Remote sensing data processing;
  • Multi-source (remote sensing) fusion method and applications;
  • Land subsidence damage identification based on deep learning;
  • Land subsidence prediction based on deep learning;
  • Land subsidence and sea-level rise.

Dr. Xiaoqiong Qin
Dr. Wei Tang
Dr. Xuguo Shi
Dr. Cheng Zhong
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

  • land subsidence
  • remote sensing
  • deformation monitoring
  • InSAR
  • GNSS
  • monitoring and prediction
  • damage mapping
  • deep learning

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Published Papers (6 papers)

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Research

26 pages, 9672 KiB  
Article
The Influence of Groundwater Management on Land Subsidence Patterns in the Metropolitan Region of Guatemala City: A Multi-Temporal InSAR Analysis
by Carlos García-Lanchares, Alfredo Fernández-Landa, José Luis Armayor, Orlando Hernández-Rubio and Miguel Marchamalo-Sacristán
Remote Sens. 2025, 17(9), 1496; https://doi.org/10.3390/rs17091496 - 23 Apr 2025
Viewed by 183
Abstract
This study investigates the relationships between surface deformations and groundwater management in the Metropolitan Region of Guatemala (MRG), a geologically complex area subjected to different types of ground deformation, integrating five municipalities around Guatemala City. Deformation patterns were characterized through Multi-Temporal Interferometric Synthetic [...] Read more.
This study investigates the relationships between surface deformations and groundwater management in the Metropolitan Region of Guatemala (MRG), a geologically complex area subjected to different types of ground deformation, integrating five municipalities around Guatemala City. Deformation patterns were characterized through Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and compared with groundwater piezometric data. The MT-InSAR technique allowed the identification of the main deformation areas in the MRG. Previously reported maximum subsidence rates ranged from −60 mm/year to −20 mm/year, with local maxima fitting with the extraction well fields of Villanueva and Petapa, in the South basin. Subsidence bowl or depression cone deformation areas were identified and located, similar to those described in the literature for other urban areas, such as Jakarta, Semarang, and Mexico City, among others. This study contextualizes these findings within the detailed hydrogeological framework of the region, highlighting the long-standing generalized exploitation of groundwater resources for urban, agricultural, and industrial uses. Historical data on water wells, piezometric levels, and groundwater flow patterns indicate that groundwater extraction has surpassed the natural recharge rates, particularly in the southern and eastern hydrological basins in the study area. This research identifies a critical need for sustainable water management, emphasizing the importance of integrating MT-InSAR into groundwater monitoring schemes. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Land Subsidence Monitoring)
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21 pages, 20519 KiB  
Article
Volume Estimation of Land Surface Change Based on GaoFen-7
by Chen Yin, Qingke Wen, Shuo Liu, Yixin Yuan, Dong Yang and Xiankun Shi
Remote Sens. 2025, 17(7), 1310; https://doi.org/10.3390/rs17071310 - 6 Apr 2025
Viewed by 270
Abstract
Volume of change provides a comprehensive and objective reflection of land surface transformation, meeting the emerging demand for feature change monitoring in the era of big data. However, existing land surface monitoring methods often focus on a single dimension, either horizontal or vertical, [...] Read more.
Volume of change provides a comprehensive and objective reflection of land surface transformation, meeting the emerging demand for feature change monitoring in the era of big data. However, existing land surface monitoring methods often focus on a single dimension, either horizontal or vertical, making it challenging to achieve quantitative volumetric change monitoring. Accurate volumetric change measurements are indispensable in many fields, such as monitoring open-pit coal mines. Therefore, the main content and conclusions of this paper are as follows: (1) A method for Automatic Control Points Extraction from ICESat-2/ATL08 products was developed, integrating Land cover types and Phenological information (ACPELP), achieving a mean absolute error (MAE) of 1.05 m in the horizontal direction and 1.99 m in the vertical direction for stereo change measurements. This method helps correct image positioning errors, enabling the acquisition of geospatially aligned GaoFen-7 (GF-7) imagery. (2) A function-based classification system for open-pit coal mines was established, enabling precise extraction of stereoscopic change region to support accurate volumetric calculations. (3) A method for calculating the mining and stripping volume of open-pit coal mines based on GF-7 imagery is proposed. The method utilizes photogrammetry to extract elevation features and combines spectral features with elevation data to estimate stripping volumes, achieving an excellent error rate (ER) of 0.26%. The results indicate that our method is cost-effective and highly practical, filling the gap in accurate and comprehensive monitoring of land surface changes. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Land Subsidence Monitoring)
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25 pages, 25079 KiB  
Article
Subsidence Monitoring in Emilia-Romagna Region (Italy) from 2016 to 2021: From InSAR and GNSS Integration to Data Analysis
by Gabriele Bitelli, Alessandro Ferretti, Chiara Giannico, Eugenia Giorgini, Alessandro Lambertini, Marco Marcaccio, Marianna Mazzei and Luca Vittuari
Remote Sens. 2025, 17(6), 947; https://doi.org/10.3390/rs17060947 - 7 Mar 2025
Viewed by 815
Abstract
This study investigates vertical soil movement, a subsidence phenomenon affecting infrastructure and communities in the Emilia-Romagna region (Italy). Building upon previous research—initially based on leveling and GNSS observations and later expanded with interferometric synthetic aperture radar (InSAR)—this study focuses on recent data from [...] Read more.
This study investigates vertical soil movement, a subsidence phenomenon affecting infrastructure and communities in the Emilia-Romagna region (Italy). Building upon previous research—initially based on leveling and GNSS observations and later expanded with interferometric synthetic aperture radar (InSAR)—this study focuses on recent data from 2016 to 2021. A key innovation is the use of dual-geometry ascending and descending acquisitions to derive the vertical and the east–west movement components, a technique not previously applied at a regional scale in this area. The integration of advanced geodetic techniques involved processing 1208 Sentinel-1 satellite images with the SqueeSAR® algorithm and analyzing data from 28 GNSS permanent stations using the precise point positioning (PPP) methodology. By calibrating the InSAR data with GNSS measurements, we generated a comprehensive subsidence map for the study period, identifying trends and anomalies. The analysis produced 13.5 million measurement points, calibrated and validated using multiple GNSS stations. The final dataset, processed through geostatistical methods, provided a high-resolution (100-m) regional subsidence map covering nearly 11,000 square kilometers. Finally, the vertical soil movement map for 2016–2021 was developed, featuring isokinetic curves with an interval of 2.5 mm/year. The results underscore the value of integrating these geodetic techniques for effective environmental monitoring in subsidence-prone areas. Furthermore, comparisons with previous subsidence maps reveal the evolution of soil movement in Emilia-Romagna, reinforcing the importance of these maps as essential tools for precise subsidence monitoring. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Land Subsidence Monitoring)
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21 pages, 23870 KiB  
Article
Utilizing LuTan-1 SAR Images to Monitor the Mining-Induced Subsidence and Comparative Analysis with Sentinel-1
by Fengqi Yang, Xianlin Shi, Keren Dai, Wenlong Zhang, Shuai Yang, Jing Han, Ningling Wen, Jin Deng, Tao Li, Yuan Yao and Rui Zhang
Remote Sens. 2024, 16(22), 4281; https://doi.org/10.3390/rs16224281 - 17 Nov 2024
Cited by 1 | Viewed by 1244
Abstract
The LuTan-1 (LT-1) satellite, launched in 2022, is China’s first L-band full-polarimetric Synthetic Aperture Radar (SAR) constellation, boasting interferometry capabilities. However, given its limited use in subsidence monitoring to date, a comprehensive evaluation of LT-1’s interferometric quality and capabilities is necessary. In this [...] Read more.
The LuTan-1 (LT-1) satellite, launched in 2022, is China’s first L-band full-polarimetric Synthetic Aperture Radar (SAR) constellation, boasting interferometry capabilities. However, given its limited use in subsidence monitoring to date, a comprehensive evaluation of LT-1’s interferometric quality and capabilities is necessary. In this study, we utilized the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to analyze mining-induced subsidence results near Shenmu City (China) with LT-1 data, revealing nine subsidence areas with a maximum subsidence of −19.6 mm within 32 days. Furthermore, a comparative analysis between LT-1 and Sentinel-1 data was conducted focusing on the aspects of subsidence results, interferometric phase, scattering intensity, and interferometric coherence. Notably, LT-1 detected some subsidence areas larger than those identified by Sentinel-1, attributed to LT-1’s high resolution, which significantly enhances the detectability of deformation gradients. Additionally, the coherence of LT-1 data exceeded that of Sentinel-1 due to LT-1’s L-band long wavelength compared to Sentinel-1’s C-band. This higher coherence facilitated more accurate capturing of differential interferometric phases, particularly in areas with large-gradient subsidence. Moreover, the quality of LT-1’s monitoring results surpassed that of Sentinel-1 in root mean square error (RMSE), standard deviation (SD), and signal-to-noise ratio (SNR). In conclusion, these findings provide valuable insights for future subsidence-monitoring tasks utilizing LT-1 data. Ultimately, the systematic differences between LT-1 and Sentinel-1 satellites confirm that LT-1 is well-suited for detailed and accurate subsidence monitoring in complex environments. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Land Subsidence Monitoring)
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19 pages, 98931 KiB  
Article
Semi-Automatic Detection of Ground Displacement from Multi-Temporal Sentinel-1 Synthetic Aperture Radar Interferometry Analysis and Density-Based Spatial Clustering of Applications with Noise in Xining City, China
by Dianqiang Chen, Qichen Wu, Zhongjin Sun, Xuguo Shi, Shaocheng Zhang, Yi Zhang and Yunlong Wu
Remote Sens. 2024, 16(16), 3066; https://doi.org/10.3390/rs16163066 - 21 Aug 2024
Cited by 3 | Viewed by 1554
Abstract
The China Loess Plateau (CLP) is the world’s most extensive and thickest region of loess deposits. The inherently loose structure of loess makes the CLP particularly vulnerable to geohazards such as landslides, collapses, and subsidence, resulting in substantial geological and environmental challenges. Xining [...] Read more.
The China Loess Plateau (CLP) is the world’s most extensive and thickest region of loess deposits. The inherently loose structure of loess makes the CLP particularly vulnerable to geohazards such as landslides, collapses, and subsidence, resulting in substantial geological and environmental challenges. Xining City, situated at the northwest edge of the CLP, is especially prone to frequent geological hazards due to intensified human activities and natural forces. Synthetic Aperture Radar Interferometry (InSAR) has become a widely used tool for identifying landslide hazards and displacement monitoring because of its high accuracy, low cost, and wide coverage. In this study, we utilized the small baseline subset (SBAS) InSAR technique to derive the line of sight (LOS) displacements of Xining City using Sentinel-1 datasets from ascending and descending orbits between October 2014 and September 2022. By integrating LOS displacements from the two datasets, we retrieved the eastward and vertical displacements to characterize the kinematics of active slopes. To identify the active areas semi-automatically, we applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster InSAR measurement points (IMPs). Forty-eight active slopes with areas ranging from 0.0049 to 0.5496 km2 and twenty-five subsidence-dominant areas ranging from 0.023 to 3.123 km2 were identified across Xining City. Kinematics analysis of the Jiujiawan landslide indicated that acceleration started in August 2016, likely triggered by rainfall, and continued until the landslide. The extreme rainfall in August 2022 may have pushed the Jiujiawan landslide beyond its critical threshold, leading to instability. Additionally, the study identified nine active slopes that threaten the normal operation of the Lanzhou–Xinjiang High-Speed Railway, with kinematic analysis suggesting rainfall-related accelerations. The influence of anthropogenic activities on ground displacements in loess areas was also confirmed through time series displacement analysis. Our results can be leveraged for geohazard prevention and management in Xining City. As SAR image data continue to accumulate, InSAR can serve as a regular tool for maintaining up-to-date landslide inventories, thereby contributing to more sustainable geohazard management. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Land Subsidence Monitoring)
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19 pages, 10012 KiB  
Article
Retrospective Analysis of Glacial Lake Outburst Flood (GLOF) Using AI Earth InSAR and Optical Images: A Case Study of South Lhonak Lake, Sikkim
by Yang Yu, Bingquan Li, Yongsheng Li and Wenliang Jiang
Remote Sens. 2024, 16(13), 2307; https://doi.org/10.3390/rs16132307 - 24 Jun 2024
Cited by 4 | Viewed by 3531
Abstract
On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This [...] Read more.
On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Land Subsidence Monitoring)
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