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Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data (Second Edition)

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

Deadline for manuscript submissions: 28 May 2025 | Viewed by 1198

Special Issue Editors

Dr. Sen Du

E-Mail Website
Guest Editor
Instituto de Geociencias (IGEO), Spanish National Research Council, 28040 Madrid, Spain
Interests: InSAR processing and its application; offset tracking; PloSAR data processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jordi J. Mallorquí

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Zhao

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Joaquín Escayo Menéndez

E-Mail Website
Guest Editor
School of Land Surveying, Geodesy and Mapping Engineering, Technical University of Madrid (UPM), 28031 Madrid, Spain
Interests: InSAR processing and its application; data fusion of GNSS and InSAR
Special Issues, Collections and Topics in MDPI journals

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. From a technical perspective, more advanced algorithms are proposed based on the advantage of multiple polarization, the combination of different sensors and datasets, etc. In terms of the applications, 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. They enable 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 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, as well as 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;
  • co-seismic 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 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

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Related Special Issue

Published Papers (2 papers)

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Research

22 pages, 8459 KiB  
Article
Integrating InSAR Data and LE-Transformer for Foundation Pit Deformation Prediction
by Bo Hu, Wen Li, Weifeng Lu, Feilong Zhao, Yuebin Li and Rijun Li
Remote Sens. 2025, 17(6), 1106; https://doi.org/10.3390/rs17061106 - 20 Mar 2025
Viewed by 323
Abstract
The rapid development of urban infrastructure has accelerated the construction of large foundation pit projects, posing challenges for deformation monitoring and safety. This study proposes a novel approach integrating time-series InSAR data with a multivariate LE-Transformer model for deformation prediction. The LE-Transformer model [...] Read more.
The rapid development of urban infrastructure has accelerated the construction of large foundation pit projects, posing challenges for deformation monitoring and safety. This study proposes a novel approach integrating time-series InSAR data with a multivariate LE-Transformer model for deformation prediction. The LE-Transformer model integrates Long Short-Term Memory (LSTM) to capture temporal dependencies, Efficient Additive Attention (EAA) to reduce computational complexity, and Transformer mechanisms to model global data relationships. Deformation monitoring was performed using PS-InSAR and SBAS-InSAR techniques, showing a high correlation coefficient (0.92), confirming the reliability of the data. Gray relational analysis identified key influencing factors, including rainfall, subway construction, residential buildings, soil temperature, and hydrogeology, with rainfall being the most significant (correlation of 0.838). These factors were incorporated into the LE-Transformer model, which outperformed univariate models, achieving a mean absolute percentage error (MAPE) of 2.5%. This approach provides a robust framework for deformation prediction and early warning systems in urban infrastructure projects. Full article
(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data (Second Edition))
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17 pages, 47934 KiB  
Article
Enhanced Phase Optimization Using Spectral Radius Constraints and Weighted Eigenvalue Decomposition for Distributed Scatterer InSAR
by Jun Feng, Hongdong Fan, Yuan Yuan and Ziyang Liu
Remote Sens. 2025, 17(5), 862; https://doi.org/10.3390/rs17050862 - 28 Feb 2025
Viewed by 563
Abstract
Eigenvalue decomposition (EVD) of covariance matrices or coherence matrices has been employed to suppress noise in phase information, and this approach has shown some effectiveness in data processing. However, while this method helps attenuate noisy phase components, it also tends to significantly degrade [...] Read more.
Eigenvalue decomposition (EVD) of covariance matrices or coherence matrices has been employed to suppress noise in phase information, and this approach has shown some effectiveness in data processing. However, while this method helps attenuate noisy phase components, it also tends to significantly degrade the true deformation phase information, which can be detrimental in certain applications. To address this issue, this paper proposes an optimal eigenvalue decomposition phase optimization method, incorporating a spectral radius-constrained covariance matrix construction, named SREVD. This method constructs a covariance matrix using spectral radius constraints and then selects optimal eigenvectors from the covariance matrix for weighted combination, yielding the final optimized phase. The advantages of this approach (1) include the use of spectral radius constraints to obtain a stable covariance matrix, and (2) rather than using the eigenvector associated with the maximum eigenvalue for phase optimization, the interferometric phase is reconstructed by a weighted combination of eigenvectors selected through eigenvalue-based optimization. Experimental analysis conducted in a mining area in Datong, Shanxi Province, China, yields the following conclusions: compared to the original interferogram and the traditional EVD-optimized interferogram, the proposed SREVD method demonstrates superior noise suppression. After optimization with SREVD, the density of monitoring points has been significantly improved. The final number of selected points is 9.06 times that of StaMPS and 1.3 times that of EVD optimization, which can better reflect the topographic changes in the study area. Full article
(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data (Second Edition))
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