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Remote Sensing
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Advanced InSAR Techniques for Geohazard Monitoring and Risk Evaluation
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Advanced InSAR Techniques for Geohazard Monitoring and Risk Evaluation

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1154

Special Issue Editors

Dr. Bochen Zhang

E-Mail Website
Guest Editor
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Interests: InSAR; UAV-InSAR; ground-based radar interferometry; geohazards; infrastructures
Special Issues, Collections and Topics in MDPI journals
Dr. Cunren Liang

E-Mail Website
Guest Editor
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: SAR; InSAR; microwave scattering mechanism; tectonics; earthquakes
Special Issues, Collections and Topics in MDPI journals
Dr. Siting Xiong

E-Mail Website
Guest Editor
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518107, China
Interests: InSAR; AI; land subsidence; image understanding
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wu Zhu

E-Mail Website
Guest Editor
Department of Geomatics, School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China
Interests: InSAR; geohazards identification and monitoring; drone modeling; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Interferometric Synthetic Aperture Radar (InSAR) has become a well-developed technique for monitoring and assessing geohazards. Its high precision and wide coverage in detecting ground deformation make it invaluable for understanding and monitoring natural hazards, such as earthquakes, landslides, volcanic eruptions, and subsidence, and their implications for infrastructure. The increasing volume of satellite data enables the acquisition of long-term ground deformation, which is crucial for monitoring and interpreting geohazards. Advances in big data and deep learning techniques further enhance the mechanism interpretation and risk evaluation of geohazards. Integrating advanced InSAR techniques with AI algorithms holds significant potential for monitoring, interpreting, and predicting geohazards.

This Special Issue aims to report advanced InSAR techniques to monitor and evaluate geohazards. Topics may cover advanced/enhanced InSAR data processing, interpretation with AI, early warning, risk assessment of geohazards, etc. Geohazard applications may be diverse, including earthquakes, landslides, volcanoes, and ground deformation related to geological, hydrological, and urbanization processes.

Articles may address topics including, but not limited, to the following:

  • Advanced algorithms for InSAR data processing;
  • Enhancing InSAR data processing and interpretation with AI;
  • Earthquake monitoring and modelling;
  • Landslide detection and monitoring;
  • Volcanic activity and eruption prediction;
  • Geohazards in urban and its implications for infrastructures;
  • Deformation related to other geological and hydrological processes;
  • Early warning and risk assessment of geohazards.

Dr. Bochen Zhang
Dr. Cunren Liang
Dr. Siting Xiong
Prof. Dr. Wu Zhu
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

  • InSAR
  • data fusion
  • artificial intelligence
  • geohazards
  • deformation monitoring and modelling
  • risk evaluation

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Published Papers (1 paper)

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Research

17 pages, 13031 KiB  
Article
Accurate Deformation Retrieval of the 2023 Turkey–Syria Earthquakes Using Multi-Track InSAR Data and a Spatio-Temporal Correlation Analysis with the ICA Method
by Yuhao Liu, Songbo Wu, Bochen Zhang, Siting Xiong and Chisheng Wang
Remote Sens. 2024, 16(17), 3139; https://doi.org/10.3390/rs16173139 - 26 Aug 2024
Viewed by 857
Abstract
Multi-track synthetic aperture radar interferometry (InSAR) provides a good approach for the monitoring of long-term multi-dimensional earthquake deformation, including pre-, co-, and post-seismic data. However, the removal of atmospheric errors in both single- and multi-track InSAR data presents significant challenges. In this paper, [...] Read more.
Multi-track synthetic aperture radar interferometry (InSAR) provides a good approach for the monitoring of long-term multi-dimensional earthquake deformation, including pre-, co-, and post-seismic data. However, the removal of atmospheric errors in both single- and multi-track InSAR data presents significant challenges. In this paper, a method of spatio-temporal correlation analysis using independent component analysis (ICA) is proposed, which can extract multi-track deformation components for the accurate retrieval of earthquake deformation time series. Sentinel-1 data covering the double earthquakes in Turkey and Syria in 2023 are used to demonstrate the effectiveness of the proposed method. The results show that co-seismic displacement in the east–west and up–down directions ranged from −114.7 cm to 82.8 cm and from −87.0 cm to 63.9 cm, respectively. Additionally, the deformation rates during the monitoring period ranged from −137.9 cm/year to 123.3 cm/year in the east–west direction and from −51.8 cm/year to 45.7 cm/year in the up–down direction. A comparative validation experiment was conducted using three GPS stations. Compared with the results of the original MSBAS method, the proposed method provides results that are smoother and closer to those of the GPS data, and the average optimization efficiency is 43.08% higher. The experiments demonstrated that the proposed method could provide accurate two-dimensional deformation time series for studying the pre-, co-, and post-earthquake events of the 2023 Turkey–Syria Earthquakes. Full article
(This article belongs to the Special Issue Advanced InSAR Techniques for Geohazard Monitoring and Risk Evaluation)
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