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InSAR for Geohazard Monitoring: From Deformation Detection to Risk Assessment

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 781

Special Issue Editors


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Guest Editor
School of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: landslide; floods; artificial intelligence; remote sensing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: InSAR/time-series; InSAR; infrastructure health monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Instituto D. Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Interests: SAR; InSAR; GNSS; earth deformation; multispectral images; machine learning; transfer learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geohazards, including landslides, subsidence, earthquakes, permafrost thaw–freeze, and volcanic activities, pose significant threats to human lives, infrastructure, and the environment. Interferometric Synthetic Aperture Radar (InSAR) has emerged as a powerful remote sensing technique for monitoring surface deformation with millimeter-scale precision over large areas and long periods. Thus, InSAR technology has become an indispensable and dynamic key method for diverse geological hazard monitoring.

This Special Issue will showcase recent advancements in InSAR technology and its applications in geohazard monitoring, focusing on the entire pipeline from deformation detection to risk assessment.

We invite contributions that address both theoretical and practical challenges, including but not limited to the following:

  • Advanced InSAR methodologies for improving deformation measurement accuracy and spatial-temporal resolution;
  • Multi-source data integration (e.g., combining InSAR with GPS, LiDAR, and optical remote sensing) for enhanced deformation monitoring and interpretation;
  • Case studies demonstrating InSAR applications in landslides, ground subsidence, seismic activity, volcanic deformation, and glacier dynamics;
  • Early warning systems and risk assessment models leveraging InSAR data for proactive geohazard management;
  • Challenges and solutions for InSAR implementation in complex environments (e.g., vegetated areas, steep terrains, and urban settings).

This Special Issue will foster collaboration among researchers, engineers, and policymakers to advance the use of InSAR technology for sustainable geohazard mitigation. We welcome original research articles, reviews, and technical notes that contribute to the evolving discourse on geohazard monitoring and risk reduction.

Key Topics:

  • Advanced InSAR algorithms and data processing techniques;
  • Integration of InSAR with other geospatial technologies;
  • Geohazard case studies (landslides, subsidence, earthquakes, volcanoes, etc.);
  • InSAR-based early warning systems and risk assessment;
  • Overcoming limitations of InSAR in challenging environments.

Prof. Dr. Xianmin Wang
Dr. Zhengjia Zhang
Dr. João Catalão Fernandes
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 250 words) can be sent to the Editorial Office for assessment.

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 technology
  • geohazard monitoring
  • surface deformation
  • early warning
  • risk assessment

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Published Papers (1 paper)

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Research

22 pages, 18423 KB  
Article
Quantitative Stability Assessment of Landslides Following the 2024 Zixing Rainstorm Using Time-Series InSAR
by Bing Sui, Yu Fang, Dongdong Li, Zhengjia Zhang, Leishi Chen, Dongsheng Du and Tianying Wang
Remote Sens. 2026, 18(6), 929; https://doi.org/10.3390/rs18060929 - 19 Mar 2026
Viewed by 361
Abstract
In July 2024, a major rainfall-induced landslide disaster occurred in Zixing county, Hunan Province, triggering more than 4000 landslides with a total area exceeding 21 km2. The scale of this hazard underscores a critical need for long-term stability assessment of the [...] Read more.
In July 2024, a major rainfall-induced landslide disaster occurred in Zixing county, Hunan Province, triggering more than 4000 landslides with a total area exceeding 21 km2. The scale of this hazard underscores a critical need for long-term stability assessment of the affected slopes. While previous studies have primarily used optical remote sensing to map landslide distributions, quantitative evaluation of post-failure movement dynamics remains limited. This study developed an integrated monitoring framework that combines time-series SBAS-InSAR displacement measurements (using Sentinel-1 data from August 2024 to September 2025) with deep learning-based optical interpretation, rainfall analysis, and geological data. Our approach enables the quantitative, region-scale stability assessment of the Zixing landslide cluster one year after the initial event. Experimental results reveal sustained surface displacement with rates ranging from −30 to 30 mm/year, and localized displacements exceeding 40 mm/year. Notably, over 48% of the mapped landslides are classified as active or critically active, indicating widespread, ongoing instability. Correlation analysis further establishes precipitation as a key driver of accelerated movement. Beyond the Zixing case, this work provides a transferable methodology for assessing long-term post-disaster landslide behavior, offering direct value for regional hazard management and early-warning systems. Full article
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