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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 5994

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 (5 papers)

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Research

22 pages, 6898 KiB  
Article
Long-Term Monitoring of Landslide Activity in a Debris Flow Gully Using SBAS-InSAR: A Case Study of Shawan Gully, China
by Jianming Zhang, Xiaoqing Zuo, Daming Zhu, Yongfa Li and Xu Liu
Remote Sens. 2025, 17(9), 1580; https://doi.org/10.3390/rs17091580 - 29 Apr 2025
Viewed by 320
Abstract
Shawan Gully historically experienced recurrent debris flow events, resulting in significant losses of life and property. The Nuole and Huajiaoshu landslides are two major high-elevation landslides in Shawan Gully, serving as primary sources of debris flow material. To monitor landslides movements, this study [...] Read more.
Shawan Gully historically experienced recurrent debris flow events, resulting in significant losses of life and property. The Nuole and Huajiaoshu landslides are two major high-elevation landslides in Shawan Gully, serving as primary sources of debris flow material. To monitor landslides movements, this study used interferometric synthetic aperture radar (InSAR) and Sentinel-1 SAR imagery acquired between 2014 and 2023 to analyze surface deformation in Shawan Gully. Prior to InSAR processing, we assessed the InSAR measurement suitability of the involved SAR images in detail based on geometric distortion and monitoring sensitivity. Compared to conventional SBAS-InSAR results without preprocessing, the suitability-refined datasets show improvements in interferometric phase quality (1.55 rad to 1.41 rad) and estimation accuracy (1.45 mm to 1.18 mm). By processing ascending, descending, and cross-track Sentinel-1 SAR images, we obtained multi-directional surface displacements in Shawan Gully. The results reveal significant deformation in the NL1 region of Nuole landslide, while the northern scarp and the foot of the slope exhibited different movement characteristics, indicating spatially variable deformation mechanisms. The study also revealed that the Nuole landslide exhibits a high sensitivity to rainfall-induced instability, with rainfall significantly changing its original movement trend. Full article
(This article belongs to the Special Issue Advanced InSAR Techniques for Geohazard Monitoring and Risk Evaluation)
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18 pages, 52971 KiB  
Article
Frequent Glacial Hazard Deformation Detection Based on POT-SBAS InSAR in the Sedongpu Basin in the Himalayan Region
by Haoliang Li, Yinghui Yang, Xiujun Dong, Qiang Xu, Pengfei Li, Jingjing Zhao, Qiang Chen and Jyr-Ching Hu
Remote Sens. 2025, 17(2), 319; https://doi.org/10.3390/rs17020319 - 17 Jan 2025
Viewed by 810
Abstract
The Sedongpu Basin is characterized by frequent glacial debris movements and glacial hazards. To accurately monitor and research these glacier hazards, Sentinel-1 Synthetic Aperture Radar images observed between 2014 and 2022 were collected to extract surface motion using SBAS-POT technology. The acquired temporal [...] Read more.
The Sedongpu Basin is characterized by frequent glacial debris movements and glacial hazards. To accurately monitor and research these glacier hazards, Sentinel-1 Synthetic Aperture Radar images observed between 2014 and 2022 were collected to extract surface motion using SBAS-POT technology. The acquired temporal surface deformation and multiple optical remote sensing images were then jointly used to analyze the characteristics of the long-term glacier movement in the Sedongpu Basin. Furthermore, historical meteorological and seismic data were collected to analyze the mechanisms of multiple ice avalanche chain hazards. It was found that abnormal deformation signals of glaciers SDP1 and SDP2 could be linked to the historical ice avalanche disaster that occurred around the Sedongpu Basin. The maximum deformation rate of SDP1 was 74 m/a and the slope cumulative deformation exceeded 500 m during the monitoring period from 2014 to 2022, which is still in active motion at present; for SDP2, a cumulative deformation of more than 300 m was also detected over the monitoring period. Glaciers SDP3, SDP4, and SDP5 have been relatively stable until now; however, ice cracks are well developed in SDP4 and SDP5, and ice avalanche events may occur if these ice cracks continue to expand under extreme natural conditions in the future. Therefore, this paper emphasizes the seriousness of the ice avalanche event in Sedongpu Basin and provides data support for local disaster management and disaster prevention and reduction. Full article
(This article belongs to the Special Issue Advanced InSAR Techniques for Geohazard Monitoring and Risk Evaluation)
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23 pages, 28195 KiB  
Article
Slow-Moving Landslide Hazard Assessment Using LS-Unilab Deep Learning Model with Highlighted InSAR Deformation Signal
by Xiangyang Li, Peifeng Ma, Song Xu, Hong Zhang, Chao Wang, Yukun Fan and Yixian Tang
Remote Sens. 2024, 16(24), 4641; https://doi.org/10.3390/rs16244641 - 11 Dec 2024
Viewed by 1080
Abstract
Slow-moving landslides are often precursors of catastrophic failure, posing a major threat to human life and property safety. Interferometric synthetic aperture radar (InSAR) has become a crucial tool for investigating slow-moving landslides hazard because of its high-precision detection capability for slow surface deformation. [...] Read more.
Slow-moving landslides are often precursors of catastrophic failure, posing a major threat to human life and property safety. Interferometric synthetic aperture radar (InSAR) has become a crucial tool for investigating slow-moving landslides hazard because of its high-precision detection capability for slow surface deformation. However, landslides usually occur in alpine canyon areas and vegetation coverage areas where InSAR measurements are still limited by temporal and spatial decorrelation and atmospheric influences. In addition, there are several difficulties in monitoring the multiscale characterization of landslides from the InSAR results. To address this issue, this paper proposes a novel method for slow-moving landslide hazard assessment in low-coherence regions. A window-based atmosphere correction method is designed to highlight the surface deformation signals of InSAR results in low-coherence regions and reduce false alarms in landslide hazard assessment. Then, the deformation annual velocity rate map, coherence map and DEM are used to construct the InSAR sample set. A landslide hazard assessment model named Landslide-SE-Unilab is subsequently proposed. The global–local relationship aggregation structure is designed to capture the spatial relationship between local pixel-level deformation features and global landslides, which can reduce the number of missed assessments and false assessments of small-scale landslides. Additionally, a squeeze-and-excitation network is embedded to adjust the weight relationship between the features of each channel in order to enhance the performance of network evaluation. The method was evaluated in Kangding city and the Jinsha River Valley in the Hengduan Mountains, where a total of 778 potential landslides with slow deformation were identified. The effectiveness and accuracy of this approach for low-coherence landslide hazard assessment are demonstrated through comparisons with optical images and previous research findings, as well as evaluations via time-series deformation results. Full article
(This article belongs to the Special Issue Advanced InSAR Techniques for Geohazard Monitoring and Risk Evaluation)
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18 pages, 7633 KiB  
Article
Coastal Reclamation Embankment Deformation: Dynamic Monitoring and Future Trend Prediction Using Multi-Temporal InSAR Technology in Funing Bay, China
by Jinhua Huang, Baohang Wang, Xiaohe Cai, Bojie Yan, Guangrong Li, Wenhong Li, Chaoying Zhao, Liye Yang, Shouzhu Zheng and Linjie Cui
Remote Sens. 2024, 16(22), 4320; https://doi.org/10.3390/rs16224320 - 19 Nov 2024
Viewed by 994
Abstract
Reclamation is an effective strategy for alleviating land scarcity in coastal areas, thereby providing additional arable land and opportunities for marine ranching. Monitoring the safety of artificial reclamation embankments is crucial for protecting these reclaimed areas. This study employed synthetic aperture radar interferometry [...] Read more.
Reclamation is an effective strategy for alleviating land scarcity in coastal areas, thereby providing additional arable land and opportunities for marine ranching. Monitoring the safety of artificial reclamation embankments is crucial for protecting these reclaimed areas. This study employed synthetic aperture radar interferometry (InSAR) using 224 Sentinel-1A data, spanning from 9 January 2016 to 8 April 2024, to investigate the deformation characteristics of the coastal reclamation embankment in Funing Bay, China. We optimized the phase-unwrapping network by employing ambiguity-detection and redundant-observation methods to facilitate the multitemporal InSAR phase-unwrapping process. The deformation results indicated that the maximum observed land subsidence rate exceeded 50 mm per year. The Funing Bay embankment exhibited a higher level of internal deformation than areas closer to the sea. Time-series analysis revealed a gradual deceleration in the deformation rate. Furthermore, a geotechnical model was utilized to predict future deformation trends. Understanding the spatial dynamics of deformation characteristics in the Funing Bay reclamation embankment will be beneficial for ensuring the safe operation of future coastal reclamation projects. Full article
(This article belongs to the Special Issue Advanced InSAR Techniques for Geohazard Monitoring and Risk Evaluation)
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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 1872
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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