Monitoring and Modelling of Geological Disasters Based on InSAR Observations: 3rd Edition
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 232
Special Issue Editors
Interests: Disaster and infrastructure monitoring; InSAR; point cloud processing; photogrammetry
Special Issues, Collections and Topics in MDPI journals
Interests: InSAR; UAV-InSAR; ground-based radar interferometry; geohazards; infrastructures
Special Issues, Collections and Topics in MDPI journals
Interests: InSAR; Geological disaster; Disaster monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In our first two Special Issues, opened from 2021 to 2023, we published 28 state-of-the-art research articles covering diverse topics in InSAR and geological disasters (https://www.mdpi.com/journal/remotesensing/special_issues/InSAR_Geological_Disasters; https://www.mdpi.com/journal/remotesensing/special_issues/ES569JPZ29). These excellent reports significantly contribute to further developments in the monitoring and modeling of geological disasters using InSAR techniques. Meanwhile, an increasing number of scholars are expressing their willingness to submit and publish their research output on this topic. Therefore, we are launching a third edition of this Special Issue.
Interferometric synthetic aperture radar (InSAR) has demonstrated its potential in monitoring geological disasters related to subsidence, landslides, earthquakes, and volcanoes. Such monitoring results provide significant information for further physical modeling, driving mechanism interpretation, developments in early warning technology, and the management and formulation of policies by authorities and stakeholders. Recently, more advanced InSAR methods have been developed for geological disaster monitoring and modeling. For instance, the integration of multi-sensor SAR data improves the temporal resolution. Advanced distributed scatterer interferometry algorithms increase the possibility of measuring low-coherent areas. Introducing machine/deep learning improves the quality of phase unwrapping and decreases errors in InSAR processing. Deep neural networks even make it possible to directly invert the geophysical parameters of disasters from SAR interferograms. Uncertainty analysis of InSAR results further increases the readability of monitoring results.
This Special Issue will publish studies covering different applications by InSAR technique, especially the monitoring and modeling of geological disasters. Topics may cover any aspect from ground displacement monitoring to inversion of geophysical parameters. Multi-source data integration (e.g., InSAR, GNSS, and ground sensors), advanced InSAR approaches, geological disaster modeling, and other relevant issues are all welcome. Monitoring results based on InSAR techniques promote the development of remote sensing science and expand the scope of remote sensing technique applications.
Articles may address, but are not limited to, the following topics:
- Multisource monitoring data integration;
- Geohazard detection;
- Disaster catalog compilation;
- Parameter inversion;
- Innovative InSAR applications;
- Advanced InSAR algorithms.
Dr. Chisheng Wang
Dr. Bochen Zhang
Dr. Chuanhua Zhu
Dr. Bin Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- InSAR
- geological disaster
- disaster monitoring
- disaster modeling and interpretation
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