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Advanced Satellite Remote Sensing for Geohazards
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Advanced Satellite Remote Sensing for Geohazards

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4210

Special Issue Editors

Prof. Dr. Keren Dai

E-Mail Website
Guest Editor
1. The State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
2. The College of Earth and Planet Science, Chengdu University of Technology, Chengdu 610059, China
Interests: InSAR; landslide monitoring; geohazard assessment
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Roberto Tomás

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Alicante, Campus de San Vicente del Raspeig s/n, 03080 Alicante, Spain
Interests: rock mechanics; landslides; infrastructures; remote sensing; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ruya Xiao

E-Mail Website
Guest Editor
School of Earth Sciences and Engineering, Hohai University, 8th Focheng West Road, Nanjing 211100, China
Interests: InSAR; time series analysis; InSAR atmospheric correction; deformation monitoring; geodesy; GNSS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi He

E-Mail Website
Guest Editor
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Interests: InSAR; spatiotemporal prediction of land subsidence; landslide hazard risk assessment; deep learning
Dr. Roberta Bonì

E-Mail Website
Guest Editor
Department of Science, Technology and Society (STS), University School for Advanced Studies (IUSS), 27100 Pavia, Italy
Interests: engineering geology; remote sensing; landslides; InSAR; monitoring; modelling

Special Issue Information

Dear Colleagues,

Satellite remote sensing has emerged as a pivotal tool in monitoring and understanding geohazards worldwide. This Special Issue aims to explore advanced techniques in satellite remote sensing, such as Synthetic Aperture Radar (SAR) and multispectral data, for detecting and monitoring geohazards such as landslides, land subsidence, and cryospheric disasters. These techniques offer unprecedented capabilities in spatial and temporal monitoring, providing critical insights into the dynamics and impacts of geohazards on both natural and human environments.

This Special Issue aims to explore recent advancements and innovative applications of advanced satellite remote sensing techniques for geohazard analysis. It aligns with the scope of Remote Sensing by emphasizing the integration of satellite remote sensing technologies with geosciences and environmental studies. This Special Issue seeks to advance methodologies in satellite remote sensing data processing, validation techniques, and modelling approaches, ultimately contributing to an improved understanding and management of geohazards at local, regional, and global scales.

Case studies demonstrating the application of satellite remote sensing in real-world geohazard scenarios.

Advanced SAR data and InSAR techniques for landslide detection and monitoring.

Satellite remote sensing for revealing land subsidence phenomena.

Glaciers, frozen soil thawing dynamics, and other cryospheric disaster monitoring using SAR and optical remote sensing.

Integration of InSAR with other remote sensing data (e.g., optical imagery, LiDAR) for comprehensive geohazard assessment.

Review articles synthesizing recent advancements and future directions in satellite remote sensing for geohazard studies.

Prof. Dr. Keren Dai
Prof. Dr. Roberto Tomás
Prof. Dr. Ruya Xiao
Prof. Dr. Yi He
Dr. Roberta Bonì
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

  • satellite remote sensing
  • geohazard monitoring
  • interferometric SAR (InSAR)
  • landslide detection
  • subsidence monitoring
  • glacier dynamics
  • remote sensing applications
  • satellite imagery
  • geospatial analysis

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Published Papers (4 papers)

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20 pages, 12809 KiB  
Article
Visual Prompt Learning of Foundation Models for Post-Disaster Damage Evaluation
by Fei Zhao, Chengcui Zhang, Runlin Zhang and Tianyang Wang
Remote Sens. 2025, 17(10), 1664; https://doi.org/10.3390/rs17101664 - 8 May 2025
Viewed by 331
Abstract
In response to the urgent need for rapid and precise post-disaster damage evaluation, this study introduces the Visual Prompt Damage Evaluation (ViPDE) framework, a novel contrastive learning-based approach that leverages the embedded knowledge within the Segment Anything Model (SAM) and pairs of remote [...] Read more.
In response to the urgent need for rapid and precise post-disaster damage evaluation, this study introduces the Visual Prompt Damage Evaluation (ViPDE) framework, a novel contrastive learning-based approach that leverages the embedded knowledge within the Segment Anything Model (SAM) and pairs of remote sensing images to enhance building damage assessment. In this framework, we propose a learnable cascaded Visual Prompt Generator (VPG) that provides semantic visual prompts, guiding SAM to effectively analyze pre- and post-disaster image pairs and construct a nuanced representation of the affected areas at different stages. By keeping the foundation model’s parameters frozen, ViPDE significantly enhances training efficiency compared with traditional full-model fine-tuning methods. This parameter-efficient approach reduces computational costs and accelerates deployment in emergency scenarios. Moreover, our model demonstrates robustness across diverse disaster types and geographic locations. Beyond mere binary assessments, our model distinguishes damage levels with a finer granularity, categorizing them on a scale from 1 (no damage) to 4 (destroyed). Extensive experiments validate the effectiveness of ViPDE, showcasing its superior performance over existing methods. Comparative evaluations demonstrate that ViPDE achieves an F1 score of 0.7014. This foundation model-based approach sets a new benchmark in disaster management. It also pioneers a new practical architectural paradigm for foundation model-based contrastive learning focused on specific objects of interest. Full article
(This article belongs to the Special Issue Advanced Satellite Remote Sensing for Geohazards)
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18 pages, 5919 KiB  
Article
Automatically Detected CSES Ionospheric Precursors Before Part of the Strong Aftershocks of the 23 January 2024 Wushi MS 7.1 Earthquake in Northwest China
by Mei Li, Hongzhu Yan and Tianyu Liu
Remote Sens. 2024, 16(22), 4182; https://doi.org/10.3390/rs16224182 - 9 Nov 2024
Cited by 1 | Viewed by 832
Abstract
Earthquake prediction is still a large challenge worldwide so far. In this paper, an automatic detection method was put into service immediately after the Wushi MS 7.1 earthquake on 23 January 2024 to weekly detect possible CSES (China Seismo-Electromagnetic Satellite) precursory information [...] Read more.
Earthquake prediction is still a large challenge worldwide so far. In this paper, an automatic detection method was put into service immediately after the Wushi MS 7.1 earthquake on 23 January 2024 to weekly detect possible CSES (China Seismo-Electromagnetic Satellite) precursory information before impending aftershocks. An electron perturbation with an enhanced magnitude of 38.3% was first detected on 24 January 2024 at night orbit 33175 and the corresponding variations in different plasma parameters measured at this orbit presented a typical feature of electron depletion or plasma bubble with an abrupt decrease and then an increase after one minute. The Kp index was also checked during this period and the values reached 3.7 once on 23 and 24 January, which indicates that these ionospheric variations probably originated from solar activities instead of three strong aftershocks with a magnitude more than five in the following three days. However, uncertainties still exist. Then, an electron perturbation with amplitude of 24.6%, as well as an O+ one of 27.3%, was successfully searched automatically at the same revisiting orbit 33251 on 3 February 2024 in a magnetically quiet period. These two plasma variations, as well as ones of other ionospheric parameters, were characterized by highly synchronous properties, which increase the availability as seismic precursors. However, no obvious variations were observed at other revisiting orbits or other orbits near the aftershock areas during this period. An aftershock with magnitude of MS 5.3 and the strongest one of MS 5.8 took place on 24 and 25 February, respectively, 20 days after and 1000 km away. Full article
(This article belongs to the Special Issue Advanced Satellite Remote Sensing for Geohazards)
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18 pages, 6634 KiB  
Article
Mini-Satellite Fucheng 1 SAR: Interferometry to Monitor Mining-Induced Subsidence and Comparative Analysis with Sentinel-1
by Shumin Feng, Keren Dai, Tiegang Sun, Jin Deng, Guangmin Tang, Yakun Han, Weijia Ren, Xiaoru Sang, Chenwei Zhang and Hao Wang
Remote Sens. 2024, 16(18), 3457; https://doi.org/10.3390/rs16183457 - 18 Sep 2024
Cited by 4 | Viewed by 1349
Abstract
Mining-induced subsidence poses a serious hazard to the surrounding environment and infrastructure, necessitating the detection of such subsidence for effective disaster mitigation and the safeguarding of local residents. Fucheng 1 is the first high-resolution mini-satellite interferometric Synthetic Aperture Radar (SAR) launched by China [...] Read more.
Mining-induced subsidence poses a serious hazard to the surrounding environment and infrastructure, necessitating the detection of such subsidence for effective disaster mitigation and the safeguarding of local residents. Fucheng 1 is the first high-resolution mini-satellite interferometric Synthetic Aperture Radar (SAR) launched by China in June 2023. In this study, we used Fucheng 1 SAR images to analyze mining-induced subsidence in Karamay by InSAR Stacking and D-InSAR. The findings were compared with Sentinel-1A imagery to evaluate the effectiveness of Fucheng 1 in monitoring subsidence and its interferometric performance. Analysis revealed significant mining-induced subsidence in Karamay, and the results from Fucheng 1 closely corresponded with those from Sentinel-1A, particularly regarding the extent of the subsidence. It is indicated that the precision of Fucheng 1 SAR imagery has reached leading standards. In addition, due to its higher resolution, the maximum detectable deformation gradient (MDDG) of Fucheng 1 is 2.15 times higher than that of Sentinel images. This study provides data support for the monitoring of mining-induced subsidence in the Karamay and give a theoretical basis for the application of Fucheng 1 in the field of Geohazard monitoring. Full article
(This article belongs to the Special Issue Advanced Satellite Remote Sensing for Geohazards)
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15 pages, 11753 KiB  
Technical Note
Risk Assessment of Ground Subsidence in Foshan (China) Based on the Integration of SBAS-InSAR Observations and Inducing Factors
by Shouyong Yi, Guilin Lai, Min Wang, Zongsheng Zhang, Yuming Chen, Ningling Wen and Xianlin Shi
Remote Sens. 2025, 17(1), 108; https://doi.org/10.3390/rs17010108 - 31 Dec 2024
Viewed by 928
Abstract
In recent years, the rapid development of the construction economy in Foshan City has led to increasingly apparent geological phenomena of ground subsidence, causing damage to buildings and infrastructures, lowering groundwater level, soil degradation, and environmental pollution. These issues have severely constrained the [...] Read more.
In recent years, the rapid development of the construction economy in Foshan City has led to increasingly apparent geological phenomena of ground subsidence, causing damage to buildings and infrastructures, lowering groundwater level, soil degradation, and environmental pollution. These issues have severely constrained the economic development of Foshan City. This paper utilized SBAS-InSAR technology to monitor ground deformation in Foshan City over the 2017–2022 period and identified 214 potential subsidence hazards. Additionally, integrating extensive surface deformation data from a long time series establishes a risk assessment index system for ground subsidence, primarily driven by construction activities. Further, the mechanism of the ground subsidence was analyzed systematically. Meanwhile, introducing the analytic hierarchy process method (AHP) to assess the risk of Foshan City, the results showed that the proportion of area from high-risk to low-risk zones is 8.55%, 8.64%, 11.79%, 22.38% and 48.64%, respectively. Moreover, this paper proposed corresponding measures to prevent and control ground subsidence. The evaluation results can provide a scientific reference for future geological disaster prevention and management in Foshan City. Full article
(This article belongs to the Special Issue Advanced Satellite Remote Sensing for Geohazards)
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