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Early Warning Systems and Real-Time Monitoring for Geohazards by Remote Sensing Techniques

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 5338

Editors

Dr. Yingfeng Zhang

E-Mail Website
Guest Editor
State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: remote sensing; earthquake; insar
Dr. Jianfei Zang

E-Mail Website
Guest Editor
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Interests: high precision satellite navigation and positioning; GNSS seismology
Prof. Dr. Chong Xu

E-Mail Website
Guest Editor
National Institute of Natural Hazards, Ministry of Emergency Management of China, Bejing 100085, China
Interests: landslides; GIS; remote sensing; natural hazards
Special Issues, Collections and Topics in MDPI journals
Dr. Qi Ou

E-Mail Website
Guest Editor
COMET, Global Change Institute, School of GeoSciences, University of Edinburgh, Edinburgh, UK
Interests: InSAR; active tectonics; global warming; human activities

Special Issue Information

Dear Colleagues,

Geohazards, such as earthquakes, volcanic eruptions, and landslides, continue to pose significant threats to human lives and infrastructure, making the need for effective detection and risk management more urgent than ever. Remote sensing technologies, including visible satellite imagery, InSAR, and GNSS, offer distinct advantages—enabling the real-time monitoring of large areas, detecting subtle surface changes, and providing critical data to predict and mitigate the impact of these hazards. These tools have revolutionized our ability to monitor seismic, volcanic, and landslide activities, offering valuable insights into ground deformation, fault movements, and volcanic behavior—often identifying early warning signs before or during an event.

However, integrating these technologies into operational early warning systems remains a challenge. Issues such as data accuracy, temporal resolution, and the complexity of combining multiple data sources must be addressed to enhance system effectiveness. Moreover, advancements in remote sensing data analysis, particularly the increasing potential of AI techniques, are expected to significantly improve early warning capabilities and deepen our understanding of geohazard mechanisms in the near future.

This Special Issue aims to showcase the latest developments in early warning systems and the real-time monitoring of geohazards through remote sensing technologies. Specifically, we focus on methodologies and case studies that highlight how these technologies can be applied to enhance geohazard monitoring and improve early warning systems. Through this issue, we seek to inspire innovation and foster collaboration in order to strengthen disaster preparedness and response globally.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Earthquakes, volcanic eruptions, and landslides;
  • Remote sensing technologies, e.g., visible satellite imagery, InSAR, and GNSS;
  • Geohazards real-time monitoring;
  • Ground deformation and fault movements;
  • Geohazards early warning systems;
  • Data accuracy and temporal resolution;
  • AI techniques in remote sensing;
  • Geohazards preparedness and response.

Dr. Yingfeng Zhang
Dr. Jianfei Zang
Prof. Dr. Chong Xu
Dr. Qi Ou
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-anonymized 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

  • earthquakes
  • volcanic eruptions
  • landslides
  • earth observation
  • geohazards
  • optical imagery
  • SAR imagery
  • UAV
  • hazard detection and mapping
  • geohazard monitoring
  • geohazard modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 3237 KB  
Article
Rapid Strong Earthquake Magnitude Estimation Based on Near-Field High-Rate GNSS Data Using Deep Learning
by Guohong Zhang, Chuanchao Huang, Xinjian Shan, Dingwen Zhang and Wenhuan Kuang
Remote Sens. 2026, 18(15), 2444; https://doi.org/10.3390/rs18152444 - 23 Jul 2026
Viewed by 489
Abstract
Strong earthquakes cause severe casualties and economic losses. Accurate and rapid magnitude estimation can enable timely emergency response and effectively mitigate earthquake disasters. Current mainstream algorithms rely on broadband seismic or strong-motion data, but during strong earthquakes, near-field clipping and a limited P-wave [...] Read more.
Strong earthquakes cause severe casualties and economic losses. Accurate and rapid magnitude estimation can enable timely emergency response and effectively mitigate earthquake disasters. Current mainstream algorithms rely on broadband seismic or strong-motion data, but during strong earthquakes, near-field clipping and a limited P-wave time window lead to significant underestimation of magnitudes. High-Rate Global Navigation Satellite System (HR-GNSS) data, which directly records ground displacement, can overcome the near-field clipping issue and holds significant potential for magnitude estimation in moderate-to-large earthquakes. With the application of deep learning in seismology, combining HR-GNSS data with deep learning can enable fast and accurate magnitude estimation. By simulating numerous earthquake ruptures and their HR-GNSS displacement waveforms, we trained a deep learning model for near real-time magnitude estimation. Results show high accuracy for moderate earthquakes (Mw 6.3+) in single-fault scenarios. In multi-fault scenarios, without requiring prior source location knowledge, the model quickly estimates magnitudes for major earthquakes (Mw 7.0+) near predefined faults based on GNSS displacement waveforms. Testing on the 2019 Ridgecrest earthquake sequence indicates that within 30 s, the estimated magnitudes stabilize close to the actual magnitudes with an error of less than 0.15 magnitude units. Compared to the peak displacement (Pd) method based on broadband seismic waves and the HR-GNSS regression method, our approach offers superior timeliness and accuracy without needing prior epicenter location information. Uncertainty tests show that for large earthquakes (Mw 7.2+), the method achieves 99% accuracy within 40 s. Overall, the model performs best for Mw 7.2+ events, retains limited applicability for Mw 6.3–7.2 events, and is less stable below Mw 6.3. Full article
(This article belongs to the Special Issue Early Warning Systems and Real-Time Monitoring for Geohazards by Remote Sensing Techniques)
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16 pages, 3651 KB  
Article
Coseismic and Postseismic Deformations of the 2023 Turkey Earthquake Doublet
by Chaoya Liu, Hongru Li, Huili Zhan, Shaojun Wang and Ling Bai
Remote Sens. 2025, 17(21), 3573; https://doi.org/10.3390/rs17213573 - 29 Oct 2025
Cited by 1 | Viewed by 1653
Abstract
On 6 February 2023, an earthquake doublet of Mw 7.8 and Mw 7.5 occurred in southeastern Turkey and caused surface ruptures over 350 km for the eastern Anatolian fault (EAF) and 150 km for the Surgu fault (SF), respectively. Over 3700 Mw > [...] Read more.
On 6 February 2023, an earthquake doublet of Mw 7.8 and Mw 7.5 occurred in southeastern Turkey and caused surface ruptures over 350 km for the eastern Anatolian fault (EAF) and 150 km for the Surgu fault (SF), respectively. Over 3700 Mw > 3.0 aftershocks occurred within 5 months following the earthquake doublet, indicating that postseismic stress adjustment is evident. Here, we utilize InSAR technology to investigate the earthquake doublet in terms of its coseismic and postseismic deformations and to estimate the changes in Coulomb stress. We found that the postseismic surface deformation is consistent with the coseismic rupture, characterized by left-lateral strike-slip movement. The coseismic deformations (>5 m) are concentrated in the central-eastern (Pazarcik and Erkenek) segments in the EAF and the central (Cardak) segment in the SF. Notably, the maximum coseismic slip (up to 10 m) and the largest postseismic slip (∼0.5 m) both occurred on the Cardak segment. Postseismic deformations (>0.05 m) are concentrated in the northeastern Erkenek segment and southwestern Amanos segment of the EAF, as well as the eastern Dogansehir segment of the SF. Compared with the coseismic deformation, the postseismic slip compensated for the insufficient deeper slip of the southwestern Amanos segment of the EAF and the central Cardak segment of the SF. Additionally, the postseismic slip extended the rupture area to both the northeast of the Dogansehir segment along the SF and the epicentral area of the 2020 Mw 6.7 earthquake along the EAF. The postseismic afterslip largely reduced the potential seismic hazard of the seismic gap between the eastern end of the coseismic rupture of the 2023 Mw 7.8 earthquake and the epicentral area of the 2020 Mw 6.7 earthquake, as well as the southwestern Amanos segment of the EAF and the eastern Dogansehir segment of the SF. Full article
(This article belongs to the Special Issue Early Warning Systems and Real-Time Monitoring for Geohazards by Remote Sensing Techniques)
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31 pages, 39226 KB  
Article
Hybrid Machine Learning and SBAS-InSAR Integration for Landslide Susceptibility Mapping Along the Balakot–Naran Route, Pakistan
by Ibad Ullah, Zhanlong Chen, Muhammad Afaq Hussain, Safeer Ullah Shah and Nafees Ali
Remote Sens. 2025, 17(20), 3464; https://doi.org/10.3390/rs17203464 - 17 Oct 2025
Cited by 6 | Viewed by 2030
Abstract
Natural hazards such as landslides are among the most harmful and recurring hazards to infrastructure, communities, and the environment around the world. In Pakistan, the Balakot Valley is prone to severe landslides, especially along the Balakot–Naran route, which is a major economic and [...] Read more.
Natural hazards such as landslides are among the most harmful and recurring hazards to infrastructure, communities, and the environment around the world. In Pakistan, the Balakot Valley is prone to severe landslides, especially along the Balakot–Naran route, which is a major economic and tourist route. This route requires accurate landslide susceptibility mapping (LSM) to mitigate landslide risk. However, existing approaches mainly rely on statistical methods, which do not sufficiently address the complexity of spatial patterns and characteristics between landslide conditioning factors (LCFs) and their prevalence. In this study, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements of slope deformation (Vslope) were employed to update the landslide inventory. Following this update, an LSM was generated to examine the causal variables that are associated with landslide occurrences. Several machine learning (ML) classifiers, which include Adaptive Boosting (AdaBoost), Light Gradient Boosting (LightGBM), Extreme Gradient Boosting (XGBoost), and a hybrid (ADA + LGBM + XGB), are utilized for mapping landslide susceptibility. A total of 14 LCFs were considered, with 70% of the dataset being trained and 30% tested. To evaluate the significance of these variables, Recursive Feature Elimination (RFE) and the Shapley Additive Explanations (SHAP) were used. Results indicate that the hybrid model exhibits superior efficiency in the area under the curve (AUC) (88.00%), precision (84.69%), accuracy (84.52%), F1-score (84.69%), and recall (84.70%). The hybrid classifier, when combined with InSAR predictions, generates an improved LSM for the route. In conclusion, the improved LSM can effectively identify areas that are prone to landslides along the Balakot–Naran route. Full article
(This article belongs to the Special Issue Early Warning Systems and Real-Time Monitoring for Geohazards by Remote Sensing Techniques)
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