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Remote Sensing
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Multi-Source Remote Sensing Integrations in Geological Hazards Research
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Multi-Source Remote Sensing Integrations in Geological Hazards Research

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: 31 May 2026 | Viewed by 1444

Special Issue Editors

Dr. Jie Zhao

E-Mail Website
Guest Editor
School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
Interests: mathematical geology; remote sensing of resources and environment; mineral resources exploration; geological big data integration applications
Dr. Shufang Tian

E-Mail Website
Guest Editor
School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
Interests: remote sensing geology; hyperspectral remote sensing and microwave remote sensing; remote sensing big data processing and integrated applications
Dr. Ying Zhang

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: earthquake forecasting; statistical seismology; earthquake precursor; AI-based earthquake forecasting
Dr. Die Hu

E-Mail Website
Guest Editor
Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
Interests: geothermal resource assessment; thermal infrared remote sensing; muti-scale ecological evaluation

Special Issue Information

Dear Colleagues,

Due to various geological processes, geological hazards and their secondary effects generally involve crustal movements, topographic changes, and the instability of geological structures, such as landslides, mudslides, ground collapses, earthquakes, etc. Under the combined effects of global climate change and extensive anthropogenic activities, geological hazards pose an aggravating negative impact on human life, property, and environment. In order to effectively reduce disaster-related risks, it is vital to perform geohazard modeling, analysis, prediction, and assessment. In the fields of geological hazards, multi-source remote sensing (RS) data possess the benefits of large spatial coverage, frequent temporal updates, superior monitoring accuracy, and advanced modeling and analysis.

Multi-source remote sensing (RS) integration promotes accuracy and efficiency in geohazard monitoring, forecasting, and assessment by combining optical, radar (synthetic aperture radar, SAR), LiDAR, thermal infrared, and other RS data to achieve information reciprocity at the data, feature, or decision levels. With a focus on developing novel data fusion methods or applying multi-source RS data to investigate the process of geological structure and surface variations, it is crucial to advance the understanding of geohazard formation mechanisms underlying the Earth’s dynamic systems. Given the abundance of information derived from multi-source RS data, researchers can make theoretical or technical contributions to geohazard assessment and secondary disaster warnings, as well as to regional sustainable development, which is in line with this journal’s scope.

Therefore, this Special Issue is dedicated to exploring innovative data fusion methods or multi-source RS applications in geological hazard research. Both review articles and research articles are encouraged.

Dr. Jie Zhao
Dr. Shufang Tian
Dr. Ying Zhang
Dr. Die Hu
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

  • data fusion
  • landslide
  • mudslide
  • ground collapse
  • earthquake
  • hazard monitoring
  • hazard assessment
  • hazard prediction

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Published Papers (2 papers)

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Research

33 pages, 12224 KB  
Article
Unsupervised Clustering of InSAR Time-Series Deformation in Mandalay Region from 2022 to 2025 Using Dynamic Time Warping and Longest Common Subsequence
by Jingyi Qin, Zhifang Zhao, Dingyi Zhou, Mengfan Yuan, Chaohai Liu, Xiaoyan Wei and Tin Aung Myint
Remote Sens. 2025, 17(23), 3920; https://doi.org/10.3390/rs17233920 - 3 Dec 2025
Viewed by 348
Abstract
Urban land subsidence poses a significant threat in rapidly urbanizing regions, threatening infrastructure integrity and sustainable development. This study focuses on Mandalay, Myanmar, and presents a novel clustering framework—Dynamic Time Warping and Trend-based Longest Common Subsequence with Agglomerative Hierarchical Clustering (DTLCS-AHC)—to classify spatiotemporal [...] Read more.
Urban land subsidence poses a significant threat in rapidly urbanizing regions, threatening infrastructure integrity and sustainable development. This study focuses on Mandalay, Myanmar, and presents a novel clustering framework—Dynamic Time Warping and Trend-based Longest Common Subsequence with Agglomerative Hierarchical Clustering (DTLCS-AHC)—to classify spatiotemporal deformation patterns from Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) time series derived from Sentinel-1A imagery covering January 2022 to March 2025. The method identifies four characteristic deformation regimes: stable uplift, stable subsidence, primary subsidence, and secondary subsidence. Time–frequency analysis employing Empirical Mode Decomposition (EMD) and Discrete Fourier Transform (DFT) reveals seasonal oscillations in stable areas. Notably, a transition from subsidence to uplift was detected in specific areas approximately seven months prior to the Mw 7.7 earthquake, but causal relationships require further validation. This study further establishes correlations between subsidence and both urban expansion and rainfall patterns. A physically informed conceptual model is developed through multi-source data integration, and cross-city validation in Yangon confirms the robustness and generalizability of the approach. This research provides a scalable technical framework for deformation monitoring and risk assessment in tropical, data-scarce urban environments. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Integrations in Geological Hazards Research)
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25 pages, 11479 KB  
Article
Improved Pixel Offset Tracking Method Based on Corner Point Variation in Large-Gradient Landslide Deformation Monitoring
by Dingyi Zhou, Zhifang Zhao and Fei Zhao
Remote Sens. 2025, 17(19), 3292; https://doi.org/10.3390/rs17193292 - 25 Sep 2025
Viewed by 673
Abstract
Aiming at the problems of feature matching difficulty and limited extension application in the existing pixel offset tracking method for large-gradient landslides, this paper proposes an improved pixel offset tracking method based on corner point variation. Taking the Jinshajiang Baige landslide as the [...] Read more.
Aiming at the problems of feature matching difficulty and limited extension application in the existing pixel offset tracking method for large-gradient landslides, this paper proposes an improved pixel offset tracking method based on corner point variation. Taking the Jinshajiang Baige landslide as the research object, the method’s effectiveness is verified using sentinel data. Through a series of experiments, the results show that (1) the use of VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarisation information combined with the mean value calculation method can improve the accuracy and credibility of the circling of the landslide monitoring range, make up for the limitations of the single polarisation information, and capture the landslide range more comprehensively, which provides essential information for landslide monitoring. (2) The choice of scale factor has an essential influence on the results of corner detection, in which the best corner effect is obtained when the scale factor R is 2, which provides an essential reference basis for practical application. (3) By comparing traditional normalized and adaptive window cross-correlation methods with the proposed approach in calculating landslide offset distances, the proposed method shows superior matching accuracy and sliding direction estimation. (4) Analysis of pixels P1, P2, and P3 confirms the method’s high accuracy and reliability in landslide displacement assessment, demonstrating its advantage in tracking pixel offsets in large-gradient scenarios. Therefore, the proposed method offers an effective solution for large-gradient landslide monitoring, overcoming limitations of feature matching and limited applicability. It is expected to provide more reliable technical support for geological disaster management. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Integrations in Geological Hazards Research)
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