Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing in Earth Surface Changes and Deformations Caused by...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide (Third 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: 10 May 2025 | Viewed by 2717

Special Issue Editors

Prof. Dr. Yi Wang

E-Mail Website
Guest Editor
Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing image processing; deep learning; geological data intelligent learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jun Hu

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: InSAR; PolInSAR; 3-D deformation mapping; geohazard monitoring and interpreting; earthquake; landslide
Special Issues, Collections and Topics in MDPI journals
Dr. Weile Li

E-Mail Website
Guest Editor
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Interests: landslide detection; landslide monitoring; landslide prediction; landslide risk assessment; remote sensing; InSAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The occurrence of earthquake and landslide events often leads to significant surface changes, and understanding these changes is of great significance for post-disaster early warning, prevention, and risk management. In addition, surface deformations before, during, and after earthquake and landslide events provide useful information for the interpretation and evaluation of disasters. With the rapid development of earth observation technology, the types of hyperspectral, multi-polarization, high spatial, and temporal resolution sensors are becoming increasingly abundant, and the volume of data is increasing explosively, providing important support for the monitoring and investigation of earth surface changes and deformations.

Contactless devices are not invasive, and they allow for measuring without access to the study area, which is a superior advantage as earth surface changes and deformations often occur in remote areas and can be potentially dangerous or accessible with difficulty. Today, remote sensing data play a large role in geosciences. With recent advancements in technologies such as UAVs, multi-band high-resolution satellite images, and multi-polarization microwave-based SAR images coupled with state-of-the-art machine learning tools, the application of observing and mapping earth surface changes and deformations has become more popular.

The aim of this Special Issue is to collect the most recent research on remote sensing applications in earth sciences. In particular, this Special Issue is dedicated to satellite, aerial, and terrestrial contactless devices for the observation and evaluation of earth surface changes and deformations caused by earthquakes and landslides, as well as new processing techniques related to remote sensing. We invite you to submit scientific, technological, or review articles focused on recent research within one or more of these topics:

  • Detection of earth surface changes—multitemporal remote sensing;
  • Mapping, modeling, and/or monitoring approaches in earth surface changes and deformations;
  • Evaluating the earth surface status and creating novel solutions by integrating remote sensing and GIS techniques;
  • Remote sensing of earthquake and landslide deformation monitoring.

This is the third edition of this Special Issue; experts and scholars in related fields are welcome to submit their original works to this Special Issue.

https://www.mdpi.com/journal/remotesensing/special_issues/Earthquake_and_Landslide_II

https://www.mdpi.com/journal/remotesensing/special_issues/Earthquake_Landslide

Prof. Dr. Yi Wang
Prof. Dr. Jun Hu
Dr. Weile Li
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

  • remote sensing
  • earth surface change
  • surface deformation
  • earthquakes
  • landslides
  • hazard detection
  • hazard mapping
  • hazard evaluation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

21 pages, 20361 KiB  
Article
The Seismic Surface Rupture Zone in the Western Segment of the Northern Margin Fault of the Hami Basin and Its Causal Interpretation, Eastern Tianshan
by Hao Sun, Daoyang Yuan, Ruihuan Su, Shuwu Li, Youlin Wang, Yameng Wen and Yanwen Chen
Remote Sens. 2024, 16(22), 4200; https://doi.org/10.3390/rs16224200 - 11 Nov 2024
Viewed by 445
Abstract
The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced [...] Read more.
The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced two major earthquakes in 1842 and 1914, each with a magnitude of M71/2. In contrast, the Hami Basin on the southern margin of the Eastern Tianshan has no historical records of any major earthquakes, and its seismic potential, mechanisms, and future earthquake hazards remain unclear. Based on satellite image interpretation and field surveys, this study identified a relatively recent and well-preserved seismic surface rupture zone with good continuity in the Liushugou area of the western segment of the Northern Margin Fault of the Hami Basin (HMNF), which is the seismogenic structure responsible for the rupture. The surface rupture zone originates at Kekejin in the east, extends intermittently westward through Daipuseke Bulake and Liushugou, and terminates at Wuzun Bulake, with a total length of approximately 21 km. The rupture zone traverses the youngest geomorphic surface units, such as river beds or floodplains and first-order terraces (platforms), and is characterized by a series of single or multiple reverse fault scarps. The morphology of fault scarps is clear, presenting a light soil color with heights ranging from 0.15 m to 2.13 m and an average displacement of 0.56 m, suggesting that this surface rupture zone likely represents the most recent seismic event. Comparison with historical earthquake records in the Eastern Tianshan region suggests that the rupture zone may have been formed simultaneously with the Xiongkuer rupture zone by the 1842 M71/2 earthquake along the boundary faults on both sides of the Barkol Mountains, exhibiting a flower-like structural pattern. Alternatively, it might represent a separate, unrecorded seismic event occurring shortly after the 1842 earthquake. The estimated magnitude of the associated earthquake is about 6.6~6.9. Given that surface-rupturing earthquakes have already occurred in the western segment, the study indicates that the Erdaogou–Nanshankou section of the HMNF has surpassed the average recurrence interval for major earthquakes, indicating a potential future earthquake hazard. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide (Third Edition))
►▼ Show Figures

Figure 1

19 pages, 17650 KiB  
Article
Automatic Landslide Detection in Gansu, China, Based on InSAR Phase Gradient Stacking and AttU-Net
by Qian Sun, Cong Li, Tao Xiong, Rong Gui, Bing Han, Yilun Tan, Aoqing Guo, Junfeng Li and Jun Hu
Remote Sens. 2024, 16(19), 3711; https://doi.org/10.3390/rs16193711 - 5 Oct 2024
Viewed by 891
Abstract
Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in [...] Read more.
Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide (Third Edition))
►▼ Show Figures

Figure 1

Other

Jump to: Research

22 pages, 113207 KiB  
Technical Note
Landslide Hazard Analysis Combining BGA-Net-Based Landslide Susceptibility Perception and Small Baseline Subset Interferometric Synthetic Aperture Radar in the Baige Section in the Upper Reaches of Jinsha River
by Leyi Su, Liang Zhang, Yuannan Gui, Yan Li, Zhi Zhang, Lu Xu and Dongping Ming
Remote Sens. 2024, 16(12), 2125; https://doi.org/10.3390/rs16122125 - 12 Jun 2024
Viewed by 816
Abstract
The geological and topographic conditions in the upper reaches of the Jinsha River are intricate, with frequent occurrences of landslides. Landslide Susceptibility Prediction (LSP) in this area is a crucial aspect of geological disaster risk management. This study constructs an LSP model using [...] Read more.
The geological and topographic conditions in the upper reaches of the Jinsha River are intricate, with frequent occurrences of landslides. Landslide Susceptibility Prediction (LSP) in this area is a crucial aspect of geological disaster risk management. This study constructs an LSP model using a Convolutional Neural Network (CNN) based on a Bilateral Aggregation Guidance (BAG) strategy, termed BGA-Net. A comprehensive landslide hazard analysis, integrating static landslide susceptibility zonation with dynamic surface deformation monitoring, was therefore conducted. The study area selected was the upper reaches of the Jinsha River, particularly the site of the Baige landslide. The BGA-Net model was first proposed for LSP generation, achieving an accuracy exceeding 85%, while the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology was jointly applied to comprehensively analyze the dynamic geological hazard risk at a regional scale. The final results were presented in a lookup table format and mapped to delineate and grade each risk level. The results show the method is practical, with high feasibility. Compared with traditional machine learning methods, the BGA-strategy-oriented CNN model more effectively differentiated the extremely low- and extremely high-susceptibility areas, enhancing decision-making processes. Full article
(This article belongs to the Special Issue Remote Sensing in Earth Surface Changes and Deformations Caused by Earthquake and Landslide (Third Edition))
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop