applsci-logo

Journal Browser

Journal Browser

Imaging Geodesy Technologies and Applications in Geohazard Monitoring and Risk Assessment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 2035

Special Issue Editors

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: multi-source data remote sensing for landslide deformation monitoring; geological hazard monitoring; radar interferometry
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: satellite gravity data processing and hydrological applications; multi-source geodetic data fusion and comprehensive disaster reduction

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: InSAR/time-series; InSAR; infrastructure health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to climate change and rapid urban expansion, the number of geohazards, e.g., land subsidence, sinkhole, landslide, collapse and debris flow, has significantly increased. Once triggered, geohazards can be very destructive or even fatal, which threatens the sustainable development of our society. However, we lack knowledge of geohazards, e.g., their distribution, kinematics and mechanisms. Therefore, monitoring and assessing the risks of geohazards becomes an urgent task for geohazard management and prevention.

Imaging geodesy technologies, such as Synthetic Aperture Radar (SAR), Optical Remote Sensing and Light Detection And Ranging (LiDAR), play increasingly virtual roles in geohazards monitoring and mapping. The ongoing advances of image processing, numerical modeling and machine/deep learning algorithms provide new opportunities for a better understanding of geohazards. The main objective of this Special Issue is to present the progress and state-of-the-art approaches in algorithm development and scientific exploitation of imaging geodesy technologies to retrieve information about geohazards. Topics of interest include, but are not limited to, the following:

  • SAR/InSAR processing and applications;
  • Optical remote sensing geohazard mapping;
  • LiDAR geohazard mapping;
  • Machine/deep learning based geohazards analysis;
  • Multi-source data fusion for geohazard monitoring;
  • Integration of imaging geodesy products with numerical and analytical geotechnical models;
  • Risk analysis models.

Dr. Xuguo Shi
Prof. Dr. Yunlong Wu
Dr. Zhengjia Zhang
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. Applied Sciences 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 2400 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

  • imaging geodesy
  • SAR/InSAR
  • optical remote sensing
  • LiDAR
  • machine learning
  • deep learning
  • geohazards detection
  • geohazards monitoring
  • risk assessment

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 policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 21868 KiB  
Article
Rain-Induced Landslide Hazard Assessment Using Inception Model and Interpretability Method—A Case Study of Zayu County, Tibet
by Leyi Su, Yuannan Gui, Lu Xu and Dongping Ming
Appl. Sci. 2024, 14(12), 5324; https://doi.org/10.3390/app14125324 - 20 Jun 2024
Cited by 1 | Viewed by 1499
Abstract
Geological landslide disasters significantly threaten the safety of people’s lives and property. Landslides are a significant threat in Zayu County, Tibet, resulting in numerous geological disasters, including the 1950 earthquake that caused significant casualties and river blockages. More recent landslides have caused substantial [...] Read more.
Geological landslide disasters significantly threaten the safety of people’s lives and property. Landslides are a significant threat in Zayu County, Tibet, resulting in numerous geological disasters, including the 1950 earthquake that caused significant casualties and river blockages. More recent landslides have caused substantial economic losses and infrastructure damage, posing ongoing risks to the local population and their property. Landslide hazard assessment is a critical task in geological disaster prevention and mitigation. This study applied the Inception model to assess landslide hazard in the Zayu area. The Inception model excels at capturing multi-scale features efficiently through its architecture. Fifteen disaster-causing factors were selected as the primary indicators for landslide susceptibility assessment. On this basis, the Inception model was used for landslide susceptibility assessment. Combined with daily precipitation data in the Zayu area, the landslide hazard assessment of the “25 April 2010, heavy rainstorm in Zayu, Tibet” was completed. Back Propagation Neural Network (BPNN), Residual Neural Network (ResNet), Convolutional Neural Network (CNN), and Visual Geometry Group-16 (VGG-16) were introduced for comparison of the fitting effects, and SHapley Additive exPlanations (SHAP) was used for interpretability analysis. The comparative experimental results show that the Inception model performed best in landslide susceptibility assessment and is feasible in practical use. The results also show that the most critical factors in the model were topographic wetness index (TWI), normalized difference water index (NDWI), and road density. This study is significant for assessing landslide hazard in geological landslide disaster prevention and mitigation. It provides a reference for further research and response to similar disasters. Full article
Show Figures

Figure 1

Back to TopTop