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Geological Hazard Monitoring, Identify, Predict, and Risk Assessment Using Geographic Information Science and Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: 25 January 2025 | Viewed by 10

Special Issue Editors

School of Earth Sciences, Lanzhou University, Lanzhou 730000, China
Interests: debris flow; hazard monitoring; hazard identification; hazard prediction

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Guest Editor
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100086, China
Interests: InSAR; emergency and automated monitoring; earthquake; natural disaster chain
Special Issues, Collections and Topics in MDPI journals
School of Earth Sciences, Lanzhou University, Lanzhou 730000, China
Interests: landslide; InSAR; radar remote sensing analysis; geological hazards

Special Issue Information

Dear Colleagues,

Geological hazards, such as earthquakes, landslides, debris flows, ice lake collapses, and land subsidence, pose serious threats to human life, property, and the environment. With the advancement of technology, geographic information science (GIS) and remote sensing (RS) technology are playing increasingly important roles in geological hazard risk management. This Special Issue will delve into the use of GIS and remote sensing technology for the monitoring, identification, prediction, and assessment of geological hazards.

The main topics include the extraction, monitoring, and identification of geological hazard features based on remote sensing imagery, the application of GIS in geological hazard risk assessment, the construction and optimization of geological hazard prediction models, and advancements in multi-source data fusion technology.

By compiling the latest research findings and application cases, this Special Issue aims to provide a scientific basis and technical support for geological hazard prevention and control, thereby enhancing disaster warning and emergency response capabilities and reducing the losses caused by geological hazards.

The extraction, monitoring, and identification of geological hazard features based on remote sensing imagery;

The application of GIS in geological hazard risk assessment;

The construction and optimization of geological hazard prediction models;

Advancements in multi-source data fusion technology.

Dr. Yan Zhao
Dr. Yongsheng Li
Dr. Yi 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. 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

  • geological hazards
  • GIS and remote sensing technology
  • risk management
  • hazard monitoring
  • hazard identification and prediction
  • multi-source data fusion

Published Papers

This special issue is now open for submission.
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