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Journals
Geosciences
Special Issues
Geospatial Analysis for Disaster Risk Monitoring and Assessment
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Geospatial Analysis for Disaster Risk Monitoring and Assessment

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 4811

Special Issue Editors

Dr. Atsuko Nonomura

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Guest Editor
Faculty of Engineering and Design, Kagawa University, 2217-20 Hayashi, Takamatsu City 761-0396, Japan
Interests: landslide risk assessment and hazard mapping; remote sensing; disaster risk reduction activities; community resilience
Prof. Dr. Shuich Hasegawa

E-Mail Website
Guest Editor
Faculty of Engineering, Kagawa University, 2217-20 Hayashi, Takamatsu City 761-0396, Japan
Interests: lanslide risk assesment; earthquake risk assesment; disaster risk reduction; active tectoncis; disaster education; geological and geomorpholigical approach; community resillience
Dr. Ranjan Kumar Dahal

E-Mail Website
Guest Editor
Central Department of Geology, Tribhuvan University, Kirtipur, Kathmandu, Nepal
Interests: disaster risk reduction and management; lanslide risk; earthquake risk; crustal dynamics; disaster education; landslide topography (DSGD); community resillience in mountainous region

Special Issue Information

Dear Colleagues,

Natural disasters, such as landslides and floods, cause serious damage to society. In order to mitigate damage as much as possible, disaster risk reduction and management is indispensable. Since natural hazards mostly depend on factors of geology and topography, geospatial analysis for disaster risk monitoring and assessment is pivotal. Therefore, geosciences can provide a first line of defense against natural disasters.

This Special Issue on “Geospatial Analysis for Disaster Risk Monitoring and Assessment” aims to bring together new insights into geospatial analysis for disaster risk monitoring and assessment, as well as new innovative approaches in disaster risk management that will ultimately reduce the threat of natural disaster. State-of-the-art research papers and case studies that reflect advances in geospatial analysis for disaster risk assessment and technologies related to hazard mapping and early warning, with emphasis on near-field geospatial analysis related to risk from natural disaster, are very welcomed.

This Special Issue aims to cover, without being limited to, the following areas:

  • Geospatial analysis for disaster risk monitoring and assessment
  • Tools and applications for disaster risk monitoring and assessment
  • Case studies that reflect the advances in geospatial analysis for disaster risk assessment
  • Activities to raise public awareness for disaster risk reduction

Dr. Atsuko Nonomura
Prof. Dr. Shuich Hasegawa
Dr. Ranjan Kumar Dahal
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. Geosciences is an international peer-reviewed open access monthly 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 1800 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

  • GIS analysis for disaster risk assessment
  • Remote sensing for disaster risk monitoring
  • Hazard mapping to raise public awareness for disaster risk reduction
  • Activities to raise public awareness for disaster risk reduction

Published Papers (2 papers)

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Research

21 pages, 9778 KiB  
Article
3D Probabilistic Modelling and Uncertainty Analysis of Glacial and Post-Glacial Deposits of the City of Saguenay, Canada
by Mohammad Salsabili, Ali Saeidi, Alain Rouleau and Miroslav Nastev
Geosciences 2021, 11(5), 204; https://doi.org/10.3390/geosciences11050204 - 7 May 2021
Cited by 5 | Viewed by 2403
Abstract
Knowledge of the stratigraphic architecture and geotechnical properties of surficial soil sediments is essential for geotechnical risk assessment. In the Saguenay study area, the Quaternary deposits consist of a basal till layer and heterogeneous post-glacial deposits. Considering the stratigraphic setting and soil type [...] Read more.
Knowledge of the stratigraphic architecture and geotechnical properties of surficial soil sediments is essential for geotechnical risk assessment. In the Saguenay study area, the Quaternary deposits consist of a basal till layer and heterogeneous post-glacial deposits. Considering the stratigraphic setting and soil type heterogeneity, a multistep stochastic methodology is developed for 3D geological modelling and quantification of the associated uncertainties. This methodology is adopted for regional studies and involves geostatistical interpolation and simulation methods. Empirical Bayesian kriging (EBK) is applied to generate the bedrock topography map and determine the thickness of the till sediments and their uncertainties. The locally varying mean and variance of the EBK method enable accounting for data complexity and moderate nonstationarity. Sequential indicator simulation is then performed to determine the occurrence probability of the discontinuous post-glacial sediments (clay, sand and gravel) on top of the basal till layer. The individual thickness maps of the discontinuous soil layers and uncertainties are generated in a probabilistic manner. The proposed stochastic framework is suitable for heterogeneous soil deposits characterised with complex surface and subsurface datasets. Full article
(This article belongs to the Special Issue Geospatial Analysis for Disaster Risk Monitoring and Assessment)
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12 pages, 4019 KiB  
Article
Validation of an Index for Susceptibility to Earthquake-Induced Landslides Derived from Helicopter-Borne Electromagnetic Resistivity and Digital Elevation Data
by Atsuko Nonomura, Shuichi Hasegawa, Tatsuya Abe, Sakae Mukoyama and Yoshiyuki Kaneda
Geosciences 2021, 11(2), 95; https://doi.org/10.3390/geosciences11020095 - 19 Feb 2021
Viewed by 1883
Abstract
Catastrophic earthquake-induced landslides can occur on slopes composed of loosened and fractured rock masses. Although it is impossible to prevent such landslides, estimation of the susceptibility of slopes to earthquake-induced landslides is useful for risk management. An index of susceptibility to earthquake-induced landslides [...] Read more.
Catastrophic earthquake-induced landslides can occur on slopes composed of loosened and fractured rock masses. Although it is impossible to prevent such landslides, estimation of the susceptibility of slopes to earthquake-induced landslides is useful for risk management. An index of susceptibility to earthquake-induced landslides (ISEL) was developed by using helicopter-borne electromagnetic resistivity data. However, the ISEL has not yet been validated through the analysis of pre-earthquake data. In this study, ISEL values were estimated from resistivity and digital elevation data obtained in 2013 around Mt. Aso, Kyushu, before the 2016 Kumamoto earthquake. Although most of the landslides around Mt. Aso during the 2016 Kumamoto earthquake were mass movements of volcanic tephra layers, some of them occurred on loosened rock masses. Landslide susceptible areas at loosened rock masses are the target for ISEL value estimation. Our results validated the effectiveness of the ISEL as a predictor of earthquake-induced rock mass landslides. Full article
(This article belongs to the Special Issue Geospatial Analysis for Disaster Risk Monitoring and Assessment)
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