Special Issue "Advances in Remote Sensing and GIS for Natural Hazards Assessment"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editor

Special Issue Information

Dear Colleagues,

Natural risk assessment is one of the disciplines that has seen the greatest advances in the field of GIS and remote sensing in recent years. The implementation of more sophisticated analysis methodologies, more accurate remote sensing systems, more innovative damage assessment protocols, etc. are some of the various tools that have improved the management of these phenomena. This Special Issue seeks contributions involving innovative approaches or relevant case studies regarding natural hazards assessment related to GIS and remote sensing in topics such as:

- Earthquakes and landslides;

- Flooding and tsunamis;

- Wildfires;

- Hurricanes and similar meteorological phenomena;

- Extreme drought and other risks associated with climate change.

Innovative methodologies, frameworks, or significant results from relevant case studies related to all these topics are welcome, but similar ones may also be considered for publication if they fit within the scope of this Special Issue.

Dr. Salvador García-Ayllón Veintimilla
Guest Editor

Guidance References

Chalkias, C.; Ferentinou, M.; Polykretis, C. GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method. ISPRS Int. J. Geo-Inf. 2014, 3, 523-539.

Fernández-Guisuraga, J. M.; Suárez-Seoane, S.; Calvo, L. Modeling Pinus pinaster forest structure after a large wildfire using remote sensing data at high spatial resolution. For. Ecol. Manage. 2019, 446, 257–271.

Garcia-Ayllon, S. Long-Term GIS Analysis of Seaside Impacts Associated to Infrastructures and Urbanization and Spatial Correlation with Coastal Vulnerability in a Mediterranean Area. Water 2018, 10, 1642.

García-Ayllón, S.; Tomás, A.; Ródenas, J.L. The Spatial Perspective in Post-Earthquake Evaluation to Improve Mitigation Strategies: Geostatistical Analysis of the Seismic Damage Applied to a Real Case Study. Appl. Sci. 2019, 9, 3182.

Li, X.; Yu, L.; Xu, Y.; Yang, J.; Gong, P. Ten years after Hurricane Katrina: monitoring recovery in New Orleans and the surrounding areas using remote sensing. Sci. Bull. 2016, 61, 1460–1470.

Poursanidis, D.; Chrysoulakis, N. Remote Sensing, natural hazards and the contribution of ESA Sentinels missions. Remote Sens. Appl. Soc. Environ. 2017, 6, 25–38

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 papers will be 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 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

  • natural hazards assessment
  • GIS
  • remote sensing
  • earthquake
  • landslide
  • flooding
  • tsunami
  • climate change
  • wildfire
  • hurricane

Published Papers (1 paper)

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

Research

Open AccessArticle
Post-Disaster Recovery Monitoring with Google Earth Engine
Appl. Sci. 2020, 10(13), 4574; https://doi.org/10.3390/app10134574 - 01 Jul 2020
Cited by 1
Abstract
Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal [...] Read more.
Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Assessment)
Show Figures

Figure 1

Back to TopTop