Special Issue "Advances in Remote Sensing for Disaster Research: Methodologies and Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Assoc. Prof. Dr. Erick Mas
E-Mail Website1 Website2
Guest Editor
Laboratory of Remote Sensing and Geoinformatics for Disaster Management, International Research Institute of Disaster Science, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572 Japan
Interests: disaster mitigation; damage assessment; UAV; disaster simulation
Prof. Dr. Shunichi Koshimura
E-Mail Website1 Website2
Guest Editor
Laboratory of Remote Sensing and Geoinformatics for Disaster Management, International Research Institute of Disaster Science, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572 Japan
Interests: Earth observation; numerical modelling; disaster management; early warning; tsunami; flood; earthquake
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Growing attention has been given to the use of satellite- or aircraft-based sensor technologies to detect and classify objects on Earth. The acquisition of information through remote sensing technologies has been applied in numerous fields, including disaster research and disaster management. Remote sensing has been applied to detection, monitoring, and response to disasters due to natural hazards. It has also provided the opportunity to identify urban vulnerabilities and exposure to possible disasters.

This Special Issue invites paper contributions highlighting recent advances in methodologies and applications of remote sensing to disaster research. Research focusing on earthquake, tsunami, and flood disasters is encouraged, but other types of disasters are welcome. We encourage submissions of review and original research articles related, but not limited, to satellite remote sensing, aerial image analysis, unmanned aerial vehicle (UAV) technology, etc. that focus on the following topics:

  • Gathering data for vulnerability and exposure analysis;
  • Post-disaster field survey using drones;
  • Damage assessment and mapping;
  • Disaster recovery monitoring;
  • Earth observation (EO) for humanitarian aid;
  • AI algorithms applied on remote sensing data for disaster research;
  • Public participation in scientific disaster research (citizen science);
  • Other topics related to remote sensing and disaster research.

IMPORTANT NOTE:

“Remote Sensing” is the Media Partner of the World Bosai Forum/International Disaster Risk Conference 2019, to be held in Sendai (WBF2019).

Although this Special Issue is open to other contributions, it also includes some of the outcomes of the session “Innovative Remote Sensing Technologies for Enhancing Disaster Management”, held at the WBF2019.

http://www.worldbosaiforum.com/2019/english/

Assoc. Prof. Dr. Erick Mas
Prof. Dr. Shunichi Koshimura
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 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. 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 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

  • Disaster research
  • Remote sensing
  • Disaster management
  • Satellite remote sensing
  • Unmanned aerial vehicle (UAV)
  • Aerial photo
  • Machine learning
  • Damage assessment
  • Disaster recovery
  • Drone field survey

Published Papers (1 paper)

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Research

Open AccessArticle
A Modular Processing Chain for Automated Flood Monitoring from Multi-Spectral Satellite Data
Remote Sens. 2019, 11(19), 2330; https://doi.org/10.3390/rs11192330 - 08 Oct 2019
Abstract
Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. We present a generic processing chain that covers all modules required for operational flood monitoring from multi-spectral satellite data. [...] Read more.
Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. We present a generic processing chain that covers all modules required for operational flood monitoring from multi-spectral satellite data. This includes data search, ingestion and preparation, water segmentation and mapping of flooded areas. Segmentation of the water extent is done by a convolutional neural network that has been trained on a global dataset of Landsat TM, ETM+, OLI and Sentinel-2 images. Clouds, cloud shadows and snow/ice are specifically handled by the network to remove potential biases from downstream analysis. Compared to previous work in this direction, the method does not require atmospheric correction or post-processing and does not rely on ancillary data. Our method achieves an Overall Accuracy (OA) of 0.93, Kappa of 0.87 and Dice coefficient of 0.90. It outperforms a widely used Random Forest classifier and a Normalized Difference Water Index (NDWI) threshold method. We introduce an adaptable reference water mask that is derived by time-series analysis of archive imagery to distinguish flood from permanent water. When tested against manually produced rapid mapping products for three flood disasters (Germany 2013, China 2016 and Peru 2017), the method achieves ≥ 0.92 OA, ≥ 0.86 Kappa and ≥ 0.90 Dice coefficient. Furthermore, we present a flood monitoring application centred on Bihar, India. The processing chain produces very high OA (0.94), Kappa (0.92) and Dice coefficient (0.97) and shows consistent performance throughout a monitoring period of one year that involves 19 Landsat OLI ( μ Kappa = 0.92 and σ Kappa = 0.07 ) and 61 Sentinel-2 images ( μ Kappa = 0.92 , σ Kappa = 0.05 ). Moreover, we show that the mean effective revisit period (considering cloud cover) can be improved significantly by multi-sensor combination (three days with Sentinel-1, Sentinel-2, and Landsat OLI). Full article
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