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Special Issue "Earth Observation to Support Disaster Preparedness and Disaster Risk Management"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 December 2017

Special Issue Editors

Guest Editor
Dr. Fabian Löw

Federal Office of Civil Protection and Disaster Assistance, Division I.1 Crisis Management—principles and IT-processes, National focal point of Copernicus Emergency Management Service (EMS) in Germany, Provinzialstraße 93, 53127 Bonn, Germany
Website | E-Mail
Interests: copernicus; disaster management; remote sensing image classification
Guest Editor
Prof. Dr. Siquan Yang

National Disaster Reduction Centre of China (NDRCC), Ministry of Civil Affairs (MoCA)
E-Mail
Phone: +86(0)1052811005
Interests: disaster risk management; Earth observation; crowd sourcing
Guest Editor
Prof. Dr. Günter Strunz

German Aerospace Centre (DLR), German Remote Sensing Data Centre (DFD), Geo-Risks and Civil Security, Oberpfaffenhofen, 82234 Weßling, Germany
Website | E-Mail
Interests: disaster management; early warning systems; risk assessment
Guest Editor
Prof. Dr. Zhenhong Li

School of Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
Website | E-Mail
Phone: +44 (0) 191 208 5704
Interests: InSAR atmospheric correction models, advanced InSAR time series techniques, high-rate GNSS, landslides (slope instability), stability monitoring of man-made infrastructure
Guest Editor
Dr. Joachim Post

United Nations Office for Outer Space Affairs (UNOOSA), United Nations Platform for Space-based Information for Disaster Management and Emergency Response—UN-SPIDER, UN Campus, Platz der Vereinten Nationen 1, 53113 Bonn, Germany
Website1 | Website2 | E-Mail
Interests: space technology and application; disaster management; knowledge management; development cooperation
Guest Editor
Dr. Juan Carlos de Villagrán de Léon

United Nations Office for Outer Space Affairs (UNOOSA), United Nations Platform for Space-based Information for Disaster Management and Emergency Response—UN-SPIDER, UN Campus, Platz der Vereinten Nationen 1, 53113 Bonn, Germany
Website1 | Website2 | E-Mail
Interests: space technology and application; disaster management; knowledge management; development cooperation
Guest Editor
Prof. Dr. Shunichi Koshimura

Laboratory of Remote Sensing and Geoinformatics for Disaster Management, International Research Institute of Disaster Science, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-0845 Japan
Website | E-Mail
Interests: Earth observation; numerical modelling; disaster management; early warning; tsunami; flood; earthquake
Guest Editor
Dr. Roberto Tomas

Department of Civil Engineering, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain
Website | E-Mail
Interests: land subsidence; landslides; InSAR; LiDAR; building monitoring
Guest Editor
Dr. Peter Spruyt

European Commission, DG -Joint Research Centre (JRC), Institute for the Protection and Security of the Citizen (IPSC), Global Security and Crisis Management Unit
Website | E-Mail
Guest Editor
Dr. Michael Judex

Federal Office of Civil Protection and Disaster Assistance, Division I.1 Crisis Management—principles and IT-processes, National focal point of Copernicus Emergency Management Service (EMS) in Germany, Provinzialstraße 93, 53127 Bonn, Germany
Website | E-Mail
Interests: copernicus; disaster management; remote sensing image classification

Special Issue Information

Dear Colleagues,

Globally, the impacts of climate change, scarcity of natural resources, and natural disasters, represent enormous challenges for society, governmental, and non-governmental organizations. With the recent adoption of the 17 Sustainable Development Goals (SDGs, see https://sustainabledevelopment.un.org/sdgs/) adopted by world leaders at the 2015 UN Sustainable Development Summit, and the call by the UN Secretary General for a “revolution” in the use of (geo)data for sustainable development, geospatial technologies have tremendous potential to effectively and efficiently monitor SDG progress and to support the implementation of the development agenda at all levels.

Disasters are increasing in frequency and severity in the modern world, and their impacts on human lives and the economy are accelerating due to growing urbanization and increasing extreme weather events. In this regard, disaster risk reduction (DRR) is one essential mean to achieve sustainable development. Likewise, a paradigm change from emergency response to disaster risk reduction and preparedness is supported by a variety of institutions and political frameworks. A prominent example is the Sendai Framework for DRR 2015–2030, which explicitly promotes the use of Earth observation (EO) as a way to gather data that is needed to elaborate information on hazard exposure, vulnerability and risk and hence as an indispensable source of information to support decision-making related to disasters.

EO has been widely applied to disaster risk management (including disaster preparation, response, recovery and mitigation). Data collection and processing methods have advanced substantially. Freeing data archives ranging back over more than 30 years (for example Landsat/NASA) and EO programs like Copernicus provide a plethora of various types of satellite data and products. Such advances need to find their way in applications related to DRR, including in the indicators to monitor advances in these areas. EO from ground and space platforms and related applications represent a unique platform to observe and assess how risks have evolved in recent years, as well as to track the reduction in the level of exposure of communities to (natural) hazards over the years.

This Special Issue is focused on EO for supporting disaster risk management. It will draw from ongoing advancements, novel developments of methodologies, and best case studies demonstrating the use of EO technology for contributing to the generation of relevant information regarding risk and vulnerability and its changes over time. We encourage submitting manuscripts related to the use of space-based information that can contribute to monitoring hazards, to tracking changes in exposure to (natural) hazards, and of vulnerable elements over the years. Further, we welcome submitting manuscripts that discuss the use of EO data from an application point of view, its implementation potential, including current obstacles and challenges using space-based information.

With these issues in mind, we invite you to submit manuscripts about your recent research, as well as review papers, with respect to the following topics (not limited):

  • EO algorithm development, automation, implementation, and validation for tracking changes and dynamics in exposure to (natural) hazards and of vulnerable elements over time and space;
  • EO for multi-hazard early warning systems and examples of implementation/contribution to risk reduction;
  • Case studies demonstrating the use of Copernicus and/or other satellite data in support of risk management;
  • EO for measuring and monitoring disaster-relevant SDGs and Sendai indicators;
  • EO for supporting Sendai priority for action 1 “Understanding disaster risk” and priority 4 “Enhancing disaster preparedness” including measurement and monitoring of global targets and defined indicators.

Dr. Fabian Löw
Prof. Dr. Siquan Yang
Prof. Dr. Günter Strunz
Prof. Dr. Zhenhong Li
Dr. Joachim Post
Dr. Juan Carlos de Villagrán de Léon
Dr. Shunichi Koshimura
Dr. Roberto Tomas
Dr. Peter Spruyt
Prof. Dr. Michael Judex
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 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 1600 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

  • Copernicus
  • disaster risk management (DRM)
  • disaster risk reduction (DRR)
  • early warning
  • Earth observation (EO)
  • exposure
  • preparedness
  • Sendai framework
  • sustainable development goals (SDGs)

Published Papers (2 papers)

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Research

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Open AccessArticle Effect of Label Noise on the Machine-Learned Classification of Earthquake Damage
Remote Sens. 2017, 9(8), 803; doi:10.3390/rs9080803
Received: 30 May 2017 / Revised: 17 July 2017 / Accepted: 28 July 2017 / Published: 4 August 2017
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Abstract
Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due
[...] Read more.
Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, we look at how mislabeled training data, or label noise, impact the quality of rubble classifiers operating on high-resolution remotely-sensed images. We first study how label noise dependent on geospatial proximity, or geospatial label noise, compares to standard random noise. Our study shows that classifiers that are robust to random noise are more susceptible to geospatial label noise. We then compare the effects of label noise on both pixel- and object-based remote sensing classification paradigms. While object-based classifiers are known to outperform their pixel-based counterparts, this study demonstrates that they are more susceptible to geospatial label noise. We also introduce a new labeling tool to enhance precision and image coverage. This work has important implications for the Sendai framework as autonomous damage classification will ensure rapid disaster assessment and contribute to the minimization of disaster risk. Full article
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Other

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Open AccessTechnical Note Mapping Vulnerable Urban Areas Affected by Slow-Moving Landslides Using Sentinel-1 InSAR Data
Remote Sens. 2017, 9(9), 876; doi:10.3390/rs9090876
Received: 23 July 2017 / Revised: 8 August 2017 / Accepted: 18 August 2017 / Published: 23 August 2017
PDF Full-text (41330 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Landslides are widespread natural hazards that generate considerable damage and economic losses worldwide. Detecting terrain movements caused by these phenomena and characterizing affected urban areas is critical to reduce their impact. Here we present a fast and simple methodology to create maps of
[...] Read more.
Landslides are widespread natural hazards that generate considerable damage and economic losses worldwide. Detecting terrain movements caused by these phenomena and characterizing affected urban areas is critical to reduce their impact. Here we present a fast and simple methodology to create maps of vulnerable buildings affected by slow-moving landslides, based on two parameters: (1) the deformation rate associated to each building, measured from Sentinel-1 SAR data, and (2) the building damage generated by the landslide movement and recorded during a field campaign. We apply this method to Arcos de la Frontera, a monumental town in South Spain affected by a slow-moving landslide that has caused severe damage to buildings, forcing the evacuation of some of them. Our results show that maximum deformation rates of 4 cm/year in the line-of-sight (LOS) of the satellite, affects La Verbena, a newly-developed area, and displacements are mostly horizontal, as expected for a planar-landslide. Our building damage assessment reveals that most of the building blocks in La Verbena present moderate to severe damages. According to our vulnerability scale, 93% of the building blocks analysed present high vulnerability and, thus, should be the focus of more in-depth local studies to evaluate the serviceability of buildings, prior to adopting the necessary mitigation measures to reduce or cope with the negative consequences of this landslide. This methodology can be applied to slow-moving landslides worldwide thanks to the global availability of Sentinel-1 SAR data. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Paper Title: Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Review of the Current Status
Author: G. J-P. Schumann (*,1,2,3) et al.
Affiliation: (1) School of Geographical Sciences, University of Bristol, Bristol, UK; (2) INSTAAR, University of Colorado Boulder, Boulder, CO, USA
(3) Remote Sensing Solutions, Inc., Monrovia, CA, USA;
Abstract: Floods are among the top-ranking natural disasters in terms of annual cost in insured and uninsured losses. Since high-impact events often cover spatial scales that are beyond traditional regional monitoring operations, remote sensing, in particular from satellites, presents an attractive alternative. Over recent years, the science and applications of remote sensing of floods has become mature enough to deliver products and services for decision-making and operational applications, such as flood disaster response assistance. However, this is not without important obstacles, and the main challenge now lies in maintaining adequate application readiness levels and high interoperability of products and services being delivered. In order to better understand the needs for flood disaster assistance from an end-user perspective as well as the challenges this poses to scientists and product developers, this paper briefly reviews existing satellite products and services made available to end-users active in flood disaster response and then critically discusses requirements for improving operational assistance during flood disasters using satellite remote sensing products.

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Fax: +41 61 302 89 18
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