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Earth Observation to Support Disaster Preparedness and Disaster Risk Management

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

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 107225

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Special Issue Editors

Dr. Fabian Löw

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Guest Editor

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
Interests: copernicus; disaster management; remote sensing image classification

Prof. Dr. Siquan Yang

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Guest Editor

National Disaster Reduction Centre of China (NDRCC), Ministry of Civil Affairs (MoCA)
Interests: disaster risk management; Earth observation; crowd sourcing

Prof. Dr. Günter Strunz

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Guest Editor

German Aerospace Centre (DLR), German Remote Sensing Data Centre (DFD), Geo-Risks and Civil Security, Oberpfaffenhofen, 82234 Weßling, Germany
Interests: disaster management; early warning systems; risk assessment

Prof. Dr. Zhenhong Li

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Guest Editor

College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, China
Interests: synthetic aperture radar (SAR); interferometric SAR (InSAR); time series; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Joachim Post

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Guest Editor

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
Interests: space technology and application; disaster management; knowledge management; development cooperation

Dr. Juan Carlos De Villagrán De Léon

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Guest Editor

Prof. Dr. Shunichi Koshimura

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Guest Editor

Disaster Geo-Informatics Laboratory, International Research Institute of Disaster Science, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572, Japan
Interests: earth observation; numerical modeling; disaster management; early warning; tsunami; flood; earthquake
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Roberto Tomás

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Guest Editor

Department of Civil Engineering, University of Alicante, Campus de San Vicente del Raspeig s/n, 03080 Alicante, Spain
Interests: rock mechanics; landslides; infrastructures; remote sensing; geotechnical engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Peter Spruyt

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Guest Editor

European Commission, DG -Joint Research Centre (JRC), Institute for the Protection and Security of the Citizen (IPSC), Global Security and Crisis Management Unit

Dr. Michael Judex

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Guest Editor

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 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

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

Published Papers (14 papers)

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Research

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8 pages, 1994 KiB

Open AccessCommunication

Earth Observation Actionable Information Supporting Disaster Risk Reduction Efforts in a Sustainable Development Framework

by Alberto Lorenzo-Alonso, Ángel Utanda, María E. Aulló-Maestro and Marino Palacios

Remote Sens. 2019, 11(1), 49; https://doi.org/10.3390/rs11010049 - 29 Dec 2018

Cited by 15 | Viewed by 4982

Abstract

Disaster risk reduction (DRR) is a high priority on the agenda of main stakeholders involved in sustainable development, and earth observation (EO) can provide useful, timely, and economical information in this context. This short communication outlines the European Space Agency’s (ESA) specific initiative to promote the use and uptake of satellite data in the global development community: Earth Observation for Sustainable Development (EO4SD). One activity area under EO4SD is devoted to disaster risk reduction (EO4SD DRR). Within this project, a team of European companies and institutions are tasked to develop EO services for supporting the implementation of DRR in International Financial Institutions’ (IFI) projects. Integration of satellite-borne data and ancillary data to generate insight and actionable information is thereby considered a key factor for improved decision-making. To understand and fully account for the essential user requirements (IFI and client states), engagement with technical leaders is crucial. Fit-for-purpose use of data and comprehensive capacity building eventually ensure scalability and long-term transferability. Future perspectives of EO4SD and DRR regarding mainstreaming are also highlighted. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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19 pages, 28952 KiB

Open AccessArticle

2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

by Veronika Gstaiger, Jiaojiao Tian, Ralph Kiefl and Franz Kurz

Remote Sens. 2018, 10(12), 2054; https://doi.org/10.3390/rs10122054 - 17 Dec 2018

Cited by 8 | Viewed by 6614

Abstract

Large-scale events represent a special challenge for crisis management. To ensure that participants can enjoy an event safely and carefree, it must be comprehensively prepared and attentively monitored. Remote sensing can provide valuable information to identify potential risks and take appropriate measures in order to prevent a disaster, or initiate emergency aid measures as quickly as possible in the event of an emergency. Especially, three-dimensional (3D) information that is derived using photogrammetry can be used to analyze the terrain and map existing structures that are set up at short notice. Using aerial imagery acquired during a German music festival in 2016 and the celebration of the German Protestant Church Assembly of 2017, the authors compare two-dimensional (2D) and novel fusion-based 3D change detection methods, and discuss their suitability for supporting large-scale events during the relevant phases of crisis management. This study serves to find out what added value the use of 3D change information can provide for on-site crisis management. Based on the results, an operational, fully automatic processor for crisis management operations and corresponding products for end users can be developed. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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16 pages, 3509 KiB

Open AccessArticle

Classification of Landslide Activity on a Regional Scale Using Persistent Scatterer Interferometry at the Moselle Valley (Germany)

by Andre Cahyadi Kalia

Remote Sens. 2018, 10(12), 1880; https://doi.org/10.3390/rs10121880 - 24 Nov 2018

Cited by 22 | Viewed by 4421

Abstract

Landslides are a major natural hazard which can cause significant damage, economic loss, and loss of life. Between the years of 2004 and 2016, 55,997 fatalities caused by landslides were reported worldwide. Up-to-date, reliable, and comprehensive landslide inventories are mandatory for optimized disaster risk reduction (DRR). Various stakeholders recognize the potential of Earth observation techniques for an optimized DRR, and one example of this is the Sendai Framework for DRR, 2015–2030. Some of the major benefits of spaceborne interferometric Synthetic Aperture Radar (SAR) techniques, compared to terrestrial techniques, are the large spatial coverage, high temporal resolution, and cost effectiveness. Nevertheless, SAR data availability is a precondition for its operational use. From this perspective, Copernicus Sentinel-1 is a game changer, ensuring SAR data availability for almost the entire world, at least until 2030. This paper focuses on a Sentinel-1-based Persistent Scatterer Interferometry (PSI) post-processing workflow to classify landslide activity on a regional scale, to update existing landslide inventories a priori. Before classification, a Line-of-Sight (LOS) velocity conversion to slope velocity and a cluster analysis was performed. Afterwards, the classification was achieved by applying a fixed velocity threshold. The results are verified through the Global Positioning System (GPS) survey and a landslide hazard indication map. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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24 pages, 23040 KiB

Open AccessArticle

What Rainfall Does Not Tell Us—Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress

by Markus Enenkel, Carlos Farah, Christopher Hain, Andrew White, Martha Anderson, Liangzhi You, Wolfgang Wagner and Daniel Osgood

Remote Sens. 2018, 10(11), 1819; https://doi.org/10.3390/rs10111819 - 16 Nov 2018

Cited by 19 | Viewed by 5015

Abstract

Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety nets, are gaining importance as complementary sources of information. This study concentrates on the analysis of satellite-derived multi-sensor soil moisture (ESA CCI, Version v04.2), the evapotranspiration-based Evaporative Stress Index (ESI), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates in nine East African countries. Based on spatial correlation analysis, we found matching spatial/temporal patterns between all three datasets, with the highest correlation coefficient occurring between October and March. In large parts of Kenya, Ethiopia, and Somalia, we observed a lower (partly negative) correlation coefficient between June and August, which was likely caused by issues related to cloud cover and the volume scattering of microwaves in sandy, hot soils. Based on simple linear and logit regression analysis with annual, national maize yield estimates as the dependent variable, we found that, depending on the chosen period (averages per year, growing or harvesting months), there was added value (higher R-squared) if two or all three variables were combined. The ESI and soil moisture have the potential to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface in operational parametric insurance projects. For the development and calibration of WII and RCC, this means that better proxies for historical and potential future drought impact can strengthen “drought narratives”, resulting in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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20 pages, 4022 KiB

Open AccessArticle

Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time

by Daniele Ehrlich, Michele Melchiorri, Aneta J. Florczyk, Martino Pesaresi, Thomas Kemper, Christina Corbane, Sergio Freire, Marcello Schiavina and Alice Siragusa

Remote Sens. 2018, 10(9), 1378; https://doi.org/10.3390/rs10091378 - 30 Aug 2018

Cited by 36 | Viewed by 6798

Abstract

Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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19 pages, 15778 KiB

Open AccessArticle

Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series

by Stephanie Olen and Bodo Bookhagen

Remote Sens. 2018, 10(8), 1272; https://doi.org/10.3390/rs10081272 - 13 Aug 2018

Cited by 44 | Viewed by 6558

Abstract

The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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19 pages, 7268 KiB

Open AccessEditor’s ChoiceArticle

Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment

by Guy J-P. Schumann, G. Robert Brakenridge, Albert J. Kettner, Rashid Kashif and Emily Niebuhr

Remote Sens. 2018, 10(8), 1230; https://doi.org/10.3390/rs10081230 - 06 Aug 2018

Cited by 93 | Viewed by 12137

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 approach. Since the 1970s, there have been many studies in the scientific literature about mapping and monitoring of floods using data from various sensors onboard different satellites. The field has now matured and hence there is a general consensus among space agencies, numerous organizations, scientists, and end-users to strengthen the support that satellite missions can offer, particularly in assisting flood disaster response activities. This has stimulated more research in this area, and significant progress has been achieved in recent years in fostering our understanding of the ways in which remote sensing can support flood monitoring and assist emergency response activities. This paper reviews the products and services that currently exist to deliver actionable information about an ongoing flood disaster to emergency response operations. It also critically discusses requirements, challenges and perspectives for improving operational assistance during flood disaster using satellite remote sensing products. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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19 pages, 5985 KiB

Open AccessArticle

An Observation Task Chain Representation Model for Disaster Process-Oriented Remote Sensing Satellite Sensor Planning: A Flood Water Monitoring Application

by Chao Yang, Jin Luo, Chuli Hu, Lu Tian, Jie Li and Ke Wang

Remote Sens. 2018, 10(3), 375; https://doi.org/10.3390/rs10030375 - 01 Mar 2018

Cited by 7 | Viewed by 5408

Abstract

An accurate and comprehensive representation of an observation task is a prerequisite in disaster monitoring to achieve reliable sensor observation planning. However, the extant disaster event or task information models do not fully satisfy the observation requirements for the accurate and efficient planning of remote-sensing satellite sensors. By considering the modeling requirements for a disaster observation task, we propose an observation task chain (OTChain) representation model that includes four basic OTChain segments and eight-tuple observation task metadata description structures. A prototype system, namely OTChainManager, is implemented to provide functions for modeling, managing, querying, and visualizing observation tasks. In the case of flood water monitoring, we use a flood remote-sensing satellite sensor observation task for the experiment. The results show that the proposed OTChain representation model can be used in modeling process-owned flood disaster observation tasks. By querying and visualizing the flood observation task instances in the Jinsha River Basin, the proposed model can effectively express observation task processes, represent personalized observation constraints, and plan global remote-sensing satellite sensor observations. Compared with typical observation task information models or engines, the proposed OTChain representation model satisfies the information demands of the OTChain and its processes as well as impels the development of a long time-series sensor observation scheme. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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19 pages, 10514 KiB

Open AccessArticle

Sensitivity and Limitation in Damage Detection for Individual Buildings Using InSAR Coherence—A Case Study in 2016 Kumamoto Earthquakes

by Ryo Natsuaki, Hiroto Nagai, Naoya Tomii and Takeo Tadono

Remote Sens. 2018, 10(2), 245; https://doi.org/10.3390/rs10020245 - 06 Feb 2018

Cited by 23 | Viewed by 5672

Abstract

In this paper, evaluation results are presented for multi-temporal interferometric coherence analysis using a Synthetic Aperture Radar (SAR) for damage assessment in an urban area. The latest space-borne SARs potentially have a high enough spatial resolution to assess individual buildings. However, interferometric coherence analysis has not been evaluated for its limitation in sensitivity and size of damaged buildings. In particular, the correlation between the coherence analysis and the damage level referred to by architectural assessments has been an open question. In this paper, analytical results using ALOS-2 PALSAR-2 datasets are presented from the 2016 Kumamoto earthquakes in Japan. For reference, building damage was assessed throughout the central urban area and specifically at a catastrophically damaged district. The results show that the buildings should be larger than a window size of the coherence for damage detection, and the damage level should be larger than Level-2 of 5, classified with the European Macroseismic Scale 1998 (EMS-98). Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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16044 KiB

Open AccessArticle

A GIS-Based Procedure for Landslide Intensity Evaluation and Specific risk Analysis Supported by Persistent Scatterers Interferometry (PSI)

by Silvia Bianchini, Lorenzo Solari and Nicola Casagli

Remote Sens. 2017, 9(11), 1093; https://doi.org/10.3390/rs9111093 - 26 Oct 2017

Cited by 21 | Viewed by 6630

Abstract

The evaluation of landslide specific risk, defined as the expected degree of loss due to landslides, requires the parameterization and the combination of a number of socio-economic and geological factors, which often needs the interaction of different skills and expertise (geologists, engineers, planners, administrators, etc.). The specific risk sub-components, i.e., hazard and vulnerability of elements at risk, can be determined with different levels of detail depending on the available auxiliary data and knowledge of the territory. These risk factors are subject to short-term variations and nowadays turn out to be easily mappable and evaluable through remotely sensed data and GIS (Geographic Information System) tools. In this work, we propose a qualitative approach at municipal scale for producing a “specific risk” map, supported by recent satellite PSI (Persistent Scatterer Interferometry) data derived from SENTINEL-1 C-band images in the spanning time 2014–2017, implemented in a GIS environment. In particular, PSI measurements are useful for the updating of a landslide inventory map of the area of interest and are exploited for the zonation map of the intensity of ground movements, needed for evaluating the vulnerability over the study area. Our procedure is presented throughout the application to the Volterra basin and the output map could be useful to support the local authorities with updated basic information required for environmental knowledge and planning at municipal level. Moreover, the proposed procedure is easily managed and repeatable in other case studies, as well as exploiting different SAR sensors in L- or X-band. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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34609 KiB

Open AccessArticle

Effect of Label Noise on the Machine-Learned Classification of Earthquake Damage

by Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen and Timothy C. Havens

Remote Sens. 2017, 9(8), 803; https://doi.org/10.3390/rs9080803 - 04 Aug 2017

Cited by 26 | Viewed by 6706

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 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

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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Review

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30 pages, 1164 KiB

Open AccessFeature PaperReview

Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review

by Saman Ghaffarian, Norman Kerle and Tatiana Filatova

Remote Sens. 2018, 10(11), 1760; https://doi.org/10.3390/rs10111760 - 07 Nov 2018

Cited by 57 | Viewed by 13997

Abstract

Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods. While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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Other

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7 pages, 897 KiB

Open AccessFeature PaperPerspective

Volunteered Geographic Information for Disaster Risk Reduction—The Missing Maps Approach and Its Potential within the Red Cross and Red Crescent Movement

by Stefan Scholz, Paul Knight, Melanie Eckle, Sabrina Marx and Alexander Zipf

Remote Sens. 2018, 10(8), 1239; https://doi.org/10.3390/rs10081239 - 07 Aug 2018

Cited by 30 | Viewed by 9703

Abstract

For the last few years, the increasing need for humanitarian support has led to increasing demand and responsibilities for the international humanitarian system. This trend raises questions regarding the use of alternative and complementary data sources and potential additional actors and communities that could be involved in support efforts and cover some of the tasks of humanitarian organizations. The article provides an overview of the Red Cross and Red Crescent movement, their practices and activities as well as current needs and challenges. The article illustrates the potential of OpenStreetMap and digital volunteers for humanitarian activities, with a particular focus on disaster risk reduction in the scope of the Missing Maps project. The background and objective of the collaborative project as well as its potential and impact for the Red Cross and Red Crescent movement are elucidated. The conclusion and outlook section presents future plans and visions to make further use of the potential of the Missing Maps approach in additional sectors and contexts. Full article

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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41330 KiB

Open AccessTechnical Note

Mapping Vulnerable Urban Areas Affected by Slow-Moving Landslides Using Sentinel-1 InSAR Data

by Marta Béjar-Pizarro, Davide Notti, Rosa M. Mateos, Pablo Ezquerro, Giuseppe Centolanza, Gerardo Herrera, Guadalupe Bru, Margarita Sanabria, Lorenzo Solari, Javier Duro and José Fernández

Remote Sens. 2017, 9(9), 876; https://doi.org/10.3390/rs9090876 - 23 Aug 2017

Cited by 74 | Viewed by 9850

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 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

(This article belongs to the Special Issue Earth Observation to Support Disaster Preparedness and Disaster Risk Management)

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