This article was downloaded by: [UQ Library] On: 30 September 2013, At: 03:47 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Environmental Hazards Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tenh20 Free or low-cost geoinformatics for disaster management: Uses and availability issues Richard M. Teeuw a , Mathias Leidig a , Charlotte Saunders a & Naomi Morris a a Centre for Applied Geosciences, School of Earth and Environmental Sciences , University of Portsmouth , Burnaby Building, Burnaby Road, Portsmouth , PO1 3QL , UK Published online: 02 Nov 2012. To cite this article: Richard M. Teeuw , Mathias Leidig , Charlotte Saunders & Naomi Morris (2013) Free or low-cost geoinformatics for disaster management: Uses and availability issues, Environmental Hazards, 12:2, 112-131, DOI: 10.1080/17477891.2012.706214 To link to this article: http://dx.doi.org/10.1080/17477891.2012.706214 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms- and-conditions
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This article was downloaded by: [UQ Library]On: 30 September 2013, At: 03:47Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Environmental HazardsPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tenh20
Free or low-cost geoinformaticsfor disaster management: Uses andavailability issuesRichard M. Teeuw a , Mathias Leidig a , Charlotte Saunders a &Naomi Morris aa Centre for Applied Geosciences, School of Earth andEnvironmental Sciences , University of Portsmouth , BurnabyBuilding, Burnaby Road, Portsmouth , PO1 3QL , UKPublished online: 02 Nov 2012.
To cite this article: Richard M. Teeuw , Mathias Leidig , Charlotte Saunders & Naomi Morris(2013) Free or low-cost geoinformatics for disaster management: Uses and availability issues,Environmental Hazards, 12:2, 112-131, DOI: 10.1080/17477891.2012.706214
To link to this article: http://dx.doi.org/10.1080/17477891.2012.706214
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.
This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions
Free or low-cost geoinformatics for disaster management: Uses andavailability issues
Richard M. Teeuw∗, Mathias Leidig, Charlotte Saunders and Naomi Morris
Centre for Applied Geosciences, School of Earth and Environmental Sciences, University of Portsmouth,Burnaby Building, Burnaby Road, Portsmouth, PO1 3QL, UK
(Received 19 December 2010; final version received 17 May 2012)
The disaster management applications of geographical information systems and remote sensingare examined relative to the disaster cycle, in pre-disaster, crisis and post-disaster contexts. Wefocus on the uses and limitations of free or low-cost data and software. A wide range ofgeospatial datasets are currently freely available, from digital elevation models (DEMs) andthematic digital maps, to multispectral satellite imagery and virtual globes, such as BingMaps. Maps of hazardous terrain and vulnerable features can be derived from sets ofsatellite data such as shuttle radar topography mission DEMs and Landsat imagery. Thederived maps are particularly useful for district scale (1:25 to 1:100 K) disaster managementin low-income countries. Detailed maps (i.e. better than 1:25 K scale) of hazardous terrainand vulnerable features generally require expensive high-resolution satellite imagery oraerial photography.
Although the Internet allows the distribution of free or low-cost geospatial data, softwareand training materials, there are still some countries with limited Internet access. Dataintegration, spatial/temporal analysis and map production are also limited by the frequentlyhigh price of geoinformatic software, making it a priority to develop suitable Free andOpen-Source Software.
This review focuses on the disaster management uses of geospatial data and software that are freeof cost or of low cost (i.e. costing less than US$100). Such an approach is clearly useful to low-income countries, which are on average 20 times more vulnerable to natural disasters than richercountries, measured by impact on GDP per head (World Bank: www.worldbank.org).
1.1. Remote sensing
Sensors on aircraft or satellites can provide an effective means of mapping and monitoringhazards and vulnerable features, before, during and after a disaster (e.g. Kelmelis, Schwartz,Christian, Crawford, & King, 2006; Zeil, 2003). Most remote-sensing systems use multispectraloptical sensors, detecting in visible and infrared wavelengths. A major limitation of opticalsensors is that they cannot detect through cloud and they operate primarily during daytime.
However, radar sensors can penetrate cloud cover and can detect at night, they are also sensitive tosoil moisture and are particularly useful when mapping and monitoring flood events (e.g. Teeuw,2007).
Between 2000 and 2011, the number of civilian space-borne remote-sensing systems more thandoubled, from 47 to 108 (http://eoedu.belspo.be/en/satellites/launch_chrono.html, 24.02.2011).Table 1 provides a summary of high-resolution (0.5–5 m pixel) and medium-resolution (5–100m pixel) satellite data that are of use for disaster management. Satellite remote sensing is oftenthe most up-to-date source of information during a disaster (Zhang and Kerle, 2008) and canprovide synoptic data, allowing district-scale damage assessments to be rapidly undertaken, evenif disaster-affected areas are inaccessible for ground surveys.
From a disaster management perspective – where good communication is essential – it isworth noting that optical imagery is easy to understand, relative to topographic maps, as illus-trated by the widespread public use of Google Earth. Andrews Deller (2007) used colourAdvanced Spectral and Thermal Emission Radiometer (ASTER) imagery draped over a DEMto produce three-dimensional (3D) views of hazardous terrain in rural Ethiopia: the resulting‘birds eye’ views were readily understood by local people who had no prior experience ofimage interpretation or map-reading.
1.2. Geographical information systems
A geographical information system (GIS) enables the capturing, integrating, manipulating, ana-lysing and displaying of data that are spatially referenced to the Earth. GIS is more than just atool for database management and computer-based mapping, it is particularly useful formerging data from diverse origins, such as the socio-economic and geoscience datasets that areneeded when assessing risk of disaster. Furthermore, GIS is an ideal tool for disaster managementbecause of the many spatial aspects of disasters, from the distribution of hazardous features, to theselection and modelling of evacuation routes, or to assessing changes before and after the impactof a disaster. GIS-based modelling can provide a better understanding of the processes causingdisasters, helping to improve the effectiveness of disaster management (e.g. Buechele et al.,2006; Dawson et al., 2009). Some disaster management applications of GIS are summarized inTable 2, in the context of the main stages of the disaster cycle.
2. Uses of free or low-cost geoinformatics in disaster management
Free or low-cost data are of no use if poorly funded users cannot afford the geoinformatic softwareneeded for data processing, analysis and map production. Access to free geospatial data, free soft-ware and free geoinformatic tutorials can be provided via the Internet, so a further requirement islow-cost Internet access. Another requirement is ‘peopleware’: staff trained in the use of geospa-tial technologies and the processing of geoinformatic data. If these items (discussed in detailbelow) are in place, then conditions are favourable for the long-term sustainable use of geospatialdata for disaster management, what we term as sustainable geoinformatics.
2.1. Free or low-cost remote-sensing data
Satellite imagery with high-to-moderate resolution (i.e. pixels in the 1 × 1 to 100 × 100 m range) canbe grouped into three price ranges: expensive, low-cost, or free of charge, as summarized in Table 1.Landsat or ASTER imagery can be processed to produce maps at the district level (1:25,000–1:100,000 scale) of key information for disaster management, such as population distribution, landcover and terrain (e.g. Jensen and Cowen, 1999; Teeuw, 2007; Baud, Kuffer, Pfeffer, Sliuzas, &
Table 1. Types of satellite remote-sensing data: their specifications, availability and cost.
SensorOwner/operator Spectral/spatial resolution
Temporalresolution
Swath Data dissemination Cost
Free or low-cost dataASTER (Advanced
Spectral andThermal EmissionRadiometer)
JAXA (Japan)/NASA (USA)
VNIR: 3 bands, 15 m 16 Days 60 km Land ProcessesDistributed ActiveArchive Centre(LPDAAC) orWarehouse InventorySearch Tool (WIST) orGlobal Land CoverFacility (GLCF)
$85 Free forregistered scienceusers
SWIR: 6 bands, 30 mTIR: 5 bands, 90 m
ASTER G-DEM JPL/NASAJAXA
30 m N/A N/A LPDAAC or WIST Free
DMSP NGDC (USA) Various sensors withnumerous products
N/A N/A NOAA/NGDC Free
specifically – globalnightlights
Landsat-5 (thematicmapper, TM)
USGS (USA) VNIR-SWIR: 6 bands,30 m
16 Days 185 km GLCF or USGS Free
TIR: 1 band 120 mLandsat-7 (enhanced
thematic mapper,ETM+)
USGS (USA) Pan: 15 m 16 Days 185 km GLCF or USGS Free (nb. sensor notoperatingcorrectly sinceMay 2003)
VNIR-SWIR: 6 bands,30 m
TIR: 2 bands 60 m(differential gain)
MODIS NASA (USA) Bands 1–2: 250 m Mostly 1–2days, butNDVI andEVI both 16days)
2,330 km NASA EOS Data Gatewayor LPDAAC, or WIST
FreeBands 3–7: 500 mBands 8–36: 1,000 m
SRTM NASA (USA) DTED L2 30 m (USAonly); DTED L1 90 m(rest of World)
N/A N/A GLCF Or CIGAR-CSI Free
TopSat DMC (UK) Pan: 3 m 1 Day 17 km Infoterra/EADS FreeRGB: 3 bands, 6 m 12 km
Medium-cost data
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ALOS – AVNIR-2 JAXA (Japan) 4 bands (VNIR) 10 m 2 Days 70 km Eurimage E500ALOS –PalSAR JAXA (Japan) L-Band; multi-
polarization; 10–100 m,mode dependant
2 Days 30–350 km SpotImage E500
ALOS – PRISM JAXA (Japan) Pan: 2.5 m 2 Days 35 km Eurimage E500DMC (disaster
monitoringconstellation)
Various VNIR: 4 bands 30 m Daily 600 km Infoterra £0.082/km2
6 satellites in constellation DMC Archive: £0.32–£0.008/km2
ENVISAT-ASAR ESA C-Band: 5 polarizationmodes; 30 m–1,000 m
35 Days 5–400 km MDA Associates orSpotImage
£280–£426E300 (Archive)
Expensive dataCARTO-SAT ISRO (India) Two pan sensors, 2.5 m,
with stereo capability5 Days 30 km GeoEye Not yet available
EO-1 Hyperion NASA (USA) 220 bands (036–2.6 mm),30 m
16 Days HS 7.6 km EROS EO1 programmedon request, freefor successfulresearchproposals
FORMO-SAT NSPO (China) Pan 2 m Daily 24 km SpotImage Archive: E2,500–4,000;
VNIR 4 bands 8 m Program: E3,500–5,000
GeoEye-1 GeoEye (USA) Pan: 0.5 m 2–3 Days 15.2 km Eurimage $25/ km2 (minimum100 km2), less forarchived data
VNIR: 4 bands, 1.65 m
IKONOS GeoEye (USA) Pan: 1 m 3–5 Days 11 km Eurimage $20/ km2 or $10,00/km2 archiveVNIR: 4 bands, 4 m
Quickbird Digital Globe(USA)
Pan: 0.6 m 1–3.5 Days 16.5 km Eurimage $14–$20/km2
N/A 60 km at ndir120 km×120 kmbi-strip Or60 km×180 kmtri-strip
SpotImage N/A
Pan-sharpened: 1.5 mcolour merge combiningpanchromatic and fourmultispectral bands
Pleiades 1: 03/2011 CNES (France) Pan: 0.7 m at nadir 26 days 20 km (nadir) N/A N/A2: 03/2012 Multispectral: 2.8 m
For more on exiting and planed sensors the reader is referred to: http://www.eohandbook.com/ ‘. . . provides the most up-to-date and comprehensive overview of existing and upcomingsatellite missions, their instruments and measurements of more than 30 space agencies worldwide. Prepared under the auspices of ESA on behalf of the Committee on Earth ObservationSatellites, the report features details of 267 earth-observing satellite missions and 785 instruments that are currently operating or planned for launch in the next 15 years’ (source: http://www.esa.int).N/A, not applicable.
Table 2. Disaster management applications of GIS and remote sensing, in the context of the disaster cycle.
Pre-disaster/preparedness Examples
Imagery from meteorological satellites, for regionalearly warning maps. The vegetation detectioncapabilities of meteorological satellites and Landsathave been used in the management of drought andfamine, as well as locust infestations, e.g. theFamine Early Warning System
Smith and Petley (2009): http://www.fews.net
Maps showing the distribution of hazardous terrainand vulnerable features, produced from the analysisof medium-resolution to high-resolution remotesensing data
Theilen-Willige (2006), Tauenbock et al. (2008),Dyke et al. (2011)
GIS-generated vulnerability maps, from census data(e.g. social deprivation indices), topographic maps,land cover maps and ground-based socio-economicsurveys
Edwards, Gustafsson, and Naslund-Landenmark(2007), Cutter et al. (2008), Baud et al. (2010)
Modelling ‘what if’ scenarios and thus helpingemergency planners or disaster managers with theirdecision-making; e.g. the Geospatial DynamicResponse Assessment Tool uses volcanic hazardmaps, historic data, satellite imagery, DEMs andflow simulations to assess eruption scenarios
Paresch, Cavarra, Favavli, Gianini, and Meriggi(2000), Renschler and Sheridan (2002)
Using GIS to identify neighbourhoods facingevacuation transport difficulties.
Cova and Church (1997), Gunes and Kovel (2000)
Reclassify digital geological and land cover maps,producing Resource Maps: features that will aiddisaster recovery (e.g. woodland for timber or fuel,water resources, flat ground for new housing).
Andrews Deller (2007), Teeuw (2007)
Disaster event/crisis responseRegional damage maps: useful for assessing the extent
and severity of various impacts, e.g. use of remotesensing to show the extent of tsunami damage afterthe Indian Ocean Tsunami
Stevens (2005), Teeuw (2005); Belward et al.(2007), Mcadoo et al. (2007), Roemer, JirapongJeewarongkakul, Kaiser, Ludwig, and Sterr(2010)
Using airborne sensors or very fine resolution satelliteimagery for rapid search operations and damageassessments – high data costs typically limit thistype of survey to urban areas
Thomas, Hendrix, and Congalton (2003), Saito(2004), Brown et al. (2011)
Near real-time mapping for disaster management: thisrequires frequently updated data
Gomez-Fernandez (2000), Kelmelis et al. (2006),Zhang and Kerle (2008)
Inventory maps of building damage, populationdistribution, transport infrastructure etc.
Paresch et al. (2000), Haynes, Barclay, andPidgeon (2007), Crowley et al. (2010)
Daily situation maps, using satellite imagery as base-maps for ground-based updates of disaster reliefdata: this is now standard practice at disaster crisisresponse centres
For examples, see: www.mapaction.org.uk
Post-disaster/recoveryOpen Street Map mapping of road networks, buildings
and critical infrastructure (e.g. Haiti, 2010)http://wiki.openstreetmap.org/wiki/WikiProject_
HaitiGIS-based change detection, for example showing
changes in the distribution of displaced people; thespread of diseases; the reconstruction of buildingsand infrastructure
For examples, see: www.mapaction.org.uk
Local-scale monitoring of refugee camps, using aerialphotography or high-resolution space imagery
Bjorgo (2000), Giadia, de Groeve, Ehrlich, andSoille (2003)
Karrup-pannan, 2010). Within a few days of the 2004 Indian Ocean Tsunami, many remote-sensingscientists contributed to a NASA-coordinated programme of regional inundation mapping, based onthe processing of shuttle radar topography mission (SRTM) data (Teeuw, 2005). Since then, research-ers have used SRTM, Landsat enhanced thematic mapper (ETM)+ and ASTER data have been used tomodel and map tsunami run-up distances and inundation zones; densely populated areas and proxi-mity to high-ground flood refuge sites, as well as impacts of seawater on inundated farm land (e.g.Belward et al., 2007; Mcadoo et al., 2007; Theilen-Willige, 2006). Figure 1 shows an example of geo-hazard, vulnerability and risk mapping, derived from Landsat ETM+ imagery and SRTM DEM data.The ASTER sensor, with its DEM capability and better spectral resolution than Landsat, can providemore information and potentially better hazard, vulnerability and risk maps. However, even the rela-tively low cost of US$85 for each 60 × 60-km ASTER scene, could be too expensive for a low-income country with many thousands of square kilometres to map. In 2009, NASA/METI releaseda freely available, near-global ASTER DEM (GDEM) with 30 m pixels, a major improvement onthe 90 m pixels of the SRTM data that are available for non-USA territories.
The crisis response stage of disasters was not adequately supported by remote-sensing data priorto 2004, due to most high-resolution imagery being expensive, with slow rates of data acquisition,data processing and delivery. Unmanned aerial vehicles, from kites through to drones, are increas-ingly being used as platforms for remote-sensing systems and can provide detailed real-timeviews of disaster-affected area at low cost relative to aerial surveys of very high-resolution satelliteimagery (e.g. Jackson, Parameswaran, Buford, Lewis, & Roy, 2006; Maza, Caballero, Capitan,Martınez-De-Dios, & Ollero, 2010; Quaritsch et al., 2010). However, unmanned aerial vehicleremote sensing is a relatively novel application in the disaster management sector: a number of ope-rational issues are still to be sorted, such as operating in windy conditions, telemetric downloading ofdata or geocorrection of oblique-view imagery.
Since 2004, the United Nations’ International Charter on Space and Major Disasters (referredto below as ‘the Charter’) has enabled the free provision of satellite imagery to those managingdisaster-hit areas, via agencies such as UNOOSA and UNOSAT (Stevens, 2005; Zhang and Kerle,2008). Consequently, disaster management maps derived from satellite imagery, typicallyshowing the areas affected, centres of population and transport links, can be produced within2 days of a disaster. The Charter has had over 200 activations and has greatly assisted disastermanagement (www.disasterscharter.org/new_e.html).
It is pertinent to note here that the satellite images distributed via the Charter are pre-processedand distributed as jpeg files (i.e. bitmaps). A jpeg image can be opened without the need of specialistsoftware and can be rapidly distributed due to its small file size, even in regions with slow Internetconnections. Hence, jpeg is the format of choice when maps are produced with the aim of distributingthem to many persons or agencies. However, the changes that can be made to jpeg maps are limited tosimple image enhancements, rather than complex image processing. Consequently, some relieforganizations want the Charter to provide access to unprocessed multispectral satellite data, sothat they can rapidly create their own sets of user-specific maps (McWilliam, 2010, personalcommunication).
2.2. Free or low-cost digital map data
Global-coverage digital topographic maps, preferably at scales in the 1:10,000 to 1:100,000 range,are key elements of GIS-based mapping and modelling for disaster management (e.g. Eakins,Taylor, Carignan, & Kenny, 2010). Particularly important items for such maps are informationon population distribution, transport infrastructure, critical facilities (such as hospitals), the locationsof large buildings (for shelter and food/water distribution) and parks or open areas to pitch tents fordisplaced people. The 2010 Haiti earthquake highlighted that street maps are essential for navigation
within disaster-impacted areas: buildings collapsed, but the road network remained. Some key web-sites for obtaining free geospatial data that are of use for disaster management are summarized inTable 3.
2.3. Timely data
It is widely recognized that GIS can facilitate crucial decision making in the early stages of dis-aster relief operations (FEMA, 2003). Data and GIS analysis are needed for many users, notablycommand and control centres, first-responders, public information managers and (later) recoveryworkers and reconstruction planners (Gunes and Kovel, 2000). The Haiti earthquake demon-strated the value of crowd-source mapping, with relief workers using GPS and Open StreetMap freeware to rapidly create maps of damaged neighbourhoods (see http://wiki.openstreetmap.org/wiki/WikiProject_Haiti).
Maps provided via the Charter are useful for assessing damage severity, locating refuge areasfor displaced people and assessing proximity to resources. However, the Charter maps rarely
Figure 1. Examples of disaster preparedness mapping: (a) landslide hazard, (b) population vulnerabilityand (c) risk of disaster from landslides. The maps were derived from Landsat ETM+ multispectral satelliteimagery and SRTM DEM data after approximately 6 hours of processing, using ERDAS Imagine software.Image (d) is a 3D view of the study region (the Vera Basin, SE Spain), with the landslide hazard mapimported into Google Earth for easier visualization and the addition of other geospatial data layers.
arrive frequently enough to be useful for crisis management: maps that were updated on a dailybasis would be ideal. Crowley (2010), participating in an e-conference on crisis management afterearthquakes, reported that maps of areas in Sumatra affected by the 2004 Tsunami might havetaken only 2 days to produce, but it then often took many days, sometimes weeks, for fieldworkers to get requested maps. For instance: ‘. . . back in January 2005, as late as the end ofweek 2, my friends were still fighting to get useful imagery post-tsunami. They made specificrequests for bathymetry of the harbor, downed bridges, and a pre-post comparison of night-time infrared of the population centers (so that they could get a very rough estimate of wherepeople were congregating). They got the first item, but never received the last two. The bathy-metric imagery did not arrive until week 3, after a long and tortuous release process from theUSG over a ‘for official use only, no-foreigners’ classification prevented its release to the UN.And even then, it arrived on 60 CDs as dumb JPEGS in Powerpoint . . . requiring UN fieldstaff to spend lots of time processing them’ (Crowley, 2010).
Table 3. Geospatial data sources.
Website – name Website – address Information or data provided
CIESIN – SEDAC(Socioeconomic and EarthScience Data Centre)
http://sedac.ciesin.org/ ‘SEDAC’s mission is to develop andoperate applications that support theintegration of socioeconomic and earthscience data and to serve as an‘information gateway’ between the earthsciences and social sciences’
NASA – WIST (WarehouseInventory Search Tool)
https://wist.echo.nasa.gov/ Main download portal for all kind of NASAdata, including the NASA/METIASTER-DEM (GDEM)
UN – data http://data.un.org/ Various (33) United Nations databasesproviding information about populationin cities to Internet availability oreducation all over the world
USGS - Global VisualizationViewer
http://glovis.usgs.gov/ Data catalogue of the USGS providingaccess to LANDSAT, Aster MODIS andother satellite derived products
The website has many links to software andsoftware projects related to geography inparticular GIS, GPS and Google Earth
OSGeo – The Open SourceGeospatial Foundation
http://www.osgeo.org/ ‘Created to support and build the highest-quality open source geospatial software.Our goal is to encourage the use andcollaborative development ofcommunity-led projects’. The websitelinks to various software projectscovering:
† Web mapping (e.g. Mapserver)† Desktop Applications (e.g. GRASS,
‘OSSIM provides advanced geo-spatialprocessing capabilities through a state ofthe art C++ software library. A number oftools, applications, and examples areincluded with the distribution’
Another limiting aspect of the Charter is that it is designed for disaster response, it is a reactivetool. However, disaster risk reduction requires proactive, ‘pre-disaster’ mapping, with the pro-duction of disaster preparedness maps, showing hazardous terrain, vulnerability and risk of dis-aster, as well as resources that would be of use for recovery and reconstruction. The production ofdisaster preparedness maps would greatly benefit from the free supply of satellite imagery, par-ticularly with regard to data-impoverished low-income countries. A first step towards theCharter providing data for disaster preparedness mapping was in 2008, when satellite radarimagery was supplied for observing cloud-covered volcanic terrain on Montserrat, amidst con-cerns that an eruption could affect thousands of the island’s inhabitants.
2.4. Free or low-cost software
Partly in response to the high cost of the main commercial GIS software (e.g. ArcGIS, MapInfo),there has been increasing use of GIS that utilizes Free and Open-Source Software (FOSS; Jasiek,2003; http://www.opensourcegis.org/). The functionality of free or low-cost software with GISand/or image-processing capabilities is summarized in Table 4. For a more detailed review,with case studies and tutorials, the reader is referred to a useful handbook on free and low-costsoftware for humanitarian and disaster management applications, produced by the MapActionNGO (www.mapaction.org).
2.5. Public geoinformatic expertise
GIS technology has had a slow uptake in low-income countries due to a lack of hardware,resources and human capital. However, simple, low-cost GISs can allow local authorities tomore effectively incorporate local knowledge and ensure community participation. To achievethis in a long-term sustainable way, planners and disaster managers in developing countriesneed to be trained in the application of GIS and remote sensing. Regional workshops are an effec-tive way of providing ‘hands-on’ training on the uses of free or low-cost geoinformatics for dis-aster risk reduction. Easy-to-use sets of tutorials are essential for effective usage of the softwareand equipment Good examples are the tutorials developed by UNITAR and researchers at ClarkeUniversity (USA), for IDRISI and by staff at the University of Twente (ITC, the Netherlands) forIntegrated Land & Water Information System (ILWIS). Since 2007, the United Nations Platformfor Space-based Information for Disaster Management and Emergency Response has raisedawareness about potential disaster management applications of satellite remote-sensing data byrunning regional outreach events and training workshops (http://www.oosa.unvienna.org/oosa/en/unspider/recentworkshops.html).
Various innovations in geospatial technology over the past 20 years (e.g. GPS and in-carnavigation systems, virtual globes and Internet road maps) have broadened the geoinformaticuser-base, from a few GIS and remote-sensing specialists, to members of the general public.Smart phones, such as those produced by Apple or Samsung, have a built-in GPS, cameraand Internet access: these currently cost many hundreds of US$, but low-cost variants, retailingat less than US$100, are now being developed. Even very basic mobile phones have proven tobe very effective tools in disaster management via the sending of text messages. Crowley,Johnson, and Schuyler (2010) highlight the use of the Ushahidi crowd-sourcing freeware tocollate text messages sent by people trapped in collapsed buildings after the Haiti Earthquake,from which locations were then passed on to search and rescue teams. Text messages have alsoproven useful with the management of drought and famine, allowing farmers to downloadweather forecasts or check the prices that they would get for their produce at differentmarkets (Ewbank, 2011). Disaster crisis managers have also made effective use of mobile
∗Software can be combined, but does not include a map server with the default installation.
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devices and their Internet links. For instance, an iPhone application has been developed forCommand, Control, Communications, Computing and Information (C4I: Prendergast, 2009).Such command systems are currently expensive but are set to become progressively cheaperwith each new generation of mobile devices.
An aspect of the wider involvement of people in geoinformatics is the increased use of parti-cipatory GIS (PGIS). With PGIS, public experiences and local knowledge contribute to scientificknowledge and guide community risk assessment (e.g. Peters-Guarin, McCall, & van Westen,2011; see McCall, 2008, for an overview). Williams and Dunn (2003), Cronin et al. (2004)and Barclay et al. (2008) observed that PGIS involves seemingly incompatible datasets, particu-larly with regard to projects in developing countries, notably:
. Conflicts between conventional digital data and local knowledge
. Knowledge representation issues, particularly with regard to mental maps
. Appropriate data frameworks: notably detailed/quantitative vs. ‘low tech’/qualitative
. Researchers with limited experience of working together (e.g. development scientists,physical scientists, NGOs, community leaders and GIS practitioners)
. Limited involvement of less powerful community members (e.g. women and those who donot own land).
There has recently been a major shift in the provision of digital map data, a movement awayfrom a few centralized sources of digital map data, with many people contributing to the devel-opment of an Internet map database: this is sometimes called crowd-sourcing and the product canbe a geo-wiki. Examples of geo-wikis can be found at: http://www.kcl.ac.uk/schools/sspp/geography/research/emm/geodata/geowikis.html and http:// www.geo-wiki.org/index.php. Geo-wikis have been applied to disasters, for instance, in the aftermath of the 2006 Kashmir earth-quake: locals phoned road access details to overseas relatives, who posted that information asa geo-wiki map layer on Google Earth (McWilliam, 2010, personal communication). In the after-math of the 2010 Haiti earthquake, disaster managers in the Port au Prince region were faced withtwo major problems, a lack of digital street maps and a devastated urban landscape, which greatlyhindered navigation, search-and-rescue efforts, transportation of relief supplies and damageassessments. Within days of the Haiti earthquake, a geo-wiki originally developed in Britainfor producing free street maps was being used by crisis response teams to rapidly map the disas-ter-impacted areas (http://wiki.openstreetmap.org/wiki/WikiProject_Haiti). An example of theOpen Street Map mapping of Haiti is shown in Figure 2, with the focus on medical centresand camps for internally displaced people. There were three consecutive disasters in Haitiduring 2010: the devastating earthquake, followed a few weeks later by a hurricane and flooding,and then a cholera epidemic. Mapping the locations of displaced people was therefore a crucialaspect of the disaster management in Haiti.
2.6. The Internet and geospatial data
Added value has been given to numerous geospatial datasets that are freely available via the Inter-net, using algorithms that process those datasets to produce maps that can easily be viewed viavirtual globes. For instance, an algorithm for the global SRTM DEM dataset, allows the extentof a 4 m rise in sea level to be viewed along any coastline on Earth, as an overlay on Google Earthimagery (http://www.kcl.ac.uk/schools/sspp/geography/research/emm/geodata/sealevel.html).Internet-based virtual globes, also known as digital globes or geo-browsers, such as GoogleEarth, NASA World Wind and Microsoft Virtual Earth (Bing Maps), provide a user-friendlyinterface, with the capacity to overlay layers of digital map data and digital imagery onto
global DEMs (e.g. Whitmeyer, Nicoletti, & De Paor, 2010). Most virtual globes provide easy-to-use3D viewing and virtual flyovers, aiding the identification and mapping of hazardous terrain (e.g.Teeuw et al., 2009a, Teeuw, Solana, Dewdney, Rust, & Robertson, 2009b) and vulnerable features,highlighting ‘at risk’ areas and helping to reduce the risk of disaster, as illustrated in Figure 1.
Effective responses to disasters require rapid, coordinated responses from disaster manage-ment teams, aided by a GIS that has access to good-quality, relevant data (ArcNews, 2002).This is increasingly being achieved via Internet map servers, with many users accessing geospa-tial data from remote servers. An example from the water resources sector is the AguaAndesproject, hosted by Kings College London (see http://www.policysupport.org/links/aguaandes).The AguaAndes server provides free, non-copyright global data, with a spatial resolution of upto 1 ha, which can be processed via associated free modelling software (e.g. running ‘what if’scenarios for population increases or climate changes) and imported into geobrowsers, such asGoogle Earth or Google Maps. Voinov et al. (2010) argue that the modelling of complexsystems, such as the interactions between the socio-economic and bio-physical systems foundin disasters, requires an open-source approach and community-level modelling. This involvesthe free supply of copyright-free data from web-servers, using standardized software, data hand-ling, model outputs, data distribution and user interfaces. A single set of international standards iskey to the global uptake of open source computing: this is being facilitated by the Open GeospatialConsortium (http://www.opengeospatial.org/).
The disaster response to the Indian Ocean Tsunami led to some innovative Internet mappingdevelopments. Lembo, Bonneau, and O’Rourke (2008) discuss a low-cost, Integrated InternetMap Server (IIMS), developed by Cornell University. The IIMS was built around manifold (anopen source GIS that costs c. US$ 250, see Table 4): it can access GIS map data, web serversstoring multi-gigabyte remote-sensing imagery and dynamic data on warehouse databases. TheIIMS provided an Internet portal for mission-critical data to be rapidly assembled and accessed, aspart of a command and control centre for disaster management staff. Honda and Ninsawat (2005)used OGC software to link WMS servers and provide 14 organizations with various datasets for dis-aster management (e.g. maps, satellite images, aerial photographs, field survey data, socio-economicrecords): they found major savings in cost, data collection time and damage assessment time, as well asreduced duplication (see http://www.mapserver.hondalab.star.ait.ac.th/tsunami).
Figure 2. Example of Open Street Map mapping from Port au Prince, Haiti. Medical centres are shown(circled red crosses), along with temporary camps for internally displaced people (blue square with whitetent symbol).
A World Bank project, titled Disaster Risk Evaluation and Management (DREAM), uses bothsatellite and ground-based technologies, supported by NASA’s World Wind virtual globe, toevaluate disaster risk and assist disaster mitigation activities in Central America. DREAM usesrecent geoinformatic developments, such as 3D city modelling and the inclusion of ‘crowd-source’ public activity data from smartphones, such as photos and GPS locations. However,the DREAM approach has some limiting issues, notably its focus on mapping in urban and indus-trial areas, which requires detailed datasets. Consequently, DREAM is relatively expensive, withhigh costs (e.g. for data, storage, processing experts), although there might be solutions throughpartnerships and the selling of the results to private companies (Dyke et al., 2011). Another issuewith DREAM is that the data collection can be time consuming. For instance, archives of high-resolution optical imagery often have many gaps due to cloud cover: requests to the data suppliersfor new imagery to fill those gaps often take weeks to complete.
2.7. Free and easy Internet access?
Internet access in many low-income countries is often scarce and expensive, with low capacity.This severely limits access to archives of geoinformatic data, such as free satellite data fromthe Global Land Cover Facility, and is a major handicap to both development and disaster man-agement (e.g. Ernst, Kervyn, & Teeuw, 2008). Shortfalls in the capacity of the Internet are par-ticularly problematic in areas recently affected by a disaster event. For instance, the intensemedia coverage during the search and rescue phase of the 2010 Haiti earthquake disaster resultedin saturation of Internet bandwidth and satellite communication systems, disrupting some of thecrisis management activities.
2.8. Accuracy and effectiveness issues
Many factors affect the effectiveness and accuracy of maps produced from satellite imagery: adetailed review is beyond the scope of this paper. Accuracy assessments of geoinformatic datacan be divided into two categories: primary data, such as satellite imagery or DEMs, andderived data, such as thematic maps or geomorphometric indices. For instance, Kervyn,Kervyn, Goossens, Rowland, and Ernst (2007) compared the accuracy of SRTM and ASTERDEM data for volcanic cones in Hawai’i: they found that both DEMs over-estimated elevationsin vegetated zones, relative to the 1:24,000 USGS topographic map of the study area.
With regard to the accuracy of thematic maps derived from remotely sensed data, variousstudies have considered the wide range of approaches to accuracy assessment, the necessarydata requirements and the difficulties encountered (e.g. Stehman, 2000, 2001, 2006; Foody,2002; Allouche, Tsoar, & Kadmon, 2006; Congalton and Green, 2008). The choice and eventhe usefulness of such accuracy assessment is still the subject of debate (e.g. Foody, 2008).For instance, accuracy assessment for urban land cover mapping range from: ‘. . . none ofthe parameters expresses the consistency with reality, which still needs to be assessed by com-paring classification results with on-site samples . . . ’ (Hofmann, Strobl, & Nazarkulova, 2011);to 71–86 per cent accuracy (Taubenboeck, Esch, Wurm, Roth, & Dech, 2010, Esch, Felbier,Roth, & Dech, 2011) or even 97 per cent accuracy (Taubenboeck, Wegmann, Roth, Mehl, &Dech, 2009, 2011). Such variations in accuracy assessments are largely dependent on thespatial resolution of the remotely sensed data and the size of the study area (Cihlar, 2000).Foody (2008) identified the misinterpretation of pixels during the selection of referencesamples as a major problem for accuracy assessments. Mixed pixels can have a negativeeffect on accuracy if the land cover classification algorithm does not allow for multiple land-cover classes; it is therefore recommended that such classification uses pattern-based indices(Taubenboeck et al., 2011) or hybrid methods (Bernardini et al., 2010).
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Accuracy assessments in the natural hazards and disasters domain face many difficulties,notably the requirement for expertise amongst the assessor(s) in disciplines as diverse as clima-tology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography (Tau-benboeck et al., 2011). Another issue is the kind of hazard (landslide, flood etc.) and its scale:high-resolution data are required for local studies, whereas medium-resolution datasets may besufficient for regional studies. A key requirement when assessing the accuracy of predicted hazar-dous events is a database of past events, such as the geomorphological features of affected areasand the extent of damage. Such a database has been done for Switzerland (Hilker, Badoux, &Hegg, 2009), but such studies are the exception rather than the rule, as they generally involvetime-consuming and expensive research.
Although accuracy assessment is frequently used in projects that process satellite imagery forland cover classification, relatively little research has done into the accuracy of predicted areas ofimpact shown on disaster preparedness maps with the areas actually affected by a given hazardevent. The reasons for this are not clear: the scarcity of studies might simply reflect a scarcityof examples to study. However, it might also reflect a reluctance of researchers, or disastermanagers, or politicians, to highlight inadequate preparation or an underestimation of risks.
3. Conclusion and recommendations
During the past decade, the disaster management sector has seen tremendous advances in theavailability, quality and delivery times of free or low-cost geospatial data. There have beenmajor improvements in the geoinformatic functionality of freeware, as well as the associated hard-ware, from PCs and laptops, to GPS receivers and mobile phones (the latter now owned by ca. 5billion people globally). There appears to be a trend, from a few GIS experts providing infor-mation for a few disaster managers, towards many GIS users providing information for a few dis-aster managers and many community-based disaster management committees. The Internet hasassisted these changes, via the provision of archive remote-sensing data and map data viamulti-terabyte servers and web mapping systems, or the easy incorporation and 3D visualizationof satellite imagery and maps in geo-browsers. On the other hand, many low-income countriesface ‘data poverty’ (Baban, Thomas, Canisius, & Sant, 2008) because their Internet capacity islimited, often with expensive low-capacity connections and slow baud rates.
This study has attempted to review the use of geoinformatics for disaster management appli-cations. The past decade has seen a tremendous growth in the availability of geoinformatic data-sets that are of use for emergency planning and disaster management. Issues with the high costand slow delivery of some datasets have improved (e.g. by the growing use of crowd-sourceddata from smart phones), or will be in the near future (e.g. via the free optical and radar datafrom the forthcoming Sentinel series of satellites). That said, many challenges remain before asystem of sustainable geoinformatics is available to assist with disaster management: these aresummarized below:
(i) We need more free geoinformatic software for disaster management applications. This isimproving rapidly, particularly with QGIS providing a free alternative to ArcGIS.However, more geoinformatic FOSS is needed, for instance to carry out object-basedimage analysis, which could greatly improve the identification and mapping of hazar-dous terrain and vulnerable features in remotely sensed imagery.
(ii) The provision of free satellite data needs to be improved. Free satellite data are needed fordisaster preparedness maps, not just the post-disaster provision of the Charter. Unmannedaerial vehicles (from kites through to drones) could provide low-cost solutions to detailedreal-time remote sensing, for instance, with urban damage assessments or rapid surveys ofrefugee camps.
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(iii) The ‘peopleware’ component of geoinformatics needs to be strengthened, with the pro-vision of free training materials that use disaster management examples. Public partici-pation in disaster management could be increased via public awareness campaigns abouthow to use various aspects of geoinformatics, from crowd sourcing of data using smartphones, through to posting of damage data on virtual globes, such as Google Earth.
(iv) Improved global Internet provision is needed, particularly in low-income countries, forthe greater use of geoinformatics in disaster management. Poor Internet links limit theimport and uploading of data, as well hindering geoinformatic training via distancelearning. Removing these Internet ‘bottlenecks’ should be an international priority, asit will pave the way towards more effective and sustainable use of geoinformatics fordisaster management.
AcknowledgementThe authors thank the Leverhulme Trust for funding to support this research (Research Project GrantF/00678/K).
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