Improving Fire Behaviour Data Obtained from Wildfires

21 Pages·2017·0.83 MB·English

Article
Improving Fire Behaviour Data Obtained
†
from Wildfires
AlexanderI.Filkov1,2,* ID,ThomasJ.Duff1 ID andTrentD.Penman1
1 SchoolofEcosystemandForestSciences,UniversityofMelbourne,Creswick3363,Australia;
[email protected](T.J.D.);[email protected](T.D.P.)
2 BushfireandNaturalHazardsCooperativeResearchCentre,Melbourne3002,Australia
* Correspondence:alexander.fi[email protected];Tel.:[phone]
† ThispaperisanextendedversionofReportNo.319.2017“Determiningthresholdconditionsforextreme
firebehaviour”.
Received:19December2017;Accepted:6February2018;Published:9February2018
Abstract: Organisations that manage wildfires are expected to deliver scientifically defensible
decisions. However,thelimitedavailabilityofhighqualitydatarestrictstherateatwhichresearch
canadvance. Thenatureofwildfirescontributestothis: theyareinfrequent,complexevents,occur
with limited notice and are of relatively short duration. Some information is typically collected
duringwildfires,however,itisoftenoflimitedquantityandmaynotbeofanappropriatestandard
forresearch. Hereweargueforaminimumstandardofdatacollectionfromeverywildfireeventto
enhancetheadvancementoffirebehaviourresearchandmakeresearchfindingsmoreinternationally
relevant. First,weanalysetheinformationroutinelycollectedduringfireeventsacrossAustralia.
Secondly,wereviewresearchmethodologiesthatmaybeabletosupplementexistingdatacollection.
Based on the results of these surveys, we develop a recommended list of variables for routine
collection during wildfires. In a research field typified by scarce data, improved data collection
standardsandmethodologieswillenhanceinformationqualityandallowtheadvancementinthe
developmentofqualityscience.
Keywords: datacollectionandmanagement;standard;firebehaviour;researchutilization
1. Introduction
Wildfires can result in substantial social, economic and environmental impacts, and recovery
activitiesmaytakemanyyears. Forexample,anillegalcampfireinCalifornia’sGarrapataStatePark,
inJuly2016,ignitedthemostexpensivefireinUShistory,costingmorethan250millionUSdollars[1]
FiresinAustraliahaveresultedinmasshouselossesinthestatesofVictoriain2009[2]and2015[3],
Western Australia in 2011, and New South Wales in 2013 [4]. The total annual economic cost of
bushfiresinVictoriaisestimatedtobeapproximately180millionAustraliandollars[5]. Thesecosts
have been forecast to double over the next 40 years to $378 million [6]. It is important to develop
strategiesthatareabletoreducetheriskoflossandtherebydecreasetheeconomic,environmental
andsocialimpactsofwildfire.
The occurrence and behavior of fires are driven by complex processes. Wildfires, and their
associatedmanagementactivities,havecomplexfinancial,socialandenvironmentalimpacts. Here,
we focus on fire behaviour alone, however recent research indicates [7–9] that there is a need for
improvedquantitativeinformationandtoolsinawiderangeofmanagementareas. Thereareafew
nationalormultinationalsystems[10,11]providingbasicfirebehaviourinformationduringwildfires,
suchasfiresize, hotspotsorburnedarea. Thereareindividualattemptstoimprovethissituation,
predominantlyinthecollectionofdatapost-fire. Forexample,theUSNationalInstituteofStandards
andTechnology(NIST)[7]isdevelopingwildland-urbaninterface(WUI)datacollectionmethodology
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Forests 2018,9,81 2of21
andfirstgenerationtoolsforimprovedriskassessmentandriskmitigationinWUIcommunitiesat
riskfromwildfires.
Firesimulationsystems[8,9,12,13]havebeendevelopedaspartofmanagementdecisionsupport
systemsandarevitaltoassessingfirerisktopeopleandproperty. Mostofthesesimulationtoolsare
basedonempiricalfireforwardrateofspread(FROS)modelsanddonotnecessarilyemulatephysical
processes. EmpiricalFROSmodelswerepredominantlydevelopedusingobservationsofexperimental
firesburninginconditionsthatallowthefirestobesafelymanaged. Asaresult,datarepresentingthe
conditionsunderwhichdamagingwildfiresoccurwererarelyincluded. Indeed,currentoperational
firespreadmodelsassumethatfiresburnatanapproximatelyconstant(quasi-steady)rateofspread
under a specific set of environmental conditions (e.g., Rothermel [14], Canadian FBP system [15],
Cheneyetal.[16],CSIROGrasslandfirebehaviourmodel[17]). However,underextremeweather
conditionsthereareemergentformsoffirebehaviourthatcanrapidlychangefireprogressionand
intensity,includingphenomenasuchasplumedominatedspread,vortexstructuresandmassspotting
events[18,19]. Consequently,simulationtoolsthatutilisetheseFROSmodelsarenotabletoemulate
thesedynamicwildfirebehaviours.
Firebehaviourandmanagementresearchcannotdevelopfullywithoutbetterquantificationofthe
variousfirebehaviourphenomenathatoccurundermoderateandextremeweatherconditions. Todo
sorequirescomprehensiveandaccuratedata[20]. Experimentalresearchintointensefirebehaviour
cannotbeundertakenasthesefirescannotbesafelymanaged;asaresult,alternativesourcesofdata
arerequiredandtheonlyopportunitytocollectinformationaboutfiresundermoderateandextreme
conditionsistocollectobservationsatwildfiresastheyoccur. Case-studyfiresarecommonlyused
inresearch[2,21,22]. However,dataisusuallycollatedfromvarioussourcespostevent,hencedata
availabilityandqualityishighlyvariable. Thereiscurrentlynoformalprocedureforensuringdata
collectedduringandpost-fireisappropriateformeetingresearchrequirements(consistent,accurate,
correctandcompletedata). Withoutnewdataregardingwildfirebehaviour,fireresearch,thefuture
developmentoffiresimulationtoolsandtheassociateddecisionsupportsystemswillbeunableto
improvesignificantly.
Fireinformationcollectedbymanagementagenciesvariesbyjurisdictionandfiresize. Insmall
fires, agenciesmayrecordsimpledetailssuchasignitionlocation[23–26], finalfireperimeterand
fire area [27]. For large fires that have substantial impacts, data may be extended to include fire
severity[28–31],fireprogression[32,33]andimpact[34–36]. However,muchofthisinformationis
collectedandcollatedpostevent. Duringfirestherearemanytransientfirebehaviourphenomenathat
cannotbeeasilyreconstructedpostevent. Theseincludespotting/firestorms,firetornado/whirls,
lateralvortices,junctionzones(jumpfires),eruptivefires,independentcrownfires[37],conflagrations,
downbursts,andpyro-convectiveevents[18,19],amongothers.
Informationaboutfirebehaviourisbestcollectedasfiresoccur,however,thereiscurrentlyno
agreedsetofstandardsormethodologiesthatdefine(a)whatinformationneedstobecollectedduring
firesand(b)whencollected,whatdatastandardsareappropriate[27]. Datacollectedduringafire
maybediscardedifitisnotrequiredbyanorganisation. Asaresult,datathataresavedwillonlybea
subsetoftheinformationavailableduringanincident.
Inthispaperweargueforaminimumstandardofdatacollectionduringallwildfires. Doing
thiswouldenablefirebehaviourphenomenatobedocumentedandanalysed. Furthermore,ifsuch
data collection were to be undertaken in a standardised manner across Australia or worldwide,
it would enhance interagency collaboration, increase the research potential of datasets and make
researchfindingsmorebroadlyrelevant. Todothiswefirstanalysethecurrentinformationroutinely
collected during fire events for most states in Australia. Secondly, we provide an overview of
someexistingresearchmethodologiesthathavethepotentialtoberoutinelyusedforobservations
duringfires. Finally, weprovidesomeinitialrecommendationsofvariablesthatwouldideallybe
consideredforroutinecollectionduringwildfires. WhilewefocusprimarilyonAustralianagencies,
therecommendationsarerelevantforagenciesworldwide.
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2. DataCollectioninAustralia
Australia is a diverse continent with ecosystems ranging from tropical rainforests through to
desertenvironments. Firesoccuratvaryingintervalsandintensitiesacrossthecountry[38]. Land
andfiremanagementistheresponsibilityofstate-levelgovernments(whichincludesixstatesand
twoterritories). TheindustrybodyAFAC(TheAustralasianFireandEmergencyServiceAuthorities
Council) endeavours to bring together fire and land management agencies across Australia and
New Zealand to provide a co-ordinated response to fire and emergency management. To date,
therehasbeennonationalpolicydevelopedfocusedondatacollectionandmanagementduringfires.
Tounderstandwhatdataarecollectedduringwildfires, weapproachedrepresentativesfrom
allfireandlandmanagementagenciesinAustralia(Table1). Representativesofstateagencieswere
contactedviaemailandtelephoneandaskedtocompleteaguidedsurvey(AppendixA).Therewere
multipleagenciesfromeachstateasfiremanagementresponsibilitiesaretypicallydividedbyland
tenure. Specifically,weasked:
• Whatinformationiscollectedandstoredduringfires? (TableA1);
• Howfrequentlyaredatacollected? (TableA2);and
• Doesthisinformationcollectionvarybetweenfiresunderdifferentconditions? (TableA2)
Responses (Appendix B) were received from Australian Capital Territory (ACT, Table A3),
New South Wales (NSW, Table A4), Queensland (QLD, Table A5), South Australia (SA, Table A6),
Victoria(VIC,TableA7)andWesternAustralia(WA,TableA8). Noresponseswerereceivedfrom
Tasmania(TAS)andtheNorthernTerritory(NT).Wheremultipleagenciesrespondedfromthesame
state,ifatleastoneoftheagenciesinthestatecollectsacertaintypeofdatathevariablewasconsidered
‘collected’bythestate.
Table1.ListoffiremanagementagenciesinAustraliathatwereapproachedinrelationtothecollection
ofdataduringfires.
StateorTerritory Agency
ParksandConservationService
ACT
RuralFireService(RFS)
NationalParksandWildlifeService
NSW RuralFireService(RFS)
DarwinCentreforBushfireResearch
NT
BushfiresNT
QueenslandParksandWildlifeService
QLD
QueenslandFireandEmergencyServices(FES)
DepartmentofEnvironment,WaterandNaturalResources(DEWNR)
SA
CountryFireService(CFS)
ForestryTasmania
TAS
TasmaniaFireService
CountryFireAuthority(CFA)
VIC
DepartmentofEnvironment,Land,WaterandPlanning(DEWLP)
WA DepartmentofParksandWildlife(DPAW)
ACT: Australian Capital Territory; NSW: New South Wales; NT: Northern Territory; QLD: Queensland;
SA:SouthAustralia;TAS:Tasmania;VIC:Victoria;WA:WesternAustralia.
Asfiresarecomplexeventsandtherearemanysourcesofdata,inthesurveysweclassifiedfire
dataintothebroadtypesdefinedinTable2.
Theresponsesinrelationtothefiredatawerebrokenintothreecategoriesrelatingtoincident
sizeasdeterminedbytheAustralasianInter-serviceIncidentManagementSystem(AIIMS)/Incident
ControlSystem(ICS)system:
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• Smallfire(Level1)—characterisedbybeingabletobecontrolledthroughlocalorinitialresponse
resourceswithinafewhoursofnotification;
• Mediumfire(Level2)—aremorecomplexeitherinsize,resources,riskorcommunityimpact.
Mayrequireinteragencyresponse;
• Largefire(Level3)—areprotracted,largeandresourceintensive. Theymayaffectcommunity
assetsand/orpublicinfrastructure,andattractsignificantcommunity,mediaandpoliticalinterest.
Wefoundthattheamountofinformationcollectedincreaseswithincreasingfiresize(Figure1).
Basicinformationthatissimpletocollectsuchasignitionlocation,incidenttypeandfinalperimeters
arerecordedbyatleastoneagencyinallstates.Datatypesthataremorecomplextocollect(suchasfire
perimeters)orhavetechnologicalrequirements(suchasforwardlookinginfrared(FLIR)camera-based
data) are collected in fewer states. This is due in part to the differing technical capabilities of the
states(forexample,somestatesdonothaveaccesstoaircraftwithlinescanandinfraredequipment).
Theremoredetailedquantitativedata(whichisimportantforconductionanalysisoffirebehaviour)
such as weather radar, progression isochrones, FLIR video, linescans, are generally only collected
occasionally(Figure1b). Apartfromfiresizes,itisunclearwhatstimulatesthecollectionofsuchdata.
Ifthesedataareonlycollectedfromfiresofaspecificnature,itmayresultinbiasesthataffectanalysis
andinterpretationofthefrequencyofextremefirebehaviour.
Whenaskedwhatkindofdatashouldbecollectedroutinelyinthefuture,thehighestnumber
ofresponses,irrespectivetofiresize,werereceivedforfirebehaviourtype,weatherradarandlocal
weather(Figure1c). Fromoursurveys,wealsoidentifiedthatthereisahighdegreeofvariationin
thewaydataarecurated. Whilewewereunabletoconductquantitativeanalysis,itisevidentthat
itisstoredinavarietyofways(e.g.,hardcopies,localservers,onlinedatarepositories). Databases
arenotsharedbetweenstatesandrarelybetweenagencieswithinthesamestate,andinformation
storageisnotcentralised;i.e.,differentcategoriesoffiredatamaybestoredindifferentsystemsorat
differentphysicallocations. ForexampleinSouthAustraliadataarestoredinanIncidentdatabase,
logbooks,afirebehaviouranalystserver,aCorporateGISdatabase,theCriticalResourceIncident
InformationManagementSystemOnlineNetwork(CRIIMSON),theSAComputerAidedDispatch
(SACAD)system,theAustralasianIncidentReportingSystem(AIRS,andIncidentManagementTeams
reports(IMTs). Foraccesstoeachdatasource,separatepermissionsaretypicallyrequired. Evenif
dataareofhighqualityandcorrectlyscoped,difficultyinaccessmayhinderfirebehaviourscience.
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Table2.Categoriesanddefinitionsusedinfiredatacollectionsurveys.
DataType Definition
Incidenttype ThelevelofIncidentScaleasdeterminedbytheAIIMS/ICSsystem1
GPStracks GlobalPositioningSystemrecordsrecordedbytranspondersmountedonfirefightingvehicles.Thismayincludegroundbasedvehiclesoraircrafts
Suppressionstrategies Detailspertainingtothemethodsandstrategiesoffirefightingused
Containment Detailsrelatingtotheeffectivenessoffirecontainmentlinesatdifferenttimesduringthefire
Finalperimeters Mapsorsurveysofthefinalburnedarea
Ignitionpoint/points Detailsaboutwherethefirestarted
Situationreports Duringafire,firefightingagenciesroutinelyreportonthestatusofthefire(includingfirebehaviourandareaaffected)
Firebehaviourobservations Informationfromfirefightersandgroundobserversrecorded
Privatepropertylosses Thelossesofprivateproperty(e.g.,houses,fences)
Localweatherobservations Informationrecordedatornearthefireusingportableweatherstations
Urbaninfrastructure Detailsrelatingtoinfrastructureimpactedbythefire
Responsestructures Detailsrelatingtothecommandandcoordinationofthefiresuppressioneffort
Fuelcondition Observationsrelatingtotheconditionofthefuelatthefire,includingthenatureandwhetherthereisevidenceofpriorfires(firehistory)
Weatherradar DatacollectedbytheAustralianBureauofMeteorologyrainradarillustratingthenatureoffiresmokeplumes
Progressionisochrones Archivesofmapscreatedatdifferenttimesduringthefireaspartoffirefightingefforts
Postfireimpacts Detailsinrelationtofireimpactstovaluesatlarge
Satelliteimages Satelliteimagesfromaroundthetimeofthefire(includebefore,duringandafter)
FLIR ImagesandvideofromlowaltitudeaircraftmountedFLIR(Forwardlookinginfrared)cameras2
Linescans ImagesfromhighaltitudeaircraftmountedInfraredlinescansystems3
1AIIMSistheAustralasianInter-serviceIncidentManagementSystem[39].ThecoreoftheAIIMSistheIncidentControlSystem(ICS)thataimstoprovideanintegratedstructureto
managetheresponsetoanyemergencyincidentthatcanbeusedbyanyorganisationinvolvedintheresponse.2FLIRcamerasareelectro-opticalthermalimagingdevicesthatdetectheat
andprovideavisualrepresentationofsmallpartsofafire.3Infraredlinescansystemisapassiveairborneinfraredrecordingsystem,whichscansacrossthegroundbeneaththeflightpath,
addingsuccessivelinestotherecordastheaircraftadvancesalongtheflightpath.
ForeFsotrses2ts0 12801,89,, 98,1 x FOR PEER REVIEW 6 of6 2o1f 21
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Incident type Aircraft GPS tracks Suppression strategies Contained/Escaped Vehicle GPS tracks Final perimeters Ignition point/points Response structures Fire behaviour type obs Fencing/house losses Weather Forecasts Local weather obs Urban infrastructure Situation reports Fuel/fire history Weather radar Progression isochrones Post fire impacts Satellite images FLIR video Linescans
Collect routinely
(a)
6
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Incident type Aircraft GPS tracks Suppression strategies Contained/Escaped Vehicle GPS tracks Final perimeters Ignition point/points Response structures Fire behaviour type obs Fencing/house losses Weather Forecasts Local weather obs Urban infrastructure Situation reports Fuel/fire history Weather radar Progression isochrones Post fire impacts Satellite images FLIR video Linescans
Collect occasionally
(b)
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Incident type Aircraft GPS tracks Suppression strategies Contained/Escaped Vehicle GPS tracks Final perimeters Ignition point/points Response structures Fire behaviour type obs Fencing/house losses Weather Forecasts Local weather obs Urban infrastructure Situation reports Fuel/fire history Weather radar Progression isochrones Post fire impacts Satellite images FLIR video Linescans
Should be collected routinely
Small fire Medium fire Large fire
(c)
Figure 1. Responses from fire and land management agencies in Australia. Clustered columns show
Figure1.ResponsesfromfireandlandmanagementagenciesinAustralia.Clusteredcolumnsshow
the number of states, which collect specific data type routinely (a), occasionally (b) or should collect
thenumberofstates,whichcollectspecificdatatyperoutinely(a),occasionally(b)orshouldcollect
routinely (c). The responses are given for small, medium and large fires.
routinely(c).Theresponsesaregivenforsmall,mediumandlargefires.
Forests 2018,9,81 7of21
3. InnovationinDataCollection
Themanagementofinformationduringactivewildfiresisanundoubtablechallengetomanagers.
However, with recent technological developments, it is likely to become simpler to collect some
information. Thereareawiderangeofmethodsthathavebeendevelopedintheresearchspacethat
havenotyetbeenadaptedforoperationalusebyfiremanagementagencies. Researchwillalways
produce more methods than agencies will adopt, however methods that can be demonstrated to
efficiently provide meaningful data are likely to be considered. For a new method to be adopted,
ideally it should offer (1) a tangible immediate benefit to the agency utilising it; (2) a long-term
benefittotheagencythroughimproveddecisionsupportasaresultofresearchoutputs;(3)feasible
implementationwithintheoperationalcontextand(4)easeofuse. Researchersandagenciesneedto
workmorecloselytoidentifysuchmethodologiesanddevelopstrategiesfordatacollectionthatensure
thequalityofthedatarecordedwhileminimisingcostanddisruptiontotheagencies. Inthissection,
wereviewanumberofrecentinnovationsthathavethepotentialtoassistwithbothmanagementand
science. SomeofthesearealreadyinuseinpartsofAustralia.
Perhaps the greatest recent advance in fire behaviour research are data derived from remote
sensingbefore,duringandafterthefire. Remotelysenseddataprovideresearchersameanstoquantify
patternsofvariationinspaceandtime. Theutilityofthesedatadependsonthescaleofapplication.
Satellitesandaircraftarethemainsourcesofthesedata. Multi-temporalremotesensingtechniques
basedonspaceandairbornesensorshavebeeneffectivelyemployedtoassessandmonitorlandscape
changeinarapidandcost-effectivemanner[40,41]. Remotelysenseddatahavebeenusedtodetect
activefires[42,43];mapfireextentsscales[44–47];estimatesurfaceandcrownfuelloading[48,49];
assessactivefirebehaviour[50–52]andexaminepost-firevegetationresponse[53,54].
Firebehaviourandmeasuresofthefuelconsumedhavebeenquantifiedthroughtheanalysis
ofthermalinfraredimagery[55–57]. Infrared(IR)sensorsandInfraredLineScanningSystemson
aircraftsallowlandmanagerstodetectactivelyburningareas,spotfires,estimatetheenergyradiated
fromthefireasitburnsandtoanalysefirebehaviour. Theseapproachesallowforthedeterminationof
keyparametersofthefire,suchasintensity,size,rateofspread,hazardsandotherfactorsrelevantto
suppressionactivitiesandlogistics. LineScanningSystemshavebeenusedformanyyearsforfire
mappingforfirefightingpurposes[58]. However,todatethesystematicuseofthemtocollectfire
behaviourdatahasbeenlimited. Whenroutinelycollected,progressionisochroneswillsignificantly
simplifytheprocessoffirereconstructionandimprovefiresimulationtoolvalidation. Mappeddata
willalsoprovideanunderstandingofhowspatialprocesseslikeclimate,topography,andvegetation
dynamics influence fire behaviour and regimes. Combining these data with information on fire
behaviourtypeandevidenceof“unusual”behaviour,suchasextremefirebehaviour,isvital.Routinely
collecting information about fire intensity, fire front depth, spotting ignitions and “unusual” fire
behaviourwillhelptobetterunderstandfirebehaviourandimproveoperationalandphysicalmodels.
Anothersysteminoperationaluseforfirefightingthathashadlimitedadoptionforsystematic
datacollectionistheuseoflowaltitudeIRfireobservation. OperationallyinAustralia,aircraftusea
singleIRsensorwhichcandetectfirefrontsorhotspotsandfirebrandsbutnotboth. Mostimaging
techniquesintendedtodetecttheheatsignatureoffirearebasedonMWIR(MediumWavelength
Infrared)andTIR(ThermalInfrared)(TIRbandincludesspectrumfrombothMWIRandLWIR(mainly
LWIR)spectralregions[59])sensors[60]. UsingasingleIRsensorisproblematicasthesignalvaries
with emissivity, there is considerable incident energy and only a small fraction of the pixel may
correspond to the fire. Using multi-spectral methods can solve this problem. For example, in the
USA,theairbornefiredatagatheringisderivedfrommulti-spectraldataacquiredbyautonomous
modular line-scanner sensors (AMS) operating in shortwave (SWIR), MWIR and LWIR spectral
regionsandprovidingenhanceddynamicrangeinsupportofactivefireimaging[60]. Also,byusing
a multispectral approach the fire radiative power, fire fractional area and temperature can be
estimated[61]. Furthermore,suchsystemscanviewthroughsmoke,allowingthenatureofember
generationandtransporttobeobserved.
Forests 2018,9,81 8of21
Arelativelyrecentsetofmethodsusedinresearchbutnotyetinoperationalfiremanagementis
the3DvisualisationandmeasurementofwildfiresmokeplumesandtheatmosphereusingLIDAR
(LIghtDetectionAndRanging)[62–64],SODAR(SOnicDetectionAndRanging)[65,66]andRADAR
(RAdio Detection and Ranging) [63,67–70]. These methods extract vertical profiles of the smoke
plumes,aswellasrecordthemovementofwindsandhotgasesfromthefire. Suchinformationis
criticalforscientiststounderstandfirebehaviour—inparticulartherapidaccelerationthatoccurswith
somefiresastheybecomelarge. Theintensityandevolutionofconvectiveplumesiscriticalinthe
understandingofloftingandspottingoffirebrands,whereplumestructurebeginstoplayanimportant
roleinhowthefirebrandsarespatiallydistributed. Anumberofstudieshavealsocharacterisedsmoke
plumebehaviourusinginformationderivedfromsatellitedata[71–73]. Informationonsmoke-plume
heightsandtheirdynamicsandtheserelateddatawillallowforimprovementsinsmokedispersion
andairqualitymodels.
WeatherRADAR[67–70]andLIDAR[62–64]havealsobeenusedforvisualizingactivefiresin
context of dynamic broad scale weather events, understanding plume formation and estimation
of it characteristics. As weather RADARs are maintained over large parts of Australia as part
of rain monitoring, they have very broad coverage and scan at a high frequency. Extreme fire
weather features like sudden wind changes, the escalation of a plume into a pyrocumulonimbus
(PyroCb)(orCumulonimbusFlammagenitus(CbFg)accordingtothenewInternationalCloudAtlas,
https://cloudatlas.wmo.int) or the advent of dry thunderstorms and associated lightning are all
importanteventstobeconsideredduringamajorbushfireeventbutarerarelycapturedusingexisting
methods. Ground-basedscanningsystemssuchasRADARcanbeconsideredanimportantauxiliary
toolfordetectingunauthorisedburningandforestfires,addingsignificantvaluetotheinformationfor
decision-makinginmonitoring,detectingandsuppressingwildfires. Anadvantageofusingweather
RADARtoanalysefireisthatthenetworkisalreadyinplaceandmaintainedforanotherpurpose.
Consequently,barrierstoitsadoptionarelow.
Remotesensingmethodshaveprovidedamajorstepforwardindatacollectionandunderstanding
firebehaviour.Methodsforcollectingthesedataarealsounderconstantdevelopment.Twomajorareas
areworthhighlighting. Firstly,asnewsatellitesarelaunchedthequalityandquantityofdataavailable
will increase. In Australia, research and management have both used the Advanced Very High
ResolutionRadiometer(AVHRR)imageryandtheModerateResolutionImagingSpectroradiometer
(MODIS)onTerra(1999)andAqua(2002)[74]. ThelaunchoftheJapanMeteorologicalAgency(JMA)
Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard in October
2014 presents a significant opportunity to improve the timeliness of satellite fire detection across
Australia. The near real-time availability of images, at a ten minute frequency, may also provide
contextualinformation(backgroundtemperature)leadingtoimprovementsintheassessmentoffire
characteristics[43].Secondly,unmannedaerialvehicles(UAVs,commonlyknownasdrones)asremote
sensing platforms have the great potential to increase the efficiency of data acquisition, but their
applications are still at an experimental stage [75–77]. UAV remote sensing has low material and
operationalcosts,flexiblecontrolofspatialandtemporalresolution,high-intensitydatacollection,
andareductionofrisktocrews. AsthecomplexityofUAVsandsensorsincreases,sowillourability
tocapturehighresolutionspatialdataatwildfires. Anadditionaladvantageisthattheycanbeusedin
conditionsthatwouldbehazardoustohumanhealth;particularlyaroundfastmovingfiresorwhere
thereisunstableweather.
Table 3 shows innovations for which adoption can bring immediate benefit to fire science
andmanagement.
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Table3.Listofinnovationsinwildfiredatacollectionwithimmediatebenefits.
Innovation DataSource Platform Features Advantages Disadvantages Usage
AdvancedHimawari Satelliteimages,dNBR1, Largeareaofdetection,
Imager NDVI2,fireseveritymaps Himawari-8satellite Pre-,real-timeandpostfireevents tenminutefrequency Notenoughspatialresolution Partiallyinuse
InfraredLine
Progressionisochrones Aircrafts,helicopters Realtimedatacollection Hightemporalandspatialresolution Smallareaofdetection Partiallyinuse
ScanningSystems
MultipleInfrared(IR)
IRvideoandimages Aircrafts,helicopters Realtimedatacollection Hightemporalandspatialresolution Highcost Prototypes
Sensors
2D/3Dvisualisationandmeasurementof Influenceofterrain,reduction
LIDAR,SODARand Ground-basedand Realtimeweathermeasurements,
2D/3Dimages wildfiresmokeplumesandtheatmosphere, ofspatialresolutionwith Occasionallyinuse
RADAR mobilesystems firedetection
Hightemporalresolution distance,highcost
Influenceofterrain,reduction
WeatherRADAR 2Dimages Ground-basedsystem Firedetection,plumedevelopment Broadcoverage,highfrequency ofspatialresolutionwith Occasionallyinuse
distance
Lowmaterialandoperationalcosts,flexible
Drones,remotecontrol Lowoperationaltime,
UAVs IR/visualvideoandimages Usageathighriskareas controlofspatialandtemporalresolution, Prototypes
aircraftsandhelicopters undevelopedpolicy
high-intensitydatacollection
1dNBRistheNormalizedBurnRatio.2NDVIistheNormalizedDifferenceVegetationIndex.
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4. ChallengesofDataCollectionandanIdealDataset
ExperienceoffirebehaviourdatacollectionfromdifferentfireagenciesinAustraliashowedthat
thereareasubstantialamountofdataacrossevents. However,thesedataareofteninconsistentand
limitquantitativeanalysisoffirebehaviour. Forexample,linescanningfrequencyvariessignificantly
during a fire, from several times to only a single scan per day. Aircraft infrared video consists of
fragmentsoffirefrontdependingonthepreferencesofthepilotanddoesnotshowthefirefrontor
spottingdevelopment. Afurthercomplicationisthesourceofdata. Forexample,weatherrecordsare
oftenderivedfromweatherstations,whicharesparselylocatedacrossthelandscape. Datafromthese
stationscandiffersignificantlyfromweatheratthefirelocation,whichmayonlybe10kmaway. Also,
mismatchesintemporalandspatialresolutionofdatabetweenmultiplesourcescreatechallengesin
aligningdataformeaningfulanalysis.
Ourpaperisfocusedonmeasuringdataforanalysisofvariousfirebehaviourphenomena—how
tounderstandthemandtakethemintoaccountinoperationalandphysicalbasedmodels. Theideal
dataset for these analyses would be recorded every 5–15 min and include information about fire
progression(linescansandgroundobservations),infraredvideoinSWIR,MWIRandLWIRspectral
regions of fire front and spot fires, RADAR and LIDAR measurements, high resolution satellite
images(<100m),photoandvideooffireandplumedevelopmentandgroundweatherobservations.
Such data would allow researchers to catch even short lifetime phenomena and dynamic effects,
suchasextremefirebehaviourswhichcanhavedevastatingconsequences. Unfortunately,whilethis
datasetisdesirableitisunrealisticasitrequireshugehumanandequipmentresources,whicharevery
limitedduringwildfires.
Asastartingpointwerecommendafocusonparticularcategories(Table4). Thesecategories
are those that will provide the greatest information gains, with minimum additional resources.
Ourfocuslistisinrelationtoalltypesoffirebehaviour,butparticularlyextremefirebehaviour—the
phenomena that only occur at large scales and under severe conditions that cannot be safely
replicatedexperimentally.
Anysystemorsetofmeasuresmustbeaccompaniedbythedevelopmentofarobustdatastorage
and sharing system. The development of such system could greatly reduce data discoverability
issuesforresearchandgovernmentalinquires. Theinformationneedsforfirebehaviorresearchare
not necessarily the same of those needed for managing the control of wildfires. Control requires
informationathightemporalfrequencies,butdoesnotnecessarilyrequirethedegreeofaccuracyor
precisionrequiredforresearch. However,intelligencegatheringinfrastructuresareinplaceforfire
control,and,attheveryleast,theinformationcurrentlybeingcollectedcouldbearchivedinawayto
makeitsuitableforfutureanalysis. Muchoftheinformationcurrentlygatheredduringafirebyafire
managementagencyisstoredinsomeform,howeveronlyasmallproportioniscentralisedandcan
beeasilyaccessed. Acentralisedand/orstandardiseddatastorageapproachwouldstreamlinethis
processandresultinbettermanagementandresearchoutcomes. Furthermore,consistencyindata
storageandmanagementshouldresultinimproveddatasharingbetweenfiremanagementagencies.
From a research perspective this should allow for more comprehensive datasets to be developed,
therebyincreasingtheapplicationofresearchresults. Asastartingpoint,datacouldbecollectedin
eachstatefireagencywiththeperspectivetocreateanationaldatacollectionsystem. Also,alldata
formatsshouldcomplywiththeInternationalSystemofUnits(SI).Suchasystemwouldprovidea
numberofchallengesintermsofcollationandmanagementofthedata,butitisbeyondthescopeof
thispapertodiscusstheseissues. OfthedatasourcesinTable4,muchoftheinformationisalready
beingcollected(e.g.,fireobservations,linescans),sothereisthepotentialforrapidlyimprovingdata
availableforresearch. Whiletheinformationcurrentlycollectedmaynotbeofasuitablestandardfor
research,integratingscientificdatacollectionintoexistingsystemsismuchmorelikelytobesupported
bymanagers,incontrasttodemandingnewdatacollectionactivitiesthatcompetefortheresources
beingusedforfirecontrol.