a fundable hyspiri mission: wildfire data products from the ... · a fundable hyspiri mission:...
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AfundableHyspIRIMission:Wildfiredataproductsfromtheairbornecampaign
E.NatashaStavros
JetPropulsionLaboratoryCaliforniaInsFtuteofTechnologynatasha.stavros@jpl.nasa.gov
(c)2015CaliforniaInsFtuteofTechnology.Governmentsponsorshipacknowledged. CL#15-4845
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AFmeofrapidecosystemchange• Climate• Land-use• Invasivespecies• Disturbance,whichcanexpeditechange
1.IdenFfyasubjectofinterest
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KeyDisturbance:Fire
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FireDataavailabletoabroadernon-remotesensingcommunity
• MODIS/VIIRSAcFveFireDetecFon• Landsat
– MTBS.gov(1984-2yearsbeforepresent)overtheUSA– Rastersofprocessed“Level3”-likedataproductsofoperaFonallyusefulfireseveritymetrics
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2.Providethenewdatainafamiliarformat
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a)#Fire#severity#data#Water#bodies#King#Fire#LiDAR#pre8fire#data#AVIRIS#and#MASTER#pre8#and#post8fire#and#LIDAR#post8fire#data#
b)#RimFire(2013)
KingFire(2014)
Serendipity–Pre-HyspIRIairbornecampaigncapturedtwomegafiresbefore,duringandafer
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Megafires• Ofenverylarge• HavelasFngecological,social,andeconomicimpact
• Uniqueclimatologyandbehavior,andasmallsamplesize=unpredictableanddifficulttomanageforandduring
Stavrosetal.(2014),InternaFonalJournalofWildlandFire
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Megafireoccurrenceisincreasing
So,thereisgrowing
interestfromtheecologicalandpoliFcalcommuniFes
aboutunderstandingfireslikeRim&
KingFire
Region IPCC scenario
Likelihood of megafire for 2031-2060 compared to
1979-2010 Easter Great Basin
RCP 4.5 2.054 RCP 8.5 2.47
Northern California
RCP 4.5 1.353 RCP 8.5 1.381
Northern Rocky Mtns.
RCP 4.5 1.531 RCP 8.5 1.928
Pacific Northwest
RCP 4.5 3.136 RCP 8.5 4.401
Rocky Mountains
RCP 4.5 4.694 RCP 8.5 4.769
Southern California
RCP 4.5 1.499 RCP 8.5 1.655
Southwest
RCP 4.5 2.042 RCP 8.5 2.024
Western Great Basin
RCP 4.5 1.882 RCP 8.5 1.951
Stavrosetal.(2014),ClimaFcChange
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StavrosEN,TaneZ,KaneVR,VeraverbekeS,McGaugheyB,LutzJA,RamirezC,McGaugheyRJ(inreview)UnprecedentedremotesensingdatafrombeforeandaferCaliforniaKingandRimMegafires.NatureScien.ficData.
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DocumentediniFaldataprocessingstreamData$Product$Development$
$$$$$$$$$$$$$$$$$$$$
Data$collec1on$$$$$$$$$$$$$$$$$$$$$$
AVIRIS$Level$2$surface$
reflectance$
MASTER$Level$1b$radiance$
LiDAR$Point$Cloud$
Data$Processing$$$$$$$$$$$$$$$$$$$$$$
Georeferencing$
Flightline$Nomaliza1on$
Level$2I$Raster$Mosaick$
Level$3I$ENVI$v5.0$Metric$Calcula1ons$
MASTER$Level$2$
emissivity$and$land$surface$temperature$
Atmospheric$Correc1on$
Topographic$Correc1on$
Georeferencing$
Layerstacking$
Level$2I$Raster$Mosaick$
Level$3I$Metric$Calcua1ons$(Matlab)$
USFS$Data$Fusion$SoSware$(v3.0)$
Grid$Alignment$between$acquisi1ons$
Level$2I$LiDAR$return$summary$sta1s1cs$=$$Structural$Metrics$
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dNBR
Landsat MASTER AVIRIS
NDVI
Landsat MASTER AVIRIS
NDVIClassificaFonsQualitaFve:VisualcomparisonforRimFire,June2014of
operaFonalmetrics
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1.AVIRISgain:Imagingspectroscopy(IS)ismostadvantageouswhenthefullspectralsignatureisused
Char Green Vegetation Substrate NPV a b R2 RMSE a b R2 RMSE a b R2 RMSE a b R2 RMSE AVIRIS (All) 0.97 0.02 0.69 0.12 1.01 0 0.95 0.05 0.98 0.01 0.82 0.10 0.97 0.01 0.84 0.09 OLI 0.77 0.05 0.46 0.16 1.00 0 0.88 0.08 0.77 0.03 0.49 0.18 0.79 0.07 0.40 0.15 AVIRIS (multispectral)
0.68 0.07 0.38 0.18 1.00 0 0.85 0.09 0.68 0.05 0.39 0.16 0.68 0.07 0.37 0.17
!
Burned'fraction'
AVIRIS'(all)' OLI' AVIRIS'
(multispectral)'AVIRIS'(all)' ' <0.0001% <0.0001%
OLI' <0.0001% ' 0.0012%AVIRIS'
(multispectral)' <0.0001% 0.0175% '
• SpectralunmixingfromAVIRISissingificantlybenerthanLandsat/OLI
• TofullybenefitfromtheadvantagesofIS,advancedanalysistechniquesarerequired
VeraverbekeS,StavrosEN,HookSJ(2014)AssessingfireseverityusingimagingspectroscopydatafromtheAirborneVisible/InfraredImagingSpectrometer(AVIRIS)andcomparisonwithmulFspectralcapabiliFes.RemoteSensingofEnvironment154,153–163.
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2.MASTERgain:InformaFongainfromthehighspaFalresoluFon,mulF-bandthermal
infraredFireradiaFvepowerisaproxyforfireintensity
Increasing*Informa.on*
A)* B)* C)*
High*heat**Low*heat*
NIROPSproxy
NIROPSvs.MODIS
MASTER
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3.UFlizaFonofAVIRIS,MASTER,andLiDARdata:Fuelmodeldevelopment
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FuelModel• AclassificaFonthatrepresentsthefueltype,condiFon,amountandstructure
• Inputintofirebehaviormodelsusedinreal-Fmedecisionmaking
• Haveassociatedemissionfactors• LANDFIREisthecurrentindustrystandard,updated~2-10years– Landsat– DynamicvegetaFonmodels– Environmentalgradients
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Ques9onHowrepresentaFveareLANDFIREfuelmodels(derivedfrommodels)ofactualforeststructureandcomposiFon?
HypothesisVegetaFontype(AVIRIS)andstructure(LiDAR)areenoughtoderivefuelmodels
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Methods• Mapinputs:
– LiDAR:slope,aspect,elevaFon+L4-L7structuralmetrics– AVIRIS:dominantvegetaFontype(wMESMA)
• Extrapolatepre-KingFireLiDARtofullextentusingpost-fireLiDAR
• ClusterLiDARmetrics(excludingL7),usingunsupervisedk-means,10iteraFons,13classificaFons
QuadraFcregressionmodelswherey=pre-fireLiDARandx=post-fireLiDAR R2
L4:standarddeviaFonofLiDARreturnsabove2m 0.79
L5:heightof95thpercenFleofLiDARreturns 0.86L6:heightof25thpercenFleofLiDARreturns 0.53L7:percentfracFonalcoverforreturnsabove2m 0.22
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AssignuniqueLiDARcluster+AVIRISdominantvegetaFontypetothe13Andersonfuelmodelsbasedontheir
descripFonFM4$ FM4$ FM6$
FM10$ FM9$ FM5$
FM9$ FM8$ FM1$
FM10$ FM10$ FM9$
FM10$ FM10$ FM9$
White$Fir$
Sierran$Mixed$Conifer$
Ponderosa$Pine$
Montane$Hardware$
Montane$Chaparral$
$1 $ $ $$$$$$$$2 $ $ $$$$$$$$$$$$3$
FM2$ FM2$ FM1$
FM2$ FM2$ FM1$
Annual$Grassland$
Barren$
Burnt$
FM8$ FM7$ FM2$
ClosedFCone$Pine$
FM9$ FM8$ FM1$
FM4$ FM4$ FM6$
Mixed$Chaparral$
$1 $ $ $$$$$$$$2 $ $ $$$$$$$$$$$$3$LiDAR$Class$
Height$(m
)$
L4$=$Height$standard$deviaPon$above$2$m$L5$=$Height$p95$for$all$returns$above$2$m$L$6$=$Height$p25$for$all$returns$above$2$m$L5$–$L6$=$Canopy$depth$from$p25$to$p95$
Montane Hardwood
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FuelModelQualitaFveAssessmentVisualMaps:AVRISI-LIDAR(2013)vs.LANDFIRE(2012)
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FireBehaviorsimulaFonusingCAWFE–inprogressLANDFIRE
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FuelModelQuanFtaFveAssessmentAVIRIS-LiDARvs.LANDFIREclassificaFonusingCohen’sKappashow
“slight”agreement
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Thereisagoodvisualcomparisonandthe
quanFtaFveassessmentshowsslightlysimilarfuelmodelclassificaFon,buthowdoAVIRIS-LiDARorLANDFIRErepresentactualforest
structureandcomposiFon?
insituForestInventoryAssessment(FIA)comparisontoAVIRIS-LiDARvs.LANDFIRE–INPROGRESS
Staytuned…
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Discussion/Conclusions
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Acknowledgements• JaniceCoen(NCAR)• HarshvardhanSingh(IndianInsFtuteofSpaceScienceandTechnology)• RobertE.McGaughey&CarlosRamirez(USDAFS)• VanKane(UW)• ZacharyTane(UCSB)• DaveSchimel(JPL)
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BACK-UP:LIDAREXTRAPOLATION
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RimFire(moderateseverity)
KingFire(highseverity)
LiDAR• Unlessthefirescorches,foreststructuredoesnotchangeimmediatelypost-fire
• LiDARintensitycanbeinformaFve,butrequiresnormalizaFonofmanyparameters
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UseFireseverity(AVIRISdNBR)tounderstandrelaFonshipsbetweenpre-andpost-firestructure
(-0.25–0.1)
(0.1–0.27)
(0.27–0.66)
(>0.66)
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