SatelliteRemoteSensingapplicationsforLandslideHazardMonitoring
Ashutosh Limaye,EricAnderson,BrianReevesNASA/SERVIRScienceCoordinationOffice
https://ntrs.nasa.gov/search.jsp?R=20170011111 2020-07-24T11:29:08+00:00Z
Outline
• BackgroundonSERVIR• NASA’sworkingloballandslidehazardmonitoring• Countrycasebriefsonlandslidemonitoringusingremotesensing• CasestudyonrelationshipbetweenfiresandlandslidesinNepal• CEOSLandslideDisasterWorkingGroupPilot• Areasforcollaboration
SERVIRisajointdevelopmentinitiativeofNASAandUSAID,workinginpartnershipwithleadingregionalorganizationsaround theglobe,tohelpdevelopingcountriesuseinformationprovidedbyEarthobservingsatellitesandgeospatialtechnologiestoaddressFood
Security,WaterandDisasters,WeatherandClimate,andLandUse/LandCoverChange.
Preventing seafood-borne illnesses in
Central America by mapping harmful
microalgae
Supporting food security in Nepal by
monitoring agricultural drought
Conserving forests in eastern and southern
Africa by mapping land cover and land use
change
Protecting lives in South/Southeast Asia by
monitoring and forecasting intense
thunderstorms
Helping herders and farmers in West Africa by
detecting ephemeral water bodies
TheCurrentSERVIRHubNetwork
SERVIR’sapproachtodisasterriskreduction
WaterandWater-relatedDisastersThematicServiceAreaofSERVIR
ü Shiftingfromproductcreationtoservicedesignanddelivery
ü Improvingscientificandtechnicalrigorofservicesthroughexternal“TechnicalAssessmentGroups”
ü BringingmoreinnovativeandappropriatesciencefromtheUS• 118USinstitutionsacrossallthematicserviceareas
ü Enhancingcollaboration acrossSERVIRhubs
SERVIR’sapproachtodisasterriskreduction
RoleofSERVIRinNASAEarthScienceDisastersProgram
üMatchneedsonthegroundwithtechnicalexpertisethatEarthscientistscanprovide
üBuildcapacityofagenciesaroundtheworldtouseEarthobservationinformation
üProvidefeedbacktoNASAontheutilityofscienceproductsfordisastermanagement
üProvideinputfromtheinternational“applications”communityperspective
NASA’sworkingloballandslidehazardmonitoring
• TheglobalLandslideHazardAssessmentforSituationalAwareness(LHASA) model isdevelopedtoprovidesituationalawarenessoflandslidehazardsforawiderangeofusers.[1]
• Considersweightedsatellite-derivedprecipitation(GPMIMERG),roads,deforestationandburning,tectonicfaults,bedrockconditions,andslope
• “GlobalLandslideNowcast”isupdateddaily
• NASAGloballandslidecatalog[2]• developedwiththegoalofidentifyingrainfall-triggeredlandslideeventsaroundtheworld,regardlessofsize,impactsorlocation
[1]Stanley,T.,andD.B.Kirschbaum (2017),Aheuristicapproachtogloballandslidesusceptibilitymapping,Nat.Hazards,1–20, doi:10.1007/s11069-017-2757-y.http://link.springer.com/article/10.1007%2Fs11069-017-2757-y
[2]Kirschbaum,D.B.,Adler,R.,Hong,Y.,Hill,S.,&Lerner-Lam,A.(2010).Agloballandslidecatalogforhazardapplications:method,results,andlimitations.NaturalHazards,52(3),561–575.doi:10.1007/s11069-009-9401-4.https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog-Export/dd9e-wu2v
https://pmm.nasa.gov/applications/global-landslide-model
Casebriefs
1. 2009LandslidesinElSalvador,andfollow-onhazardsanalysis2. 2015Gorkha earthquakeinNepal3. AppliedresearchforbetterunderstandingoflandslidehazardsinRwanda
Casebrief:2009ElSalvadorlandslides
• ConvergenceofatropicalstorminthePacificandalowpressuresystemintheAtlanticledtoextremelyintenseandprolongedrainfall,andresultingfloodsandlandslides
Datafrom
disconnecteddecision
supporttoolsare
difficulttoassimilate
andcanprovide
conflicting
information
Masswastingsusceptibility
Laharinundationzone
(Anderson,2013)• Charteractivationinvolvingrapidresponsemapping• Value-addedproductssupportedreconstructionplan• Realizedthatfollow-onappliedresearchwasneeded
Casebrief:2015Gorkha earthquakeinNepal
• 4312landslidesidentifiedfrom10satellites:fewerlandslidesthanexpectedforanearthquakeofthismagnitude,possiblyduetomuchlessshakingatthesurface(Kargel etal.2016)
• Networkanalysisshowingvolunteerscientists&analysts(red)andconnectionswithuser/decisionmakingagencies(green)(Schumannetal.2016)
NASA/GSFC
Casebrief:AppliedresearchforbetterunderstandingoflandslidehazardsinRwanda
• FromUSGeologicalSurvey,weneedtoknow4thingsaboutlandslides1.Whenwilltheyhappen? 2.Wherewilltheystart?3.Wherewillthego? 4.Whatcouldbeaffected?
254landslideeventsidentifiedthroughvisualinterpretationofhighresolutionimagesinGoogleEarth
Preliminaryhazardmapderivedthroughlogisticregressiontesting(Piller andAnderson2015)
Possiblenextsteps:Considernewwaystocollectcrowd-sourceddata(e.g.,SpaceAppsChallenge)
Casestudy:Nepalfire-landsliderelationship
Overarchingquestion:CanwedetectanyrelationshipsbetweenfiresandlandslidesinNepal,asseenfromthesatelliteremotesensingperspective?
• Justification• Post-firelandslideprobabilityisoftenconsideredinU.S.GreatBasin• BurnedAreaEmergencyResponse(BAER)teamsassesspostfirethreatstolives,property,andresources
• Apparentlackofresearchintofire/landslidelinkagesinNepal
• FiresinNepal• ProlongeddryseasonsandlowerwinterprecipitationinNepalhaveincreasedwildfireincidences
• FireisamajorcauseofforestdegradationinNepal NASA Earth Observatory
(2016)
Casestudy:Nepalfire-landsliderelationship
• Studyareas:• Koshi Basin,Nepal– rainfall-triggeredlandslides(ICIMODdatabase)• Gorkha earthquakeaffectedareainNepal(Kargel etal2016)
• Researchquestions• Istherearelationshipbetweenfirefrequency/severityandlandslideoccurrence?
• Howdoestherelationshipchangewhenconsideringrainfall- vs.earthquake-triggered landslides?
Casestudy:Nepalfire-landsliderelationship
• Approach• Testresponseofrainfall-triggeredlandslides(ICIMOD/Koshi basin)andearthquake-triggeredlandslides(Kargel etal.2016)toseveralenvironmentalfactors,includingnormalizedburnratio(NBR)derivedbyLandsat7from2003to2015,usinglogisticregressionapproach
• Potentialexplanatoryvariables:
Variable Abbrev. Data Source Spatial Res Time SummaryNormalized Burn Ratio NBR LANDSAT 7 30 m 2003-2015 (SWIR-NIR)/(SWIR+NIR)Fire Occurrence Fires MODIS MCD45A1 500 m 2003-2015 (Fires)/(catchment)Drainage Density DD ALOS 5m DEM 5 m (str length)/(As)Topographic Wetness Index * TWI ALOS 5m DEM 5 m ln(As/tanβ)
Sediment Transport Index * STI ALOS 5m DEM 5 m (As/22.3)m (sinβ/0.0896)n
Stream Power Index * SPI ALOS 5m DEM 5 m AstanβPopulation Density Pop Dens Landscan 1 km 2010 (People)/(catchment)Height Above Nearest Drainage HAND ALOS 5m DEM 5 m Vertical distanceSlope Slope ALOS 5m DEM 5 m (rise)/(run)Euclidean Distance to Streams Eucl Str ALOS 5m DEM 5 m Straight line distanceAspect Aspect ALOS 5m DEM 5 m Direction of slopeProfile Curvature Prfl Crv ALOS 5m DEM 5 m Parallel to dir. max slopePlan Curvature Plan Crv ALOS 5m DEM 5 m Perpindicular to max slopeFlow Accumulation Flow Acc ALOS 5m DEM 5 m Accum. pixel x pixel flowCHIRPS CHIRPS CHIRPS Monthly 0.05° 2003-2015 Average monthly accum.CHIRP CHIRP CHIRP Monthly 0.05° 2003-2015 Average monthly accum.
As = surface area of catchment; β = slope in degrees; m = 0.6; n = 1.3(Moore et al, 1988)
Casestudy:Nepalfire-landsliderelationship
Results forearthquake-induced landslides:5%improvementconsideringNBR
NotconsideringNBR
ConsideringNBR
Predicted Accuracy0 1
Observed 0 304 223 57.70%1 125 594 82.60%
Overall Accuracy: 72.1%
Overall Accuracy: 77.3%
Predicted Accuracy0 1
Observed 0 363 211 63.20%1 118 601 83.40%
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Casestudy:Nepalfire-landsliderelationship
Results forrainfall-induced landslides:negligibledifferenceconsideringNBR
NotconsideringNBR
ConsideringNBR
Overall Accuracy: 59.4%
Predicted Accuracy0 1
Observed 0 564 426 57.00%1 378 613 61.90%
Overall Accuracy: 60.6%
Predicted Accuracy0 1
Observed 0 591 399 59.70%1 381 610 61.60%
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Y=−&. *9? +8. *:# ∗ ;<5 +&. &8' ∗ ,-./0 +&. &&9 ∗ 23456
Casestudy:Nepalfire-landsliderelationship
Knownlimitations
• Rainfall-inducedcase• Exactlandslidedatesunknown;therefore,cannottesttimingofexplanatoryvariables
• Earthquake-inducedcase• Onlyconsideredonemajortriggeringevent(2015Gorkha earthquake)
• Givenunknownspecificdatesofmostlandslidesinstudyset,wehadtoconsiderburningoveralongperiodoftime(versussingleburnevents)
• Furtherdatacollectionontimingandlocationofburning,triggerfactors(e.g.,rainfall,earthquakes),andlandslideeventscouldshedmorelightonfire-landsliderelationships
Formore,seeReeves(2017):https://ntrs.nasa.gov/search.jsp?R=20170001625
CEOSLandslidePilot
MaingoalsTodemonstratetheeffectiveexploitationofEarthobservations(EO)dataandtechnologiestodetect,mapandmonitorlandslidesandlandslidepronehillsides,indifferentphysiographicandclimaticregions.
ToapplysatelliteEOacrossthecycleoflandslidedisasterriskmanagement,includingpreparedness,situationalawareness,responseandrecoverywithadistinctmulti-hazardfocusoncascadingimpactsandrisks.
• Co-leads• Dr.DaliaKirschbaum,NASAGoddardSpaceFlightCenter,Maryland,USA• Dr.JonathanGodt,LandslideHazardsCoordinator,U.S.GeologicalSurvey,Colorado,USA• Dr.Jean-PhilippeMalet,SchoolandObservatoryofEarthSciences,UniversityofStrasbourg,France• Dr.SigridRoessner,GFZGermanResearchCentreforGeosciences,Germany
CEOSLandslidePilot
Threeobjectives(2016-2019):1. EstablisheffectivepracticesformergingdifferentEarthObservationdata(e.g.optical
andradar)tobettermonitorandmaplandslideactivityovertimeandspace.2. Demonstratehowlandslideproducts,models,andservicescansupportdisasterrisk
managementformulti-hazardandcascadinglandslideevents.3. Engageandpartnerwithdatabrokersandenduserstounderstandrequirementsand
userexpectationsandgetfeedbackthroughtheactivitiesdescribedinobjectives1-2.
Twomainfocusregions:NepalandthePacificNorthwestUnitedStates,includingWashingtonandOregon.
Plansfortheexperimentalregionsarestillindevelopment,butinclude:SoutheastAlaska,China,theCaribbean(HaitiandLesserAntilles),Peru,andIndonesia.http://ceos.org/ourwork/workinggroups/disasters/landslide-pilot/
Areasforcollaboration• CEOSLandslidesPilot– Earthobservationsfocus
• SERVIR:ADPCandHubConsortiummembersareconductingadditionalconsultationsandneedsassessmentswithstakeholdersintheregiontodesignfutureservices.Aretherewaystocollaboratewithotherinternationaltechnicalinstitutionstocollectivelyaddresslandslideriskmanagement?
• WeareinterestedinfeedbackandfindingwaystoconnectresearchandapplicationstobroaderNASAresources,includingfuture:
• NISAR– NASA-ISROSyntheticApertureRadarmission• SWOT– SurfaceWaterOceanTopographymission• Landsat9
• AGUFallMeeting2017sessionsNH018: Landslidedynamics:hazardandriskassessment,triggering,modeling,in-situobservations,andremotesensing:https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session23681
Thankyou