Rff Rpt Measurement and Monitoring

FAGAN AND DEFRIES 18

CorrectingSARimageswithaccurate,groundlevelDEMsgreatlyimprovesbackscatterinterpretationandallowsdirectforestheightmeasurementusinginterferometry(Ticehurstetal.2004;Kellndorferetal.2004,).Thedevelopmentofaglobal,groundlevelDEMforforestedregionswouldenableasignificantadvanceintheabilityofSARtomeasureforests(Kellndorferetal.2004;BourgineandBaghdadi2005).

LightDetectionandRanging(LIDAR)

LIDARsystemsemitcoherentlightandmeasurethetimingofsignalreturnfromtheEarthssurface.Theyareessentiallylaseraltimeters,orrangefinders,thatmeasurethedistancetotargetswithgreatprecisionbytiminghowlonganemittedlasertakestoreflectoffthegroundtarget(Dubayahetal.2000).ThetargetsthatLIDARsamplesaresmallareascalledfootprints,andthelasersreflectfromfoliageandwoodybiomassaswellasfromthegroundsurface.CurrentLIDARsystemsdifferfromSARsystemsinthattheysamplefootprintsatdiscontinuousintervals,ratherthanreceivewavereflectionsfromentirelandscapes(Figure10).InterpolationbetweenLIDARsamplingfootprintsisnecessarytogeneratevegetationcanopymapsorDEMs(Lefskyetal.2002b;Figure11a).TheresolutionofthistechnologydependsonthesamplingdensityofLIDARfootprints.

Figure10.PotentialLIDARsamplingfootprintsfortheDESDynImission

Note:Examplefootprintsaredotsindicatedontheinset.Footprintsnotto30kmscale.LIDARfootprintswillbetakenjustalongtheredlines.Source:Obertoetal.2008.CourtesyofNASA.

LIDARsystemsdifferintheirfootprintsize,firingfrequency,andtheinformationrecordedfromthelaserpulse.TherearecurrentlytwomaintypesofLIDARsystems:smallfootprint,discretepostingsystems,andlargefootprint,waveformrecordingsystems(Lefskyetal.2002b).Athirdexperimentaltype,flashLIDAR,scanslargeareasandreceivesreturnsinasimilarfashiontoSAR


(Swansonetal.2009).Becausethistypeisexperimental,wewillnotdiscussitfurtherinthisreport.

SmallfootprintLIDARssamplesmallareas(submeterwidth)athighdensity(multiplefootprintspermeter,Figure11a).Theyrecordsingleormultiplereturntimes,orposts,fromthereturnedlaserpulse(e.g.,firstreturn,lastreturn,and/orthepeaklaserreturns,Figure11a).AlargenumberofsmallfootprintLIDARmeasurementsallowcreationofveryrealisticmapsofbareareasandthetopsofvegetatedcanopies(Lefskyetal.2002b;Figure11b).SmallfootprintLIDARpulsesareabletopenetratetothegroundthroughdensevegetatedcanopiesonlyiftheyarespacedveryclosely(Clarketal.2004b).ThelargenumberofcloselyspacedfootprintsrequiredtoimageaforestcanopycurrentlylimitssmallfootprintLIDARtoaerialsurveyingofsmallswaths(e.g.,5km;Swansonetal.2009;Dubayahetal.2000).

Figure11aandb.ReturnsfromSmallFootprintLIDAR

a

b

Source:CourtesyofImageTreeCorporation.


Figure11c.ExampleWaveformfromaLargeFootprintLIDAR

Note:Thegraphontheleftindicatestheactualwaveformdatacollectedbythesensor,andtheimageisillustrativeoftheinteractionstakingplace.Source:CourtesyoftheJetPropulsionLaboratory,NASA.

Itispossibletorecordmoreinformationfromeachlaserpulse,andlargefootprintLIDARsuselaserpulseslargeenoughtopenetrateindividualtreecanopies(e.g.,1030metersindiameter,Figure11c).Fromeachfootprint,thefirstlaserreturnindicatesthetopofthevegetatedcanopy,thelastlaserreturnindicatesthegroundelevation(Lefskyetal.2002b),andtheamountofreturnedenergypertimeindicatestheheightandareaofthereflectingsurfaces.Bydisplayingthedistributionofsignalreturnsbyreturntime,LIDARsystemscreateawaveformimageofthevegetationstructureandtopographyatthesamplingpoint(Figure11c).LIDARwaveformsareusedtocreatehighlyaccurate,threedimensionalmodelsofactualforeststructure;forestheight,abovegroundbiomass,andotherquantitiescanbeestimatedfromlargefootprintLIDAR(Lefskyetal.2002b;Dubayahetal.2000).

TheutilityofLIDARforforestmappingisonlylimitedbythewidthanddensityoffootprints.Atwideintervals,significantinterpolationisnecessarytoestimateforestheightorunderlyingtopography.Accuracyislowerinheterogeneousareas(Hudaketal.2002).Whilesmallfootprint,aerialLIDARtypicallysamplesasmallareaveryintensively,largefootprint,aerialLIDARmaysample25mfootprintsspacedat50mintervals.LIDARmissionsmustbalancefootprintwidth,number,andspacingtomeasurelargeareasaccurately.AerialLIDARsystemsarecommonlyusedinbothscientificandcommercialforestmeasurement;theseprimarilyaresmallfootprintsensors,whichdespitetheirlimitedimageswathhaveimpressivedetailoverimagedforests(Figure11b).

Becauseofthedistanceofsatellitesfromtheearthandtheamountofareathesatellitearedesignedtocover,satellitebasedLIDARmissionsarelargefootprintandwidelyspacedbetweenorbitalswaths(e.g.,50mfootprintsspacedat13kmintervals)(Obertoetal.2008;NASA2009a;Figure10).ThereiscurrentlyonlyonesatellitemountedLIDARsensor,ICESatGLAS,andalthoughitslargefootprint(~65m)wasnotexplicitlydesignedforforestmeasurement,themissionisbeing


usedtoestimateforestbiomassandimproveDEMs(NASA2009a;Nelsonetal.2009).FollowonmissionstothisLIDARmissionarebeingplanned,butthemissionscanbeexpectedtohaveshortlifetimesofonlythreetofiveyearsbecausethelasersburnoutafterasetnumberofuses(NASA2009a).

2.3 Putting it All Together: Remote Sensing Fusion Remotesensingforforestmeasurementusesmeasuredquantities(e.g.,canopygreenness

andradarbackscatter)toestimateotherquantitiesofinterest(leafwatercontent,forestbiomass).Incorporatingmorethanonesourceofremotesensingdata,knownasimagefusion,typicallyimprovesestimatesofforestproperties.Theseestimatescanalsobeimprovedbyincludinginformationfromgeographicallyreferenceddatasetsorganizedusinggeographicinformationsystems(GIS;Figure12a).Forlackofabetterterm,wedefinetheintegrationofremotesensingandGISdataasGISfusion.GISfusionisstandardpracticeinremotesensing.Imagefusionhasonlyrecentlyincreasedinprevalence.Bothpracticesgreatlyincreasethepowerandutilityofremotelysenseddata.

Figure12a.ExampleofGISRemoteSensingFusion

Note:Undisturbedforests(greengrid)canbemappedbyoverlayingGISdataonroadnetworks(blue)ontoasatellitederivedforestmap.Source:GOFCGOLD2008.


Figure12b.ExampleofImageFusion

CoarsefineImageFusion

Note:Inthiscase,alowerresolutioncolorSPOTimageisbeingfusedwithahigherresolutionSPOTpanchromaticimage,inatypeofcoarsefineimagefusionknownaspansharpening.Noticetheboatwakesthatappearonlyinthehigherresolutionpanchromaticandfusedimages.Source:Short2009.


2.3.1ImageFusion

Satelliteimageryfromtwoormoresensorscanbecombinedtocreateasingleimage(ordataset)withhigherresolutionand/orutility(Treuhaftetal.2003;Walkeretal.2007;Royetal.2008;Dalponteetal.2008;Saatchietal.2007).Alltypesofimagescanbefused.Fusionreferstothecreationofnewmapsbycombiningdatafromoverlappingimages(Figure12b).Thetwomostcommontypesoffusionsareactivepassiveandcoarsefine,butthepotentialforimagefusionislimitedonlybyavailabledata.

Passiveandactivesensorsmeasureverydifferentquantitiesabouttheearthssurfaceanddifferinuses.Becauseapassivesensormeasuresreflectancefromsurfaceswithdifferentphysicalandchemicalproperties,thesensormeasureslightabsorptionbygreen,photosynthesizingleaves.Thedegreeoflightabsorptionbyphotosynthezingleavesisaproxyforestimatingforestproductivity(therateofbiomassproduction).Anactivesensorcanestimatesecondaryforestbiomassbymeasuringthethreedimensionalstructureofforests.Becauseproductivityispositivelycorrelatedwithbiomassinlowbiomassforests(KeelingandPhillips2007),fusingapassiveimagewithanactiveSARimagewouldyieldbetterestimatesofproductivityandbiomassthaneitherimagebyitself.

Forpassivesensors,currentlimitsondatastorageandthecapabilityofsensorsmeanthatnoonesensorcansimultaneouslymaximizepixelresolution,spectralresolution(numberofbands),andareaofcoverage(Anderssonetal.2009;Jensen2007).Thus,itisdifficulttocoverlargeareaswithhighresolutionimagerywithouthavingtomosaic(orcombine)thousandsofdiverseimagestakenatdifferenttimes(Anderssonetal.2009;Figure13).Eachimagemustbecorrectedfordifferencesinsolarilluminationandatmospherichaze,andsuchcorrectionisoftendifficult.Onewaytoimprovetheresolutionofwideswathsensorsistofusehighresolutionimagerywithcoarseresolutionimagery.Inpansharpeningfusion,thehighresolutionimageryisusedtoincreaseblackandwhite(panchromatic)detailinthecoarsercolorimage(Figure12b;Qianetal.2007).Intrainingfusion,highresolutionimagesareusedtocorrecttheestimatesofthecoarserresolutionimageryinaniterativeprocess.Bothofthesemethodscandramaticallyimprovetheresolutionandutilityofcoarserresolutionimagery(Qianetal.2007;e.g.,Hansenetal.2003).

2.3.2GISFusion

FusingGISdatawithremoteimagesisroutine(e.g.,Jensen2007).UseofGISallowsorganizationanddisplayofdatasetswithexactlyknowngeographiclocations(georeferenceddata).Theselocationscanbegroundderived(e.g.,citybuildingdatasets)ortakendirectlyfromremotelysensedimagery(e.g.,road,landcover,orDEMdatasets).IntegratingGISdatawithremotesensingimagerymakesnewanalysespossible(e.g.,roadsandlogging;Asneretal.2006)andcanimproveestimatesfromremotesensingdata(e.g.,climateandbiomass;Baccinietal.2004).Georeferenceddataareusedregularlytoquantifyandimprovetheaccuracyofremotesensinganalysesandpredictionsinaprocessknownasgroundtruthing.Groundtruthdataarehighaccuracydatausedtoevaluate,orvalidate,theaccuracyofmapsderivedfromsatelliteimagery.Thesedataareacrucialcomponentofremotesensing(seeSection3.1.3).TheincreasingintegrationofGISandremotesensinghasfacilitateddisplayandcommunicationofsatelliteimagerybetweenscientistsandforthepublic,aswitnessedbytheexplosivegrowthinmappinginsocietyandthewidespreaduseofGoogleEarth,MicrosoftsBingMaps,andotherproducts.


Figure13.MosaicofModerateResolutionLandsat7Images

Source:ImagecourtesyofU.S.GeologicalSurveyGlobalVisualizationViewer(GLOVIS).

2.4 An Overview of Current and Near-Term Earth-Observing Technology Therearemanyearthobservingsatellites(EOS)currentlyinorbit,bothactiveandpassive,

includingapproximately110nonmilitarysatellites(UCS2009).Thereareabout75distinctsensortypesfocusedonobservingtheearthssurfacewithcoarseorhigher(


onlybriefly.Whenanindividualsensorspanscategoriesdependingonitsmodeorband,weplaceitinthemoredominantcategoryforthatsensor.WegatheredallofthedataandspecificationsonsatellitesfromthesourcesnotedintheAppendixandpubliclyavailablewebsites.

2.4.1CurrentSensorTechnology

MultispectralpassivesatellitesmakeupthevastmajorityofEOSandhavebeenincreasinginspatialresolution,spectralresolution,andreturnfrequencyinrecentyears.Spatialresolutionhasincreasedinmoderatespatialresolutionsatellites,buttheadventofhighspatialresolution,pointablecommercialsatelliteshaschangedthecostandavailabilityofhighresolutionimagery.Theseincreasesinspatialresolutionhaveoftencomeatthecostofimageswath(seeFigure6)buttechnologyisimprovingoverall,asillustratedbythewideswathbutmoderateresolutionoftheDMCconstellation.Thereareafewhyperspectralsatellitescurrentlyinoperation(e.g.,theU.S.ModerateResolutionImagingSpectroradiometer(MODIS),theHyperioninstrumentontheU.S.EarthObservingMission1,andtheCompactHighResolutionImagingSpectrometer(CHRIS)ontheEuropeanSpaceAgencysProjectforOnboardAutonomy(PROBA)spacecraft),butingeneralmostsatellitesmeasurebetweenoneand12bands(Appendix).Severalnotablecoarseresolutionsatelliteconstellationshavebeeninoperationfordecades,includingAVHRRandGOES,theU.S.launchedGeostationaryOperationalEnvironmentalSatellites.Therecentlaunchingofmoremoderatetohighresolutionconstellations,suchasMODIS,theDMCseries,andtheRapidEyeseries,isdramaticallychangingreturntimesforforestmeasurement(Appendix).WhereasLandsatimagesonceevery16days,andcloudfreeimagesinsomeareasonlyoccuronceayear(Olanderetal.2008;Asner2001),theDMCconstellationhasdailyimagingcapability(seetheAppendix).

RecentandupcomingSARsensorlauncheswillincreasethenumberofactivesensorsinorbit.ModernSARsatelliteshavehigherandmoreflexiblespatialresolution,greaterimageswathflexibility,andmorepolarizationmodesthanprevioussatellites(seetheAppendix).JapansAdvancedLandObservingSatellite(ALOS),whichincludesanLbandsensorwithfourpolarizationmodes,hasbeenusedforglobalforestcovermappingsinceitslaunchin2007(Olanderetal.2008).ThereisonlyonefunctioninglandobservingLIDARsatellite.ThebiggestbarriertowidespreadusageofactivesensorsremainsthetechnicaldifficultyofprocessingSARdata,butthecostofSARdata,althoughequaltosomeopticalsensors,isalsoabarriertoglobalmapping(Patenaudeetal.2005).

Fromthestandpointofglobalforestmeasurement,themostsignificantrecentadvanceinremotesensinganalysisiseconomic,nottechnological.Asofearly2009,theU.S.Landsatarchive(19722009)becamefreelyavailable,thusenablingglobalandtemporalanalysisonmoderateresolutionimagery(Olanderetal.2008).TheregionalBrazilianCBERSarchive(19992009)ofimageryisalsonowfreeinmostdevelopingcountries(GOFCGOLD2008;Powelletal.2007).Currently,thesetwofreeimageryseriesofferthehighestresolutioncoverageperdollar,andtheyhaveagoodtemporalrecord(Figure6).InconjunctionwithotherfreeMODISandAVHRRdata,itisnowpossibletosurveyglobalforestsdailywithcoarseresolutionimagesandcreatemoderateresolutionforestmapswithmonthlytoannualfrequency.TheLandsatDataContinuityMission(andtheupcomingLandsat7imagerymosaics,seebelow)willcontinuethisfreedatapolicyandmakeitpossibletodevelopalongterm,globalrecordoflanduseandlandcover(Lovelandetal.2008).


CoarseResolutionPassiveSensors

Coarseresolutionpassivesensorshaveapixelsizeof2001,500m,receiveradiationintheopticalthroughthermalrange,andhaveimageswathsrangingfrom360kmtothefullearthdisk(Figure6,Appendix).Itwouldtakebetween14and40AVHRRor2580MODISimagestocoverthe13.4billionhectare(ha)globallandarea(estimatesareinexactforcoarseresolutionimagesthatincludelandandwater;Table3).Becausecoarseresolutionisusefulformonitoringweatherandclimate,manycoarseresolutionsensorsarepartsoflargeconstellations.Forexample,theAVHRRsensorconstellationincluded12satellitesbetween1978and2009,includingseveralsatellitesoperatingsimultaneously(Jensen2007).Althoughmanycoarseresolutionsensorsonlyhaveafewvisibleornearinfraredbandsatresolutionstoocoarsetodetectmostlandcoverchange,theveryhighrepeattimesmakethesesensorsidealformonitoringphenology,fire,andotherephemeralevents.Theadventofmoderatecoarsesensors(e.g.,MODIS,MERIS,250300m)hasimprovedgloballandcovermapping(Heroldetal.2008).ArecentlylaunchedhyperspectralcoarseresolutionsensormountedontheIndianMicrosatellite(IMSHySI)haspotentialforlandcovermappingaswell(Appendix).

Coarseresolutionsensorsaremountedonbothpolarorbitingsatellitesandgeostationarysatellites.Althoughgeostationarysatellitesimagetheentireearthdiskinrealtime,currentsensorsareultracoarse(>1.5km)inresolutionandonlytheGOESseriesisdescribedhereasanexampleintheAppendix.Thereareseverallongterm,similargeostationarysatellitesseriessupportedbytheUnitedStates(GOES),Europe(Meteostat),Russia(Elektro),andChina(FY3and4).

Table3.CostsofImageryforWalltoWallSamplingoftheGlobalLandArea

SatelliteSensor SensorType(pixelsize)

NumberofImagesRequired

Priceperkm2(inUS$,2009)

EstimatedTotalCost

MODIS Coarseresolution(2501000m)

2580 Free $0

Landsat7ETM Moderateresolution(28.5m)

3921 Free,formerly$0.02in2008

$0,formerly$2,683,700

GeoEye1 Highresolution(1.65m)

580,787 $25(forabasicimageofEurope)

$3,354,625,000

ALOSPALSAR Coarsestresolutionmode(100m)

1096 $0.0017 $230,075

Note:CreatedfromAppendix1forselectedsensors;thenumberofrequiredimagesisanestimatebasedonthe134,184,000km2globallandarea(seetext).Priceschange;theseestimatesarefor2009.ALOSPALSARwouldlikelybedegradedto400mresolutionforaccuracy.

ModerateResolutionPassiveSensors

Moderateresolution(10200mpixel)passivesensorscanreceiveopticalthroughthermalradiationandhaveimageswathsrangingfrom60890km(Figure6,Appendix).Itwouldtakeapproximately3,921LandsatimagestocoverthegloballandareaTable3.Therearecurrentlyfivemajormoderateresolutionsatelliteseries:Landsat,theSatellitePourLObservationdelaTerre(SPOT),CBERS,theIndianRemoteSensingsatellite(IRS),andtheDMCseries(Achardetal.2007;Powelletal.2007).Inaddition,China,Argentina,Thailand,andRussiaallsupportindividual


moderateresolutionsatellites,butitisunclearwhetherChinasdataareavailabletothepublic.Additionally,thereareseveralexperimentalmoderateresolutionsatellites,mostnotablythehyperspectralHyperionandCHRISsensors,andthesehaveshownpromiseforhyperspectralimagingasameansofdistinguishinglandcovers(Ustinetal.2004).

TheLandsatseries(satellites17,1972present;Jensen2007)istheworkhorseforgloballandcoveranalysis.Unfortunately,Landsat5isonlytransmittingdatainregionswithgroundstations,andLandsat7hadascanlinefailurein2003thatcausedblackstripestoappearinthesidesofitsimages.TheUnitedStatesGeologicSurvey(USGS)isworkingtomosaic(thatis,combine)Landsat7imagestocorrectthescanlineproblem,butthenextLandsatisnotduetobelauncheduntil2012(Lovelandetal.2008).Acontinualrecordofgloballandcoverrequiresfillingthe20032012Landsatdatagap.Tthereareseveralpossibleapproaches.TheAdvancedSpaceborneThermalEmissionandReflectionRadiometer(ASTER,launchedin1999)isamoderatecostsatellitethathasnearglobalcoverage(Jensen2007).SPOT(15,1986present;Jensen2007)isasatelliteserieswithglobalcoveragebutdataarerelativelyexpensivetopurchase(Figure6).TheChina/BrazilEarthResourceSatelliteseries(CBERS,threesatellites,1999present;Jensen2007)isagrowingconstellationofsatelliteswithfreeimagery,buttheimageryisfocusedoverBrazil,China,andmostrecently,Africa.TheIndianResourceSatelliteseries(IRS,sevensatellites,1988present;Jensen2007)isalarge,longrunningconstellationwithglobalimagecoverageavailableatsomecost.Finally,theDMCseriesisaconstellationofsatellitesfromdifferentcountriesinthesameorbit;imagesfromdailyoverflightsareprovidinganimportantcommercialbackuptoLandsat(GOFCGOLD2008).

HighResolutionPassiveSensors

Highresolution(0.310mpixel)passivesensorsreceiveopticalthroughnearinfraredradiationandhaveimageswathsrangingfrom11to70km(Figure6,Appendix).Therehavebeenanumberofcommercialandnationallaunchessincethefirsthighresolutionsatellite,IKONOS,launchedin1999,andtheseadditionstothefleetofEOShaveledtotheformationofcommercialhighresolutionconstellations.TheseconstellationsincludehighresolutioninstrumentsoperatedbyseveralU.S.companies,includingGeoEyeandDigitalGlobe.Oneofthechallengesofusinghighresolutionimageryforglobalforestmappingisthatapproximately580,787GeoEye1imagesarerequiredtocoverthegloballandarea(Table3).Mostofthehighresolutionsatelliteshavefivebands;fournarrowbandsinthevisibleandnearinfrared,andonehigherresolutionpanchromaticbandthatcanhavesubmeterresolution(fornationalsecurityrequirements,panresolutionisactuallydegradedto0.5monU.S.commercialsatellites).Therehavebeenseveralrecentlaunchesofsolelypanchromaticsensorswithprimaryapplicationsindefenseandmapping.Mosthighresolutionsensorsarepointable,decreasingtheireffectivereturntimefrom,forexample,26daysto3days.Thisresultsinspottyglobalcoverage,particularlyforforests,asthecoverageisoftenconcentratedindenselypopulatedandlesscloudyareas.Therecentdevelopmentofcheapmicrosatellitesthatcanbelaunchedasaconstellation(e.g.,RapidEye)orasanondemandsensor(e.g.,theU.K.smicrosatellite,TopSat)coulddramaticallyincreasetheglobalcoverageandreturntimeofhighresolutionsensors(KramerandCracknell2008).


ActiveSensors

Activesensors(SARandLIDAR)currentlymakeupasmallproportionoflandobservingsatellites;thereareapproximatelynineSARsensorsandoneLIDARsensorinorbit(Appendix).Activesensorshaveresolutionsrangingfrom11,000mandimageswathsrangingfrom10500km;itwouldtakeapproximately1,096ALOSimagestocoverthegloballandarea.Inthelastdecade,SARsatellitetechnologyhasimprovedtofourpolarityoptionsandvariableresolution(withsmallerswathsathigherresolutions)indifferentimagingmodes.Until2006,allpubliclyavailableSARsatellitescarriedCbandsensors(Jensen2007)exceptfortheLbandJERS1missionthatranfrom19921997,theshortlivedSeasat(Lband)andAlmaz(Sband)missions,andthebriefSRTMandSIRA,SIRB,andSIRC/Xshuttlemissions.TheCanadianRadarsatserieshasbeengatheringCbandSARimagerysince1995,andtheEnvisatASARhasbeengatheringCbandaswellsince2002.Themostrecentsatellitelaunches,ALOS(LBand),TerraSARX(Xband),andCOSMOSkyMed(Xband),haveincreasedthediversityofSARbandsinorbit.ALOSprovidesglobalcoverage,TerraSARXisthefirstsatelliteinaninterferometricconstellation,andCOSMOSkyMedisamicrosatelliteSARconstellationwithavarietyofmodes.WhenevaluatingthespatialresolutionofSARsatellites,givenintheAppendix,itisimportanttorememberthattheireffectiveresolutionafterspeckleaveragingwillbethreetofourtimesmorecoarse,approximately50100m.

ThefirstgroundmeasuringLIDARaltimeter,ICESat,waslaunchedin2004andafollowonisplanned.WhiletheswathsICESatmeasuresdonotcovertheentirelandsurface,theinstrumenthasprovidedusefulinformationformodelingice,elevation,andforeststructure(NASA2009a).

2.4.2ExpectedSensorTechnology,20092015

Futuresatellitelauncheswillfillcurrentdatagaps,providefollowonmissions,populateconstellations,andprovidenewfunctionality.ThediversityofcountrieswithsatellitesinorbitwillincreasewithnewsatellitesfromSpain,Turkey,Taiwan,theUnitedArabEmirates(UAE),Sweden,Ukraine,Malaysia,andNigeria.ThemajorityoflauncheswillcomefromChina,theUnitedStates,countriesintheEuropeanUnion,Argentina,andIndia,withnotablesmallercontributionsfromBrazil,Russia,Canada,andJapan(Appendix).

Amongfollowonmissions,theLDCM,CBERS3,andtheultrahighresolutionGeoEye2meritmentionfortheiradvancedtechnology.Bothnationalandcommercialhighresolutionopticalmissionswillcontinuetoincreaseinnumberasmicrosatellitetechnologylowersthecostsofproduction(CEOS2009;Olanderetal.2008;Kramer2008).SeveralnewSARconstellationsareplanned(TerraSAR,Sentinel1,Radarsat,ALOS,andSAOCOM1series).TheSentinel2series,amoderateresolutionopticalconstellation,isalsoplanned.Therewillbeseveralnewmoderateresolutionhyperspectralsensors(EnMap,HyspIRI,TESHYS,SACF,andPRISMA).Mostnotably,ofthe87orsolandobservingmissionsplannedbetween2009andlate2015,25willmountSARsensors,twowillmountLIDAR,andonewillmountbothadramaticincreaseinthenumberanddiversityoffunctioningactivesensors.

Overall,itislikelythatspatialresolution,returntimes,andSARcapabilitieswillimproveoverthe20092015period,withsignificantnewhyperspectral,InSAR,andLIDARfunctionality.Despitetheunpredictablenatureofgovernmentdatapoliciesandimageryprices,aswellasuncertainties


relatingtolaunchschedulesandsuccess,thenextsixyearswilllikelybringasignificantexpansioninthecapacityofremotesensingtomonitortheearthssurface.

ExpectedCoarseResolutionPassiveSensors

Approximately27coarseresolutionsensorsareplannedforlaunchbetween2009and2015,butitisunclearifdatawillbepubliclyavailablefromninesensorstobelaunchedbyChina.Mostlauncheswillcontinueconstellations(e.g.,GOES,FY3),withtheAVHRRsensorcontinuingonMetOpAandMetOpB.TheUnitedStatesplanstolaunchanewsatelliteseries,NPOESS,asareplacementtotheMODISandAVHRRsatellites,butNPOESShaslowermaximumresolutionthanMODISandmaybemoresuitableasanAVHRRfollowup(TownshendandJustice2002).TwosensorsthatmaybemoresuitablesuccessorstoMODISaretheGCOMC1(with11bandsat250mresolution)andtheSentinel3series(with21bandsat300mresolution).Mostexpertsagreethatitwillbedifficulttomatchthecombinationofresolution,revisittime,andfreeimagerythatMODIScurrentlyoffersforgloballandcoveranalysis.

InadditiontoGCOMC1andtheSentinel3series,therewillbeafewtechnicalimprovementsincoarseresolutionsensors.GISATwillmountacoarsesensor,amoderatesensor,andacoarseresolutionhyperspectralsensor.SACDwillbeacoarseresolutionthermalinfraredsensortomeasurethetemperatureandenergyreleaseoffires.Mostsignificantly,realtimemonitoringofvegetationandproductivitywillbecomepossibleafter2014,withthelaunchofvisibleandnearinfrared(VNIR)geostationarysensorswitharesolutionof1km.Thesesensors,startingwithGOESR,offerasignificantimprovementovercurrenttechnology.

ExpectedModerateResolutionPassiveSensors

Atleastfifteensatellitesbearingmoderateresolutionsensorsareplannedforlaunch.TheseareNigeriaSat2,Amazonia1,RASAT,SACF,ARGO,CBERS3,CBERS4,KanopusV,Resourcesat2,Resourcesat3,LDCM(Landsat8),GISAT,HyspIRI,Sentinel2A,andSentinal2B.ThemultispectralARGO,LCDM,Resourcesat2,GISAT,andSentinel2sensorsareexpectedtocollectglobaldata.Severalhyperspectralmoderateresolutionsensorsareunderdevelopment.Ofthesixplannedhyperspectralsensors(EnMap,HyspIRI,TESHYS,GISAT,PRISMA,andSACF),onlyHyspIRIhasaswathsizesuitedtofrequentglobalcoverage.Iflaunchingagenciessupportglobaldatacollection,thisincreasingdiversitywillsafeguardglobalcoverageagainstthelossofasinglesatellite.ResolutionintheSPOTserieshasimprovedtothepointwhereSPOT5andSPOT6areconsideredwideswath,highresolutionsensors.TheLDCMwillhaveninebandsandmaintaincontinuityinacriticalimageryseries,IndiasResourcesat3willhave23mresolutionandanextrawideswathof700km,andtheSentinel2serieswillbeanadvanceinsensortechnology,withhighresolution(10m),13bands,awideswath(240km),andthreededicatedbandsforatmosphericcorrection.IftheLDCMfailstolaunchoroperateproperly,aninterruptioninfreeaccesstoglobalimageryislikely.

ExpectedHighResolutionPassiveSensors

Althoughitisdifficulttoanticipatecommerciallaunches,thenextseveralyearswillseeanincreaseinthenumberofstandard(fourbandVNIR,resolution110m)highresolutionsensors(Appendix).Severalcountrieshavetakenadvantageofthelowercostofmicrosatellitestocreatehighresolutionsensorswithtechnicalassistancefromaerospacecompanies(e.g.,DubaiSat(UAE)


andMalaysiasRazakSat).MicrosatellitetechnologyhasledtothehighresolutionRapidEyeconstellationofsixsatellites.TheconstellationwillbecompletedbytheadditionofthesatelliteARGOinmid2009.Withhighrevisittimesandalargerswaththanmanyhighresolutionsatellites(78km),RapidEyehasthepotentialtodevelopglobalcoveragequickly(RapidEye2009).

Someimprovementsinhighresolutionsensorcapabilitiesandlowerdatacostsareexpected.WorldView2willhavesubstantialspectralandspatialresolutionwitheightbandsintheVNIRand1.8mresolution.VENUSwillalsoofferhighspectralandspatialresolution,with12bandsintheVNIRwitharesolutionof5.3m.Pleiades1andPleiades2areextremelypointable(+/50)andwilladdflexible,highresolutioncapacitytotheSPOTseries.SPOT6willalsocontinuetheSPOTseries,withawideswath(60km)andhigherresolution(8m,2mpan).GeoEye2willhavethehighestspatialresolution(0.25m)inthe20092015interval.PRISMAwillintegratea2.5mresolutionpancamerawithhyperspectralimagery,andtheSentinel2series(discussedabove)willhavehighresolutionimagery(10m)intheVNIRwithaverywideswath.Finally,CBERS3andCBERS4willcombinehighresolutionimagerywithamoderateresolutionimagerandacoarseresolutionthermalimager.

ExpectedActiveSensors

SARandLIDARsensorswillmakeupathirdofnewlaunchesfrom20092015,withanincreaseinthediversityofSARbands(C,X,S,L,andpossiblyP),functions(InSARanddualInSAR/LIDAR),andnumberofSARconstellations.TheTerraSARX(Xband)constellationwillflyininterferometricformationtoproduceahighresolutiondigitalelevationmodelforcommercialpurposes.Theresolutionwillbe1mbeforespeckleaveraging.TheCOSMOSkyMEDconstellation(Xband)willbeintegratedwiththeplannedSAOCOM1constellation(Lband)fortwicedailyoverflightsintwobands.TheRadarsatConstellation(Cband)willcontinuethelongrunningRadarsatseriesofimageryanddecreaserevisittime(eoPortal2009).TheSentinel1constellation(Cband)willhaveaninterferometricmodeandvariableswathwidth.Finally,thelaunchofALOS2willcreateahighquality,Lbandconstellation.

Thereareseveraladvancesinactivesensortechnologyplannedforlaunch,includingavariableC/Xbandsensor(DMSAR),interferometricsensors(InSAR,severalsatellites),aLIDARfollowonmission(GLAS),ahighresolutionSbandsensor(HJ1C),aLIDARInSARintegratedLbandsensor(DESDynI),andpotentiallyaPbandinterferometricsensor(BIOMASS).Ofthese,DESDynI,GLAS,andBIOMASSmeritspecialmentionfortheirpotentialtoinformforestmeasurementandmonitoring.DESDynIwouldbethefirstintegratedInSARLIDARmission,anditwouldcombinetherelativestrengthsofInSARandLIDARtocreateunparalleledmeasurementofecosystemstructureandforestheight.GLASwouldcontinueglobalLIDARmeasurement,thusincreasingtheaccuracyofglobalDEMestimates.BIOMASSiscurrentlyasensorconceptunderconsiderationforlaunchinlate2015orearly2016andwouldconsistofaPband,interferometricSARspecificallydesignedtomeasureforestbiomassinrealtime.

2.4.3ACautionaryNoteonPlanningandSatelliteTechnology

Althoughplannedlaunchesaregoodindicatorsoffuturepossibilitiesforearthobservation,successfulsatellitelaunchandoperationareneverguaranteed.Satellitelaunchesareusuallydelayedbybudgetaryandconstructionexigencies.Satellitesregularlyfailtolaunch(examples


includethelaunchfailuresofLandsat6,IKONOS1,andtheOrbitalCarbonObservatory,in1993,1999,and2009,respectively).Onceinorbitandfunctioning,satelliteshaveexpectedoperatinglifespansandoftenoutlivethese,buttheiractualoperatinglifespanisamatterofchance.AninstrumentonLandsat7hadamajorproblemfouryearsafterlaunch,whileinstrumentsonLandsat5arestillfunctioning24yearsafterlaunch(Powelletal.2007;Lovelandetal.2008).Satellitebornesensorscandegradeandshiftinqualityovertime,aswell,andtheirimageproductsrequireregularcalibrationandvalidation.Processinganddistributingthedatageneratedbythesensorsareasimportantashavingthesatelliteinspace;manydataaredifficulttoobtainorprocessorarenotmadewidelyavailable.Asaresult,longterm,globalforestmonitoringplanscannotrelyonsinglesatellitestocarryoutthebulkofdatacollection.


Chapter 3. Technical Considerations in Global Forest Monitoring Theuseofremotesensingtoprovideaccurate,longtermglobalforestmeasurementsrequires

severaltechnicalandlogisticchoices.Agreementisrequiredaboutwhatactuallyconstitutesaforestandwhichforestquantitiestomeasure.Theselectionofsatelliteimageryandimageanalysismethodsmustbeguidedbytheforestmeasurementsofinterestandbybasicprinciplesofscientificinquiry,suchasaccuracy,repeatability,andlongevity.Todetectchanges,itisnecessarytocreatereferenceforestmaps.Aconsistentsurveymethodmustbeselectedthatbalancesanalysistime,coverage,andaccuracy.Finally,thelogisticsofimageprocessing,cost,andgroundtruthingmustbeconsidered(SanchezAzofeifaetal.2009;Achardetal.2007;Gibbsetal.2007;Olanderetal.2008).

3.1 Measuring Forests Globally Anyglobalefforttomonitortheworldsforestsmustbeginwithcommondefinitionsand

commonmeasurements.Definingaforestisnotassimpleataskasitmightseem,giventhewidevariationinforestecosystemsfromcountrytocountryandthedynamicnatureofforestregrowthafterhumandisturbance.Forexample,atwhattreedensityshouldanopenecosystemofshorttreesandgrassbeclassifiedasaforest?Isaclearcutdeforestation?Isanabandonedtreeplantationaforest?

Tomeasureforestseffectively,wemustbeabletocomparegroundmeasurementstakenbyforestersandforestecologiststothosetakenbyremotesensingmeasurements.Todothis,wemustbeclearaboutwhichforestcharacteristicsweareinterestedinmeasuring.

3.2DefiningForests

Theexactdefinitionofforestversusnonforesthasimportantconsequencesforthemonitoringofdeforestationandforestdegradationandpoliticaleffortstoreducecarbonemissions.Currently,onlytheFAOusesasingleglobalstandarddefinitionofforest.In2005,theFAOdefinedaforestasaminimumcoverof10percent,heightof5m,andareaof0.5m(FAO2006).UndertheKyotoProtocol,participatingcountriesdefineforestswithintheirbordersbyselectingonevalueofminimumarea,treeheight,andcanopycoverfromthefollowingrange:0.051hectare(ha)area,25mminimumheightatmaturity,andminimumcrowncoverof1030percent(UNFCC2001).2

DeforestationisdefinedbytheUnitedNationsFrameworkConventiononClimateChange(UNFCCC)asasemipermanentconversionofforestedlandtootheruses.Thisdefinitionassumesthatregrowthwillnotoccurquicklyandexcludestemporarydeclinesincanopycoverduetologging(UNFCC2001).Remotesensinghasdifficultydistinguishingareasofdeforestationfromintactforestwhentheintactforestdefinitionincludesaverylowcanopycoverthreshold(e.g.,10percent).Theoppositeistrueforforestdegradation,whichisdefinedasadecreaseinforestcanopycoverwithoutdeforestation;lowercanopycoverthresholdsincreasethelikelihoodofdetectingdegradation(e.g.,adecreasefrom90percentto10percentcover).

Thelackofaglobalstandarddefinitionforforestcovermakesitdifficulttocreateforestmapsthatcanbereadilyusedbyallcountries,buttheFAOstandardmaybeausefulstartingpoint.

2Anupdatedlistofforestdefinitionsbycountrycanbefoundathttp://cdm.unfccc.int/DNA/index.html.


Ideally,globalforestmonitoringeffortswillcreatemapsthatcanberesampledbyinterestedcountriestofittheirforestdefinitions,suchasasetofprocesseddataknownasthevegetationcontinuousfieldsproductfromMODIS(Hansenetal.2003).

3.1.2ForestMeasurementandtheForestIdentity

Historically,forestmeasurementhasbeengroundbased,asdescribedinChapter1.Forestersandforestecologistshavemeasuredseveralforestquantitiesincludingthese:

biodiversityspeciesrichnessandevennessinanarea;

canopyqualitiesopenness,leafareaindex(LAI),leafwatercontent,andphenology);

structuralvariablestreedensity,basalarea,treeheight,andtreediameteratbreastheight(DBH);

forestfloorqualitieslitterfall,soilcarboncontent,andseedlingdensity;and

ecophysiologicalcharacteristicstranspirationrate,treerespiration,waterstress,andnitrogenflux.

Anorganizingprinciple,theForestIdentity(Kauppietal.2006),isacentralthemeintheremainderofthisreport.TheIdentityrelatesfourforestattributes(area,volume(densityofgrowingstock),biomass(themassoflivingmaterial),andsequesteredcarbon)andissummarizedindetailbyWaggonner(2009).TheForestIdentityattributesarerelated:forestarea(ha)canbeconvertedintovolume(cubicmeters,orm3)usingstemdensity(m3/ha);volume(m3)canbeconvertedtobiomass(kilograms,orkg)usingwooddensity(kg/m3);andbiomass(kg)canbeconvertedtocarbon(kgC)usingcarbondensity(kgC/kg).Asdiscussedearlier,anysuccessfulglobalforestmonitoringprojectwillhavetointegrateremotesensingandgrounddata.ThesimplicityoftheForestIdentitymakesitstraightforwardtoconvertbetweengroundmeasurementsandremotesensing(Table4;Kauppietal.2006).ThecomponentsoftheForestIdentitytietogetherfourscientificallyandeconomicallyimportantforestcharacteristicsandprovideausefulstartingpointforglobalforestmonitoring.

3.1.3ComparingForestInventoryandRemoteSensingMeasurement

Thesameforestquantities(e.g.,biomass)areestimateddifferentlybygroundforestinventoryandbyremotesensing(Table4).Forestinventorytypicallymeasurestreeabundance,diameter,crownwidth,species,andheight(Song2007;Chaveetal.2005).AlthoughitispossibletomeasureallofthequantitiesoftheForestIdentitydirectlythroughdestructiveharvesting,forestersgenerallyuseallometricequationstoestimatevolume,biomass,andcarbonfromtreediameter,speciesspecificwooddensity,and/orheight,andthenextrapolatetotheentireforest(Chaveetal.2005;Gibbsetal.2007,seeTable4).Allometricequationshavebeendevelopedfortropicalforests(Chaveetal.2004)andhigherlatitudeforests(TerMikaelianandKorzukhin1997)throughexhaustivemeasurementandselectiveharvestingandweighingoftrees.Someallometricequationsestimatetotalforestbiomassdirectlyfromtreediameterbutgivenlargespeciestospeciesvariationinwooddensity,theseequationscanbemarkedlyinaccurate(Bakeretal.2004;Chaveetal.2005).Thereissignificantvariationbetweenthebiomassestimatesofdifferentallometricequationsandthisvariationcanbeasignificantsourceoferrorinstandbiomassestimates(Bakeretal.2004;Chaveetal.2005).Scalingupfromstandlevelestimatesofbiomasstoregional


estimatesoftenhaslowaccuracyduetolocalvariabilityinforestcoveranddensity(Houghtonetal.2001;Houghton2005).

Inremotesensing,satellitemeasurementsofforestareaandstructureareusedtoestimatevolume,biomass,orcarbon(Table4).Theseestimatescanbesupplementedbyothermeasuresofforestleafarea,productivity,andflammabilityandincorporatedintoaforestmodelthatcanyieldimprovedestimates(vanderWerfetal.2006;Table4).Satellitederivedforestmetricsaretakenacrossanentireregionmakingextrapolationunnecessary.Twoimportantconsiderationspertainingtoremotesensingmeasurementsofforestsaretheimportanceofcorrelationandthenecessityofaccurategrounddataforoverallaccuracy.

Table4.HowForestInventoryandRemoteSensingEstimatetheForestIdentity

ForestIdentity ForestInventory RemoteSensing

Area Measuredlocally,atonetime,inonetoafewforesttypes.

Measuredregionallyandrepeatedly,distinguishingforesttypesandages(Optical,SAR).

Volume Estimatedusingdiameteratbreastheight,treeheight.

Estimatedfrommeasuresofforestheight/structure(SAR,LiDAR).

Biomass Estimatedfromvolumeandwooddensitymeasurements.Extrapolatedregionally

Estimatedfromarea&foreststructure.Estimatesareimprovedbymeasuresofforestflammability,productivity,leafarea,phenology,andgasflux.

Carbon Estimatedfrombiomassandcarbondensitymeasurements.Extrapolatedregionally.

Sameasbiomass.Estimatesaresitespecific,acrossentireregions.

Remotesensingmeasuresreflectedspectra;forestareaandthehorizontalandverticalstructureofforestscanbemeasureddirectlyfromthesereflectedspectra.Fieldworkorhigherresolutionimagerycanbeusedtogenerategroundtruthdatatoassesstheaccuracyoftheseforestareaandstructuremeasurements(Jensen2007).RemotesensingenablesestimatesoftheothermetricsintheForestIdentity(volume,biomass,andcarbon)by:

obtainingforestinventorymeasurements,

correlatingspectrawiththosemeasurements,and

extrapolatingfromthesecorrelationstotherestoftheforest.

Forexample,existingfieldmeasurementsofbiomassarerequiredtocreateforestwidepredictionsofbiomassfromsatelliteimagery;anotherindependentsetoffieldmeasurementsisrequiredtoassesstheaccuracyofthesepredictions.

Fieldestimatesofvolume,biomass,andcarbonarederivedfromallometricequations.Theseequationscanbeinaccurateorbiased(e.g.,bynotincludingspeciesspecificwooddensity;Bakeretal.2004).Remotesensingcanonlybeasaccurateasitsgroundtruthdata.Itispossibleforremotesensingtoaccuratelypredictbiasedestimatesofbiomassfromstandardallometricequationsthat,


forexample,donotincorporatespeciesspecificdifferencesinwooddensity.Thus,itisdifficulttoassessthereportedaccuracyofremotesensingpredictionsofforestvolume,biomass,andcarbon.Becauseestimatingthetruevalueoftargetsisquitedifficult,andonlythecorrelationsbetweenremotesensingandgroundbasedestimatesarereportedintheliterature,wehavechosentoreportthepublishedaccuracyofremotesensingestimates.Theiraccuracyissummarizedhereaseithercorrelationcoefficientsorthepercentageofvarianceexplainedbyregressionequations.Furtherrefinementsingroundbasedmeasurementofforestbiomasswillleadtomoreaccurate,unbiasedestimatesoftruevaluesfrombothforestinventoryandremotesensing.

3.2 Criteria for a Global Forest Monitoring Program Severalprinciplesguideconsiderationindesigningaforestmonitoringprogramthataimsto

measuretheworldsforestsonaregularbasis.Oneprincipleisaccuracy,discussedmorefullybelow.Anydatageneratedbyaforestmonitoringprogramwillhavepoliticalandscientificconsequencesandshouldmeetorexceedstandardsofaccuracyforremotesensing.Anotherprincipleisrepeatability,requiringconsistencywithearlierscientificefforts(e.g.,forestdefinitions),transparencywithscientificmethodology,andpublicavailabilityofsourcedata(e.g.,imagery).Thedataandconclusionsfromaforestmonitoringeffortwillbemostusefultoscientists,politicians,andforestersifthedataareverifiable,extendable,andrelevant.Athirdprincipleislongevity;reliable,longtermdataonforestsarerareandthecreationofaremotesensingbased,globalrecordofforestchangewouldbeasignificantscientificaccomplishment(Grainger2009).Thislastguidingprinciplehassignificantconsequencesforprogramdesign.

Satellitefailureisunpredictable,sodesigningaforestmonitoringprogramthatdependsonthesuccessfullaunchoroperationofasinglesatelliteisrisky.Thebeststrategyforlongtermcontinuousmonitoringistouseastandardspatialandspectralresolutioncollectedbyseveraldifferentsatelliteserieslaunchedcontinuouslyoverdecades.Forexample,the30mresolution,sixbandLandsatsensordataformathasbeencollectedforafewdecadesanditcanbeapproximatedbyseveralsatelliteseries(e.g.,SPOT,Avnir,andIRS)(Powelletal.2007).The1.1kmresolution,multibandAVHRRsensordataformatisalsosupportedlongtermandcanbeapproximatedbyanumberofsatelliteconstellations,includingSPOT,thegeostationaryconstellations,andtheChineseFYseries(seetheAppendix).TheSARCbandhasbeentakenat30mresolutionbyseveraldifferentsatellitesoverthelasttwodecades,andCbandsensorsareplannedfornewlaunches(Appendix).Allofthesedataformatswouldbegoodcandidatesforlongtermdataseriesonforestcharacteristics,andifnecessarytheycouldbefusedwithotherdatatoincreasetheirutility(e.g.,Hansenetal.2008b).

Thesedataformatscurrentlyhavetheadvantageofbeingfreelyavailable,whichfacilitatesprogramfinanciallongevityandincreasestransparency.Althoughseveralcommercialsatelliteseries(SPOT,GeoEye)alsohavelongterm,highqualityimageryarchives,therightstoreleasetheseimagestothepublicarelimited.Theroleofthesedatainaglobalmonitoringprogramrequiresfundingandameansofallowingthedatatobesharedtomaximizetheirusefulness.

3.3 Developing and Evaluating Reference Forest Maps Todetectchangeinforestcharacteristicsofinterest,baselinereferencemeasurementsare

required.Biomassestimatesderivedfromremotesensingarerareformanyregionsandaccurate


historicalreferencemapsofforeststructureorbiomasscurrentlydonotexist(Olanderetal.2008;Gibbsetal.2007;and,e.g.,Baccinietal.2008).Bycontrast,itispossibletodevelopmoderateresolutionreferencemapsofforestareadatingto1972(Olanderetal.2008).Formoredetailonthistopic,seeChapter4.

3.3.1AccuracyinForestMapping

Accuracyconsiderationsareparamountindetectingchangesintheworldsforests(Grainger2008;Gibbsetal.2007).Therecanbeerrorsofomission(notdetectingorlocallyunderestimatingforestquantities)andcommission(falsepositivedetectionsorlocaloverestimatesofforestquantities).Wereportherethegreaterofthetwotypesoferrorwhenassessingpredictionaccuracy.Forforestarea,accuracyisdefinedasthepercentageofpixelsintheremotesensingimagerythatarecorrectlyidentifiedwithrespecttolandcovertype,sothelowestoverallaccuracy(meanomissionorcommissionerroracrosslandcoverclasses)isusedwherepossible.Forforestvolume,biomass,andcarbon,wereportthematchbetweenpredictionsfromremoteimageryandobservedgroundmeasurements(i.e.,thepercentageofvarianceingroundtruthdataexplainedbyregressionequations).Ingeneral,remotesensingestimatesofforestareahavehighaccuracy3whileestimatesofforeststructureandbiomassarelessaccurate(DeFriesetal.2007;Olanderetal.2007;Rosenqvistetal.2003).

3.3.2ReferenceMapsforCarbon

Approximately50percentofvegetationbiomassiscarbonbutthereisgreatuncertaintyinestimatesofglobalbiomassstocks(seeSection4.4).TheguidelinesissuedbytheIntergovernmentalPanelonClimateChange(IPCC)describethreeacceptabletiersofforestmeasurement.Eachtierisincreasinglyaccurateandprecisebutalsomorecomplexandexpensiveinthemonitoringrequirements(GOFCGOLD2008;seeTable5foranoverview).Conservativeprinciplesofestimationcanprovideusefulestimatesofforestareaandbiomassforpolicymakers(Achardetal.2007;Grassietal.2008;GOFCGOLD2008).Conservativeprinciplesdictatethat,atierlowerthanrequiredcouldbeusedoracarbonpoolcouldbeignoredifitcanbedemonstratedthattheoverallestimateofreducedemissionsare[sic]likelytobeunderestimated(GOFCGOLD2008,12).Wherelargelevelsofuncertaintyexistinbiomassestimates,discountingtheestimatedforestcarbonwouldbeanappropriateconservativeresponse.Forexample,ifestimatesofforestcarbonvarybetween60and90tonsperhectare,60couldbeusedasadefaultregionalvalue(Tier1)orcouldbefurtherdiscountedto40mt/hatoaccountforuncertainty.

3.3.3ForestInventoryandReferenceMaps

Becauseforestinventorydatacancurrentlyestimatelocalbiomassmoreaccuratelythanremotelysenseddata,referencemapsforforestbiomassandotherquantitiescouldbedevelopedfromgrounddatabyinterpolatingbetweenmeasurementplots(Gibbsetal.2007).Thisapproach

3Inthisreport,theoverallaccuracyofsatelliteimageryanalysisisdesignatedbyfivestandardadjectives:veryhighaccuracy(>90percent),highaccuracy(>80percent),acceptableaccuracy(>70percent),lowaccuracy(5070percent),andpooraccuracy(


hasmanyadvantageswhereforestinventorydataexistanditcanbereadilyintegratedwithsatellitedataonforestarea(Gibbsetal.2007).

Table5.IPCCTierDescriptionforCountryLevelCarbonReporting

Tiersforemissionsfactors:ChangeinCStocks

1.IPCCdefaultfactors.

2.Countryspecificdataforkeyfactors.

3.DetailednationalinventoryofkeyCstocks,repeatedmeasurementsofkeystocksthroughtimeormodeling.

Note:Tier3isthemostdetailed.Source:GOFCGOLD2008.

Figure14.ExampleForestTypeMapthatCouldBeUsedforStratificationofForestInventoryPlots

Source:GOFCGOLD2008.


Strictplottoplotinterpolationacrosslargeareasisinaccurateasitignoresspatialvariationinforesttypesandages(Houghtonetal.2001;Gibbsetal.2007).Stratifyingforestinventoriesacrossarangeofforesttypesandages,andtheninterpolatingonlywithinthemeasuredforesttypesandages,increasestheaccuracyofforestinventoryreferencemaps(Gibbsetal.2007;seeFigure14).Thestratificationapproach,althoughmoreaccurate,requiresregionalmapsofforesttypeandforestagefromremotesensinganalyses.Remotesensingprovidesestimatesofforestareaquitewell;however,distinguishing(orclassifying)differentforesttypesandageshashadvariableaccuracy.Imagefusionsandnewimageanalysismethodsmayincreasetheaccuracyofforestclassifications.ThesemethodsarediscussedfurtherinSection4.1.

3.4 Survey Methods for Determining Forest Area and Type Threedistinctstrategiescanbeemployedinusingremotesensingtomeasureforestsglobally:

gridsampling,changestratifiedrandomsampling,andwalltowallsampling(Olanderetal.2008;Achardetal.2007;DeFriesetal.2007).Ingridsampling,moderateresolutionimagesamplesareanalyzedatregularspacingacrossalargearea(seeFigure15).ExamplesincludetheFAOs2010ForestResourcesAssessmentwhichwillsampletheglobeatonedegreeintervals.Theadvantageofgridsamplingisthatitgivesrepresentativeestimatesoflandcoveroverlargeareaswithrelativelylittleimageprocessing.Themaindisadvantageisthattheapproachmayundersampledeforestedareasbecausesuchareasarenotrandomlydistributedandarelikelytobeconcentratedinjustafewareas(Achardetal.2007).Areastratifiedsamplingisrandomsamplingthatisstratifiedbyforesttype,forestcharacteristics,orlandcoverchange.Inchangestratifiedsampling,moderateresolutionsamplingisheaviestinareasthatcoarseresolutionimageryhasindicatedischangingrapidly.AnexampleistheNASALandCoverandLandUseChangeprogramatSouthDakotaStateUniversity.TheprogramusesfrequentMODISimagerytodetectchangeandthenstratifiesLandsatsamplestomakeaccurateestimatesoflandcoverchange(Hansenetal.2008a;seeFigure16a).MODIScannotbeuseddirectlytoestimatelandcoverchangesincemostchangeoccursatsubpixelscales(Achardetal.2007;SanchezAzofeifaetal.2009).Theadvantagesofchangestratifiedrandomsamplingarethatitmaximizeschangedetectionforagivensetofimagingprocessingresourcesandyieldsunbiasedmeasurementsofforestchange.Thisapproachislesssuitedforsamplingcharacteristicsofstandingforests.

Thethirdapproach,walltowallsampling,completelymapsaregionwithimagery.ExamplesincludeBrazilsPRODESprogram(seeFigure16b)andIndiasbiennialforestsurveyaswellongoingeffortsintheUnitedStates(NLCD),Europe(CORINE),Canada,NewZealand,Australia,andSouthAfrica(DeFriesetal.2007;Heroldetal.2007).Walltowallsamplingisidealfordetectinglandcoverchangeandshiftsindeforestation(oftenreferredtoasleakage)betweencountries,butthemethodislogisticallychallengingintheneedtoacquire,process,andanalyzethenumberofimagesrequiredtocoverthegloballandarea.


Figure15.GridSamplingwithModerateResolutionImages

Note:EachdotisthecenterofasingleIRSimage.Source:GOFCGOLD2008.

Figure16a.ExampleofResolutionStratifiedSampling

Note:Moderateresolutionsamplesareconcentratedinareasofchange(red)determinedfromcoarserresolutionimagery.Source:GOFCGOLD2008.


Figure16b.ExampleofWalltoWallSamplingwithModerateResolutionImageryinBrazil

Note:Noticethedifferenceinilluminationandcloudcoverbetweenmoderateresolutionimages.Source:GOFCGOLD2008.

3.5 Price and Logistics Thelargequantitiesofimageryassociatedwithglobalforestmeasurementwillnecessitate

budgetarychoices,automatedprocessingchains,anddeliberationonselectionofresamplingintervals(Achardetal.2007;DeFriesetal.2007;GOFCGOLD2008;Olanderetal.2008;Rosenqvistetal.2003).Table3summarizesthecostsofimageryrequiredtodowalltowallsamplingofglobalforestsatdifferentresolutions.Highresolutionimagerywouldbeveryexpensiveatcurrentprices,asdiscussedindepthinSection4.2.2forLIDAR.Automatedanalysisofimageryisquickandstandardizesresults,butthelargenumberofimagesrequiredtocovertheworldsforestsatmoderateresolutionwillrequirelargeprocessingresourcesforautomatedclassification(Anderssonetal.2009;DeFriesetal.2007;Mayauxetal.2005).Althoughmoderncomputersarecapableofprocessingthisamountofimagery,eithersignificantinvestmentorcomputingpartnershipsarerequired(GOFCGOLD2008).Theresamplingintervalcanbeaffectedbytheamountoftimerequiredtoprocesseachyearsimagery;theBrazilianNationalInstituteforSpaceResearch(INPE),forexample,prioritizeshighchangeareasforearlyprocessingtoensuretimelyrevisits(Achardetal.2007).

Decisionsonhowoftentosampletheworldsforestsshouldtakeintoaccountprocessingtimesandtheavailabilityofcloudfreepassiveimagery(GOFCGOLD2008).Cloudfree,globalLandsatmosaicscontainimageryeverythreetofiveyearsbutcloudfreeMODISmosaicscanbecreatedseasonallyduetotheirhighreturntime(e.g.,Mortonetal.2005).Cloudspersistinsomeareaseveninostensiblycloudfreeimages,necessitatingfusionwithSARimagerytopenetratecloudsincertaintropicalareas(Olanderetal.2008;Lindquistetal.2008;Rosenqvistetal.2008).TheALOSforestmapperprogram,notably,willproduceglobal,cloudfreeforestcovermapsannuallyfrom2007onward(J.Kellndorfer,pers.comm.).Withprocessingandclouddelays,aglobalforestmonitoringeffortmaybecapableofproducingannualorbiannualforestmapsatcoarseresolution;withouttheuseofSAR,moderateresolution,cloudfreemapsmaytakeseveralyearstoproduce.


Itisexpensivetointegrateremotesensingestimateswithforestinventorydata,LIDARdata,andhighresolutionimageryyetselectivegroundtruthingisabsolutelycriticaltoevaluateaccuracy(Gibbsetal.2007;GOFCGOLD2008;Olanderetal.2008;Rosenqvistetal.2003).Highresolutionimagerycollections,includingGoogleEarth,canbeusedassubstitutesforgroundtruthinglandcoverestimates(e.g.,Gibbsetal.2007;Readetal.2003;Helmeretal.2009;Bicheronetal.2008),butgrounddataarerequiredforbiomassandcarbonestimates(Gibbsetal.2007).

Incontrasttolocallyaccurateanalyses,forwhichconclusionsaregeographicallyrestricted,globalremotesensinganalysessacrificelocalaccuracyforgreaterglobalaccuracy.Aglobal,dispersednetworkofgroundtruthdataisnecessarytoquantifythisaccuracy(e.g.,Bicheronetal.2008).Largeamountsofgroundtruthdataalreadyexistinsomeregionsandforestersandforestecologistscontinuetocollectmoreinmanyorganizedforestsamplingprograms(Herold2009).Collectingandcoordinatinggroundtruthdataforglobalforestmeasurementwouldbeausefulscientificundertaking(Heroldetal.2008).


Chapter 4. An Overview of Remote Sensing Capabilities for Forest Measurement: Current and Near-Term Technologies

Intheprecedingchapterswenotedseveralkeylimitationsofremotesensing:(a)tradeoffsinswathandresolutionincurrentsensortechnology(Section3.2),(b)thesmallnumberofactivesensorscurrentlyinorbit(Section2.4.1),(c)therelevanceofinexpensiveimageryforfrequentglobalcoverage(Section3.5),(d)thewisdomofredundancyinsatelliteselection(Section2.4.3),and(e)theneedforgroundtruthingtovalidatetheaccuracyofconclusionsfromremoteimagery(Section3.5).Inlightoftheselimitations,wenextconsiderthecapabilityofremotesensingtomeasuretheglobalforestspecifically,globalforestarea,foreststructure,andotherforestpropertiesinTable4.

4.1 Forest Area: Current Measurement Methods Globalforestareaisoftenmeasuredastwoclasses(forest/nonforest)orbinned(thatis,

categorized)intohomogenousforesttypesthatdonotdistinguishtreeplantationsordisturbedforests(Hansenetal.2008a;Bicheronetal.2008).Forestsarequitedistinctfrommostnonforesttypesoflandcover,soforest/nonforestareacanbemeasuredwithopticalandSARsensorswithahighdegreeofaccuracy(from80>90percent).Distinguishingmorethantwolandcoverclasses,suchasforestsofdifferentagesorcomposition,canstillresultinhighaccuracy,butclassificationaccuracyusuallydecreaseswithanincreasingnumberofforestclasses(Foody2002)orwhenalargerareaisexamined(Olanderetal.2008).Theseresultsmeanthatremotesensingisideallysuitedtodetectforestremoval(deforestationorclearcutting)butlesswellsuitedtodetectforestthinningorforestreplacementbyindustrialtreeplantations(DeFriesetal.2007;Sanchezetal.2009).Challengesremainindistinguishingprimaryforestsfromtreeplantationsandoldersecondaryforests(Kimesetal.1999;Sanchezetal.2009,Songetal.2007;Thenkabailetal.2004),andindetectingforestdegradation,whichisareductioninforestcanopycoverorbiomassthatdoesnotresultincompleteforestclearing(DeFriesetal.2007).Weexplorethesetopicsfurtherbelow.

4.1.1SensorTypesandMeasuringForestArea

Eachsensortypehasdifferentstrengthsandweaknessesformeasuringforestarea.Coarseresolutionsensorshavethegreatestcapabilityforglobalcoverageandhighreturntimes(Achardetal.2007;Rosenqvistetal.2003),buttheirpixelsizecausesthemtomissthemajorityofdeforestationevents(Olanderetal.2008;DeFriesetal.2002;Sanchezetal.2009;Mortonetal.2005).Highresolutionsensorsenablemeasurementoftreecrownareaandfinedelineationofforestcompositionanddisturbance,buttheirsmallswathslimittheirutilityforrepeated,cloudfreeglobalmeasurement(Anderssonetal.2009).Bybalancingalargeswathsizewithspatialresolutionthatisabletodetectthemajorityoflandusechange,moderatepassivesensorsareconsideredthebestcompromiseforregionallandcovermonitoring(Achardetal.2007;Olanderetal.2008;Anderssonetal.2009).Finally,hyperspectralandpolarizedSARsensorshaveimprovedtheabilitytodistinguishamongforesttypesandmapforestcover(DeFries2008;Thenkabailetal.2004;HoekmanandQuinones2000).Analyzingtheirimagerycanbetechnicallydifficult,however(Kasischkeetal.1997;Ustinetal.2004).Futurerefinementsinimagefusiontechniquesandnewsatellitetechnologycanbeexpectedtoimprovemeasurementsofforestareaandtype.


4.1.2ForestAreaversusMeasuringChangeinForestArea

Measuringforestareaisdistinctfrommeasuringchangesinforestarea,forbothpracticalandquantitativereasons.Practically,increasesinforestareaoftenresultfromlandcovertypesthatarequitespectrallydistinctfromtheoriginalforestandwouldnotbeclassifiedasforestarea.Intemperateandtropicalareas,woodyencroachmentintograsslandscreatesspectrallyandstructurallydistinctforestsinunexpectedareasandforestregrowthonabandonedfarmscreatesdistinctsecondaryforests(Houghton2005).Deforestationresultsintheconversionofforeststoavarietyofagriculturallandcovers,includingspectrallysimilartreeplantations.Tropicalforestregrowthisveryrapid,makingtentotwentyyearoldforestsdifficulttodistinguishfromprimaryforestonasatelliteimage(seeSection4.1.5).

Fromaquantitativestandpoint,measuringchangeinforestareaisdistinctfromclassifyingtwolandcovermaps.Toillustratewhy,considerthemostbasiclandcoverchangemethod,postclassification;quantifyingthechangesbetweentwoclassifiedlandcovermaps.Inthismethod,highclassificationaccuracyiscriticalbecausequantifyinglandcoverchangebycomparingtwolandcovermapsmultipliestheirrespectiveerrors(Luetal.2004).AmultitudeofothermethodsofquantifyinglandcoverchangeovertimearereviewedatlengthbyLuetal.(2004).Thereareseveralgeneralmethodsforgloballandcoveranalysis.Imagedifferencingsubtractstwoimagesandthenclassifiestheareasthathaveexperiencedchangesinspectralcharacteristics(e.g.,greenness).Combinedanalysisfusesbothimagesinthesamedataset,andthenclassifiesthecombineddataset,labelingthechanges.Hybridanalysisidentifieswhichpixelshavechangedbetweendatesandclassifiesonlythechangedareas(Luetal.2004).

Inthediscussionofmeasuringforestareabelow,wefocusprimarilyontheaccuracyofclassifiedlandcovermapsbecausedistinguishinglandcovertypesisanecessaryfirststepinaccuratelyclassifyingchangesinforestarea.

4.1.3CoarsePassiveSensorsandForestArea

Therehavebeennumerousglobalandregionaleffortstomapforestsusingcoarseresolutionsensors,andtheseareablyreviewedbyothers(seeTable4inHeroldetal.2008;Table1inMayauxetal.2005;andTable2inAchardetal.2007).Effortstocreategloballandcovermapshaveusedfoursatellites:AVHRR,MODIS,SPOTVegetation,andEnvisat/MERIS.Twomaintypesofproductshaveresultedfromthesemappingprograms:landcoverclassificationsandtreecovercontinuousfields(AVHRRandMODISonly).Alloftheseproductshavehistoricallybeenfreelyavailablefordownload,increasingtheiruseingloballandcoveranalysis.

Thetreecovercontinuousfieldsmapsareapartofanefforttoproducecontinuous,subpixelestimatesofvegetationtraitsfromAVHRRandMODISdata(Hansenetal.2003).TheseeffortsusehigherresolutionLandsatandIKONOSdatatotrainanalgorithmthatcalculatespercenttreecoverforeachcoarseresolutionpixel.Thiscoarsefinefusionmethodyieldsestimatesofforestcoverthatcanbeadaptedforanyforestdefinitionandiscapableofcoarsedistinctionsofforestcoverbasedonleaftypeanddensity(Hansenetal.2003).

Landcoverclassificationsassignonelandcoverclassperpixelandhavebeenongoingsince1992(AVHRR),2000(MODIS,SPOTVegetation,and2005(MODIS,SPOTVegetation,andEnvisat/MERIS)(Heroldetal.2008).CurrentlytheEnvisat/MERIS300mresolutionGLOBCOVER


productisthehighestresolutiongloballandcovermap.Mapsoftropicalforestshavealsobeenproducedat250mresolutionfromMODIS(Bicheronetal.2008;Hansenetal.2008a).Evenjustasstaticsnapshots,theseglobalmapshavebeenimportantinclimateandcarbonmodeling(DeFries2008;Achardetal.2007).

Thesecoarseresolutionmappingeffortshaveseverallimitations.Thenumberoflandcoverclassesdiffersbetweenmappingeffortsandlandcoverclassificationsareoftendifficulttoassignindisturbed,heterogeneous,ortransitionalecosystems(Heroldetal.2008;Mayauxetal.2006).Simple,coarseresolutionforest/nonforestclassificationshavebetweenacceptableandhighaccuracy(LatifovicandOlthof2004;Hansenetal.2003).Coarseresolutionclassificationswithmultiplelandcovercategorieshavehadpoortolowaccuracyinrepresentingactuallandcover(e.g.,GLOBCOVERhas67percentaccuracy)(Bicheronetal.2008;LatifovicandOlthof2004;Mayauxetal.2006).Theselandcoverclassifications(e.g.,GLC2000;GLOBCOVER)donotalwaysagreewitheachother(Girietal.2005).Figure17indicatestwodifferentlandcovermaps,theGLC2000andMODIScontinuousfields,whichdisagreemarkedlyinCentralAmerica.ThescientificeffortGeowiki.orgistryingtousevolunteerinputandhighresolutionimagerytoreviseconflictsbetweengloballandcovermapsandimprovetheirclassificationaccuracy(IIASA2009).

Thecapacityofcoarseresolutionsensorstodetectforestclearingandforestfiresinalmostrealtimeisparticularlyimportanttoslowingdeforestationinthetropics,whereitisdifficulttomonitorremoteforests.Weexaminethislaterinthechapter(Section4.4.2).ThefrequentreturntimeofMODISpermitsphenologybasedmappingoftropicaldeforestationwith89percentaccuracy,forexample(Mortonetal.2005).Withonedayimageprocessing,INPEusesMODISimageryinarealtimedeforestationpreventionprogramcalledDETEXtopreventlargescale,illegalclearingintheBrazilianAmazon(DeFries2008;Herold2009).Coarseresolutionimagerycanonlydetectcompleteclearingandonlydetectsalowpercentageofactualdeforestationthatisgreaterthan1020hainsize(Sanchezetal.2009;Achardetal.2007).InCostaRica,forexample,only10percentofdeforestationwasdetected(Sanchezetal.2009).Coarseresolutionimageryisbestused,therefore,asadetectionorsamplingtooltotargethigherresolutionsensors(i.e.,thestratifiedsamplingprogrammentionedinSection3.4)(DeFriesetal.2007).

Theavailabilityoffrequent,free250300mresolutionimageryhasimproveddeforestationdetectionandlandcovermappingbecausetheimagerymatchesthescale(6.25to9ha)oflargescaleforestclearingsforfarmingandranchingandtheapproachcandetectchangesofaround1020hainsize(Figure18,Achardetal.2007).TheNPOESSsatelliteseriesisintendedtoreplacetheMODISandAVHRRsatellites,butNPOESSwillhave400mto1kmresolution(TownshendandJustice2002)limitingittodetectonlyextremelylargeclearingsbetween16hato100hainsize.TheJapaneseGCOMC1(with11bandsat250mresolution)andtheEuropeanSentinel3series(with21bandsat300mresolution)maybebettersuitedtocontinuetheMODISandEnvisat/MERISrecord(Appendix).Itwillbedifficulttoequalthecombinationofresolution,revisittime,andfreeimagerythattheMODISTerraandAquaconstellationcurrentlyofferstogloballandcoveranalysis.


Figure17:CoarseResolutionClassificationDisagreements

Notes:ForestcoverfromMexicotoPanamaclassifiedbyGLC2000andbyMODIS(A)andthedisagreementbetweenthem(B).TheredcircleidentifiesahotspotofdisagreementinGuatemalaandElSalvador.ThewebtoolallowsonetovalidatethesedisagreementsusinghighresolutionsatelliteimageryandGeoTaggedPictures(forexample,Panoramio)(C).Source:CreatedusingGeoWiki,2009.


Thefrequentrevisittimeofcoarseresolutionsensorsallowsforneardailymonitoringofphenology,importantforlandcoverclassificationandfordetectingfutureclimatechangeasdescribedinChapterOne(Mortonetal.2005;Goetzetal.2005).Repeatedpassesthatcaptureseasonalleafdynamicscanimprovedetectionofseasonaltropicalforests(e.g.,Mortonetal.2005)andcoarseresolutionimagerywillremainusefulforforestmeasurementwellintothefuture.Giventheusefulnessofthevegetationcontinuousfieldsandthepotentialforautomatedsubpixeldetectionsofdeforestation(e.g.,DETER),coarsefineimageryfusionsareanexcellentmethodtoincreasetheresolutionofcoarseresolutionimagery(e.g.,Hansenetal.2003).Nextgenerationgeostationarysatellites(GOESR,FY4)areexpectedtoimproveto1kmresolutionintheVNIRsometimearound2014.Thischangewouldenablerealtimemonitoringoflargescaledeforestation,fires,andphenologyandcouldprovidedramaticallyimprovedinputstocarbonandclimatemodels(Chuvieco2008;DeFries2008).

4.1.4ModeratePassiveSensorsandForestArea

Moderatepassivesensorshaveresolutiononascaleappropriateforforestmanagement(Figure18).Thesensorsareabletocaptureforestchangesatthescaleofonehectareinlocalanalysesandtwotofivehectaresinregionalanalyses(Olanderetal.2008;Achardetal.2007).Forest/nonforestcovercanbedistinguishedbymoderatesensorswithgreaterthan90percentaccuracyinlocalareas.Overlargerareasaccuracydeclinestobetween85and90percent(Olanderetal.2008).Severalforestmappingeffortsusemoderateresolutionimages;theseincludesubnationalandnationalwalltowallmapping(Achardetal.2007;Heroldetal.2008)andglobalsamplingefforts(Mayauxetal.2005).Herold(2009)reviewsnationalmappingeffortsextensively.

GlobalwalltowallcollectionsofmoderateresolutionimagerycouldbeconstructedatconsiderableexpensefromIRS,SPOT,orDCMdata,buttheLandsatglobalmosaicsaretheonlyfree,publiclyavailableglobalimagery(Achardetal.2007).Asof2009,cloudfree,globalLandsatmosaicsfor1975,1990,2000,and2005havebeencreatedandeffortsareunderwaytocreateanerrorfreeLandsat7mosaicfor2010(Lindquistetal.2008;Hansenetal.2008b).TheLandsatmosaicscontaindatemetadataforeachcomponentimagetoallowforcorrectinterpretationforexamplewhencalculatinglocaldeforestationrates.TheseglobalLandsatmosaicshavebeensuggestedbyseveralauthorsasexcellentsourcesforforestareareferencemapsforREDD(Gibbsetal.2007;Olanderetal.2008;Achardetal.2007).

AlthoughtheglobalLandsatmosaicsofferopportunitiesformappingglobalforests,numerouschallengesariseinanalyzingthemforforestarea.Becauseofobstructionsincoverageincloudyareas,mostnonconstellationmoderatesensors(i.e.,allexcepttheIRSandDCMseries)havedefactoreturntimesofayearormoreintropicalareas(Asner2001;Olanderetal.2008).Thislimitcanseriouslycompromiseeffortstomapseasonalforests,especiallyintropicalareas,becauseofthedifficultyindistinguishingleaflessforestsduringdryseasonswhenmostimageryisacquired(Sanchezetal.2009;Asner2001).TodeveloptheglobalLandsatmosaics,scientistswereforcedtoknittogetherimagestakenoverseveralyears(e.g.,19861991forthe1990mosaic).Eventhenunacceptablelevelsofcloudcoverpersistedonabout2530percentoftheLandsatmosaicimagesinEcuador(Olanderetal.2008)andin16percentofmosaicimagesintheCongoBasin(Lindquistetal.2008).Asidefromthedetectionofseasonalforests,automatedclassificationofLandsat


mosaicsrequirescorrectionforthevarietyofsunilluminationangles(knownasradiometriccorrection)andthecreationofglobalorregionalalgorithmstoclassifydifferentlandcovers(Luetal.2004;Schroederetal.2006;Bicheronetal.2008).Landsatclassificationalgorithmsdevelopedinoneregionareunlikelytobeaccurateinanotherregioneveniftheregionsaresimilar(e.g.,Foodyetal.2003).

Figure18.ResolutionofSensorsRelativetoEcologicalMeasurements

Notes:Thedottedboxesindicatethetemporalandspatialscaleofecologicalprocessesandhumandisturbances.Thesolidcoloredboxesshowthespatialscaleandtemporalrecordlengthofselectedsatelliteandairbornesensors(GOESdullred;MODISgreen;LiDAR/Radarpinkpurple;Landsat/EO1yellow;IKONOS/Quickbird/Airbornelightblue).Source:Chambersetal.2007.

Regionalwalltowallmappingefforts,globalsamplingefforts,andoneglobal,moderateresolutionlandcovermap(GeoCoverLC;Cunninghametal.2002)indicatethatthesechallengescouldbeovercome.ThecurrentmoderateresolutionGeoCoverLCmaphasmixedglobalaccuracy(72percent)andverybroadforestclasses(Cunninghametal.2002).Anaccurate,detailed30mresolutionforestmapwouldbeasignificant,tenfoldimprovementingloballandcovermapping(Bicheronetal.2008).Reprocessingtheglobal30mLandsatarchive(1983present)tocreateaseasonappropriate,cloudfree,andradiometricallycorrectedimagerytimeseriesisfeasiblewithcurrenttechnology,althoughtheprojectwouldbecomputationallyintensive.AlgorithmshavebeendevelopedrecentlytoeliminatecloudsandcloudshadowsandimprovetheLandsatmosaicsbyfusingtogether,orcompositing,timeseriesofLandsatandMODISimages(Lindquistetal.2008;Royetal.2008;Hansenetal.2008b).Inaddition,automatedradiometriccorrection,whichisoftendifficultinallforestimages,hasbeendemonstratedonaseriesofLandsatimages(Schroederetal.2006;Xianetal.2009).


4.1.5ModerateResolutionImageryandLandCoverClassification

Theliteratureonlandcoverclassificationwithmoderateresolutionimageryisextensive(partiallyreviewedinSanchezetal.2009;Patenaudeetal.2005;Olanderetal.2008;andCoppinetal.2004).Becauseclassificationaccuracygenerallydecreaseswithanincreasingnumberofclasses(Foody2002),weusethelandcoverclassificationhierarchyofAndersonetal.(1976)whenreferringtotheaccuracyofdifferentclassifications.Inthishierarchy,aLevelIclassificationdistinguishesbasicclasses(forest,nonforestvegetation,agricultural,urban)andaLevelIIclassificationdistinguishesmorelandcoverclasses,includingforesttypes.Asmentionedabove,LevelIaccuracyinexcessof85percentisusuallyobservedwithmoderateresolutionimagery,withhigheraccuracyathigherresolution(SalajanuandOlsen2001;Petersonetal.2009).Generally,accuracydecreaseswithanincreasingnumberofclasses.PublishedLevelIIclassificationshaverangedfrom65to85percentaccuracy(referencesinPatenaudeetal.2005).Ingeneral,extendingLevelIIclassificationalgorithmsdevelopedinoneregiontootherregionsisatechnicalchallengethatisnotalwaysmet(Sanchezetal.2009;Lovelandetal.2002;Xianetal.2009;Roganetal.2008;Foodyetal.2003).

Distinguishingamongforesttypes,forestages,degradedandintactforests,andtreebasedagroecosystemscanalsobechallenging(Sanchezetal.2009;Olanderetal.2008;Patenaudeetal.2005).Intemperateforests,SalajanuandOlsen(2001)wereabletoclassifyaforestedareainMichigantoLevel1with8991percentaccuracy,butaccuracydecreasedto7784percentfordistinguishing10foresttypes.InSiberiasBaikalregion,Petersonetal.(2009)wereabletodistinguishfourborealforestclasseswith8098percentaccuracy.Intropicalforests,Thenkabailetal.(2004)foundthatmoderateresolution,multispectralsensorshadpoor(4050percent)accuracyindistinguishingnineforesttypesandages.Bycontrast,Sesnieetal.(2008)wereabletodiscriminate17forestclasseswith93percentaccuracyusingafusionofspectral,DEM,andclimaticGISlayers.Sesnieetal.(2008)had69percentaccuracywithspectralclassesalone,indicatingthatdetailedLevelIIclassificationintropicalareasmayrequireGISfusionorhyperspectraldata.Weexplorethisfurtherbelow(Section4.1.6).

Inbothtropicalandtemperatezones,moderateresolutionimagerycannotdistinguishforestregrowthfrommatureforestafter1530yearsofgrowth(Steininger1996;Nelsonetal.2000;Songetal.2007;FiorellaandRipple1993).Partiallyloggedorclearedforestshavelongbeendifficulttodistinguishfromintactforests(Saderetal.2003;WilsonandSader2002;Achardetal.2007),butrecently,promisingspectralanalysistechniqueshavebeendevelopedtomapsubpixelforestdegradationintheBrazilianAmazonusingmoderateresolutionimagery(Asneretal.2005;Souzaetal.2005b).

Inthetropics,distinguishingforestsfromtreebasedagriculture(i.e.,agroforestry,treeplantations)isdifficultusingmoderateresolutionimagery;theplantationsoftenappearsimilartosecondaryforests(Sanchezetal.2009).InMalaysia,BabanandYosuf(2001)wereabletodistinguishrubberplantationswith74percentaccuracyandfoundthatplantationsweremostoftenconfusedwithintactforests.Sesnieetal.(2008)wereabletodistinguishtreeplantationswithapproximately90percentaccuracyinCostaRica,butaccuracydecreasedto55percentwhentheentireimagewasexaminedacrossdiverseplantationtypes.Kuplichetal.(2000)wereabletodistinguishplantedEucalyptusandPinusforestswith89percentaccuracyinafragmented


Brazilianagriculturallandscape.IneasternEcuador,Santosetal.(2008)usedLandsat7todoaLevelIIclassificationofanoilpalmsecondaryforestlandscapewith77percentaccuracy.

DespitethesechallengesinLevelIIclassificationofforests,therehasbeensignificantprogressinclassificationofmoderateresolutionimageryinrecentyears(Coppinetal.2004).Onepromisingapproachistheclassificationoflandcovertrajectoriesovertime(ratherthanstaticimages)madepossiblebytherecentopeningoftheLandsatimagearchive(Kennedyetal.2007;Maseketal.2008;Coppinetal.2004;Songetal.2007).Forexample,Helmeretal.(2009)mappedageclassesoftropicalforestregrowthwithveryhighaccuracybyusingtheentire,37yearLandsatarchivetoageregrowthonpreviouslyclearedpixels.Thisnewtrajectoryclassificationmethod,inadditiontothecompositingmethodsmentionedabove(Hansenetal.2008b),underscorestheimportanceoflong,continuoustimeseriesofmoderateresolutionimagerytolandcoveranalysis(andthecriticalnatureofthesuccessoftheLDCM).

4.1.6HyperspectralSensors:ForestAreaandBeyond

Hyperspectralimages,althoughtheyhavelimitedglobalcoverage,aremarkedlybetter(e.g.,4070percentbetter)thanmultispectralmoderateresolutionimagesindistinguishingandmappingdiverseforesttypesandotherlandcovers(Thenkabailetal.2004;UstinandXiao2001;Goodenoughetal.2003)..Eventhoughthepotentialisstillbeingexplored,hyperspectralimagesalreadyhaveawidediversityofapplicationsduetotheabilitytomeasuretheabsorptionoflightbychemicals.Thisabilityinturnallowsdirectmeasurementofleafchemistryandthequantificationofphotosyntheticandnonphotosynthetic(woody)cover(AsnerandMartin2009;Chambersetal.2007;Ustinetal.2004).HyperspectralimageryimprovesmeasurementofLAI(Leeetal.2004;Schlerfetal.2005),forestproductivity(viacanopynitrogen;Smithetal.2002;OllingerandSmith2005),canopystructure(ArroyoMoraetal.2009),anddroughtstress(Asneretal.2004).Theimageryalsomakesnewmeasurementsofforestdiversitypossibleincludingcanopyleafecophysiology(Asneretal.2009b)andthediscriminationofindividualspeciesandspeciesgroups(Goodwinetal.2005;Asneretal.2008;Clarketal.2005).Moderateresolutionhyperspectralimagery,whichwillbecomewidespreadinthenextfewyearsastheEnMap,HyspIRI,andseveralothersatellitesarelaunched,hasthepotentialtorevolutionizeforestbiodiversitymeasurementsinanewscienceofremotespectranomics(AsnerandMartin2009).Althoughmanynewhyperspectralsensorswillhaverelativelynarrowswathsandmaybeunsuitedforglobalforestmeasurement,theHyspIRIsensorwillhavea90kmswath(Appendix)andislikelytomarkedlyimprovetheaccuracyoflandcovermapping.

4.1.7HighResolutionPassiveSensors:ForestAreainFocus

Becausehighresolutionpassivesensorshaveanarrowswathandpointable,interruptedcoverage,therearecurrentlynocontinuousgloballandcovermapsorcloudfreeimagemosaicsathighresolution(Loarieetal.2007).GeoEyesconstellationwillbemappingtheentireglobeinthenextfewyears(MarkBrender,pers.comm.),butcloudfreeimageryisunlikelytoresult(Asner2001;Olanderetal.2008).SPOT5hascontinuousglobalcoverageat10mresolutionbuttheimageryhasnotbeenanalyzed.RapidEye,ahighresolutionconstellationwithdailyrepeatcapacity(launchedin20082009),hasthepotentialtodevelopcloudfree,continuousglobalmaps(RapidEye2001).


Currently,GoogleEarthhasthelargestcollectionofglobalhighresolutionimagery,buttheimageryisfromavarietyofsatelliteandairbornesourceswithdifferingresolutionsandundiscloseddates(Potere2008).GoogleEarthmaynotyetbeusefulforstrictchangedetectiononaglobalscale,buttheapproachisveryusefulforlandcovervalidationandvisualization(e.g.,IIASA2009;Bichersonetal.2008;Helmeretal.2009;Olanderetal.2006).Thesameresultistrueforhighresolutionimageryingeneral:theimageryisaninefficientwaytomapglobalforestsduetoirregularspatialsampling(Loarieetal.2007;Anderssonetal.2009),relativelyhighcost(Olanderetal.2008;Anderssonetal.2009),datasharingrestrictions,andradiometriccorrectionchallenges(e.g.,variablecanopyshadowing)(Goetzetal.2003;Anderssonetal.2009).Currentpoliciesaboutaccesstodatarestrictthesharingoforiginaldatabutallowsharingofdataproducts,suchasbasicmaps,foralmostallhighresolutionimagery.However,theimageryisusefulforidentifyinglocallandcovers(Wulderetal.2004)andforestdisturbance(Thompsonetal.2008;Wulderetal.2008;deSouzaandRoberts2005),andevenfollowingthepopulationdynamicsofindividualtreespecies(Clarketal.2004a).Highresolutionimageryhasimmenseutilityforgroundtruthinglowerresolutionimagery,bothasafinescalevalidationtoolandastrainingdataforalgorithms(e.g.,Hansenetal.2003;Wangetal.2005).

Althoughhighresolutionimagerydoesnothavethespectralresolutionofothersensors(mostnotablymissingtheSWIRbands),theimagerycontainsadditionalinformationontheshapeandtextureofobjectsinthelandscapethatlowerresolutionimagerylacks(Goetzetal.2003).TheaccuracyofhighresolutionimageryindistinguishingLevelIlandcoverclassesinforestedlandscapesisveryhighintherange95percentandgreater(Townsendetal.2009).Assuch,theimageryisoftenusedasatreecovervalidationforlowerresolutionimageryeithervisually(e.g.,Kozaketal.2008;Neighetal.2008)orquantitatively(Knornetal.2009lHansenetal.2003lWangetal.2005).Becausespectralinformationislimited(oftenfourbands)andspatialalgorithmsarestillunderdevelopment,highresolutionimageryislessaccurateatLevelIIclassifications(~7090percent;Oumaetal.2008;Mallinisetal.2008;Gergeletal.2007;andMoralesetal.2008).Thehighresolutionsensorsarecapableofdistinguishingsmallscalelandcoversanddisturbancesthatdonotappearinmoderateresolutionimagery,suchasriparianstrips(Gergeletal.2007),pestoutbreaks(Wulderetal.2008a),andlogging(deSouzaandRoberts2005),andthevisualinterpretationofthehighresolutionimageryisstraightforward(Wulderetal.2004).Automatedcrowndelineationtechniqueshavebeendevelopedrecently(Broadbentetal.2008;Palaceetal.2008),andhighresolutionimageryshowspotentialforusingcrownsizetoclassifysecondaryforeststhatareolderthan20years(Clarketal.2004a;Kayitakireetal.2006).

4.1.8SARSensorsandPassiveSARFusionsforForestArea

SARhaspotentialforglobalforestmappingandSARcapabilitieshavebeenextensivelyreviewedelsewhere(Patenaudeetal.2005;Rosenqvistetal.2003;Lu2006;Balzter2001;andKasischkeetal.1997).Here,wesimplysummarizethemainconclusionsofpriorresearchandfocusonrecentdevelopmentsinSARresearch.SARdistinguisheslandcoverbasedonthestrengthofbackscatter,thevariationinbackscatteroverspace(texture),and,dependingontheanalysis,temporalvariationinbackscattersignatures.RegionalmapsofforestcoverhavebeenderivedfromSARsensorsincludingtheGlobalRainforestMap(JERSsensor;Rosenqvistetal.2000)andtheCentralAfricaMosaicprojectmaps(ERSsensors;DeGrandietal.1999).TheALOSPathfinder


missionhascreatedthefirstglobalLbandSARforestmapat50mresolutionandthemissionwillcontinuetodosoannually(J.Kellndorfer,pers.comm.).

ScientistsfullyappreciatethepotentialofsatelliteSARsensorstodistinguishlandcover;theircapabilitiesarewellknownfromanalysisofdatafromadvancedairborneSARsensorsandthemultipolar,multibandSARsensor(C,X,andLband)aboardthe1994SIRC/Xshuttlemission(Kasischkeetal.1997;vanderSandenandHoekman1999).Ingeneral,singlebandSARsensorswithonepolarization(e.g.,ERS1,JERS1)havevariableaccuracy(lowtoveryhigh)inLevelIclassificationsdependingontopography,surfacemoisture,andthestructuralcomplexityofthelandscape(Patenaudeetal.2005;Kellndorferetal.1998;PodestandSaatchi2002).AsthenumberofpolarizationsorSARbandsincreases,LevelIaccuracycanbeveryhigh(>90percent),rivalingorexceedingpassivesensors(Dobsonetal.1995a;Kellndorferetal.1998;Patenaudeetal.2005).LevelIIaccuracyisalsoverydependentonthenumberofpolarizationsandbandsemployed,achievingaccuraciesbetween7090percent(Kasischkeetal.1997;Kellndorferetal.1998).Dobsonetal.(1995a),notably,haveveryhighLevelIandIIregionalaccuracywithpolarized,multibanddatainMichiganforests.Saatchietal.(1997),withmultipolarSIRC/Xdata,areabletomeasureAmazonforestswithveryhigh(92percent)LevelIaccuracyandacceptable(72percent)LevelIIaccuracy.TheLbandJERS1andALOSPALSARsensorshavebeenshowntohaveveryhighLevelIaccuracywhenclassifyingseveraldifferentforestedregions(Thieletal.2006;Saatchietal.2000;Kellndorferetal.2008).BecauseofthehighSARreflectionfromwater,longerwavelengthsensorshavecreatedhighlyaccuratemapsofmangrovesandfloodplainforests(Costa2004).

TheaccuracyofSARsensorsisaffectedbyecosystemstructure(Almeidaetal.2007;Kellndorferetal.1998,2004),variabilityinsoilandvegetationmoisture(Kasischkeetal.1997),andtopography(Kellndorferetal.1998).Highresolutiondigitalelevationmapsarenecessarytocorrectfortopographicaldistortions;accuracydecreasesmarkedlywithouteffectivecorrection(Kellndorferetal.1998;Ticehurstetal.2004).Becauseofitshighmoisturesensitivity,theCbandistheleastusefulforlandcoverclassifications,althoughitsutilityforLevelIclassificationincreaseswhenmultiplepolarizationsareused(Saatchietal.1997;vanderSandenandHoekman1999;Kasischkeetal.1997).

RecentresearchinSARlandcovermappinghasinvestigatedthepotentialofopticalSARfusions(Saatchietal.2007;Santosetal.2008)andInSARforimprovedlandscapeclassification(Balzter2001;EngdahlandHyyppa2003;ParkandChi2008).InterferometricSARcanimproveclassificationsbyaddingvegetationheightinformationtootherSARmeasuresliketextureandbackscatter(Balzter2001).OpticalSARfusionsshowspromiseforsynergisticlandscapeclassification(Kasischkeetal.1997;Kuplichetal.2000)InIndiandryforests,fusionofmoderateresolutionpassiveandEnvisatASARCbanddataimprovedLevelIIclassification(>90percent)(ChandandBadarinath2007).InAmazonia,Kuplichetal.(2000,2006)foundLandsatSARfusionsimprovedLevel2classificationsoverLandsatorSARalone,andSaatchietal.(2007)usedaSARopticalfusiontoderivevegetationandbiomassclassesfortheentirebasin.Santosetal.(2008)foundthataLevelIIclassificationofoilpalmplantationsandforestswasimprovedbyapassiveSARfusion,to90percentfrom76percent(SAR)and77percent(Landsat).IncentralAfrica,Mayauxetal.(2000)usedAVHRRandERSdatafusiontoclassifyforests,savannas,mangroves,swampforests,andtreeplantationswith75percentaccuracy.


GiventhelargenumberofSARconstellationsplannedforlaunchinthe20092015period,itislikelythatInSAR,multibandSARfusions,andopticalSARfusionswillbecomeincreasinglyimportanttechniquesinlandcoverclassification.

4.2 Forest Structure: Current Measurement Methods Wenextreviewtheuseandrelativeaccuracyofdifferentremotesensingmethodstomeasure

verticalandhorizontalforeststructure,withanemphasisonforestheight.Remotesensorsaresensitivetoforeststructure,which,inconjunctionwitharea(Table4),isusedtoestimateforestvolume(Section4.3),biomass(Section4.4),andcarbon(Section4.5).

Forestsarehighlycomplexthreedimensionalstructures.Forestheightcanvarymarkedlyoveradefinedarea(i.e.,atree,plot,pixel,orstand).Inthisreport,wedefinethehorizontalandverticalcomplexityofforestsurfacesasforeststructure;bothactiveandpassivesensorscanmeasurevariationinforeststructure.Verticalforeststructurecanbecharacterizedbyforestheight,canopydepth,leafdensityprofiles,andothermeasurements.Horizontalforeststructurecanbecharacterizedbycanopywidth,treedensity,andtreearchitecture.

Becauseofgeneralitiesintreestructurefromhydraulicconstraints(Westetal.2009),forestheightallowsforaccurateestimationofvolume,biomass,andotherforestproperties(Lefskyetal.2002b),alldiscussedfurtherbelow.Forestheightcaneitherbemeasureddirectlybyremotesensingorestimatedindirectlyfromallometricrelationshipswithotherremotelysensedmeasurements(e.g.,canopywidth;Song2007).Exactmeasurementsofforestheightmustderivetwokeyparametersforagivenarea:groundelevationandcanopytopheight.Therearemultipleapproachestomeasuringcanopytopheightandgroundelevation.Theseapproachesincludepassivestereo,LIDAR,InSARgrounddifferencing,andpolarimetricInSAR(Sextonetal.2009;Balzter2001;StOngeetal.2008).

4.2.1StereoMeasurementsofForestStructure

Multiangle(stereo)passivesensorsareusedtoestimatethethreedimensionalstructureofobjectsfromtwoormoreoffsetobservations,muchlikeSARinterferometry(Jensen2007).MultianglesensorsincludeMISR;ASTER;PROBACHRIS;anyofthepointable,highresolutionsatellites(e.g.,GeoEye,Spot5);andaeriallymountedstereodigitalcameras.Highresolutionstereophotographyhasalonghistoryofmeasuringobjectheightfromaerialimages(Jensen2007).Highresolutionstereoimageshavebeeneffectivelyusedtomeasurecanopytopheightinforestswithhighaccuracy(Brownetal.2005;StOngeetal.2008)andtodevelopanewglobal,canopytopDEMfrommoderateresolutionASTERimages(ERSDAC2009).AlthoughtheaccuracyofstereoimageryinestimatingcanopytopheightisexceededbyLIDARandsomeInSARmeasurements,theapproachhasbeenproposedasameanstoextendLIDARresultstolandscapes(St.Ongeetal.2008;Dineretal.2005).HighresolutionstereoimageryislikelytobecheaperthansmallfootprintLIDARonaglobalscaleyetithasseverelimitationsintermsofcosts,datausagerights,andcloudfreecoverage(seeSection4.1.6),andcouldonlyprovidevalidationdataformonitoringtheglobalforest.

Coarserresolutionmultianglesensors(e.g.,MISR,PROBACHRIS,POLDER,repeatpassMODIS)requirelocallyfittedalgorithmstomodelhowreflectionandabsorptionfrommultipleanglesrelate


tosubpixelcanopyheightandstructure(Dineretal.2005).Simplemetricsofvarianceinreflectionhavebeenusedtosuccessfullyestimatetreedensityandheightinopenforests(Sedanoetal.2008;Heiskanen2006),andMISRestimatesofheightandroughnesshavebeenusedtoimproveLevelIIclassificationaccuraciesinforests(Galvaoetal.2009;LiuandKafatos2007).Choppingetal.(2008)recentlyusedacomplexcanopymodeltoestimatetreeheightandbiomassinArizonawithacceptabletohighaccuracy(r2valuesof0.69and0.81,respectively)fromMISRdata,andKimesetal.(2006)foundthatlargefootprintLIDARestimatesofforestheightwerecloselyrelated(r2=0.89)toAirMISRheightpredictionsinNewHampshire.Althoughcoarseresolution,multiangleimageanalysisisintheexperimentalstage,theanalysishaspotentialforrelativelylowcost,globalestimatesofforeststructure(Choppingetal.2008;Dineretal.2005;Kimesetal.2006).Theneedforlocalizedmodelcreationandextensivevalidation,andpossibleinaccuraciesinclosedcanopyforests,makeitunclearwhethercoarseresolution,multianglesensorswillyieldglobalforeststructuremeasurementsinthenextdecade(Kimesetal.2006;Choppingetal.2008;Schulletal.2007).

4.2.2LIDARMeasurementsofForestStructure

BothsmallfootprintandlargefootprintLIDARprovideexcellentmeasurementsofverticalforeststructurethatexceedtheaccuracyofotherremotesensingmeasurements(Sextonetal.2009;Hydeetal.2006;Nelsonetal.2006;StOngeetal.2008;Balzteretal.2007;Wolteretal.2009;Hyyppaetal.2008).LIDARmeasurementsmeetstandardsofaccuracyforgroundbasedmeasurementsforforestcanopytopheight(Deanetal.2009;Naessetetal.2004),andLIDARmeasurementsaredistinctivebecausetheyalsomeasuregroundelevationatthesametime,allowingaccuratecalculationsofforestheight.LargefootprintLIDARshavemeasuredforestcanopyheightwithgreaterthan75percentaccuracyacrossawidevarietyofforesttypes(Lefskyetal.2002a,b;Meansetal.1999;Hydeetal.2005;Lefskyetal.2007).Airplanemounted,smallfootprintLIDARshavehighaccuracy(>80percent)inmeasuringcanopyheight(Balzteretal.2007;Clarketal.2004b)andcanmeasurehorizontalcanopystructureandothercharacteristicsaswellduetotheirhighsamplingdensity(Lefskyetal.2002b;Hyyppaetal.2008).SmallfootprintLIDARsmakeexcellentgroundtruthingdatasetsbuttheirsmallswathmakesglobalmeasurementchallenging(Dubayahetal.2000;Balzteretal.2007).Althoughlargeareascouldbesurveyedviaaerial,smallfootprintLIDAR,itwouldtakeafleetoffiveplanes1063cloudfreedaystosurveyBrazils8.5millionsquarekilometers(Swansonetal.2009).Giventhecloudinessofthetropics,realflighttimemightbesixyearsforcompletecoverage.

Global,walltowallcoveragebyLIDARsensorsisprobablyunlikelyinthenearfuture;evenwithlongrange,unmannedaerialsystems(UAS),walltowallglobalLIDARcoveragewouldbelogisticallydifficult.Obstaclesincludetherequirementofobtainingpermissiontoflyovercountries,theamountofflyingneededforglobalcoverage,andhighcostsofpurchasingandrunningmultipleUASsensors(roughly$460millionforfoursensorsoverfiveyears[Swansonetal.,2009]).However,threemethodsofglobalsamplingbyLIDARarepossible.ThefirstiscoarseresolutionsamplingbyLIDARsatellitesdescribedabove(Section2.2.2,Figure10).Thesecond,whichwouldcostapproximately$80million,couldbeaccomplishedbyusingasingleUASwithLIDARsensorstoconducttargetedsamplingofadiversityofforesttypesinselectedcountries.ThethirdismountingaLIDARsensoronasatellite.


ThelargefootprintICESatGLASistheonlyLIDARsensorinorbitandtheglobal,wideintervalcoverageisbeingusedforforestmeasurement(Figure19).ThewidespacingbetweenLIDARshotsmakesorbitalLIDARslikeGLASunsuitedforDEMcreationorwalltowallsamplingofforestheight,butGLASLIDARfootprintsarewellcorrelatedgloballywithforestheight(r2=0.83,Lefskyetal.2007).Theseapproachesprovideanimportantsourceofdataforvalidationandfusionwithotherimagery(Lefskyetal.2002b;Baccinietal.2008;Nelsonetal.2009).Forexample,GLASdatahavebeencombinedwithMODIS(Nelsonetal.2009;Boudreauetal.2008)andLandsat(Helmeretal.2009)datatocreateaccurateregionalbiomassandheightestimates.

Figure19.GLASFootprintSpacinginAfrica

Notes:Eachblackdotrepresenta65mwidthLIDARfootprint;only30percentofallfootprintsarerepresentedhere.Source:Baccinietal.2008.

Inthe20092015timeframe,ICESatwillreachtheendofitsoperatinglifetimeandisexpectedtobefollowedbytheICESatIIandDESDynILIDARsatellites.DESDynIisasatellitemissionplannedforlaunchin2014andcarryingalargefootprintLIDARaswellastwoLbandInSARsystemswithmultiplepolarimetry.TheLIDARcapabilitywillbeusedinconjunctionwithInSARtoachieveaccurate,moderateresolutionforestheightmeasurementsonaglobalscale(Donnellanetal.2008).AccuratemeasurementsofgroundelevationmayalsoresultdependingonadvancesinprocessingInSARdata(ESA2009).


4.2.3InSARMeasurementsofForestHeight

SeveraldifferentmethodsusingSARinterferometrycanderiveforestheight.(Balzter2001;Sextonetal.2009).CanopytopheightfromshortwavelengthInSAR(e.g.,theSRTMmission)canbesubtractedfromindependentmeasuresofthegroundelevation(Kellndorferetal.2004;Walkeretal.2007).ShortwavelengthInSARestimatesofcanopytopheightcanbesubtractedfromlongwavelengthInSARestimatesofgroundelevation(Neeffetal.2005).PolarimetricinterferometricSAR(POLinSAR)usesinterferometric,polarizedSARandmultiplemeasurementstomodeltheverticalstructureandheightofforestsfromSARscatteringmodels(Treuhaftetal.2000;LeToanetal.2008).POLinSARisstillunderdevelopment,althoughthetechniquehasshownpromiseforheightestimationacrossadiversityofforesttypes(LeToanetal.2008;ESA2009).

Sexton(2009)comparestheaccuracyofdifferentInSARmeasurementtechniques.Ingeneral,allInSARmeasurementsunderestimateforestheightbecausetheymeasuretothephasescatteringcenter,whichisusuallybelowcrownlevel.Thisbiascanbecorrected(Saatchietal.,inreview).InaforestinNorthCarolina,Sexton(2009)foundthatshortlongwavelengthdifferencing(XandP)wasmoreaccuratethanSRTMgrounddifferencingbutbothInSARmethodswerelessaccuratethanLIDAR.HealsofoundthatSARdifferencingwasleastaccurateinshorterstands(duetogroundreturnofcanopytopwavelengths)especiallyfortheSRTMgroundmethod.Regionalcalibrationwouldbenecessarytocorrectforthisbiasinheightestimation(Sextonetal.2009).

GiventhenumberofSARsatellitestobelaunchedinthenextsixyears,InSARmeasurementofcanopytopheightwillincreaseinfrequencyandglobalcoverage(Zinketal.2007).CurrentsatellitesonlyhavethecapacitytodotemporalinferometryandseveralupcomingSARconstellationsandfollowonsatelliteswillcontinueandextendthiscapacity(Section2.4.2).FuturecapacityforspatialinterferometrywillcomefromanInSARsatellite(DESDynI)andseveralformationSARconstellations(e.g.,TerraSARX;seetheAppendix).Ultimately,theutilityofinterferometricSARinmeasuringglobalforestheightwilldependonthecreationofahighresolution,groundlevelglobalDEM(Walkeretal.2007)orthedevelopmentofmultiband,multipolarizedimagedatasetsthatallowtheestimationofforestheightthroughPOLinSARorshortlongwavelengthsubtractionalgorithms(Neeffetal.2005;Sextonetal.2009;ESA2009).TheGLASandDESDynILIDARswillimproveglobalDEMs(butnotatahighspatialresolution)andthelargenumberofSARsatellitestobelaunchedwillallowforInSARsynergiesbetweenC,X,L,andpossiblyPbandsensors(NASA2009a;ESA2009;Saatchietal.inreview;Patenaudeetal.2005).

Developmentofanaccurate,highresolutionglobalDEMwouldallowforthecreationofreferencesmapsofforestheightusingCbandInSARfromtheERS,RADARSAT,orSRTMmissions(Kellndorferetal.2004).AgroundlevelDEMcouldbeachievedbyalongband(VHF)SARsensor,LIDARsaturationoftheearthssurfacefromsatelliteandairbornesensors(viatheproposedLISTsatellite,orusinghighelevation,unmanneddronescall

Rff Rpt Measurement and Monitoring

Documents

Transcript of Rff Rpt Measurement and Monitoring