Measurement, comparison, and use of remotely derived Leaf Area Index predictors PDF
Preview Measurement, comparison, and use of remotely derived Leaf Area Index predictors
MEASUREMENT,COMPARISON,ANDUSEOFREMOTELY DERIVEDLEAFAREAINDEXPREDICTORS By RYANR.JENSEN ADISSERTATIONPRESENTEDTOTHEGRADUATESCHOOL OFTHEUNIVERSITYOFFLORIDAINPARTIALFULFULLMENT OFTHEREQUIREMENTSFORTHEDEGREEOF DOCTOROFPHILOSOPHY UNIVERSITYOFFLORIDA 2000 ACKNOWLEDGMENTS Adissertationisnevertheresultofoneperson’sefforts. Rather,itisthe culminationofmanypeopleworkingtogetherforacommongoal. Iwishtothankmy committeemembers,PeterWaylen,NigelSmith,andFrancisPutzforteachingmehow toconductresearchandwriteadissertation. Iwouldespeciallyliketothankmy committeechair,MichaelBinford,forprovidingmewiththeguidance,freedom,and knowledgetocompletethisdissertation. Myfamilyhasbeeninstrumentalinallowingmetocompletethiswork. Mywife, Tricia,andmytwodaughters.SavannahandSierra,havesufferedpatientlythroughmany dayswhendadwaseitherawaydoingfieldwork,orworse,typingonthecomputerinour livingroom. Thankyougirls! Iwouldalsoliketothankmyfather,JohnJensen,for reviewingthisworkmanytimes. Finally,Iwouldliketothanktheofficestaffandmy fellowgraduatestudentsfortheirsupport. 11 TABLEOFCONTENTS page ACKNOWLEDGEMENTS ii ABSTRACT v CHAPTERS 1 INTRODUCTION 1 2 STUDYAREA 9 LongleafPine/TurkeyOakSandhills ,9 SandPineScrub ,13 Hammocks ,14 OtherNorthFloridaCommunities 15 3 BACKGROUNDANDLITERATUREREVIEW 16 BiophysicalRationale ,16 RemoteSensingofVegetation 16 LeafAreaIndex 24 VegetationIndices 26 LAIandRemoteSensingVegetationIndices 29 ArtificialNeuralNetworks 30 NetworkArchitecture 33 NetworkTraining 35 NeuralNetworksandRemoteSensing 37 SatelliteImagery 38 4 METHODS 40 Goals 40 Hypotheses 40 Methods 42 FieldLAIMeasurements .42 BrightnessValueExtraction .43 MultipleRegressionofSixTMSpectralBands .43 VegetationIndicesandLAI. .44 ArtificialNeuralNetworkandLAI 44 OverallAccuracy 44 5 RESULTS 46 RegressionAnalyses .46 Hi ArtificialNeuralNetwork .59 OverallAccuracy 70 Discussion 75 6 ECOLGOGICALSTUDIES 77 LeafAreaIndex,Fire,andSuccessionin LongleafPine/TurkeyOakSandhills. 79 ComparisonsoftheUrbanForestsinGainesvilleandOcala,FL 92 6 DISCUSSIONANDCONCLUSION 106 Discussion 106 OtherSensors 108 Conclusions 110 FutureResearch 114 APPENDICES A COMPUTATIONOFLOCALTASSELEDCAPCOEFFICIENTS 112 B GISPROGRAMUSEDTOINTERPOLATEBRIGHTNESSVALUES 115 C CREATIONOFLAISURFACEIMAGESUSINGANNS 121 REFERENCES 122 BIOGRAPHICALSKETCH 133 IV AbstractofDissertationPresentedtotheGraduateSchool oftheUniversityofFloridainPartialFulfillmentofthe RequirementsfortheDegreeofDoctorofPhilosophy MEASUREMENTANDCOMPARISONOFREMOTELY DERIVEDLEAFAREAINDEXPREDICTORS INNORTHCENTRALFLORIDA By RyanR.Jensen May2000 Chair:MichaelW.Binford MajorDepartment:Geography Environmentalchangeoccursinresponsetobothnaturalandanthropogenic causes. Astheworld’shumanpopulationcontinuestoincrease,anthropogenicchange willalsoincrease. Thesechangesaffectthehealthandvigorofforeststhroughoutthe world,includingthoseinnorthcentralFlorida. LeafAreaIndex(LAI),theamountof leafareaperunitgroundarea,isanimportantbiophysicalvariablethatisdirectlyrelated toratesofatmosphericgasexchange,biomasspartitioning,andproductivity. While globalandlocalmodelsthatmapbiophysicalparametersareprevalentintheliterature, landscapetoregionalscalemodelsarelesscommon. Therefore,theabilitytomapand monitorLAIoverlandscapetoregionalscaleareasisessentialforunderstandingmedium scalebiophysicalpropertiesandhowthesepropertiesaffectbiogeochemicalcycling, biomassaccumulation,andprimaryproductivity. Thisstudydevelopsandverifies severalnewmodelstoestimateLAIusinginsitufieldmeasurementsthroughoutnorth centralFlorida,LandsatThematicMapperremotelysensedimagery,remotelyderived vegetationindices,simpleandmultipleregression,andartificialneuralnetworks (ANNs). Thisstudyconcludesthatwhilemultiplebandregressionandregressionwith V individualvegetationindices(NormalizedDifferenceVegetationIndex,SoilAdjusted VegetationIndex,SimpleRatio,andGreennessVegetationIndex)canestimateLAI,the mostaccuratewaytoestimateregionalscaleLAIistotrainanANNusinginsituLAI dataandremotesensingbrightnessvaluesmeasuredfromsixdifferentportionsofthe electromagneticspectrum. ThenewANNmethodofestimatingLAIisthenappliedtotwoforestecology studies. ThefirststudyanalyzesLAIinlongleafpine/turkeyoaksandhillsasafunction oftimesincelastbum. Itconcludesthatintheabsenceoffire,sandhillLAIincreases, andthismaybeusefulforidentifyingwhereprescribedbumsneedtobedone. The secondstudyestimatesandcomparesurbanforestLAIinOcalaandGainesville,Florida asafunctionofexistingtreeordinancesinthetwocities. ItconcludesthatGainesville’s urbanforesthashigherLAIvaluesprincipallybecauseofaverystricttreeordinance. VI CHAPTER 1 INTRODUCTION Environmentalchangeoccursinresponsetobothnaturalandanthropogenic events. Naturaleventsincludemigrationofcontinents,buildinganderosionof continents,sealevelchange,theElNino-southemoscillation,hurricanes,tornados,fire, landslides,variationsofsolarintensity,andevenmeteorstrikes. Anthropogenicevents includelandcover/landuseconversion,increasedcarbondioxideemissionthroughfossil fuelburningandlandconversion,andfiresuppression(CurranandFoody,1994). Asthe world’shumanpopulationcontinuestogrow,human-inducedenvironmentalchangewill increase. Theseeventsareoftenwellstudiedatthelocalscalewhereobservationand contemplationhaveoccurredformillenniaandattheglobalscalewheresignificant amountsofresearchhavebeendonesincetheearly1960s. However,thisunderstanding rarelyextendstothelandscapeandregionalscalesasitisbasedonlimitedresearchand lacksrobustmodelsandrelevantdata(CurranandFoody,1994). Knowledgeof biophysicalcharacteristicsofvegetationatthelandscapeandregionalscalesisnecessary todescribeenergyandmassfluxesattheEarth’ssurfaceusingglobalcirculationmodels, carboncyclemodels,andwatermodels(WeissandBaret,1999). Theneedforsuch knowledgeisnowaccepted(IGBP,1990)andmodelsmustbedevelopedthatincorporate regionalscalemodels(Curran,1994). Manyoftheecologicalquestionsconcerningtherelationshipbetweenboth naturalandanthropogeniceffectsontheenvironmentrequireaccurateestimatesof biophysicalvariables. Biophysicalvariablesincludeecosystemcharacteristicssuchas 1 2 primaryproduction,totalplantbiomass,componentsofthenitrogencycle,and evapotranspirationratesthatdescribeecosystempropertiesandfunctions(Aberand Melillo,1991). AbiophysicalvariableofprincipalimportanceisLeafAreaIndex(LAI) definedasm^ofleafperm^ofground(Curran,1994;PierceandRunning,1998; Lymbumer,L,P.J.BeggsandC.R.Jacobson.,2000). Leavesrepresentthefundamental photosyntheticorganoftreesandotherplants. Inputstoleavesincludeshort-andlong- wavelengthradiation,water,andnutrientsfromthesoil. Outputsincludelong- wavelengthradiation,conductionandconvection,transmissionoflight,transpiration, reflectance,andenergystoredincarboncompounds(sugars)forexporttotheremainder oftheplant(AberandMelillo,1991). Becauseleavesaretheprimaryorgansforcanopy- atmospheregasexchange,LAIisakeyvariableinatmosphericcirculationmodels (Ruimy,A.,B.Saugier,G.Dedieu,1994). Leavesfixcarbondioxide(CO2)during photosynthesis,andreleaseCO2duringrespiration. Thebalancebetweenthetwois knownasnetcarbonfixationandwhenextrapolatedatthelandscapescalethishelpsto explainlarge-scalecarbondynamics. Oneimportantwaythatbiophysicalvariables,such asLAI,aremeasuredatthelandscapescaleisthroughremotesensing. Satelliteremotesensing,definedasthemeasurementandanalysisofearth phenomenafromspace,hasbeenusedinmanyvegetationstudies(e.g.,Bonan,1993; Curran,1994;Gong,Ruiliang,andYu,1997). Remotelysenseddatafromsatellites possessavarietyofattributesusefulforlarge-scalevegetationmapping. Forexample, satelliteremotesensingdataprovideasynopticviewoflandscape-toregional-scaleareas andarecollecteduniformlyinspaceandtimeinanondestructivemanner(Green, Mumby,Edwards,Clark,andEllis,1997;FriedlandBrodley,1997). Satelliteremote 3 sensingcancontinuouslymonitortheearth’ssurfacethroughorbitingplatformsthat containsensors,whichmeasureemittedorreflectedenergy(AberandMelillo,1991). Despiteitsimportance,knowledgeofandabilitytomeasurelandscape-scale*LAI usingremotelysenseddataislimitedbecause: (i) CurrentmethodsofinsituLAImeasurementarelaborintensive,makingit difficulttomeasurethroughoutlargegeographicareas,renderinguseofthese methodsbeyondthestandlevelimpractical(Fassnacht,Gower,MacKenzie, Nordheim,andLillesand,1997). (ii) Adequatespatialandtemporalsamplingisdifficulttoachieveusingground measurementtechniquestomonitorlandscapescaleLAI(WeissandBaret,1999). (Hi) Present-dayfieldLAIdataaredifficulttorelatetohistoricalremotesensordata becauseforestschangeoverthetimeelapsedbetweensceneacquisitionandLAI measurement.(LillesandandKeifer,1996;Jensen,2000). (iv) StandardmethodsforestimatingLAIfromsatelliteremotesensingdataare inadequateacrosstherangeofLAIbecausesomestudieshavefoundathreshold LAIvalueof2-4wherecommonlyusedvegetationindicesfailtoincreasein relationtoincreasedLAIvalues(e.g.,theNormalizedDifferenceVegetation Index(Figure1-1;CarlsonandRipley,1997;Datt,1998;LawrenceandRipple, 1997). Interception,scattering,andemissionofelectromagneticradiationbyleavesare closelyrelatedtoforestcanopystructure(Mather,1987). IfthespatialaspectsofLAI TurnerandGardner(1991)definelandscapeasthelandformsorecosystemsofaregionintheaggregate, ortothelandsurfaceanditsassociatedhabitatsatscalesofhectarestomanysquarekilometers. 4 0.8 0.7 0.6 0.5 NDVI 0.4 0.3 0.2 0.1 0 Figure1-1.Leaf-Area-Index(LAI)saturationinrelationtotheNormalizedDifference VegetationIndex(adaptedfromCarlsonandRipley,1997).
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