Asner etal RSE 2014 (remote sensing of multiple canopy chemicals and LMA in Amazonian ...

13 Pages·2014·5.21 MB·English

by  Gregory P. Asner

RemoteSensingofEnvironment158(2015)15–27
ContentslistsavailableatScienceDirect
Remote Sensing of Environment
journal homepage: www.elsevier.com/locate/rse
Quantifying forest canopy traits: Imaging spectroscopy versus
field survey
⁎
GregoryP.Asner ,RobertaE.Martin,ChristopherB.Anderson,DavidE.Knapp
DepartmentofGlobalEcology,CarnegieInstitutionforScience,260PanamaStreet,Stanford,CA94305,USA
a r t i c l e i n f o a b s t r a c t
Articlehistory: Spatialandtemporalinformationonplantfunctionaltraitsarelackinginecology,whichlimitsourunderstanding
Received16June2014 ofhowplantcommunitiesandecosystemsarechanging.Thisproblemisacuteinremotetropicalregions,where
Receivedinrevisedform13November2014 informationonplantfunctionaltraitsisdifficulttoascertain.WeusedCarnegieAirborneObservatoryvisible-to-
Accepted14November2014
shortwaveinfrared(VSWIR)imagingspectroscopywithlightdetectionandranging(LiDAR)toassessthefoliar
Availableonlinexxxx
traitsofAmazonianandAndeantropicalforestcanopies.Wecalibratedandvalidatedtheretrievalof15canopy
foliarchemicalsandleafmassperarea(LMA)acrossanetworkof791-hectarefieldplotsusinganewVSWIR-
Keywords:
LiDARfusionapproachdesignedtoaccommodatetheenormousscalemismatchbetweenfieldandremotesens-
Amazonbasin
Canopychemistry ingstudies.TheresultsindicatethatsparseandhighlyvariablefieldsamplingcanbeintegratedwithVSWIR-
CarnegieAirborneObservatory LiDARdatatoyielddemonstrablyaccurateestimatesofcanopyfoliarchemicaltraits.Thisnewairborneapproach
Chemometrics addressestheinherentlimitationsandsamplingbiasesassociatedwithfield-basedstudiesofforestfunctional
Datafusion traits,particularlyinstructurallyandfloristicallycomplextropicalcanopies.
Hyperspectral ©2014ElsevierInc.Allrightsreserved.
LiDAR
Tropicalforest
1.Introduction biological and structural diversity within and across communities,
whichwhencombined,maylimitourunderstandingofplantfunctional
Plantfunctionaldiversityexpressesmanyecologicalprocessesrang- diversityandassembly.Onlyonestudyhassampledthemanythou-
ingfromnaturalselectiontoCO exchangebetweenthebiosphereand sandsofAmazoniantreespeciesrequiredtoresolveanassemblypat-
2
atmosphere.Despiteitscentralimportancetoevolutionaryandecolog- ternofcanopychemicaltraits(Asner,Martin,etal.,2014),butlikeall
icalresearch,ourknowledgeofplantfunctionaldiversityremainsvery otherfieldstudies,itcouldnotprovidespatiallydetailedinformation.
limitedinspaceandtime.Specifically,amajorknowledgegaphasde- Asaresult,potentialchangesintropicalforestfunctionalpatternscon-
velopedbetweensmall-scalefieldstudies(≤1ha)ofplantfunctional tinuetogounobserved,withcascadinglimitationsonourabilityto
traitsandbroad-scale,remotelysensedestimatesofvegetationproper- modelfuturechangesinforestcompositionandfunctionalprocesses
ties.Ontheonehand,fieldstudiesprovideanunderstandingofthe (Simonson,Coomes,&Burslem,2014).
inter-relationshipsbetweenfunctionaltraits,andthewaysthatplants Incontrasttothelimitationsoffieldstudies,remotesensingofAm-
expressthosetraits.Incontrast,remotelysenseddatafromsatellites azonianforestshasprovidedspatiallycontiguousinformation over
haveprovenreliableforestimatingchangesinvegetationcoverand time,buttheinformationhasmostlybeenlimitedtoforestcover,defor-
structure,withfarlesssuccessinmeasurementsoffunctionaldiversity. estationanddisturbance(e.g.,Achardetal.,2014;Souza,Roberts,&
Thisscale-gapinfunctionalecologyisexemplifiedinAmazonianfor- Cochrane,2005).Farfewerstudieshaveconsideredforestbiomass
ests.Forexample,anarrayoftree-andplot-scalestudieshavebeenun- and phenology (e.g., Baccini et al., 2012; Samanta et al., 2010), or
dertakentoassessanimportantaspectofplantfunctionaldiversity— plantcommunitycomposition(Chambersetal.,2007;Tuomistoetal.,
canopychemistry(e.g.,Cuevas&Medina,1988;Fyllasetal.,2009; 2003).Nonehavequantitativelymappedforestcanopychemistry,leav-
Reich,Ellsworth,&Uhl,1995),yetcollectivelytheliteraturehasprovid- inguswithnodirectwayoflinkingleafstudiestolarge-scaleprocesses
edlittlespatialinformationonthechemicaldiversityofAmazonforest orenvironmentalgradientsthroughouttheregion.
communities.Moreover,field-basedfoliartraitstudiesarehighlysus- QuantificationofcanopychemicalpatternsinAmazonianecosys-
ceptibletothecontributionsofmostlyunknown,spatially-explicitenvi- temsisneededtoadvanceourunderstandingoffunctionalbiogeogra-
ronmentalfiltersliketheunderlyinggeologyandsoils,andvariable phy,andtoobservefunctionalchangeovertime.Giventheenormous
geographicextentoftheAmazonregion,remotesensingwillbethe
⁎ Correspondingauthor.Tel.:[phone]. onlywaytodevelopanunderstandingofchangingfunctionaldiversity.
E-mailaddress:[email protected](G.P.Asner). However, remote sensing ultimately requires a connection to field
http://dx.doi.org/10.1016/j.rse.2014.11.011
0034-4257/©2014ElsevierInc.Allrightsreserved.
16 G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27
measurementsforpurposesofcalibration,validationandinterpreta- interdependentfunctionalprocesses.Chlorophyllsandcarotenoidsfa-
tion.Likemosttropicalforestregions,Amazoniapresentsagreatchal- cilitatelightcaptureandphoto-protection.Nitrogen(N)andphospho-
lengetopairfieldmeasurementswithremotelysenseddata(Fig.1). rus (P) are required for carbon fixation, growth metabolism and
Bearinmindthattheseforestcanopiesreach40ormoremetersin nucleicacids.Metabolicelementslikerock-derivedmacronutrients
heightonvariableterrain,andarefoundinstructurallycomplexassem- (e.g.,Ca,K,Mg)andmicronutrients(e.g.,B,Fe)supportmultipleleaf
blagescomprisedofthousandsofspecies,oftenwithnotwolocally functions such as carbon allocation (Demarty, Morvan, & Thellier,
neighboringtreesofthesamespecies.Itissimplyinfeasibletosample 1984).Solublecarbon(C)—comprisedofsugars,starchandpectin—
enoughofthecanopy—whateverthetraitofinterest—inawaythat issynthesizedastheinitialenergystorefortheplant(Chapin,1991;
directlylinksfieldandremotelysensedataatascalecommensurate Evans,1989).Leafstructuralcompoundsincludingligninandcellulose
tolarge-areamappingapplications.Weneednewremotesensingtech- aregeneratedtosupportstrengthandlongevity,andtodecreasepalat-
niquesthatarecompatiblewiththerelativelysparsewaythatforests abilitytoherbivores(Melillo,Aber,&Muratore,1982).Intropicalcano-
aresampledinthefield. pies, phenolic compounds are synthesized primarily for chemical
Herewefocusedourfunctionaltraitworkonawidevarietyofforest defenseofleaves(Coley,Kursar,&Machado,1993).Lightcaptureand
canopychemicalcompoundssynthesizedinleavestosupportmultiple, growthchemicalsarealsocoordinatedwithvariationinleafmassper
Fig.1.Thereexistsafundamentalmismatchbetweenfieldandremotesensingstudiesofforestcanopytraits.Intropicalforests,whichcontainthousandsofspeciescoexistingasverytall
canopies,andoftenincomplexterrainandremoteregions,fieldworkmustnecessarilybecarriedoutinatacticalorselectivefashion.Majorsamplingtrade-offsexistatregional,landscape,
plot,tree,branchandleaflevels.(a)Inthecaseofcanopychemistry,individualtreessuchasthispalmareclimbed,andfoliagefromoneormorebranchesislaboriouslycollectedfor
transportandanalysisinlaboratories.(b)Fieldplotsaresampled,oftensparsely,duetoissuesoflimitedcanopyaccess,aswellastocontrolforverticallightgradientsinthecanopy
thatadduncertaintytocanopytraitstudies.Fieldsamplingintensity,asshownhere,mayvarywidelyfromplottoplot,fromcommonlysparsetorarelycompleteinventory.(c)From
above,aircraftandsatelliteremotesensinginstrumentsarealsochallengedbyhighlyvariableilluminationconditionsthatareconvolvedtovariablecanopystructure.Additionally,
someportionsofmostforestcanopiesareleaflessatthetimeofobservation,asshowningraycrownsintheaerialphotography.Methodsareneededtobridgethehugegapbetween
therealitiesoffieldcollectionandthoseofremotesensingobservations.
G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27 17
area(LMA)andwatercontent(Wrightetal.,2004).Thischemicalport- Inthecaseofforests,theoryandmodelssuggestthatcanopychem-
folioexpressesmultiplestrategiesundertakenbyplantstomaximize istrycanbeestimatedwithimagingspectroscopyunderspecificmea-
fitnessoverthelifetimeoftheindividualorspecies. surement conditions. First, the spectrometer must be capable of
Remotesensingofcanopychemistryhasprimarilybeenattempted acquiringhigh-fidelitymeasurements,definedbyveryhighsignal-to-
with imaging spectroscopy, also known as hyperspectral imaging noise,detectoruniformityandinstrumentstability,torecordminute
(Curran,1989;Ustin,Roberts,Gamon,Asner,&Green,2004;Wessman, variations in the spectral radiance features of vegetation (Asner &
Aber,Peterson,&Melillo,1988b).Imagingspectrometersmeasurethe Green, 2001; Green et al., 1998). Second, there must be sufficient
solarradiationreflectedfromasurfaceinnarrow,contiguousspectral plantmaterialcross-sectionandopticaldepthpresentedtotheimaging
channelscoveringabroadwavelengthregion(Goetz,Vane,Solomon,& sensorforthefoliarchemicaltraitstobemostfullyexpressedinthe
Rock,1985).Recentlytwocontrastingviewpointshaveemergedintheef- measuredspectra.Whencanopiesareopticallythin,orwithlowleaf
forttoestimatecanopychemicalpropertiesfromimagingspectroscopy. areaindex(LAI),ascanbethecaseinwater-limitedenvironments,veg-
Ontheonehand,appliedstudiesindicatethatfoliarchemicaltraitscan etationstructuremaybeamoreprominentcontributortoreflectance
be estimated from spectral reflectance and absorptance features thanfoliarchemistry(Jacquemoudetal.,2009).Incontrast,LAIandcan-
(Kokaly,Asner,Ollinger,Martin,&Wessman,2009;Townsend,Foster, opycoverareoftenhighintropicalforests,andopticallythickcanopies
Chastain,&Currie,2003;Ustinetal.,2009).Ontheotherhand,modeling affordthebestobservingconditionsfordevelopingcanopychemicales-
worksuggeststhatthespectralsignaturesofplantcanopiesaresome- timates from imaging spectroscopy (Asner, 2008; Atkinson, Foody,
timesdominatedbystructuralvariation(Knyazikhinetal.,2013),leaving Curran,&Boyd,2000).Nonetheless,variationinLAIhasmoreofanef-
itunclearastowhethercanopychemistrycanberetrievedfromimaging fectonreflectanceinthenear-infrared(e.g.800–1200nm)thanit
spectroscopyinthepresenceoftheseconfoundingeffects. doesinthevisible(400–700nm)orshortwave-infrared(N1300nm)
Fig.2.TopographicmapofPeruwithgenerallocation(circles)of791-hectareCarnegieSpectranomicsProject(CSP)fieldplotsusedforcalibrationandvalidationofcanopyfunctional
traitsintheAndestoAmazonregion.
18 G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27
wavelengthranges(Myneni&Asrar,1993),andthisneedstobeconsid- todetermineLMA.Theprotocolforscanning,water,andLMAdetermina-
eredwhenselectinginstrumentationandmethodsofspectralanalysis. tionisprovidedontheCSPwebsite(http://spectranomics.ciw.edu).
Third,therelativeimportanceoffoliarchemistryandcanopystructure LaboratoryprotocolsforallchemicalassaysweregivenbyAsnerand
onthespectralreflectancesignaturesofcanopiesisspatiallydependent; Martin (2011) and Asner, Martin, et al. (2014), and see http://
Intra-andinter-canopygapsdiminishthecontributionoffoliarchemi- spectranomics.ciw.edu.Abriefoverviewisprovidedhere.Driedleaves
calvariationonremotelysensedreflectance(Gerard&North,1997). weregroundandanalyzedforconcentrationsofP,basecationsand
Finally,thesun-to-canopyilluminationandviewingconditionsmust micronutrients(Ca,K,Mg,B,Fe)usingInductivelyCoupledPlasma
becontrolledforinordertoextractcomparablespectralsignatures spectroscopy(ICP-OES;ThermaJarrel-Ash,IRISAdvantage,Waltham,
acrossimages,landscapesandovertime. MA, USA) after microwave digestion in nitric acid solution (CEM
Toaddressthemanyfactorsthatmakeremotesensingofcanopy MARSXpress;Matthews,NC,USA).TotalCandNweredeterminedon
chemistrydifficultwithimagingspectroscopy,wedevelopedamethod drysampleusingacombustion-reductionelementalanalyzer(Costec
thatlinkstheoften-sparse,field-basedsamplingofforestcanopieswith AnalyticalTechnologiesInc.Valencia,CA,USA).Cellulose,ligninand
anewmethodforsamplingthespectralpropertiesofthecanopies.We sol-Cwereassayedusingsequentialdigestionofincreasingacidityin
testedthemethodusingairbornehigh-fidelityimagingspectroscopy anAnkomfiberanalyzer(AnkomTechnology,Macedon,NY,USA)and
alonga3000melevationgradientstretchingfromlowlandAmazonia arepresentedonanash-freedrymassbasis.Totalphenolsweremea-
totreelineinthePeruvianAndes.Withnewmeasurementsfromthe sured from the frozen leaf disks colorimetrically using the Folin–
CarnegieAirborneObservatory—AirborneTaxonomicMappingSys- Ciocalteau method following extraction in 95% methanol and 48 h
tem,orCAO-AToMS(Asneretal.,2012),weattemptedtoremotely darkincubation(Ainsworth&Gillespie,2007).
quantify15foliarchemicalsandLMA.Indoingso,weevaluatedthepre-
cisionandaccuracyofremotelysensedfoliartraitscomparedtothose 2.3.Imagingspectrometerdata
derivedfromfieldandlaboratorytechniques.
AirborneremotesensingdatawereacquiredinAugust–September
2.Methods 2011,2012and2013usingCAO-AToMS(Table1,Fig.3),whichincludes
ahigh-fidelityvisible-to-shortwaveInfrared(VSWIR)imagingspec-
2.1.Studyregion trometerandaduallaser,waveformLiDAR(Asneretal.,2012).Wecol-
lectedthedataovereachstudylandscapefromanaltitudeof2000m
Ourstudyincorporatedfieldandairbornemeasurementsfromfor- a.g.l.,anaverageflightspeedof55–60ms−1,andamappingswathof
estedlandscapesstretchingfromthewesternAmazonianlowlandsto 1200m.TheVSWIRspectrometermeasuresspectralradiancein480
submontaneandmontaneforestsofthePeruvianAndes(Fig.2).Across channelsspanningthe252–2648nmwavelengthrangein5nmincre-
theselandscapes,elevationincreasesfromabout100mtomorethan ments(full-widthathalf-maximum).TheVSWIRhasa34°field-of-
3000m abovesealevel.Meanannualprecipitationvariesb1100to viewandaninstantaneousfield-of-viewof1mrad.At2000ma.g.l.,
N5000mmyr−1,andmeanannualtemperaturerangesfrom13°Cto theVSWIRdatacollectionprovided2.0mgroundsamplingdistance,
26°C(Table1).SoilsinthelowlandlandscapesrangefromUltisolson orpixelsize,throughouteachstudylandscape.TheLiDARhasabeam
clayterrafirme(terrace)substratestoInceptisolsonalluvialfloodplains, divergencesetto0.5mrad,andwasoperatedat200kHzwith17°
andtowhitesandyEntisols.IntheAndeanlandscapes,soilsaremostly scanhalf-anglefromnadir,providingswathcoveragesimilartothe
classifiedasInceptisolsandEntisols.Vegetationatallsitesishumid, VSWIRspectrometer.Becausetheairbornedatawerecollectedalong
broadleaftropicalforest.Canopiesaredenselyfoliated,withLAIranging
fromabout3–8(Girardinetal.,2010).Fractionalinterceptedphotosyn-
theticallyactiveradiation(fIPAR)oftenexceeds98%(Asner,Anderson, Table1
etal.,2014). Descriptiveinformationfor791-hectarefieldplotsusedforcalibrationandvalidationof
CarnegieAirborneObservatory(CAO)spectroscopicremotesensingofcanopychemical
traitsandleafmassperarea.Siteabbreviationsaregivenfollowedbythenumberofplots
2.2.Fieldplots withineachsite.SitesareorganizedbytheyeartheywereimagedbytheCAO.Soilorders
followtheU.S.DepartmentofAgriculture(USDA)soiltaxonomysystem.
ThroughouttheAndes-to-Amazonstudyregion,weestablished791-
Yearmapped Site Soilorder Elevation(m) MAP(mm) MAT(°C)
hectarefieldplotsforuseincalibrationandvalidationofourremotesens-
ingmethod(Fig.2,TableS1).Likeinmosttropicalforests,exceptional 2011 SUC-01;1 Ultisol 116 2754 26.2
ALP-01;1 Ultisol 131 2760 26.3
taxonomicandstructuraldiversity,andcanopyinaccessibility,madeitin-
JEN-11;1 Ultisol 131 2700 26.6
feasibletosamplealltreespeciesinourfieldplots.Ineachplot,arangeof SUC-05;1 Ultisol 132 2754 26.2
sampling intensities, from 3 to 38 trees with full sunlight canopies JEN-12;1 Entisol 135 2700 26.6
(TablesS2–S4),wasselectedforcollection(Fig.1).Theseplotsarepart ALP-30;1 Entisol 142 2760 26.3
CUZ-03;1 Inceptisol 205 2600 24.7
oftheCarnegieSpectranomicsProject(CSP)network,andhavebeende-
TAM-06;1 Inceptisol 215 2600 24.0
scribedindetailbyAsner,Martin,etal.(2014).Highlyvariablenumbers
TAM-09;1 Inceptisol 220 2600 24.0
oftreecollectionsreflecttherealityofcanopyaccess,bothintermsofter- TAM-05;1 Ultisol 223 2600 24.0
rainandtreeclimbing. PJL-01;1 Ultisol 420 5000 23.1
Leafcollectionswereconductedusingtree-climbingtechniques.For PJL-02;1 Entisol 632 5000 23.1
SPD-02;1 Inceptisol 1527 4628 18.5
eachtree,twofullysunlitbranchesatthetopofthecanopywereselected
SPD-01;1 Inceptisol 1776 4341 18.5
andcut,sealedinlargepolyethylenebagstomaintainmoisture,storedon TRU-08;1 Inceptisol 1800 4341 18.5
iceincoolers,andtransportedtoalocalsiteforprocessingwithin3h,and TRU-04;1 Inceptisol 2758 2678 13.0
usuallylessthan30min.Asubsetoffullyexpandedleaveswasrandomly TRU-03;1 Inceptisol 3043 2678 13.0
selectedfromthebranchesforscanningtodeterminefreshleafarea,and 2012
weighingtorecordfreshanddryweightsaswellasleafwaterconcentra- ESC-A;3 Ultisol 945 1055 27.1
tion.Additionalleaveswereselectedforovendryingat70°Candanother ESC-B;11 Entisol 945 1055 27.1
foracquisitionoffreshleafdiskstobeimmediatelyfrozento-80°Cinliq- 2013
uidN.Bothsubsetsweremaintainedintheirstabilizedstateforsubse- LA-A;21 Ultisol 260 2700 24.0
quent chemical analyses in the Carnegie Spectranomics Library, LA-B;11 Inceptisol 260 2700 24.0
SPA;16 Inceptisol 1500 4628 18.5
Stanford,CA,USA.Valuesoffreshleafareaweredividedbydryweight
G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27 19
adjacentflightlineswith50%overlap,theLiDARpointdensitywastwo differencebetweentheDTMandDSMyieldedadigitalcanopymodel
lasershotsm−2,or8shotsperVSWIRpixel. (DCM)ofvegetationheightaboveground.
TheLiDARdatawereusedtopreciselyortho-geolocatetheVSWIR TheVSWIRdatawereradiometricallycorrectedfromrawDNvalues
data, and to provide a means to mask canopy gaps and shadows, toradiance(Wsr−1m−2)usingaflat-fieldcorrection,radiometriccal-
waterandexposedsoilintheVSWIRdata.Toachievethis,thelaser ibrationcoefficientsandspectralcalibrationdatacollectedinthelabora-
rangeswerecombinedwiththeembeddedhighresolutionGlobalPosi- tory.ThestandardizedGPSpulse-per-secondmeasurementwasusedto
tioningSystem-InertialMeasurementUnit(GPS-IMU)datatodeter- preciselyco-locateVSWIRspectralimagerytotheLiDARdatacollection.
minethe3-Dlocationsoflaserreturns,producinga‘cloud’ofLiDAR Wecreatedacameramodeltodeterminethethree-dimensionalloca-
data. The LiDAR data cloud consists of a very large number of tionandfield-of-viewofeachsensorelement,andcombineditwith
georeferencedpointelevationestimates(cm),whereelevationisdeter- standardizedtiminginformation,fordataco-registration.Asmoothed
minedrelativetoareferenceellipsoid(WGS1984).Weusedthese bestestimateoftrajectory(SBET),theLiDARDTM,andthecamera
pointstointerpolatearasterdigitalterrainmodel(DTM)fortheground modelwerethenusedtoproduceanimagegeometrymodelandobser-
surfaceofeachlandscape.Thiswasachievedusinga10m×10mkernel vationaldatacontaininginformationonsolarandviewinggeometryfor
passedovereachflightblock,withthelowestelevationestimateineach eachimagepixel.Theseinputswereusedtoatmosphericallycorrectthe
kernelassumedtobeground.Subsequentpointswereevaluatedby radianceimageryusingtheACORN-5model(ImspecLLC,Glendale,CA,
fittingahorizontalplanetoeachofthegroundseedpoints.Iftheclosest USA).ToimproveaerosolcorrectionsinACORN-5,weiterativelyranthe
unclassifiedpointwasb5.5°andb1.5mhigherinelevation,itwasclas- modelwithdifferentvisibilitiesuntilthereflectanceat420nm(which
sifiedasground.Thisprocesswasrepeateduntilallpointswithinthe isrelativelyconstantforvegetatedpixels)was1%.Reflectanceimagery
blockwereevaluated.Thedigitalsurfacemodel(DSM)wasbasedonin- wascorrectedforcross-trackbrightnessgradientsusingabidirectional
terpolationsofallfirst-returnpoints.Themeasurementofthevertical reflectancedistributionfunction(BRDF)modelingapproachdescribed
Fig.3.Examplesofcolor-infraredcompositeimagesofCarnegieAirborneObservatoryVisibletoShortwaveInfrared(VSWIR)imagingspectrometerdataof1-hectarestudyplots(whiteboxes)
onlandscapesrangingfromlowlandAmazoniatotheAndeantreeline.SitenamesandelevationsareincludedinTable1.(Forinterpretationofthereferencestocolorinthisfigurelegend,the
readerisreferredtothewebversionofthisarticle.)
20 G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27
byColgan,Baldeck,Féret,andAsner(2012).TheVSWIRimagerywas LiDARobservations.Theco-alignedVSWIRandLiDARdatawereproc-
thenorthorectifiedtotheLiDARDCM. essedtogethertodevelopasuitabilitymapforleaftraitestimationat
aprescribedgridcellsize(Fig.4).Weselected1-hectareresolution
2.4.Linkingfieldandremotelysenseddata gridcellstomatchthesizeofmanytypicalforestfieldplots(Peacock,
Baker,Lewis,Lopez-Gonzalez,&Phillips,2007),andthespectralsigna-
Themethodforcalibration,validationandmappingofcanopychem- tureofeach1-hectarecellwasderivedbyaveragingthespectraofall
icaltraitsandLMAispresentedinFig.4.Thegoalsofthemethodareto 2-meterresolutionVSWIRmeasurementsthatpassedthefollowingfil-
allowfortheautomatedcompilationofcomparableimagingspectrom- tering criteria: (i) Normalized Difference Vegetation Index (NDVI)
eterdatasetsoverlargegeographicareaswhileminimizingthelocal- ≥0.8;(ii)vegetationheight≥2.0m;and(iii)minimalintra-orinter-
scaleeffectsofsun-sensor-canopygeometry,inter-andintra-crown canopyshadeintheVSWIRpixel.Throughdetailedsurveyofmore
shading,forestgaps,andterrain-relatedartifacts.Inaddition,themeth- than7millionhaofVSWIRimageryoverAndeanandAmazonianfor-
odmustbecompatiblewiththenecessarilysparsesamplingofforest ests,wehavefoundthataminimumNDVIthresholdof0.8ishighlycon-
canopiesinthefield. servative,allowingmosttropicalcanopyfoliageintothetraitanalysis,
Toachievethisgoal,wedevelopedadata-fusionapproachfacilitated whileexcludingareasofun-foliatedcanopy.The2-meterminimum
bythecollectionandinter-calibrationofboresight-alignedVSWIRand height requirement removes bare ground and short non-forest
Fig.4.Pre-screeningof(a)CAOVisible-to-ShortwaveInfrared(VSWIR)imagingspectrometerdatausing(b)embeddedLightDetectionandRanging(LiDAR)dataoncanopyheightand
gaps.(c)MinimumNDVIthresholdof0.8ensuressufficientfoliarcoverineachanalysispixel.(d)CombiningLiDARandsolar-viewinggeometry,amaskisgeneratedtoremovepixelsin
shade,andgroundandwatersurfaces.(e)Theresultingsuitabilityimageprovidesanindicationofpixelsthatcanbeusedforchemometricanalysis.Hereweonlyusedpixelsinthetop50%
ofthesuitabilityindex.
G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27 21
vegetationsuchasexposedgrasscover.Theshademaskisderivedfrom WeusedPartialLeastSquaresRegression(PLSR;Haaland&Thomas,
araytracingmodelthatpreciselyidentifiescanopylocationinunshad- 1988)toquantitativelylinktheairborneVSWIRspectroscopytofield-
edandunobstructedviewoftheVSWIRspectrometer(Asneretal., collected,lab-assayedfoliartraits.ThePLSRapproachisbeneficialbe-
2007).ThisLiDAR-basedshademaskremovesVSWIRpixelsthatare causeitutilizesthecontinuousspectrumasasinglemeasurementrath-
fullyorpartiallyshadedbyadjacentfoliage,branchesorcrowns.To- erthaninaband-by-bandtypeofanalysis(Boulesteix&Strimmer,
gether,thesefiltersprovidedapixel-by-pixel(2.0m)suitabilitymap 2006;Martens,2001).Toavoidstatisticalover-fitting,thenumberofor-
withineach1-hectaregridcell,fromwhichaveragespectralreflectance thogonalspectraldimensionsorvectorsusedinthePLSRanalysiswas
wascomputed(Fig.S2).Thisfilteringtechniquehastheadvantageofre- estimatedbyminimizingthePredictionResidualErrorSumofSquares
ducingthetotalcanopyanalysisareatoalevelanalogoustothe“sunlit (PRESS)statistic(Chen,Hong,Harris,&Sharkey,2004).The PRESS
canopyfoliage”criterionoftenusedinfieldcollectionsofforestcanopy statisticwascalculatedthroughaleave-one-outcross-validationproce-
foliage.However,italsoremoveswithin-plotvariationincanopyspec- dureforeachPLSRmodelthatwasrun.Thiscross-validationprocedure
tralreflectanceproperties,whichmaybeconsideredimportantinother iterativelygeneratesN−1regressionmodelswhilereservingtherest
higherspatialresolution(b1ha)studies. ofthesamplefrom theinputdatasetuntiltherootmeansquared
Ahistogramreportingthefractionoftheforestcanopythatpassed error(RMSE)forthePRESSstatisticisminimized.Theprecisionandac-
through the filtering process in each of the 79 1-hectare plots is curacyofthePLSRmodelforeachfoliartraitwereassessedbasedonthe
showninFig.5a.ThemeanvalueforsuitableVSWIRsamplingineach coefficientofdetermination(R2)andtheRMSE,respectively,between
1-hectareplotwas52%.Incontrast,onaverageonly5%ofeachplot remotelysensedandfield-measuredtraitvalues.
wascoveredbycanopiesthatweresampledinthefield(Fig.5b).This TotesttherobustnessandrepeatabilityofthePLSR–PRESSapproach
inherentmismatchinscalecannotbedirectlyovercomeduetotheex- with1-hectareresolutionfilteredandbrightness-normalizedVSWIR
tremedifficultyoffieldsampling.However,bycreatingsimilarsam- data,wesplitthe79forestplotsintocalibration(n=55)andvalidation
plingconditionsforfield-accessibleandremotelysensedportionsof (n=24)subsetseachspanningtherangeoftraitvalues.Tominimize
full-sunlight,highlyfoliatedcanopiesineachplot,thescale-gapbe- modelredundancy,werandomlyselected70%oftheplotsfromthe
tweenthesedisparatedatatypeswaseffectivelyminimized. 55plotstogeneratethePLSRmodels.Foreachiterationandleaftrait,
FollowingthepreparationofthefilteredVSWIRreflectancespectra thePLSR-PRESSmethodwasrun1000timesonthecalibrationdatato
(Fig. 6), we convolved them to 10-nm bandwidth and applied a determineproducer-basedcalibrationprecisionandaccuracy(Serbin,
brightness-normalizationadjustment(Fig.S1).Brightnessnormaliza- Singh, McNeil, Kingdon, & Townsend, 2014). The PLSR equations
tionutilizes‘spectralangle’tomitigatedifferencesinbrightnessthat resultinginrobustmodels(R2Nthemeanvalueofthe1000iterations)
mayarisefrominternalcanopyshade,whichisproportionaltoLAI werethenusedtoestimateeachcanopytraitinthe24validationplots.
(Kruseetal.,1993;Myneni,Ross,&Asrar,1989).Thisreducesthecon- Thisprovidedawaytocalculateameanandstandarddeviationofeach
tributionofvaryingLAItochemometricdeterminationsoffoliartraits remotelysensedtraitascomparedagainstthefield-basedvalue.
fromremotelysenseddata(Feilhauer,Asner,Martin,&Schmidtlein,
2010).Theresultingspectraweretrimmedatthefarends(b410nm, 3.Results
N2450nm)ofthemeasuredwavelengthrange,aswellasinregions
dominatedbyatmosphericwatervapor(1350–1480,1780–2032nm). 3.1.Canopychemicalvariation
The foliar chemistry and LMA of sunlit canopy trees and lianas
spannedaverywiderangeofvaluesatbothsiteandregionalscales
(Tables2,S2–S4).Ourrangeofvaluesmeetsthatwhichhasbeenre-
ported in global and cross-biome synthesis studies (Poorter,
Niinemets,Poorter,Wright,&Villar,2009;Wrightetal.,2004),aswell
as in a much larger Andes and Amazon canopy chemical data set
(Asner,Martin,etal.,2014).Bycoveringabroadrangeofchemicaldi-
versityamongtaxaandacrosssites,wehadasufficientdatasettoade-
quatelytestthegeneralrelationshipsbetweenfoliartraitsandimaging
spectroscopyforhumidtropicalforests.
3.2.Calibration
The1-hectarefilteredandbrightness-normalizedVSWIRdata
calibratedwellagainstasuiteoffoliartraitsrepresentinglightcap-
tureandgrowth,structureanddefense,andmaintenanceandme-
tabolism(Table2).Alllightcaptureandgrowthtraits,including
chlorophyll a + b, carotenoids, N, P, LMA, water and soluble C
showedexcellentcalibrationperformances,withR2=0.55–0.71and
%RMSE=5–16%,where%RMSEiscalculatedintermsoftheoriginal
value for each trait as 100 ∗ RMSE / μ . Variances among the
trait trait
1000 PLSR model runswere about8–18%,bothintermsof R2and
RMSE. Thenumberof spectral(latent) vectorsselectedamong the
1000PLSRmodeliterationswasalsoconsistent.
Among the structure-defense traits, lignin and total C were
calibratedwiththehighestprecision(R2=0.54–0.71)andaccuracy
Fig.5.(a)Histogramshowingthefractionof2-meterCAOVSWIRpixelsthatpassedthe (%RMSE=2.7–14.9%)(Table2).Individualcalibrationrunsindicated
pre-screeningcriteria(Fig.4)ineachofthe791-hectarefieldplots.Onaverage,only awiderrangeofperformancesascomparedwiththoseinvolvingthe
52%ofthe2500VSWIRpixelsperhectarewereconsideredappropriateforspectroscopic
lightcaptureandgrowthtraits.Phenolsandcellulose,however,had
analysis.(b)Histogramshowingthefractionofeach1-hectareplotrepresentedinthe
field-basedcanopysamplingeffort.Onaverage,just3–5%oftheareaoftheplotscould weakercalibrationperformances,andyetremainedhighlysignificant
besampledinthefieldduetotheinaccessibilityoftalltropicalforestcanopies. (pb0.001).Forfoliarmaintenance-metabolismtraits,Caperformed
22 G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27
Fig.6.(a)Examplesofcanopyreflectancespectraderivedfromthemeannumberofpixelssuitableforchemometricanalysisfollowingpre-screeningwithineach1-hectareplot.Zoom
imagesofspectraareprovidedfor(b)visibleand(c)shortwave-infraredregionstorevealsubtlefeaturesassociatedwithvaryingchemicalconcentrations.
the best in terms of precision (R2 = 0.79 ± 0.17) and accuracy traits,thenear-IRplayedanadditionallyimportantrole,particularlyin
(%RMSE=16.9%)(Table2).Othercationcalibrationswerehighlysig- the1000–1300nmrange.Moreover,the2000–2500nmrangecontrib-
nificant(pb0.001),butexhibitedlowerprecisionandaccuracy.We utedtothecalibrationofthespectraldatatoLMAandsolubleC.The
foundthatthenumberoflatentspectralvectorsautomaticallyselected otherchemistriesrepresentingstructure-defenseandmaintenance-
amongthe1000modelrunsvariednarrowlyformosttraits,withafew metabolismtraitgroupshadregressioncoefficientsthatalsorequired
exceptionssuchasforCaandtotalC.Greatervariationinthenumberof multiplespectralfeaturesacrossthe400–2500nmwavelengthrange.
latentvectorsindicatesinstabilityinthespectral-to-chemicalrelation- Noneofthechemicaltraitshadthesamespectralweightings,andthe
shipsamongmodelruns. vastmajorityofchemicaltraitsrequiredportionsofthespectrumin
ThestandardizedPLSRcoefficientsindicatedthatallwavelengthre- combinationsthatwerenothighlycorrelatedamongPLSRcoefficients
gionswereimportantformostofthefoliartraits(Fig.7).Inthisfigure, (TableS5),suggestingauniqueroleofeachtraitindeterminingthe
departuresfromthezero-lineindicatespectralfeaturesthataremost spectralreflectancemeasuredfromtheVSWIRspectrometer.
importantindeterminingthefoliartraitofinterest.Therewasconsis-
tencyinthespectralregionsrequiredforchlorophylla+b,carotenoid 3.3.Validation
and N calibrations as indicated in the correlation between PLSR
weightings(TableS5).Specifically,thevisibleregionalongthe700- Validationmodelsindicatedhighlysignificantresultsforalltraits,
nm‘rededge’andintheblue(400–500nm)provedtobeimportant withtheexceptionofK(pb0.001;Table3).Thebestperformances
contributorstothePLSRmodelsforthesetraits.Therewerealsoimpor- wereachievedforphotosyntheticpigments,N,P,LMA,FeandtotalC.
tantcontributionsfromtheshortwave-IRregion,particularlyinthe Regressedagainstfielddata,theirR2valuesrangedfrom0.39–0.58and
1500–1700nmrange,indicativeofprotein–Ninteractionswithsolarra- %RMSEof14–34%.Asomewhatlowerperforminggroupintermsofpre-
diation(Curran,1989). cisionincludedwater,solubleC,ligninandcellulose,althoughtheirrela-
Wealsofoundthatthevisibleandshortwave-IRwerecriticaltothe tiveaccuraciesremainedrelativelyhigh(%RMSE=6–24%).Thepoorest
estimationofP,LMA,solubleCandwater(Fig.7).However,forthese performerswerephenolsandbasecations(%RMSE=24–63%).
G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27 23
Table2
Calibrationofcanopychemicaltraitsandleafmassperarea(LMA)usingairbornehigh-fidelityvisible-to-shortwaveinfrared(VSWIR)spectroscopy.Mean±standarddeviationofPLSR
modelresults(R2;RMSE)calculatedrandomlyselectedfieldplotscontainingthecanopytraitrangeshown.
R2 RMSE %RMSE Vectors Traitrange
Lightcaptureandgrowth
Chlorophyllab⁎(mgg−1) 0.70±0.07 0.84±0.10 15.66 5(1) 2.65–8.53
Carotenoids⁎(mgg−1) 0.63±0.08 0.16±0.02 13.15 5(1) 0.69–1.84
N⁎(%) 0.54±0.09 0.30±0.03 14.47 5(1) 1.28–3.33
P⁎(%) 0.71±0.10 0.02±0.00 16.59 6(2) 0.06–0.26
LMA⁎(gm−2) 0.69±0.08 11.87±1.55 9.99 5(2) 76.0–180.0
Water(%) 0.49±0.13 2.95±0.38 5.22 5(2) 44.3–63.9
Solublecarbon(%) 0.49±0.14 4.40±0.87 9.16 4(3) 36.0–71.9
Structureanddefense
Phenols(mgg−1) 0.33±0.10 20.30±1.77 18.37 1(1) 51.0–156.2
Lignin(%) 0.51±0.15 3.51±0.62 14.94 3(2) 9.2–33.5
Cellulose(%) 0.38±0.12 2.33±0.34 14.34 3(3) 8.2–21.7
Totalcarbon(%) 0.69±0.16 1.35±0.35 2.67 6(3) 42.1–54.6
Maintenanceandmetabolism
Ca⁎⁎(%) 0.79±0.17 0.14±0.06 16.99 9(5) 0.04–2.96
B⁎(μgg−1) 0.53±0.07 7.31±0.89 43.33 5(1) 4.74–55.98
Fe⁎(μgg−1) 0.56±0.09 12.39±1.94 27.29 4(1) 21.8–120.1
K⁎(%) 0.42±0.22 0.15±0.04 24.57 4(3) 0.36–1.51
Mg⁎(%) 0.34±0.19 0.06±0.01 31.78 3(3) 0.07–0.50
RMSE=rootmeansquareerrorinunitsoftheoriginalchemicalassays.
%RMSE=RMSEexpressedasapercentageofthemeanvalueoftheleaftrait.
R2=regressioncoefficientforK-foldcross-validationdatausedduringPLSRanalyses.
Vectors=numberofspectralweightingvectors,ororthogonaldegreesoffreedom,fromtheVSWIRdatausedforthechemicaldetermination.
⁎ Indicatesthatchemicalvalueswerenaturallog-transformedforPLSRanalysis.
⁎⁎ Square-rootofchemicalvalue.
4.Discussion Thebestchancetoisolatetheeffectsoffoliartraitsoncanopyreflec-
tancesignaturesreliesonreducingtherelativecontributionofcanopy
WehavefoundthatasuiteofcanopyfoliarchemicaltraitsandLMA structure,andbycontrollingforsolarandviewinggeometry.Reliable
canbeestimatedusingairbornehigh-fidelityvisible-to-shortwavein- plant functional trait mapping can be achieved by pre-screening
frared(VSWIR)imagingspectroscopy.Becausefieldtechniquesare portionsofthecanopyforgaps,lowLAI,andshadows.WhilelowLAI
oftenseverelyunder-samplefoliartraitsinforests,therebystruggling portionsofthecanopycanberemovedwithasimpleNDVIfilter,gaps
toprovidesystematic,repeatestimatesofcanopypropertiesatthe andshadowsrequiremoredetailedstructuralinformation.Asnerand
standlevel,imagingspectroscopycangreatlyreducetheinherentlimi- Martin(2008)firstshowedhowLiDARcaneffectivelybeusedforthis
tationsandsamplingbiasesassociatedwithfieldwork.Thisissuehas task,butonlyiftheLiDARdataaretightlyalignedwiththespectrometer
beenparticularlyproblematicintallstatured,floristicallycomplextrop- datatoprovideprecisesun-to-canopy-to-sensorgeometryforevery
icalforests,wheredataqualitycontinuestogomostlyunmonitored, pixel (Asner et al., 2012). This technique, combined with LiDAR-
and where geographic sampling biases limit the interpretability of derivedminimumvegetationheightdata,resultsinasuitabilitymap
plot-networkdata. athighspatialresolution(e.g.,2m)withinwhichthemostappropriate
spectraldatacanbeselectedforchemometricanalysis(Fig.4).Bycarry-
ingoutthesesteps,thepre-screenedvegetationcanopiesbecomemore
4.1.Observingconditionsforremotesensingoffunctionaltraits comparableoverlargetractsofotherwisehighlyvariablecanopystruc-
turalandterrainconditions.
Remotesensingofcanopychemistryisnotanewidea,andenormous Inthisstudy,theNDVI≥0.8filterwasconservative,allowinginto
progresshasbeenmadesincethelate1980stowardthegoalofdevelop- thecalibrationandvalidationatleast95%ofthecanopydatawithin
ingsensorsandtechniquestoremotelyprobethechemicalcontentof the791-hectarefieldplotsspreadthroughoutAmazonianandtheAn-
vegetation (Homolová, Malenovsky, Clevers, García-Santos, & deanforests(datanotshown).The2-meterminimumheightfilterre-
Schaepman,2013;Kokalyetal.,2009;Martin,Plourde,Ollinger,Smith, movedwaterbodies,clearings,landslideareas,andlargegaps.The
&McNeil,2008;Ustinetal.,2009;Wessman,1992).However,thecon- majorityofthefiltering,however,wasdrivenbytheLiDAR-derived
volutionofvegetationstructure,foliarchemistryandobservationgeom- intra-andinter-crownshademasks.Usingthisapproach,ourcalibra-
etry(sunandsensor)hasalsoimpededprogressinunderstandinghow tionandvalidationunderwentenormousfilteringdowntoabout52%
besttoisolatefoliarchemistrywithinremotelysenseddata.Variation ofeachforestplot,butwithwidevariationbetweenplots(20–100%;
incanopystructure,particularlyLAI,leafangledistribution,andwithin- Fig.5a).
canopygapscandominateopticalreflectancesignatures(Asner,1998). Beyondthespatialfilteringapproachusedhere,additionalmeasures
Therealsooftenexistsspatialcovarianceofcanopystructureandchem- canbetakentomaximizetheexpressionoffoliartraitsinimagingspec-
istry(Ollinger,2011).Moreover,currentcanopyreflectancemodelswill, trometerdata.Oneapproachisbrightnessnormalization,whichhelps
virtuallybytheirformulation,simulateadiminishedeffect offoliar reducethecontributionofvaryingLAIandfoliarclumpingoncanopy
chemistry on spectral-optical signatures, particularly in the near- spectralreflectance(Feilhaueretal.,2010).Throughbrightnessnormal-
infrared(Knyazikhinetal.,2013),butthisislargelyanartifactofthein- izationoftheVSWIRspectrathatpassedthespatialfilteringstep,the
abilityofcurrentmodelstosimulatethemultiplechemicalconstituents expressionoffoliartraitsshouldbemaximized.Ourpre-screeningand
thatdrivefoliarspectralproperties,especiallyintheshortwave-infrared brightnessnormalizationapproachfacilitatedthecalibrationofmulti-
(Curran,1989;Feretetal.,2008;Jacquemoudetal.,2009). plefoliarchemicaltraitsandLMAagainstVSWIRimagingspectrometer
24 G.P.Asneretal./RemoteSensingofEnvironment158(2015)15–27
Fig.7.MeanandstandarddeviationofspectralweightingvectorsforPLSRchemometricresultsfor1000iterationsreportedinTable2.(a)Chlorophylla+b(chla+b);(b)carotenoids;
(c)nitrogen;(d)phosphorus,(e)leafmassperarea(LMA);(f)water;(g)solublecarbon(sol-C),(h)phenols;(i)lignin;(j)cellulose,(k)totalcarbon;(l)calcium;(m)boron;(n)iron;
(o)potassium;(p)magnesium.(Forinterpretationofthereferencestocolorinthisfigure,thereaderisreferredtothewebversionofthisarticle.)
data(Table2).Rarelyhasasuiteoffoliartraitsbeencalibratedagainst substratesandclimaticconditionsacrossthewesternAmazon(Asner,
airborneimagingspectrometerdata,andthedemonstrablyhighperfor- Martin,etal.,2014).
manceisduetothreeinteractingfactors.First,asstated,pre-screening
andbrightnessnormalizationreducesnoisecausedbyvariablecanopy 4.2.Assessingremotesensingperformance
structureandshade.Second,ourfield-collectedfoliartraitdataalsocon-
trolledforfullsunlightcanopyposition,sothefielddataweremadera- Thecalibrationexerciserevealedhighlysignificantquantitativelink-
diometricallyandbiologicallymorecompatiblewiththefilteredVSWIR agesbetweenVSWIRspectroscopyandasuiteoffoliarfunctionaltraits
data.Third,ourfielddataspantherangeofknowncanopychemicalvar- thatmediatelightcapture,growth,structure,defense,maintenanceand
iationinterrestrialplants.TheissueofchemicalrangeinthePLSRmeth- metabolism.Modelcalibrationperformancesvarieddependingupon
odologycannotbeover-emphasized;awidechemicalinputrangeis therandomselectionofinputfieldplots,andareingeneralagreement
requiredinasimilarwaytotherequirementsoflaboratoryassaycali- withpreviousPLSRcalibrationstudiesatbothleafandcanopyscales
brations(Martens,2001).Ourcanopychemicaldatacoveranenormous (Curran,1989;Knoxetal.,2011;Kokalyetal.,2009;Martin&Aber,
rangeofvalues,reflectingevolutionaryprocessesthathaveledtothedi- 1997;Martinetal.,2008;Petersonetal.,1988;Skidmoreetal.,2010;
versification of species and communities on contrasting geologic Ustinetal.,2009).Ourrandommodelselectionapproachprovideda