Seasonality and drought effects of Amazonian forests observed from multi-angle satellite data

14 Pages·2015·3.47 MB·English

by  Hilker, Thomas

Seasonality and drought effects of Amazonian forests observed from
multi-angle satellite data
de Moura, Y. M., Hilker, T., Lyapustin, A. I., Galvão, L. S., dos Santos, J. R.,
Anderson, L. O., ... & Arai, E. (2015). Seasonality and drought effects of
Amazonian forests observed from multi-angle satellite data. Remote Sensing of
Environment, 171, 278-290. doi:10.1016/j.rse.2015.10.015
10.1016/j.rse.2015.10.015
Elsevier
Version of Record
http://cdss.library.oregonstate.edu/sa-termsofuse
RemoteSensingofEnvironment171(2015)278–290
ContentslistsavailableatScienceDirect
Remote Sensing of Environment
journal homepage: www.elsevier.com/locate/rse
Seasonality and drought effects of Amazonian forests observed from
multi-angle satellite data
YhasminMendesdeMouraa,⁎,ThomasHilkerb,AlexeiI.Lyapustinc,LênioSoaresGalvãoa,
JoãoRobertodosSantosa,LianaO.Andersond,e,CélioHelderResendedeSousab,EgidioAraia
aNationalInstituteforSpaceResearch(INPE),AvenidadosAstronautas,1758SãoJosédosCampos,SP,Brazil
bOregonStateUniversity,CollegeofForestry,Corvallis,OR97331,USA
cNASAGoddardSpaceFlightCenter,Greenbelt,MD20771,USA
dNationalCentreforMonitoringandEarlyWarningofNaturalDisasters(CEMADEN),EstradaDoutorAltinoBondensan,500,SãoJosédosCampos,SP,Brazil
eEnvironmentalChangeInstitute,UniversityofOxford,OxfordOX13QY,UK
a r t i c l e i n f o a b s t r a c t
Articlehistory: SeasonalityanddroughtinAmazonrainforestshavebeencontroversiallydiscussedintheliterature,partiallydue
Received25March2015 toalimitedabilityofcurrentremotesensingtechniquestodetectitsimpactsontropicalvegetation.Weusea
Receivedinrevisedform9October2015 multi-angleremotesensingapproachtodeterminechangesinvegetationstructurefromdifferencesindirection-
Accepted22October2015
alscattering(anisotropy)observedbytheModerateResolutionImagingSpectroradiometer(MODIS)withdata
Availableonline5November2015
atmosphericallycorrectedbytheMulti-AngleImplementationAtmosphericCorrectionAlgorithm(MAIAC).
Ourresultsshowastronglinearrelationshipbetweenanisotropyandfield(r2=0.70)andLiDAR(r2=0.88)
Keywords:
basedestimatesofLAIevenindensecanopies(LAI≤7m2m−2).Thisallowedustoobtainimprovedestimates
Amazon
Drought ofvegetationstructurefromopticalremotesensing.WeusedanisotropytoanalyzeAmazonseasonalitybased
Anisotropy onspatiallyexplicitestimatesofonsetandlengthofdryseasonobtainedfromtheTropicalRainfallMeasurement
Greening Mission(TRMM).Anincreaseinvegetationgreeningwasobservedduringthebeginningofdryseason(across
Browning ~7%ofthebasin),whichwasfollowedbyadecline(browning)laterduringthedryseason(across~5%ofthe
MAIAC basin).Anomaliesinvegetationbrowningwereparticularlystrongduringthe2005and2010droughtyears
MODIS (~10%ofthebasin).Weshowthatthemagnitudeofseasonalchangescanbesignificantlyaffectedbyregional
differencesinonsetanddurationofthedryseason.Seasonalchangesweremuchlesspronouncedwhen
assumingafixeddryseasonfromJunethroughSeptemberacrosstheAmazonBasin.Ourfindingsreconcile
remotesensingstudieswithfieldbasedobservationsandmodelresultsastheyprovideasounderbasisforthe
argumentthattropicalvegetationgrowthincreasesduringthebeginningofthedryseason,butdeclinesafter
extendeddroughtperiods.Themulti-angleapproachusedinthisworkmayhelpquantifydroughttolerance
andseasonalityintheAmazonianforests.
©2015ElsevierInc.Allrightsreserved.
1.Introduction couldalterspeciescomposition,biodiversity(Asner&Alencar,2010;
Asner,Nepstad,Cardinot,&Ray,2004,April20;Phillipsetal.,2009)
Vulnerabilityoftropicalforeststoclimatechangehasreceivedbroad andplantproductivity(Aragaoetal.,2007;Gattietal.,2014;Meir,
attentionbythescientificcommunityasincreaseinequatorialseasur- Metcalfe,Costa,&Fisher,2008).
facetemperature(SST)canleadtolongerdryseasons(Fuetal.,2013; Overthelastdecade,theAmazonregionhasexperiencedtwosevere
Marengo,Tomasella,Alves,Soares,&Rodriguez,2011)andmorefre- droughts,onein2005andanotherin2010(Marengoetal.,2011).How-
quent,severedroughtevents(Lewis,Brando,Phillips,vanderHeijden, ever,thebroadscaleresponseofvegetationtotheseeventsremains
&Nepstad,2011;Malhi,Aragão,Galbraith,etal.,2009;Malhi,Aragão, controversial.Saleska,Didan,Huete,anddaRocha(2007)reportedan
Metcalfe,etal.,2009;Marengoetal.,2008).Thefeedbacksofsuch increaseingreenness(higherEVI)forthe2005drought,aresultthat
drying on global climate change could be substantial; the Amazon wassubsequentlychallenged(Atkinson,Dash,&Jeganathan,2011;
rainforestaloneaccountsforabout15%ofglobalphotosynthesisand Samantaetal.,2010).Xuetal.(2011)observedawidespreaddecline
hostsperhapsaquarteroftheworld'sterrestrialspecies(Malhietal., ingreeningforthe2010drought.Similarly,theprevailingviewofsea-
2008).Fieldstudieshaveindicatedthatsuchextremedroughtevents sonalityofvegetationhasrecentlybeendiscussed.Severalfindings
(Brandoet al., 2010;Graham,Mulkey,Kitajima, Phillips,&Wright,
⁎ Correspondingauthor. 2003; Huete et al., 2006; Hutyra et al., 2007; Myneni et al., 2007;
E-mailaddress:[email protected](Y.M.deMoura). Samantaetal.,2012;Wagner,Rossi,Stahl,Bonal,&Hérault,2013)
http://dx.doi.org/10.1016/j.rse.2015.10.015
0034-4257/©2015ElsevierInc.Allrightsreserved.
Y.M.deMouraetal./RemoteSensingofEnvironment171(2015)278–290 279
supporttheviewthatphotosyntheticactivityincreasesinitiallyduring 2.Materialandmethods
thedryseasoninresponsetoanincreaseinincidentphotosynthetically
active radiation (PAR). However, a recent study based on NASA's 2.1.Quantificationofmulti-anglescattering
ModerateResolutionImagingSpectroradiometer(MODIS)(Morton
etal.,2014)arguedthatseasonalchangesaredrivenbyartifactsofthe MAIACdatawereobtainedfrom12MODIStiles(h10v08toh13v10,
sun-sensorgeometry. spanning10°Nto20°Sinlatitudeand80°Wto42°Winlongitude)
Agrowingbodyofliteraturesuggestsuncertaintiesinremote from Terra and Aqua satellites between 2000 and 2012. MAIAC is
sensing of atmospheric aerosol loadings (Samanta et al., 2010, basedonMODISLevel1B(calibratedandgeometricallycorrected)ob-
2012)anddeficienciesinclouddetection(Hilkeretal.,2012)tobe servations,whichremovemajorsensorcalibrationdegradationeffects
partiallyresponsibleforthesecontradictingresults.Whileprogress present in earlier collections (Lyapustin et al., 2014). MAIAC grids
hasbeenmadeaddressingsomeofthesechallengesbyusingalterna- MODISL1Bdatatoa1kmresolution,andaccumulatesmeasurements
tivedatasets(Hilkeretal.,2014)orhigherspatialresolutionimagery ofthesamesurfaceareafromdifferentorbits(viewgeometries)for
(Zelazowski,Sayer,Thomas,&Grainger,2011),observationsbased upto16daysusingamovingwindowapproach.Thesedataareused
onremotelysensedvegetationindicesarelimitedintheirabilityto toderivespectralregressioncoefficientsrelatingsurfacereflectancein
detectchangesinvegetationcoverduetoawell-documentedsatu- theblue(0.466μm)andshortwaveinfrared(2.13μm)foraerosolre-
rationeffectinareaswithhighbiomassandleafarea(Carlson& trievals,andtoobtainparametersofsurfacebi-directionalreflectance
Ripley,1997). distributionfunction(BRDF)(Lyapustinetal.,2011;Lyapustin,Wang,
Asanalternativetoobservationsfromonlyoneviewangle,the Laszlo,Hilker,etal.,2012).Assumingthatvegetationisrelativelystable
combinationofmultipleviewanglesmayprovidenewopportunities duringthisperiod,thesurfacedirectionalscatteringcanbecharacterized
to mitigate these saturation effects, and allow better insights into usingtheRoss-ThickLi-Sparse(RTLS)BRDFmodel(Roujean,Leroy,&
seasonal and inter-annual changes of tropical forests. Biophysical Deschamps,1992).Duringperiodsofrapidsurfacechange(e.g.,green-
changesin thecanopy structure affect thedirectional scattering of uporsenescence)MAIACfollowsanapproachoftheMODISBRDF/
lightandtheseeffectsareobservablefrommulti-angulardata(Chen, albedo algorithm (MOD43, Schaaf et al., 2002) to scale the BRDF
Menges,&Leblanc,2005).Withtheadvanceofmulti-angularsensors model with the latest measurement to adjust the magnitude of
such as theMulti-angleImaging SpectroRadiometer (MISR) (Diner reflectancewhileassumingthattheshapeofBRDFdoesnotchange
etal.,1998),progresshasbeenmadeindescribingthedependenceof significantly. This approach preserves spectral contrasts of actual
reflectance on observation angles (Barnsley, Settle, Cutter, Lobb, & surfacecharacteristics.
Teston,2004;Dineretal.,1998).Forinstance,theangularcomponent OneadvantageofusingtheRTLSmodelratherthanreflectancedi-
ofsurfacereflectance(anisotropy)hasbeenlinkedtoopticalproperties rectlyisthepossibilitytomaintainconstantsun-observergeometry
and geometric structure of the target (Widlowski et al., 2004; and extrapolate measurements to the principal plane to describe
Widlowski,Pinty,Lavergne,Verstraete,&Gobron,2005)suchascanopy backscatterandforwardscatterdirections.Inthisstudy,weselecteda
roughness(Strahler,2009),leafangledistribution(Roujean,2002),leaf viewzenithangle(VZA)of35°ratherthantheabsolutehotspotlocation
areaindex(LAI)(Walthall,1997)andfoliageclumping(Chenetal., atVZA=45°inordertokeepthemodeledreflectanceclosertotheac-
2005). tualrangeofanglesobservedbyMODIS,therebyminimizingpotential
The theoretical basis for the influence of canopy structure on errorsresultingfromextrapolationoftheBRDF.Forlandvegetatedsur-
multi-anglereflectancehasbeendeveloped(Bicheron,1999;Chen, faces,directionalscatteringdominatesintheNIRregionduetothehigh
Liu,Leblanc,Lacaze,&Roujean,2003;Gao,2003;Leblancetal.,2005; absorptionofvisiblelight.RatherthanobtaininganisotropyoftheNIR
Mynenietal.,2002).However,multi-anglereflectanceisnoteasily bandalone,wecalculatedforwardandbackscatterforblue,redand
obtainedfromtraditionalsurfacereflectancealgorithms,evenwhen NIR reflectance and then obtained the Enhanced Vegetation Index
data is acquired from multiple viewangles.Pixel basedalgorithms (three-bandversionEVI)forbothdirections.Theobjectiveofusing
often assume a Lambertian reflectance model, which reduces the EVIratherthansurfacereflectanceofagivenbandwastominimize
anisotropyofthederivedsurfacereflectance(Lyapustin&Muldashev, theeffectofnon-photosyntheticallyactiveelementswhileoptimizing
1999;Wangetal.,2010)thusdecreasingtheabilitytodetectdirectional thesensitivitytogreencanopystructure.Itcan,however,beshown
scattering(Hilkeretal.,2009). thatdifferencesbetweenforwardandbackwardscatterEVIislargely
Newmethodsforprocessingremotesensingdata,suchastheMulti- the result of differences in scattering in the near infrared region
AngleImplementationofAtmosphericCorrection(MAIAC),canhelp (Moura,Galvão,dosSantos,Roberts,&Breunig,2012).
overcomethislimitationbyusingaradiativetransfermodelthatdoes ThespectralerrorofMAIACsurfacereflectancewasevaluatedasthe
notmakeaLambertianassumption(Lyapustin&Knyazikhin,2001). standarddeviationbetweenobservedsurfacereflectance(BRF)and
MAIACisacloudscreeningandatmosphericcorrectionalgorithmthat BRDFmodelprediction1)overatime(usinganareaof100×100km
usesanadaptivetimeseriesanalysisandprocessingofgroupsofpixels toobtainsufficientstatisticsgivenhighcloudcoverinAmazonia)and
toderiveatmosphericaerosolconcentrationandsurfacereflectance.A 2)inspace(pixelbypixel)fortheexampleoftwo30dayperiods,in
detaileddescriptionofthealgorithmcanbefoundinLyapustinetal. JuneandSeptember.
(2011),Lyapustin,Wang,Laszlo,Hilker,etal.,(2012).Inthispaper,
wetakeadvantageofMAIACtostudychangesinanisotropyacrossthe 2.2.LiDARbasedestimatesofleafareaindex
AmazonBasinusingthirteenyearsofmulti-angleMODISobservations.
Wedefineanisotropyasdifferenceinreflectancebetweenthebackscat- Estimates of anisotropy were validated against existing and
tering(relativeazimuthangle(RAA)=180°)andtheforwardscatter- independentfieldobservationsofLAI(n=16)obtainedfromthelitera-
ing (RAA = 0°) directions for a fixed view and sun zenith angle. ture(Andreae,2002;Figueraetal.,2011;Domingues,Berry,Martinelli,
Estimatesofsuchdefinedanisotropywerethenrelatedtofieldand Ometto,&Ehleringer,2005;Doughty&Goulden,2000a,b;Galvaoetal.,
LiDAR-basedestimatesofLAIinordertovalidateitsrelationtovegeta- 2011;Malhi,Aragão,Galbraith,etal.,2009;Malhi,Aragão,Metcalfe,
tionstructure.Ourobjectivesweretodemonstratespatialandtemporal et al., 2009; Negrón Juárez, da Rocha, Figueira, Goulden, & Miller,
changesinanisotropy,particularlyduringtheonsetofthedryseasonas 2009;Restrepo-Coupe et al., 2013; Scurlock, Asner, & Gower, 2001;
ameasureofchangesinvegetation.Were-visitedthetwolastmajor Zanchietal.,2009),andAirborneLaserScanning(ALS)asanexample
droughtsintheAmazonBasin(2005and2010)toevaluateanomalies ofameasureofcanopystructure.LiDARdatawereacquiredbytheSus-
inanisotropyandinvestigatevegetationresponsetothesedrought tainableLandscapesBrazilprojectsupportedbytheBrazilianAgricultural
eventsonamonthlybasis. ResearchCorporation(EMBRAPA),theUSForestService,USAID,andthe
280 Y.M.deMouraetal./RemoteSensingofEnvironment171(2015)278–290
USDepartmentofState.AdetaileddescriptionoftheLiDARdatacanbe averageofMODIStileh12v09(south-centralAmazon)asanexample.
foundathttp://mapas.cnpm.embrapa.br/paisagenssustentaveis.Inorder Wevariedthesolarzenithanglebetween45°(whichisthedefault
toallowacomparisonbetweenLiDARbasedLAIandanisotropy,the angleforBRDFnormalizationinMAIAC)and25°degrees(whichis
areaofairborneLiDARacquisitionwasfirstsubdividedinto1×1km more commonly found in tropical latitudes). Results shows strong
tilesmatchingtheMODISpixels.Theprobabilityofcanopygapwithin seasonalvariationsforredandNIRreflectance(variationsintheblue
eachtilewasthendeterminedasthesumofthetotalnumberofhits bandarenotshown),andresultingEVI,irrespectiveofthemodeled
downtoaheightz,relativetothetotalnumberofindependentLiDAR SZA.WhilereflectanceandEVIanisotropyincreasedwithincreasing
shots(N)(Lovell,Jupp,Culvenor,&Coops,2003;Reading,Bedunah,& SZA,theseasonaldifferenceremainedsimilar.Theseasonalrobustness
Amgalanbaatar,2006): withrespecttoagivenSZAmaybeexplainedbythefactthattheregion
aroundhotspotanddarkspotareaisrelativelysmooth(Fig.1a,cande),
1−z¼Xzmax#z makingestimatesofseasonalanisotropyrelativelyinsensitivetothe
j particularsun-sensorconfigurationselected,aslongasthisconfigura-
PgapðzÞ¼ zN¼j ð1Þ tionremainsconstant.
Figs.A.2andA.3provideaquantitativeanalysisofthestandard
where#zisthenumberofhitsdowntoaheightzabovetheground(or deviation(σ)betweenobservedsurfacereflectanceandBRDFmodel
therangetowhichthegapprobabilityistaken).Finally,theleafarea prediction.Fig.A.2showsthebehaviorofthestandarddeviationover
profile L(z) was modeled as a logarithmic function of P (Lovell atime,usinganareaof100×100kmareaasanexample(65°0′0″W,
gap
etal.,2003)assuminganexponentialextinctionoflightwithinthe 5°0′0″S).Themeanstandarddeviationswere0.005and0.019forthe
canopy: redandNIRreflectance,respectively,whichisabout10–15%ofthe
(cid:2) (cid:3) seasonalchangesillustratedinFig.1aandb.Slightseasonalvariability
LðzÞ¼−log PgapðzÞ : ð2Þ inσwasfound,likelyasaresultofincreasedcloudcoverduringthe
wetseason.Fig.A.3illustratesthespatialvariabilityofthestandard
Adetaileddescriptionofthemethodappliedcanbefoundin(Coops deviationinJune(Fig.A.3a)andSeptember(Fig.A.3b).Forreasonsof
etal.,2007). brevityonlythevariabilityinEVIispresented.SimilartoFig.A.2,the
standarddeviationbetweenobservedEVIandmodelpredictionpre-
2.3.EstimatingonsetanddurationofAmazondryseasons sentedinFig.A.3wasonaverageabout5–10%oftheobservedseasonal
changes(compareFig.1).
Themostcommonperiodusedintheliteraturefordescribingdry Fig. 2 illustrates the spatio-temporal variation in anisotropy by
seasonsacrossAmazoniaisJunethroughSeptember(Saleskaetal., meansofitsfirstprincipalcomponent(PC1)fortheperiodbetween
2007; Samanta et al.,2010; Xu et al., 2011). It is, however, widely 2000 and 2012. The droughts years 2005 and 2010 were excluded
acknowledgedthattheactualonsetanddurationofthedryseason fromthisanalysistoonlyrepresentnormalyearvariations.PC1ex-
variesgreatlyacrosstheAmazonBasin(Silvaetal.,2013).Inordertoin- plainedabout89%ofthetotalvarianceinanisotropy;consequently,
vestigatetheeffectsofregionalvariabilityinprecipitation,onsetand wefocusonthisfirstcomponenttoillustrateregionalvariabilityin
length of dry season were calculated for each year using monthly thedataset.Redandyellowrepresentareaswithrelativelyhigheran-
estimatesofwaterdeficitfromprecipitationobtainedfromTropical isotropy,whilegreentowhiteshowareaswithrelativelyloweranisot-
RainfallMeasuringMission(TRMM)(3B43v7and7A,at0.25°spatial ropy.NotabledifferenceswerefoundnotonlybetweentheAmazonian
resolution).TRMMdatahasbeenextensivelyusedtocharacterizethe rainforestandnon-forestedsavannahregions,butalsowithinthefor-
seasonal and inter-annual variability in rainfall across the Amazon estedareaitself(comparetraditionalvegetationindicesforinstancein
region (Aragao et al., 2007). Dry season months were determined Hilkeretal.,2014,Fig.S2).Highanisotropywasfoundpredominantly
byusingtheassumptionthatmoisttropicalforeststranspireabout inthemoredenselyforestedareasinnorthernandeasternAmazonia
100mm·month−1(Anderson,2012;Aragaoetal.,2007):Whenrainfall whereastheopenforesttypesinthesouthernregionsyielded,onaver-
dropsbelow100mm·month−1,evapotranspirationexceedsprecipita- age,lowervaluesofanisotropy.ThepointsymbolsinFig.2illustratethe
tion,andsoilwateravailabilitydeclines(Borchert,1998;A.Strahler& plotlocationsforfieldbasedobservationsofLAIandLiDARderived
D.Jupp,1990;Williamsetal.,1998). measurements.WhilethetotalnumberofindependentLAIestimates
islimitedacrossremoteforestedareassuchastheAmazonBasin,the
3.Results fieldlocationsindicatedinFig.2representedareasonablerangeof
foresttypeswithintheAmazonrainforest.
TheleftcolumninFig.1showsanexampleofaBRDFsurfacefittedto Measuresofanisotropywerestronglyrelatedtoindependentfield
retrieveforwardandbackscatterforredreflectance(a)andNIRreflec- observationsofLAI(n=16)obtainedfromtheliterature(Fig.3a,r2=
tance(c).WealsoillustratedaBRDFsurfacecalculatedforEVI(e)to 0.70pb0.05)andLAIestimatesderivedfromairborneLiDAR(Fig.3b,
demonstratetheanisotropyofthisindex.TheRTLSsurfacesareshown r2=0.88pb0.05).Importantly,bothrelationshipswerefoundtobe
fora1×1kmareaofAmazonforest(65°0′0″W,5°0′0″S)usingall linear,atleastwithintheobservedrangeofLAI≤7m2m−2andyielded
observations acquired between January 1 and 14, 2006. The polar animproveddescriptionofstructureindenselyvegetatedareascom-
coordinatesrepresenttheviewzenithandazimuthangles,thez-axis paredtoestimatesobtainablefromnadirEVIimagesalone(Fig.3c).
showsthecorrespondingreflectance(ρ)intheredandNIRbands, TherelationshipbetweenbothfieldmeasuredandLiDARLAIestimates
andEVI,respectively.TheblackdotsrepresenttheMODISobservations withanisotropyfollowedalmosttheidenticallinearfunctionalform,
thatwereusedtoparameterizetheBRDFsurface.Theredandbluedots whichallowedustodescribeleafareaacrossarangeofvegetation
showthemodeledforwardandbackscatterreflectance(Fig.1,leftcol- typeswithintheAmazonianrainforestfrombothdatasources.
umn)withafixedsunobservergeometry(SZA=45°,VZA=35°, Inadditiontoalargeheterogeneityinvegetationstructure(Fig.2),
RAA=180°inthebackscatterdirectionandSZA=45°,VZA=35°, ouranalysisconfirmedalsoalargevariabilityinprecipitationacross
RAA=0°intheforwardscatterdirection).Wefittedonesuchsurface thebasin(Villaretal.,2009).Fig.4representsestimatesofmonthly
foreachpixeland14-dayperiodtoderivebi-weeklyanisotropyacross waterdeficit.Areaswithlowwaterdeficitareshowninblue,whereas
theAmazonBasin. red indicates high water deficits. Areas with no water deficit are
Fig.1b,dandfshowtemporalvariationsinanisotropy(forred,NIR presentedwithoutcolor.Highlevelsofwaterdeficitwerefoundinthe
andEVI,respectively)fordifferentsunobservergeometriestoverify northernAmazonregionmainlybetweenJanuaryandMarch,corre-
therobustnessofthemethodapplied.Thetimeseriesshowsaspatial spondingtothedryseasoninthenorthernhemisphere,whereasMay
Y.M.deMouraetal./RemoteSensingofEnvironment171(2015)278–290 281
Fig.1.ModeledBRDFsurfacefora1×1kmareaofAmazonforest(65°0′0″W,5°0′0″S)forredreflectance(a),NIRreflectance(c)andEVI(e).TheblackdotsrepresenttheactualMODIS
observationsaccumulatedovera14dayperiod,thebluedotrepresentsthemodeledforwardscatterdirection(darkspot),thereddotrepresentsthemodeledbackscatterdirection
(hotspot).Fig.1b,dandfshowatimeseriesofanisotropy(red,NIRandEVI,respectively)usingthemeantimeseriesofMODIStileh12v09.Sun.zenithangles(SZA)variedbetween
45°and25°degreestoinvestigatethesensitivitywithrespecttothesunsensorconfiguration.
toAugustmarkedthedryseasonmonthsacrosslargepartsofthesouth- changes are labelled “greening”, negative changes are labeled
ernhemisphere.Overall,thelargestwaterdeficitwasfoundduringJune “browning”;allchangesarenormalizedwithrespecttotheirstandard
andJuly(focusingonthesouth-easternborderoftheAmazon),whereas deviations).Absolutechanges(non-normalized)inanisotropyatbegin-
thelowestlevelsofwaterdeficitwereobservedduringMarch,with ningandendofdryseasonarepresentedinA.1.Onlythosechangesthat
precipitationexceeding100mmmonth−1almostacrosstheentire exceededtheRMSEofthefieldvalidation(Fig.3aandb)arepresented.
basin.Thebeginningandlengthofdryseason(Fig.5aandb,respective- Non-forestedareaswereexcludedfromallanalysisusingtheMODIS
ly)variedaccordinglyandfollowedasouth-westnorth-eastgradient land cover product (collection 5, Friedl et al., 2010). Fig. 6a uses a
withupto5monthsofwaterdeficitinthesouth-west.Bycontrast, fixeddryseasonassumptionfromJunethroughSeptembertoderive
largeareasofAmazonasstate,centralAmazon,showed,onaverage, conventionalmeasuresofgreening/browning.Fig.6bshowschanges
no water deficit during the observed years (gray area in the map, ofgreening/browningbasedononsetandlengthofdryseasonderived
comparealsoSteege&Pitman,2003). fromwaterdeficit.Inbothcases,thedroughtyearsof2005and2010
Anisotropy changes in response to these seasonal variations in wereexcludedfromtheanalysestoreflect“normalyear”situations.In
precipitationareshowninFig.6.Fig.6aandbshowtotaldifferences caseofthefixeddryseasonassumption(Fig.6a),negativenetchanges
inanisotropybetweenbeginningandendofthedryseason(positive inanisotropywerefoundlargelyinthewestandnorth-westernregion
282 Y.M.deMouraetal./RemoteSensingofEnvironment171(2015)278–290
Fig.2.Thefirstprincipalcomponentofanisotropybetween2000and2012.Thedroughtsyears2005and2010wereexcluded.ThelocationsofthefieldandLiDARestimatesofLAIare
shown.LiDARestimateswereobtainedfromSustainableLandscapeProjectinthreelocations:AdolphoDuckeForestReserve,Amazonasstate,Brazil(⊗);RioBrancomunicipality,Acre
State,Brazil( )andTapajósNationalForest,ParáState,Brazil(⦾).TheotherfieldestimatesofLAIwerecollectedfromtheliterature:Malhi,Aragão,Galbraith,etal.(2009),Malhi,Aragão,
Metcalfe,etal.(2009)(•),Dominguesetal.(2005)(○),DoughtyandGoulden(2008c)(*),NegrónJuárezetal.(2009)(x),Andreaeetal.(2002)(□),Zanchietal.(2009)(◊),Restrepo-
Coupeetal.(2013)(Δ),Figueraetal.(2011)(b),Scurlocketal.(2001)(˃),Galvaoetal.(2011)(+).
oftheAmazonBasin,whilesmallgreeningeffectswereobservedinthe moremonthswhenaccountingforactualdryseasononset.Monthly
Amapástateregionandsouth-centralAmazonia.Whenconsideringthe changes in LAI (as deviations from annual means) were calculated
specificlengthofdryseason(Fig.6b),itbecomesapparentthatmostof usingthelinearrelationshipstofieldandLiDARmeasurementspresent-
theareashowingnetbrowningeffectsdidactuallynotexperiencea edinFig.3.ThedashedlineinFig.6canddrepresentestimatesbasedon
seasonalwaterdeficit(Fig.6a),atleastonaveragewithinthetime therelationshipwithfieldobservations(Fig.3a),whilethesolidline
periodobserved.NetgreeningeffectsshowninFig.6bweresimilarto representsestimatesbasedontherelationshipwithLiDARobservations
thosepresentedinFig.6a.However,regionally,considerabledifferences (Fig.3b).Consistentwiththenetchangesinareaofgreeningandbrow-
werefound,particularlyinthesouthwesternpartofthestudyarea. ning,ourresultssuggestedthattotalleafareaincreasesduringthe
Comparedtothenormalizedresults,non-normalizeddifferencesinan- beginningofthedryseasonbyonaverage0.2m2m−2acrossthebasin,
isotropybetweenthebeginningandendofdryseasonwereprominent whileLAIdroppedbelowtheannualmeanafterabout2monthsofdry
acrossmostoftheAmazonBasin(A.1),whichcanbeexplainedby season (0.1 m2 m−2). However, these results should be interpreted
therelativelysmallRMSEobtainedfromthevalidationdataset(Fig.3a carefullyaschangesinLAIvariedgreatlyacrossspaceandmayalsobe
andb). theresultofchangesinotherstructuralparameters(seediscussion).
Fig.6canddshownetgreeningandbrowningeffectsinpercentage Thetwodroughtyearsresultedinstrongbrowningeffects;spatial
oftotalareapermonthofdryseason.Greeningandbrowningeffects andtemporalpatternsofanomaliesinanisotropyforthe2005and
weredefinedaspercentageofpixelswithsignificantincrease/decrease 2010droughtsarepresentedinFig.7.Forbothyears,onlythespecific
inanisotropy(≥2σ)comparedtotheannualmean.Fig.6cusesafixed beginningandendofthedryseason(basedonthewaterdeficit)are
dryseasonassumption(JunethroughSeptember),whileFig.6dshows shown.Fig.7showsanomaliesi.e.deviationsfromthenormalyearpat-
changesbasedononsetofdryseasonderivedfromwaterdeficit.In ternspresentedinFig.6;allanomalieswerenormalizedtothestandard
caseofFig.6d,wealsoshowthelastmonthbeforeawaterdeficitwas deviation(≥2σ)oftheyears2000–2012,excluding2005and2010.
observed,inordertoillustratechangesinphotosyntheticactivitywith Whilepositiveandnegativeanomalieswereapproximatelybalanced
thereduction of rainfall towardstheendof therainy season.Both atthebeginningofthedryseason(Fig.7candd),negativeanomalies
estimatesshowedincreasedanisotropyduringthebeginningofthe inanisotropyoutweighedpositiveeffectsafteraboutthreemonths,
dryseasonwithabout2%ofareaexperiencing“greening”whenusing especially during 2010, with negative anomalies being three times
a fixed dry season assumption and over 5% of total area greening largerthanpositiveones.EstimatesofderivedLAIshowedsmallposi-
whenexplicitlyconsideringdryseasononsetforeachpixel.Inboth tiveanomaliesduringthebeginningofthe2005droughtperiod,but
analyses,greeningeffectsturnedintonetbrowningeffectsafteran confirmed large negative effects with extended dry season length.
extendedlengthofdryseason,reachingabout7%oftheareaafter3or In 2010, change was similar to normal years (anomalies were
Y.M.deMouraetal./RemoteSensingofEnvironment171(2015)278–290 283
Fig.3.RelationshipbetweenanisotropyandLAI;a)fromfieldvaluescollectedintheliterature(seeFig.2),andb)fromLiDARestimates.c)Relationshipbetweendirectionallynormalized
(nadir)EVIandLAI.ThecorrelationswereperformedusingthedatesdescribedinthefielddatawiththeclosestMODISacquisitionsavailable.ThelocationoftheplotsareprovidedinFig.2.
RMSEforFig.3aandbwere0.08and0.02(unitsofanisotropy),respectively.
small)duringthefirstfewmonthsofthedryseason.However,areas sourcesfromundetectedcloudstogriddinguncertainties.Itshouldfur-
with6monthsofdryseasonshowednegativedeviationfromnormal theraccountforlimitationsoftheRTLSmodeltodescribetheBRDF
yeardeclineinstructure(Fig.6d)ofanadditional−0.2m2m−2across shapeandanisotropyoftheMAIACdata.
thebasin(Fig.7d). Ourresultssuggestthatstructuralinformationofvegetationmaybe
obtainedfrequentlyoverlargeareasfromMODIS.Furtherresearch,
4.Discussion however,willbeneededtoinvestigatepotentialsforotherecosystems
andregions.TheBRDFmodelselectedinthisstudyallowedustoderive
Thisstudyusedmulti-angleobservationsfromtheMODISinstru- seasonalanisotropyindependentofthesunobservergeometry,which
menttoinvestigatespatialandtemporalvariabilityinvegetationstruc- isanimportantconsiderationforseparatingvegetationseasonality
tureacrosstheAmazonBasin.Whiletherangeofviewanglesacquired fromartifactsduetoseasonalchangesinthesun/sensorconstellation
fromMODISisrelativelysmall(Fig.1),astheinstrumentwasnotspecif- (Mortonetal.,2014).
icallydesignedformulti-angleacquisitions,anisotropystillprovidedan While it is acknowledged that vegetation may change over a
effectivemeanstocharacterizevegetationstructureacrosstheAmazon 14dayperiod,asusedinourBRDFapproach,thistechniqueshould
forest.Changesinthesun-sensorconfigurationovertheyeardonotal- stillallowustoobservemostseasonalityofvegetationandhasprov-
waysallowtomodelforwardandbackscatteringobservationswithin en useful in other composite products (Huete et al., 2002; Schaaf
thesamplingrangeoftheMODISinstruments.However,theanalysis et al., 2002). Data scarcity may prevent frequent updates of BRDF
presentedinFig.1hasdemonstratedarelativerobustnesswithrespect shapes,whichinsomecaseslimittheabilitytodetermineanisotropy
totheselectedsun-sensorconfiguration.Thestandarddeviationbe- and may lead to misinterpretation of changes in canopy structure.
tweenobservedandmodeledMAIACreflectance(Figs.A.2andA.3) However, analysis of observation frequency across the Amazon
wasabout10%oftheobservedvariationinanisotropy(Figs.6and7), Basin(Hilkeretal.,2015)hasshownthatMAIACprovidesonaverage
confirmingtheabilityofourapproachtodetectseasonalandinter- between10and60observationsinanygivenmonthfromTerraand
annual changes. The results were further within the range of the Aqua, respectively, which should allow stable BRDF inversions for
RMSEreportedinFig.3,therebyconfirmingthesignificanceoftherela- most pixels. Other methods to derive multidirectional reflectance
tionshiptocanopystructure.Theapproachshouldaccountforerror (Franch,Vermote,Sobrino,&Fédèle,2013;Schaafetal.,2002)have
284 Y.M.deMouraetal./RemoteSensingofEnvironment171(2015)278–290
Fig.4.Monthlyestimatesofwaterdeficit(inmmmonth−1),basedonTRMMobservationsfrom1998to2012.Areaswithlowwaterdeficitareshowninblue,whereastheredcolorin-
dicateshighwaterdeficits;areaswithnowaterdeficitarepresentedwithoutcolor.
been published. Their usefulness to derive MODIS anisotropy will D.L.B.Jupp,1990).Ourstronglinearrelationshipfoundbetweenanisot-
havetobeaddressedseparately. ropyandLAIestimates(Fig.3aandb,r2=0.70,r2=0.88pb0.05,
Theabilityofmulti-angleobservationtoderivevegetationstructural respectively)confirmsthesefindings.
attributesiswellsupportedbypreviousstudiesoftemperateecosys- TheRMSEfortherelationshipsbetweenanisotropyandfieldobser-
tems(Chen etal.,2003;Gao,2003).Multi-angledata decreasethe vationshasallowedustolinkseasonalchangesinanisotropywith
dispersionandsaturationingeometricallycomplexcanopies(Zhang, changesinvegetationstructure.However,wedoacknowledgethat
Tian,Myneni,Knyazikhin,&Woodcock,2002)andarethereforebetter otherstructuralvariablescaninfluenceseasonalpatternsofanisotropy
suitedtodescribethethreedimensionalstructureofforestscompared indifferentways.Forinstance,anisotropyisalsoaffectedbycanopy
to mono-angle acquisitions (Chen & Leblanc,1997;A.H.Strahler& roughness(Strahler,2009),leafangledistribution(Roujean,2002)
Fig.5.Beginning(a)andlength(b)ofdryseasonacrosstheAmazoncalculatedonperpixelbasisusingmonthlywaterdeficits.Thisapproachwasperformedforeachyearseparatelyin
ordertoconsiderinter-annualvariability.Thefigureshowsmeanonsetandlengthofdryseasonforallyears.
Y.M.deMouraetal./RemoteSensingofEnvironment171(2015)278–290 285
Fig.6.(a)SpatialdistributionofchangesinanisotropynormalizedbythestandarddeviationusingadryseasonperiodfromJunetoSeptember(forallyearsbetween2000and2012,ex-
cept2005and2010).Thegrayregionsrepresentsnodryseasonornon-forestedareas.(b)Spatialdistributionofchangesinanisotropynormalizedbythestandarddeviationusingspecifc
beginandendofdryseasonbasedonthewaterdeficitmaps.Figurescanddshowthecorrespondingchangesingreening(bluebars)andbrowning(redbars)bymonthsofdryseason
(p=0.05).ThedashedlinesinFigurescanddrepresentthenetchangesinLAI(averagedacrossthebasin)modeledbythelinearrelationshipbetweenanisotropyandLAI(Fig.2a).The
solidlineshowsthecorrespondingestimatesbasedonthemodelderivedfromLiDAR(Fig.2b).
andfoliageclumping(Chenetal.,2005).Furthermore,theselected of Amazon forests as it increases the sensitivity of optical observa-
approachofmodelingLAIfromLiDARdepends,tosomeextent,onfoot- tionstochangesacrossdensevegetationtypes.Thisshouldconsider-
printsizeandpointdensity.Whilethetechniqueutilizedherehasbeen ably improve our understanding of Amazon forest seasonality and
validatedelsewhere(Coopsetal.,2007,Lovelletal.,2003),differences droughttolerance.
inecosystemtypesandLiDARconfigurationmayaffectLAIestimates. ThefindingspresentedinFigs.4and5suggestthatrainfallpatterns
Asaresult,thefindingspresentedwithrespecttochangesinleafarea inAmazonianforestsvariedgreatly,causingdifferencesinseasonality
shouldbeinterpretedwithcareandshouldbeunderstoodmoreasan acrosstheregion.Estimatesofwaterdeficit(Fig.5aandb),followeda
exampleofhowanisotropymaybelinkedtostructuralvariables.Further south-westnorth-eastgradientwithupto5monthsofwaterdeficit
analysiswillberequiredwithrespecttochangesinanisotropyasaresult inthesouth-westwhereaslargeareasofAmazonasstateshowed,on
ofchangesincanopyroughnessorotherparameters.Nonetheless,the average,nowaterdeficitduringtheobservedyears(compareSteege
linearfunctionalformsuggeststhatmulti-angleobservationsmaypro- &Pitman,2003).Previousresearchhassuggestedthatvegetationsea-
videanopportunitytoaddresscurrentlimitationscausedbysaturation sonalitymayfollowthisgradientclosely,ashigherprecipitationlevels
ofconventional(nadir)vegetationindices(Hueteetal.,2006;Knyazikhin supporthigherleafareasinthewetterregionswhilevegetationin
etal.,1998)atleastwithintherangeofobservedLAI(≤7m2m−2). drier areas is limited by available soil water(Myneni et al., 2007).
While mono-angle observations have been shown to indicate Wateravailabilitymayfurthercontributetochangesinthespatialdis-
levelsof vegetation greenness, theyare less well suitedtodescribe tributionofleaves(Guanetal.,2015;Malhado&Costa,2009;Schurr
thethreedimensionalstructureofforestcanopies(Chen& Leblanc, etal.,2012;terSteegeetal.,2006;Wagneretal.,2013).Also,thelength
1997; A.H. Strahler & D.L.B. Jupp, 1990). The selected approach ofdryseasonhasbeenshowntocorrelatewithabovegroundbiomass
usinganisotropymayprovidenewinsightsintostructuralvariability (Saatchi,Houghton,DosSantosAlvala,Soares,&Yu,2007)andtree
286 Y.M.deMouraetal./RemoteSensingofEnvironment171(2015)278–290
Fig.7.Spatialdistributionofthestandardizedanomaliesinanisotropyfor2005(a)and2010(b),consideringspecificallybeginandendofdryseason(basedonthewaterdeficitmaps).
Thegrayregionsrepresentsnodryseasonornon-forestedareas.Figurescanddshowthecorrespondinganomaliesingreening(bluebars)andbrowning(redbars)bymonthsintodry
season(p=0.05).CirclesrepresentsanapproximationoftheepicentersofthedroughtsdescribedbyLewisetal.(2011).ThedashedlinesinFigurescanddrepresenttheanomaliesinLAI
(averagedacrossthebasin)modeledbythelinearrelationshipbetweenanisotropyandLAI(Fig.2a).Thesolidlineshowsthecorrespondingestimatesbasedonthemodelderivedfrom
LiDAR(Fig.2b).
speciescomposition(terSteegeetal.,2006;Wagneretal.,2014).Esti- observationstodeterminewhetherachangeissignificantornot,but
matingvegetationseasonalityfromwaterdeficitprovidedasimple didnotnormalizebythestandarddeviation.OurresultsinFig.A.1
buteffectiveapproachtocapturethisregionalvariabilityinprecipita- showedmuchincreaseseasonalitywhenusingtheRMSEoffieldand
tion.Otherapproaches,forexample,basedonavailablephotosyntheti- LiDAR data (Fig. 3a and b) to determinesignificance. Also, Myneni
callyactiveradiation(PAR)arepossibleandmayresultindifferent etal.(2007)calculatedseasonalityasthedifferencebetweenthemax-
definitionofdryandwetseason. imum4-monthaverageLAIinthedryseasonminustheminimum
Whileourfindingsareingoodagreementwithpreviousreports 4-month average LAI in the wet season for those regions with dry
(Steege&Pitman,2003),itshouldbenotedthatinthenorthernpart seasonslongerthan3months.Forallotherregions,theycalculated
oftheAmazon,cloudcoverisconsiderablyhigherthaninthesouth, seasonalityasthedifferencebetweenthedry-seasonaverageLAIand
whichmayincreasemeasurementnoiseintheTRMMdataandcontrib- theminimum4-monthaverageLAIinthewetseason.
utetolargerspatialvariationinonsetandlengthofdryseasonobserved. Theseasonalityinanisotropy(Figs.1,6)cannotbeexplainedbydi-
ThefindingsprovidedinFigs.2,A.1and6suggestlargespatialand rectionaleffects,asallobservationshavebeennormalizedtoafixedfor-
seasonalvariabilityofAmazonianforests.Thisresultrelateswellto wardandbackscattergeometry(Lyapustin,Wang,Laszlo,&Hilker,
previousstudiesonvegetationstructureandseasonalityoftheAmazon 2012).OppositefindingsbasedonconventionalMODISdata(Morton
(terSteegeetal.,2006;Villaretal.,2009)aswellasestimatesofabove- etal.,2014)willrequirefurtheranalysistobeaddressedseparately.
groundcarbon(Saatchietal.,2011).Considerationofthisvariability Onepossibleexplanationmightbenoiseinthedataset(Hilkeretal.,
willbecriticalforinterpretingthebiophysicalresponsesofvegetation 2012)renderingresidualchangesbelowastatisticalsignificancelevel.
tochangesinclimate. Changes in anisotropy (greening/browning) during the dry season
TheresultspresentedinFigs.6and7confirmseasonalswingsinthe (Figs.A.1,6and7)coincidedwellwithpreviousreportsonAmazonsea-
Amazon(Mynenietal.,2007).Whilethetotalareawithsignificant sonality.Theresultssupporttheviewthatphotosyntheticactivityini-
change(Fig.6)isrelativelysmallcomparedtoMynenietal.(2007), tiallyincreasesduringthedryseasoninresponsetoanincreasein
thesevariationscanbeexplainedbythedifferenceinmethodsapplied. incidentPAR(Brandoetal.,2010;Grahametal.,2003;Hueteetal.,
First,Mynenietal.(2007)usedtheRMSEoftherelationshiptofield 2006;Hutyraetal.,2007;Malhi,Aragão,Galbraith,etal.,2009,Malhi,