package Phenopix: for A image-based R vegetation phenology Agricultural and Forest Meteorology

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ContentslistsavailableatScienceDirect

Agricultural and Forest Meteorology

jo u rn al h om ep a g e :w w w . e l s e v i e r . c o m / l o c a t e / a g r f o r m e t

Phenopix: A R package for image-based vegetation phenology

Gianluca Filippa

^a,∗

, Edoardo Cremonese

^a

, Mirco Migliavacca

^b

, Marta Galvagno

^a

, Matthias Forkel

^b,1

, Lisa Wingate

^c

, Enrico Tomelleri

^d

, Umberto Morra di Cella

^a

, Andrew D. Richardson

^e

aEnvironmentalProtectionAgencyofAostaValley,ARPAValledAosta,ClimateChangeUnit,Italy

bMaxPlanckInstituteforBiogeochemistry,DepartmentBiogeochemicalIntegration,Jena,Germany

cINRA,UMRISPA,Bordeaux,France

dInstituteforAppliedRemoteSensing,EURAC,Bolzano,Italy

eHarvardUniversity,DepartmentofOrganismicandEvolutionaryBiology,Cambridge,MA,USA

a r t i c l e i n f o

Articlehistory:

Received15October2015 Receivedinrevisedform 29December2015 Accepted7January2016

Keywords:

Imageanalysis Communityecology Pixel-basedanalysis Phenology

a b s t r a c t

InthispaperweextensivelydescribenewsoftwareavailableasaRpackagethatallowsfortheextraction ofphenologicalinformationfromtime-lapsedigitalphotographyofvegetationcover.ThephenopixR packageincludesallstepsindataprocessing.Itenablestheuserto:drawaregionofinterest(ROI)onan image;extractredgreenandbluedigitalnumbers(DN)fromaseasonalseriesofimages;depictgreen- nessindextrajectories;ﬁtacurvetotheseasonaltrajectories;extractrelevantphenologicalthresholds (phenophases);extractphenophaseuncertainties.

Thesoftwarecapabilitiesareillustratedbyanalyzingoneyearofdatafromaselectionofsevensites belongingtothePhenoCamnetwork(http://phenocam.sr.unh.edu/),includinganunmanagedsubalpine grassland,atropicalgrassland,adeciduousneedle-leafforest,threedeciduousbroad-leaftemperate forestsandanevergreenneedle-leafforest.Oneofthenoveltiesintroducedbythepackageisthespatially explicit,pixel-basedanalysis,whichpotentiallyallowstoextractwithin-ecosystemorwithin-individual variabilityofphenology.Weexaminetherelationshipbetweenphenophasesextractedbythetraditional ROI-averagedandthenovelpixel-basedapproaches,andfurtherillustratepotentialapplicationsofpixel- basedimageanalysisavailableinthephenopixRpackage.

1. Introduction

Traditional monitoring of plant phenology relies on direct human observations of discrete phenological events, or phenophases, such as bud-burst, ﬂowering, autumn decolour- ing,and leaf-fall(e.g.Lechowicz,1984;Richardsonet al.,2006;

Galvagnoetal.,2013;Migliavaccaetal.,2008;Filippaetal.,2015).

Such observations are typically made on a limited number of individualorganisms,acrossalimitedgeographicarea(i.e.,often ata speciﬁc researchsite). On theotherhand, satelliteremote sensingallowsobserving landsurfacephenology onregionalto globalscalesbuthasalimitedrepresentativenessforphenological changesatecosystemorspecies-level(WhiteandNemani,2006;

Delbartetal.,2005;Busettoetal.,2010;Hufkensetal.,2012;Forkel

∗ Correspondingauthor.

E-mailaddress:g.ﬁlippa@arpa.vda.it(G.Filippa).

1 Nowat:TechnischeUniversitäenDepartmentofGeodesyandGeoinformation ResearchGroupRemoteSensing,Vienna,Austria.

etal.,2015).Atanintermediatescale,near-surfaceremotesensing of phenologymakes useof radiometricinstruments orimaging sensors.Near-surfaceremotesensingquantiﬁes,athightemporal resolution,and withaﬂexible degreeofspatialintegration(i.e., the potentialtolook acrossthe canopy asa whole, but at the sametimefocusonindividualorganisms),seasonalchangesinthe opticalpropertiesofvegetatedsurfaces(e.g.Jenkinsetal.,2007;

Richardsonetal.,2007;Soudanietal.,2012).Recentstudieshave demonstratedthesoundnessofdigitalcamerasasmulti-channel imagingsensors(Richardsonetal.,2009;Klostermanetal.,2014;

Wingateetal.,2015;Migliavaccaetal.,2011).

Intherecentliterature,differentapproacheshavebeenusedto processdigitalimagesofthevegetationcanopy.Severalresearchers haveexhaustivelyaddressedtheissuesofdataqualityanddata ﬁltering in order to reduce noise in the seasonal trajectories of greenness (e.g. Sonnentag et al., 2012; Julitta et al., 2014;

Migliavacca et al., 2011). Other authors focused on curve ﬁt- ting/smoothing methodsfor extracting datesfromphenological time-series(i.e.phenophases,seee.g.Zhangetal.,2003;Becketal., 2006;Guetal.,2009;Elmoreetal.,2012;Klostermanetal.,2014 http://dx.doi.org/10.1016/j.agrformet.2016.01.006

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Table1

MaincharacteristicsoftheselectedPhenoCamsites.

SiteID Coords(^◦) Elev(m) PFT Cameratype Dominantspecies

Torgnon-ND 45.8N7.6E 2160 GRA NikonD5000 Nardusstricta

Kamuela 20.0N−155.7E 850 GRA Stardot C3/C4grasses

Bartlett 44.1N−71.3E 268 DBF Axis211 Acerrubrum;Fagusgrandifolia

Harvard 42.5N−72.1E 340 DBF Stardot Quercusrubra;Acerrubrum

Mammothcave 37.2N−86.1E 226 DBF Stardot Quercussp.;Caryasp.

Torgnon-LD 45.8N7.6E 2091 DNF NikonD5000 Larixdecidua

Harvardhemlock 42.5N−72.2E 345 ENF Stardot Tsugacanadensis

PFT:plantfunctionaltype,GRA:grassland,DBF:deciduousbroadleafforest,DNF:deciduousneedle-leafforest,ENF:evergreenneedle-leafforest.

andthegreenbrownRpackage).Thefullexploitationoftheexisting plethoraofmethodsandapproacheswouldbeneﬁtfromacompre- hensiveframeworkinwhichtocomparethemacrossecosystem typesandclimateconditions.

Phenologicalnetworksbasedontheanalysisofdigitalimages aregrowingworldwide. IntheUS,PhenoCam networkincludes

over200sitesthatuseastandardcameraandfollowastandardpro- tocol(phenocam.sr.unh.edu/webcam/).InEurope,theEUROPHEN networkconsistsofabout60ﬂuxsitesequippedwithdigitalcam- eras(Wingateetal.,2015).Similarnetworksarealsopresentin Australia(www.aceas.org.au,Brownetal.inpreparation)andAsia (NasaharaandNagai,2015).Theincreasinguseofrepeateddigital

Fig.1. AsampleimagefromallPhenoCamsitesincludedinthisstudymaskedonthechosenregionofinterest(ROI),alongwiththeseasonaltrajectoriesofﬁlteredGCCin year2013.EcosystemtypeabbreviationsareasinTable1.

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photographyforphenologicalresearchhighlightstheneedofeasy- to-use,opensourceandﬂexiblesoftwaretoolsfortheprocessing oftheimages.Additionally,theexistingnetworksarepoorlycoor- dinatedintermsofcameratypes,settingsandsamplingprotocols, thuspresentingabigchallengetobuildaﬂexibletoolcapableof facingthelargediversityofecosystems,imagequalityandsetups.

In this paper we present a collection of functions packed in a software available as a R package (R Core Team, 2015), calledphenopix(http://r-forge.r-project.org/projects/phenopix/).

We will ﬁrst showthe main features of thepackage and then illustrateitsapplicationonaselectionofsitesbelongingtothePhe- noCamdataset.Lastly,asectionfocusedonpixel-basedanalysis will:(1)examinetherelationshipbetweenphenologicalthresh- olds(phenophases)extractedfromtheaverageseasonaltrajectory ofgreennessoveraregionofinterest(ROI-averagedapproach)and fromeachpixelofsuchregion(pixel-basedapproach);(2)illustrate potentialapplicationsofpixel-basedimageanalysistodiscriminate betweensubtlydifferentphenologicalseasonaltrajectorieswithin thesameimagescene.

2. PhenoCamsites

The study sites used to illustrate the functionality of the phenopix package belong to the PhenoCam network (phenocam.sr.unh.edu/webcam/)andareillustratedinTable1andFig.1.

3. Mainfunctions

Thetypicalwork-flowofthephenopixpackageissummarised in theflowchartshown in Fig.2. First, one(ormore) region(s) ofinterest(ROI)is(are)chosen,thendigitalcolournumbersare extractedfromtheROIofeachimage,andprocessedtoobtaina seasonaltimeseries.Afterfilteringthetime series,dataarefit- tedwitheitheradoublelogisticequationorasmoothingcurve,

onwhich phenological thresholds (phenophases) areextracted.

Finally,uncertaintyoftheﬁtandofphenophasescanbecomputed.

3.1. Regionsofinterest(ROIs)

Thesceneofthepicturerarelyincludesonlythetargetedvege- tationcanopy,thustheuserwillwanttochooseaparticularregion withinthe scenefor analysis.Even more often,more thanone regionmaybeofinterest,forexampleinamixedforestonemight independentlyanalysedifferentdeciduousspeciesandevergreen trees(e.g.Ahrendsetal.,2008).ThefunctionDrawROI()allowsthe usertodrawoneormoreregionsofintereston-screen,usingthe mousecursoronachosenreferencepicture.

3.2. Extractvegetationindices

Fromthedigitalcolourvaluesofeachimagethegreenchromatic coordinate(G_CC)iscomputed.G_CCisavegetationindexderived fromphotographicimageandquantiﬁesthegreennessrelativeto thetotalbrightness.G_CCiscomputedasfollows:

GCC= GDN

RDN+GDN+BDN (1)

whereGDN,RDN,BDNarethegreen,redandbluedigitalnumbers, respectively (Gillespieet al.,1987).Similarly, chromaticcoordi- natesof redandblue (R_CC andB_CC)are alsocomputed.Several indicesbasedonRGBcolourshavebeendevelopedinthelastyears, includingforexamplethegreenexcessindex(GEI)(Woebbecke et al.,1995; Mizunumaet al.,2013).Someauthorsalso useda combinationofG_CCandR_CCtoextractautumnphenophases(e.g.

Klostermanetal.,2014).Forsimplicity,allsubsequentanalyseswill befocusedonG_CCbutphenopixallowstheanalysisofthewhole varietyofcolourindices,includingthecomputationofnewones.

Fig.2.Thework-flowoftheprocessingchaininthephenopixRpackage.Redandblueobjectsdenotespecificfeaturesofpixel-basedandROI-averagedapproaches,respectively.Pixel-basedapproachproducesaphenophasemap(left),whereasROI-averagedapproachresultsinaGCCseasonaltrajectoryandacertainnumberofphenophases, dependingonthemethodchosen(right).TheexampleisfromTorgnon-NDgrasslandsiteinyear2013.(Forinterpretationofthereferencestocolourinthisfigurelegend, thereaderisreferredtothewebversionofthisarticle.)

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Table2

Doublelogisticmodeloptionsavailableinphenopix,alongwiththeiroptionnamesasimplementedinthepackage.Theﬁrsttwomethods wereﬁrstimplementedinthegreenbrownRpackageanddescribedinForkeletal.(2015).

Equation Reference Optionname

f(t)=mn+(mx-mn)·

₁

1+e(−rsp∗(t−sos))+_1+e(rau∗(t−eos))¹

Becketal.(2006) ‘beck’

f(t)=m1+(m2−m7t)·

1

1+e^(m³^−t)/m⁴− ¹

1+e^(m⁵^−t)/m⁶

Elmoreetal.(2012) ‘elmore’

f(t)=(a1t+b1)+(a2t²+b2t+c)·

1

[1+q1e^{−h1(t−n1)}]v1− ¹

[1+q2e^{−h2(t−n2)}]v2

Klostermanetal.(2014) ‘klosterman’

f(t)=y₀+ ^a¹

[1+e−(t−t01)/b1]c1

− ^a²

[1+e−(t−t02)/b2]c2

Guetal.(2009) ‘gu’

Table3

Methodsforphenophaseextractionavailableinphenopix.

Name Phenophases Description

trs sos,eos,los,pop,mgs,peak,msp,mau Basedonauser-deﬁnedthresholdofseasonaldevelopmentofGCC

derivatives asTRSplusrspandrau Basedonlocalextremesintheﬁrstderivative

klosterman Greenup,Maturity,Senescence,Dormancy Basedonlocalextremesintherateofchangeofcurvaturek(Kline,1998) gu UD,SD,DD,RD,prr,psr Basedonacombinationoflocalmaximaintheﬁrstderivative(Guetal.,2009) sos:startofseason,eos:endofseason,los:lengthofseason,pop:peakofseasonposition,peak:maximumseasonalGCC,mgs:meangrowingseasonGCC,msp:meanspring GCC,mau:meanautumnGCC,rsp:rateofspringgreenup,rau:rateofautumnsenescence,UD:upturndate,SD:stabilisationdate,DD:downturndate,RD:recessiondate, prr:peakrecoveryrate,psr:peaksenescencerate.

ThefunctionextractVIs()extractsrawred,greenandblue digitalnumbersfromeachpixelintheROI,andcomputescolour chromaticcoordinatesasinEq.(1).Vegetationindicescanbecom- putedonROIswith2approaches (Fig.2):(1) theROI-averaged approach:colour chromatic coordinatesare computed foreach pixeland thenaveragedover thewholeROI, and(2) thepixel- basedapproach,whereeachpixelbelongingtotheROIisanalysed separately(Section 5).Theprocedureisrepeatedforeachimage inthearchive.Atime-stampisretrievedfromthefilenameofthe imageandatimeseriesofthecomputedindicesisreturned.Specific rulesmustbefollowedinnamingtheimagefiles,andthereaderis referredtothepackagehelppagesformoredetails.

3.3. Dataﬁltering

Dataretrievedfromimagesoftenneedrobustmethodsforﬁlter- ingthetimeseries.Badweatherconditions,lowilluminationand dirtylensesareamongthemostcommonissuesthatdetermine noiseinthetimeseriesofvegetationindices.Previousstudies(e.g.

Sonnentagetal.,2012;Julittaetal.,2014;Migliavaccaetal.,2011;

Papaleetal.,2006)providethebackgrounduponwhichtheﬁltering techniquesimplementedinphenopixwerechosen.Wedesigned afunctionautoFilter()basedon5differentapproaches.

(1)AﬁltercallednightwhichremovesG_CCrecordslowerthana certainthreshold,bydefault0.2.Thisﬁltersremovesnightdataor imageswithveryscarceillumination(e.g.earlymorningorsunset images,cloudydays,etc.).

(2)Aﬁltercalledblue,basedonbluechromaticcoordinate(B_CC) (Julittaetal.,2014).First,hourlyBCCdataisaggregatedatadaily resolutionanddailymeansandstandarddeviationsarecomputed.

Aquantile (bydefault the5th percentile)is then calculatedon standarddeviations.Thisthresholdisthenaddedandsubtracted fromthedaily meanB_CC togeneratea seasonal envelope.Raw datafallingoutsidethisenvelopearediscarded.Theblueﬁlterwas designedtoremoveimageswithdominantcloudsand/orsnowon thecanopy,becausethebluechromaticcoordinatewasfoundtobe verysensitivetosuchconditions.

(3)Aﬁltercalledmad,followingthemethodofPapaleetal.

(2006),anoutlierdetectionbasedonthedouble-differencedtime series,usingthemedianofabsolutedeviationaboutthemedian (MAD)thatisarobustoutlierestimator.

Fig. 3.Illustration of the methods used to extract phenological thresholds (phenophases)inaseasonalGCCtrajectory(f(t)).(a)trsmethod:Phenophasesare definedasthedayofyear(DOY)whenthe50%GCCisreachedeitherduringgreenup (sos)andautumn(eos),theDOYofmaximumGCCiscalledpop;(b)derivatives method:PhenophasesaredefinedastheDOYwhenf(t)showstheabsolutemax- imumandminimum;(c)Klostermanmethod:Phenophasesaredefinedasthetwo localmaxima(greenup)andtwolocalminima(autumn)intherateofchangein curvaturek(Kline,1998;Klostermanetal.,2014)andarenamedafterZhangetal.

(2003);(d)Gumethod:Maximumandminimumoff(t)areusedtodeﬁneslopes ofrecoveryandsenescencelinestangenttothecurves(greenandbrownlines).

Theintersectionbetweentheselinesandbaselineandmaxlinedefinethefour phenophasesintheoriginalformulation(Guetal.,2009).Toaccountforthemid seasondecreaseinGCCwehavefurtherdefinedaplateaulineasalinearfittoGCC

valuesbetweenSDandDD,inordertoadjustthedeﬁnitionofphenophaseDD.For phenophaseabbreviationsanddetailsseeTable3.(Forinterpretationoftherefer- encestocolourinthisﬁgurelegend,thereaderisreferredtothewebversionofthis article.)

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Fig.4. AllfittingandphenophasemethodsappliedtoHarvarddeciduousforestGCCseasonaltrajectoryinyear2013.ThisplotistheoutputfromthefunctionsgreenExplore() andplotExplore().Plotsinthesamerowsharethesamefittingmethod,whereasthoseinthesamecolumnsharethesamephenophaseextractionmethod.TheRMSEfor eachfittingisalsoannotatedinthefirstcolumn.

(4)Aﬁltercalledspline,followingthemethodofMigliavacca etal. (2011),based onrecursivesplinesmoothingand residual computationfollowedbyremovalofoutliersfallingoutsideagiven residualenvelope.

(5)Aﬁltercalledmax,followingthemethodofSonnentagetal.

(2012),basedontheidentiﬁcationofthe90thpercentilevaluesin three-daysmovingwindows.

Filterscanbeappliedaloneorinsequencesothattheusercan choosethebestcombinationoffiltersthatsuitstheimagearchive tobeprocessed.Additionally,each filterhasitsown discarding criteriathatmaybetunedbytheuseraccordingtothequalityof theinputGCC timeseries.Thedefault behaviourof thefiltering functionistousea sequenceofnight, splineand maxfilters. In general,thissequencewillbeeffectiveenoughtoproperlyfilter theGCCtimeseries.Theuserisadvisedtoapplythemaxfilterto effectivelyminimisetheimpactofchangesinsceneillumination (Sonnentagetal.,2012).

3.4. FitacurvetotheG_CCseasonalcourseandextract phenophases

Theextractionofphenophasesis donein twosteps(follow- ingtheapproachofForkeletal.,2015;Klostermanetal.,2014).

First, a curve is fitted tothe G_CC seasonalcurve to reducethe influenceofsingleobservationandtobettercapturetheseasonal behaviour.Ina secondstepdifferentextractionmethodscanbe usedtoretrievephenophasemetricsfromthefittedseasonalcurve.

Weselectedfourdifferentdoublelogisticequationstobeincluded inthepackage (Table2).Theequationsdifferin thenumberof parameterstobeoptimisedandhenceintheflexibilityofthefitting curves.For athoroughexaminationofthefittingsandexplana- tionofcurveparameters,seethecorrespondentpublications.In

additiontothedoublelogisticequations,anapproachbasedona smoothedcubicsplineisalsoavailable.Therearecases,e.g.with verynoisytimeseriesorweaksignal(lowseasonalamplitudein greenness)where thedoublelogisticﬁtsfail. Insuchcases,the splinemethodistheonlypossibilitytoextractaseasonaltrajectory.

Aftercurvefitting,asuiteofdifferentapproachesisavailable toextractphenophases,assummarisedinTable3,andillustrated inFig.3.Thephenophaseextractionmethodsleadtothedefini- tionofdatesthatmayhavemarkedlydifferentecologicalmeanings andbeusefulforseveralapplicationsinthefieldofenvironmental

Fig.5. Phenophasesasdeterminedbythebreakpointanalysis(PhenoBP()function)applied toKamuela grasslandGCC seasonal trajectoryinyear2013. The thicknessoftheverticaldashedlinesisproportionaltotheuncertaintyofthe extractedbreakpoints(samescaleasthex-axis).Uncertaintyisestimatedusing thefunctionconfint()asthe95%conﬁdenceinterval.

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Fig.6. TheuncertaintyanalysisappliedtoHarvarddeciduousforestGCCseasonaltrajectoryinyear2013.Thegreylinesrepresentthe500simulatedcurvesonwhich phenophasesareextracted.Shadedareasonphenophasesrepresent10thand90thpercentilesofthe500replications.DataareﬁttedusingtheKlostermanmethod.

Phenophasesareextractedwith(a)thetrsmethod;(b)thederivativesmethod,(c)Klostermanmethod,(d)Gumethod.

sciences.Howeverthediscussionofsuchmeaningsisbeyondthe objectivesofthispresentationpaper,andthereaderisreferredto thepublicationslistedinTable2forfurtherinformation.Hereitis importanttopointoutthattheavailabilityofverysimplemeth- ods(e.g.trs)andmoresophisticatedones(e.g.Klostermanmethod) allowtheusertochoosetheextractionmethodbettersuitedtothe phenologicaltrajectoryoftheinvestigatedecosystemand/orbased onthequalityoftheinputdata.

Thecombinationoffourfittingmethodsplusthecubicspline smoothingwiththefourphenophaseextractionmethodsproduces 20differentavailablecombinationsinoutputfromasingleyearly GCCtimeseries.Aspecificfunction(greenExplore())isdesigned togiveanoverviewofallfittingsandphasesforagivendataset (Fig.4).TheRMSEforeachfitisalsoshown,sothattheusercanuse itasacriterionforfitselection.Oncethefitischosen,byexamin- ingtheplotalongarowtheusercanevaluatewhichphenophase methodismoresuitabletotheG_CCtimeseries.Thepossibilityto combinedifferentfittingandphenophasemethodstoaGCCtime seriesprovidesa framework inwhich tocompare indetail the differentmethodscurrentlyinuse.Thisneedwashighlightedby Keenanetal.(2014)inarecentattempttolinkforestphenologyas describedbytime-lapsephotographyandphysiologyasdescribed bytraditionalplanttraitmeasurements.

In addition tothe above described methods, phenopix also implements a phenophase extraction method (function Phe- noBP())basedonlinearpiecewiseregressionandcorrespondent breakpointsinthetimeseries(Wingateetal.,2015).Thismethod wasdesignedtoaccommodatemultiplegreeningpeaksduringthe sameseason,whichistypical,forexample,ofwaterlimitedecosys- temssuchasKamuela(Fig5)ormanagedgrasslandsandcroplands.

4. Estimationoftheuncertainty

Traditionally,theanalysisofdigitalimagesforphenologyhas rarely included the estimation of uncertainty on phenophase

extraction,despiteitsparamountimportance(e.g.fortheevalu- ationofmethodrobustnessandasinputdatafortheoptimisation ofprocess-basedorlandsurfacemodels,wherephenologyiscur- rentlypoorlyrepresented(Richardsonetal.,2013)).

Thephenopixpackageprovidestwoapproachesfortheesti- mationofuncertainty,oneforthefitteddoublelogisticequations and onefor the smoothingsplinemethod. For thefittedequa- tionstheresidualsbetweenthemodelfitandtheobserveddata areusedtogeneraterandomnoise andfitting isappliedrecur- sivelytorandomly-noisedoriginaldata.Thisprocedureresultsin anensembleofcurvesandcurveparametersonwhichphenophase extractionisperformed,thusprovidinganuncertaintyestimateof phenophases.Forthesplinesmoothingmethod,theuncertaintyis estimatedbycombiningarandomnoiseasforthefittedequations withmultiplechangesinthespline’sdegreesoffreedom,toaccount forthearbitrarinessintheirchoice.Inparticular,splinedegreesof freedomaresetat5%ofthetimeserieslengthandthenletvary recursivelybetween1%and5%ofthetimeserieslengthtogener- atetheensembleofsmoothingcurvesonwhichtheuncertaintyis estimated.Inadditiontotheresidualsmethod,forthefittedequa- tionsonly,wearealsodevelopingamethodbasedontheHessian matrixofthecurveparameters.

Table4

Estimatedcomputationtime(hours)requiredtocompletedifferentstepsofthe pixel-basedanalysisprocessing10,000pixelsfromaseasonaltimeseriesof5000 imagesusingonesingleprocessoronthreedifferentcomputers.

Step 3.07GHz 2.60GHz 2.13GHz

Filtering 28 49 52

Spline 0.02 0.02 0.04

Beck 36 63 69

Elmore 0.7 0.8 1.5

Klosterman 10 16 19

Gu 22 37 42

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Fig.6 shows theuncertainty analysisfor Harvard deciduous 2013 GCC seasonal trajectory, with 500 replicates. The ﬁtting methodappliedinthiscasewasKlosterman.Bydefault,thecon- ﬁdenceintervalisrepresentedbythe10thand90thpercentileof thedistributionofallextractedphenophases,butotheroptionsare available.Inanycase,theusercanaccesstheuncertaintydatatables of(1)curveequationparametersand(2)extractedphenophasesto customisethecomputationoftheuncertaintyenvelope.

5. Phenopixapplication:spatialanalysis

Basedonananalysisrunonallsitesincludedinthispaperexcept forKamuela(i.e.sixyear-sites),wepresentinthisparagraphthe applicationofthepixelbasedanalysistoinvestigatethespatial variabilityofphenologywithinaROI(pixel-basedapproach).

Todate,fewstudieshaveexploredthepossibilityofanalyzing asetofimagespixelbypixelratherthanaveragingcolourvalues ontheoverallregionofinterest(Julittaetal.,2014;IdeandOguma, 2013).Therefore,anemergentquestionishowtheROI-averaged analysisisrepresentativeofallROIpixelsin termsofextracted phenophases?And, is this relationship consistent acrossﬁtting methodsandecosystemtypes?Thephenopixpackageallowsthe usertoperformpixelbasedimageanalysisandprovidesanumber offunctionstodisplayandanalysetheresults.Itmustbenoted thatpixel-by-pixelanalysisiscomputationallyintense.Wereport

inTable4thecomputationtimesrequiredtoanalyseaROIcontain- ing10,000pixelsonaseasonaltimeseriesof5000imageswithall fitsavailableinphenopixfor3differentcomputers,usingonesingle processor.Computationtimemaybeashighas70h,withverylow valuesforsplinesmoothingandhigherfordatafilteringandfitted equations.However,parallelcomputationavailableinthespatial functionsofphenopixenableconsiderablyreducedcomputation timescomparedtothosereportedinTable4.

We evaluatethe relationship between approaches (i.e. ROI- averagevs pixel-based) andfitting/phenophase methods across sitesinFig.7.Twodifferentfittingmethods(namelyKlosterman andspline)appliedtoeachpixeloftheROIsandthenaveraged areingoodagreementwitheachother(Fig.7,firstabovediagonal panel),withaslightlyhighercorrelationforspringthanforautumn phases.Theerrorbarsforthisrelationshipsuggestahighervariabil- ityforautumnthanforspringphenophases(firstbelowdiagonal panel). WhencomparingtheROI-averaged and thepixel-based approach,thebestrelationshipisfoundwiththeKlostermanfitting methodappliedtobothapproaches,withcorrelationcoefficientsof 0.97and0.75forspringandautumnphenology,respectively.How- ever,therelationshipbetweensplinefitappliedpixel-by-pixeland KlostermanfitappliedtotheaverageROIalsoshowsverygood agreement.Incontrast,thesplinemethodappliedtotheaverage ROI(lastcolumninFig.7)hasaconsistentlyweakerrelationship withallothermethods,resultinginearlierautumnphasesanda muchhighervariability.

Fig.7. Scatterplotmatrixshowing:(a)abovethediagonal,therelationshipbetweenphenophasesextractedwithtwodifferentapproaches(pixel-basedandROI-averaged) andtwodifferentfittingmethods(splinesmoothingandKlostermanfit),and(b)belowthediagonal,errorsassociated.Errorbarsforpixelapproachrepresentthemean absolutedeviationofallpixels,whereaserrorbarsforROI-averagedapproachrepresenttheuncertaintyasdescribedinSection4.Notethatthescaleforerrorbarsis providedinthefirstbelow-diagonalpanel.Differentsymbolsdenotedifferentphenophaseextractionmethods.Closesymbolsareforspringphenologyandopensymbols forautumnphenology.DifferentcoloursrepresentdifferentPFTs(abbreviationsforPFTsareasinTable1).DatafromallsitesexceptforKamuelawereusedinthisanalysis.

(Forinterpretationofthereferencestocolourinthisﬁgurelegend,thereaderisreferredtothewebversionofthisarticle.)

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Acrossphenophasemethods(differentsymbolsinFig.7),there isnoevidenceofasystematicbiasbetweenthetwoapproachesfor anyparticularphaseextractionmethod.Acrossecosystemtypes, needle-leafforest(ENF,i.e.harvardhemlocksite)showsaconsis- tentdeparturefromthe1:1lineeveninthebestrelationships(third panelfromleftintheﬁrstrowofFig.7),probablyduetothelower signaltonoiseratiointheseasonaltrajectoryofevergreentrees.

However,themoststrikingdifferenceistheconsistentlylowercor- relationforautumnthanforspringphases,suggestingthatinthe sameecosystemandpossiblyalsowithinasinglespeciesautumn phasesaremorevariablethanspringphases.

Insummary,thefastersplinesmoothingappliedpixel-by-pixel leadstotheidentificationofphenophasesinsubstantialagreement withthemorecomputationally-intenseKlostermanfit.Addition- ally,beingmoreflexible,thesplinefittingleadstoamuchlower numberofunfittedpixelscomparedtotheKlostermanfit.Hence, fromthisanalysis,splinesmoothing ispreferredover curvefit- tingforpixel-by-pixelanalysis.FortheROI-averagedapproach,itis stronglypreferabletoperformcurvefittingoversplinesmoothing, providedthattheG_CCseasonaltrajectorydoesnotshowmultiple peaks,relatedtoe.g.waterstressormanagementpractices.

Oneinteresting applicationof thepixel-basedanalysisstems from theexamination of the frequency distribution of a given phenophaseacrossallpixels.Whenaphenophaseshowsabi-(or multi-)modalityandaconsistentspatialdistribution,pixelsmay begroupedaccordingtothevaluesofone(ormore)phenophase(s)

Fig.8. ResultsofthespatialanalysisconductedonTorgnon-NDgrasslandsite.(a) Mapofthespatialdistributionofmaturity(Klostermanﬁttingmethod,Kloster- manphenophasesmethod),withdarkercoloursindicatingearlieroccurrenceofthe phase(inDOYs).(b)Averageseasonaltrajectoryofpixelsclusteredaccordingtothe bimodaldistributionofmaturity.Patcheswithforbsdominatingshowanearlier maturitycomparedtograsses(dashedlines).Intheinset,densitydistributionof Maturityphaseacrossallpixels.Shadedareasdenotethematurityintervalsused tosubsetROIpixels.Theresultingsubsetswereinturnusedtoextracttheseasonal trajectoriesofforbs-andgrass-dominatedportionsoftheROI.(Forinterpretation ofthereferencestocolourinthisﬁgurelegend,thereaderisreferredtotheweb versionofthisarticle.)

andseparateseasonaltrajectoriescanbeobtained(Fig.8).Byapply- ingthisproceduretotheTorgnon-NDgrasslandsitewewereable toidentifyabimodaldistributioninmaturityphase(i.e.theendof springgrowth),asextractedafterKlostermancurveﬁtting.Wethen selectedpixelswithmaturityonsetdatesfallingaroundthetwo modes(i.e.DOY180±3andDOY205±3)andcomputedanaverage trajectoryforthesetwogroups.Thiswaspossiblebecauseinthe pixel-basedapproacheachpixelhasassociatedcurveparameters toreconstructtheseasonaltrajectory.Theresultingaveragetrajec- toriesaremarkedlydistinctaroundtheseasonalpeak,andreﬂect thedifferentecologyofforbsandgrassesoccurringoververyshort distances(<20cm)atthisgrasslandsite(Julittaetal.,2014).Fur- therinterpretationofthisbehaviourisbeyondtheobjectiveofthis

Fig.9.ResultsoftheclusteranalysisrunonBartlettpixel-basedphenophases.(a) SampleimagefromDOY275,(b)distributionofclustersintheROI,(c)density distributionofGreenupandDormancyphasesacrossallpixels,and(d)averagedsea- sonaltrajectoriesforthetwoclusters.Allextractedphases(fromthefourmethods presentedinSection3)wereusedininputtoclustering.

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paper,butthispreliminaryanalysishighlightsthepotentialuseof spatiallyexplicitimageprocessingtoidentifydifferentphenology relatedtodifferentplantfunctionaltypes.

Moresophisticatedclusteringmethodscanbeappliedtopixel values in order to distinguish different phenological patterns.

For example,attheBartlett site (amixeddeciduousforest)we might expect a different phenology in early or late flushing species (Fig. 9a). With a cluster analysis (Hartigan and Wong, 1979)using phenophases extracted withall available methods asaninputmatrix,wewereabletoclearlydistinguishtwopor- tionsofthecanopy(Fig.9b).Inthisexampletheclusteranalysis wasusedtodefinetwosub-ROIs,thatreenteredtheprocessing chain(ROI-averaged approach,Fig.2)andledtotheidentifica- tionoftwomarkedlydifferentseasonaltrajectories(Fig.9cand d).Beech-dominated portions ofthecanopy (cluster2)show a lateryellowing/browningcomparedtomaple,prevailingincluster 1(Richardsonetal.,2009).

6. Summaryandoutlook

WepresentedanewRpackagecalledphenopixthatcollects themost up-to-date processing techniquesfor repeated digital photography of vegetationcanopy in thecontext of phenological and ecological research. Together with the basic and well establishedprocessingsteps,thepackageimplementsseveralnew features, including the possibility to combine different ﬁtting andphenophasemethods,tocomputeuncertaintyonextracted phenophases,andtoconductpixel-by-pixelanalysis.Phenophase maps obtained by the pixel-based approach may represent a promisingtoolfor avarietyofecologicalanalyses,includingfor example:(1)thestudyofspecies-speciﬁcphenology;(2)theexami- nationofspatialvariabilityofapparentlyhomogeneousecosystems suchasgrasslands;(3)thepotentialtodiscriminatebetweenover- storyand understory or age-relateddifferences in phenological patternsofopencanopyforests.

Onemajorfuture stepintheevolutionofphenopix package includesthecomputationofthesocalledcamera-NDVI,inthefor- mulationproposedbyPetachetal.(2014).

The phenopix R package offers a suite of standardised and reproducibleprocessingcodethatmaybeadopted,deployedand furtherdevelopedbytheexistingphenocamerasnetworksworld- wide.

Acknowledgments

ADRacknowledgessupportfromthe NationalScienceFoun- dation, through the Macrosystems Biology program (grant EF-1065029).

ThepackagewasdevelopedintheframeworkoftheALCOTRA InterregProjectn227e-PHENORetifenologichenelleAlpi.

WethankMorganFurze,HarvardUniversity,forassistancewith editingthemanuscriptandtwoanonymousreviewersforvaluable commentsonapreviousversionofthemanuscript.

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