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SILVA FENNICASilva Fennica vol. 50 no. 1 article id 1518

Category: research note

www.silvafennica.fiISSN-L 0037-5330 | ISSN 2242-4075 (Online)

The Finnish Society of Forest Science Natural Resources Institute Finland

Francesco Chianucci

A note on estimating canopy cover from digital cover and hemispherical photography

Chianucci F. (2016). A note on estimating canopy cover from digital cover and hemispherical photography. Silva Fennica vol. 50 no. 1 article id 1518. 10p.

Highlights• Comparisonoffisheye(DHP)andcover(DCP)photographyforestimatingcanopycover.• Digitalphotographicestimatesvalidatedagainstartificialimageswithknowncover.• AccuracyofcoverestimatesfromDHPisinfluencedbymeangapsizeandactualcover.• AccuracyofcoverestimatesfromDCPisnotinfluencedbymeangapsizeandactualcover.

AbstractFastandaccurateestimatesofcanopycoverarecentralforawiderangeofforestrystudies.Asdirectmeasurementsareimpractical,indirectopticalmethodshaveoftenbeenusedinforestrytoestimatecanopycover.Inthispapertheaccuracyofcanopycoverestimatesfromtwowidelyusedcanopyphotographicmethods,hemisphericalphotography(DHP)andcoverphotography(DCP)wasevaluated.CanopycoverwasapproximatedinDHPasthecomplementofgapfractiondataatnarrowviewingzenithanglerange(0°–15°),whichwascomparablewiththatofDCP.The methodologywastestedusingartificialimageswithknowncanopycover;thisallowedexploringtheinfluenceofactualcanopycoverandmeangapsizeoncanopycoverestimationfromphotog-raphy.DCPprovidedrobustestimatesofcanopycover,whoseaccuracywasnotinfluencedbyvariationinactualcanopycoverandmeangapsize,basedoncomparisonwithartificialimages;bycontrast,theaccuracyofcoverestimatesfromDHPwasinfluencedbybothactualcanopycoverandmeangapsize,becauseofthelowerabilityofDHPtodetectsmallgapswithincrown.TheresultswerereplicatedinbothDHPandDCPimagescollectedinrealforestcanopies.Finally,theinfluenceofcanopycoveronfoliageclumpingindexandleafareaindexwasevaluatedusingatheoreticalgapfractionmodel.ThemainfindingsindicatethatDCPcanovercomethelimitsofindirecttechniquesforobtainingunbiasedandpreciseestimatesofcanopycover,whicharecomparabletothoseobtainablefromdirect,morelabour-intensivetechniques,beingthereforehighlysuitableforroutinemonitoringandinventorypurposes.

Keywords foliagecover;crowncover;forestcanopy;fisheyephotography;coverphotographyAddresses ConsiglioperlaRicercainAgricolturael’Analisidell’EconomiaAgraria–ForestryResearchCentre,vialeSantaMargherita80,52100Arezzo,ItalyE-mail fchianucci@gmail.comReceived4November2015Revised2December2015Accepted7December2015Available at http://dx.doi.org/10.14214/sf.1518

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1 Introduction

Canopycoverisacommonlyusedvariableinforestry(Jennings1999;Rautiainenetal.2005).ThisvariableisstronglyrequiredforaccuratemodellingofleafareaindexL using radiative transfer theory (Majasalmietal.2014;Nilson1999;NilsonandKuusk2004).Inaddition,canopycoverisamajordeterminantofforestreflectancefromopticalremotesensingdata(Dawsonetal.1999).It is also often included in national forest inventories (Angelini et al. 2015). Accordingly, accurate in situ estimatesofcanopycoverarecentralforawiderangeofforestrystudies.

As direct measurements are impractical and time-consuming, canopy cover was oftenestimatedfromopticalinstrumentsusingarestrictedzenithanglerange(typicallyabout0°–15°).Althoughtheapproachissomewhatincorrect,inthatmeasurementsarenotstrictlyvertical,useofnarrow-angleofviewisoftenconsideredduetoitssimplicity.Forexample,thewidelyusedhemisphericalsensorssuchasLAI-2000PlantCanopyAnalyzer(Li-COR,Lincoln,NE,USA)ordigitalhemisphericalphotography(alsocalledfisheyephotography;DHP)havebeenfrequentlyemployedtoobtainanestimateofcanopycoverfromgapfractiondataatnarrowviewingzenithanglerange(Korhonenetal.2006;Kuchariketal.1999;Rautiainenetal.2005;SeedandKing2003).However,thegapfractionreadingsobtainedatthisviewweretypicallynoisyinhemispheri-calsensors,becauseoflimitedspatialsamplingatthisview.Whilesomestudieshaveproposedcorrectionforinsufficientsamplingusingtheseinstruments(e.g.,NilsonandKuusk2004),Mac-farlaneetal.(2007)recentlyproposeddigitalcoverphotography(DCP),arestricted-viewanglemethod.Theauthorsuseda70mmequivalent-focal-lengthtoobtainveryfinespatialresolutionatanapproximately30°fieldofview(FOV),whichwascomparablewiththeFOVoftheuppermostringofLAI-2000.TheresultingcombinationofhighresolutionandmainlyverticalsamplinginDCPallowedtoseparatetotalgapfractionintolarge,between-crownsgapsandsmall,within-crowngaps,leadingtodistinctestimatesofcanopycover(crowncoverandfoliagecover;Macfarlaneetal.2007,butseealsosection2.1).However,althoughsomestudiesspeculatedthatDCPcanyieldmoreaccurateestimateofcanopycoverthanDHP(PekinandMacfarlane2009;Ryuetal.2010),owingmainlytoitshigherzenithalresolution,nopreviousstudieshaveevaluatedtheaccuracyofcoverestimatesfromboththesemethodscomparedwithknownreferencecanopycoverdata.

Theobjective of this studywas testing the accuracyof digital canopyphotography forestimatingforestcanopycover.Forthepurpose,estimatesobtainedfromcoverandhemispheri-calphotographywerecomparedwiththoseobtainedfromanartificialtargetwithknowncanopycover;thisallowedexploringtheinfluenceofactualcanopycoverandmeangapsizeoncanopycover estimation from digital photography.Resultsfrombothphotographicmethodswerealsocomparedinrealforestcanopies.Finally,theimpactofcanopycoveronmodellingcanopyattrib-uteslikeclumpingindexandleafareaindexwasillustratedusingatheoreticalgapfractionmodel(Nilson1999).

2 Material and methods

2.1Definitionofcanopycover

Canopycoverisfrequentlydefinedastheaverageproportionofgroundsurfacecoveredbytheverticalprojectionoftreecrowns(Jenningsetal.1999).However,twodistinctvariablescanbedefineddependingonwhethergapsinsidecrownenvelopeswereconsideredornot.Traditionalmeasuresofcanopycoverconsidercrownsasnon-transparentenvelopes,i.e.consideringwithin-crownsgapsaspartofthecanopy;thisisequivalenttothedefinitionofcrown coverbyMacfarlane

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et al. (2007). Conversely, foliage cover isdefinedasthecomplementoftotalgapfraction,takingintoaccountbothwithin-andbetween-crownsgaps.Fortheremainderofthestudy,canopycoverandcrowncoverareusedassynonym,whilefoliagecoverisexplicitlymentionedtodistinguishfromthetraditionaldefinitionofcover.

2.2Artificialcanopycovermeasurements

TheideaofmeasuringanartificialtargettovalidateopticalmethodshasbeenrecentlyproposedbySongetal.(2014)andMacfarlaneetal.(2014).Inthisstudyreferencecanopycovermeasure-mentswereobtainedbygeneratingartificialcanopyimageswithknowncanopycover.AnRroutine(RCoreDevelopmentTeam,Vienna)wasusedtorandomlyfillingblank210×210mmimageswithblack-circles(simulatingadarkcanopy)witharadiusof3mmeach.Twentyscenesweregeneratedcorrespondingtoanartificial(known)canopycoverrangingfrom0.12to0.90,whichrepresentsarealisticrangeofcanopycoverinnaturalforeststands.Imageswerethenprintedandplacedinalevelledsurface.Coverandhemisphericalimageswerethenacquiredinovercastskyconditionsbyorientingthecameraperpendiculartothesurface.ThewidelyusedcanopydigitalcameraNikonCoolpix4500,eitherwithorwithout thefisheye lens,wasplacedatadistancetorepresentthestandardatthecentreoftheimagewithinaFOVofabout30°.HemisphericalimageswereacquiredwiththecameraequippedwithaFC-E8fisheyelensconvertersettoF1(equivalent focal length 38 mm), aperture-priority mode (A), minimum aperture (F 5.3), one-stop underexposure,asrecommendedbyMacfarlaneetal.(2014)toimprovecontrastinimages,andcenter-weightedexposuremetering.Cover imageswereacquiredwith thefixed lensset toF2(equivalentfocallengthof70mm),aperture-prioritymode(A),minimumaperture(F9.6),one-stopunderexposureandmatrixexposuremetering.Lensvignettingwasassumednegligible inbothcamerasetups(Langetal.2010).Theimageswerecroppedtofitthestandard’sextent.Thebluechannelofeachimagewasclassifiedusingasinglebinarythresholding(RidlerandCalvard1978)fromthe‘rtiff’packageinR(RCoreDevelopmentTeam,Vienna).Canopycoverwasthenestimatedastheratioofblackpixelsovertotalpixels.

Toevaluatetheinfluenceofgapsizeoncanopycovermeasurements,eightimageswerefurthergeneratedbyapplyingacheckerboardpattern(canopycoverof0.5)witharbitrarilyvaryingcheckerboardsize(simulatinggapsize)from0.02%to0.2%oftheimagearea(sidewidthfrom3to30mm).Theimageswerethenprintedandacquiredusingthesameprotocolabove.

2.3Actualcanopymeasurements

Fisheyeandcoverimageswereacquiredunderovercastskyconditionsinten0.5–1hadecidu-ous stands in Central Italy (main species Fagus sylvatica L., Quercus cerris L., Castanea sativa Mill.)thathadacanopycoverrangingfrommediumtomoderatelydense.Thedatawerecollectedfromthreeforestssiteswhichweresampledin2011and2012inapreviousstudy(Chianucciet al. 2015).

Dependingonplotsize,9–15fisheyeandcoverimageswereacquiredineachplotwiththeNikonCoolpix4500alongagridofsamplepoints.Camerasettingsandimageclassificationwereasdescribedabove,withtheexceptionthatthegammafunctionwassetto1.0(ChianucciandCutini2013).CanopycoverinDHPwascalculatedatthecomplementofgapfractionestimatedat0°–15°zenithanglerange.

Onceclassified,DCPimageswereanalyzedusingWinscanopy2012a(RegentInstruments,Ste-Foy,Quebec,Canada).Twocanopycoverestimateswereobtainedusingagap-sizedistribu-tionapproach;gapslargerthan0.3%oftheimageareawereclassifiedasbetween-crownsgaps;

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thegapareathresholdwassetbasedonapreviousstudy(Chianuccietal.2014).ConsistentlywiththeterminologyofMacfarlaneetal.(2007),crowncoverwasestimatedasthefractionofpixelsthatdonotlieinbetween-crownsgapsandfoliagecoverwasestimatedasthecomplementof total gap fraction.

2.4 Gap fraction model

TheimpactofcanopycoveronleafareaindexandclumpingindexretrievalwasevaluatedusingthegapfractionmodelofNilson(1999)andgapfractiondataestimatedfromLAI-2000PlantCanopyAnalyzer.Themodeldeterminesgapfractionasfollows:

P(θ ) = exp −c(θ )NS(θ )⎡⎣ ⎤⎦ (1)

where

c(θ ) = − ln[1− (1− P1(θ ))(1−GI )1−GI

(2)

and

P1(θ ) = exp−G(θ )(L+ BAI )NS(θ )cosθ

⎡

⎣⎢

⎤

⎦⎥ (3)

whereN is tree density (trees m–2), S( θ ) is the area of projection of the average tree crownenvelopeat thezenithangleθ, c( θ ) isanauxiliaryparameterwhichcorrects themeancrowncoverage, P1( θ )thegapfractionwithinasinglecrown,G( θ ) is the foliage projection function, L istheleafareaindex,BAIisthebranchareaindex,GIistheFisher’sgroupingindexoftreedistributionpattern.Themodelestimatesclumpingindex(Ω,Eq.4)andleafarea(L, Eq. 5) as:

Ω(θ ) = c(θ )NS(θ )cosθG(θ )(L+ BAI )

(4)

and

L = −N(κ +α )G(θ )

S(θ )× ln 1−1− exp (1−GI ) lnP(θ )NS(θ )⎡⎣ ⎤⎦

1−GI⎡

⎣⎢⎢

⎤

⎦⎥⎥0

π /2

∫ cosθ sinθdθ (5)

whereκ istheshoot-levelclumpingindexandα isthebranchareatoleafarearatio.Toapplytheformulas, in addition to the estimated value of P( θ ), the model requires input parameters includ-ingstanddensity,treeheight,crowndepth,shoot-levelclumping,foliageprojectionfunctionandcanopycover.Themodelwasruninthebirch(Betula pendula Roth)standinJärvselja,Estonia,belongingtotheRAMI(RadiationtransferModelIntercomparison)testsites,whichhavebeenwidelyusedtobenchmarkingradiativetransfermodelling(e.g.Kuusketal.2009;Piseketal.2011).Alltheinputparameters(includingcanopycover)wereobtainedfromtheworksbyKuusketal.(2009)andNilsonetal.(2011);theinputcanopycoverwasthenvariedtoevaluatetheinfluenceoftheseattributesonLandΩ,theotherinputparametersheldconstant.Ascanopycoverdirectlyinfluencesthegroupingindex(GI),theinputcanopycoverwasvariedwithinarangesuitabletoallowthetheoreticalformulastogivereliableresults,i.e.,whereGI(S(0)) < 1.

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3 Results

Bothphotographicmethodsprovidedaccurateestimatesofcanopycover,whichsignificantlyagreedwiththoseobtainedfromartificialimageswithknowncanopycover(Pearson’sr-test, p < 0.01). Nonetheless,thetwomethodsshoweddifferentperformancedependingonactualcanopycover(Fig.1a)andmeangapsize(Fig.1b). DHPshowedalargertendencytounderestimatecanopycover,inparticularinmedium-densecanopies(Fig.1a);inaddition,DHPshowedanincreasingtendencytounderestimatecanopycoverwithdecreasinggapsize(Fig.1b).Closerinspectionofartificialchessboard-patternedimagesrevealedthatDHPpossessedmoremixedpixels(Fig.2),beinghighlysensitivetogapfragmentation,becausemixedpixelsarelocatedmainlyonthecanopy-skyedges.Thiseffect,inturn,haveastrongimpactonpixelclassificationsincefragmentedcanopyimagesaremorepronetobemisclassifiedintoskyorcanopyduringthresholding(Fig.1).Conversely,DCPpossessedveryfewmixedpixels(Fig.2),andshowedlowervariationsinestimatedcoverassociatedwithactualcanopycover(Fig.1a)andmeangapsize(Fig.1b).

Comparisonsofbothphotographicmethodsappliedinrealforestcanopieswereinagree-mentwith resultsobtained fromartificial images;DHPyielded larger canopycoverestimatescomparedwithfoliagecoverestimatedfromDCP,indicatingthatmanysmallgaps(i.e.,within-crowngaps)wereeitherclassifiedasfoliageduringthresholdingorwerenotdetectedatallinDHP,becauseofthelowerzenithalresolutionoffisheyeimages(Fig.3).AbetteragreementwasobservedbetweencanopycoverfromDHPandcrowncoverinDCP(Fig.3),becauselargebetween-crownsgapweremore-easilyidentifiedinfisheyeimages.

Atheoreticalgapfractionmodel(Nilson,1999)indicatedthatcanopycoverwasinverselycorrelatedwiththegroupingindex(Pearson’sr-test p < 0.05)andthereforewiththefoliageclump-ingindex,thesparsercanopiesexhibitingmoreclumpeddistributionoffoliageandthedensercano-piesexhibitingmorerandomlydistributedfoliage(Fig.4).Inaddition,canopycoverwasstronglycorrelatedwithleafareaindexinvertedfromgapfractiondata–thehigherthecanopycover,thesmallertherespectiveinvertedleafareaindexvalue.Inoursimulationincreasingcanopycoverfrom0.65to0.95decreasedtheestimatedleafareaindexbyvaluesrangingfrom24%to51%.

Fig. 1. a) Differencebetween theactualandmeasuredcanopycovercalculatedfromartificialcanopy imageswithknowncanopycoverusingdigitalhemispherical(DHP)andcover(DCP)photography;b)Variationofcanopycoverwithmeangapsizecalculatedfromartificialcheckerboard-patternedcanopyimagesusingdigitalhemispherical(DHP)andcover(DCP)photography.Meangapsizerangesfrom0.02%(3mm)to0.2%(30mm)oftheartificialcanopyimage area.

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Fig. 2. HistogramsofDCP(top)andDHP(bottom)imagesofartificialcheckerboard-patternedcanopyimageswithdifferentmeangapsize.Thecolorclassesinlegendindicatesdifferentmeangapsizerangingfrom0.02%(3mm,navy)to0.2%(30mm,blue)oftheartificialcanopyimagearea.

Fig.3.Canopycover fromhemisphericalphotography(y-axis)vs either foliagecover (left)orcrowncover (right)calculatedfromcoverphotography(x-axis)instudiedstandsinItaly.Thedashedlineindicatesthe1:1relationship.

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Fig. 4. VariationinfoliageclumpingindexestimateswithviewzenithangleatdifferentassumedcanopycovervaluesfortheRadiativeTransferModelIntercomparison(RAMI)birchstandinJärvselja, Estonia.

4 Discussion

ThestudydemonstratedthatDCPprovidesrobustestimatesofcanopycover,whoseaccuracyislargelyunaffectedbyvariationinactualcanopycoverandmeangapsize.Bycontrast,theaccuracyofDHPwasaffectedbybothactualcanopycoverandmeangapsize,mainlybecauseitslowernearlyverticalresolution,whichresultsinalowerabilityindetectingsmallgapsnearthezenithandameasurementofaquantitythatisanalogoustosomethingbetweencrownandfoliagecover(PekinandMacfarlane2009).Ithasgenerallybeennotedthatlowresolutionimageshavemoremixedpixels(Blennow1995;Leblancetal.2005;Macfarlane,2011),whichcouldobscuresmallgapswithincanopies.Thiseffect,inturnmaypreventtheaccuracyofgapsizedistributionesti-mates,particularlyinhighlyfragmentedcanopies(Songetal.2014).ThisimpliesthatuseofDHPisparticularlycriticalindensecanopies,whicharemainlycharacterizedbysmallwithin-crowngaps;DHPmaybeunabletodetectgapsinsuchcanopies,leadingtoanincreasinglyprobabilityto achieve saturated canopy cover values. This effect, in turn, may prevent the inversion of canopy attributesfromtransmittancedata,sincethelogarithmofzerogapfractionisundefined.Therefore,DCPappearsparticularlysuitedindenseforestcanopies,becauseitshigherabilitytodetectsmallgapsallowsmoreaccuratecanopycoverretrievalinthesestands.Inaddition,thehigherverticalresolutionofDCPallowsseparatingtotalgapfractionintolarge,between-crownsgapsandsmall,within-crowngaps,leadingtoeffectiveestimatesofcanopytransmittance(Rautiainenetal.2005).Further,previousstudiesdemonstratedthatDCPwaslesssensitivetocameraexposure,lensvignet-tingandskyluminancethanDHP(Macfarlaneetal.2007;Macfarlane,2011).

Canopycoverhascertainlyaneffectonaveragegapfractionanditsangulardistribution–thehigherthecanopycover,thesmallerthetotalgapfraction(Nilson2011).Inaddition,thetheoreticalgapfractionmodelindicatedthatcanopycoverinfluencesgapsizeanditsvariance;sparser canopies exhibitedmore clumpeddistributionof foliage,whichcanbe attributed to alargerfrequencyinlargegaps(ChenandCihlar1995)andalargervariationingapsizeoccur-ringatincreasingcanopyspaceavailability.Conversely,densercanopiesshowedmorerandomlydistributed foliage, probably because the lower number of small gaps occurring at saturatingcanopy density (Macfarlane 2011). As the variation in foliage clumping corresponds to different

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proportion in sunlight and shaded leaves, it implies that accurate canopy cover estimates are criticalforreliablemodellingfluxesofcarbon,waterandenergy,andtheirdistributionwithinthe canopy (Chen et al. 2012).

Tosumup,thestudyhascomparedtwophotographicmethodsforestimatingcanopycoverinforeststands.ThemainfindingsindicatethatDCPisaneffectivetoolforestimatingforestcanopycover.Asthemethodissimple,rapidandcost-effective,itcanbeusedforroutinemeasuresandmonitoringofcanopycoverinawiderangeofforestryapplications.

Acknowledgments

TheauthorisindebtedwithTiitNilsonandJanPisekfortheirfruitfuldiscussionsandsuggestionsthatimprovedtheoriginaldraftofthepaper.Theauthoralsowishtothanktheanonymousreview-ersfortheirhelpfulsuggestions.Thestudywassupportedbytheresearchproject“ALForLab”(PON03PE_00024_1)co-fundedbythe(Italian)NationalOperationalProgrammeforResearchandCompetitiveness(PONR&C)2007–2013,throughtheEuropeanRegionalDevelopmentFund(ERDF)andnationalresource(RevolvingFund-CohesionActionPlan(CAP)MIUR).

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