Measuring β‐diversity by remote sensing: A challenge for ...€¦ · ment of remote sensing tools (Rocchini & Neteler, 2012) for diversity estimate in which the concept of β-diversity

Methods Ecol Evol 201891787ndash1798 wileyonlinelibrarycomjournalmee3 emsp|emsp1787copy 2018 The Authors Methods in Ecology and Evolution copy 2018 British Ecological Society

Received22September2017emsp |emsp Accepted11November2017DOI 1011112041-210X12941

I M P R O V I N G B I O D I V E R S I T Y M O N I T O R I N G U S I N G S A T E L L I T E R E M O T E S E N S I N G

Measuring β-diversity by remote sensing A challenge for biodiversity monitoring

Duccio Rocchini123 emsp|emspSandra Luque4 emsp|emspNathalie Pettorelli5 emsp|emspLucy Bastin6 emsp|emsp Daniel Doktor7emsp|emspNicolograve Faedi38emsp|emspHannes Feilhauer9 emsp|emspJean-Baptiste Feacuteret4 emsp|emsp Giles M Foody10 emsp|emspYoni Gavish11 emsp|emspSergio Godinho12emsp|emspWilliam E Kunin13 emsp|emsp Angela Lausch7 emsp|emspPedro J Leitatildeo1415 emsp|emspMatteo Marcantonio16emsp|emspMarkus Neteler17 emsp|emsp Carlo Ricotta18 emsp|emspSebastian Schmidtlein19emsp|emspPetteri Vihervaara20emsp|emsp Martin Wegmann21 emsp|emspHarini Nagendra22

1CenterAgricultureFoodEnvironmentUniversityofTrentoSMicheleallrsquoAdige(TN)Italy2CentreforIntegrativeBiologyUniversityofTrentoPovo(TN)Italy 3DepartmentofBiodiversityandMolecularEcologyFondazioneEdmundMachResearchandInnovationCentreSMicheleallrsquoAdige(TN)Italy4UMR-TETISIRSTEAMontpellierMaisondelaTeacuteleacutedeacutetectionMontpellierCedex5France5InstituteofZoologyTheZoologicalSocietyofLondonLondonUK6SchoolofComputerScienceAstonUniversityBirminghamUK7DepartmentComputationalLandscapeEcologyHelmholtzCentreforEnvironmentalResearchndashUFZLeipzigGermany8DepartmentofComputerScienceandEngineeringUniversityofBolognaBolognaItaly9InstitutfuumlrGeographieFriedrich-AlexanderUniversitaumltErlangen-NuumlrnbergErlangenGermany10SchoolofGeographyUniversityofNottinghamNottinghamUK11SchoolofBiologyFacultyofbiologicalScienceUniversityofLeedsLeedsUK12InstituteofMediterraneanAgriculturalandEnvironmentalSciences(ICAAM)UniversidadedeEvoraEvoraPortugal13SchoolofBiologyUniversityofLeedsLeedsUK14DepartmentLandscapeEcologyandEnvironmentalSystemAnalysisTechnischeUniversitaumltBraunschweigBraunschweigGermany15GeographyDepartmentHumboldt-UniversitaumltzuBerlinBerlinGermany16DepartmentofPathologyMicrobiologyandImmunologySchoolofVeterinaryMedicineUniversityofCaliforniaDavisCAUSA17MundialisGmbHampCoKGBonnGermany18DepartmentofEnvironmentalBiologyUniversityofRomeldquoLaSapienzardquoRomeItaly19KarlsruherInstitutfuumlrTechnologie(KIT)InstitutfuumlrGeographieundGeooumlkologieKarlsruheGermany20NaturalEnvironmentCentreFinnishEnvironmentInstitute(SYKE)HelsinkiFinland21DepartmentofRemoteSensingRemoteSensingandBiodiversityResearchGroupUniversityofWuerzburgWuerzburgGermanyand22AzimPremjiUniversityBangaloreIndia

Correspondence DuccioRocchini Emailsducciorocchinigmailcom ducciorocchinifmachit

Present addressLucyBastinKnowledgeManagementUnitJointResearchCentreoftheEuropeanCommissionIspraItaly

HandlingEditorFrancescaParrini

Abstract1 BiodiversityincludesmultiscalarandmultitemporalstructuresandprocesseswithdifferentlevelsoffunctionalorganizationfromgenetictoecosystemiclevelsOneofthemostlyusedmethodstoinferbiodiversityisbasedontaxonomicapproachesandcommunityecologytheoriesHowevergatheringextensivedatainthefieldisdifficultduetologisticproblemsespeciallywhenaimingatmodellingbiodiversitychangesinspaceandtimewhichassumesstatisticallysoundsamplingschemesInthiscontextairborneorsatelliteremotesensingallowsinformationtobegatheredoverwideareasinareasonabletime

2 MostofthebiodiversitymapsobtainedfromremotesensinghavebeenbasedontheinferenceofspeciesrichnessbyregressionanalysisOnthecontraryestimatingcompositionalturnover(β-diversity)mightaddcrucial informationrelatedtorela-tiveabundanceofdifferentspeciesinsteadofjustrichnessPresentlyfewstudieshaveaddressedthemeasurementofspeciescompositionalturnoverfromspace

3 Extendingonpreviouswork inthismanuscriptweproposenoveltechniquestomeasure β-diversity from airborne or satellite remote sensingmainly based on (1)multivariatestatisticalanalysis(2)thespectralspeciesconcept(3)self-organizing

1788emsp |emsp emspenspMethods in Ecology and Evolu13on ROCCHINI et al

1emsp |emspINTRODUCTION

Biodiversitycannotbefullyinvestigatedwithoutconsideringthespa-tialcomponentofitsvariationInfactitisknownthatthedispersalofspeciesoverwideareasisdrivenbyspatialconstraintsdirectlyrelatedtothedistanceamongsitesAnegativeexponentialdispersalkernelisusuallyadoptedtomathematicallydescribetheoccupancyofnewsitesbyspeciesasfollows

wheredik=distancebetweentwolocationsi and k and aisaparam-eterregulatingthedispersalfromlocalizedareas(lowvaluesofa)towidespreadones (highvaluesofaMeentemeyerAnackerMarkampRizzo2008)

Inthissensedistanceacquiresasignificantroleinecologytoesti-matebiodiversitychangeHencespatiallyexplicitmethodshavebeenacknowledged inecologyforprovidingrobustestimatesofdiversityatdifferenthierarchical levelsfromindividuals (TyrePossinghamampLindenmayer2001)topopulations(Vernesietal2012)tocommu-nities(RocchiniAndreiniButiniampChiarucci2005)

Whendealingwithspatialexplicitmethodsremotesensingimagesrepresent a powerful tool (Rocchini et al 2017) particularlywhencoupling information on compositional properties of the landscapewithitsstructure(Figure1)Remotesensinghaswidelybeenusedforconservationpracticesincludingverydifferenttypesofdatasuchasnightlightsdata(Mazoretal2013)LandSurfaceTemperatureesti-matedfromMODISdata (MetzRocchiniampNeteler2014)spectralindices(Gillespie2005)

Mostoftheremotesensingapplicationsforbiodiversityestima-tionhave reliedon theestimateof localdiversityhotspots consid-eringlandusediversity(Wegmannetal2017)orcontinuousspatialvariabilityofthespectralsignal(Rocchinietal2010)Thisismainlygrounded in the assumption that a higher landscape heterogeneityis strictly related to a higher amount of species occupying differ-ent niches (Scmheller et al in press) However given two sites s1 and s2 the finaldiversity isnotonly relatedto thespeciesspectralrichnessof s1 and s2 but overall to the amountof shared speciesspectralvaluesInotherwordsthelowerthetheirintersections1caps2the higherwill be the total diversity while the lowest total diver-sitywill be reachedwhen s1caps2 = s1cups2 Such intersectionhasbeen

widelystudiedinecologyafterthedevelopmentofβ-diversitytheory(Whittaker1960)

Tuomisto etal (2003) demonstrated the power of substitutingdistance in Equation 1 by spectral distance to directly account forthe distance between sites in an environmental space instead of amerelyspatialoneHoweverwhile spectraldistanceexamplesexistwhenmeasuring theβ-diversity amongpairs of sites (eg RocchiniHernaacutendezStefanoniampHe2015)fewstudieshavetestedthepossi-bilityofmeasuringβ-diversityoverwideareasconsideringseveralsitesatthesametime(howeverseeAlahuhtaetal2017HarrisCharnockampLucas2015)Thisisespeciallytruewhenconsideringthedevelop-mentofremotesensingtools(RocchiniampNeteler2012)fordiversityestimateinwhichtheconceptofβ-diversityisstillpioneering

The aim of this paper is to present themost novelmethods tomeasureβ-diversityfromremotelysensedimagerybasedonthemostrecentlypublishedecologicalmodelsInparticularwewilldealwith(1)multivariatestatisticaltechniques(2)theapplicabilityofthespec-tralspeciesconcept(3)multidimensionaldistancematrices(4)met-ricscouplingabundanceanddistance-basedmeasures

Thismanuscriptisthefirstmethodologicalexampleencompassing(and enhancing)most of the availablemethods for estimating β-di-versityfromremotelysensedimageryandpotentiallyrelatethemtospeciesdiversityinthefield

2emsp |emspMULTIVARIATE STATISTICAL ANALYSIS FOR SPECIES DIVERSITY ESTIMATE FROM REMOTE SENSING

UnivariatestatisticshavebeenusedtodirectlyfindrelationsbetweenspectralandspeciesdiversityHowevertheamountofvariabilityex-plainedbysinglebandsvegetationindicesversusspeciesdiversityisgenerallyrelativelylowduetothefactthatdifferentaspectsrelatedtothecomplexityofhabitatsmightactinshapingdiversityfromdis-turbanceandlanduseatlocalscalestoclimateandelementfluxesatglobalscales

Ordination techniques are designed to quantitatively describemultivariategradualtransitionsinthespeciescompositionofsampledsitesMeasuringthedistancebetweentwosamplingsitesinthemulti-dimensionalordinationspaceisagoodproxyofthechangeinspeciescompositionWhenthismeasureisrelatedtothegeographicaldistance

(1)F=

NsumK=1

eminusdik

a

featuremaps(4)multidimensionaldistancematricesandthe(5)RaosQdiversityEachofthesemeasuresaddressesoneorseveralissuesrelatedtoturnovermeas-urementThismanuscript is thefirstmethodologicalexampleencompassing (andenhancing)mostoftheavailablemethodsforestimatingβ-diversityfromremotelysensedimageryandpotentiallyrelatingthemtospeciesdiversityinthefield

K E Y W O R D S

β-diversityKohonenself-organizingfeaturemapsRaosQdiversityindexremotesensingsatelliteimagerysparsegeneralizeddissimilaritymodelspectralspeciesconcept

emspensp emsp | emsp1789Methods in Ecology and Evolu13onROCCHINI et al

betweentheconsideredsitesthebetadiversityatthisparticularscalecanbeassessed

Of thevariousavailableordination techniquesdetrendedcorre-spondenceanalysis(DCAHillampGauch1980)isparticularlysuitablefor such analyses The axes (ie gradients) of the DCA ordinationspacearescaledinSDunitswhereadistanceof4SDisrelatedtoafullspecies turnoverThisenablesaversatileanalysis thateasily revealswhethertwosampledsitesstillhavespeciesincommon

Several studieshavemapped theordinationspaceusing remotesensing data (eg Feilhauer amp Schmidtlein 2009 Feilhauer FaudeampSchmidtlein2011Feilhaueretal2014GuSinghampTownsend2015Harris etal 2015 Leitatildeoetal 2015Neumannetal 2015SchmidtleinampSassin2004SchmidtleinZimmermannSchuumlpferlingampWeiss2007)Forthispurposetheaxesscoresofthesampledsitesare regressed against the corresponding canopy reflectance values

extractedfromair-orspaceborneimagedataTheresultingmultivar-iateregressionmodelsoneperordinationaxisandmostoftengener-atedwithmachine learning regression techniques are subsequentlyappliedontheimagedataforaspatialpredictionofordinationscoresEachpixeloftheimagedataisassignedtoaspecificpositionintheordinationspacethatindicatesitsspeciescompositionTheresultinggradientmapsareapowerfultoolforanalysesofbetadiversityacrossdifferent spatial scales (Feilhauer amp Schmidtlein 2009 Hernandez-Stefanonietal2012)

AsimpleanalysisofthevariabilityoftheDCAscoresinadefinedpixelneighbourhood(ieamovingwindow)resultsinaefficientbetadiversityassessmentThespatialscaleofthisassessmentcanbevariedeitherbyresamplingthegradientmaptoacoarserspatialresolution(iepixelsize)orbychangingthekernelsizeoftheconsideredpixelneighbourhood Such techniques have been further developed egfor spatial conservationprioritizationprogrammes such asZonation(Moilanenetal2005MoilanenKujalaampLeathwick2009)

Figure2showsanexampleofaDCA-basedassessmentofbetadiversityonaverylocalscale(10m)followingtheapproachdescribedinFeilhauerandSchmidtlein(2009)Theanalysedlandscapeisamo-saicofraisedbogsfenstransitionmiresandMoliniameadowsForadetaileddescriptionofthedataandsitepleaserefertoFeilhaueretal(2014)andFeilhauerDoktorSchmidtleinandSkidmore(2016)

Analyses like this require two different datasets (1) a sampleoffielddatathatisrepresentativeforthevegetationinthestudiedarea and is used to generate theordination space (2) imagedatawithasufficientspectral resolutiontodiscriminatethevegetationtypeswithintheordinationspaceandwithaspatialresolutionthatisinlinewiththesamplingdesignofthefielddata(Feilhaueretal2013)

F I G U R E 1 emsp Anexampleofhowtocoupleinformationoncompositionalpropertiesofthelandscapebyopticaldatatogetherwithstructural(3D)propertiesbylaserscanningLiDARdata

F I G U R E 2 emsp β-diversityassessmentwithacombinationofordinationtechniquesandremotesensing(a)Three-dimensionaldetrendedcorrespondenceanalysis(DCA)ordinationspaceofn=100vegetationplotssampledinraisedbogsfenstransitionmiresandMoliniameadowsinthealpinefoothillsofSouthernGermanyAninter-plotdistanceof4SDcorrespondstoafullspeciesturnover(b)MapsoftheordinationaxesresultingfromaspatialpredictionbasedoncanopyreflectanceEachpixelhasapredictedpositionintheordinationspacethatisindicatedbyitscolourThecolourschemecorrespondsto(a)Themaphasaspatialresolutionof2times2m2whichisinlinewiththesampledplotsize (c)CumulativechangeratesalongthethreeDCAaxesina5times5pixelneighbourhoodAhighchangerateindicatesahighbetadiversity


Using these data the continuous spatial variability of the spec-tralsignalintheimagepixelsistranslatedintoaspatiallycontinuousmeasureofspeciescompositionTheadvantagesofthisapproachareobvioussincethediversityanalysesareconductedinthefloristicgra-dientspacetheresultingmeasuresresemblefieldstudiesandarethuseasiertointerpretthanspectralproxiesandclosertothepointofviewofmanyend-usersFurthermoretheanalysisofordinationscoresindefinedpixelneighbourhoodsisnotrestrictedtoasinglespatialscalebutofferstheopportunitytoimplementassessmentsofbetadiversityonmultiplescales

3emsp |emspTHE SPECTRAL SPECIES CONCEPT

ThespectralspeciesconcepthasbeenproposedbyFeacuteretandAsner(2014a) tomap bothα and β component of the biodiversity usinga unique framework It is rooted in the convergence between twootherconceptsthespectralvariationhypothesis(SVH)proposedbyPalmer Earls Hoagland White andWohlgemuth (2002) and theplantopticaltypesproposedbyUstinandGamon(2010)sustainedbythetechnologicaladvances in thedomainofhighspatial resolu-tionimagingspectroscopyTheSVHstatesthatthespatialvariabilityin the remotely sensed signal that is the spectral heterogeneity isrelatedtoenvironmentalheterogeneityandcouldthereforebeusedasapowerfulproxyofspeciesdiversitySVHhasbeentestedindif-ferentsituations(Rocchinietal2010)andconclusionsshowthattheperformanceofthisapproach isverydependentonseveralfactorsincludingtheinstrumentcharacteristics(spectralspatialandtempo-ral resolution) the typeofvegetation investigatedand themetricsderivedfromremotelysensedinformationtoestimatespectralheter-ogeneityPlantopticaltypesrefertothecapacityofsensorstomeas-ure signals that aggregate information about vegetation structurephenology biochemistry andphysiology Therefore this concept isalsotightly linkedtotheperformancesofthesensorandfindspar-ticularechowiththeincreasinguseofhighspatialresolutionimagingspectroscopyfortheestimationandidentificationofmultiplevegeta-tionproperties

Thedetailsprovidedbyhighspatialresolutionimagingspectros-copyare sufficient toperformanalysesofplantoptical traitsat theindividual treescale inorder todifferentiate treespeciesobtain in-formationaboutleafchemicaltraitsandestimatetheαcomponentofbiodiversity(AsnerampMartin2008AsnerMartinAndersonampKnapp2015ChadwickampAsner2016ClarkampRoberts2012ClarkRobertsampClark2005FeacuteretampAsner2013VaglioLaurinetal2014)TheseresultsillustratethatspectralinformationcanberelatedtotaxonomicorfunctionalinformationofthevegetationwhichsupportstheSVHunderthehypothesisthatthemetricsusedtocomputespectralhet-erogeneityandagivencomponentofvegetationdiversityareprop-erlydefinedHowevertheseapplicationsarecurrentlylimitedbytheimportantamountoffielddatarequiredtotrainregressionorclassi-ficationmodelswhich isalsodirectly linkedtotheir lowgeneraliza-tionabilityintimeandspaceUnsupervisedapproachesthenappearasvaluablealternatives for theanalysisofecosystemheterogeneity

(Baldeck amp Asner 2013 Baldeck etal 2014 Feilhauer Faude ampSchmidtlein2011FeacuteretampAsner2014b)asecologicalindicatorsofα and βdiversityatlandscapescaleusuallyrequireoneorseverallevelsofabstractionbeyondthecorrecttaxonomicidentification(TuomistoampRuokolainen2006)

Clustering(properlypre-processed)spectralinformationshouldre-sultinpixelsfromthesamespeciesnaturallygroupingtogetherratherthandistributing randomlyamongclustersFeacuteretandAsner (2014a)proposedagroupingmethodaimingatassigninglabelstopixelsbasedon multiple clustering of spectroscopic data acquired at landscapescaleThesepixelslabelledwithasetoftheso-calledspectralspeciescan thenbeused straightforwardly in order to computevarious di-versitymetricssuchasShannonindexforαdiversityandBray-Curtisdissimilarity forβ diversityThepre-processing stage is divided intoseveralstagesAftermaskingallnon-vegetatedpixelsanormalizationbased on continuous removal is applied to each pixel and over thefullspectraldomainthenaprincipalcomponentanalysisisperformedonthecontinuouslyremovedspectraldataThenormalizationreduceseffectsduetochangesinilluminationcanopygeometryandotherfac-torsunrelatedtovegetationwhileenhancingthesignalcorrespondingtovegetationThecomponentsincludingindividual-specificinforma-tionarethecomponentsof interestTheycanbe identifiedaftervi-sual inspectionorautomated routines if initialdata showsufficientsignaltonoiseratioOncealimitednumberofcomponentshavebeenselectedk-means clustering is then applied to a certain number ofsubsetsandforeachof thesesubsetscentroidsarecomputedandeachpixelintheimageislabelledbasedontheclosestcentroidTherepetitionofclusteringbasedonvarioussubsetsoftheimagetendstominimizetheriskofassigningcentroidstoirrelevantgroupsofpixelsExperimental results showed that the averaging of diversity indicescomputedfrommultiplecentroidmapscanbeseenasananalogoustosignalaveragingwhichconsists in increasingsignaltonoiseratiobyreplicatingmeasurementsForeachrepetitiontheclosestcentroidcorrespondstothespectralspeciesandforeachspatialunitofagivensizethespectralspeciesdistribution isderivedinordertocomputeanydiversitymetricrequiringeitherinformationatthelocalscaleorcomparisonofinformationacrossspatiallydistantplots

Theconceptsofspectralspeciesandspectralspeciesdistributionhavebeentestedsuccessfullyona limitednumberofsituationsandtypesofecosystems(seeRocchinietal2016forareviewandLauschetal 2016 for an application to similar concepts) As an exampleFeacuteretandAsner(2014a)showedabilitytoproperlyestimatelandscapeheterogeneityatmoderatespatialscaleuptofewdozensquarekilo-metersovertropicalforestsbasedonhighspatialresolutionimagingspectroscopy (Figure 3) A generic parameterization of the methodshowed robust performances for α diversity mapping across spaceandtimebutmappingβdiversityacrosslargespatialscalesusingim-agesacquiredduringdifferentairbornecampaignremainschallengingwhichleadstoanunsolvedproblemwhenconsideringoperationalre-gionalmappingIntheperspectiveofglobalmonitoringofbiodiversityandgiventheunprecedentedremotesensingcapacityallowedbytheCopernicusprogramincludingtheSentinel-2multispectralsatellitesseveralotherchallengesareforeseenandcurrentlyinvestigatedThe


influenceofdecreasedspatialandspectralresolutionontheabilitytoproperlydifferentiateecologicallymeaningfulspectralspeciesacrosslandscapesandoverregionswillneedtobeinvestigatedTheapplica-tionofthisconceptbeyondtropicalforestsandsavannaecosystemsshouldalsobeinvestigatedasitmaynotholdwhenappliedonmoder-atelydiverseecosystemsorsystemswithindividualswhoseindividu-alshavedimensionswellbelowtheresolvingpoweroftheinstrument

4emsp |emspSELF-ORGANIZING FEATURE MAPS

TheKohonenself-organizingfeaturemap(SOFMKohonen1982)isaneuralnetworkthatmaybeusedtoundertakeunsupervisedcluster-ingofdataCriticallytheinputtoaSOFMcanbealargemulti-variatedatasetsuchasmaybeacquiredonspeciesfromquadrat-basedfieldsurveysTheSOFMsummarizesthedata ina lowtypicallytwodi-mensionaloutput(Figure4)Inthisoutputspacethedataforindivid-ualquadratsaretopologicallyorderedmdashwithsitesthataresimilarclosetogetherwhilethoseofhighlydifferentspeciescompositionaremoredistantBecause thedatasites in theoutputspacearearrangedby

F I G U R E 3 emsp Spectralspeciescanbeidentifiedinahyper-ormultispectralimagebyspatialclusteringmethodandtheirdistributioncanbemappedSuchmapscanfurtherbeusedtoapplylocal-basedheterogeneitymeasurements(α-diversity)aswellasiterativedistance-basedmethodstobuildβ-diversitymapsReproducedfromFeacuteretandAsner(2014a)

F I G U R E 4 emsp Aself-organizingfeaturemapcanbebuiltstartingfromaninputlayeregthepresenceorabsenceofatreespeciesorofapeculiarspectralvalue)whichisconnectedtoeveryunitintheoutputlayerbyaweightedconnectionTheself-organizingfeaturemapusesunsupervisedlearningtomapthelocationoffieldsiteswithintheoutputspaceonthebasisoftheirrelativesimilarityinspeciesorspectralcompositionRedrawnfromFoodyandCutler(2003)

Output layer

Input units


relativesimilaritytheoutputspacemayalsobeusedtoaggregateorclassifyadatasetAssuchtheSOFMisattractiveasanon-parametricclusteringanalysisandasameanstoundertakeanordination(ChonParkMoonampCha1996)

ASOFMisunlikesomeoftheapproachesusedcommonlyincom-munityecologynotconstrainedbyassumptionsrelatingthestatisticaldistributionofthedatausedTheSOFMusesunsupervisedlearningtoproduceatopologicallyorderedoutputspaceinwhichthesamplesarearrangedspatiallyinrelationtotheirrelativesimilarityinspeciescompositionTheSOFMthusperformsanon-parametricordinationanalysis(Foody1999)TheproductionofaclassificationbyaSOFMcomprisestwomainstages(GiraudelampLek2001)Aniterativeanaly-sisinwhichtime-decayingparametersthatcontrolnetworklearningandthesizeoflocalneighbourhoodslocatedaroundoutputunitsisusedForthistheusermustspecifyanumberofkeyparameterssuchasthesizeandshapeofthenetworknumberofiterationsoftheal-gorithmthelearningrateanditsrateofdeclineandaneighbourhoodparameterTheneedforsuchparameterscanaddsomeuncertaintytotheanalysisWhile therearenoformal rules to follow in thede-signofaSOFMtherearerecommendationsforthedeterminationofSOFMparametersettings (GiraudelampLek2001)Afurtherconcernis thatasanunsupervisedclassifier theclassesdefinedmaynotal-waysbethemostusefulforaninvestigationInadditionthenatureof theanalysismeans thedirectionof thegradientscannotbecon-trolled(Fritzke1995)buttheanalysisperformswellincomparisontopopularordinationtechniquessuchasPCAandDCA(FoodyampCutler2003)TheSOFMmayalsouseavarietyofdifferentdatatypessuchaspresenceabsenceabundanceorimportancevaluesandcansolvecomplexnonlinearproblems(GiraudelampLek2001)

5emsp |emspMULTIDIMENSIONAL DISTANCE MATRICES GDMS AND SGDMS

Oneofthemostwidespreadmethodsforassessingdiversityisusingdistancematrices (LegendreBorcardampPeres-Neto2005) IndeedearlyworkbyWhittaker (1960)suggested thatβ-diversitycouldbequantifiedbydissimilaritymatricesamong(pairsof)sitesFurthermoretheManteltest(MantelampValand1970)designedtoestimatetheas-sociationbetweentwo independentdissimilaritymatriceshasbeenwidelyusedtocorrelateacommunitycompositiondissimilaritymatrixwithanenvironmentdissimilarityonethusprovidingusefulinsightsinto community composition and turnover (Legendre etal 2005Tahvanainen2011)

Generalized dissimilarity modelling (GDM Ferrier Manion Elith ampRichardson2007)canbeconsideredasanextensionoftheManteltestwhichisabletoaccommodatemultidimensionalenvironmentaldatatobecomparedwiththecompositionaldataGDMsalsoallowforthepredictionofcompositionalturnoveraswellasforegenvironmentalclassificationconstrainedtothecompositionaldissimilarity(Ferrieretal2007Leathwicketal2011)InGDMthecompositionaldissimilaritiesbetweenallpairsofsamplesaremodelledasafunctionoftheirrespectiveenvironmentaldis-tancesThisisdonethroughalinearcombinationofmonotonicI-splinebasis

functionsundertheassumptionthatincreasingenvironmentaldissimilarity (egalongagradient)canonlyresultinincreasingcompositionaldissimilar-ityThismethodisthuswellsuitedformeasuringandmappingβ-diversityandisthusbecomingwidelyusedinconservationscienceandmacroecol-ogyandrecentlybeensubject toseveraldevelopmentsaswedescribebelow

One such development is the phylogenetic GDM (phylo-GDMRosauer etal 2014) which incorporates phylogenetic dissimilari-ties intoGDMandallows foranalysingandpredictingphylogeneticβ-diversity thus linking ecological and evolutionary processes Thismethod can provide novel insights into themechanisms underlyingcurrentpatternsofbiologicaldiversity(GrahamampFine2008)Anotherrecent development of GDM is the multi-site GDM (MS-GDMLatombeHuiampMcGeoch2017)whichextendsGDMsfrompairwisetomulti-sitedissimilaritymodellingInsuchapapertheauthorstestedMS-GDMbymeansofbothconstrained(monotonical)additivemod-elsand I-splinesalthoughwithnoconclusive results relating to thebestmethod overallThey concluded however thatwhen applyingMS-GDMtoahighnumberofsamplestheycouldbetterexplainthedriversofspeciesturnoverAlsoanimportantdevelopmentofGDMis the Bayesian bootstrapGDM (BBGDMWoolley Foster OrsquoHaraWintleampDunstan2017)designedtocharacterizeuncertaintyingen-eralizeddissimilaritymodelsThisapproachallowsbetterrepresentingtheunderlyinguncertainty inthedatabyestimatingthevarianceinparametersbasedontheavailabledata

FinallyanimplementationofGDMwhichwascreatedparticularlyfordealingwithhigh-dimensional (andpotentiallyhigh-collinear) re-motesensingdataasinputinGDMisthesparsegeneralizeddissim-ilaritymodel (SGDMFigure5Leitatildeoetal2015)Thismethod isatwo-stageapproachthatconsistsofinitiallyreducingtheenvironmen-talspace(egreflectancedata)bymeansofasparsecanonicalcorrela-tionanalysis(SCCAFigure5WittenTibshiraniGrossampNarasimhan2013)andthenfittingtheresultingcomponentswithaGDMmodelTheSCCAisaformofpenalizedcanonicalcorrelationanalysisbasedontheL1(lasso)penaltyfunctionandisthusdesignedtodealwithhigh-dimensionaldataThetwoalgorithmsarecoupledinawaythattheSCCApenalizationisselectedthroughaheuristicgridsearchman-ner in order tominimize the cross-validate rootmean square errorin the dissimilarities predicted by the GDM In this procedure thehigh-dimensionalenvironmentaldata (suchascoming fromtimese-riesofmultispectralorhyperspectraldata)aresubjecttoasupervisedordinationapproachthatreducestheirdimensionwhilecapturingtheaxesofvariationthatmostcorrelatetothoseofthecommunitycom-positionalmatrixSGDMhasbeensuccessfullyusedformodellingandmappingthecompositionalturnoverofbothanimalandplantspeciesusingseveraldifferentsourcesofremotesensing(andauxiliary)data(Leitatildeoetal2015LeitatildeoSchwiederampSenf2017)

6emsp |emspRAOrsquoS Q DIVERSITY

Most of the previously shown metrics are based on the distanceamong pixel values in a multidimensional spectral space None


of them considers the relative abundance of such pixel values in aneighbourhood

Bycontrastabundance-basedmetricssuchastheShannonentropycouldoutputsimilarresultsdespiteavariabledistanceamongpixelval-uesAsanexampleconsidera3times3matrixofremotelysenseddata

composedbythefollowingvalues

thenconsideradifferentmatrix

FromaShannonsentropyperspectivesuchmatricesareequalintermsofheterogeneityTheShannonsentropyisindeedbasedontherelativeabundance(andrichness)ofasampleanditsvalueis2197forboth thematricesThisvalue equalling thenatural logarithmofthenumberofclasses(pixelvalues)isalsoShannonsmaximumtheo-reticalvaluegivena3times3matrixduetothelackofidenticalnumbersinthematricesThisexampleexplicitlyshowsthataccountingforthedistanceamongvaluesandtheirrelativeabundanceiscrucialtodis-criminateamongareasintermsofmeasured(modelled)heterogeneity

Oneof themetrics accounting for both the abundance and thepairwisespectraldistanceamongpixelsistheRaosQdiversityindexasfollows

wheredij=spectraldistanceamongpixels i and j and p = proportion ofoccupiedarea

Hence RaosQ is capable of discriminating among the ecologi-caldiversityofmatrices(3)and(4)turningouttobe459and9070respectivelyAppendix S1provides an example spreadsheet to per-formthecalculationwhilethecompleteRcodeisstoredintheGitHubrepositoryhttpsgithubcommattmarspectralrao

Wedecidedtomakeuseofacasestudytohighlightthe impor-tanceofconsideringthedistanceamongpixelvaluesinremotesenseecologicalapplicationTheperformanceofRaosQindexindescrib-ing landscape diversitywas tested in a complex agro-forestry land-scapelocatedinsouthernPortugalAtestsitewithanareaofabout10times10km2(centroidlocatedat38∘39prime1074primeprimeN8∘12prime5230primeprimeW)wasselectedtoconducttheanalysisInthisareaasavanna-likeecosystemcalledmontadooccupiesabout40ofthetestsitefollowedbytra-ditionalolivegrovespasturesvineyardsandirrigatedmonocultures(egcornfields)Montadoisspatiallycharacterizedbythevariabilityofitstreedensity(egGodinhoGilGuiomarNevesampPinto-Correia2016)andthegradientbetweenlowandhightreedensityoverspacecanleadtodifferentstructuralheterogeneityandhabitatdiversity

Within the test site polyculture under the small farming con-text (eg vegetable gardens orchards and cereal crops) is an im-portantfeatureofthislandscapebygeneratingahighcompositionalandconfigurational spatialheterogeneity (Figure6)Themaingoalinusingthiscasestudy is todemonstrate thepotentialandeffec-tivenessoftheRaosQindexinproducingaccuratelyremotesens-ing-based maps of spatial diversity over such complex landscapeFor this study a cloud-free Sentinel-2A (S2A) image acquired onAugust 8 2016wasused to compute theNDVI at a 10m spatialresolutionTheS2Aimagedownloadaswellastheatmosphericcor-rection (DOSmethod)were performed using the Semi-automaticClassificationplugin(SCP)implementedintheQGISsoftware(QGISDevelopmentTeam2016)

TheNDVIwasusedasinputdataforRaosQindexcomputationusingawindowsizeof3times3pixelsTheperformanceoftheRaosQwascomparedtotheShannonEntropyindex(ShannonsH)whichisoneofthesimplestandwidelyusedremotesensing-baseddiversitymeasures for landscape heterogeneity assessment (Rocchini etal2016)To investigatewhetherbothdiversity indicesdifferbetween

(2)

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18 20 23

19 21 22

⎞⎟⎟⎟⎠

(4)

⎛⎜⎜⎜⎝

10 121 227

1 40 251

7 100 149

⎞⎟⎟⎟⎠

(5)Q =

sumsumdij times pi times pj

F I G U R E 5 emsp AnexampleofthesparsegeneralizeddissimilaritymodelapproachRemotesensingdataandbiodiversitydatainthefieldcanbecoupledbysparsecanonicalcorrelationanalysis(SCCA)toproducecanonicalcomponentsandacommunitydissimilaritymatrixwhicharethenusedtobuildageneralizeddissimilaritymodel(GDM)tofinallyderiveaβ-diversitymap

0 0

SCCA

GDM

GDM

Remote sensing data Biodiversity data

Canonical components Community dissimilarity

Beta-diversity map


land cover types one-wayANOVA testswere performedThis ap-proachwas used for analysing the degree of dissimilarity betweenRaosQandShannonH indexacross twohighcomplex landcovertypes (1) montado and (2) polyculture To do so a sample of 60squareswith 250times250m2 sizewas randomly selected over thesetwolandcovertypesEachsquarerepresentsasampleof625S2ANDVI pixels thus corresponding to a total of 37500pixels overthe60 squares For the comparisonbetweenboth indices the co-efficient of variation (CV) was calculated for each 250times250m2 Regarding the RaosQ performance Figure 6 clearly points to thesignificant improvementsshownbyRaosQ indexcomparedtotheShannonH index indescribing the spatialdiversity Inparticular itcanbeseenthroughtheFigure6 thatRaosQ indexcanhighlightdifferentgradientsofspatialdiversityofmontadoareaswhichpres-enthightreedensityvariability(Figure6)andthushighspatialhet-erogeneityOne-wayANOVAtestsrevealedthatbothindicesvaluesweresignificantlydifferentbetweenthetwolandcovertypes(mon-tado F=5033p lt 001 polyculture F=8898plt001)Overall

theobtainedresultsdemonstratethecapabilityofRaosQindex inproducingaccuratelandscapediversitymapsinacomplexlandscapesuchastheMediterraneanagro-forestrysystems

7emsp |emspCONCLUSION

In this paper we showed several methods based on ecologicalβ-diversitywhichcanbeinvestigatedbyremotesensingthroughthecalculationofecosystemheterogeneitytoestimatethespatialvariabil-ityofbiodiversityWhenthereisawiderangeofheterogeneityforex-amplewhenthedataincludehomogeneousandheterogeneouszonesnosinglemeasuremightcaptureallthedifferentaspectsofβ-diversity(egBaselga2013)Thatiswhywesuggestedinthismanuscriptmul-tivariateandmultidimensionalmethods(egmultivariatestatisticsandmultidimensionaldistancematrices)basedonthespectralsignalanditsvariabilityoverspacetoaccountfordifferentaspectsofdiversityalsoincludingdistance-andabundance-basedmethods(egtheRaosQ)

F I G U R E 6 emsp UpperpanelsSentinel-2Ascene(August82016)andderivedNDVIfortheagro-forestrysystemstestsitelocatedinsouthernPortugalLowerpanelsresultsfromShannonsHandRaosQindicescomputationShannonindextendstooverestimatethelandscapediversitywhencomparedtotheRaosQindex


Biodiversitymeasuredasspeciesrichnessisoftenusedforconser-vationpurposeshencetheimportanceofavoidinganunder-orover-estimate has been highlighted (Chiarucci etal 2009) Furthermorepairwisedistance-basedmethodsmightbeprofitablyusedtodetectnotonlydiversityhotspotsinanareabutalsothevariationofbiodi-versityoverspaceandpotentiallyovertimeoncemultitemporalsetsofimagesareused

In this paperwe focused on optimizingmeasures of β-diversitybasedonremotesensingdataSuchmeasuresmightbeusedtoregressspeciesdiversityagainstremotelysensedheterogeneitybasedonnewregressiontechniqueswhichmaximizethepossibilityofpredictingthezones in a study area or at larger spatial scales of peculiar conser-vationvalueAsanexampleshrinkageregressionrecentlyappliedinbiodiversityconservation(AuthierSarauxampPeron2017)couldallowadirectfocusonhabitatmodellingwhichisoneofthemajorstrengthsofremotesensing(GillespieFoodyRocchiniGiorgiampSaatchi2008)MoreoversuchanalysismightbeperformedinaBayesianframeworkallowingto(1)modelmultidimensionalcovariateswithnon-stationaryvariationoverspace(RandellTurnbullEwansampJonathan2016)suchasthebandsofsatelliteimagesand(2)modeltheerrorsintheoutputandtheirvariationoverspace(Rocchinietal2017)

Aspreviouslystatedthesuggestedmethodsforβ-diversityes-timation from remote sensing aremainly based on distances butthey could be effectively translated to relative abundance-basedmethodsAsanexampleRocchinietal(2013)introducedthepos-sibility of applying generalized entropy theory to satellite imageswithonesingleformularepresentingacontinuumofdiversitymea-sures changing one parameter One of the best examples in thisframeworkcouldbe theuseofHill numbers inwhichdiversity isexpressedasfollows

whereS=numberofsamplespixelsandpi=relativeabundanceofaspeciesspectralvaluevaryingtheparameterqqDvariesaccordinglyinseveraldiversity indiceseg forq = 0 qD is thesimplenumberofspeciesforlim(q)=1qDequalsShannonsentropyetc(HsiehMaampChao2016)

Furthermore connectivity analysis might also be taken into ac-count (Moilanen etal 2005 2009) For instance a remote sens-ing-based connectivity network among different sites based onβ-diversitymeasurescouldbeappliedfortheestimateof landscapeconnectivityandconsequentgeneticflowasdemonstratedbyVernesietal(2012)Ithasalsobeenshownthatcommunityrelatedbiodiver-sityindicatorsareoftenmissingfromcurrentmonitoringprogrammes(Vihervaaraetal2017)thusmethodssuchasremotesensing-basedRaosQdiversityappliedforvariousecosystemsmightimproveother-wisechallengingmonitoringofbiologicalcommunities

Withthismanuscriptwehopetostimulatediscussionontheavail-ablemethodsforestimatingβ-diversityfromremotelysensedimagerybyproposinginnovativetechniquesgroundedonecologicaltheory

ACKNOWLEDGEMENTS

WearegratefultothehandlingEditorandtotwoanonymousreview-erswhoimprovedwithskillfulsuggestionsapreviousversionofthepresentmanuscriptDRwaspartiallysupportedby (i) theEU-LIFEproject LIFE14ENVIT000514 FutureForCoppices (ii) the H2020project ECOPOTENTIAL (Grant Agreement no 641762) iii) theH2020TRuStEE-TrainingonRemoteSensingforEcosystemmodEl-ling-project(GrantAgreementno721995)

AUTHORSrsquo CONTRIBUTIONS

All authors contributed to the development and writing of themanuscript

DATA ACCESSIBILITY

Partofthedataandcorrespondingoriginalsourcesareavailableatthefollowinghttpsdoiorg105061dryaddg31k

ORCID

Duccio Rocchini httporcidorg0000-0003-0087-0594

Sandra Luque httporcidorg0000-0002-4002-3974

Nathalie Pettorelli httporcidorg0000-0002-1594-6208

Lucy Bastin httporcidorg0000-0003-1321-0800

Hannes Feilhauer httporcidorg0000-0001-5758-6303

Jean-Baptiste Feacuteret httporcidorg0000-0002-0151-1334

Giles M Foody httporcidorg0000-0001-6464-3054

Yoni Gavish httporcidorg0000-0002-6025-5668

William E Kunin httporcidorg0000-0002-9812-2326

Angela Lausch httporcidorg0000-0002-4490-7232

Pedro J Leitatildeo httporcidorg0000-0003-3038-9531

Markus Neteler httporcidorg0000-0003-1916-1966

Carlo Ricotta httporcidorg0000-0003-0818-3959 Martin Wegmann httporcidorg0000-0003-0335-9601

Harini Nagendra httporcidorg0000-0002-1585-0724

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AsnerGampMartinR(2008)Spectralandchemicalanalysisoftropicalfor-estsScalingfromleaftocanopylevelsRemote Sensing of Environment1123958ndash3970httpsdoiorg101016jrse200807003

AsnerGPMartinREAndersonCBampKnappDE(2015)QuantifyingforestcanopytraitsImagingspectroscopyversusfieldsurveyRemote Sensing of Environment15815ndash27httpsdoiorg101016jrse2014 11011

(6)qD=

(Ssumi=1

pqi

) 1

1minusq


AuthierMSarauxCampPeronC(2017)VariableselectionandaccuratepredictionsinhabitatmodellingAshrinkageapproachEcography40549ndash560httpsdoiorg101111ecog01633

BaldeckCampAsnerG(2013)Estimatingvegetationbetadiversityfromairborne imaging spectroscopy and unsupervised clustering Remote Sensing52057ndash2071httpsdoiorg103390rs5052057

BaldeckCAampColganMSFeacuteretJ-BLevickSRMartinREampAsner G P (2014) Landscape-scale variation in plant communitycomposition of an African savanna from airborne species mappingEcological Applications2484ndash93httpsdoiorg10189013-03071

BaselgaA(2013)Multiplesitedissimilarityquantifiescompositionalhet-erogeneity among several sites while average pairwise dissimilaritymaybemisleadingEcography36124ndash128httpsdoiorg101111 j1600-0587201200124x

Chadwick K amp Asner G (2016) Organismic-scale remote sensing ofcanopyfoliar traits in lowlandtropical forestsRemote Sensing887httpsdoiorg103390rs8020087

ChiarucciABacaroGRocchiniDRicottaCPalmerMWampScheinerSM (2009) Spatially constrained rarefaction Incorporating the au-tocorrelatedstructureofbiologicalcommunitiesinsample-basedrar-efactionCommunity Ecology 10 209ndash214 httpsdoiorg101556comec102009211

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ClarkM LampRobertsDA (2012) Species-level differences inhyper-spectral metrics among tropical rainforest trees as determined bya tree-based classifier Remote Sensing 4 1820ndash1855 httpsdoiorg103390rs4061820

ClarkMRobertsDampClarkD(2005)HyperspectraldiscriminationoftropicalrainforesttreespeciesatleaftocrownscalesRemote Sensing of Environment96375ndash398httpsdoiorg101016jrse200503009

FeilhauerHampSchmidtleinS(2009)MappingcontinuousfieldsofalphaandbetadiversityApplied Vegetation Science12429ndash439httpsdoiorg101111j1654-109x200901037x

FeilhauerHFaudeUampSchmidtleinS(2011)CombiningIsomapordi-nationandimagingspectroscopytomapcontinuousfloristicgradientsin a heterogeneous landscape Remote Sensing of Environment 1152513ndash2524httpsdoiorg101016jrse201105011

FeilhauerHThonfeldFFaudeUHeKSRocchiniDSchmidtleinS(2013)Assessingfloristiccompositionwithmultispectralsensorsacomparisonbasedonmonotemporalandmultiseasonal fieldspectraInternational Journal of Applied Earth Observation and Geoinformation21218ndash229httpsdoiorg101016jjag201209002

FeilhauerHDoktorDLauschASchmidtleinSSchulzGampStenzelS(2014)MappingNatura2000habitatsandtheirlocalvariabilitywithremote sensingApplied Vegetation Science17 765ndash779httpsdoiorg101111avsc12115

FeilhauerHDoktorDSchmidtleinSampSkidmoreAK(2016)MappingpollinationtypeswithremotesensingJournal of Vegetation Science27999ndash1011httpsdoiorg101111jvs12421

FeacuteretJ-BampAsnerGP (2013)Tree speciesdiscrimination in tropicalforests using airborne imaging spectroscopy IEEE Transactions on Geoscience and Remote Sensing5173ndash84httpsdoiorg101109TG RS20122199323

FeacuteretJ-BampAsnerGP(2014a)Mappingtropicalforestcanopydiversityusing high-fidelity imaging spectroscopy Ecological Applications 241289ndash1296httpsdoiorg10189013-18241

FeacuteretJ-BampAsnerGP(2014b)MicrotopographiccontrolsonlowlandAmazonian canopy diversity from imaging spectroscopy Ecological Applications241297ndash1310httpsdoiorg10189013-18961

FerrierSManionGElithJampRichardsonK(2007)Usinggeneralizeddissimilaritymodelling toanalyseandpredictpatternsofbetadiver-sityinregionalbiodiversityassessmentDiversity and Distributions13252ndash264httpsdoiorg101111j1472-4642200700341x

FoodyGM(1999)Applicationsoftheself-organisingfeaturemapneuralnetworkincommunitydataanalysisEcological Modelling12097ndash107httpsdoiorg101016s0304-3800(99)00094-0

FoodyGMampCutlerMEJ(2003)TreebiodiversityinprotectedandloggedBorneantropicalrainforestsanditsmeasurementbysatelliteremote sensing Journal of Biogeography30 1053ndash1066 httpsdoiorg101046j1365-2699200300887x

FritzkeB(1995)GrowinggridndashAselforganizingnetworkwithconstantneighborhoodrangeandadaptationstrengthNeural Processing Letters29ndash13httpsdoiorg101007bf02332159

GillespieTW (2005) Predictingwoody-plant species richness in trop-ical dry forests a case study from South Florida USA Ecological Applications1527ndash37httpsdoiorg10189003-5304

Gillespie T W Foody G M Rocchini D Giorgi A P amp Saatchi S(2008)Measuring andmodeling biodiversity from spaceProgress in Physical Geography32203ndash221httpsdoiorg10117703091333 08093606

GiraudelJLampLekS(2001)AcomparisonofSOMalgorithmandsomeconventional statistical methods for ecological community ordina-tion Ecological Modelling 146 329ndash339 httpsdoiorg101016s0304-3800(01)00324-6

GodinhoSGilAGuiomarNNevesNampPinto-CorreiaT (2016)Aremote sensing-basedapproach toestimatingmontadocanopyden-sityusingtheFCDmodelAcontributiontoidentifyingHNVfarmlandsin southern Portugal Agroforestry Systems 90 23ndash34 httpsdoiorg101007s10457-014-9769-3

GrahamCHampFinePVA(2008)PhylogeneticbetadiversityLinkingeco-logicalandevolutionaryprocessesacrossspaceintimeEcology Letters111265ndash1277httpsdoiorg101111j1461-0248200801256x

GuHSinghAampTownsendPA(2015)Detectionofgradientsoffor-estcompositioninanurbanareausingimagingspectroscopyRemote Sensing of Environment 167 168ndash180 httpsdoiorg101016jrse201506010

HarrisACharnockRampLucasRM(2015)Hyperspectralremotesens-ingofpeatlandfloristicgradientsRemote Sensing of Environment16299ndash111httpsdoiorg101016jrse201501029

Hernandez-Stefanoni J L Gallardo-Cruz J A Meave J A RocchiniDBello-PinedaJampLoacutepez-MartiacutenezJO (2012)Modelingα- and β-diversityinatropicalforestfromremotelysensedandspatialdataInternational Journal of Applied Earth Observation and Geoinformation19359ndash368httpsdoiorg101016jjag201204002

Hill M O amp Gauch H G 1980 Detrended correspondence analysisAn improvedordination techniqueVegetatio42 47ndash58 httpsdoiorg101007978-94-009-9197-2_7

HsiehTCMaKHampChaoA(2016)iNEXTAnRpackageforrarefactionandextrapolationofspeciesdiversity(Hillnumbers)Methods in Ecology amp Evolution71451ndash1456httpsdoiorg1011112041-210x12613

KohonenT(1982)Analysisofasimpleself-organizingprocessBiological Cybernetics44135ndash140httpsdoiorg101007bf00317973

LatombeGHuiCampMcGeochMA(2017)Multi-sitegeneraliseddis-similaritymodellingUsingzetadiversitytodifferentiatedriversofturn-overinrareandwidespreadspeciesMethods in Ecology and Evolution8431ndash442httpsdoiorg1011112041-210x12756

LauschABannehrLBeckmannMBoehmCFeilhauerHHackerJMhellipCordAF (2016)Linkingearthobservationandtaxonomicstructural and functional biodiversity Local to ecosystem perspec-tives Ecological Indicators 70 317ndash339 httpsdoiorg101016jecolind201606022

LeathwickJRSnelderTChaddertonWLElithJJulianKampFerrierS(2011)Useofgeneraliseddissimilaritymodellingtoimprovethebiolog-icaldiscriminationofriverandstreamclassificationsFreshwater Biology5621ndash38httpsdoiorg101111j1365-2427201002414x

LegendrePBorcardDampPeres-NetoPR(2005)Analyzingbetadiver-sityPartitioningthespatialvariationofcommunitycompositiondataEcological Monographs75435ndash450httpsdoiorg10189005-0549


LeitatildeoPJSchwiederMSuessSCatryIMiltonEJMoreiraFhellipHostert P (2015) Mapping beta diversity from space Sparse gen-eralised dissimilarity modelling (SGDM) for analysing high-dimen-sionaldataMethods in Ecology and Evolution6764ndash771httpsdoiorg1011112041-210x12378

LeitatildeoPSchwiederMampSenfC(2017)sgdmAnRpackageforperform-ingsparsegeneralizeddissimilaritymodellingwithtoolsforgdm ISPRS International Journal of Geo-Information623httpsdoiorg103390ijgi6010023

MantelNampValandRS(1970)AtechniqueofnonparametricmultivariateanalysisBiometrics26547ndash558httpsdoiorg1023072529108

MazorTKarkSPossinghamHPRocchiniDLevyYRichardsonAJampLevinN(2013)Cansatellite-basednightlightsbeusedforconser-vationThecaseofnestingseaturtlesintheMediterraneanBiological Conservation 159 63ndash72 httpsdoiorg101016jbiocon201211 004

MeentemeyerRKAnackerBLMarkWampRizzoDM(2008)Earlydetectionofemergingforestdiseaseusingdispersalstimationandeco-logical nichemodeling Ecological Applications18 377ndash390 httpsdoiorg10189007-11501

MetzMRocchiniDampNetelerM(2014)Surfacetemperaturesatthecontinental scaleTracking changeswith remote sensingatunprece-dented detail Remote Sensing63822ndash3840httpsdoiorg103390rs6053822

MoilanenA FrancoAMA Early R Fox RWintle B amp ThomasC D (2005) Prioritizing multiple-use landscapes for conservationMethods for large multi-species planning problems Proceedings of the Royal Society B Biological Sciences 272 1885ndash1891 httpsdoiorg101098rspb20053164

MoilanenA Kujala H amp Leathwick J R (2009)The zonation frame-workandsoftware forconservationprioritization InAMoilanenKWilson amp H P Possingham (Eds) Spatial conservation prioritization Quantitative methods amp computational tools (pp 196ndash210) OxfordOxfordUniversityPress

NeumannCWeissGSchmidtleinSItzerottSLauschADoktorDampBrellM(2015)Ecologicalgradient-basedhabitatqualityassessmentfor spectralecosystem monitoring Remote Sensing 7 2871ndash2898httpsdoiorg103390rs70302871

PalmerMWEarlsPGHoaglandBWWhitePSampWohlgemuthT(2002)QuantitativetoolsforperfectingspecieslistsEnvironmetrics13121ndash137httpsdoiorg101002env516

QGISDevelopmentTeam(2016)QGISgeographicinformationsystemOpenSourceGeospatialFoundationAvailableathttpqgisosgeoorg

RandellDTurnbullKEwansKampJonathanP(2016)Bayesianinfer-ence for nonstationary marginal extremes Environmetrics 27 439ndash450httpsdoiorg101002env2403

Rocchini D Petras V Petrasova A Horning N Furtkevicova LNetelerMhellipWegmannM(2017)OpendataandopensourceforremotesensingtraininginecologyEcological Informatics4057ndash61

RocchiniDampNetelerM(2012)Letthefourfreedomsparadigmapplyto ecology Trends in Ecology amp Evolution 27 310ndash311 httpsdoiorg101016jtree201203009

RocchiniDAndreiniButiniSampChiarucciA (2005)Maximizingplantspecies inventory efficiency by means of remotely sensed spectraldistancesGlobal Ecology and Biogeography14 431ndash437 httpsdoiorg101111j1466-822x200500169x

Rocchini D Balkenhol N Carter G A Foody G M Gillespie TWHeK ShellipNetelerM (2010)Remotely sensed spectral heteroge-neityasaproxyofspeciesdiversityRecentadvancesandopenchal-lenges Ecological Informatics 5 318ndash329 httpsdoiorg101016jecoinf201006001

RocchiniDDelucchiLBacaroGCavalliniPFeilhauerHFoodyG M hellip Neteler M (2013) Calculating landscape diversity with

information-theorybased indicesAGRASSGISsolutionEcological Informatics1782ndash93httpsdoiorg101016jecoinf201204002

Rocchini D Hernaacutendez Stefanoni J L amp He K S (2015) Advancingspecies diversity estimate by remotely sensed proxiesA conceptualreview Ecological Informatics 25 22ndash28 httpsdoiorg101016jecoinf201410006

RocchiniDBoydDSFeacuteretJBFoodyGMHeKSLauschAhellipPettorelliN(2016)Satelliteremotesensingtomonitorspeciesdiver-sityPotentialandpitfallsRemote Sensing in Ecology and Conservation225ndash36httpsdoiorg101002rse29

Rocchini D Garzon-Lopez C X Marcantonio M Amici V BacaroG Bastin L hellip Rosaacute R (2017) Anticipating species distributionsHandlingsamplingeffortbiasunderaBayesianframeworkScience of the Total Environment584ndash585 282ndash290 httpsdoiorg101016jscitotenv201612038

RosauerDFFerrierSWilliamsKJManionGKeoghJSampLaffanSW (2014)PhylogeneticgeneraliseddissimilaritymodellingAnewapproachtoanalysingandpredictingspatialturnoverinthephyloge-neticcompositionofcommunitiesEcography3721ndash32httpsdoiorg101111j1600-0587201300466x

SchmellerDWeatherdonLLoyauABondeauABrotonsLBrummittNhellipReganE(inpress)AsuiteofessentialbiodiversityvariablesfordetectingcriticalbiodiversitychangeBiological Reviews

SchmidtleinSampSassinJ(2004)MappingcontinuousfloristicgradientsingrasslandsusinghyperspectralimageryRemote Sensing of Environment92126ndash138httpsdoiorg101016jrse200405004

Schmidtlein S Zimmermann P Schuumlpferling R amp Weiss C (2007)MappingthefloristiccontinuumOrdinationspacepositionestimatedfromimagingspectroscopyJournal of Vegetation Science18131ndash140httpsdoiorg101111j1654-11032007tb02523x

TahvanainenT (2011)Abrupt ombrotrophication of a boreal aapamiretriggered by hydrological disturbance in the catchment Journal of Ecology201199

Tuomisto H amp Ruokolainen K (2006) Analyzing or explaining betadiversityUnderstanding the targets of differentmethods of anal-ysis Ecology 87 2697ndash2708 httpsdoiorg1018900012-9658 (2006)87

TuomistoHPoulsenARuokolainenKMoranRQuintanaCCeliJampCanasG (2003) Linking floristicpatternswith soil heterogeneityandsatellite imagery inEcuadorianAmazoniaEcological Applications13352ndash371httpsdoiorg1018901051-0761(2003)013[0352lfp wsh]20co2

TyreAJPossinghamHPampLindenmayerDB(2001)InferringprocessfrompatternCanterritoryoccupancyprovide informationabout lifehistoryparametersEcological Applications111722ndash1737httpsdoiorg1023073061091

Ustin S L amp Gamon J A (2010) Remote sensing of plant functionaltypesNew Phytologist186795ndash816httpsdoiorg101111j1469- 8137201003284x

VaglioLaurinGChanJC-WChenQLindsellJACoomesDAGuerriero LhellipValentiniR (2014)Biodiversitymapping in a tropi-calWestAfricanforestwithairbornehyperspectraldataPLoS ONE9e97910httpsdoiorg101371journalpone0097910

VernesiC RocchiniD Pecchioli ENetelerMVendraminGGampPaffetti D (2012) A landscape genetics approach reveals ecolog-ical-based differentiation in populations of holm oak (Quercus ilexL)attheirnorthernmostdistributionEDGEBiological Journal of the Linnean Society107458ndash467httpsdoiorg101111j1095-8312 201201940x

VihervaaraPAuvinenA-PMononenLToumlrmaumlMAhlrothPAnttilaShellipVirkkalaR (2017)Howessentialbiodiversityvariablesandre-motesensingcanhelpnationalbiodiversitymonitoringGlobal Ecology and Conservation 10 43ndash59 httpsdoiorg101016jgecco2017 01007


WegmannMLeutnerBFMetzMNetelerMDechSampRocchiniD(2017)rpiAGRASSGISpackageforsemi-automaticspatialpatternanalysis of remotely sensed land cover dataMethods in Ecology and Evolutioninpresshttpsdoiorg1011112041-210x12827

Whittaker R H (1960) Vegetation of the SiskiyouMountains OregonandCaliforniaEcological Monographs30280ndash338httpsdoiorg10 23071943563

Witten D Tibshirani R Gross S amp Narasimhan B (2013) PMAPenalized multivariate analysis R package version 109 RetrievedfromhttpsCRANR-projectorgpackage=PMA

Woolley SN C Foster SDOrsquoHaraTDWintle BA ampDunstanP K (2017) Characterising uncertainty in generalised dissimilaritymodels Methods in Ecology and Evolution 8 985ndash995 httpsdoiorg1011112041-210x12710

SUPPORTING INFORMATION

Additional Supporting Information may be found online in thesupportinginformationtabforthisarticle

How to cite this articleRocchiniDLuqueSPettorelliNetalMeasuringβ-diversitybyremotesensingAchallengeforbiodiversitymonitoringMethods Ecol Evol 201891787ndash1798 httpsdoiorg1011112041-210X12941


1emsp |emspINTRODUCTION

Biodiversitycannotbefullyinvestigatedwithoutconsideringthespa-tialcomponentofitsvariationInfactitisknownthatthedispersalofspeciesoverwideareasisdrivenbyspatialconstraintsdirectlyrelatedtothedistanceamongsitesAnegativeexponentialdispersalkernelisusuallyadoptedtomathematicallydescribetheoccupancyofnewsitesbyspeciesasfollows

wheredik=distancebetweentwolocationsi and k and aisaparam-eterregulatingthedispersalfromlocalizedareas(lowvaluesofa)towidespreadones (highvaluesofaMeentemeyerAnackerMarkampRizzo2008)

Inthissensedistanceacquiresasignificantroleinecologytoesti-matebiodiversitychangeHencespatiallyexplicitmethodshavebeenacknowledged inecologyforprovidingrobustestimatesofdiversityatdifferenthierarchical levelsfromindividuals (TyrePossinghamampLindenmayer2001)topopulations(Vernesietal2012)tocommu-nities(RocchiniAndreiniButiniampChiarucci2005)

Whendealingwithspatialexplicitmethodsremotesensingimagesrepresent a powerful tool (Rocchini et al 2017) particularlywhencoupling information on compositional properties of the landscapewithitsstructure(Figure1)Remotesensinghaswidelybeenusedforconservationpracticesincludingverydifferenttypesofdatasuchasnightlightsdata(Mazoretal2013)LandSurfaceTemperatureesti-matedfromMODISdata (MetzRocchiniampNeteler2014)spectralindices(Gillespie2005)

Mostoftheremotesensingapplicationsforbiodiversityestima-tionhave reliedon theestimateof localdiversityhotspots consid-eringlandusediversity(Wegmannetal2017)orcontinuousspatialvariabilityofthespectralsignal(Rocchinietal2010)Thisismainlygrounded in the assumption that a higher landscape heterogeneityis strictly related to a higher amount of species occupying differ-ent niches (Scmheller et al in press) However given two sites s1 and s2 the finaldiversity isnotonly relatedto thespeciesspectralrichnessof s1 and s2 but overall to the amountof shared speciesspectralvaluesInotherwordsthelowerthetheirintersections1caps2the higherwill be the total diversity while the lowest total diver-sitywill be reachedwhen s1caps2 = s1cups2 Such intersectionhasbeen

widelystudiedinecologyafterthedevelopmentofβ-diversitytheory(Whittaker1960)

Tuomisto etal (2003) demonstrated the power of substitutingdistance in Equation 1 by spectral distance to directly account forthe distance between sites in an environmental space instead of amerelyspatialoneHoweverwhile spectraldistanceexamplesexistwhenmeasuring theβ-diversity amongpairs of sites (eg RocchiniHernaacutendezStefanoniampHe2015)fewstudieshavetestedthepossi-bilityofmeasuringβ-diversityoverwideareasconsideringseveralsitesatthesametime(howeverseeAlahuhtaetal2017HarrisCharnockampLucas2015)Thisisespeciallytruewhenconsideringthedevelop-mentofremotesensingtools(RocchiniampNeteler2012)fordiversityestimateinwhichtheconceptofβ-diversityisstillpioneering

The aim of this paper is to present themost novelmethods tomeasureβ-diversityfromremotelysensedimagerybasedonthemostrecentlypublishedecologicalmodelsInparticularwewilldealwith(1)multivariatestatisticaltechniques(2)theapplicabilityofthespec-tralspeciesconcept(3)multidimensionaldistancematrices(4)met-ricscouplingabundanceanddistance-basedmeasures

Thismanuscriptisthefirstmethodologicalexampleencompassing(and enhancing)most of the availablemethods for estimating β-di-versityfromremotelysensedimageryandpotentiallyrelatethemtospeciesdiversityinthefield

2emsp |emspMULTIVARIATE STATISTICAL ANALYSIS FOR SPECIES DIVERSITY ESTIMATE FROM REMOTE SENSING

UnivariatestatisticshavebeenusedtodirectlyfindrelationsbetweenspectralandspeciesdiversityHowevertheamountofvariabilityex-plainedbysinglebandsvegetationindicesversusspeciesdiversityisgenerallyrelativelylowduetothefactthatdifferentaspectsrelatedtothecomplexityofhabitatsmightactinshapingdiversityfromdis-turbanceandlanduseatlocalscalestoclimateandelementfluxesatglobalscales

Ordination techniques are designed to quantitatively describemultivariategradualtransitionsinthespeciescompositionofsampledsitesMeasuringthedistancebetweentwosamplingsitesinthemulti-dimensionalordinationspaceisagoodproxyofthechangeinspeciescompositionWhenthismeasureisrelatedtothegeographicaldistance

(1)F=

NsumK=1

eminusdik

a

featuremaps(4)multidimensionaldistancematricesandthe(5)RaosQdiversityEachofthesemeasuresaddressesoneorseveralissuesrelatedtoturnovermeas-urementThismanuscript is thefirstmethodologicalexampleencompassing (andenhancing)mostoftheavailablemethodsforestimatingβ-diversityfromremotelysensedimageryandpotentiallyrelatingthemtospeciesdiversityinthefield

K E Y W O R D S

β-diversityKohonenself-organizingfeaturemapsRaosQdiversityindexremotesensingsatelliteimagerysparsegeneralizeddissimilaritymodelspectralspeciesconcept

























Output layer

Input units
























(2)

⎛⎜⎜⎜⎝

x11 x12 x13

x21 x22 x23

xd1 xd2 xd3

⎞⎟⎟⎟⎠

(3)

⎛⎜⎜⎜⎝

10 13 15

18 20 23

19 21 22

⎞⎟⎟⎟⎠

(4)

⎛⎜⎜⎜⎝

10 121 227

1 40 251

7 100 149

⎞⎟⎟⎟⎠

(5)Q =



0 0

SCCA

GDM

GDM



Beta-diversity map














ACKNOWLEDGEMENTS




DATA ACCESSIBILITY


ORCID















REFERENCES




(6)qD=

(Ssumi=1

pqi

) 1

1minusq








































































































Output layer

Input units
























(2)

⎛⎜⎜⎜⎝

x11 x12 x13

x21 x22 x23

xd1 xd2 xd3

⎞⎟⎟⎟⎠

(3)

⎛⎜⎜⎜⎝

10 13 15

18 20 23

19 21 22

⎞⎟⎟⎟⎠

(4)

⎛⎜⎜⎜⎝

10 121 227

1 40 251

7 100 149

⎞⎟⎟⎟⎠

(5)Q =



0 0

SCCA

GDM

GDM



Beta-diversity map














ACKNOWLEDGEMENTS




DATA ACCESSIBILITY


ORCID















REFERENCES




(6)qD=

(Ssumi=1

pqi

) 1

1minusq






























































































Output layer

Input units
























(2)

⎛⎜⎜⎜⎝

x11 x12 x13

x21 x22 x23

xd1 xd2 xd3

⎞⎟⎟⎟⎠

(3)

⎛⎜⎜⎜⎝

10 13 15

18 20 23

19 21 22

⎞⎟⎟⎟⎠

(4)

⎛⎜⎜⎜⎝

10 121 227

1 40 251

7 100 149

⎞⎟⎟⎟⎠

(5)Q =



0 0

SCCA

GDM

GDM



Beta-diversity map














ACKNOWLEDGEMENTS




DATA ACCESSIBILITY


ORCID















REFERENCES




(6)qD=

(Ssumi=1

pqi

) 1

1minusq






















































































Output layer

Input units
























(2)

⎛⎜⎜⎜⎝

x11 x12 x13

x21 x22 x23

xd1 xd2 xd3

⎞⎟⎟⎟⎠

(3)

⎛⎜⎜⎜⎝

10 13 15

18 20 23

19 21 22

⎞⎟⎟⎟⎠

(4)

⎛⎜⎜⎜⎝

10 121 227

1 40 251

7 100 149

⎞⎟⎟⎟⎠

(5)Q =



0 0

SCCA

GDM

GDM



Beta-diversity map














ACKNOWLEDGEMENTS




DATA ACCESSIBILITY


ORCID















REFERENCES




(6)qD=

(Ssumi=1

pqi

) 1

1minusq







































































































(2)

⎛⎜⎜⎜⎝

x11 x12 x13

x21 x22 x23

xd1 xd2 xd3

⎞⎟⎟⎟⎠

(3)

⎛⎜⎜⎜⎝

10 13 15

18 20 23

19 21 22

⎞⎟⎟⎟⎠

(4)

⎛⎜⎜⎜⎝

10 121 227

1 40 251

7 100 149

⎞⎟⎟⎟⎠

(5)Q =



0 0

SCCA

GDM

GDM



Beta-diversity map














ACKNOWLEDGEMENTS




DATA ACCESSIBILITY


ORCID















REFERENCES




(6)qD=

(Ssumi=1

pqi

) 1

1minusq




























































































(2)

⎛⎜⎜⎜⎝

x11 x12 x13

x21 x22 x23

xd1 xd2 xd3

⎞⎟⎟⎟⎠

(3)

⎛⎜⎜⎜⎝

10 13 15

18 20 23

19 21 22

⎞⎟⎟⎟⎠

(4)

⎛⎜⎜⎜⎝

10 121 227

1 40 251

7 100 149

⎞⎟⎟⎟⎠

(5)Q =



0 0

SCCA

GDM

GDM



Beta-diversity map














ACKNOWLEDGEMENTS




DATA ACCESSIBILITY


ORCID















REFERENCES




(6)qD=

(Ssumi=1

pqi

) 1

1minusq





























































































ACKNOWLEDGEMENTS




DATA ACCESSIBILITY


ORCID















REFERENCES




(6)qD=

(Ssumi=1

pqi

) 1

1minusq























































































ACKNOWLEDGEMENTS




DATA ACCESSIBILITY


ORCID















REFERENCES




(6)qD=

(Ssumi=1

pqi

) 1

1minusq

















































































































































































































Documents

Measuring β‐diversity by remote sensing: A challenge for ...€¦ · ment of remote sensing tools (Rocchini & Neteler, 2012) for diversity estimate in which the concept of β-diversity