The Earth Observation Data for Habitat Monitoring (EODHaM) system

International

Journal

of

Applied

Earth

Observation

and

Geoinformation

j ou rn a l h o m ep a g e :w w w . e l s e v i e r . c o m / l o c a t e / j a g

The

Earth

Observation

Data

for

Habitat

Monitoring

(EODHaM)

system

Richard

Lucas

_,

_Palma

_Blonda

_,

_Peter

_Bunting

_,

_Gwawr

_Jones

_,

_Jordi

_Inglada

_,

Marcela

Arias

_,

_Vasiliki

_Kosmidou

_,

_Zisis

_I.

_Petrou

_,

_Ioannis

_Manakos

_,

_Maria

_Adamo

_,

Rebecca

Charnock

_,

_Cristina

_Tarantino

_,

_Caspar

_A.

_Mücher

_,

_Rob

_H.G.

_Jongman

_,

Henk

Kramer

_,

_Damien

_Arvor

_,

_{Jo ¯ao}

_Pradinho

_Honrado

_,

_Paola

_Mairota

a_{CentreforEcosystemSciences,SchoolofBiological,EarthandEnvironmentalScience,TheUniversityofNewSouthWales,HighStreet,Kensington,}

NSW2052,Australia

b_{NationalResearchCouncil–InstituteofIntelligentSystemsforAutomation(CNR-ISSIA),ViaG.Amendola122,70126Bari,Italy} c_{InstituteofGeographyandEarthSciences,AberystwythUniversity,Aberystwyth,CeredigionSY233DB,UnitedKingdom} d_{CESBIO(CNES/CNRS/UPS/IRD),18,AvenueEdouardBelin,31401ToulouseCedex9,France}

e_{InformationTechnologiesInstitute(ITI),CentreforResearch&TechnologyHellas,6thkmHarilaou–Thermi,57001Thessaloniki,Greece} f_{Alterra,WageningenUR,Droevendaalsesteeg3,6708PBWageningen,TheNetherlands}

g_{IRD-UMR228ESPACE-DEV,MTD-Montpellier,500rueJean-Franc¸oisBreton,34093MontpellierCedex,France}

h_{InBIO/CIBIO&FaculdadedeCiênciasdaUniversidadedoPorto,EdifícioFC4(Biologia),RuadoCampoAlegre,s/n,4169-007Porto,Portugal} i_{DepartmentofAgro-EnvironmentalandTerrestrialSciences,UniversityofBari,AldoMoro,ViaOrbona4,70126Bari,Italy}

a

r

t

i

c

l

e

i

n

f

o

a

b

s

t

r

a

c

t

Tosupportdecisionsrelatingtotheuseandconservationofprotectedareasandsurrounds,theEU-funded BIOdiversitymulti-SOurcemonitoringSystem:fromSpaceTOSpecies(BIOSOS)projecthasdeveloped theEarthObservationDataforHAbitatMonitoring(EODHaM)systemforconsistentmappingand mon-itoringofbiodiversity.TheEODHaMapproachhasadoptedtheFoodandAgricultureOrganizationLand CoverClassificationSystem(LCCS)taxonomyandtranslatesmappedclassestoGeneralHabitat Cate-gories(GHCs)fromwhichAnnexIhabitats(EUHabitatsDirective)canbedefined.TheEODHaMsystem usesacombinationofpixelandobject-basedprocedures.The1stand2ndstagesuseearthobservation (EO)dataalonewithexpertknowledgetogenerateclassesaccordingtotheLCCStaxonomy(Levels1to 3andbeyond).The3rdstagetranslatesthefinalLCCSclassesintoGHCsfromwhichAnnexIhabitattype mapsarederived.AnadditionalmodulequantifieschangesintheLCCSclassesandtheircomponents, indicesderivedfromearthobservation,objectsizesanddimensionsandthetranslatedhabitatmaps(i.e., GHCsorAnnexI).ExamplesareprovidedoftheapplicationofEODHaMsystemelementstoprotected sitesandtheirsurroundsinItaly,Wales(UK),theNetherlands,Greece,PortugalandIndia.

©2014TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/3.0/).

Introduction

Landuseremainsasignificantdriverofhabitatdegradationand removalandassociatedlossesinbiodiversity.In Europe,Natura 2000siteswereestablishedtohaltsuchlosses(Mücheretal.,2006; Mücher,2009;EC,2011)butincreasinglypressuresfromwithin butparticularlyaroundthesesitesareleadingtodeteriorationof thehabitatstheyweredesigned toprotect (Lomba etal.,2013;

∗ Correspondingauthor.Tel.:+390805929433.

E-mailaddresses:richard.lucas@unsw.edu.au(R.Lucas),blonda@ba.issia.cnr.it (P.Blonda),pfb@aber.ac.uk(P.Bunting),jordi.inglada@cesbio.cnes.fr(J.Inglada), zpetrou@iti.gr(Z.I.Petrou),imanakos@iti.gr(I.Manakos),adamo@ba.issia.cnr.it (M.Adamo),tarantino@ba.issia.cnr.it(C.Tarantino),sander.mucher@wur.nl (C.A.Mücher),damien.arvor@ird.fr(D.Arvor),jhonrado@fc.up.pt(J.P.Honrado).

Mairotaetal.,2013;Vicenteetal.,2013).Quantitativeassessments ofthechangingextentandqualityofhabitatsandthethreatsand pressuresaffectingthesearethereforeurgentlyneeded.

As many protected sites and their surrounds are changing rapidly,theuseofEarthObservation(EO)datacombinedwithlocal knowledgeof thesiteshasbeenadvocatedfor monitoring.The benefitofEOdataisthat theseare acquiredin differentmodes (e.g.,optical,radarandLIDAR)andoftenroutinelyatvarious spa-tialandtemporalscales.However,experts(e.g.,ecologists,reserve wardens,vegetationsurveyors)withgoodknowledgeofthesites beingobservedareoftenneededtoensurethattheinterpretation oftheEOdataisaccurateandthatclassificationoutputsare appro-priateforconservationpurposes.Commonobstaclestoachieving thislinkhaveoftenincludedpartialknowledgeofhabitatsandof theneedsofusersbyEOscientists,andskepticismamongstthe potentialusersontheabilityofEOdatatodelivertheinformation http://dx.doi.org/10.1016/j.jag.2014.10.011

Fig.1. TheFAOlandcoverclassificationschemeencompassingLevels1to3andbeyond.

neededforpracticalconservationandmanagement.Therefore,the knowledgeofbothpartiesneedstobetappedinordertooptimize mapsandmaximizebenefitforpracticalapplications(Lucasetal., 2006,2011;Blondaetal.,2013).Afurtherobstacleisthattherehas beennosystematicapproachtotheclassificationofhabitatsfrom EOdatathatisapplicabletoallsitesandavailableasastandard. Indeed,manyareasaremappedandmonitoredusingarangeof differentdatasources,typesandclassificationschemes. Further-more,theschemesusedhaveoftengenerated classesoflimited valueforconservationpurposes.

Toaddresstheseissues,theFP7-fundedBIOSOSprojectsought todeveloptheEarthObservationDataforHabitatMonitoring (EOD-HAM)system,withthisprovidingastandardizedframeworkfor consistentlandcoverandhabitatmappingandmonitoringinside and around protected areas with particular focus onEuropean Natura2000sitesandtheirsurroundings.Akeycomponentofthe systemistheinclusionofdecisionruleswithinahierarchical clas-sificationstructurewiththesegeneratedfromexpertknowledge frombothecologistsandremotesensingscientists.Thispaper con-veystheframeworkoftheEODHaMsystemandprovidesexamples ofelementsasappliedtoselectedsites.

TheEODHaMsystemoverview Overallstructure

TheEODHaMsystemadoptstheFoodandAgriculture Organi-zation’s(FAO)LandCoverClassificationSystem(LCCS)(diGregorio andJansen,2005;Fig.1),whichshowstheclosestcorrespondence ofanycommonclassificationscheme(Tomasellietal.,2013)to thehabitattaxonomyofGeneralHabitatCategories(GHCs)(Bunce et al.,2008).This hasbeentested previously in the context of habitatandbiodiversitymonitoring(Bunceetal.,2013a)and pro-videsausefulframeworkforEOandinsitudataintegrationfor AnnexImapping.ByusingGHCsincombinationwithinformation onenvironmentalvariables(e.g.,biogeographicalregions,surface moisture)andondominantorindicatorspecies,AnnexIcategories (Bunceetal.,2013b)canbedelineatedalthoughend-user interac-tionisoftenarequirementatthisstage.

TheEOcomponentoftheEODHaMsystem(Fig.2)isbasedon geographicobject-basedimageanalysis(GEOBIA;HayandCastilla, 2008;Blaschkeand Strobl,2001)and alsoincorporatesa pixel-basedanalysis.Thesystemiscomprisedof(a)datainputinvolving preparation and pre-processing (orthorectification, radiometric, atmosphericand/ortopographiccorrection),(b) spectralfeature extraction, segmentationand classification toLCCSLevel2 (1st stage),(c)classificationtoLevel3andbeyond(2ndstage),withthis

involvingexpertknowledge,and(d)translationoftheseclassesto GHCsandAnnexIClasses(3rdstage)ofconservationimportance (EUHabitatsDirective).Anadditionalmodulefocusesonchange detectionandvalidationofoutputs,whichincludemapsofland cover,habitatsandchangesinthese.Theoutputproductsfeed sub-sequentlyintomodulesthatperformecologicalmodelingatthe landscapelevel,biodiversityindicatorsextraction,andbiodiversity indicatorschangedetection.

The processing within the EODHaM is automated, withthe exception of threshold value determination, and is undertaken primarilyusingtheRemoteSensingandGISLibrary(RSGISLib) soft-ware(Buntingetal.,2014),theGeospatialDataAbstractionLibrary (GDAL),andtheORFEOToolbox(Ingladaand Christophe,2009), withXMLand PYTHONscripting. Theclassificationsystemalso makesuseoftheKEAimagefileformat(BuntingandGillingham, 2013),whichallowsforprocessingwithinarasterattributetable (RAT).WithintheRAT,whichhasbeendevelopedsuchthatlarge datasetscanbeefficientlyanalyzed(Clewleyetal.,2014),allpixels ofthesameobjectsharethesameID.Thistableisfirstpopulated withimagedataandderivedproducts(e.g.,vegetationindices)and classcodesareaddedprogressivelyastheclassificationproceeds, withthefinalattributionbeingtheLCCS,GHCsandAnnexIclasses fordifferentperiodsintime.Thisallowschangestobedetected. Allattributescanbereadilyqueriedtofinetuneanystepinthe classificationprocess.

EOdatarequirements

TheEODHaMsystemwasdevelopedforusewithveryhigh res-olution(VHR)optical(includinghyperspectral)data,buthasthe benefit ofbeingable toingestdatafrom anysensor (including radarandLIDAR)andatanyspatialscaleprovidedthatinformation extracted(e.g.,onplantleafphenologyortypeorwaterinundation extent)isrelevanttotheclassificationprocessandaccuratewithin acceptablelimits.AtVHR,theWorldview-2iscurrentlythesensor ofchoicebecausetheeightbandsofspectraldataallowcalculation ofawiderrangeofspectralindicesandimagesacquisitionscan betargetedtoperiodsthatarephenologicallyoptimalforthe dis-criminationoflandcoverclassesandthedetectionofchange.When usingopticaldata,thestandardprocessingprovidesdataexpressed inunitsoftopofatmosphere(TOA)reflectancebutthepreferenceis toremovetheeffectoftheatmospheresodataareprocessedto sur-facereflectance(e.g.,usingthe6Scode;Vermoteetal.,2003)and correctedfortopographicand/orbidirectionaleffects(e.g.,using thealgorithmofShepherdandDymond(2003).

Thesystemhastheadvantageofbeingabletobringin knowl-edge fromthe users (e.g.,whether water bodies are persistent

→

→

→

→

→

Fig.2. OverviewoftheEOcomponentswithintheEODHaMsystem.ThesystemishierarchicalandtopdownandcommenceswiththeclassificationofLevels1and2,with thisbasedonspectralprocessingalone,andcontinuestoLCCSLevel3andbeyond.ThefinalLCCScategoriesarethentranslatedtoGHCsandsubsequentlytoAnnexI.Change analysesarethenperformed.

orotherwise)andalsofromotherdatasources,includingdigital terrainmodels(DTMs),canopyheightmodels(CHMs),estimates ofthenumberofvegetationstrata,andoutputsfromhydrological models(e.g.,inundationextent).Thematicinformationrelatingto thedistributionofurbaninfrastructure,productionagricultureand forestry,andwaterbodies(e.g.,reservoirs)canalsobeincluded. Inthelattercase,ahighlevelofgeometriccorrectionaccuracyis essential.

Fordetailedclassification,informationontheseasonal pheno-logicalbehaviorofvegetation(Fisheretal.,2006;Bhandarietal., 2012;ZhuandWoodcock,2012)isapre-requisitetodistinguish manyoftherequiredlandcoverandhabitattypes.However, phen-ologydiffersdependingontheenvironmentbeingconsidered.For example,inthehumidtropics(e.g.,BrazilianAmazonia),onlyone imageisgenerallyneededbecauseofthegenerallylowvariation inspectralreflectanceovertime.However,forotherareas,what istermedasa‘pre’ora‘post’flushanda‘peak’flushimageare requiredwiththeserelatingtotheperiodsoflowestandhighest productivityofvegetation,respectively. InnorthernEurope,the preandpeakflushimageswouldbeacquiredintheearlyspring andmidsummerrespectively,whereasintheMediterranean,the periodsofacquisitionwouldbeassociatedwiththespring(peak flush)andsummer(postflush).Thiswouldalsobethecasein sea-sonalforests(e.g.,inIndia)wherethereisadistinctdry(preor postflush)seasonandawetseason(peakflush).Insomeregions (e.g.,southern Italy, Wales, India)with seasonalenvironments, moreacquisitionswouldbeneededtodefinethedifferentland cover categoriesand associatedhabitats withtheseacquiredin whatis termedhereas‘transition’periods.Theseoftenpresent thebestopportunitiesfordiscriminatingspecifichabitats, partic-ularlywheretheseareonlydistinguishablespectrallyforavery shortperiodoftime.Multi-dateimageryisalsorequiredtocapture hydrologicalcyclesandparticularlytheperiodicityofinundation, ortodealwiththeseasonalmanagementofsomelanduseclasses (e.g.,ploughingandotherpracticesofannualfarmingcycles).In theLCCSclassification,forexample,waterseasonalityand persis-tenceareconsidered.Waterseasonalityisdefinedonthebasisof sitesbeinginundatedfor2–4monthsormorethan4monthsand waterloggedareas arealsodistinguished.For naturaland artifi-cialwaterbodies,waterpersistenceisdefinedonthebasisofthese beingnon-perennial(1–3months,4–6monthsand7–9months) orperennial(>9months).Tidalareas(withdiurnalvariations)and inundationwithincultivated areasare furtherconsidered.Such informationcanbeobtainedfromtemporalimagery,byreferencing hydrologicalmodelsorfromlocalknowledge.

TheEODHaM1ststage

The EODHaM 1st stage uses only spectral information for extracting objects,segmenting theimagery, and classifyingthe landscapetoLCCSLevel2(Fig.2).Thethreeessentialstepsinthe

EODHaM1ststageare(a)initialextractionofdistinctand identi-fiablefeaturesofvaryingsizeanddimensionwithinthelandscape followedby(b)segmentationoftheremainingareastodividethe landscapeintospectrallyhomogeneousunitsand,oncecompleted, (c)aclassificationofobjectswithintheimagedscene.

Objectextraction

Whilst many studies have focused on segmentingan entire image(e.g.,Lucasetal.,2006),alimitationisthatthealgorithms usedperformwellindelineatingsomelandscapeobjectsbutrarely allobjectsof interest. Indeed,many segmentation methodsare notadaptedtodetectthevarietyofgeographicalentities compris-ingacomplexscene(Marceauetal.,1994).BlaschkeandStrobl (2001)alsoconsiderthatsegmentsinanimagewillnever repre-sentmeaningfulobjectsatallscalesandaddressallapplications, andrecommendamulti-scalesegmentationapproach.

Forthisreason,theEODHaMsystemfirstautomaticallyextracts recognizableobjectsfromtheimagepriortosegmentationthrough an extractionprocedure that utilizesspecific spectral bands or derivedindices(Ariasetal.,2013).Theseobjects,observedinVHR data,includeindividualtrees,hedgerows,roadsandponds.Their detectionistypicallyvalidatedthroughreferencetoexisting the-matic layers(e.g.,buildingsextentorground observations;e.g., oftreecrowns).For example,inWales,over76%ofmobileand staticcaravansweredetectedusingthisapproach.However,the successinextractiondependsuponfactorssuchastheground sur-facetopographyandsolarilluminationaswellasthecontrastofthe featureswithothersinthescene.Thereliabilityofextractionwill alsodecreaseforcompositeobjects(e.g.,farmyards).Asthe resolu-tiondecreases,mostoftheseobjectsbecomelessdistinct(Marceau etal.,1994).Hence,thenumberofobjectsofdifferenttypethatcan beextractedfrom,forexample,10mSPOTHRGor30mLandsat sensordatadecreasewiththespatialresolution.

Within the EODHaM system, and prior to segmentation of VHRimagery,algorithmsforautomaticallyextractingobjects cor-responding to individual tree crowns and clusters of crowns, buildings(including caravans)andhedgerowshavebeen devel-oped within the ORFEO Toolbox. These utilize individual or combinationsofbands,derivedindices(e.g.,entropy)or context-sensitive features such as geometric (e.g. area, compactness, elongatedness), morphological, topological(e.g., adjacency) and non-topological(e.g.,distancebetweenobjects)attributes(Arias et al.,2013).For extracting hedgerows,for example,thresholds ofHaralicktexturemeasuresandbinarymorphologicaloperators (e.g.,dilation,erosionandclosure)areused;connectionsarethen madebetweensegmentssuchthathedgerowsareformed.These thresholdshavebeendefinedwithreferencetoVHRimageryfrom arangeofEuropeansitesbutcanbevariedbytheuser.An exam-pleofdelineatedtreecrownsandclustersofcrownswithinbotha naturalandmanagedsettingisprovidedinFig.3.Dependingupon

Fig.3.Treecrownsandclustersoftreecrownsdelineated(usingfeatureextractionbasedonapixel-basedanalysis)in(a)olivegrovesneartheKalamasDeltainGreeceand (b)anaturalsettingnearRemondesinPortugal.

theirspectraluniformity,largerfieldunitscanalsobedelineated. However,thisiscompromisedwhereboundariesareindistinctora largenumberofobjectswithahighspectraldiversityoccurwithin eachunit.

Segmentation

Followingautomatedextractionoflandscapeobjects,two algo-rithmsareavailablewithintheEODHaM1ststageforsegmenting theremainingareas,withthesebasedprimarilyonspectral infor-mation.However,LIDARdatacanbeintegratedasthisoftenleads toimprovementsinthesegmentationofforestareas.Within RSG-ISLib,andforimagesegmentation,aclusteringandsmallobject eliminationalgorithmisappliedwhilsttheORFEOToolboxuses thealgorithmofComaniciuandMeer(2002).

At this stage, the concept of ‘small’ and ‘large’ objects is introduced.‘Small’objectsaretypicallythosethatcanbediscerned within theimagery and include (in the case of the VHR data) previously extracted objects (e.g.,buildings, trees, hedgerows). These‘smallobjects’areextractedusingtheproceduresoutlined intheobjectextractionsection.However,smallobjectscanalso beobtainedfromwithintheremainderoftheimageby parame-terizingthesegmentationalgorithmssuchthattheobjectsizeis commensuratewithmanyof thefinerbutoftenless distinctor recognizableelementsofthelandscape(e.g.,patchesofshrubor marshygrasslandsinotherwisedryfields).Existingthematic lay-ers(e.g.,representingbuildingsor fieldboundaries)canalsobe integratedwithinthesegmentationprocess,whichoftenresults in splitting of objects. However,the benefit is that the result-ingobjectsalignwithunits(e.g.,roads) thathave alreadybeen mapped.Aseparatesegmentationisthenperformedfortheentire imagetogenerate‘large’ objects,which canbeassociatedwith well-definedlandscapeandmanagement units,includingfields, forestryplantations,urbaninfrastructure (e.g.,airport runways, largeindustrialbuildings)andreservoirs.Theselargerobjectsare generated through parameterization of the segmentation algo-rithm and delineation of specific features (e.g., field or forest boundaries)can often be validated through reference to exist-ingthematiclayers.Thelandscapeobjectsalreadyextracted(e.g., buildings,individualtrees)areignoredinthissegmentation pro-cesssuchthatthese largerunitsarecaptured in theirentirety. However,reference can be made interchangeably betweenthe largeandsmallobjectsthroughtheRATofboththelargeandsmall objectlayers.Thesmallandlargesegmentationgeneratedusingthe RSGISLibcodeisillustratedinFig.4.Inthissegmentation,which

isbasedonanunsupervisedK-meansclusteringoftheimagery, objectsbelowacertainsizeareeliminatedfollowingeachiterations andassociatedpixelsarereassignedtonewclustersinsubsequent iterations.Thenumberofclusters,maximumnumberofiterations anddegreesofspectralchangerequiredbythealgorithmcanbe alteredfromdefaultvaluesascantheminimumsizeofobjectsto beeliminated.Thealgorithmgivesthesameresultwiththesame inputparameters(Clewleyetal.,2014).

Linkingsmallandlargeobjects

Largeobjectscontainsmallerobjects,whichareoftenrelatively homogeneous(spectrally)andspatiallyrelated(Couclelis,2010). Forexample,olivegroves(thelargeobjects)containolivetrees, witheachindividualshowingdifferentcharacteristics(e.g.,crown dimensions,height,speciestype)orsharingattributeswithother similarindividuals (e.g.,orientation,distanceto).Insomecases, however,thelargerobjectmaynotcaptureallofthecomponents ofacompositefeature(e.g.,anairportorcaravanpark)ina land-scapeasitisnotsufficientlywelldelineated.Thisisparticularlythe casewherethecompositefeatureiscomprisedofalargenumberof smallerobjectswithdifferingspectralcharacteristics.Inthiscase, alargeobjectcanbeconstructedfromthesmallersimplerobjects (e.g.,treesofdifferentspeciestypeorbuildings)(Couclelis,2010). Asanexample,acaravanparkmightconsistofindividualcaravans butalsoroadsandgrassvergesandlawns.Hence,byfirst classi-fyingandthencombiningtheseobjects(iftheycanbeextracted) basedon,forexample,proximitybyobjecttypeandconnectivity,a largerobjectcanbeformedanddescribed.Anotherexamplewould beanairportwithrunways,grass areas,terminalbuildingsand roadinfrastructure.Throughthesetwoapproaches,largeobjects aredefinedanddescribed.

ClassificationintheEODHaM1ststage

TheEODHaM1ststageinvolvesclassificationatthepixeland smallobject leveland, in itscurrent implementation, utilizesa sequenceofdecisionrules,withtheseminimizedtoincludeonlya narrowrangeofspectralindicesthatallowdiscriminationofLCCS Level1andLevel2categories(i.e.vegetatedandnotvegetated ter-restrialandaquatic).Whilstthesequenceofrulesanddatalayers usedisset,thethresholdsappliedaresubjecttochangedepending upontheusers’aprioriknowledgeandinterpretationofthescene althoughtheyaregenerallysimilarwithinandoftenbetween envi-ronments(e.g.,TemperateorMediterranean).Atthepixellevel,

Fig.4. ThemeanWorldview-2reflectance(nearinfrared2,redandblueinRGB)associatedwith(a)smalland(b)largeobjectsforamixedforestandagriculturallandscape innorthernPortugal.Featuresextractedpriortosegmentationarealsoindicatedwithtreesdepictedasgreenandurbanareasasblack.

simplebinarymasksrepresenting,forexample,theextentof veg-etationcoveraregenerated. However,atthesmallobjectlevel, therasterattributetable(RAT)associatedwiththeKEAformatis accessedforclassification(BuntingandGillingham,2013).Within theRAT,eachsmallobjecthasauniqueIDwithallpixels associ-atedwitheachobjecthavingthesamecharacteristics.Eachobject withintheRATis thenprogressivelyattributedwiththemeans ofreflectancedataandderived indicescalculatedfromthe pix-elscontained,binarymaskinformation(e.g.,water,notwater)and ultimatelytheclassassigned(e.g.,vegetated,non-vegetated).The largeobjectlayerusedlateralsousestheRAT.

Pixellevel

Classificationatthepixellevelisundertakentogeneratebinary masksoftheextentofdifferentvegetativestates(Table1)andopen water,basedonindicessuchastheNormalizedDifference Veg-etationIndex(NDVI),Plant SenescenceReflectanceIndex(PSRI) andtheWaterBandIndex(WBI)(SimsandGamon,2002;Fig.5). Theseareusedtodiscriminategreen(photosynthetic)andbrown (non-photosynthetic;deadorsenescent)vegetationbutoptions arealsoavailableformappingtheextentofotherstates,including non-submerged,submerged(e.g.,algae)orburnt.Oncemapped, all vegetated states are merged into a vegetated category and remainingpixelsassociatedwithanon-vegetatedcategory.Within thevegetationcategory,woodyvegetationisdifferentiatedusing theratiooftheblueand greenreflectanceortheLIDARcanopy heightmodel(CHM),inpreparationforsubsequentclassification atLevel4.Wheredualormulti-seasonimagesareavailable,the

pixel-levelclassificationofthedifferentvegetativestatesateach timestepisusedtodeterminetheextentofevergreen,deciduous andalsolowproductivityvegetation(e.g.,inaquaticenvironments) andhenceindicatephenology,whichisacomponentoftheLCCS classification.

Smallobjectlevel

Atthesmallobjectlevel,non-vegetated areasareassociated with(a)openwaterandurbaninfrastructure,(extractedfromthe image eithera priori (e.g.,buildings; seeobject extraction sec-tion) or classified using the indices given in Table 2), and (b) remainingobjectsnot classifiedasvegetationusing theindices giveninTable1.Thislattercaseavoidsseparateclassificationof the wide range of non-vegetated surfacesthat are common to manyscenes.Asecondcomponentthenidentifiesaquaticsurfaces, withtheseincludingtheopenwater areasusedforthe classifi-cationofnon-vegetationbutalsosubmergedandnon-submerged aquatic vegetation.Theselatter categories are oftendifficult to differentiatebecauseofenvironmentalvariability(e.g.,extentof inundation,type(water,iceorsnow)andturbidity)andmay war-rant theuse of more specific spectral indices, digital elevation modelsorancillaryinformation(e.g.,theoutputfromhydrological models).Oncedefinedasaquatic,allremainingobjectsareassigned asterrestrialusinganinverserule.TheclassificationatLCCSLevel 2(i.e.,vegetatedornon-vegetatedterrestrialoraquatic)isthen achievedthroughcrosstabulationoftheareasofvegetationand non-vegetationwiththosethatareaquaticandterrestrial.

Table1

Indicesusedintherule-basedimplementationoftheEODHaMSystemforidentifyingvegetatedstates.

Greenvegetation Formula Othervegetativestates Formula Woodyvegetation Formula NDVI _NIR1−R NIR1+R PSRI a RE−G NIR1 BG B G Greennessc G R REPrel b _ RE−(NIR2−R) CHM d _HEIGHT FDIe _ NIR1−(RE+C) a_{Non-photosynthetic/senescent(brown)vegetation.} b_{Non-submergedaquaticvegetation.}

c _{Submergedaquaticvegetation(usedincombinationwiththeWBI;seeTable2).} d _{CanopyHeightModel(e.g.,derivedfromLIDARdata).}

Fig.5. (a)theNDVIand(b)thePSRIrepresentingareasofgreenandbrownvegetationrespectivelyand(c)theWBI,LeCesine,Italy.Thescalebarindicatesvaluesranging fromlow(black)tohigh(white).

TheEODHaM2ndstage

TheEODHaM 2ndstage hastwo maincomponents focusing onclassification toLCCS Level3 and, subsequently, Level 4. In

itscurrentimplementation,theclassificationutilizesuser-defined thresholdsofreflectancedataor derivedindices butotherdata can beincorporated includingDigital Elevation Models (DEMs) andderived measures(such asDTMsandCHMsobtainedfrom,

Table2

Indicesusedintherule-basedimplementationoftheEODHaMsystemforidentifyingnon-vegetatedareas.

Water Formula Soil Formula Urban Formula

NDWI _C−NIR2 C+NIR2 NDSI G−Y G+Y NHFD RE−C RE+C WBI B _NIR1 Brightness B+G+R+NIR1 4 DSMa _Height

A11 A12 A23 A24 B15 B16 B27 B28 Primarydescriptors 1 Lifeform1,2,3 _{  } 2 Cover1,2 _{ } 3 Height1,2,3 _{ } 4 Spatialdistribution1,2,3 _ 5 Leaftype1 _{ } 6 Phenology1 _{ } 7 Lifecycle1 _ 8 Stratification2 _{ } 9 Spatialaspects1,2,3 _{ } 10 Cropcombination1,3 _{ }

11 Cover-relatedculturalpractices  

12 Waterseasonality1,3 _{ } 13 Surfaceaspect1,2 _{ } 14 Macropattern1,2 _{ } 15 Physicalstatus1,3 _{ } 16 Persistence1,3 _{ } 17 Depth1,2 _{ } 18 SedimentLoad1 _{ }

Additionalenvironmentaldescriptors

20 Landform1,2 _{     } 21 Climate       22 Altitude2,3 _{     } 23 Lithology/Soils       24 Erosion1,2 _{     } 25 Cropcover/density1 _{ } 26 Waterquality1 _ 27 Surfaceaspect1,2 _ 28 Vegetation1,2 _{  } 29 Croptype1,3 _{ } 30 Floristicaspect1 _{ } 31 Builtupobjects1,2,3 _ 32 Salinity3 _{ }

LCCScomponentsarederivedfrom1_{opticaland/or}2_{LIDARdata.Somecodescanbeallocatedusing}3_{polarimetricorinterferometricradardata,eithersingularlyorin}

combinationwithopticalorLIDARdata.Allinformationcanbeobtainedoraugmentedbygroundsurveysorinsitudata/knowledge.Notalllayersrequirepopulationfora classificationtobegenerated.

forexample,LIDAR),andthematicinformation(e.g.,presenceof

buildings).SuchinformationisintegratedintotheRAT.Thematic

informationmaybeincludedasanumericvalue(e.g.,indicating

overlaporotherwise)orastring.Thisprovidesawide rangeof

informationthatcanbeexploitedbytheusersforlandcoverand

subsequentlyhabitatdescriptionthroughtheEODHaMprocess.For

theclassificationatLevel3andparticularlyLevel4,expert

knowl-edgeoftheinformationcontentofremotesensingdatainrelation

tothelandcoversbeingconsideredcanbeincorporatedintothe

rules(e.g.,indefiningthresholdbandsandvalues;Adamoetal.,

2014).

ClassificationtoLevel3

TheLCCSLevel3classificationrequiresthatthelandscapebe differentiated according to elements that are cultivated, man-agedorartificialornatural orsemi-natural.Thesystemfocuses onclassifyingthelandscapethrough referencetotheextracted context-sensitiveobjects(e.g.,largeobjectscontainingindividual treesinrows)orexistingthematic(e.g.,cadastral,infrastructure) layers.

Initially,objectsinthesmallobjectlayerareassociatedwith theclasstheyrepresentusing thethematiclabelappliedwhen thesewereextracted(e.g.,trees,buildings).IntheRATforthelarge objectlayer,theproportionofthearearepresentedbythesesmall objectsiscalculated,withthispotentiallyexpandabletoinclude thenumberofobjects,theirorientationandsoon.Somemeasures arecalculatedtoallowfordescriptionofobjectsordisambiguation. Forexample,roundnessisameasureofhowdifferentashapeis

fromacircleandthismeasurecanbeusedtoidentifyanddescribe treecrowns(e.g.,olivetrees).Suchinformationcanthenbeusedto classifyforestplantations(basedontheproportionofpixels repre-sentingwoodyvegetation),orchards(basedonthesizeanddensity oftrees)andurbanareas(basedonthenumber,sizeanddensity ofbuildings)andhencemanagedandcultivatedareas.Eachlarge objectisalsoassociatedwithafieldsizeclassastheLCCS differen-tiatesbetweensmall(≤8ha),medium(≥8haand≤20ha)andlarge (>20ha)areas.Akeycriterionhereistoensureappropriate delin-eationofthelargerenclosingobjects,whichcanbeproblematic (e.g.,whendealingwitholivegroves)andmaynecessitatetheuse ofexistingancillaryinformation(e.g.,relatingtofieldboundaries). Onceareasareassociatedwiththeselabels(i.e.,‘terrestrial’or ‘aquatic’, ‘vegetated’or ‘not vegetated’, ‘cultivated, managedor artificial’,or‘naturalorsemi-natural’),thesearecross-tabulated togenerateaclassificationof Level3categories (e.g.,terrestrial vegetated cultivated/managed; see Fig.1).The accuracy in the classificationofthesecategoriesneedstobehighasseparate clas-sificationsofeachareperformedsubsequentlywhen classifying beyondLevel3.

ClassificationbeyondLevel3

Whenclassifying beyondLevel3, thirty-two separatelayers (columnsintheRAT)aregenerated,whicharepopulated subse-quentlywithclasscodesdefinedwithintheLCCS(Table3).These codesarethencombinedwithintheRATtogeneratethefinal classi-ficationlabel.Asanexample,elevencodesareusedforvegetation. In thefirstinstance,woody(A1)and herbaceous(A2)lifeforms

Fig.6.Classificationsofvegetationasafunctionof(a)heightand(b)coverbasedonLIDARandVHRopticaldatarespectivelyand(c)theresultingLCCSclassificationfor Veluwe,theNetherlands.

andcryptograms(A7)aredifferentiated.Inthewoodycategory, trees(A3)andshrubs(A4)areseparatedwhilstintheherbaceous category,forbs(A5)andgraminoids(A6)aredistinguished. Cryp-togramsaredividedintolichens(A8codeifterrestrialvegetated (A2)andA10ifaquaticvegetated(A24))andmosses(A9ifA12; A11ifA24).Lifeformsarethencategorizedaccordingtotheircover (A12–A20codes,fordifferentpercentagecovers),height(B1–B10, fordifferentvegetationheights),spatialdistribution(continuous

(C1)or fragmented(C2)), leaf type (broad-leaved (D1), needle-leaved(D2)oraphyllous(D3)),seasonality/leafstrategy(evergreen (E1),deciduous(E2),semi-evergreen,E3))and/orlifecycle(annual (E5)orperennial(E6))andstratification(one,twoormorelayers; F1,F2etc.).Theassignedcodesarethencombinedandtranslated toameaningfuldescription;forexample,A12.A3.A11.B5.C1.D1.E1 translatestosemi-naturalopencontinuoushighbroadleaved ever-greenforest.Anexampleof theclassification fortheVeluwein

Fig.7.Areasofthegrassland(GRA),whichcanbedifferentiatedwheretheseoccur adjacent(withaborder)tourbanbuildings(URB).

theNetherlandsisprovidedinFig.6.Inthiscase, the classifica-tionbenefitsfromtheinclusionofLIDARdataastheLCCSdescribes vegetatedareasonthebasisoftheirheight(withdivisionsat0.03, 0.3,0.5,0.8,3,5,7,14,and30m)andcover(withdivisionsat1, 4,15,40and65%).TheLIDARpointclouddatacanalsobebinned onaperunitareabasistodifferentiatevegetationwithoneortwo ormorelayers,withthisdescribingtheirstratification(Miuraand Jones,2010;Buntingetal.,2013).Watercansimilarlybedescribed inrelationtoitsstate(water,iceorsnow),persistence,depth, sed-imentloadandsalinity.IntheEODHaMSystem,notalllayersneed tobepopulatedtogeneratethefinalclassification.Forexample, informationonleaftype(D1,D2orD3)maybemissingorwoody shrubsareunabletobedistinguishedfromtreesandhenceremain aswoody(A1).Hence,landcovermapscanstillbegeneratedeven thoughsomeLCCScodesarenotavailable.

EODHaM3rdstage:translationtoGHCs

Inthe3rdstageofEODHaM,GHCsandAnnexIhabitatmaps areautomaticallyproducedbasedonatranslationfromtheLCCS classes extracted in theprevious stages. Mapping relationships and discrepancies in definitions between the LCCS and GHCs taxonomiesareoutlinedinKosmidouetal.(2014).Several one-to-manyrelationshipsfromLCCStoGHCsclasseshavebeenobserved. Toresolvesuchambiguities,additionalsite-specificexpertrules, usingancillarydata,morphologicalandtopologicalfeaturesand contextual information (e.g., proximity, adjacency), have been identifiedandreportedbyAdamoetal.(2014),withthese inte-grated within the EODHaM system. For example, herbaceous graminoids(CHE)adjacenttobuildings(URB),regardlessof den-sity,aredescribedasUrban/Herbaceous(GRA;Fig.7),asdiscussed byBunceetal.(2011).LIDARdatacanalsobeusedtodeterminethe GHCsrelatingtolow,medium,tallorforestphanaerophytes(i.e., LPH,MPH,TPHandFPHrespectively;Bunceetal.,2008,2011).In caseLIDARdataarenotavailable,textureanalysismeasures(i.e., localentropy)areusedassurrogatestodiscriminatevegetation heightcategories(Petrouetal.,2012,2014a;Adamoetal.,2014).

Toincrease therobustness and transferability of the frame-worktolandscapesindifferentgeographicallocations,ascheme

Therationaleistohandleuncertaintyintheoutcomeofexpertrules andmissinginformationandcounteractboththepotentialnoise afflictionofthedataandinaccuraterulethresholdsprovidedbythe experts.Usingalinearmembershipfunction,eachpotentialGHC classeventofacertainobjectisgivenabasicprobabilityassignment value.Incaseinadequateinformationisavailabletodiscriminate among certainpotentialGHCclasses,eventsconsistof multiple GHCclasses.Beliefandplausibilityvaluesarethenassignedtoeach event.Thefinaleventisselectedastheonewiththesmallest num-berofclassesundertherequirementofminimumbeliefvalueof 0.75or0.94,foreventswithsingleormultipleclassesrespectively (Petrouetal.,2014b).

TheEODHaMapproachhasbeenextendedtoincludethe extrac-tionofAnnexImaps,withrulesetsdefinedforthetranslationof LCCStoGHCandtoAnnexIclasses(usingGHCqualifiers)(Tomaselli etal.,2013).TheLCCSattributesandGHCqualifiersreferto addi-tionallayersofinformation(e.g.,lithology,moisture,soilaspect, acidity,elevation,climate),whichhelptoresolveambiguitiesinthe classificationtoAnnexI.Wheneversuchdatalayersare unavail-able,multipleAnnexIclassesmayresultwhentranslated from someGHCsclasses.

Several approaches to asses the accuracy of the land cover and habitatclassificationsare available,withthesebased upon theLCCCclasscomponents,thecombinedLCCSclasscodes, the GHCstranslatedfromtheseand/ortheAnnexIcategories. Accu-racycanbeassessedagainstinsitudataorotherremotesensing data(e.g.,aerialphotography).Asanexample,forthesites con-sidered,usersandproducersaccuracies(Congalton,1991)forthe LCCSclassesweregenerallygreatest(typicallyover97%)for homo-geneous classes(e.g.,coniferous plantations,openwater,active raisedbogintemperateenvironments)butleast(sometimesaslow as_∼30%;closetochance)formorecomplexand heterogeneous landcovers(e.g.,naturalaquaticperennialgraminoidsinItalyand mossesonfloodedlandinWales).

Changedetectionmodules

Awiderangeoftechniqueshavebeendevelopedfordetecting changeusingEOdata(Singh,1989;Bovoloetal.,2012).Common amongsttheseareimageandclassdifferencing,changevector anal-ysis,andcrosscorrelationanalysis(KoelnandBissonnette,2000; Blaschke,2010;Chenetal.,2012).WithintheEODHaMsystem,six typesofchangeassessmentareconsidered,withthesebasedon theuseoftheRATmatrixoftheKEAfileformat:changesin(i)LCCS classes(orGHCs),(ii)LCCScomponentcodes,(iii)thenumberof extractedobjectsbelongingtothesamecategory(e.g.,buildings), (iv)objectsizeandgeometry(splittingormerging),(v)EOdata andderivedmetrics(e.g.,LIDAR-derivedheightortheNDVI),and (vi)calculatedlandscapeindicators.Changedetectionispossible, even whensomeinformation onhabitatsor indicatorsis miss-ing.ExamplesofchangesthataredetectedareprovidedinFig.8, wherebyindividualcaravansaretrackedovertime(delineatedtree crownscouldsimilarlybemappedovertimetoquantifylosses)and changesinclassassignment(vegetationtowater)andthePSRIare indicated.

Discussion

UseoftheLCCSandGHCstaxonomies

WhenclassifyinglandcoversfromEOdata,acommonapproach has been to take training areas for pre-defined classes (e.g.,

Fig.8. Classificationsofchange(circled)inCorsFochno,Wales,showing(a)anincreaseinthenumberofextractedobjects(inthiscase,mobilecaravans),(b)waterinundation (blue)fromfloodingandtides,withthisdeterminedasachangeintheLCCSclasslabelandsub-code,and(c)diebackofstandsofP.australis(whitershades)betweenTime 1andTime2,asindicatedbyanincreaseinthePSRI.ThesechangeobservationsgivelandmanagersevidenceofthechangesoccurringwithinandsurroundingtheNatura 2000site.

broadleaveddeciduousforest)and usethestatistics fromthese toprovideaclassificationresult(e.g.,Muchoneyetal.,2000;Xie etal.,2008).TheEODHaMsystemtakesa differentapproachby classifyingthecomponentsofeachLCCSclass(e.g.,broadleaved, deciduousand forest)basedonarange ofinputdata(airborne, spaceborne, thematic) and then combining these to provide a meaningful description(i.e.,broadleaveddeciduousforest).The mainbenefitisthattheclasses,whichcanbenumerous,are rel-evanttoanylocationworldwide(byvirtueofusingtheLCCSand GHCtaxonomies)andtheclassificationcanbeappliedatanyspatial scaleprovidedthatfeatureswithinthelandscapecanbeadequately resolvedandappropriate spectraldata (intermsof wavelength regions)areavailable.Hence,sensorssuchastheWorldview-2, Quickbirdand/orLandsatdatacanbeusedtoclassifysitesranging insizefromseveraltohundredsofkm2_{.Whereavailable,LIDAR}

datahave alsobeen integrated primarily to classifyvegetation

heightandcover.Whereonlycoarserspatialresolutiondataare available,suchasprovidedbytheLandsatsensors,the classifica-tionofsomeelementsofthelandscape(e.g.,buildings,hedgerows) maynotbeachieved.Ingeneral,thesequence ofrulesusedfor classificationremainsthesamebutthethresholdvaluesmayvary dependingupon theimage dataused,prevailingenvironmental conditionsandlocalknowledge.Thresholdsaregenerally trans-ferabletootherregionswhenusingdatafromthesamesensors, withpreferencegiventoimageryacquiredpriortoandduringthe peakvegetationflushperiod.

Detectionofchange

Mostchangedetectiontechniquestypicallyfocusonjustone elementofthechangeprocess, whetherit bea changeinclass (e.g.,toindicateprocessesofdeforestation)orreflectancevalues

cessconsidersdifferencesintheLCCSclasscodes(includingtheir components),thenumbers,sizesanddimensionsofobjectsinthe landscape,anddifferencesinimagedatavaluesandderived meas-ures.Assuch,theEODHaM systemprovidesusers witha more completeinsightintothechangesthatareoccurringwithin pro-tectedareasandtheirsurroundsandalsoresultsthatareeasierto interpret.Ratherthanstatingonlythatachangehastakenplace, thechangeitselfisdescribedindetail.

Limitationsofapproach

Thegreatest difficultyinthemapping oftheLCCSclasses is thedifferentiationofaquaticvegetationaswellascultivatedand managedareas.Thiscanbepartlyovercomebydeveloping context-sensitive rules or referring to existing ancillary data layers or hydrologicalmodels.Inthecaseofancillarylayers,cadastral infor-mationcanbeusedtoidentifyvegetationoccurring,forexample,in riceorcottonfields.Whilsttheclassificationiscurrentlyrule-based, outputsfromotherclassificationprocedures(e.g.,supervised, sup-portvectormachines)canbeused.Forexample,componentsof theseclassifications(e.g.,mapsof forestextent,waterorurban areas)canbeusedasdirectinputtotheEODHaMsystem.A fur-therlimitationisthatsubpixelproportions(e.g.,ofplantfunctional types)arenotyetintegratedwithintheclassification.Thesecan, however,bedeterminedaprioribasedon,forexample,spectral end-memberunmixingorfuzzyclassificationandaddedtotheRAT. EachobjectwouldthenbeclassifiedaccordingtotheLCCS taxon-omybasedonthedominantclass(e.g.,woodyshrubs,graminoids, forbs)orbygenusorspecies,withinformationontherelative pro-portionsoftheremaininglifeforms,generaorspeciesoccurring withintheobjectalsoretainedforfurtheranalysis.

Summaryandconclusions

TheEODHaMsystem(whichisoutlinedinFig.2)iscomprised of softwarethat allows for classification withinthe framework oftheFAOLCCSandsubsequenttranslationoftheLCCSclasses (Level3andbeyond)toGHCsand AnnexIhabitats(takinginto accountambiguities).Thesetaxonomiesarerecognizedfortheir globalapplication.TheEODHaMsystemwasdevelopedusingVHR databutdatafrommoderateresolutionopticalsensorscanalso beusedasthesameLCCStaxonomyisapplied.However,thereis ascaledependencyonthenumberandtypesofclassesthatcan bediscriminated.Classificationscanalsobeundertakenbasedon knowledgeoftheuserwithouttheneedforextensivegroundtruth datasets.Changedetectionmodulesareavailablewiththesebased onchanges inLCCScodes andclasses,spectral dataand indices andcountsofextractedobjects(e.g.,trees,buildings).Thesystem islargelyautomated,withuserinteractionrequiredprimarilyto fine-tunethethresholdsusedintherule-basedsystem.Thesystem benefitsfrominteractionsbytheendusersinrefiningtherulesused forclassificationandselectingancillarydata(e.g.,tidallevels) rele-vanttothediscriminationoflandcovertypes.Overall,theEODHaM systemprovidesaframeworkandpotentiallyoperationalapproach tothemonitoringofprotectedareasandtheirsurroundsfromEO data.

BasedonEOdataavailability,themethodshavebeen success-fullyappliedtoNatura2000sitesandtheirsurroundsinWales,the NetherlandsandItalyandhavebeenevaluatedatotherlocations inGreece,Portugaland India,thereby coveringarangeof envi-ronmentalandbiogeographiccontexts.Ineachcase,aconsistent

source software and uses a diversity of EO and also ancillary datasets.Thesystemcanbereadilyadoptedbymanagersof pro-tectedsitesandtheirsurroundsforconsistentlandcover,habitat andultimatelybiodiversitymonitoringwithinandbetweensites andimprovingtheeffectivenessofpolicyandmanagementwhilst copingwithnationalandinternationalreportingobligations.

References

Adamo,M.,Tarantino,C.,Tomaselli,V.,Kosmidou,V.,Petrou,Z.I.,Manakos,M., Lucas,R.M.,Mücher,C.A.,Veronico,G.,Marangi,C.,DePasquale,V.,Blonda, P., 2014. Expert knowledge for translating land cover/use maps to gen-eralhabitatcategories(GHC).Landsc.Ecol.29,1045–1067,http://dx.doi.org/ 10.1007/s10980-014-0028-9.

Arias,M.,Inglada,J.,Lucas,R.M.,Blonda,P.,2013.HedgerowsegmentationonVHR opticalsatelliteimagesforhabitatmonitoring.In:Proceedings,Geoscienceand RemoteSensingSymposium(IGARSS),pp.3301–3304.

Bhandari,S.,Phinn,S.R.,Gill,T.,2012.PreparingLandsatimagetimeseries(LITS)for monitoringchangesinvegetationphenologyinQueensland,Australia.Remote Sens.4,1856–1886.

Blaschke,T.,2010.Objectbasedimageanalysisforremotesensing.ISPRSJ. Pho-togram.Rem.Sens.65,2–16.

Blaschke,T.,Strobl,J.,2001.What’swrongwithpixels?Somerecent develop-mentsinterfacingremotesensingandGIS.InterfacingRemoteSens.GIS6, 12–17.

Blonda,P.,Lucas,R.,Honrado,J.P.,2013.FromSpacetospecies:Solutionsfor biodi-versitymonitoring.In:WindowonCOPERNICUS,SpecialIssueonDiscoverwhat CopernicuscandoforEuropeanregionsandcities.SuccessStories.,pp.66–73, http://copernicus4regions.eu/dirpublication.

Bovolo,F.,Marchesi,S.,Bruzzone,L.,2012.Aframeworkforautomaticand unsuper-viseddetectionofmultiplechangesinmultitemporalimages.IEEETrans.Geosci. RemoteSens.50(6),2196–2212.

Bunce,R.H.G.,Metzger,M.J.,Jongman,R.H.G.,Brandt,J.,deBlust,G.,ElenaRossello, R.,Groom,G.B.,Halada,L.,Hofer,G.,Howard,D.C.,Kováˇr,P.,Mücher,C.A., Padoa-Schioppa,E.,Paelinx,D.,Palo,A.,Perez-Soba,M.,Ramos,I.L.,Roche,P.,Skånes, H.,Wrbka,T.,2008.Astandardizedprocedureforsurveillanceandmonitoring Europeanhabitatsandprovisionofspatialdata.Landsc.Ecol.23,11–25. Bunce, R.G.H., Bogers, M.M.B., Roche, P., Walczak, M., Geijzendorffer, I.R.,

Jongman, R.H.G., 2011. Manual for Habitat Surveillance and Monitoring and Vegetationin Temperate,Mediterranean and DesertBiomes. Alterra-EBONEHandbookv20110131.http://wageningenur.nl/ebone

Bunce,R.G.H.,Bogers,M.M.B.,Evans,D.,Halada,L.,Jongman,R.H.G.,Mucher,C.A., Bauch,B.,deBlust,G.,Parr,T.W.,Olsvig-Whittaker,L.,2013a.Thesignificance ofhabitatsasindicatorsofbiodiversityandtheirlinkstospecies.Ecol.Indic.33, 19–25.

Bunce,R.G.H.,Bogers,M.M.B.,Evans,D.,Jongman,R.H.G.,2013b.Fieldidentification ofhabitatsdirectiveAnnexIhabitatsasamajorEuropeanbiodiversityindicator. Ecol.Indic.33,105–110.

Bunting,P.,Clewley,D.,Lucas,R.M.,Gillingham,S.,2014.Theremotesensingand GISsoftwarelibrary(RSGISLib).Comp.Geosci.62,216–226.

Bunting,P.,Gillingham,S.,2013.TheKEAimagefileformat.Comp.Geosci.57,54–58. Bunting,P.,Armston,J.,Lucas,R.M.,Clewley,D.,2013.SortedPulseData(SPD) Library.PartI:agenericfileformatforLIDARdatafrompulselasersystems interrestrialenvironments.Comp.Geosci.56,197–206.

Chen,G.,Hay,G.J.,Carvalho,L.M.T.,Wulder,M.A.,2012.Object-basedchange detec-tion.Int.J.RemoteSens.33(No.14),4434–4457.

Congalton,R.G.,1991.Areviewofassessingtheaccuracyofclassificationsofremote sensingdata.RemoteSens.Environ.37,35–46.

Comaniciu,D.,Meer,P.,2002.Meanshift:arobustapproachtowardsfeaturespace analysis.IEEETrans.PatternsAnal.Mach.Intell.24(5),603–619.

Couclelis,H.,2010.Ontologiesofgeographicinformation.Int.J.Geogr.Inform.Sci. 24(12),1785–1809.

Clewley,D.,Bunting,P.,Shepherd,J.,Gillingham,S.,Flood,N.,Dymond,J.,Lucas,R., Armston,J.,Moghaddam,M.,2014.Apython-basedopensourcesystemfor geo-graphicobjectbasedimageanalysis(GEOBIA)utilizingrasterattributetables. RemoteSens.6,6111–6135.

diGregorio,A.,Jansen,L.J.M.,2005.LandCoverClassificationSystem(LCCS): Classi-ficationConceptsandUserManualforSoftware.Version2.TechnicalReport8. FAOEnvironmentandNaturalResourcesServiceSeries,Rome.

EuropeanCommission,2011.OurLifeinsurance,ourcapital:anEUbiodiversity strategyto2020.COM(2011)244final.LuxemburgPublicationOfficeofthe EuropeanUnion,pp.28,http://biodiversity.europa.eu/

Fisher,J.I.,Mustard,J.F.,Vadeboncoeur,M.A.,2006.GreenleafphenologyatLandsat resolution:scalingfromthefieldtothemountain.RemoteSens.Environ.100, 265–279.

Hay,G.J.,Castilla,G.,2008.Geographicobject-basedimageanalysisGEOBIA):anew nameforanewdiscipline.In:Blaschke,T.,Lang,S.,Hay,G.J.(Eds.),Object-Based ImageAnalysis,LectureNotesinGeoinformationandCartography.Springer, Berlin,Heidelberg,pp.75–89.

Marceau,D.J.,Howarth,P.J.,Gratton,D.J.,1994.Remotesensingandthe measure-mentofgeographicalentitiesinaforestedenvironment.1.Thescaleandspatial aggregationproblem.RemoteSens.Environ.49,93–104.

Miura,N.,Jones,S.D.,2010.Characterisingforestecologicalstructureusingpulse typesandheights ofairbornelaser scanning.Remote Sens. Environ.114, 1069–1076.

Muchoney,D.,Borak,J.,Chi,H.,Friedl,M.,Gopal,S.,HodgesMorrow,N.,Strahler, A.,2000.ApplicationoftheMODISglobalsupervisedclassificationmodelto vegetationandlandcovermappingofCentralAmerica.Int.J.RemoteSens.21 (6–7),1115–1138.

Mücher,C.A.,(Ph.D.thesis)2009.Geo-spatialModellingandMonitoringofEuropean LandscapesandHabitatsUsingRemoteSensingandFieldSurveys. Wagenin-gen University, Wageningen, The Netherlands, ISBN 978-90-8585-453-1, pp.269.

Mücher,C.A.,Gerard,F.,Olschofsky,K.,Hazeu,G.W.,Luque,S.,Pino,J.,Gregor, M.,Wachowicz,M.,Halada,L.,Tompo,E.,Kohler,R.,Petit,S.,Smith,G.,Kolar, J.,2006. Spatialimpactofconservationsites(Natura2000)onlandcover changes.In:ProceedingsoftheSecondWorkshopoftheEARSeLSIGonRemote

stages.RemoteSens.Environ.81,337–354.

Tomaselli,V.,Dimopoulos,P.,Marangi,C.,Kallimanis,A.S.,Adamo,M.,Tarantino,C., Panitsa,M.,Terzi,M.,Veronico,G.,Lovergine,F.,Nagendra,H.,Lucas,R.,Mairota, P.,Mücher,S.,Blonda,P.,2013.Translatinglandcover/landuseclassifications tohabitattaxonomiesforlandscapemonitoring:aMediterraneanassessment. Landsc.Ecol.28(5),905–930.

Vermote,E.F.,Tanre,D.,Deuze,J.L.,Herman,M.,Morcrette,J.J.,2003.Second Simula-tionofthesatellitesignalinthesolarspectrum(6S).IEEETrans.Geosci.Remote Sens.35(3),675–686.

Vicente,J.R.,Fernandes,R.F.,Randin,C.F.,Broennimann,O.,Gonc¸alves,J.,Marcos,B., Pôcas,I.,Alves,P.,Guisan,A.,Honrado,J.P.,2013.Willclimatechangedrivealien invasivespeciesintoareasofhighconservationvalue?Animproved model-basedregionalassessmenttoprioritizethemanagementofinvasions.J.Environ. Manage.131,185–195.

Xie,Y.,Sha,Z.,Yu,M.,2008.Remotesensingimageryinvegetationmapping:a review.J.PlantEcol.1(1),9–23.

Zhu,Z.,Woodcock,C.,2012.Continuousmonitoringofforestdisturbanceusingall availableLandsatimagery.RemoteSens.Environ.144,152–171.