Comparison of Two Independent Mapping Exercises in the Primeiras and Segundas Archipelago ...

20 Pages·2016·5.41 MB·English

remote sensing
Article
Comparison of Two Independent Mapping
Exercises in the Primeiras and Segundas
Archipelago, Mozambique
LuisaTeixeira1,*,JohnHedley2,AurélieShapiro3andKathrynBarker4
Received:16September2015;Accepted:29December2015;Published:12January2016
AcademicEditors:StuartPhinn,ChrisRoelfsema,XiaofengLiandPrasadS.Thenkabail
1 DepartmentofPhysicalGeographyandEcosystemScience,LundUniversity,S-22362Lund,Sweden
2 EnvironmentalComputerScienceLtd.,RaymondPennyHouse,HammettSquare,Tiverton,
DevonEX166LR,UK;[email protected]
3 ConservationRemoteSensingCentre,BiodiversityUnit,WWF-Germany,10117Berlin,Germany;
[email protected]
4 ARGANSLtd.,PlymouthSciencePark,Plymouth,DevonPL68BY,UK;[email protected]
* Correspondence:[email protected]
Abstract: Productionofcoralreefhabitatmapsfromhighspatialresolutionmultispectralimageryis
commonpracticeandbenefitsfromstandardizedaccuracyassessmentmethodsandmanyinformative
studies on the merits of different processing algorithms. However, few studies consider the full
productionworkflow,includingfactorssuchasoperatorinfluence,visualinterpretationanda-priori
knowledge.Anend-usermightjustifiablyask:Giventhesameimageryandfielddata,howconsistent
wouldtwoindependentproductioneffortsbe? Thispaperisapost-studyanalysisofaprojectin
which two teams of researchers independently produced maps of six coral reef systems of the
archipelagoofthePrimeirasandSegundasEnvironmentalProtectedArea(PSEPA),Mozambique.
Bothteamsusedthesameimageryandfielddata,butapplieddifferentapproaches—pixelbased
vs. objectbasedimageanalysis—andusedindependentlydevelopedclassificationschemes. The
resultsofferauniqueperspectiveonthemapproductionprocess. Botheffortsresultedinsimilar
mergedclassesaccuracies,averagingat63%and64%,butthemapsweredistinctintermsofscale
of spatial patterns, classification disparities, and in other aspects where the mapping process is
reliant on visual interpretation. Despite the difficulty in aligning the classification schemes clear
patternsofcorrespondenceanddiscrepancywereidentified.Themapswereconsistentwithrespectto
geomorphologicallevelmapping(17outof30pairedcomparisonsatmorethan75%agreement),and
alsoagreedintheextentofcoralcontainingareaswithinadifferenceof16%acrossthearchipelago.
However,moredetailedbenthichabitatlevelclasseswereinconsistent. Mappingofdeepbenthic
coverwasthemostsubjectiveresultanddependentonoperatorvisualinterpretation,yetthiswas
one of the results of highest interest for the PSEPA management since it revealed a continuity of
benthosbetweentheislandsandtheimpressionofaproto-barrierreef.
Keywords: OBIA; pixel based; very high resolution; benthic habitat; image interpretation;
operatorsubjectivity
1. Introduction
Coralreefmappingfromremotelysensedimagesisnowawell-establishedpractice,asattested
bythemanypublishedpaperssincetheearly1990s[1],books[2,3]andavailabilityoflargesatellite
imageryarchives,andcommercialimageserviceproviders. Remotesensingmethodshavesuperseded
visualinterpretationandmanualdelineationofaerialphotography[4–6],andwiththedevelopment
RemoteSens.2016,8,52;doi:10.3390/rs8010052 www.mdpi.com/journal/remotesensing
RemoteSens.2016,8,52 2of20
ofhighspatialresolutionmultispectralsatellitesensors(pixels<5m)thespacedatanowapproach
theresolutionofaerialphotography. Thedigitalformatofsatellitedatafacilitatescomputeranalysis,
andavarietyofalgorithmsforproducinghabitatmapshavebeendescribed, frombasicper-pixel
classification[7–10]toobjectbasedimageanalyses(OBIA)[11–14].However,regardlessofthemapping
algorithmused,therearemanyaspectsofmapproductioninwhichoperatordecisionsstillaffectthe
outcome: (1)choiceofalgorithmandparameterization;(2)choiceofclassificationscheme;(3)visual
quality checking and reprocessing; and (4) final corrections of misclassifications, i.e., contextual
editing[15]. Inparticular,inthemapproductionprocessthefirstanalysisisrarelyaccepted;typically
a cycle of visual assessment, adjustment and reprocessing occurs. When a map is produced for
anend-user,manualcorrectionof“obvious”misclassificationsisobligatory,andthemappingprocess
effectivelybecomesbasedonvisualinterpretation. Whiletheliteraturecontainsmanycomparisons
ofdifferentclassificationmethods[7,8,16,17],invariablytheseareconductedbythesameindividuals
orinaworking-groupinteraction,whereoperatorsubjectivitiesareminimizedbecausetheaimisto
assessthealgorithms. However,thisdoesnotaddresstheend-usersquestion: generally,howreliable
andconsistentismapproduction?
Theconsiderationofoperatorsubjectivityinmappingqualityisrarelydiscussedinremotesensing
literature. Andréfouët[18]addressesthecomplexityinevaluatingthehumaninfluenceinthemap
productionprocess,andparticularlyoftheonesusuallyreferredtoas“experts”. Onereasonforthe
lackofinformationonoperatorsubjectivityisthatananalysisrequirestwoindependentmapping
exercisesonthesamesite,ideallyfromthesameimageryandwiththesamefielddata,andthisis
rarelyfeasible. Inthispaperwehavetakenadvantageofsuchasituationthatarosefromacooperation
betweenEuropeanSpaceAgency’s(ESA)G-ECO-MONprojecttoevaluatetheuseofremotesensing
forecosystemservices,theWorldWideFundforNature(WWF),andLundUniversity,Sweden. The
resultingproductsweretwosetsofbenthichabitatmapsforsixoftheislandsinthePrimeirasand
Segundas Environmental Protected Area (PSEPA) in Mozambique: one produced by pixel based
classificationmethodology,theotherfollowinganOBIAapproach. Thesatelliteimageryandfield
data were the same for both analyses and the habitat maps showed similar accuracies according
to their respective methodologies in comparison to field data. However, in addition to utilizing
differentalgorithms,thecompletelyindependentmapproductioncompriseddifferentclassification
schemes,differentinterpretationofthefielddata,anddifferentvisualqualityassuranceprocesses
andcontextualediting. Theresultshavenotgonethroughanypost-comparisonrevision. Thesemaps
arethereforeanexampleofthepossiblevariationfromtwospecificprovidersonthesamemapping
task,andgiveanindicationofboththedifferencesandconsistenciesbetweentwowhollyindependent
productionchains.
2. StudyArea
The Primeiras and Segundas Environmental Protected Area (PSEPA) is located in northern
Mozambique,extendingover200kmofcoastline,fromPebanetoAngoche. Theareawasdeclared
protected in late 2012, and includes mangroves, seagrass beds and diverse coral reef habitats that
supportabiodiversityrichecosystem[19].Ofthetwoarchipelagos,distributedparalleltothecoastline,
onlythenorthern,Segundas,wasincludedinthiswork. Thearchipelagoconsistsofsevenislands,but
thesouthern-most,Moma,wasexcludedfromthestudyasfielddatawerenotcollected(Figure1).
The islands are quite small, with a maximum length of about 1 km. The smaller islands
support little or no vegetation while the larger islands have some forested area. Each island is
surroundedbyfringingreefsinasemi-circleshapetothesoutheast,wheremassivecoralcolonies
occur sporadically [20]. The lagoons, made of sand, coral rubble and seagrass beds, are shallow
andsomepartsarepracticallyexposedduringlowtide[20,21]. Theislandshaverelativelyexposed
northern,easternandsouthernsides,thelatterbeingusuallysubjecttomonsooninfluencedtrade
windsheadingnortheastduringthesummerorwetseason(OctobertoMarch)andsouthwestinwinter
ordryseason(ApriltoSeptember)[22,23]. Ingeneral,themostdevelopedandspeciesdiversereefs
RemoteSens.2016,8,52 3of20
havebeenreportedinthemostshelteredregionsofthecoralreefsystems,i.e.,facingthemainland[20].
RTehmeotel o20c1a6l, w8, a52te rsareturbulent,notonlyduetoupwellingbutalsotopredominanttidalwave3s oaf n2d0
irregularseafloorbathymetry,resultinginstrongandquitevariablecurrents. Themaindirectionof
bathymetry, resulting in strong and quite variable currents. The main direction of the offshore surface
theoffshoresurfacecurrentsissoutheast, whilecurrentsatdepth(100–150meters)movetowards
currents is southeast, while currents at depth (100–150 meters) move towards north [23].
north[23].

FFiigguurree 11.. LLooccaattiioonnaall mmaapp ooff tthhee ssttuuddyy aarreeaa..
3. Material and Methods
3. MaterialandMethods
33..11.. SSaatteelllliittee IImmaaggeerryy DDaattaa
AA mmiixxttuurree ooff 44--bbaanndd WWoorrllddVViieeww22 ((rreedd,, bblluuee,, ggrreeeenn,, aanndd nneeaarr--IIRR)) aanndd QQuuiicckkBBiirrdd aarrcchhiivvee iimmaaggeerryy
wweerree uusseedd ffoorr mmaappppiinngg.. TThhee sscceenneess, ,oonnee ppeerr iissllaanndd, ,wweerree sseelleecctteedd aaccccoorrddiinngg ttoo tthhee bbeesstt aavvaaiillaabbllee
vviissiibbiilliittyy ddeeffiinneedd bbyy rreedduucceedd eexxtteenntt ooff wwhhiitteeccaappss aanndd ssuunn gglliinntt,, vviissiibbllee ddeeeepp bbeenntthhiicc ffeeaattuurreess,, aanndd
ggeenneerraallllyy cclleeaarr wwaatteerrss ((TTaabbllee 11)).. DDuuee ttoo ffrreeqquueenntt rroouugghh sseeaass aanndd tteerrrriiggeennoouuss pplluummeess,, cclleeaarr iimmaaggeerryy
ooccccuurrss iinnffrreeqquueennttllyy,, aanndd tthhee bbiiggggeesstt ttiimmee ddiissccrreeppaannccyy bbeettwweeeenn iimmaaggeerryy aanndd tthhee iinn--ssiittuu ddaattaa ccoolllleeccttiioonn
wwaass fofouurr yeyaerasr.s A. ltAhlothuoguhg ith isi tliiksellyik tehlayt tthhaet btehnethboesn hthaso sunhdaesrguonndee rsgoomnee cshoamngeecsh daunrginegs tdhuisr ipnegritohdi,s
tpheer siotrdu,ctthueraslt rcuocmtuproanlecnotms poof ntheen tssysotfetmhe, is.ey.,s tceomra,li a.en.,dc ororackla, snhdoruolcdk b,esh faoiurlldy cboenfsaiisrtleynct.o Tnhsies tiemnat.gTerhye
uimndaegrewryenutn dsetarwndeanrtds tarnaddiaormderatrdicio manedtr icseannsdors ecnosrorreccotirornec tuiosninugs icnaglicbarlaibtiroanti ocnoecfofeicffiiecnietsn tsfrformom tthhee
pprroovviiddeerr [[2244]]..
TTaabbllee 11.. SeSnesnosro, ra,caqcuqisuiitsioitnio dnatdea taenda ntdimtei maneda vnidsibviilsiitbyi laistysesassmseesnsmt feonr tthfoer imthaegeimrya cgoevryericnogv eeraicnhg
leoaccahtiolonc.a tion.
Acquisition Acquisition No No Sun Deep Areas Clear
Locations Sensor Acquisition Acquisition No NoSun DeepAreas Clear
Locations Sensor Date Time Whitecaps Glint Visible Waters
Date Time Whitecaps Glint Visible Waters
1 Baixo Santo Antonio WV-2 07.12.2009 07:36:08 X X
1 BaixoSantoAntonio WV-2 07.12.2009 07:36:08 X X
2 Mafamede WV-2 18.12.2009 07:34:16 X
2 Mafamede WV-2 18.12.2009 07:34:16 X
3 3 PPuuggaa PPuuggaa QQBB2 2 1111..0055..22001100 0077::3311::0055 XX
4 4 BBaaiixxoo MMiigguueell QQBB2 2 1111..0055..22001100 0077::3311::0055 XX
5 5 NNjojovvoo WWVV--22 0099..0011..22001100 0077::3322::5599 XX XX
6 Caldeira WV-2 29.12.2009 07:34:14 X X X
6 Caldeira WV-2 29.12.2009 07:34:14 X X X
3.2. Benthic Cover Data
In-situ point data covering the six coral reef systems were collected by the Lund University
group in two surveys, one from 15 to 17 April 2014 on the islands Mafamede, PugaPuga, Baixo Miguel
and Njovo, and the second on 10 and 11 May 2014 on the islands Caldeira and Baixo Santo Antonio.
RemoteSens.2016,8,52 4of20
3.2. BenthicCoverData
In-situ point data covering the six coral reef systems were collected by the Lund University
group in two surveys, one from 15 to 17 April 2014 on the islands Mafamede, PugaPuga, Baixo
MiguelandNjovo,andthesecondon10and11May2014ontheislandsCaldeiraandBaixoSanto
Antonio. Themethodsbywhichfielddatacanbecollectedinthisregionareaffectedbytidaland
waterclarityvariationaswellasweatherconditions. Benthiccoverwasobservedfromtheboatusing
aclearbottombucket: aqualitativedescriptionofthesubstrateandvisiblefeatureswererecorded,
verysimilartotheresultingOBIAlevel3classes. Underwaterphotographsweretakenatselected
locations to illustrate different benthic cover types. Geographical coordinates were captured with
aGarminMontana650tGPS(horizontalaccuracy˘3.65m)[25]. Datapointsweretakenfromaslow
movingboatatintervalsofabout80–150meters(asdefinedbytheGPSreceiver),butalsoaccording
toobservablechangesofthebenthiccover. Althoughinitiallyplannedastransects,therouteshad
tobeadjustedtothetidalandgeomorphologiccharacteristicsoftheislandsforsafetyreasons. Data
werecollectedbycircumnavigatingthereefcrest,definingsometransectsinthelagoonandbyvisiting
zonesofinterestpreviouslydefinedaccordingtothesatelliteimagery. Thepointcountwasintended
to be approximately 200 points per island. However, due to bad weather and unsafe navigable
conditions, thefinaldatasetvariedfrom24to139pointsperisland, totalingabout660geolocated
pointsdocumentingbenthiccover(Table2). Amoredetaileddescriptionofthesamplingmethodology
isgiveninTeixeiraetal.[26].
Table2.Fielddatadistributionperlocation.
Location GeolocatedPointsSampleSize
BaixoSanto
101
Antonio
Mafamede 176
PugaPuga 103
Njovo 127
BaixoMiguel 29
Caldeira 130
3.3. MappingMethodologies
Thetwomapproductioneffortsconsistedofaper-pixelclassificationapproachconductedunder
theG-ECO-MONproject,henceforthdenotedGEM,andanOBIAconductedbytheLundUniversity
group,denotedLund.Theresultsofbothapproachesincludedrasterclassificationmapswithper-pixel
correspondencetothesourceimages,aftertheconversionoftheLundpolygondata. Assuch,the
resultswerecomparedper-pixel. Theclassificationschemesusedweredifferent,aswerethemethods
foraccuracyassessmentwithfielddata,detailedinthefollowingsections.
3.3.1. GEMProductionEffortProcessingandClassification
First,theimagesforNjovoandBaixoSantoAntoniowerecorrectedforwatersurfacesun-glint
usingthenearinfra-red(NIR)band[27],whiletheotherimageswereconsideredsufficientlyfreeof
glintthatcorrectionwasnotrequired. Thepurposeoftheglintcorrectionistoremovewavesurface
patternsthatwouldotherwisedominatetheclassification,resultinginspectralreflectancesvalues
closertohowtheywouldbeiftheglintwasabsent,andthereforemoreinlinetothoseoftheother
images. Howeverthecorrectionisnotperfectandresidualnoisemayremain[28]. Imageswerethen
subjecttoaspatialfilteringstepwhereadjacentpixelsofsimilarreflectancewerereplacedbytheir
meanspectra,thenappliediterativelyuntilthenumberofdistinctpixelswasreducedto10%. The
spatialscaleofthemergingwasverysmall(typically3ˆ3pixels)andthisstepwasappliedtoreduce
singlepixelscalenoiseintheclassification. Followingtherecommendedpracticeofinitiallyclassifying
RemoteSens.2016,8,52 5of20
to at least twice the number of required classes, 60-class unsupervised k-means classification was
applied. The resultant codes were ascribed to a reduced classification scheme (Table 3) by visual
interpretationusinghistoricalspatiallyapproximateRapidAssessmentdocumentsandgeneralized
field data. Classification was then validated using the actual contemporary geolocated field data.
There were insufficient field data points to partition the data into training and validation sets, so
all of the field data were reserved for accuracy assessment. Use of visual interpretation and local
knowledgefortrainingdataisnotonlysometimesapracticalnecessity[29]orinherentlyuseful[30]
butitisalsoinevitable: nomapproducerwoulddiscountwhattheyknowwhenqualitychecking
theirclassificationresults. Hence,operatora-prioriknowledgeisalwaysrelevanttomapproduction
andrarelyindependentfromthedatausedforaccuracyassessment. Withthisinminditisimportant
tonotewhatbackgroundinformationinformedtheclassattributions: nomemberoftheGEMgroup
hadvisitedthesitebutseveralRapidAssessmentdocumentswereavailablefrom1997to2010that
describedthebenthiccompositioninroughlydefinedareas[21,31,32];thein-situdatalaterusedfor
accuracyassessmentwasalsoavailablebutthiswasonlyreferencedinroughgeographicalterms,i.e.,
tounderstandthenatureofthebenthiccompositioninsideversusoutsidethelagoonsandtodefinethe
classificationscheme.
Table3.HabitatclassificationschemeappliedintheGEMmappingproductioneffort.
Code Name Description
0 Unprocessed Nodata,pixelunprocessedorinvalid.
1 Land Abovewaterattimeofimageacquisition.
2 Deepwater Bottomcan’tbeseeninimage.
3 Deepsand Sandwherebottomisonlyjustvisible,typicallymorethan10mdepth.
Relativelycleansandcover,approximatelylessthan10mdepth,
4 Shallowsand
maycontainsomerubbleandverythinvegetation.
Sandwith Sandorrubblewithsomeseagrassand/ormacroalgaecover
5
thinvegetation butnotsufficienttoobscurethevisibilityofthesandsubstrate.
6 Densevegetation Densecoverofseagrassormacroalgaesufficienttoobscurethesubstratebelow.
Habitatthatcontainscoralinarelatively
Reefflat/coral
7 shelteredenvironment,typicallybehindthereefcrestand
andrubblefield
attheedgeofthelagoon.Canalsobespurandgroovezone.
Reefcrest/high Placeswherecoralisfoundinhighestdensity,typically
8
coralcover attheedgeofthereefandtopofthereefslope.
Reefslope/ Highcoralcoverregionthatslopesuptothereefcrestfromoutsidethelagoon,
9
forereef maycontainsoftcorals.Spurandgrooveformationsalsofoundhere.
Darkbenthosbarelyvisibleinimagery,atdepths10to20m,
Deepbenthic
10 theexactnatureofwhichcannotbedetermined.Couldbe
cover
vegetation(seagrassesormacroalgae)ordeepreefstructures.
Wavesorclouds Regionswherethebenthoscannotbeclassifiedbecause
11
intheimage breakingwavesorcloudsobscureitinthesourceimage.
Sandon Correspondstoareasclassedassandinthein-situdatabuttheyaretypically
12
rocksubstrate raised,probablyonconsolidatedrubble,andwiththinvegetation.
Nodepthcorrectionordepth-invariantindexcalculation[33,34]wasapplied. Giventhatthesites
wereinlargepartarelativelyflatshallowlagoon,withtheonlydepthvariationontheforereefslope,
itwasjudgedthatmergingofthe60classeswouldbeadequatetohandletheexistenceofdifferent
classesduetodepth. Finally,contextualeditingwasusedtocorrectmisclassificationsasjudgedby
visualinterpretation.
Theclassesusedwerechosenbyconsideringthestructureofthefielddata,andthedelineations
thatarosenaturallyfromunsupervisedclassificationoftheimagery.Theclassesweredefinedathabitat
RemoteSens.2016,8,52 6of20
levelandincludebothbioticcompositionandgeomorphologicalzone. Inmultispectralimageryof
fourbands(red,green,blue,andnearinfra-red),onlytwoorthreeconveysubsurfaceinformation,soit
isgenerallynotpossibletodifferentiatebetweenseagrassandmacroalgaebyspectralreflectancealone;
infact,thefielddataindicatedthatseagrassandmacroalgaeoftenoccurredtogether. Thereforethe
classificationschemeincludedclassesbasedongeneral“vegetation”whichcouldbeeitherseagrassor
macroalgae. Typicallya-prioricontextualknowledgebyusersisrequiredtoidentifyareaswhichare
dominatedbyseagrass. Mosthabitatswereonacontinuumwhereclasses“sand”,“sandwiththin
vegetation”and“densevegetation”werenotpreciselydefinableintermsofpercentagecoverbutarose
duetospectralgradationofimagepixels. Nospecificlevelofcoralcoverisimpliedintheclasses“reef
slope”,“reefcrest”and“reefflat”,butthesecanbeconsideredplaceswherelivecoralwouldbefound.
3.3.2. GEMProductionEffortAccuracyAssessment
Duetothedifficultlyinclearlydelineatingclasseswheremixedassemblagesarecommon(both
intheimageryclassificationandin-situdata)accuracyassessmentwasconductedbymergingclasses
to three basic cover types: dominated by coral, sand and vegetation (seagrass or algae). This was
also necessary because the in-situ data contained only small number of instances of some classes,
andevenwiththismerging,the“vegetation”classwasabsentinthein-situdataatthreeofthesix
islands. Confusionmatricesforthesethreeclasses(andincluding“deepwater”forthemapdata)were
constructedforthesixislands.
3.3.3. LundProductionEffortProcessingandClassification
Thefirstpre-processingstepappliedintheOBIAwastomaximizethevisualcontrastoftheimages
byacombinationofradiometriccorrection,principalcomponentanalysis,darkobjectsubtraction,sun
glintcorrectionaccordingtoHedleyetal.[27]andwatercolumncorrectionaccordingtoLyzenga[33,34].
The imagery was iteratively segmented and classified according to a three level hierarchical
classificationschemebasedonshallowwatercoralreefenvironmentresearchworkbyMumbyand
Harborne[35],Rohmann[36]andAndréfouët[37]. Theclassificationscheme(Table4)wasadaptedto
thefielddataandimagerysoastomaximizethevarietyofbenthichabitatsincluded.Segmentationwas
performedusingTrimbleeCognitionDeveloper’smultiresolutionsegmentationalgorithm,applying
adecreasingscaleparameter(100to5),whilecompactnessandcolorwerekeptalmostconstant(0.7to
0.9). Duetothevariationwithintheimages,theparameterswereadjustedtotheindividualdatasets.
ClassificationwasperformedusingtheNearestNeighborclassificationalgorithm,includingFeature
Optimization tools. Training datasets were created by the operator based on image interpretation
informedbythefielddata,togetherwithempiricalknowledgeofthecoralreefsystems.TheLundteam
hadphysicallyvisitedthesiteandcollectedthein-situdata,butinattempttopromoteindependence
fromthein-situdatasets,thetrainingsiteswereselectedfromareasnotcoveredbythefielddata.
Mapswerecreatedforeachlevelofthehierarchicalclassification,resultinginthreemapsper
reefsystem. Finally,contextualeditingwasappliedtocorrectmisclassificationsaccordingtovisual
interpretation. FurtherinformationontheimageprocessingcanbefoundinTeixeiraetal.[26].
3.3.4. LundProductionEffortAccuracyAssessment
Confusion matrices were generated for the resulting bottom cover and benthic habitat maps
(levels2and3)usingthefielddatapoints. Allclassespresentinthemapwereincludedintheaccuracy
assessment,despiteinsufficientdistributionofpointdataacrossclasses.
RemoteSens.2016,8,52 7of20
Table 4. Three level hierarchical habitat classification scheme applied in the Lund mapping
productioneffort.
Level1 Level2 Level3
GeomorphologicalZone BottomCover BenthicHabitat
Land Land Land
Shallowwaters Sand Sand
Lagoon/Reefcrest/Forereef Sand/Rubble Sand/Rubble
withSeagrass
withSeagrassandRocks
withSeagrassandRockswithBrownMacroalgae
withRocks
withRockswithBrownMacroalgae
Rock Rock
withBrownMacroalgae
withSandandRubble
withBrownMacroalgaeandSandandRubble
Coral Coral
SpurandGroove
Field
Patches
Deep(fore)reef Sand Sand
Deepbenthiccover Deepbenthiccover
Deepwater Deepwater Deepwater
Noinformation Noinformation Noinformation
3.4. MappingResultsComparison
Since there was no previous intention of conducting a comparative study, the classification
schemes were developed independently, which led to relatively few classes having a direct 1:1
correspondencebetweenthetwomapproductioneffortsandclassgroupingbeingrequired(Table5).
For geomorphological categories, level 1 in the Lund maps, identifying corresponding classes or
groupings was fairly straightforward for “Land”, “Shallow surround” (shallow areas outside the
fore reef), “Reef crest”, “Fore reef” and “Deep water”. Likewise, the bottom cover (level 2) maps
were used in the assessment of “Deep benthic cover” (dark benthos of unknown composition in
deepwater),“Coralcontaining”areas(placeswherelivecoralwouldbefound),andthreedifferent
comparisonsforbaresandorrock,“Bare1–3”. Thebenthichabitatclassesatlevel3wereconsidered
too specific to provide a relevant assessment of direct map correspondence. Therefore, it was not
possibletocomparemixedclasseswithvegetatedcoverdirectly,soallclassesthatincludedseagrass
and/or brown macroalgae were aggregated and compared to the GEM class “Dense vegetation”.
AcomparisonbetweenGEM“Densevegetation”andLund“Sand”wasalsoincludedbecauseitwas
apparentfromvisualinterpretationthatthiscouldhaveahighcorrespondence.
The maps from the two production efforts were initially compared by visual assessment,
supportedbythecalculationoftheedgesimilarityindexforthepairsofclassesreferringtocoral,
vegetation and deep benthic cover. This index, ranging from 0 to 1, represents the percentage of
overlapofclassboundaries,thusassessinggeometriccorrespondencebetweenthemapsproducts.
AsrecommendedbyLizarazo[38],theedgeswereconsideredasoverlappingwithinatolerancezone,
heredefinedasabufferofapproximatelytwotimesthesensorgroundresolution,i.e.,atotaloffour
metersforWV-2andfivemetersforQB2images.
RemoteSens.2016,8,52 8of20
Table5.Pairingofselectedclassesforcomparisons.
Comparison GEMClasses LundClasses OBIALevel
Geomorphologylevel
Land Land Land 1
Shallowsurround Shallowsand Shallowwaters 1
Reefcrest Reefcrest/highcoralcover Reefcrest 1
Forereef Reefslope/forereef Forereef 1
Deepwater Deepwater Deepwater 1
Benthichabitatlevel
Deepbenthiccover Deepbenthiccover Deepbenthiccover 2
Bare1 Sandwiththinvegetation Sand 2
Bare2 Sandoverrocksubstrate Sand 2
Bare3 Sandoverrocksubstrate Rock 2
Vegetation-sandconfusion Densevegetation Sand 2
Sand/RubblewithSeagrass
Sand/RubblewithSeagrassandRocks
Sand/RubblewithSeagrassand
RockswithBrownMacroalgae
Vegetation Densevegetation Sand/RubblewithRocks 3
withBrownMacroalgae
RockwithBrownMacroalgae,and
RockwithBrownMacroalgae
andSandandRubble
Reefcrest/highcoralcover
Coralcontaining Coral 2
andReefslope/forereef
Additionally, each island’s map was overlaid to develop confusion matrices of class
correspondencebetweentheGEMandLundproducts. Thisledtoatotalof18confusionmatrices
quantifyingthespatialagreementoftheselectedclasses.Tounderstandtheconsistencyofthemapping
resultsacrossthesixislands,WilcoxonSigned-Rankstestandlinearintercorrelationwereappliedto
thenumberofpixelscoincidingforeachselectedclasspair. Wilcoxon’sp-valueforpairedsamples
indicatestheprobabilitythattheobservedresultswouldhappenifthenullhypothesisweretrue,i.e.,
ifthemedianofthedifferencesofthesampleswerezero. Thus,smallvaluesofp-valueshowthatthere
isasystematicover-orunderestimationoverthesixislands,whereasanon-significantresultmeans
eithertheagreementwasverygoodbetweenthetwoproductionmethodsortheproductionmethods
disagreedbutinanon-systematicway. Thecoefficientofdeterminationofthelinearintercorrelation
providesanevaluationofhowmuchthequantificationresultingfromonemethodologyisreliable
asapredictorforthequantificationoftheother. Inbothproductioneffortseachislandwasmapped
independently,soevaluatingtheresultsacrossthesixislandswillrevealanysystematicbiasesinthe
productionmethods,encompassingbothmappingalgorithmandoperatordependentfactors.
Theapplicabilityofthemapsasnaturalresourcesmanagementtoolswasassessedbyevaluating
theconsistencyofthemapsinansweringhypotheticalquestionsbasedoninformationneedsfromthe
PSEPA’smanagement:
(1) Whatistheextentofcoralcontaininghabitatsperisland,andthetotalwithintheprotectedarea?
(2) What is the extent of macroalgae or seagrass areas per island, and the total within the
protectedarea?
(3) Whereareareasofdeepbenthiccovertobefound,possiblycurrentlyunknownandafuture
focusforfieldsurveys?
RemoteSens.2016,8,52 9of20
Thevaluesoftheextentsofcoral,vegetationanddeepbenthiccoverwereestimatedaccording
totheclassgroupsalreadypresented,althoughthisgroupingcouldbedifferentdependingonthe
interpretationoftheenduser,particularlyforthestudyofvegetation.
4. ResultsandDiscussion
4.1. AccuracyAssessment,EdgeSimilarityandClassAgreementResults
Accuracyassessmentwithrespecttothefielddataindicatedthemapshavevariablebutgenerally
reasonable accuracy: 56% to 72% for GEM and 43% to 93% for Lund (Table 6). These values are
considered adequate for management and planning purposes, for which a value of about 60% is
generallyrecommended[3,39]. Moreover,theobtainedresultsfallwithintherangeofvaluesfoundin
currentresearchresultsofcoralreefhabitatmapping[7,12,18,40]. Therefore,bothproductionefforts
resultedinmapsthatweregenerallyofadefensiblequalityinabenthichabitatmappingcontext.
Table6.Accuracyassessmentresultsforthepixelbasedandobjectbasedmaps.
GEM Lund
Island
GeneralizedThreeClassProduct(%) Level2(%) Level3(%)
BaixoSantoAntonio 58 91 74
Mafamede 66 93 56
PugaPuga 63 83 60
BaixoMiguel 72 44 35
Njovo 61 82 55
Caldeira 56 58 43
Afirst,roughvisualassessmentofthemappingoutputs(Figure2),conductedbyoverlapping
thethreeOBIAmapsonthepixelbasedones,allowedtheidentificationofcleardifferences. While
land and the overall shape of the coral reef system matched well, the deep benthic classes—deep
benthiccoveranddeepsand—wereoftenmoreextensiveinGEMmaps. Anotherobservationisthat
thebenthichabitat(level3)Lundmapsshowmoreclearlydelineatedareas,particularlywithinthe
lagoonandreefcrest,whileavoidingthe“saltandpepper”effect,whichisafrequentlyidentified
advantageoftheobjectbasedapproach[14,41,42].
Theedgesimilarityresultssupportthevisualinterpretation,quantifyingthedeepbenthiccover
edgecorrespondenceasrangingfrom2%to16%(Table7). Theremainingclasscomparisons,more
complex to assess visually due to the number of classes included, shows a general good level of
agreement,withanedgesimilarityindexinorderof10%to30%forcoralcontainingclassesandabove
30%forvegetationinthemajorityofthelocations.
Theconfusionmatricescomparingthemappairsshowveryhigh(>75%)classagreementfor
“Land”,“Forereef”and“Deepwater”inallthesixstudylocations(Table8). Twoofthecomparisons
relatedtosandorrocksubstrate,“Bare1”and“Bare2”,hadaveryhighagreementforabouthalf
ofthelocations,while“Bare3”(Sandoverrocksubstratevs. Rock)hadveryhighagreementonlyat
one. The“Coralcontaining”comparisonhadverygoodagreementatthreesitesandpooragreement
atonlyone. Additionally,fromthe18generatedconfusionmatrices,itwaspossibletoobservethat
“Sandonrocksubstrate”,“Densevegetation”and“Sandwiththinvegetation”displayahighlevel
ofagreementwiththeclass“Lagoon”fromtheLundclassificationscheme. Thisresultwasexpected,
andsupportsadequatespatialcorrespondenceofclasses,asthesethreearegenerallyfoundinthat
geomorphologicalzone.
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Remote 2016, 8, 52 10 of 20

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