Object-Based Mapping Of Coral Reef Habitats Using Planet Dove Satellites

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remote sensingArticleObject-Based Mapping of Coral Reef Habitats UsingPlanet Dove SatellitesJiwei Li 1 , Steven R. Schill 2 , David E. Knapp 1 and Gregory P. Asner 1, *12*Center for Global Discovery and Conservation Science (GDCS), Arizona State University, Tempe, AZ 85281,USA; jiweili@asu.edu (J.L.); dknapp4@asu.edu (D.E.K.)The Nature Conservancy, Caribbean Division, Coral Gables, FL 33134, USA; sschill@tnc.orgCorrespondence: gregasner@asu.eduReceived: 23 May 2019; Accepted: 15 June 2019; Published: 18 June 2019 Abstract: High spatial resolution benthic habitat information is essential for coral reef protection andcoastal environmental management. Satellite-based shallow benthic composition mapping offers amore efficient approach than traditional field measurements, especially given the advancements inhigh spatial and temporal resolution satellite imagery. The Planet Dove satellite constellation nowhas more than 150 instruments in orbit that offer daily coverage at high spatial resolution (3.7 m).The Dove constellation provides regularly updated imagery that can minimize cloud in tropicaloceans where dense cloud cover persists. Daily image acquisition also provides an opportunity todetect time-sensitive changes in shallow benthic habitats following coral bleaching events, storms,and other disturbances. We developed an object-based coral reef habitat mapping approach forDove and similar multispectral satellites that provides bathymetry estimation, bottom reflectanceretrieval, and object-based classification to identify different benthic compositions in shallow coastalenvironments. We tested our approach in three study sites in the Dominican Republic using 18 Doveimages. Benthic composition classification results were validated by field measurements (overallaccuracy 82%). Bathymetry and bottom reflectance significantly contributed to identifying benthichabitat classes with similar surface reflectance. This new object-based approach can be effectivelyapplied to map and manage coral reef habitats.Keywords: Planet Dove; coral reef; benthic composition; seagrass; coastal; shallow; tropical ocean1. IntroductionCoral reefs and associated shallow coastal ecosystems are among the most productive andvulnerable in the world [1]. Effective protection and management of coral reefs rely heavily onaccurate and up-to-date spatially-explicit information on shallow benthic habitats [2]. Traditionallabor-intensive field surveys offer point and transect records that can only be applied to small areas [3].While field-based methods can collect detailed information along coral reef transects, these data areoften limited to very small areas and are inadequate for monitoring large areas [4]. However, satelliteremote sensing technology, when combined with field survey data, provides a solution to repeatedlymap and monitor coral reef benthic habitats over large geographic areas [5]. A common trade-off ofremote sensing is its lower accuracy compared to field surveys.Advances in Earth observation offer benefits to coral reef habitat mapping via higher spatialresolution (pixel sizes 5 m) and increasing temporal resolution [6]. High image acquisition frequency(e.g., daily) provides increased likelihood of obtaining cloud-free scenes over tropical regions anddelivers time-sensitive data allowing detection of changes to the benthos such as large-scale coralbleaching [2,7]. In NASA MODIS image analyses, a typical portion of the cloud-free satellite imagesover reef regions ranges from 20% to 30% [8,9]. Previous coral reef studies have been conducted usingRemote Sens. 2019, 11, 1445; ensing

Remote Sens. 2019, 11, 14452 of 16mid-spatial resolution satellite images (e.g., Landsat-8, Sentinel-2) or high-resolution images with lowtemporal frequency (e.g., IKONOS, Worldview) [3,4,10–13]. Coral reef mapping could benefit fromhigh temporal frequency satellite sensors (e.g., Planet Dove).Mapping benthic composition in shallow coastal environments requires multiple inputs, includingsea surface reflectance, bottom (or benthic) reflectance, and bathymetry to identify different habitatsusing either an object- or a pixel-based approach [2,14]. In particular, bathymetry information is centralto identifying different benthic surfaces within distinct coastal geomorphic zones. Moreover, bottomreflectance retrieved from satellite images follows the removal of water column attenuation effectsusing radiative transfer modeling techniques [15]. Here, we developed a comprehensive object-basedmapping approach that provides bathymetry estimation, bottom reflectance retrieval, and object-basedclassification, all using Planet Dove satellite images. We applied and verified our approach in threecoastal sites located in the southeastern Dominican Republic using GPS-referenced underwater videoand transducer bathymetry field data. We then performed an accuracy assessment analyzing differentbenthic habitat types and bathymetric ranges. As part of this research, we also investigated the effectsthat benthic habitat reflectance, bathymetry, and water column attenuation have on Dove-derivedcoral reef habitat products.2. Materials and Methods2.1. Study SitesThree shallow coastal study sites were selected within a recently declared 8000 km2 marinesanctuary “Arrecifes del Sureste” (Latitude: 17.5 –18.8 N, Longitude: 67.8 –69.35 W).This sanctuary covers 120 km of coast and is a primary tourism hub, receiving over 4 millionvisitors annually (Figure 1). With coral reef-based tourism being a major part of the local DominicanRepublic economy, a park management plan is currently being developed to monitor and protect thesecoastal ecosystems. We chose benthic classes to conform to those used by conservation practitionerswho manage these habitats within the Dominican Republic. Our research provides an opportunityto test object-based shallow benthic composition mapping across a wide range of benthic types,geomorphic zones, and bathymetric ranges. Such products can provide baseline data, monitor changes,and inform adaptive management actions within the park.

Remote Sens.Sens. 2019,Remote2019, 11,11, 1445x FOR PEER REVIEWof 161633ofFigure 1. Planet Dove mosaic imagery (shown in RGB true color) covering three study sites withinFigure1. Planetmosaicimagery(shownin RGB truecolor) coveringthreestudysites withinthe Arrecifesdel DoveSurestemarinesanctuaryin ominicanRepublic(DR):Catalina(a), eastern Dominican Republic (b), and Saona Island (c). Field data transects are shown as terngeneralDominicanlocation of Republicthe study(b),regionprovidedin (c).theFieldlowerdatarightpanel. are shown as red lines.The general location of the study region is provided in the lower right panel.2.2. Field Data Collection2.2. FieldDataCollectionFrom30 Aprilto 4 May 2018, a total of six field transects were generated to measure and assessa diverseof benthiccompositionsthe threestudysites(CatalinatoIsland,SaonaFromarray30 Aprilto 4 May2018, a total withinof six fieldtransectsweregeneratedmeasureand Bathymetricfieldmeasurementswerecollectedusinga diverse array of benthic compositions within the three study sites (Catalina Island, Saona Island, a LowranceOK,FigureUSA) systemwith a xSonicP319 (50/200 kHz)and 10andEastern Elite7TiDominican(Tulsa,Republic,1). Bathymetricfield measurementsweretransducercollected usinga (Tulsa,Hz GPS receiverthatcollectedcontinuousdepthreadingsat 3P319pts/secalong eachtransducertransect. Inparallel,LowranceElite7TiOK,USA) systemwitha xSonic(50/200kHz)and10Hza GPS-referencedSea-Drop 6000HDreadings(Tampa, atFL,3 USA)underwatercamerawith 30 mGPSreceiver thatSeaViewercollected continuousdepthpts/secalong eachvideotransect.In parallel,avertical cable wasSeaViewerused to recordbenthichabitattypes alongtransect(Table video1). A totalof 152.4GPS-referencedSea-Drop6000HD (Tampa,FL,eachUSA)underwatercamerawithkm30of verticalbathymetricand 122benthicvideosamplesalong allmcablemeasurementswas used to sect (Table1).transects.A total of rementpointsata15minterval.Benthichabitattypeskm of bathymetric measurements and 122 benthic video samples were recorded along all transects.(coraldeep,5,300coral bathymetricback-reef/flat,measurementcoral fore-reef,gorgonian/softhardbottomFrom reefthesecrest,data,coralwe patchgeneratedpointsat a 15 algae)habitat types (coral reef crest, coral patch deep, coral back-reef/flat, coral fore-reef, gorgonian/softwerefor witheach video1).seagrassA total of3000 pointswere appliedas thetrainingcoral, classifiedhardbottomalgae, sampleseagrass(Tabledense,sparse,sand shallow,and sanddeepwithsamplesmacroalgae)for the object-basedclassification.The rification.sparsewere classifiedfor eachsampleA usedtotal forof 3,000wereWeselectedthetrainingtrainingsamplesand verificationpoints basedclassification.on depths andhabitattypes. 2,300 points wereappliedas thefor the object-basedTheremainingused for model verification. We selected the training and verification points based on depths andhabitat types.

BenthicHabitat typeDescription [16]Remote Sens. 2019, 11, x FOR PEER REVIEWField Video ExampleCoral Reef Crest is found inshallow water break zones.Table 1. Benthic composition classification scheme.The REVIEWbenthic cover consists ofRemote Sens. 2019, 11, x FOR PEERRemote Sens. 2019, 11, 1445coral build up andBenthicRemote Sens. Coral2019, 11,x FOR PEER REVIEWReefDescription [16]Field Video Exampleturf/calcareousalgae.LargeHabitat typeTable 1. Benthiccompositionclassification scheme.Crestfleshyare inCoralReefmacroalgaeCrest is foundTable 1. Benthic composition classification scheme.BenthiclargelyabsentbreakandonlyTable 1. Benthiccompositionclassification scheme.shallowwaterzones.Description[16]Field Video ExampleHabitat sBenthicFieldCoral DescriptionReef[16]Crest is[16]found inBenthic Habitat Type Habitat type DescriptionFieldVideoVideo ExampleExamplecoralobserved.build up andCoral Reefshallowwaterbreakzones.CoralDeepis Reef CrestisinCrestThe benthiccoverconsistsofcoveredwitha veneerof turffleshymacroalgaeareshallowwaterbreak zones.coralbuildup rse( 5%)largelyabsentandonlyThe benthic cover consists ofCoral swerecoralbuildupandCrestzones. TheCoralfleshymacroalgaearecoralReefbuild up rved.turf/calcareousalgae. LargeCoral Reef CrestCrestLargefleshy macroalgaelargelylargely gae.CoralPatchDeepis typicallyfleshymacroalgaeareand only picallyfoundononlytheturfcoveredwitha arginsseawardalgaea coloniessparse( 5%)small andcoralwereCoralPatch reefDeepcrest,is typicallybeyondabovecover ofthescleractiniancoral,observed.coveredwith a whereveneerlargeof turf 10mdepthhydrocoral,gorgonians,CoralPatchDeepis typicallyalgaeandasparse( 5%)mayscour theCoralPatchPatch veredwitha macroalgae.veneerof turfcoverofscleractiniancoral,veneer of turf algaeandaasparse( 5%)seafloor.DeepTypicallyfoundalgaeandsparseon( 5the%)hydrocoral,gorgonians,cover of fcrest, abovehydrocoral, gorgonians,Coral Patch DeepTypicallyfoundonthe nealgae 10 m bydepthwherelargetoCoral Patchsponges,andmacroalgae.seaward beyond exposedthe reef crest,above 10 mmarginsseawardforma semiconsolidatedwavesmayscourDeep where dthe sedmarginsseawardseafloor.Coral Back ltherubbleoriginatingbeyondreefcrest, abovereef/flatwavesmay scourtheontheshelteredmarginsfromstructuresand 10mreefdepthwherelargeseafloor.landwardof theSkeletal This structures and inatingformBacka semiconsolidated framework withCoralbondedby corallinealgae mreef structuresandCoral Back-reef/flatpatchy macroalgae.Typicallyfound esheltered marginslandwardof thereefcrest.onthe ithpatchyThis habitatalsobea foundsurroundingframeworkswithsparseof thereefcrest.formsemiconsolidatedCoralBack- may landwardmacroalgae.Typically 10%).foundor atop tmay patchyalso beframeworkwithreef/flatCoral minantlyfound surroundingorfoundatopmacroalgae.reef/flat rugoselandwardof arginsThishabitatmayalsothebecoral cover (typically orkswithslopesparseThis habitatmayalsobecarbonateframeworks.Typically found (typically 10%).foundsurroundingoratopslope of the reef crest.ModeratelyThe coral communityrugoseCoralcommunityis composedCoral Fore-reefColoniesare frameworks.predominantlyRemote Sens.2019,Fore-reef11, x FOR PEERREVIEWcarbonateis composed primarilyof Siderastrea,frameworkssparseprimarilyof withSiderastrea,small(submeter)in size.ModeratelyrugoseMontastrea, Diploria,and Colpophylliaspp.coralcover(typically 10%).Remote Sens. 2019, 11, x FOR workswithonsparseAreasofafoundframeworkCrustose corallinealgaeandfleshyalgaeColoniesare dslopeofcoralcover(typically 10%).formedofmassivecoral(Sargassum, Dictyota)alongwithgorgonianssmall(submeter)in size.Areasofalgaeaofframeworkcorallineandthe reefcrest.The fleshycoral ordominate heformedof massivealgae(Sargassum,Coral trea,Typicallyfoundon thestructuremayor massivemaynotorAreas of a ral e-reefcommunityis spp.CrustoseDendrogyra. Siderastrea,havea Livelivingveneer.Gorgonian/Soft Coral Coralnothave a Fore-reefcommunityis dmaintains the algae(Sargassum,Dictyota)primarilyof %Siderastrea,patchy( 15overall).Colpophylliaspp. Crustosepatchy ( risalong rallinealgaefleshydominate the substratebetweencorals.patchy ( lgaeand fleshyalong withgorgonianssubstratebetweencorals.algae (Sargassum, Dictyota)dominate the remainder ofalong with gorgonianssubstrate.heavilyReef frameworkdominatethe remainder ofReefframeworkheavilydominatedby macroalgaeReef framework heavilydominatedbysubstrate.HardbottomHardbottom th Algaeandoccasionalgorgonians.Coral coverCoralis typically( 5%). lowcover lowis typicallywith AlgaeCoral covertypically low( 5is %).( 5 %).Seagrass DenseSeagrass DenseDense meadows of seagrassDenseof seagrass( 60 % meadowscover) dominatedby( 60%cover)dominatedbyThalassia testudinum. OtherThalassia testudinum.Otherseagrasses(e.g., Syringodiumseagrasses(e.g.,Syringodiumfiliforme) and macroalgaefiliforme)and macroalgae(e.g.,Halimedasp.) are also(e.g., Halimedasp.) arealsopresentbut at lowerdensity.present but at lower density.Sand with less than 40%seagrassor HalimedaSand withless than cover.40%Thecommunityisseagrass or Halimeda cover.dominated by Thalassia4 of 164 of 164 of 164 of 165 of 175 of 16

Gorgonians dominate thesubstrate between corals.Hardbottomwith AlgaeHardbottomRemote Sens. 2019, 11, 1445 with AlgaeHardbottomwith AlgaeReef framework heavilydominatedby macroalgaeReef y macroalgaeReef frameworkheavilyCoralcover acroalgae( 5%).Coralcoveristypicallylowand occasional gorgonians.( 5%).Coral cover is typically low5 of 16( 5 %).1. Cont.TableBenthic Habitat TypeDescription[16] of seagrassDense meadows( 60% meadowscover) dominatedbyDenseof seagrassThalassiatestudinum.Other( 60%cover)dominatedbyDense meadows of iatestudinum.OtherDenseDensemeadowsofseagrass( 60%cover)( 60 % cover) dominated eagrassDenseby udinum.Otherseagrasses (e.g.,(e.g.,Syringodiumfiliforme)Seagrass DenseHalimedasp.) are andalsofiliforme)andmacroalgaeSeagrass Dense seagrasses (e.g., Syringodiummacroalgae esent butat lowerdensity.presentbutatlowerdensity.(e.g., Halimeda sp.) are alsoSand withthandensity.40%presentbut atlesslowerseagrassor HalimedaSand withless than d with less than 40%dominatedby ThalassiaThe40%communityisSand with lessthanseagrassorseagrassor yThalassiaHalimeda cover.TheisThe communitycommunityisSparse by ssiaSeagrass ySeagrasstestudinum but otherand macroalgaemacroalgae(Halimeda dSparseseagrasses (principallysignificantlyto sp.)Syringodiumfiliforme) andcover.contributesignificantlytomacroalgae (Halimeda sp.)Unconsolidatedsedimentcover.contribute significantly tosheets withlittleto s,orsheetswithlittleto noUnconsolidated sedimentsheets isclassinvertebrate,seagrass,to no invertebrate,seagrass,ormacroalgalsheets with little to nooroccursatdepthsandincover.Thisclasscover. This classmacroalgaloccursat allalldepthsandininvertebrate,seagrass,orSand ShallowSand Shallowall geomorphologicalzonesbut typicallyallgeomorphologicalzonesoccursat foundin more abundanceinfoundthe occursat all e Sens.2019,11,x FORPEERREVIEWSand reall geomorphological zonessideand foundin morelagoons.sideandandwithinembaymentsabundancein thesouthernlagoons.side andandwithinembaymentsField Video Example6 of 17lagoons.Coarse,andoftenrippled sandSandDeepsheetsfoundin areaswithCoarse, often rippled sand sheetsfoundinSand Deep with sparsewithsparsehigherenergyflow alongareaswith higherenergyflow alongwithMacroalgaesmall patcheswithof Halimedaalgae. ofMacroalgaesmall patchesHalimeda algae.2.3. Satellite Image ProcessingPlanet (formerly Planet Labs, Inc.) has manufactured and launched numerous miniature satellitescalled “Dove”. With a constellation of over 150 satellites, the Doves offer 3.7 m spatial resolution andcollect daily scenes. A total of 18 sun-synchronous Dove images from three satellite sensors wereselected to map benthic habitats over the study area. The single scene dimensions are approximately25 km 8 km with a spatial resolution of 3.7 m. These images were collected from 22 January to 30January 2018 and were selected based on minimal cloud cover, sun-glint, waves, and water turbidity.The Dove top-of-atmosphere (TOA) radiance in Blue (470 nm), Green (540 nm), Red (610 nm) andNear-Infrared (NIR; 780 nm) bands were used to correct atmospheric effects by using the 6S atmosphericcorrection model [17]. The resulting surface reflectance ρ(λ) in visible bands were subtracted by theNIR band in order to minimize sea surface effects to derive marine reflectance ρm (λ) as [18]:ρm (λ) ρ(λ) ρ(NIR),(1)Below-surface remote sensing reflectance (rrs ) was then calculated using method by [19]:rrs (λ) ρm (λ)/π,0.52 1.7(ρm (λ)/π)(2)

Remote Sens. 2019, 11, 14456 of 16With below-surface reflectance calculated, water bathymetry was predicted using an adaptivebathymetry estimation method with self-tuning parameters (m0 , m1 ) as described in Li et al. [20]:H m0ln(1000 rrs blue) m1 ,ln(1000 rrs green)(3)Using the modeled bathymetry, we estimated bottom reflectance by using bathymetry as inputinformation. We masked out deep open ocean according to the bathymetry (H 15 m). The totalbelow-surface remote sensing reflectance is the combination of both water column contribution (rrsC (λ))and bottom contribution at the water surface (rrsB (λ)) as [21]:rrs (λ) rrsC (λ) rrsB (λ),(4)where bottom contribution at the water surface (rrsB (λ)) is contributed by bottom reflectance (rb (λ)) as:rrsB (λ) 1r (λ)e kH ,π b(5)where k is an attenuation coefficient for the water column [19].After deriving surface reflectance ρ(λ), bottom reflectance rrsB (λ), and bathymetry H, we usedeCognition Developer 9.4 (Munich, Germany) software to segment the images using the followingparameters: Scale (150), Shape (0.1), and Compactness (0.5). We tested different combinations ofsegmentation parameters (50 Scale 200, 0.05 Shape 0.2, 0.1 Compactness 1.0) to arrive at thecombination that best captured the representation of benthic features based on the visual examination.Remote Sens. 2019, 11, x FOR PEER REVIEW7 of 16These processing steps are illustrated in Figure 2.Figure 2. Bathymetric, bottom reflectance, and benthic map retrival.Figure 2. Bathymetric, bottom reflectance, and benthic map retrival.The interpreted GPS-referenced video transects were used to identify objects that represented eachhabitat type, and the nearest neighbor classifier was applied to classify the objects. The nearest neighborclassifier was selected because it offered the best results after comparison of multiple classifiers (e.g.,nearest neighbor, support vector machine, etc.). The mean values of the blue and green bands for bothsurface reflectance and bottom reflectance and mean depth were used as object attributes for the classifier.

Remote Sens. 2019, 11, 14457 of 16The interpreted GPS-referenced video transects were used to identify objects that represented eachhabitat type, and the nearest neighbor classifier was applied to classify the objects. The nearest neighborclassifier was selected because it offered the best results after comparison of multiple classifiers (e.g.,nearest neighbor, support vector machine, etc.). The mean values of the blue and green bands forboth surface reflectance and bottom reflectance and mean depth were used as object attributes forthe classifier. For shallow to medium depths ( 10 m), a greater layer weight was placed on surfacereflectance values, and for deeper depths ( 10 m), a greater weight was placed on bottom reflectancevalues. The lower portions of water leaving radiance are contributed by the bottom reflectance atgreater depths [19,22]. Upon completing the benthic composition classification, an accuracy assessment(confusion matrix) was conducted using the remaining field validation points.3. Results3.1. Bathymetry Retrieval and Benthic Composition ClassificationThe high-resolution bathymetry information generated from Planet Dove satellite images wasconsistent with known spatial variations from shallow to deep coral reef regions and out to the deepocean. For instance, general depth trends were retrieved with an increasing depth gradient extendingfrom land to ocean. These satellite-derived bathymetry measurements supported the mapping ofbenthic geomorphologic zones, such as shallow reef crest (depth 3 m), which are found along the coastof southeastern Dominican Republic (Figure 3). Comparing the mapped bathymetry to field-basedwater depth measurements, root mean square errors (RMSE) ranged from 1.37 m to 1.98 m, and R2ranged from 0.70 to 0.91 (Catalina Island, RMSE 1.98 m, R2 0.70; Saona Island, RMSE 1.37 m,R2 0.91, east Dominican Republic, RMSE 1.72 m, R2 0.73) (Figure A1a–c). The overall accuracyRMSE was 1.5 m, sufficient to support subsequent benthic classification steps. The NIR band wasused to remove the water surface effect in the initial step of satellite image processing (Figure 2).Therefore, higher RMSE values at the Catalina Island study site were caused by poor image quality inthe near-infrared band.

Remote Sens. 2019, 11, x FOR PEER REVIEW8 of 16Therefore, higher RMSE values at the Catalina Island study site were caused by poor image quality8 of 16in the near-infrared band.Remote Sens. 2019, 11, 1445Figure 3. Bathymetric spatial information derived from Dove satellite images covering three studyFigure3. BathymetricspatialinformationfromDove satelliteDominicanimages coveringthreestudysiteswithinthe Arrecifesdel Surestemarinederivedsanctuaryin Republic:CatalinaIsland, Saona Island, and eastern Dominican Republic. The bathymetry was derived from medium toIsland,SaonaIsland, and eastern Dominican Republic. The bathymetry was derived from medium tohightidalstages.high tidal stages.The results of the image segmentation for the three study sites are shown in Figure 4. DifferentThehabitatresultstypesof thewereimagesegmentationstudy inputssites areofshownFigure 4. inedsurfaceinreflectance,bottombenthic habitatwere segmentedon thecombinedinputsof esbathymetry.The finalbasedderivedbenthichabitatmapsthe eme are shown in Figure 5. This scheme was developed for coral reef management applications,schemeprovidesare shownin FigureThis schemedevelopedcoralforreefwhicha baselineto 5.comparefuturewaschange,and ,of e,andisusefulfortheidentificationof targetedconservation areas, marine spatial planning, and ecosystem service models (Figure 5) [23].Coralconservationareas, marinespatial planning,and ecosystemservicemodels (Figure5) [23]. Coralreefsreefswere classifiedinto fore-reefand back-reefclasses whenconsideringbathymetry,reef crest,weretheclassifiedinto fore-reefandback-reefSeagrassclasses whenconsideringreef crest,lagoons,and theandspatial proximityto theshoreline.beds weremappedbathymetry,in shallow protectedspatialthe shoreline.bedswere mappedshallowlagoons,suchDRassuchas proximitythe middletoregionof Saona SeagrassIsland andnear-shoreareas ofinthePunta protectedCana regionin easternthe middle(Figure5). region of Saona Island and near-shore areas of the Punta Cana region in eastern DR(Figure 5).

Remote Sens. 2019, 11, xFOR PEER REVIEW1445Figure 4. Image segmentation results of benthic features within the three study sites.Figure 4. Image segmentation results of benthic features within the three study sites.99ofof 16

Remote Sens. 2019, 11, 1445Remote Sens. 2019, 11, x FOR PEER REVIEW10 of 1610 of 16Figure 5. Benthic habitat classification results covering three study sites within the Arrecifes del SuresteFigure 5. Benthic habitat classification results covering three study sites within the Arrecifes delmarine sanctuary in southeastern Dominican Republic: Catalina Island, Saona Island, and easternSureste marinesanctuary in southeastern Dominican Republic: Catalina Island, Saona Island, andDominicanRepublic.eastern Dominican Republic.3.2. Accuracy Assessment3.2. Accuracy AssessmentConfusion matrix results were calculated using field measurement data. Table 2a,b showsConfusionresultswere calculatedusing field errormeasurementdata. Tableshowsandthethe resultsof thematrixaccuracyassessmentand correspondingmatrix comparingthe2a-bobservedresults ofclassesthe accuracyand correspondingerror

(coral reef crest, coral patch deep, coral back-reef/flat, coral fore-reef, gorgonian/soft coral, hardbottom with algae, seagrass dense, seagrass sparse, sand shallow, and sand deep with sparse macroalgae) were classified for each video sample (Table1). A total of 3000 points were applied as the training samples for the object-based .

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