<?xml version="1.0" encoding="UTF-8" ?>
<oai_dc:dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title>3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</dc:title>
<dc:creator>Prado, E. (Elena)</dc:creator>
<dc:creator>Rodríguez Basalo, Augusto</dc:creator>
<dc:creator>Cobo, Adolfo</dc:creator>
<dc:creator>Ríos, P. (Pilar)</dc:creator>
<dc:creator>Sánchez, F. (Francisco)</dc:creator>
<dc:subject>circalittoral rocky shelf</dc:subject>
<dc:subject>underwater 3D photogrammetry</dc:subject>
<dc:subject>structure-from-motion</dc:subject>
<dc:subject>Avilés Canyon System</dc:subject>
<dc:subject>benthic habitat modeling</dc:subject>
<dc:subject>deep-learning</dc:subject>
<dc:subject>YOLO</dc:subject>
<dc:subject>Annotation of underwater images</dc:subject>
<dc:description>The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</dc:description>
<dc:description>1</dc:description>
<dc:date>2021-11-11T20:56:43Z</dc:date>
<dc:date>2021-11-11T20:56:43Z</dc:date>
<dc:date>2020</dc:date>
<dc:type>article</dc:type>
<dc:identifier>2072-4292</dc:identifier>
<dc:identifier>http://hdl.handle.net/10508/15470</dc:identifier>
<dc:identifier>10.3390/rs12152466</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights>Atribución 3.0 España</dc:rights>
<dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
<dc:rights>openAccess</dc:rights>
<dc:publisher>MDPI AG</dc:publisher>
<dc:publisher>Centro Oceanográfico de Santander</dc:publisher>
</oai_dc:dc>
<?xml version="1.0" encoding="UTF-8" ?>
<d:DIDL schemaLocation="urn:mpeg:mpeg21:2002:02-DIDL-NS http://standards.iso.org/ittf/PubliclyAvailableStandards/MPEG-21_schema_files/did/didl.xsd">
<d:DIDLInfo>
<dcterms:created schemaLocation="http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/dcterms.xsd">2021-11-11T20:56:43Z</dcterms:created>
</d:DIDLInfo>
<d:Item id="hdl_10508_15470">
<d:Descriptor>
<d:Statement mimeType="application/xml; charset=utf-8">
<dii:Identifier schemaLocation="urn:mpeg:mpeg21:2002:01-DII-NS http://standards.iso.org/ittf/PubliclyAvailableStandards/MPEG-21_schema_files/dii/dii.xsd">urn:hdl:10508/15470</dii:Identifier>
</d:Statement>
</d:Descriptor>
<d:Descriptor>
<d:Statement mimeType="application/xml; charset=utf-8">
<oai_dc:dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title>3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</dc:title>
<dc:creator>Prado, E. (Elena)</dc:creator>
<dc:creator>Rodríguez Basalo, Augusto</dc:creator>
<dc:creator>Cobo, Adolfo</dc:creator>
<dc:creator>Ríos, P. (Pilar)</dc:creator>
<dc:creator>Sánchez, F. (Francisco)</dc:creator>
<dc:subject>circalittoral rocky shelf</dc:subject>
<dc:subject>underwater 3D photogrammetry</dc:subject>
<dc:subject>structure-from-motion</dc:subject>
<dc:subject>Avilés Canyon System</dc:subject>
<dc:subject>benthic habitat modeling</dc:subject>
<dc:subject>deep-learning</dc:subject>
<dc:subject>YOLO</dc:subject>
<dc:subject>Annotation of underwater images</dc:subject>
<dc:description>The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</dc:description>
<dc:date>2021-11-11T20:56:43Z</dc:date>
<dc:date>2021-11-11T20:56:43Z</dc:date>
<dc:date>2020</dc:date>
<dc:type>article</dc:type>
<dc:identifier>2072-4292</dc:identifier>
<dc:identifier>http://hdl.handle.net/10508/15470</dc:identifier>
<dc:identifier>10.3390/rs12152466</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
<dc:rights>openAccess</dc:rights>
<dc:rights>Atribución 3.0 España</dc:rights>
<dc:publisher>MDPI AG</dc:publisher>
<dc:publisher>Centro Oceanográfico de Santander</dc:publisher>
</oai_dc:dc>
</d:Statement>
</d:Descriptor>
<d:Component id="10508_15470_1">
</d:Component>
</d:Item>
</d:DIDL>
<?xml version="1.0" encoding="UTF-8" ?>
<dim:dim schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.arvo.es/dim.xsd">
<dim:field authority="900" confidence="500" element="contributor" mdschema="dc" qualifier="author">Prado, E. (Elena)</dim:field>
<dim:field authority="900" confidence="500" element="contributor" mdschema="dc" qualifier="author">Rodríguez Basalo, Augusto</dim:field>
<dim:field authority="900" confidence="500" element="contributor" mdschema="dc" qualifier="author">Cobo, Adolfo</dim:field>
<dim:field authority="900" confidence="500" element="contributor" mdschema="dc" qualifier="author">Ríos, P. (Pilar)</dim:field>
<dim:field authority="900" confidence="500" element="contributor" mdschema="dc" qualifier="author">Sánchez, F. (Francisco)</dim:field>
<dim:field element="date" mdschema="dc" qualifier="accessioned">2021-11-11T20:56:43Z</dim:field>
<dim:field element="date" mdschema="dc" qualifier="available">2021-11-11T20:56:43Z</dim:field>
<dim:field element="date" mdschema="dc" qualifier="issued">2020</dim:field>
<dim:field element="identifier" mdschema="dc" qualifier="issn">2072-4292</dim:field>
<dim:field element="identifier" mdschema="dc" qualifier="uri">http://hdl.handle.net/10508/15470</dim:field>
<dim:field element="identifier" mdschema="dc" qualifier="doi">10.3390/rs12152466</dim:field>
<dim:field element="description" lang="en" mdschema="dc" qualifier="abstract">The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</dim:field>
<dim:field element="description" lang="es_ES" mdschema="dc" qualifier="version">1</dim:field>
<dim:field element="language" mdschema="dc" qualifier="iso">eng</dim:field>
<dim:field element="publisher" lang="es_ES" mdschema="dc">MDPI AG</dim:field>
<dim:field element="publisher" mdschema="dc" qualifier="centre">Centro Oceanográfico de Santander</dim:field>
<dim:field element="rights" lang="*" mdschema="dc">Atribución 3.0 España</dim:field>
<dim:field element="rights" mdschema="dc" qualifier="uri">http://creativecommons.org/licenses/by/3.0/es/</dim:field>
<dim:field element="rights" mdschema="dc" qualifier="accessRights">openAccess</dim:field>
<dim:field element="subject" mdschema="dc">circalittoral rocky shelf</dim:field>
<dim:field element="subject" mdschema="dc">underwater 3D photogrammetry</dim:field>
<dim:field element="subject" mdschema="dc">structure-from-motion</dim:field>
<dim:field element="subject" mdschema="dc">Avilés Canyon System</dim:field>
<dim:field element="subject" mdschema="dc">benthic habitat modeling</dim:field>
<dim:field element="subject" mdschema="dc">deep-learning</dim:field>
<dim:field element="subject" mdschema="dc">YOLO</dim:field>
<dim:field element="subject" mdschema="dc">Annotation of underwater images</dim:field>
<dim:field element="title" lang="es_ES" mdschema="dc">3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</dim:field>
<dim:field element="type" lang="es_ES" mdschema="dc">article</dim:field>
</dim:dim>
<?xml version="1.0" encoding="UTF-8" ?>
<thesis schemaLocation="http://www.ndltd.org/standards/metadata/etdms/1-0/ http://www.ndltd.org/standards/metadata/etdms/1-0/etdms.xsd">
<title>3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</title>
<creator>Prado, E. (Elena)</creator>
<creator>Rodríguez Basalo, Augusto</creator>
<creator>Cobo, Adolfo</creator>
<creator>Ríos, P. (Pilar)</creator>
<creator>Sánchez, F. (Francisco)</creator>
<subject>circalittoral rocky shelf</subject>
<subject>underwater 3D photogrammetry</subject>
<subject>structure-from-motion</subject>
<subject>Avilés Canyon System</subject>
<subject>benthic habitat modeling</subject>
<subject>deep-learning</subject>
<subject>YOLO</subject>
<subject>Annotation of underwater images</subject>
<description>The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</description>
<date>2021-11-11</date>
<date>2021-11-11</date>
<date>2020</date>
<type>article</type>
<identifier>2072-4292</identifier>
<identifier>http://hdl.handle.net/10508/15470</identifier>
<identifier>10.3390/rs12152466</identifier>
<language>eng</language>
<rights>http://creativecommons.org/licenses/by/3.0/es/</rights>
<rights>openAccess</rights>
<rights>Atribución 3.0 España</rights>
<publisher>MDPI AG</publisher>
<publisher>Centro Oceanográfico de Santander</publisher>
</thesis>
<?xml version="1.0" encoding="UTF-8" ?>
<record schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<leader>00925njm 22002777a 4500</leader>
<datafield ind1=" " ind2=" " tag="042">
<subfield code="a">dc</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="720">
<subfield code="a">Prado, E. (Elena)</subfield>
<subfield code="e">author</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="720">
<subfield code="a">Rodríguez Basalo, Augusto</subfield>
<subfield code="e">author</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="720">
<subfield code="a">Cobo, Adolfo</subfield>
<subfield code="e">author</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="720">
<subfield code="a">Ríos, P. (Pilar)</subfield>
<subfield code="e">author</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="720">
<subfield code="a">Sánchez, F. (Francisco)</subfield>
<subfield code="e">author</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="260">
<subfield code="c">2020</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="520">
<subfield code="a">The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</subfield>
</datafield>
<datafield ind1="8" ind2=" " tag="024">
<subfield code="a">2072-4292</subfield>
</datafield>
<datafield ind1="8" ind2=" " tag="024">
<subfield code="a">http://hdl.handle.net/10508/15470</subfield>
</datafield>
<datafield ind1="8" ind2=" " tag="024">
<subfield code="a">10.3390/rs12152466</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">circalittoral rocky shelf</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">underwater 3D photogrammetry</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">structure-from-motion</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">Avilés Canyon System</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">benthic habitat modeling</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">deep-learning</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">YOLO</subfield>
</datafield>
<datafield ind1=" " ind2=" " tag="653">
<subfield code="a">Annotation of underwater images</subfield>
</datafield>
<datafield ind1="0" ind2="0" tag="245">
<subfield code="a">3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</subfield>
</datafield>
</record>
<?xml version="1.0" encoding="UTF-8" ?>
<mets ID=" DSpace_ITEM_10508-15470" OBJID=" hdl:10508/15470" PROFILE="DSpace METS SIP Profile 1.0" TYPE="DSpace ITEM" schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd">
<metsHdr CREATEDATE="2022-08-19T03:42:00Z">
<agent ROLE="CUSTODIAN" TYPE="ORGANIZATION">
<name>Repositorio Institucional Digital del IEO</name>
</agent>
</metsHdr>
<dmdSec ID="DMD_10508_15470">
<mdWrap MDTYPE="MODS">
<xmlData schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:mods schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:role>
<mods:roleTerm type="text">author</mods:roleTerm>
</mods:role>
<mods:namePart>Prado, E. (Elena)</mods:namePart>
</mods:name>
<mods:name>
<mods:role>
<mods:roleTerm type="text">author</mods:roleTerm>
</mods:role>
<mods:namePart>Rodríguez Basalo, Augusto</mods:namePart>
</mods:name>
<mods:name>
<mods:role>
<mods:roleTerm type="text">author</mods:roleTerm>
</mods:role>
<mods:namePart>Cobo, Adolfo</mods:namePart>
</mods:name>
<mods:name>
<mods:role>
<mods:roleTerm type="text">author</mods:roleTerm>
</mods:role>
<mods:namePart>Ríos, P. (Pilar)</mods:namePart>
</mods:name>
<mods:name>
<mods:role>
<mods:roleTerm type="text">author</mods:roleTerm>
</mods:role>
<mods:namePart>Sánchez, F. (Francisco)</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2021-11-11T20:56:43Z</mods:dateAccessioned>
</mods:extension>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2021-11-11T20:56:43Z</mods:dateAvailable>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2020</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="issn">2072-4292</mods:identifier>
<mods:identifier type="uri">http://hdl.handle.net/10508/15470</mods:identifier>
<mods:identifier type="doi">10.3390/rs12152466</mods:identifier>
<mods:abstract>The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</mods:abstract>
<mods:language>
<mods:languageTerm authority="rfc3066">eng</mods:languageTerm>
</mods:language>
<mods:accessCondition type="useAndReproduction">Atribución 3.0 España</mods:accessCondition>
<mods:subject>
<mods:topic>circalittoral rocky shelf</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>underwater 3D photogrammetry</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>structure-from-motion</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Avilés Canyon System</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>benthic habitat modeling</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>deep-learning</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>YOLO</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Annotation of underwater images</mods:topic>
</mods:subject>
<mods:titleInfo>
<mods:title>3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</mods:title>
</mods:titleInfo>
<mods:genre>article</mods:genre>
</mods:mods>
</xmlData>
</mdWrap>
</dmdSec>
<amdSec ID="FO_10508_15470_1">
<techMD ID="TECH_O_10508_15470_1">
<mdWrap MDTYPE="PREMIS">
<xmlData schemaLocation="http://www.loc.gov/standards/premis http://www.loc.gov/standards/premis/PREMIS-v1-0.xsd">
<premis:premis>
<premis:object>
<premis:objectIdentifier>
<premis:objectIdentifierType>URL</premis:objectIdentifierType>
<premis:objectIdentifierValue>http://www.repositorio.ieo.es/e-ieo/bitstream/10508/15470/1/3DFine_scaleTerrain.pdf</premis:objectIdentifierValue>
</premis:objectIdentifier>
<premis:objectCategory>File</premis:objectCategory>
<premis:objectCharacteristics>
<premis:fixity>
<premis:messageDigestAlgorithm>MD5</premis:messageDigestAlgorithm>
<premis:messageDigest>2ba0d882434308b6b7f72d2f4124bdcb</premis:messageDigest>
</premis:fixity>
<premis:size>11273240</premis:size>
<premis:format>
<premis:formatDesignation>
<premis:formatName>application/pdf</premis:formatName>
</premis:formatDesignation>
</premis:format>
</premis:objectCharacteristics>
<premis:originalName>3DFine_scaleTerrain.pdf</premis:originalName>
</premis:object>
</premis:premis>
</xmlData>
</mdWrap>
</techMD>
</amdSec>
<fileSec>
<fileGrp USE="ORIGINAL">
<file ADMID="FO_10508_15470_1" CHECKSUM="2ba0d882434308b6b7f72d2f4124bdcb" CHECKSUMTYPE="MD5" GROUPID="GROUP_BITSTREAM_10508_15470_1" ID="BITSTREAM_ORIGINAL_10508_15470_1" MIMETYPE="application/pdf" SEQ="1" SIZE="11273240">
</file>
</fileGrp>
</fileSec>
<structMap LABEL="DSpace Object" TYPE="LOGICAL">
<div ADMID="DMD_10508_15470" TYPE="DSpace Object Contents">
<div TYPE="DSpace BITSTREAM">
</div>
</div>
</structMap>
</mets>
<?xml version="1.0" encoding="UTF-8" ?>
<mods:mods schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Prado, E. (Elena)</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Rodríguez Basalo, Augusto</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Cobo, Adolfo</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Ríos, P. (Pilar)</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Sánchez, F. (Francisco)</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2021-11-11T20:56:43Z</mods:dateAvailable>
</mods:extension>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2021-11-11T20:56:43Z</mods:dateAccessioned>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2020</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="issn">2072-4292</mods:identifier>
<mods:identifier type="uri">http://hdl.handle.net/10508/15470</mods:identifier>
<mods:identifier type="doi">10.3390/rs12152466</mods:identifier>
<mods:abstract>The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</mods:abstract>
<mods:language>
<mods:languageTerm>eng</mods:languageTerm>
</mods:language>
<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by/3.0/es/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Atribución 3.0 España</mods:accessCondition>
<mods:subject>
<mods:topic>circalittoral rocky shelf</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>underwater 3D photogrammetry</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>structure-from-motion</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Avilés Canyon System</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>benthic habitat modeling</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>deep-learning</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>YOLO</mods:topic>
</mods:subject>
<mods:subject>
<mods:topic>Annotation of underwater images</mods:topic>
</mods:subject>
<mods:titleInfo>
<mods:title>3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</mods:title>
</mods:titleInfo>
<mods:genre>article</mods:genre>
</mods:mods>
<?xml version="1.0" encoding="UTF-8" ?>
<atom:entry schemaLocation="http://www.w3.org/2005/Atom http://www.kbcafe.com/rss/atom.xsd.xml">
<atom:id>http://hdl.handle.net/10508/15470/ore.xml</atom:id>
<atom:published>2021-11-11T20:56:43Z</atom:published>
<atom:updated>2021-11-11T20:56:43Z</atom:updated>
<atom:source>
<atom:generator>Repositorio Institucional Digital del IEO</atom:generator>
</atom:source>
<atom:title>3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</atom:title>
<atom:author>
<atom:name>Prado, E. (Elena)</atom:name>
</atom:author>
<atom:author>
<atom:name>Rodríguez Basalo, Augusto</atom:name>
</atom:author>
<atom:author>
<atom:name>Cobo, Adolfo</atom:name>
</atom:author>
<atom:author>
<atom:name>Ríos, P. (Pilar)</atom:name>
</atom:author>
<atom:author>
<atom:name>Sánchez, F. (Francisco)</atom:name>
</atom:author>
<oreatom:triples>
<rdf:Description about="http://hdl.handle.net/10508/15470/ore.xml#atom">
<dcterms:modified>2021-11-11T20:56:43Z</dcterms:modified>
</rdf:Description>
<rdf:Description about="http://www.repositorio.ieo.es/e-ieo/bitstream/10508/15470/2/license_rdf">
<dcterms:description>CC-LICENSE</dcterms:description>
</rdf:Description>
<rdf:Description about="http://www.repositorio.ieo.es/e-ieo/bitstream/10508/15470/1/3DFine_scaleTerrain.pdf">
<dcterms:description>ORIGINAL</dcterms:description>
</rdf:Description>
</oreatom:triples>
</atom:entry>
<?xml version="1.0" encoding="UTF-8" ?>
<qdc:qualifieddc schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
<dc:title>3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</dc:title>
<dc:creator>Prado, E. (Elena)</dc:creator>
<dc:creator>Rodríguez Basalo, Augusto</dc:creator>
<dc:creator>Cobo, Adolfo</dc:creator>
<dc:creator>Ríos, P. (Pilar)</dc:creator>
<dc:creator>Sánchez, F. (Francisco)</dc:creator>
<dc:subject>circalittoral rocky shelf</dc:subject>
<dc:subject>underwater 3D photogrammetry</dc:subject>
<dc:subject>structure-from-motion</dc:subject>
<dc:subject>Avilés Canyon System</dc:subject>
<dc:subject>benthic habitat modeling</dc:subject>
<dc:subject>deep-learning</dc:subject>
<dc:subject>YOLO</dc:subject>
<dc:subject>Annotation of underwater images</dc:subject>
<dcterms:abstract>The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</dcterms:abstract>
<dcterms:dateAccepted>2021-11-11T20:56:43Z</dcterms:dateAccepted>
<dcterms:available>2021-11-11T20:56:43Z</dcterms:available>
<dcterms:created>2021-11-11T20:56:43Z</dcterms:created>
<dcterms:issued>2020</dcterms:issued>
<dc:type>article</dc:type>
<dc:identifier>2072-4292</dc:identifier>
<dc:identifier>http://hdl.handle.net/10508/15470</dc:identifier>
<dc:identifier>10.3390/rs12152466</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
<dc:rights>openAccess</dc:rights>
<dc:rights>Atribución 3.0 España</dc:rights>
<dc:publisher>MDPI AG</dc:publisher>
<dc:publisher>Centro Oceanográfico de Santander</dc:publisher>
</qdc:qualifieddc>
<?xml version="1.0" encoding="UTF-8" ?>
<rdf:RDF schemaLocation="http://www.openarchives.org/OAI/2.0/rdf/ http://www.openarchives.org/OAI/2.0/rdf.xsd">
<ow:Publication about="oai:repositorio.ieo.es:10508/15470">
<dc:title>3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</dc:title>
<dc:creator>Prado, E. (Elena)</dc:creator>
<dc:creator>Rodríguez Basalo, Augusto</dc:creator>
<dc:creator>Cobo, Adolfo</dc:creator>
<dc:creator>Ríos, P. (Pilar)</dc:creator>
<dc:creator>Sánchez, F. (Francisco)</dc:creator>
<dc:subject>circalittoral rocky shelf</dc:subject>
<dc:subject>underwater 3D photogrammetry</dc:subject>
<dc:subject>structure-from-motion</dc:subject>
<dc:subject>Avilés Canyon System</dc:subject>
<dc:subject>benthic habitat modeling</dc:subject>
<dc:subject>deep-learning</dc:subject>
<dc:subject>YOLO</dc:subject>
<dc:subject>Annotation of underwater images</dc:subject>
<dc:description>The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</dc:description>
<dc:date>2021-11-11T20:56:43Z</dc:date>
<dc:date>2021-11-11T20:56:43Z</dc:date>
<dc:date>2020</dc:date>
<dc:type>article</dc:type>
<dc:identifier>2072-4292</dc:identifier>
<dc:identifier>http://hdl.handle.net/10508/15470</dc:identifier>
<dc:identifier>10.3390/rs12152466</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
<dc:rights>openAccess</dc:rights>
<dc:rights>Atribución 3.0 España</dc:rights>
<dc:publisher>MDPI AG</dc:publisher>
<dc:publisher>Centro Oceanográfico de Santander</dc:publisher>
</ow:Publication>
</rdf:RDF>
<?xml version="1.0" encoding="UTF-8" ?>
<metadata schemaLocation="http://www.lyncode.com/xoai http://www.lyncode.com/xsd/xoai.xsd">
<element name="dc">
<element name="contributor">
<element name="author">
<element name="none">
<field name="value">Prado, E. (Elena)</field>
<field name="authority">900</field>
<field name="confidence">500</field>
<field name="orcid_id">0000-0003-2897-9436</field>
<field name="value">Rodríguez Basalo, Augusto</field>
<field name="value">Cobo, Adolfo</field>
<field name="value">Ríos, P. (Pilar)</field>
<field name="authority">916</field>
<field name="confidence">500</field>
<field name="orcid_id">0000-0001-9710-9114</field>
<field name="value">Sánchez, F. (Francisco)</field>
<field name="authority">662</field>
<field name="confidence">500</field>
</element>
</element>
</element>
<element name="date">
<element name="accessioned">
<element name="none">
<field name="value">2021-11-11T20:56:43Z</field>
</element>
</element>
<element name="available">
<element name="none">
<field name="value">2021-11-11T20:56:43Z</field>
</element>
</element>
<element name="issued">
<element name="none">
<field name="value">2020</field>
</element>
</element>
<element name="embargoEndDate">
</element>
</element>
<element name="identifier">
<element name="issn">
<element name="none">
<field name="value">2072-4292</field>
</element>
</element>
<element name="uri">
<element name="none">
<field name="value">http://hdl.handle.net/10508/15470</field>
</element>
</element>
<element name="doi">
<element name="none">
<field name="value">10.3390/rs12152466</field>
</element>
</element>
</element>
<element name="description">
<element name="abstract">
<element name="en">
<field name="value">The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.</field>
</element>
</element>
<element name="version">
<element name="es_ES">
<field name="value">1</field>
</element>
</element>
</element>
<element name="language">
<element name="iso">
<element name="none">
<field name="value">eng</field>
</element>
</element>
</element>
<element name="publisher">
<element name="es_ES">
<field name="value">MDPI AG</field>
</element>
<element name="centre">
<element name="none">
<field name="value">Centro Oceanográfico de Santander</field>
</element>
</element>
</element>
<element name="rights">
<element name="*">
<field name="value">Atribución 3.0 España</field>
</element>
<element name="uri">
<element name="none">
<field name="value">http://creativecommons.org/licenses/by/3.0/es/</field>
</element>
</element>
<element name="accessRights">
<element name="none">
<field name="value">openAccess</field>
</element>
</element>
</element>
<element name="subject">
<element name="none">
<field name="value">circalittoral rocky shelf</field>
<field name="value">underwater 3D photogrammetry</field>
<field name="value">structure-from-motion</field>
<field name="value">Avilés Canyon System</field>
<field name="value">benthic habitat modeling</field>
<field name="value">deep-learning</field>
<field name="value">YOLO</field>
<field name="value">Annotation of underwater images</field>
</element>
</element>
<element name="title">
<element name="es_ES">
<field name="value">3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf</field>
</element>
</element>
<element name="type">
<element name="es_ES">
<field name="value">article</field>
</element>
</element>
</element>
<element name="bundles">
<element name="bundle">
<field name="name">CC-LICENSE</field>
<element name="bitstreams">
<element name="bitstream">
<field name="name">license_rdf</field>
<field name="format">application/octet-stream</field>
<field name="size">904</field>
<field name="url">http://www.repositorio.ieo.es/e-ieo/bitstream/10508/15470/2/license_rdf</field>
<field name="checksum">1ea18580adfb39ca85ed7bae434a5f6b</field>
<field name="checksumAlgorithm">MD5</field>
<field name="sid">2</field>
</element>
</element>
</element>
<element name="bundle">
<field name="name">ORIGINAL</field>
<element name="bitstreams">
<element name="bitstream">
<field name="name">3DFine_scaleTerrain.pdf</field>
<field name="originalName">3DFine_scaleTerrain.pdf</field>
<field name="format">application/pdf</field>
<field name="size">11273240</field>
<field name="url">http://www.repositorio.ieo.es/e-ieo/bitstream/10508/15470/1/3DFine_scaleTerrain.pdf</field>
<field name="checksum">2ba0d882434308b6b7f72d2f4124bdcb</field>
<field name="checksumAlgorithm">MD5</field>
<field name="sid">1</field>
</element>
</element>
</element>
</element>
<element name="others">
<field name="handle">10508/15470</field>
<field name="identifier">oai:repositorio.ieo.es:10508/15470</field>
<field name="lastModifyDate">2022-07-22 12:24:05.49</field>
</element>
<element name="repository">
<field name="name">Repositorio Institucional Digital del IEO</field>
<field name="mail">repositorio@ieo.es</field>
</element>
</metadata>