A comparison of spectral macroalgae taxa separability methods using an extensive spectral library

Autores
Rodríguez, Yolanda Chao; Domínguez Gómez, José Antonio; Sanchez Carnero, Noela Belen; Rodríguez-Pérez, Daniel
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Remote sensing is one the most promising approaches to coastal area cartography, including mapping algae forests. After discrimination of algal communities from other benthic habitats, next step is species discrimination (from other algae). Spectral signature provides the most complete remote description to characterize any algae. In this work spectral signatures are studied from the point of view of taxa separability to assess the potential use of remote sensors to map seaweed in coastal waters. Three approaches were tested: Red-Green-Brown colorimetry (sRGB), optimal spectral boundary separation based on True Skill Statistics (TSS-OB), and pigment absorbance band detection by Derivative Spectroscopy (DS). An extensive spectral library of 36 algal species present in the Atlantic Galician coast (NW of Spain) is used to test and validate these methods. The results show that the three broad taxa of red, green and brown algae can be separated by all three methods (Cohen's kappa of 0.697, 0.891 and 0.910, respectively). The TSS-OB and the DS approaches provide almost perfect classification (despite some anomalous specimens), with DS being slightly better. The sRGB approach, useful for in situ photographic classification, also provides good results.
Fil: Rodríguez, Yolanda Chao. Universidad Nacional de Educacion a Distancia; España
Fil: Domínguez Gómez, José Antonio. Universidad Nacional de Educacion a Distancia; España
Fil: Sanchez Carnero, Noela Belen. Universidad de Vigo; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina
Fil: Rodríguez-Pérez, Daniel. Universidad Nacional de Educacion a Distancia; España
Materia
Algae Species
Colorimetry
Photosynthetic Pigments
Spectral Classification
Spectral Library
True Skill Statistics (Tss)
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/36301
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling A comparison of spectral macroalgae taxa separability methods using an extensive spectral libraryRodríguez, Yolanda ChaoDomínguez Gómez, José AntonioSanchez Carnero, Noela BelenRodríguez-Pérez, DanielAlgae SpeciesColorimetryPhotosynthetic PigmentsSpectral ClassificationSpectral LibraryTrue Skill Statistics (Tss)https://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Remote sensing is one the most promising approaches to coastal area cartography, including mapping algae forests. After discrimination of algal communities from other benthic habitats, next step is species discrimination (from other algae). Spectral signature provides the most complete remote description to characterize any algae. In this work spectral signatures are studied from the point of view of taxa separability to assess the potential use of remote sensors to map seaweed in coastal waters. Three approaches were tested: Red-Green-Brown colorimetry (sRGB), optimal spectral boundary separation based on True Skill Statistics (TSS-OB), and pigment absorbance band detection by Derivative Spectroscopy (DS). An extensive spectral library of 36 algal species present in the Atlantic Galician coast (NW of Spain) is used to test and validate these methods. The results show that the three broad taxa of red, green and brown algae can be separated by all three methods (Cohen's kappa of 0.697, 0.891 and 0.910, respectively). The TSS-OB and the DS approaches provide almost perfect classification (despite some anomalous specimens), with DS being slightly better. The sRGB approach, useful for in situ photographic classification, also provides good results.Fil: Rodríguez, Yolanda Chao. Universidad Nacional de Educacion a Distancia; EspañaFil: Domínguez Gómez, José Antonio. Universidad Nacional de Educacion a Distancia; EspañaFil: Sanchez Carnero, Noela Belen. Universidad de Vigo; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Rodríguez-Pérez, Daniel. Universidad Nacional de Educacion a Distancia; EspañaElsevier B.V.2017-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/36301Rodríguez, Yolanda Chao; Domínguez Gómez, José Antonio; Sanchez Carnero, Noela Belen; Rodríguez-Pérez, Daniel; A comparison of spectral macroalgae taxa separability methods using an extensive spectral library; Elsevier B.V.; Algal Research; 26; 9-2017; 463-4732211-9264CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.algal.2017.04.021info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2211926416304040?via%3Dihubinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:31:46Zoai:ri.conicet.gov.ar:11336/36301instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:31:47.02CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A comparison of spectral macroalgae taxa separability methods using an extensive spectral library
title A comparison of spectral macroalgae taxa separability methods using an extensive spectral library
spellingShingle A comparison of spectral macroalgae taxa separability methods using an extensive spectral library
Rodríguez, Yolanda Chao
Algae Species
Colorimetry
Photosynthetic Pigments
Spectral Classification
Spectral Library
True Skill Statistics (Tss)
title_short A comparison of spectral macroalgae taxa separability methods using an extensive spectral library
title_full A comparison of spectral macroalgae taxa separability methods using an extensive spectral library
title_fullStr A comparison of spectral macroalgae taxa separability methods using an extensive spectral library
title_full_unstemmed A comparison of spectral macroalgae taxa separability methods using an extensive spectral library
title_sort A comparison of spectral macroalgae taxa separability methods using an extensive spectral library
dc.creator.none.fl_str_mv Rodríguez, Yolanda Chao
Domínguez Gómez, José Antonio
Sanchez Carnero, Noela Belen
Rodríguez-Pérez, Daniel
author Rodríguez, Yolanda Chao
author_facet Rodríguez, Yolanda Chao
Domínguez Gómez, José Antonio
Sanchez Carnero, Noela Belen
Rodríguez-Pérez, Daniel
author_role author
author2 Domínguez Gómez, José Antonio
Sanchez Carnero, Noela Belen
Rodríguez-Pérez, Daniel
author2_role author
author
author
dc.subject.none.fl_str_mv Algae Species
Colorimetry
Photosynthetic Pigments
Spectral Classification
Spectral Library
True Skill Statistics (Tss)
topic Algae Species
Colorimetry
Photosynthetic Pigments
Spectral Classification
Spectral Library
True Skill Statistics (Tss)
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Remote sensing is one the most promising approaches to coastal area cartography, including mapping algae forests. After discrimination of algal communities from other benthic habitats, next step is species discrimination (from other algae). Spectral signature provides the most complete remote description to characterize any algae. In this work spectral signatures are studied from the point of view of taxa separability to assess the potential use of remote sensors to map seaweed in coastal waters. Three approaches were tested: Red-Green-Brown colorimetry (sRGB), optimal spectral boundary separation based on True Skill Statistics (TSS-OB), and pigment absorbance band detection by Derivative Spectroscopy (DS). An extensive spectral library of 36 algal species present in the Atlantic Galician coast (NW of Spain) is used to test and validate these methods. The results show that the three broad taxa of red, green and brown algae can be separated by all three methods (Cohen's kappa of 0.697, 0.891 and 0.910, respectively). The TSS-OB and the DS approaches provide almost perfect classification (despite some anomalous specimens), with DS being slightly better. The sRGB approach, useful for in situ photographic classification, also provides good results.
Fil: Rodríguez, Yolanda Chao. Universidad Nacional de Educacion a Distancia; España
Fil: Domínguez Gómez, José Antonio. Universidad Nacional de Educacion a Distancia; España
Fil: Sanchez Carnero, Noela Belen. Universidad de Vigo; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina
Fil: Rodríguez-Pérez, Daniel. Universidad Nacional de Educacion a Distancia; España
description Remote sensing is one the most promising approaches to coastal area cartography, including mapping algae forests. After discrimination of algal communities from other benthic habitats, next step is species discrimination (from other algae). Spectral signature provides the most complete remote description to characterize any algae. In this work spectral signatures are studied from the point of view of taxa separability to assess the potential use of remote sensors to map seaweed in coastal waters. Three approaches were tested: Red-Green-Brown colorimetry (sRGB), optimal spectral boundary separation based on True Skill Statistics (TSS-OB), and pigment absorbance band detection by Derivative Spectroscopy (DS). An extensive spectral library of 36 algal species present in the Atlantic Galician coast (NW of Spain) is used to test and validate these methods. The results show that the three broad taxa of red, green and brown algae can be separated by all three methods (Cohen's kappa of 0.697, 0.891 and 0.910, respectively). The TSS-OB and the DS approaches provide almost perfect classification (despite some anomalous specimens), with DS being slightly better. The sRGB approach, useful for in situ photographic classification, also provides good results.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/36301
Rodríguez, Yolanda Chao; Domínguez Gómez, José Antonio; Sanchez Carnero, Noela Belen; Rodríguez-Pérez, Daniel; A comparison of spectral macroalgae taxa separability methods using an extensive spectral library; Elsevier B.V.; Algal Research; 26; 9-2017; 463-473
2211-9264
CONICET Digital
CONICET
url http://hdl.handle.net/11336/36301
identifier_str_mv Rodríguez, Yolanda Chao; Domínguez Gómez, José Antonio; Sanchez Carnero, Noela Belen; Rodríguez-Pérez, Daniel; A comparison of spectral macroalgae taxa separability methods using an extensive spectral library; Elsevier B.V.; Algal Research; 26; 9-2017; 463-473
2211-9264
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.algal.2017.04.021
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2211926416304040?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082803336216576
score 13.22299

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