How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)

Autores
Olmedo Masat, Olga Magalí; Raffo, María Paula; Rodríguez Pérez, Daniel; Arijón, Marianela; Sanchez Carnero, Noela Belen
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Macroalgae have attracted the interest of remote sensing as targets to study coastal marine ecosystems because of their key ecological role. The goal of this paper is to analyze a new spectral library, including 28 macroalgae from the South-West Atlantic coast, in order to assess its use in hyperspectral remote sensing. The library includes species collected in the Atlantic Patagonian coast (Argentina) with representatives of brown, red, and green algae, being 22 of the species included in a spectral library for the first time. The spectra of these main groups are described, and the intraspecific variability is also assessed, considering kelp differentiated tissues and depth range, discussing them from the point of view of their effects on spectral features. A classification and an independent component analysis using the spectral range and simulated bands of two state-of-the-art drone-borne hyperspectral sensors were performed. The results show spectral features and clusters identifying further algae taxonomic groups, showing the potential applications of this spectral library for drone-based mapping of this ecological and economical asset of our coastal marine ecosystems.
Fil: Olmedo Masat, Olga Magalí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina
Fil: Raffo, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
Fil: Rodríguez Pérez, Daniel. Universidad Nacional de Educación a Distancia; España
Fil: Arijón, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina
Fil: Sanchez Carnero, Noela Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina
Materia
COASTAL MACROALGAE
HYPERSPECTRAL SENSORS
SPECTRAL FEATURES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/153187
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)Olmedo Masat, Olga MagalíRaffo, María PaulaRodríguez Pérez, DanielArijón, MarianelaSanchez Carnero, Noela BelenCOASTAL MACROALGAEHYPERSPECTRAL SENSORSSPECTRAL FEATUREShttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Macroalgae have attracted the interest of remote sensing as targets to study coastal marine ecosystems because of their key ecological role. The goal of this paper is to analyze a new spectral library, including 28 macroalgae from the South-West Atlantic coast, in order to assess its use in hyperspectral remote sensing. The library includes species collected in the Atlantic Patagonian coast (Argentina) with representatives of brown, red, and green algae, being 22 of the species included in a spectral library for the first time. The spectra of these main groups are described, and the intraspecific variability is also assessed, considering kelp differentiated tissues and depth range, discussing them from the point of view of their effects on spectral features. A classification and an independent component analysis using the spectral range and simulated bands of two state-of-the-art drone-borne hyperspectral sensors were performed. The results show spectral features and clusters identifying further algae taxonomic groups, showing the potential applications of this spectral library for drone-based mapping of this ecological and economical asset of our coastal marine ecosystems.Fil: Olmedo Masat, Olga Magalí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Raffo, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; ArgentinaFil: Rodríguez Pérez, Daniel. Universidad Nacional de Educación a Distancia; EspañaFil: Arijón, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Sanchez Carnero, Noela Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaMDPI AG2020-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/153187Olmedo Masat, Olga Magalí; Raffo, María Paula; Rodríguez Pérez, Daniel; Arijón, Marianela; Sanchez Carnero, Noela Belen; How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia); MDPI AG; Remote Sensing; 12; 23; 12-2020; 1-332072-4292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/12/23/3870info:eu-repo/semantics/altIdentifier/doi/10.3390/rs12233870info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:45:23Zoai:ri.conicet.gov.ar:11336/153187instacron: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 15:45:23.311CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)
title How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)
spellingShingle How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)
Olmedo Masat, Olga Magalí
COASTAL MACROALGAE
HYPERSPECTRAL SENSORS
SPECTRAL FEATURES
title_short How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)
title_full How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)
title_fullStr How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)
title_full_unstemmed How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)
title_sort How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)
dc.creator.none.fl_str_mv Olmedo Masat, Olga Magalí
Raffo, María Paula
Rodríguez Pérez, Daniel
Arijón, Marianela
Sanchez Carnero, Noela Belen
author Olmedo Masat, Olga Magalí
author_facet Olmedo Masat, Olga Magalí
Raffo, María Paula
Rodríguez Pérez, Daniel
Arijón, Marianela
Sanchez Carnero, Noela Belen
author_role author
author2 Raffo, María Paula
Rodríguez Pérez, Daniel
Arijón, Marianela
Sanchez Carnero, Noela Belen
author2_role author
author
author
author
dc.subject.none.fl_str_mv COASTAL MACROALGAE
HYPERSPECTRAL SENSORS
SPECTRAL FEATURES
topic COASTAL MACROALGAE
HYPERSPECTRAL SENSORS
SPECTRAL FEATURES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Macroalgae have attracted the interest of remote sensing as targets to study coastal marine ecosystems because of their key ecological role. The goal of this paper is to analyze a new spectral library, including 28 macroalgae from the South-West Atlantic coast, in order to assess its use in hyperspectral remote sensing. The library includes species collected in the Atlantic Patagonian coast (Argentina) with representatives of brown, red, and green algae, being 22 of the species included in a spectral library for the first time. The spectra of these main groups are described, and the intraspecific variability is also assessed, considering kelp differentiated tissues and depth range, discussing them from the point of view of their effects on spectral features. A classification and an independent component analysis using the spectral range and simulated bands of two state-of-the-art drone-borne hyperspectral sensors were performed. The results show spectral features and clusters identifying further algae taxonomic groups, showing the potential applications of this spectral library for drone-based mapping of this ecological and economical asset of our coastal marine ecosystems.
Fil: Olmedo Masat, Olga Magalí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina
Fil: Raffo, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina
Fil: Rodríguez Pérez, Daniel. Universidad Nacional de Educación a Distancia; España
Fil: Arijón, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina
Fil: Sanchez Carnero, Noela Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina
description Macroalgae have attracted the interest of remote sensing as targets to study coastal marine ecosystems because of their key ecological role. The goal of this paper is to analyze a new spectral library, including 28 macroalgae from the South-West Atlantic coast, in order to assess its use in hyperspectral remote sensing. The library includes species collected in the Atlantic Patagonian coast (Argentina) with representatives of brown, red, and green algae, being 22 of the species included in a spectral library for the first time. The spectra of these main groups are described, and the intraspecific variability is also assessed, considering kelp differentiated tissues and depth range, discussing them from the point of view of their effects on spectral features. A classification and an independent component analysis using the spectral range and simulated bands of two state-of-the-art drone-borne hyperspectral sensors were performed. The results show spectral features and clusters identifying further algae taxonomic groups, showing the potential applications of this spectral library for drone-based mapping of this ecological and economical asset of our coastal marine ecosystems.
publishDate 2020
dc.date.none.fl_str_mv 2020-12
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/11336/153187
Olmedo Masat, Olga Magalí; Raffo, María Paula; Rodríguez Pérez, Daniel; Arijón, Marianela; Sanchez Carnero, Noela Belen; How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia); MDPI AG; Remote Sensing; 12; 23; 12-2020; 1-33
2072-4292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/153187
identifier_str_mv Olmedo Masat, Olga Magalí; Raffo, María Paula; Rodríguez Pérez, Daniel; Arijón, Marianela; Sanchez Carnero, Noela Belen; How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia); MDPI AG; Remote Sensing; 12; 23; 12-2020; 1-33
2072-4292
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/12/23/3870
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs12233870
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
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
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score 13.22299

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