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dc.contributor.author
Olmedo Masat, Olga Magalí  
dc.contributor.author
Raffo, María Paula  
dc.contributor.author
Rodríguez Pérez, Daniel  
dc.contributor.author
Arijón, Marianela  
dc.contributor.author
Sanchez Carnero, Noela Belen  
dc.date.available
2022-03-10T17:21:29Z  
dc.date.issued
2020-12  
dc.identifier.citation
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  
dc.identifier.issn
2072-4292  
dc.identifier.uri
http://hdl.handle.net/11336/153187  
dc.description.abstract
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.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
MDPI AG  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
COASTAL MACROALGAE  
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HYPERSPECTRAL SENSORS  
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SPECTRAL FEATURES  
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Biología Marina, Limnología  
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Ciencias Biológicas  
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CIENCIAS NATURALES Y EXACTAS  
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Ciencias de las Plantas, Botánica  
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Ciencias Biológicas  
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CIENCIAS NATURALES Y EXACTAS  
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Oceanografía, Hidrología, Recursos Hídricos  
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Ciencias de la Tierra y relacionadas con el Medio Ambiente  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
How far can we classify macroalgae remotely? An example using a new spectral library of species from the south west atlantic (argentine patagonia)  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2021-09-17T16:44:53Z  
dc.journal.volume
12  
dc.journal.number
23  
dc.journal.pagination
1-33  
dc.journal.pais
Suiza  
dc.description.fil
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  
dc.description.fil
Fil: Raffo, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina  
dc.description.fil
Fil: Rodríguez Pérez, Daniel. Universidad Nacional de Educación a Distancia; España  
dc.description.fil
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  
dc.description.fil
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  
dc.journal.title
Remote Sensing  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/12/23/3870  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/rs12233870