Publicación:
How Far Can We Classify Macroalgae Remotely? An Example Using a New Spectral Library of Species from the South West Atlantic (Argentine Patagonia)

Vista sencilla de ítem
dc.contributor.authorOlmedo Masat, Olga Magalí
dc.contributor.authorRaffo, María Paula
dc.contributor.authorRodríguez Pérez, Daniel
dc.contributor.authorArijón, Marianela
dc.contributor.authorSánchez Carnero, Noela
dc.date.accessioned2025-12-11T15:39:05Z
dc.date.available2025-12-11T15:39:05Z
dc.date.issued2020-11-26
dc.descriptionThe registered version of this article, first published in “Remote Sensing, 12(23), (2020), 3870", is available online at the publisher's website: MDPI, https://doi.org/10.3390/rs12233870
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en “Remote Sensing, 12(23), (2020), 3870", está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/rs12233870
dc.description.abstractMacroalgae 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.en
dc.description.provenanceMade available in DSpace on 2025-12-11T15:39:05Z (GMT). No. of bitstreams: 1 How Far Can We Classify Macroalgae Remotely. DANIEL RODRIGUEZ PER.pdf: 8615796 bytes, checksum: 73560c8d11605f05ec0f705813f181ef (MD5) Previous issue date: 2020-11-26en
dc.description.sponsorshipThe authors acknowledge her funding by a national PhD scholarship granted by CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas).
dc.description.versionversión publicada
dc.identifier.citationOlmedo Masat, O. M., Raffo. M. P., Rodríguez Pérez, D., Arijón, M. y Sánchez Carnero, N., (2020). “How Far Can We Classify Macroalgae Remotely? An Example Using a New Spectral Library of Species from the South West Atlantic (Argentine Patagonia)”. Remote Sensing, 12(23), 3870; DOI:10.3390/rs12233870
dc.identifier.doihttps://doi.org/10.3390/rs12233870
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31101
dc.journal.issue23
dc.journal.titleRemote Sensing
dc.journal.volume12
dc.language.isoen
dc.page.final33
dc.page.initial1
dc.publisherMDPI
dc.relation.centerFacultad de Ciencias
dc.relation.departmentFísica Matemática y de Fluídos
dc.relation.researchgroupFísica Médica
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject2417.05 Biología marina
dc.subject.keywordsCoastal macroalgaeen
dc.subject.keywordsSpectral featuresen
dc.subject.keywordsHyperspectral sensorsen
dc.titleHow Far Can We Classify Macroalgae Remotely? An Example Using a New Spectral Library of Species from the South West Atlantic (Argentine Patagonia)en
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication6567ec87-332f-45c9-8f32-1fb7e2a6a02d
relation.isAuthorOfPublication.latestForDiscovery6567ec87-332f-45c9-8f32-1fb7e2a6a02d
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
How Far Can We Classify Macroalgae Remotely. DANIEL RODRIGUEZ PER.pdf
Tamaño:
8.22 MB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.62 KB
Formato:
Item-specific license agreed to upon submission
Descripción:
Descargar
Colecciones
Artículos y papers