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

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 hyperspe...

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Detalles Bibliográficos
Autores: Olmedo Masat, Olga Magalí, Raffo, María Paula, Rodríguez Pérez, Daniel, Arijón, Marianela, Sánchez Carnero, Noela
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/31101
Acceso en línea:https://hdl.handle.net/20.500.14468/31101
Access Level:acceso abierto
Palabra clave:2417.05 Biología marina
Coastal macroalgae
Spectral features
Hyperspectral sensors
Descripción
Sumario: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.