Data/hora: 14/06/2024 08:06:37

Provedor de dados: 	5	
País: 	France	
Título: 	Hindcast and Near Real-Time Monitoring of Green Macroalgae Blooms in Shallow Coral Reef Lagoons Using Sentinel-2: A New-Caledonia Case Study	
Autores: 	Brisset, Maele; Van Wynsberge, Simon; Andréfouët, Serge; Payri, Claude; Soulard, Benoit; Bourassin, Emmanuel; Le Gendre, Romain; Coutures, Emmanuel.	
Data: 	2021-01	
Ano: 	2021	
Palavras-chave: 	Ulva; Sentinel-2; Satellite; Remote sensing; Algal bloom; Coral reefs; Pacific lagoons.	
Resumo: 	Despite the necessary trade-offs between spatial and temporal resolution, remote sensing is an effective approach to monitor macroalgae blooms, understand their origins and anticipate their developments. Monitoring of small tropical lagoons is challenging because they require high resolutions. Since 2017, the Sentinel-2 satellites has provided new perspectives, and the feasibility of monitoring green algae blooms was investigated in this study. In the Poé-Gouaro-Déva lagoon, New Caledonia, recent Ulva blooms are the cause of significant nuisances when beaching. Spectral indices using the blue and green spectral bands were confronted with field observations of algal abundances using images concurrent with fieldwork. Depending on seabed compositions and types of correction applied to reflectance data, the spectral indices explained between 1 and 64.9% of variance. The models providing the best statistical fit were used to revisit the algal dynamics using Sentinel-2 data from January 2017 to December 2019, through two image segmentation approaches: unsupervised and supervised. The latter accurately reproduced the two algal blooms that occurred in the area in 2018. This paper demonstrates that Sentinel-2 data can be an effective source to hindcast and monitor the dynamics of green algae in shallow lagoons	
Tipo: 	Text	
Idioma: 	Inglês	
Identificador: 	https://archimer.ifremer.fr/doc/00668/77968/80184.pdf

https://archimer.ifremer.fr/doc/00668/77968/80185.zip

DOI:10.3390/rs13020211

https://archimer.ifremer.fr/doc/00668/77968/	
Editor: 	MDPI AG	
Formato: 	application/pdf	
Fonte: 	Remote Sensing (2072-4292) (MDPI AG), 2021-01 , Vol. 13 , N. 2 , P. 211 (19p.)	
Fonte: 	info:eu-repo/semantics/openAccess

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