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Get Free AccessThe bloom of pelagic Sargassum in the Atlantic Ocean has become increasingly problematic, especially when the algae have beached. A build-up of decaying beached material has damaging effects on coastal ecosystems and tourism industries. While remote sensing offers an effective tool to assess the spatial and temporal patterns of Sargassum over large spatial extents, its use so far has been limited to a broad discrimination of Sargassum species from other macroalgae and floating vegetation. Knowledge on the spatial distribution of decayed material will help to support management strategies and inform targeted removal. In this study, we aim to characterise the spectral response of fresh and decayed Sargassum and identify regions of the spectra that offer the greatest separability for the detection and classification of decayed material. We assessed the spectral response of fresh and decayed Sargassum (1) in situ on the beach and (2) in mesocosm experiments where Sargassum samples were allowed to decay over time. We found a decrease in the magnitude of reflectance, noticeably in the visible region (400–700 nm), for decayed, in contrast to fresh, Sargassum. Separability analyses also showed that most spectral bands with a wavelength > ~540 nm will be capable of discriminating between fresh and decayed material, although the near-infrared region offers the greatest degree of separability. We demonstrate, for the first time, that there are clear differences in the spectral reflectance of fresh and decayed Sargassum with potential application for remote sensing approaches.
Chris J. Chandler, Silvia Valery Ávila-Mosqueda, Evelyn Raquel Salas-Acosta, Edén Magaña‐Gallegos, Edgar Escalante Mancera, M. Reali, Betsabé de la Barreda-Bautista, Doreen S. Boyd, Sarah E. Metcalfe, Sofie Sjögersten, Brigitta I. van Tussenbroek, Rodolfo Silva, Giles Foody (2023). Spectral Characteristics of Beached Sargassum in Response to Drying and Decay over Time. Remote Sensing, 15(17), pp. 4336-4336, DOI: 10.3390/rs15174336.
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Type
Article
Year
2023
Authors
13
Datasets
0
Total Files
0
Language
English
Journal
Remote Sensing
DOI
10.3390/rs15174336
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