Seaweed Habitats on the Shore: Characterization through Hyperspectral UAV Imagery and Field Sampling

Type Article
Date 2022-07
Language English
Author(s) Diruit WendyORCID1, Le Bris Anthony2, Bajjouk Touria3, Richier Sophie2, Helias Mathieu1, Burel ThomasORCID1, Lennon Marc4, Guyot Alexandre4, Ar Gall Erwan1
Affiliation(s) 1 : Univ Brest, CNRS, IRD, Ifremer, LEMAR, 29280 Plouzané, France
2 : Centre d’Etude et de Valorisation des Algues (CEVA), 22195 Pleubian, France
3 : Ifremer, Dynamiques des Ecosystèmes Côtiers (DYNECO)/Laboratoire d’Ecologie Benthique Côtière (LEBCO), 29280 Plouzané, France
4 : Hytech-Imaging, 115 Rue Claude Chappe, 29280 Plouzané, France
Source Remote Sensing (2072-4292) (MDPI AG), 2022-07 , Vol. 14 , N. 13 , P. 3124 (26p.)
DOI 10.3390/rs14133124
WOS© Times Cited 2
Note This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management
Keyword(s) seaweeds, hyperspectral, UAVs, intertidal ecology, rocky shores, supervised classification, vegetation cover
Abstract

Intertidal macroalgal habitats are major components of temperate coastal ecosystems. Their distribution was studied using field sampling and hyperspectral remote mapping on a rocky shore of Porspoder (western Brittany, France). Covers of both dominating macroalgae and the sessile fauna were characterized in situ at low tide in 24 sampling spots, according to four bathymetric levels. A zone of ca. 17,000 m2 was characterized using a drone equipped with a hyperspectral camera. Macroalgae were identified by image processing using two classification methods to assess the representativeness of spectral classes. Finally, a comparison of the remote imaging data to the field sampling data was conducted. Seven seaweed classes were distinguished by hyperspectral pictures, including five different species of Fucales. The maximum likelihood (MLC) and spectral angle mapper (SAM) were both trained using image-derived spectra. MLC was more accurate to classify the main dominating species (Overall Accuracy (OA) 95.1%) than SAM (OA 87.9%) at a site scale. However, at sampling points scale, the results depend on the bathymetric level. This study evidenced the efficiency and accuracy of hyperspectral remote sensing to evaluate the distribution of dominating intertidal seaweed species and the potential for a combined field/remote approach to assess the ecological state of macroalgal communities.

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How to cite 

Diruit Wendy, Le Bris Anthony, Bajjouk Touria, Richier Sophie, Helias Mathieu, Burel Thomas, Lennon Marc, Guyot Alexandre, Ar Gall Erwan (2022). Seaweed Habitats on the Shore: Characterization through Hyperspectral UAV Imagery and Field Sampling. Remote Sensing, 14(13), 3124 (26p.). Publisher's official version : https://doi.org/10.3390/rs14133124 , Open Access version : https://archimer.ifremer.fr/doc/00780/89149/