**1. Introduction**

The intertidal zone hosts considerable diversity together with a great abundance of benthic organisms [1,2] and has long been monitored as a control ecosystem in ecological processes. Seaweeds are the major component of flora on temperate rocky shores, where they can commonly form extensive canopies, structuring macroalgal communities comparable to terrestrial forest systems in their arrangement [3,4]. Seaweed species are vertically distributed on the shore according to several abiotic factors such as desiccation, hydrodynamics, light and salinity, themselves largely influenced by tide oscillations [5,6]. Temperate rocky shores are globally dominated by fucoids (i.e., large Phaeophyceae from the order Fucales), from high to low levels of the shore and by kelps (i.e., large Phaeophyceae from the order Laminariales *sensu lato*) in the lower intertidal fringe and the subtidal area [7]. Along the north east Atlantic coastline, up to six successive macroalgal communities may be found [8,9], which can be reduced to 2–5 depending on the geographical area, the substratum or the hydrodynamic conditions [10].

**Citation:** Diruit, W.; Le Bris, A.; Bajjouk, T.; Richier, S.; Helias, M.; Burel, T.; Lennon, M.; Guyot, A.; Ar Gall, E. Seaweed Habitats on the Shore: Characterization through Hyperspectral UAV Imagery and Field Sampling. *Remote Sens.* **2022**, *14*, 3124. https://doi.org/10.3390/ rs14133124

Academic Editor: Stuart Phinn

Received: 29 April 2022 Accepted: 26 June 2022 Published: 29 June 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Brittany is a long-term monitored area for macroalgal diversity (approximatively 650 species [11]) and resources (e.g., Benthic Network research program since 2005). These characteristics are examples of a prime area to fully describe seaweed-dominated habitats through remote sensing. Therefore, remote sensing for macroalgal covers has undergone early development since the 1960s [12–17].

Seaweed communities have been recognized as a quality element for the classification of coastal water bodies as part of the European Water Framework Directory (WFD, 2000/60/EC; E.C., 2000 [18]) and several metrics based on the good ecological state of macroalgal communities have been developed along the European coasts [6,9,19–24]. On rocky shores, the occurrence and abundance of vegetation can easily be estimated visually through the cover-abundance scale, or percentage-cover indices, without damaging the habitat [5]. Even if these estimations are easy to implement, they may be time consuming and some locations remain difficult to reach. In this context, using remote sensing imagery for spatialization is an interesting alternative to a site-specific scale [25,26], and could help survey shifting ecosystems [27].

Both multispectral and hyperspectral imagery are routinely used on terrestrial vegetation, for instance, to estimate crop yields [28–31]. By contrast with other plants, seaweeds have a larger phylum-specific diversity of pigments, which can be discriminated by analyzing spectral characteristics at different wavelengths [32]. Pigment diversity in algae contributed to the early development of seaweed detection through airborne remote sensing [33]. Later, mapping of macroalgal communities was processed using satellite imagery (IKONOS, SPOT, Sentinel-2), with scale refining depending on the sharpness of the sensors aboard [34–37], and promoted combined airborne/ground spectra acquisition for macroalgal mapping. Another powerful tool to study coastal environments is the use of free-access satellite images, which could help to produce extensive habitat mapping, in order to observe natural variations in habitats overtime [38].

These methods enable the collection of homogeneous data over broad spatial scales but are inaccurate when applied to heterogeneous habitats, varying at a centimeter in scale [39]. Such approaches are complexified in coastal areas due to tidal variations and highly mosaic environments [40]. Furthermore, data acquisition is generally altered by the occurrence of a water layer [41] and often disturbed by atmospheric conditions (noticeably, cloud cover and light reflection). The development and easy access to both unmanned aerial vehicles (UAVs) and hyperspectral sensors further promoted the remote mapping and characterization of intertidal habitats [42–44]. Since the 1970s, automated methods (i.e., classification algorithms) have been developed to classify multi-/hyper-spectral images [45–47]. The present work focuses on an easy habitat classification through high spatial resolution pictures, obtained by a UAV and by applying commonly used algorithms. To characterize seaweed-dominated habitats, maximum likelihood (MLC) is currently the most widely used method of supervised classifications [48], along with the spectral angle mapper (SAM) [49–52].

To date, there are still few studies comparing both mapping intertidal seaweed using multispectral [38,42,53] or hyperspectral sensors on UAVs, and an accurate spatial resolution (less than 5 cm). Indeed, the majority of studies focus on kelp beds at lower resolution (spatial and/or spectral) [54–57]. Rossiter et al. (2020) [49,58] successfully classified shores using both multispectral and hyperspectral sensors focusing on the Fucales *Ascophyllum nodosum*, but did not compare sensor data with macroalgal in situ covers.

To fill these gaps existing between remote sensing and field sampling, a two-way approach was conducted: on the one hand, in situ sampling of macroalgal communities, and on the other hand, hyperspectral UAV imagery acquisition and automated classifications. In that prospect, several objectives were defined:


The working hypothesis of this study is that the two classifications, obtained from hyperspectral images, would yield similar results between the two, successfully differentiating macroalgae and would correspond to those obtained in the field. The present experiment was performed on a seaweed-dominated shore of western Brittany to test the complementarity between both approaches and to study the distribution of seaweed habitats. The aim of the study is to evaluate the correspondence between the distribution of species in macroalgal habitats obtained by both in situ sampling on the shore and hyperspectral imagery by a UAV.
