*2.6. Data Analysis*

Accuracy assessment for classification was checked using ground truth (or reference) ROIs based on the same method as the training data [63,69]. These polygons were independent of the training ROIs and their number represented one third of training ROIs. The accuracy assessment tool was used to create the confusion matrix and derive quantitative measures of accuracy (i.e., kappa coefficient, overall accuracy, user/producer accuracy, errors of commission/omission) using ENVI version 5.6.1 (Exelis Visual Information Solutions, Boulder, CO, USA). User accuracy is the probability of correct class assignment, calculated by dividing the number of correctly classified pixels by the total number of pixels in the class, and producer accuracy is the correctly classified reference pixels, calculated by dividing the number of correctly classified pixels by the total number of pixels that should be in a class.

Each grid structure was replaced using 'Advanced Digitizing toolbar' ('Move Feature' and 'Rotate Feature' options) on Qgis. Corresponding polygons were accurately positioned using pictures taken during the field sampling, in order to decrease the potential GPS error and to compare the exact same position.

To compare in situ data and classification data, vectors of the grid structure were replaced on the Porspoder image using the 'Vector to ROI' tool, and the percentage of pixels for each class in ROIs was extracted with the ROI statistics tool on ENVI.

Statistical analyses were conducted using the R environment [70]. Normality and homoscedasticity were first tested on each biological and classification variable, corresponding to seaweed species and substratum covers, with Shapiro–Wilk and F Test/Levene tests, respectively. These tests then determined what analyses were the most suitable (parametric or not). In order to represent the distribution of the replicates described by the three approaches, a distance-based redundancy analysis (db-RDA) was constructed, based on the method described by Escobar-Briones et al. (2008) [71]. Values for each class (apart from 'Water') were first converted into a distance matrix by calculating the Hellinger distance for each class in the whole dataset. Then, a principal coordinates analysis (PCoA) was performed on this matrix. The PCoA allows to convert the distance between items (distance matrix) into a map-based visualization (each item is assigned a location in a low-dimensional space, materialized by its eigenvector) in order to better understand the relation between each object. All the PCoA eigenvectors were used as input into an RDA in order to build the db-RDA. The db-RDA represents on a single plot the position of the different replicates using the PCoA eigenvalues, as well as the species (classes) and the explanatory variables (level and method).

To compare more precisely the cover of each of the classes for the three methods and for each bathymetric level, Kruskal–Wallis tests (non-parametric) were performed followed by a post-hoc Dunn test to identify variables that were statistically different.

#### **3. Results**

#### *3.1. In Situ Vegetation Cover*

Large discrepancies were observed in the covers between bathymetric levels, the lowest levels being characterized by a dominance of seaweeds, whereas bare rock showed a large occurrence in the upper level. Covers of macroalgal classes differed between the four levels (Figure 5). The Pc-Fspi level, corresponding to the upper shore (5.2–6.1 m above chart datum (CD)), was lightly vegetalized, with bare rock occupying 55% of the surface. The level was dominated by the Fucales *P. canaliculata* for about 32.5%. The remaining covers were well distributed between *F. spiralis* and red seaweeds (5% each), whereas benthic fauna (barnacles and limpets) corresponded to a cover of 2.5%.

**Figure 5.** Average covers of macroalgal groups and sessile fauna, and percentage of bare rock observed in situ at each bathymetric level. Covers are given in percentages. Fucoids and other brown species are grouped in the 'Brown' class, and erect and crustose red algae are grouped in the 'Red' class.

The An level (middle shore, 3.4–4.4 m above CD) was largely dominated by the Fucales *A. nodosum* (60%) and *F. serratus* (5.9%). Red seaweeds then covered about 25% of the surface (22.5% erect and 2.5% crustose). Bare rock and limpets completed the remaining surface (6.7% and 2.5%, respectively).

In the Fser level (lower shore, 3.1–2.3 m above CD), macroalgal covers became conspicuously dominant compared to bare rock and sessile fauna. Indeed, the cover of *F. serratus* was close to 100% (94.2%), while the rest corresponded to erect red algae (5.8%).

In the He-Ld level (2.8–1.6 m ab. CD), the distribution between macroalgal groups was equilibrated, with a co-dominance of *H. elongata* (39.2%) and erect red seaweeds (36.7%). The Laminariales *L. digitata* also presented large covers (17.5%), and in addition, there were little covers of crustose red and green seaweeds (2.5% and 4.2%, respectively).

Thus, An, Fser and He-Ld had a higher cover of Phaeophyceae (more than one half) compared to the other macroalgal groups of species (65.8%, 94.2% and 56.7% of cover, respectively). By contrast, Pc-Fspi showed only a bit more than one third of cover by Phaeophyceae (37.5%).

#### *3.2. Classifications Results*
