## 4 Results and Discussion
### Pixel Classification Using Unsupervised and Supervised Methods
Globally, the _Spartina maritima_-dominated patches were identified well with the supervised classifier but comprised a high variability in class assignment, depending on the density of the meadow and the presence of biological debris with the unsupervised one. Small, scattered patches of the invasive _Spartina anglica_ extending over the tidal flat were difficult to identify because of both the variety of interference signals and the small dimensions of the meadows towards the interior of the bay. A comparison of the ground truth of the dominant vegetation, identified through the GNSS contours, acquired on October 2016, and the corresponding classified vegetated features in the Pleiades images provided the rate of correctly classified pixels for the two _Spartina_ species, obtained with an unsupervised classifier. Overall, patches of _Spartina maritima_ dominance were identified well with the supervised classifier, with a percentage of correctly identified pixels of 74% for the image of May 2017, which increased to 96% for that of August 2016. With the supervised classifier, the strong presence of ephemeral biological perturbation on some dates (August and October) did not significantly affect the accuracy of the identification of the native species, and it was always over 74%. Using the unsupervised classification method, the correct classification of the native _Spartina_ species attained 93% and 80% for the images of April 2013 and October 2017, respectively. The identification of the invasive _Spartina anglica_ proved to be much more difficult, using both the supervised and unsupervised classifiers. Considering the results obtained using the supervised classifier, the identification of the ground truth patches was fairly successful, with accuracies ranging from 53% to 90% for the images acquired in October 2017 and August 2016, respectively. However, this class was frequently mistaken for the extensive zones of the vegetated mudflat (_Zostera_), particularly within the image from October 2017, and we obtained an overestimation of the areas assigned to the invasive _Spartina_. [PERSON] et al. (2015) also found similar uncertainties in the identification of _Spartinas_ that would be confused with the typical spectral signature of soil containing microhypotenthos, which contains significant quantities of chlorophyll. Indeed, the mudflat zone can either be vegetated in different density degrees by seagrass or present deposits of itinerant features, like algae and microhypotbentos, of several sorts. The combined presence of all these features provides complex spectral signatures that can be difficult to characterize. Thus, in this case, the only assurance we have that they do not correspond to the same species is field knowledge. We are confident that such a strong extrapolation could be reduced by increasing the number of ground truth samples of this species. Additionally, the accuracy maps of the classifications obtained for all dates (Figure 9-right panel) shows that regions classified by a higher uncertainty degree (accuracy uncertainty under 60 %) correspond to channels and zones in the tidal flat, where the mixing of features is high (vegetated and un-vegetated mud, water with microhypotenthos, sediment with different degrees of humidity, etc.). Considering the accuracy metrics provided in Table 3, only images corresponding to Springtime (April 2013 and May 2017) provide a Kappa index  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & & **April 2013** & & **August 2016** & & **October 2016** & & **May 2016** & & **October 2017** \\ \hline \multirow{2}{*}{**Overall accuracy**} & 0.87 & 0.66 & 0.79 & 0.85 & 0.56 \\
**Kappa index** & 0.81 & 0.56 & 0.72 & 0.80 & 0.46 \\
**Average** & 0.69 & 0.72 & 0.79 & 0.83 & 0.60 \\  & C1: 0.64 & C1: 0.9 & C1: 0.72 & & C1: 0.85 & & C1: 0.53 \\  & C2: 0.93 & C2: 0.96 & C2: 0.88 & & C2: 0.74 & C2: 0.78 \\
**Class accuracy** & C3: 0.97 & C3: 0.