## 4 Results and Discussion
### Pixel Classification Using Unsupervised and Supervised Methods  
Several pixel classification algorithms were applied for each of the five dates of MS Pleiades images, with the aim of identifying the dominant marsh species in the Bay of Arcachon, _Spartina maritima_ and _Spartina anglica_, and monitor the changes resulting from the invasion of the latter. Concerning the supervised classification, as previously mentioned, after preliminary tests with several classifiers, we proceeded with Random Forests, both because it is a commonly used classifier, and it provided slightly better results than the other tested classifiers. For all classes, we considered the same number of training zones, maintaining, as much as possible, a balanced number of pixels per training zone, depending on the size of the feature representative of the class. For the image of April 2013, this balance was relatively difficult to achieve, as the presence of the invasive _Spartina_ was particularly reduced in the study zone at this date. For the simulated annealing algorithm, since it is an unsupervised method, it is important to note that the algorithm class color attribution is done randomly, and class colors need to be re-assigned by the user in order to achieve an agreement between different season images and a match with the previously considered supervised classification results. Different tests were performed for each image, considering the influence of the algorithm's parameters, number of iterations, start temperature (a parameter associated with the initial range of search for pixel clustering), number of classes and transformation coefficient in order to estimate the position of the class center. Very similar results were obtained for the classifications, with differences under 5% for each class from one run to the other. Field surveys have shown that the meadow vegetation dominance did not change at the considered timescale. Therefore, the alteration of the spectral signatures and consecutive class attribution by the algorithm was mostly influenced by the seasonal appearance of transient biological features or the vegetation's phenology. It is then necessary to perform a post-classification analysis to provide the best color match between images of different seasons. Both the unsupervised and supervised pixel classifications of the overall images allowed for an accurate identification of the global high marsh zone, including all species. However, single-feature identification (species differentiation) remained difficult, and the degree of accuracy was strongly dependent on the presence of noise induced by episodic events or short-term perturbations that can alter the species spectral signal (Figure 9; Table 3). Such difficulties in the isolation of spectral features due to the presence of water and a high humidity at low marsh levels were also found, in [21], in the realization of supervised classifications of Landsat images. Given the variability of field reflectance for one matching feature (Figure 6), such a result was not completely unexpected, and this was shown by the significant differences between classifications for all dates. We observed that the larger the noise induced by seasonal perturbations, the larger the variability of the classification within the patches, particularly considering the results obtained using the unsupervised classification algorithm. Figure 9: Pixel classification using the unsupervised method, Simulated annealing (left panel), with the supervised method, Random Forests (central panel), and classification accuracy maps for the supervised classification (right panel). (**a–c**) 25 April 2013, (**d–f**) 3 August 2016, (**g–i**) 6 October 2016, (**j–l**) 24 May 2017, and (**m–o**) 7 October 2017. Black contours on the image acquired on 6 October 2016 correspond to the field ground truth GNSS vegetation contours. Color bar indicates the confidence level of class attribution in the accuracy maps.