## 5 Conclusions  
Technological evolutions of HSR multispectral satellite sensors greatly facilitates ecosystems' monitoring capability at an increasingly smaller scale. In order to make progress in the knowledge of biological invasion dynamics in salt marsh environments, we evaluated the potential of Pleiades images to assess salt marsh evolution, after a biological invasion, and discriminate the native species _Spartina maritina_ from the invasive species _Spartina anglica_ for the study site of the Arcachon Bay (South-west France). Different techniques for land use classification were investigated, and their performance was evaluated using a large set of ground truth data. As expected, the supervised methods exhibit, on average, better results than the unsupervised algorithms. Moreover, in this study, we demonstrated that classification accuracy is highly dependent on two main factors, which are the size of vegetation patches and the species phenology. Pleiades images provided a robust and consistent identification of the global marsh zone. We showed that, in the Bay of Arcachon, the arrival of the exotic species was relatively controlled and did not lead to significant changes in the marsh composition, which remained dominated by the native _Spartina_ species. On the other hand, distinguishing between species remains challenging, particularly when considering small features, such as the invasive species at an early stage of the invasion, which represents relevant information concerning invasion control issues. Our results showed that the current pixel classification methods applied to Pleiades images provide a poor performance, when the order of magnitude of the _Spartina_ sp. meadow size is lower than 10 m. However, lags in plant phenology provide temporal windows, making species distinction possible. The results highlight that a high-performance classification requires good knowledge of the field conditions and the vegetation's development. The biggest challenge is revealed to be the dissociation of established vegetation from ephemeral biological features. In periods of low biological productivity, the level of biological noise can be significantly reduced, and hence the success in species differentiation increases. Thus, the seasonal interference of other biological features and stages of development of the target vegetation can alter its spectral signature or influence the exposure of the colonized environment (bare ground). In the Bay of Arcachon, the two congener _Spartina_ species present a phenology lag, where their spectral signature is considerably different between Autumn and early Spring. The strong presence of algae between late Spring and Autumn shortens this temporal window of remotely sensed assessment. We found that a suitable time frame for the remote sensing exploitation of the _Spartina_ species distinction should be between late winter and early spring, when _Spartina anglica_ is still withered enough to be differentiated from _Spartina maritima_, and algae blooms have not yet occurred. The current pixel classification methods are highly sensitive to environmental noise. We highlight here that the consideration of particular temporal windows might be crucial for an improved assessment of the different types of marsh vegetation. Additionally, 2-meter spatial resolution data still generate significant uncertainties in the identification of vegetation patches characterized by a decametric scale. In order to better perform the identification of the invasive species, it will be necessary to evaluate the pan-sharpened products of HSR sensors, provided by Pleiades or WorldView 3 and 4, as well as coupled approaches based on pixel classification and pattern-recognition techniques. Conceptualization, R.M. and A.S.; Methodology, F.F., V.M. and B.L. ; Software, B.P., V.M., B.Y. and L.B. ; Validation, B.P. ; Formal Analysis, B.P. and B.Y. ; Investigation, B.P., F.F., V.M. and B.L. ; Resources, V.M., B.L. and L.B. ; Data Curation, B.P. ; Writing--Original Draft Preparation, B.P.