## 4 Results and Discussion
### Pixel Classification Using Unsupervised and Supervised Methods
& C3: 0.96 & C3: 0.98 & & C3: 0.98 & & C3: 0.39 \\  & C4: 0.88 & C4: 1 & C4: 0.39 & C4: 0.96 & & C4: 0.35 \\  & C5: 0.037 & & C5: 0.76 & & C5: 0.98 & & & C5: 0.64 & & & C5: 0.92 \\  & 64 & 0 36 & 0 & 0 & 90 & 0 & 3 & 7 & 72 & 8 & 0 & 20 & **85** & **2.10** & **2.5** & **0** & **53** & **0** & **47** & **0** \\
**Confusion** & 2 & 93 & 0 & 5 & 0 & 96 & 0.4 & 0 & 3.6 & 10 & 88 & 0 & 1 & **14** & **74** & **11** & **0** & **1** & **6** & **78** & **15** & **0** & **1** \\
**matrix** & 1 & 0 & 97 & 0 & 2 & 0 & 26 & 0 & 0 & 74 & 0.2 & 3.8 & 96 & 0 & 0 & **0.4** & **0**of 0.8, reinforcing that the lower level of biological noise within these images positively affects the performance of the classifier. With the unsupervised algorithm (Figure 9), on the other hand, the correct classification of meadows dominated by the _Spartina anglica_ species was between 10% and 25%. Such a low performance in the identification of the invasive species is probably primarily due to the fact that we consider several patches of small dimensions (2-4 m diameter). Secondarily, pixels within meadows of this species were often mistaken for pixels classified as vegetated mudflat and water with microphytobenthos, which is most likely due to the fact that _Spartina anglica_ vegetation cover tends to be reduced outside of the growing season. Thus, there is a high ground exposure within zones colonized by this type of vegetation, which significantly affects its radiometric signal. However, when considering only patches of larger dimensions (5-10 m diameter), the identification increases to 69.5% for the unsupervised classification of the image acquired on May 2017. The comparison of field species reflectance is measured in the NIR and Green bands (Figure 6; Table 2), and the classification results reveal an overall satisfactory class distinction, especially concerning the distinction of the native _Spartina maritina_ from the remaining vegetation. Since, in the considered area, the presence of _Spartina anglica_ was mostly reduced to isolated patches towards the inner bay or to zones of mixed vegetation in the higher marsh, the identification of the invasive species was not evident, particularly in images with a higher noise level. Considering the image of April 2013, where the vegetation did not yet present a strong seasonal development, several individual _Spartina anglica_ patches presented a spectral signal under 0.01 sr\({}^{-1}\), which is much lower than the radiometric measurements. Indeed, the phenology of this species is rather late, and at this time of the year, the plants were still withered, and the meadows presented a very low density. Other studies [14; 15] have also shown the interference of visible soil within vegetation in its reflectance, significantly lowering it. The classification of the native _Spartina_, on the contrary, provides center class values in the range of the radiometric field measurements, between 0.02 and 0.04 sr\({}^{-1}\) (NIR band), mostly depending on the meadow density. The better accuracy of the classification for the native _Spartina_ was not so unexpected, considering that this species presents a much earlier development than that of the invasive congener. The pixel-based classification allowed for the estimation of a surface of the main _Spartina maritina_ patch of 5685.5 m\({}^{2}\), whereas with the manual identification of the patch, a surface of 5378.17 (\(\pm\)87) m\({}^{2}\) was obtained for the year of 2013. Considering the monitored patch of _Spartina anglica_, followed in the long-term aerial photographs, we obtained a surface of 67.25 m\({}^{2}\), using the supervised classification, and 74.31 (\(\pm\)1) m\({}^{2}\) using manual identification for the same date.