## 1 Introduction  
Biological invasions often represent an important source of change in a system. They are mostly perceived as a possible threat to local biodiversity [1; 2; 3; 4]. Occasionally, the arrival of a new species can also carry significant benefits to ecosystem services [3; 5; 6; 7]. The consequences of biological invasions are usually difficult to predict. Their impact on ecosystems may significantly change in space and time [8] because of a variable adaptive response of the host communities. This response can induce a slowdown of the invasive species (IS) proliferation or, conversely, a sudden rapid expansion. The detection and mapping of the earliest stage of IS development and expansion often turns out to be the best strategy for anticipating and controlling their impact [9; 10]. Due to the frequency of physical disturbance, tidal flats and salt marshes present a high susceptibility to biological invasions [11]. This vulnerability particularly increases with human pressure [12]. These systems are characterized by complex interactions between geomorphological patterns and ecological structures [13] and present a highly dynamic and extensive character, which, being associated with access difficulties, make remote sensing an appealing monitoring tool [14; 15; 16]. Several studies have already demonstrated the strong potential of using optical satellite imagery in mapping intertidal vegetation [10; 13; 17; 18; 19], and particularly, for the monitoring of biological invasions [20; 21; 22]. Intertidal vegetation follows a natural spatial zonation, according to its tolerance to the different tidal levels [23; 24]. This natural zonation represents an advantage for remote sensing assessment, because it provides a clear physical separation between different species. Vegetation tends to grow in patches of clearly demarcated borders, presenting a relative homogeneity from a radiometric point of view, and the meadows of each species tend to be constant in time [14]. However, despite the relative intra-patch homogeneity, different degrees of vegetation cover and mixing can occur when the pixel size increases [14; 25]. The environmental background noise, due to the soil characteristics, water and biofilm, then generates a significant per-pixel variability of the upwelling radiative signal [26]. Further, there is also a significant time dependency on plant phenology that can largely influence species differentiation in spaceborne imagery [22; 26; 27; 28]. For these reasons, it is essential to select the most suitable satellite on the basis of instrumental characteristics. Especially, remotely sensed multispectral satellite missions can provide data with significantly different temporal, spatial and spectral resolutions [25; 29]. The selection of one resolution over the other needs to be weighted according to the objectives, as current orbiting sensors cannot yet provide simultaneous high temporal, spectral and spatial resolutions [30]. For instance, [PERSON] et al. [15] compared multispectral and hyperspectral remote sensing data efficiency in mapping salt marsh vegetation in the Venice lagoon (Italy). They showed that spatial resolution affected the pixel classification accuracy in a much more important way than spectral resolution and that hyperspectral data can contain largely redundant information. Consequently, they suggested that, for such studies, preference should be given to spatial, rather than spectral, resolution. However, a possible way to overcome these limitations is through the combination of images from sensors, providing different spatial and spectral products by the application of pan-sharpening techniques [31; 32]. The application of such techniques enables one to simultaneously take advantage of panchromatic (higher spatial resolution) and multispectral (higher spectral resolution) images. The identification and differentiation of vegetation species is commonly based on a similarity in pixel clustering, using supervised or unsupervised methods [10; 15; 28; 33].