The aim of this study is to model the seaweed species distribution with respect to environmental parameters. We focused on fixed species of algae, such as fucoids. Firstly, we identified environmental parameters, such as substratum nature, immersion time, exposure, etc., which determine the seaweed distribution. Secondly, we used field sampling to compute the distribution laws for seaweed according to the environmental parameters selected. Thirdly, we used the distribution laws and the environmental parameters to perform predictive mapping of seaweed belts with a fuzzy logic method. Seaweed presence is directly dependent on the nature of the substratum. In the intertidal domain we used an alternative, because seaweed beds can be observed directly. We detected seaweed presence with Spot satellite imagery. The second parameter is immersion time. For each elevation value (surveyed by Lidar), we converted water tidal levels into annual percentages of immersion. The third environmental variable used was exposure to waves. During the fixation phase, seaweeds cannot withstand high levels of exposure. We used a model of wave propagation to delineate areas with different exposure levels. The presence of seaweed species for each parameter was estimated from field sampling, along with 3D measurements (dGPS). Higher and lower limits of dominant seaweed belts were contoured. With reference to the three environmental variables selected, the distribution laws for each seaweed species were estimated. A classification by fuzzy logic was applied using eCognition software. Two phases were used in this method: the first phase involved segmentation to obtain polygons, each polygon being homogenous in terms of the environmental parameters selected: vegetation cover, immersion time and exposure level. During the second phase, the distribution laws estimated from field sampling were implemented and finally a membership value was calculated for each targeted species and the results were discussed.