Efficient and sustainable coastal management requires regular and accurate monitoring of ecosystem health indicators. In this context, the observation of short- and long-term changes in species distribution is a main objective of algal monitoring in the intertidal zone of coastal areas. Quantitative analysis of intertidal algal communities provides direct information on their spatial extent and status and indirect information on environmental parameters such as nutrient load or physical disturbance. However, conventional field mapping is time consuming and is limited to relatively small and accessible coastal areas whereas progress in hyperspectral techniques has significantly increased the ability to study spatial and temporal patterns of coastal vegetation. Accordingly, hyperspectral remote sensing is one of the most promising technologies for mapping coastal habitats. The objective of this study was to analyze the ability of hyperspectral remote sensing to identify macrophytic species and their association on the intertidal flat of Helgoland. On 9th May 2008, airborne data were acquired using the AISA Eagle Imaging Spectrometer, which operates in the visible and near infrared spectral domain (420-970nm) with a spectral bandwidth of 3nm. The airborne data acquisition was conducted during low tide with a ground sampling distance of 0.80 metres. Extensive field mapping provided information to validate the remote sensing data. The AISA data were pre-processed radiometrically before an atmospheric and geometric correction was conducted. Water, land and shadowed areas were masked off to isolate the intertidal zone. Then, different classification techniques were applied and tested for their potential to identify algae species and their associations, i.e. Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC). The SVM approach resulted in an overall accuracy of 76% and a kappa index of 0.78. The SAM performed poorly with an overall accuracy of 60% and a kappa index of 0.52. MLC performed best with an overall accuracy of 84% and a kappa index of 0.80. The results of the MLC are very promising and indicate that certain algae species and their associations can be identified very well (e.g. Fucus serratus and Laminaria digitata). In contrast, associations of red algae species could be distinguished with high producer accuracy (81% to 82%), but relatively high commission errors (45% to 61%) resulting in low user accuracies (39% to 55%). Besides the spectral similarity of red algal species, the patchy structure of the algae vegetation as well as the accuracy of the field mapping were limiting for all three classifiers.