Analysis of coastal marine algae communities enables to adequately estimate the state of coastal marine environment and provides evidence for environmental changes. Hyperspectral remote sensing provides a tool for mapping macroalgal habitats if the algal communities are spectrally resolvable. We compared the performance of three classification approaches to determine the distribution of macroalgae communities in the rocky intertidal zone of Heligoland (Germany) using airborne hyperspectral (AISAeagle) data. The classification results of two supervised approaches (maximum likelihood classifier and spectral angle mapping) are compared with an approach combining k-Means classification of derivative measures. We identified regions of different slopes between main pigment absorption features of macroalgae and classified the resulting slope bands. The maximum likelihood classifier gained best results (Cohan’s kappa = 0.81), but the new approach turned out as time effective possibility to identify the dominating macroalgae species with sufficient accuracy (Cohan’s kappa = 0.77), even in the heterogeneous and patchy coverage of the study area.