Fuzzy approaches provide improved spatial detection of coastal dune EU habitats

Mapping habitats on coastal dunes, crucial yet highly vulnerable ecosystems, requires objectivity and repeat- ability, which are still lacking in the implementation of the Habitats Directive. Although remote sensing offers promising solutions, the effectiveness of distinguishing habitats on coastal dunes from satellite imagery remains uncertain. In this study, we compare crisp and fuzzy classification approaches using WorldView-3 imagery to map coastal dune habitats in two Natural Parks of Tuscany (Italy). Field-collected vegetation data were classified into Annex I habitats of Habitats Directive and EUNIS habitats. Using field data as reference, we performed image classifications with a crisp method (Random Forests) and three fuzzy methods, namely Random Forests, Spectral Angle Mapper and Multiple Endmember Spectral Mixture Analysis. Metrics of overall accuracy and Mantel tests were used to compare the results. EUNIS habitats exhibited the best performance in terms of classification accuracy, likely due to the simpler classification system. We observed a great disparity among habitats, with coastal dune scrubs and white dunes generally achieving the highest accuracy. Fuzzy classifications, despite yielding lower overall accuracy than the crisp classification, provided a more realistic representation of vegetation patterns, highlighting the inherent fuzziness of vegetation in coastal dunes. Despite challenges related to image resolution and habitat heteroge- neity, combining satellite imagery with field surveys proved valuable for mapping coastal dune habitats, contributing essential data to the conservation of these fragile ecosystems. We provide a novel and effective tool, which will reduce the economic and physical efforts needed for habitat search and sampling in the field.