Assessment of Remote Sensing Techniques for Habitat Mapping in Coastal Dune Ecosystems

Bare sand and semi-fixed dunes represent ideal conditions for successionally young slack habitats that support rare species of coastal dune flora such as fen orchid (Liparis loeselii) and liverworts (e.g., Petallophyllum ralfsii). In ecologically significant and large dune systems, such as the Kenfig National Nature Reserve, UK, the identification and mapping of habitats and the provision of information on the relative proportion of sand and vegetation form the key to conservation management. To map this habitat, mapping algorithms are applied to Compact Airborne Spectro-graphic Imager (CASI) data. Per-pixel mapping was performed using the minimum distance, maximum likelihood and Mahalanobis distance classification algorithms with training data extracted for habitats at various levels of the National Vegetation Classification (NVC) scheme. Sub-pixel mapping was performed using a linear mixture model, fuzzy membership functions and neural networks, and the sub-pixel proportions of the spectral end members viz. sand, vegetation and shade/moisture were defined. Results indicate that per-pixel mapping can only be achieved for broad habitat categories that correspond to level I of the NVC. Of the algorithms used, the minimum distance, with an overall mapping accuracy of 92%, outperforms both maximum likelihood and Mahalanobis distance. Results from the sub-pixel algorithms indicate that all three techniques can be used to map the relative proportions of sand and vegetation. It is argued that both approaches provide baseline maps that are required to implement successfully an effective dune conservation programme.