Mapping natural habitats using remote sensing and sparse partial least square discriminant analysis

Abstract

This work presents a novel approach for mapping the spatial distribution of natural habitats in the ‘Foothills of Larzac’ Natura 2000 listed site located in a French Mediterranean biogeographical region. Sparse partial least square discriminant analysis was used to analyse two RapidEye data sets (June 2009 and July 2010) with the purpose of choosing the most informative spectral, textural, and thematic variables that allow discrimination of habitat classes. The sparse partial least square discriminant analysis selected relevant and stable variables for the discrimination of habitat classes that could be linked to ecological or biophysical characteristics. It also gave insight into the similarities and differences between habitat classes with comparable physiognomic characteristics. The highest user accuracy was obtained for dry improved grasslands (u = 91.97%) followed by riparian ash woods (u = 88.38%). These results are very encouraging given that these two classes were identified in Annex 1 of the EC Habitats Directive as of Community interest. Due to limited data input requirements and its computational efficiency, the approach developed in this article is a good alternative to other types of variable selection approaches in a supervised classification framework and can be easily transferred to other Natura 2000 sites.

Acknowledgements

The authors thank the botanists from the Conservatoire des Espaces Naturels du Languedoc Roussillon (CEN-LR) who helped undertake the fieldwork and provided us with reference maps on habitats.