Forest species discrimination in an Alpine mountain area using a fuzzy classification of multi-temporal SPOT (HRV) data

Forest cover maps, showing the location and the extent of different forest types, are essential tools for forest monitoring and planning. Nowadays, remote sensing is one of the most important source of forest cover classifications at different scales. In this paper, the usefulness of middle resolution images (SPOT HRV) for forest cover mapping at local scale is evaluated (1) and a fuzzy classification approach is tested for increasing forest discrimination at the specific level (2). The study was carried out in a mountain test area located in the eastern Alps of Italy within the RI.SELV.ITALIA project founded by the Italian Ministry of Agriculture and Forests. It was based on a data-set composed of two SPOT images, topographically corrected, and some ground inventory data. The SPOT images, taken in summer and autumn, were selected in order to use the phenology characteristics of the different forest species for improving the separability between evergreen and deciduous species. The bi-temporal images were firstly combined in a single multi-temporal image which was later classified using both maximum likelihood and fuzzy classification methods. In order to identify the best classification procedure, the results of both classifications were evaluated using independent ground inventory data and then compared. The fuzzy classification approach gave more accurate results in forest species discrimination, while the maximum likelihood algorithm showed some limits in classifying the forest cover in such a complex landscapes characterized by rugged terrain and by the frequent presence of mixed forests.