Testing Ikonos and Landsat 7 ETM+ Potential for Stand-Level Forest Type Mapping by Soft Supervised Approaches

Abstract

Forest types can be adopted as a suitable reference for classifying survey units within multipurpose forest resources inventories, at the properly considered level. This kind of hierarchical classification approach integrates an ecologically meaningful per-habitat perspective with practical survey, planning and management requirements. Advanced remote sensing technologies can be valuable tools for a cost-effective implementation of such an approach. In the present paper, data from high (Landsat 7 ETM+) and very high (Ikonos) spatial resolution satellite sensors were tested to understand their potential contribution supporting stand-level forest type mapping under Mediterranean conditions. Ikonos and Landsat images were used to differentiate forest coverages by so called soft classifiers: fuzzy maximum likelihood procedure for Ikonos and subpixel unmixing procedure for Landsat. Fuzzy classified images are then contrasted with forest type map made by photointerpretation of Ikonos imagery. Performances are showed and drawbacks discussed.