Spatial distribution modelling of forest attributes coupling remotely sensed imagery and GIS techniques

Needs for accurate information about forest resources can only partly be met by conventional inventories based on ground sampling. Earth observation (EO) techniques are a valuable source of information for several forest attributes which are linked to relevant spectral responses (tree species composition, stand biomass, stand density, etc.). In particular, EO can be effective for propagating forest inventory plot sample values through the landscape: sample values can be assigned to non-sampled locations according to the similarity of certain spectral features among the sampled and the non-sampled plots. A spatial modelling based on the integration of remotely sensed images and sample field measurements targeted to produce forest attributes maps is presented for a site in central Italy with more than 300 geocoded sampling field plots. Plot data of tree stemwood volume and other non-wood forest attributes came from a single-stage cluster design with 58 primary sampling units (clusters). Landsat 7 ETM+ images are used with two classification techniques (k-NN and fuzzy classifiers) to model the spatial distribution of stemwood volume (m(3)/ha) and stem density (n/ha). Modelling and mapping results are discussed.