Application of a niche-based model for forest cover classification

In recent years, a surge of interest in biodiversity conservation have led to the development of new approaches to facilitate ecologically-based conservation policies and management plans. In particular, image classification and predictive distribution modeling applied to forest habitats, constitute a crucial issue as forests constitute the most widespread vegetation type and play a key role for ecosystem functioning. Then, the general purpose of this study is to develop a framework that in the absence of large amounts of field data for large areas may allow to select the most appropriate classification. In some cases, a hard division of classes is required, especially as support to environmental policies; despite this it is necessary to take into account problems which derive from a crisp view of ecological entities being mapped, since habitats are expected to be structurally complex and continuously vary within a landscape. In this paper, a niche model (MaxEnt), generally used to estimate species/habitat distribution, has been applied to classify forest cover in a complex Mediterranean area and to estimate the probability distribution of four forest types, producing continuous maps of forest cover. The use of the obtained models as validation of model for crisp classifications, highlighted that crisp classification, which is being continuously used in landscape research and planning, is not free from drawbacks as it is showing a high degree of inner variability. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classification, may represent an useful approach to making more efficient and effective field inventories and to developing effective forest conservation policies