A Smart Procedure for Assessing the Health Status of Terrestrial Habitats in Protected Areas: The Case of the Natura 2000 Ecological Network in Basilicata (Southern Italy)

Natura 2000 is the largest coordinated network of protected areas in the world, which has been established to preserve rare habitats and threatened species at the European Community level. Generally, tools for habitat quality assessment are based on the analyses of land-use/land-cover changes, thus, highlighting already overt habitat modifications. To evaluate the general quality conditions of terrestrial habitats and detect habitat degradation processes at an early stage, a direct and cost-effective procedure based on satellite imagery (Landsat data) and GIS (Geographic Information System) tools is proposed. It focuses on the detection of anomalies in vegetation matrix (stress/fragmentation), estimated for each habitat at the level of both a single protected site and local network, to identify habitat priority areas (HPA), i.e., areas needing priority interventions, and to support a rational use of resources (field surveys, recovery actions). By analyzing the statistical distributions of standardized NDVI for all the enclosed habitats (at the site or network level), the Degree of Habitat Consistency (DHC) was also defined. The index allows the assessment of the general status of a protected site/network, and the comparison of the environmental conditions of a certain habitat within a given protected site (SCI, SAC) with those belonging to the other sites of the network. The procedure was tested over the Natura 2000 network of the Basilicata region (Southern Italy), considered as a hotspot of great natural and landscape interest. An overall accuracy of similar to 97% was obtained, with quite low percentages of commission (similar to 8%) and omission (similar to 6%) errors. By examining the diachronic evolution (1985-2009) of DHC and HPA, it was possible to track progress or degradation of the analyzed areas over time and to recognize the efficaciousness/failure of past managements and interventions (e.g., controlled disturbances), providing decision-makers with a thorough understanding for setting up the most suitable mitigation/contrast measures.