Habitat mapping and change detection in Natura2000 coastal sites in Southern Apulia

Monitoring biodiversity at habitat and landscape level is becoming widespread in Europe and elsewhere as countries establish national and international habitat conservation policies and monitoring systems. Long-term habitat mapping and change detection are essential for the management of coastal wetlands as well as for evaluating the impact of conservation policies. Earth observation (EO) data and techniques are a valuable resource for long-term habitat mapping, through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. The Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) has been identified as the most effective for translating EO-derived LC/LU classes to habitat types, since it allows a better description of natural habitats in comparison to other classification systems; moreover, LCCS has proven to be a effective tool in change detection, both at the level of conversion and modification (Tomaselli et al., 2013; Adamo et al 2014). As regards the present contribution, vegetation, LC and habitat mapping has been performed on three coastal sites belonging to the Natura 2000 and located in Southern Apulia (Italy), in years 2007 and 2015. Vegetation maps represented the baseline position for natural and semi-natural types, defined as phytosociological units in accordance with the Zurich-Montpellier method. Vegetation units were then reclassified in habitat types (according to the Annex I to the 92/43 EEC Directive and EUNIS) and in LC classes (according to Corine Land Cover and LCCS). The adopted landscape classification procedure refers to a hierarchical model with three different information levels: the vegetation unit, the habitat type, and the LC type. The mapping products were then compared, in the different acquisitions, in order to point out the ability of different taxonomies in detecting changes in vegetation and habitat types. LCCS turned out to be the most effective, highlighting changes such as height, structure and density, which were not evidenced with other classification systems.