Ecological regions or ecoregions derive from ecological classification of land and represent broad and discrete ecologically homogeneous areas within which natural communities and species interact with the physical elements of the environment. The aim of this paper is to define the ecoregions of Italy, southern Europe, based on a robust methodological process for classification and mapping. The ecoregions of Italy comprise 2 Divisions, 7 Provinces, 11 Sections and 33 Subsections and constitute the first comprehensive ecological classification of the country that integrates accurate and updated cartographies and knowledges on climate, vegetation, land units and biogeography. This classification has the strength to be adopted as a proper framework for ecological modelling, biodiversity conservation policies and sustainable territorial planning at the national and subnational level. © 2014, © 2014 The Author(s). Published by Taylor & Francis

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Traditionally fuel maps are built in terms of ‘fuel types’, thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000–2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies.

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