A key topic in landscape ecology and vegetation science is the quantitative analysis of changes in forest cover over time, through the use of geomatics monitoring tools. Ecologists and landscape researchers are pointing out that a full understanding of ecosystems and landscapes should be based on the analysis of their functioning over long time series. Under this perspective, a long-term historical reconstruction of forest cover is essential. This study has aimed at examining the long-term dynamics of forest landscapes in Italy, over the last century, using recent remote-sensing based map (2012) and an accurate historical map (1936). A forest-non forest approach has been followed by the computation of a variety of landscape metrics using two analysis tools, with the final objective of quantifying changes in forest cover patterns and in the composition of specific landscape elements. Results show that forest landscape structure has significantly changed across Italy, resulting in a general trend of decreasing fragmentation and patchiness, mainly through enlargement of existing forest patches, which have also assumed a more geometrically regular shape. In relative terms, the greatest expansion of forest areas has occurred mainly in lowland districts characterised by the highest level of human pressure in the country. © 2017 Società Botanica Italiana.

The objective of the present study is the comparison of the combined use of Earth Observation-1 (EO-1) Hyperion Hyperspectral images with the Random Forest (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) classifiers for discriminating forest cover groups, namely broadleaved and coniferous forests. Statistics derived from classification confusion matrix were used to assess the accuracy of the derived thematic maps. We demonstrated that Hyperion data can be effectively used to obtain rapid and accurate large-scale mapping of main forest types (conifers-broadleaved). We also verified higher capability of Hyperion imagery with respect to Landsat data to such an end. Results demonstrate the ability of the three tested classification methods, with small improvements given by SVM in terms of overall accuracy and kappa statistic. © 2016 by the authors; licensee Italian Society of Remote Sensing (AIT).

To support sustainable forest management, planning policies and environmental actions, it is essential to have available common and standardized geospatial information on forest structure, composition and distribution. In this paper we present a harmonized forest categories (HFCs) map of four administrative Regions located in central Italy (i.e. Marche, Abruzzo, Lazio and Molise) at a scale of 1:400,000. The study area extends over 42,246 km², 14,878 km² of which are covered by forests. Four regional forest maps were harmonized in order to produce common standardized information on composition, structure and the distribution of forests in central Italy. A forest category is a forest vegetation unit defined by the main tree species composition. In this study we adopted a nomenclature scheme composed of 16 forest and shrubland categories. This work represents the first HFCs map in Italy over a large area. The legend is also harmonized with the European Environment Agency forest types nomenclature. © 2016 Nicolò Camarretta.