Key message: Trees outside forests (TOF) have crucial ecological and social-economic roles in rural and urban contexts around the world. We demonstrate that a large-scale estimation strategy, based on a two-phase inventory approach, effectively supports the assessment of TOF’s diversity and related climate change mitigation potential. Context: Although trees outside forest (TOF) affect the ecological quality and contribute to increase the social and economic developments at various scales, lack of data and difficulties to harmonize the known information currently limit their integration into national and global forest inventories. Aims: This study aims to develop and test a large-scale estimation framework to assess ecological diversity and above-ground carbon stock of TOF. Methods: This study adopts a two-phase inventory approach. Results: In the surveyed territory (Molise region, Central Italy), all the attributes considered (tree abundance, basal area, wood volume, above-ground carbon stock) are concentrated in a few dominant species. Furthermore, carbon stock in TOF above-ground biomass is non-negligible (on average: 28.6 t ha⁻¹). Compared with the low field sampling effort (0.08% out of 52,796 TOF elements), resulting uncertainty of the estimators are more than satisfactory, especially those regarding the diversity index estimators (relative standard errors < 10%). Conclusion: The proposed approach can be suitably applied on vast territories to support landscape planning and maximize ecosystem services balance from TOF. © 2018, INRA and Springer-Verlag France SAS, part of Springer Nature.

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.

Stone pine stand of Castel Fusano (Rome) burnt on July the 4th 2000 during a huge wildfire. As a consequence of the fire an intensive natural sexual and asexual regeneration began. In order to monitor such a regeneration field surveys were carried out in 2003 and 2006 in sample plots. Remotely sensed high resolution images from Ikonos and Quick Bird were acquired for the same years. The purpose of this work is to test different methodologies for modeling existing relationships between remotely sensed images and ground collected data in order to estimate and to map both sexual and asexual regeneration. For such a purpose different methodologies were tested: step-wise Muliple Linear Regression, Neural Networks (Relevance-Vector-Machine and the Multi-Layered-Perceptron) and the k-Nearest-Neighbors. These activities were carried out within the framework of the GRINFOMED- MEDIFIRE also developing a specific software named Spatial Forest Modeler (SFM) able to analyze existing relationships between remotely sensed variables and data collected in the field in order to identify the best available models to map and estimate the studied variables acquired on the basis of a field sampling design. The present paper presents data collected in the field, analysis and modeling methods and achieved results. The SFM software is also presented.

Forest ecosystems for their important multifunctional value, need a complex and increasing amount of descriptive information to support their management. Ecological and environmental related attributes have became nowadays important as traditional ones, such as wood growing stock and basal area. The correct application of Sustainable Forest Management criteria is boosted by spatial contiguous knowledge of such attributes. For such a reason in the last years a huge number of scientific experiences in the forest area have been concentrated to study the relationship between data acquired in the field and remotely sensed multispectral images. Models based on such relationships can be used to estimate and map forest attributes acquired in the field on the basis of a statistical sampling design. can be sucould not take in consideration spatially structured data. In last years many researches have focused on possible relationships between field data and remote sensed informations derived from multispectral imagery. Modeling these relationships allows to extend inventory data to not explored surfaces. In this paper were discussed results on spatializing forest biometrical attributes, tree heterogeneity and dimensional heterogeneity assessed during an inventory of Mountain Community "Alto Molise" (IS) throw Spot 5 and Lansat TM 7 imagery. For this purpose a multilinear regression and a k-Nearest Neighbor classifier were used.