IntroduzioneNegli ultimi decenni è avvenuta una profonda trasformazione del settore forestale italiano, in linea con quanto osservato anche a livello europeo (MI-PAAF 2015). Il sorgere di nuove funzioni e ruoli produttivi e sociali attribuiti alle risorse forestali (sensu lato, incluse le aree verdi in ambito urbano, le piantagioni da legno e gli alberi fuori-foresta) hanno determinato una crescita dell'importanza, ma anche della complessità del sistema foresta-legno. In Italia esiste una significativa tradizione nella ricerca forestale e una radicata consapevolezza che l'innovazione e la competitività sono possibili grazie a un'alleanza tra iniziative di ricerca strategiche a scala nazionale con Abstract: Precision forestry: concepts, tools and perspectives in Italy.Recent advancements in informatics and communication technologies have led to an increasing employment of analytical and communication tools in forestry, including data from satellite, airborne, unmanned aerial vehichles, global positioning systems, and many sensors, devices and other geospatial tools. Precision forestry enables highly repeatable measurements, actions and processes to manage and harvest forest stands, simultaneously allowing information linkages between production and wood supply chain, involving resource managers and environmental community; all these factors are contributing to the wider goal of sustainable forest management. In this report, we review the most recent advances in the precision forestry applications and tools, with particular reference on advanced forest inventory, decision support systems, precision forest harvesting, and wood traceability. We discuss the opportunities and challenges towards implementing precision forest practices in forest management and planning and forest industry in Italy.

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Models of stand volume and biomass estimation based on LiDAR data for the main forest types in Calabria (southern Italy). The AlForLab project is part of the Cluster MEA (Materials Energy Environment) addressed to the Calabria Region. Estimating the main dendrometric variables of Calabrian forests using models based on publicly available remote sensed data is one of the main purposes of the project. This paper describes the procedures used to develop several thematic maps (raster and vector) of timber volume and phytomass to be used in planning and management activities at both regional and forest property scale, as well as for felling plans, logging projects etc. We used public LiDAR data at medium-low resolution (1.6 pts m -2 ), acquired on about 90% of Calabrian territory in the frame of a national remote sensing programme of the Italian Ministry of the Environment. Field data from the second National Forest Inventory (INFC 2005) on 311 sample points were used for model calibration, as well as new field data acquired specifically for AlForLab project on 143 angle count samples. A series of regression models to predict volume and its corresponding aboveground biomass (dry and fresh weight) were developed and digital maps at different spatial resolutions were produced, as well as their estimate uncertainties. These models and their mapping products are also an important part of the new-establishing forest Decision Support System CFOR. The adopted models, though based on the same mathematical equation, have specific coefficients for different species and groups of species, according to a forest type classification system compatible with the fourth level of Corine Land Cover. In this way it is possible to apply the models without accessing more detailed forest type maps. All estimation methods and procedures are consistent with national forest inventory models, and with the other new tools proposed by AlForLab project to estimate timber volume, such as the regional tariffs and the field sampling inventory procedures. R 2 adjusted values (for models at the highest typological detail) are between 60% and 85%, whereas uncertainties of timber volume estimate (ESS%) range from 25% (for main forest species) up to 50% (for less spread forest types). All processing steps to produce digital maps were performed on open-source environment (R and QGIS).Keywords: ALS, LiDAR, Timber Volume, Forest Biomass, Estimation Models, CHM, AlForLab Received: Feb 09, 2017; Accepted: Apr 19, 2017; Published online: May 15, 2017 Citation: Scrinzi G, Floris A, Clementel F, Bernardini V, Chianucci F, Greco S, Michelini T, Penasa A, Puletti N, Rizzo M, Turco R, Corona P, 2017. Modelli di stima del volume e delle fitomasse del soprassuolo arboreo delle principali formazioni forestali della Calabria mediante dati LiDAR. Forest@ 14: 175-187 [online 2017-05-15]

IntroduzioneNegli ultimi decenni la percezione della società nei confronti del ruolo delle risorse forestali ha subito profondi mutamenti. La gestione forestale, in passato prevalentemente orientata alla massimizzazione della produzione legnosa, si è andata configurando sulla base di nuove richieste relative alla promozione e alla salvaguardia del ruolo ecologico-ambientale, tu- Abstract: Large-scale indicators for monitoring forest diversity of the main forest types in Calabria (Italy).Recently, the Society's perception of forest resources has gone through significant changes. Forest ecosystems play a multifunctional role and host an important portion of the whole biodiversity, particularly in the Mediterranean area. Remote sensing technologies provide a unique way to obtain spatially extensive information on forest ecosystems, but relatively few studies used such information to evaluate forest habitat and biotic diversity. In this paper we evaluate the effectiveness of remote sensing to predict forest diversity by linking remotely sensed information with diversity metrics obtained from ground measurements of butterfly diversity. The field work was carried out in Calabria in four different forest types (beech, chestnut, black pine and silver fir forests). The sampling of Lepidoptera was carried out by LED light traps. We positioned 9 traps per forest type, for a total of 36 sites chosen to sample the different stages of forest succession in each forest type. Samples were carried out once a month from May to November 2015. Data from in situ butterfly measurements were compared with above ground forest biomass estimated from airborne LiDAR with NDVI estimated from Landsat 8. Results indicated that the Geometridae/Noctuideae ratio of lepidopteran communities was significantly correlated with the tree biomass, its distribution among tree size classes and the NDVI. The Geometridae/Noctuidae ratio, therefore, represents an index easy to calculate, which can be employed to integrate data acquired from remote sensing in order to obtain continuous spatial estimates of forest naturalness.