Awareness of exhaustible forest resources is not recent in human history; rather, it dates back to the late Middle Ages, when it became clear that some kind of planning was needed to utilise forest resources and to do so, assessment was necessary. Postponed in time, enlarged to a national scale and based on statistical sampling, compared to the inventory methods adopted at that time, modern NFIs are assigned to produce sound information necessary to support forest policies. Forest areas and composition, ownership, growing stock and increment, as well as management, silviculture and structural characters are among the variables assessed by NFIs. This chapter provides statistics on those variables. For areas, estimates are shown for Total wooded area, Forest, Other wooded land, and their distribution among inventory categories and forest types, which describe species composition. In addition, the chapter also addresses distribution by altitude classes. For stands characters, areas are shown by crown coverage, development stage and age class. Lastly, inventory statistics are given on the presence and amount of small trees and shrubs.

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. R2 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).

In a framework of multiple services supplied simultaneously by forests, the protection against natural hazards is one of the most important. Forests deliver conservation of natural resources, including soil and water, and other environmental services. They slow water dispersion and allow for infiltration and percolation of rainwater, which recharges soil and underground water storage. Forest cover, moreover, protects soil from wind and water erosion, avalanches and landslides. INFC collects a wide range of information related to the protective function of Italian wooded areas. This chapter shows estimates regarding such physical site characteristics, as slope, land position and aspect which, together with tree canopy coverage and terrain roughness, can condition the protective role of forests. Inventory statistics on terrain instability and hydrogeological constraint, as defined by national laws, are shown as well, the latter being a basis of most national and regional regulations on forest management. Finally, the presence of a primary designated management objective has been assessed with a particular focus on direct and indirect protection. Estimates on such attributes are shown in the last section of this chapter.

Detection and correction of LiDAR raster data from the Italian national remote sensing programme and production of a suitable CHM to forest volume estimation in Calabria (southern Italy). The AlForLab project, a Public-Private Laboratory which is part of the Cluster MEA (Materials Energy Environment) addressed to the Calabria Region (southern Italy), has gained great benefit by using LiDAR data acquired in the frame of a national remote sensing programme of the Ministry of the Environment and Protection of Land and Sea. This kind of LiDAR data, distributed in raster format and publicly available for research and non-profit purposes, have proved to be a suitable tool to support forest management. Their usage, however, has required the recognition and correction of non-forest elements included in the Digital Surface Model (DSM), like electric powerlines, wind turbines, sub-vertical rocks and viaducts. Such outliers, if remaining into the Canopy height Model (CHM), can generate potential errors in application of LiDAR-based prediction models. This paper proposes some semi-automatic pre-processing procedures, directly applicable on raster data, in order to obtain a CHM without non-forest elements. The methods described here have been developed in open-source environment (R and QGIS). The correction procedures carried out were tested in three municipalities having forest area between 1700 and 5400 ha, and characterized by different types of outliers. The performances of the methods were evaluated by comparing the estimated forest volume obtained before and after their application. Although low total volume changes were observed on the entire study areas (about 0.5%, corresponding to 1500 to 7200 m3), more significant effects, tens to hundreds cubic meters per hectare of overestimation, can occur in stands or forest compartments with a high presence of outliers. In conclusion, the proposed methods have proved to be suitable to achieve a reliable CHM for forest applications.

The INFC2015 field campaign surveyed almost 9000 sample points and assessed and measured a relevant number of variables. This chapter describes the procedures adopted to retrieve the sample points marked in the previous NFI (INFC2005) and to reach and mark sample points never located before on the ground. The chapter also describes the protocol used for classifying and measuring the variables, either at a stand level or a single item level as well as the relevant sample unit (point or plot) for measuring each variable. Some sections of the chapter describe the electronic devices and procedures used for storing the field data and sending them to the central database. It also details the way in which crews were supported remotely and the way data quality assurance was applied during the campaign, both in the field and from a distance.