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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.

Forestry originated as a science under the need to manage forests for timber production in a sustainable way. Since then, sustainability has been broadened to include a variety of values and services recognised as important for human well-being, while at maintaining a relevant productive function. Hence, silviculture exists as a need of humankind and not as a need of forests for their continued existence. Sustainability is obtained by adopting correct silvicultural practices and adjusting the planning in view of the most suitable management able to ensure the goals, for example, carbon balance under climate warming scenarios. National forest inventories provide reliable information that is needed for effective forest policies. This chapter shows area estimates on some variables important for production, such as forest accessibility, presence of roads or tracks, terrain roughness and availability for wood supply (Sect. 8.2). Silviculture practices are actualised by forest utilisation modes, which are also related to timber logging modes. Section 8.3 shows the statistics on these variables as well as those on the growing stock and the biomass annually removed from the forest. Section 8.4 shows the estimates on presence of forest planning or regulations that influence productive aspects of forests. The type of planning may vary consistently due to overlapping authorities protecting forests in Italy, so the presence of planning at different levels was emphasised.

Quantitative variable raw data recorded in the sample plots require pre-processing before the NFI estimators of totals and densities can be used to produce statistics. The objective of the plot level estimates is to estimate the variables of interest for each sample point expanded to the 1 km2 area of the cell that the point represents. The intensity and complexity of the computations vary considerably depending on the variable, the way it is obtained by the measured items (e.g., DBH measurement vs. basal area), whether all the items in the sample plot or only a subsample of them are measured, and the availability of models. The definitive result of the computations are tallies, volumes, biomass and carbon stocks but estimates of additional variables at intermediate steps may be needed (e.g., total tree height). This chapter describes the methods and the models used in INFC2015 for the estimation of the variables related to trees (e.g., tallies, basal area), small trees and shrubs (e.g., biomass, carbon stock), stumps (e.g., volume, biomass), stock variation (e.g., the wood annually produced by growth and that removed). Some of the models described were produced in view of the INFC needs, before and after it was established in 2001, while others were created during the NFI computation processes. Finally, the conversion factors needed to estimate the biomass of deadwood, saplings and shrubs were obtained through an additional field campaign of the second Italian NFI (INFC2005) and the following laboratory analyses.

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.

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.

Forests affect climate globally and have an important role in the global carbon cycle. Countries that signed the United Nations Framework Convention on Climate Change (UNFCCC) and the agreements that followed, use national forest inventory data to estimate carbon sequestration related to land use, land use changes and forestry. Five terrestrial carbon pools are relevant for the estimation of carbon stocks and carbon stock changes under the UNFCCC and the Kyoto Protocol: soil, litter, belowground and aboveground biomass, and deadwood. The second Italian NFI (INFC2005) estimated the organic carbon stock stored within four out of the five pools and confirmed the major role of soil, which stored 57.6% of Italian forest organic carbon in the four pools studied. Estimating soil carbon change is challenging if the time elapsed between two assessment periods is not long, because the expected changes are small compared to the high carbon stock already present in soils. INFC2015 updated the estimates of the carbon stored in aboveground living biomass and in deadwood. The results are shown and commented on in Sects. 12.2 and 12.3. They allow for computation of the overall aboveground biomass carbon stock, i.e., the joined contribution of those two ecosystem components. Annual variation of carbon in growing stock is also due to carbon stored via growth and carbon removed with harvesting. These entries of the balance were estimated by INFC2015 and indicate that Italian forests act as carbon sinks; this is shown in Sect. 12.4.

Aims of the study. Assessment of growth is essential to support sustainability of forest management and forest policies. The objective of the study was to develop a species-specific model to predict the annual increment of tree basal area through variables recorded by forest surveys, to assess forest growth directly or in the context of more complex forest growth and yield simulation models.Area of the study. Italy.Material and methods. Data on 34638 trees of 31 different forest species collected in 5162 plots of the Italian National Forest Inventory were used; the data were recorded between 2004 and 2006. To account for the hierarchical structure of the data due to trees nested within plots, a two-level mixed-effects modelling approach was used.Main results. The final result is an individual-tree linear mixed-effects model with species as dummy variables. Tree size is the main predictor, but the model also integrates geographical and topographic predictors and includes competition. The model fitting is good (McFadden’s Pseudo-R2 0.536), and the variance of the random effect at the plot level is significant (intra-class correlation coefficient 0.512). Compared to the ordinary least squares regression, the mixed-effects model allowed reducing the mean absolute error of estimates in the plots by 64.5% in average.Research highlights. A single tree-level model for predicting the basal area increment of different species was developed using forest inventory data. The data used for the modelling cover 31 species and a great variety of growing conditions, and the model seems suitable to be applied in the wider context of Southern Europe. Keywords: Tree growth; forest growth modelling; forest inventory; hierarchical data structure; Italy.Abbreviations used: BA - basal area; BAI – five-year periodic basal area increment; BALT - basal area of trees larger than the subject tree; BASPratio - ratio of subject tree species basal area to stand basal area; BASTratio - ratio of subject tree basal area to stand basal area; CRATIO - crown ratio; DBH – diameter at breast height ; DBH0– diameter at breast height corresponding to five years before the survey year; DBHt– diameter at breast height measured in the survey year; DI5 - five-year, inside bark, DBH increment; HDOM - dominant height; LULUCF - Land Use, Land Use Changes and Forestry; ME - mean error; MAE - mean absolute error; MPD - mean percent deviation; MPSE - mean percent standard error; NFI(s) - National Forest Inventory/ies; OLS - ordinary least squares regression; RMSE - root mean squared error; UNFCCC - United Nation Framework Convention on Climate Change.

International audience; AbstractKey messageThe increment estimation methods of European NFIs were explored by means of 12 essential NFI features. The results indicate various differences among NFIs within the commonly acknowledged methodological frame. The perspectives for harmonisation at the European level are promising.ContextThe estimation of increment is implemented differently in European National Forest Inventories (NFIs) due to different historical origins of NFIs and sampling designs and field assessments accommodated to country-specific conditions. The aspired harmonisation of increment estimation requires a comparison and an analysis of NFI methods.AimsThe objective was to investigate the differences in volume increment estimation methods used in European NFIs. The conducted work shall set a basis for harmonisation at the European level which is needed to improve information on forest resources for various strategic processes. MethodsA comprehensive enquiry was conducted during Cost Action FP1001 to explore the methods of increment estimation of 29 European NFIs. The enquiry built upon the preceding Cost Action E43 and was complemented by an analysis of literature to demonstrate the methodological backgrounds. ResultsThe comparison of methods revealed differences concerning the NFI features such as sampling grids, periodicity of assessments, permanent and temporary plots, use of remote sensing, sample tree selection, components of forest growth, forest area changes, sampling thresholds, field measurements, drain assessment, involved models and tree parts included in estimates. ConclusionIncrement estimation methods differ considerably among European NFIs. Their harmonisation introduces new issues into the harmonisation process. Recent accomplishments and the increased use of sample-based inventories in Europe make perspectives for harmonised reporting of increment estimation promising.

In the frame of a process aiming at harmonizing National Forest Inventory (NFI) and ICP Forests Level I Forest Condition Monitoring (FCM) in Italy, we investigated (a) the long-term consistency between FCM sample points (a subsample of the first NFI, 1985, NFI_1) and recent forest area estimates (after the second NFI, 2005, NFI_2) and (b) the effect of tree selection method (tree-based or plot-based) on sample composition and defoliation statistics. The two investigations were carried out on 261 and 252 FCM sites, respectively. Results show that some individual forest categories (larch and stone pine, Norway spruce, other coniferous, beech, temperate oaks and cork oak forests) are over-represented and others (hornbeam and hophornbeam, other deciduous broadleaved and holm oak forests) are under-represented in the FCM sample. This is probably due to a change in forest cover, which has increased by 1,559,200 ha from 1985 to 2005. In case of shift from a tree-based to a plot-based selection method, 3,130 (46.7 %) of the original 6,703 sample trees will be abandoned, and 1,473 new trees will be selected. The balance between exclusion of former sample trees and inclusion of new ones will be particularly unfavourable for conifers (with only 16.4 % of excluded trees replaced by new ones) and less for deciduous broadleaves (with 63.5 % of excluded trees replaced). The total number of tree species surveyed will not be impacted, while the number of trees per species will, and the resulting (plot-based) sample composition will have a much larger frequency of deciduous broadleaved trees. The newly selected trees have—in general—smaller diameter at breast height (DBH) and defoliation scores. Given the larger rate of turnover, the deciduous broadleaved part of the sample will be more impacted. Our results suggest that both a revision of FCM network to account for forest area change and a plot-based approach to permit statistical inference and avoid bias in the tree sample composition in terms of DBH (and likely age and structure) are desirable in Italy. As the adoption of a plot-based approach will keep a large share of the trees formerly selected, direct tree-by-tree comparison will remain possible, thus limiting the impact on the time series comparability. In addition, the plot-based design will favour the integration with NFI_2.

At the international level, various definitions have been established for the compilation and publication of forest resources assessment results over the last decade. These international definitions frequently rely on terms that are not precisely specified for inventory purposes and do not completely cover the requirements arising from the application of National Forest Inventory (NFI) data. Also, with respect to conventional topics such as forest area and growing stock estimation, several terms and expressions referring to individual trees are not, or are only vaguely, defined until now. Since the individual tree is the basic element of any forest resources assessment, the clarification of tree-related terms is an important part of COST Action E43 to harmonise common reporting of National Forest Inventories. Based on a review of existing definitions and on the requirements for harmonised reporting, common tree-related definitions are established. One objective of this study is to refine and enhance the applicability of available tree and shrub definitions, in particular with regard to the distinction between trees and shrubs. The study also focuses on the parts or “elements” of trees and on the distinction between these elements as they are of particular importance in growing stock and biomass definitions. Furthermore, several definitions for tree characteristics such as “living” and “standing”, as well as tree variables such as height, length, diameter at breast height, and crown projection area are adjusted with respect to NFI purposes. A concluding discussion reflects upon the reviewed, refined and newly established definitions. The definitions presented in this paper provide a firm basis for a common set of harmonised reference definitions developed by COST Action E43 and contribute to the precise and consistent use of terms.

International agreements such as the Kyoto protocol and Convention on Biological Diversity (1992), as well as, criteria and indicator processes require reports on the status of nations’ forests. Any comparison of the current status and trends of forest resources among nations presumes that the nations’ applied definitions and concepts produce comparable estimates of the status of forests. In spite of this, the FAO has already collected global information for 60 years and made noticeable efforts in creating common definitions, but forest related data are still collected using diverse definitions, even regarding basic concepts such as forest and forest area. A simple consequence is that the cross-countries estimates are not comparable. The reasons behind the differences in the definitions are diverse histories, and sometimes different use of forests. In an ideal case, national forest inventories should fulfil both national and international needs. In addition to the FAO’s Forest Resources Assessment process, other efforts are made to assess the status of forests in European countries, e.g. European Forest Information and Communication System (EFICS). EFICS produced reports about forest inventories but does not suggest any common definition or method to convert estimates from one definition to another one. This article presents principles and methods to create commonly acceptable and adoptable definitions for forest inventories. The principles and methods are demonstrated using two examples: the reference definitions of forest and growing stock. The article is based on the work of COST Action E43 (http://www.metla.fi/eu/cost/e43/).