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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.

N is known to be the most limiting element for vegetation growth in temperate and boreal forests. The expected increases in global temperature are predicted to accelerate N mineralization, therefore incrementing N availability in the soil and affecting the soil C cycle as well. While there is an abundance of C data collected to fulfill the requirements for national GHG accounting, more limited information is available for soil N accumulation and storage in relation to forest categories and altitudinal gradients. The data collected by the second Italian National Forest Inventory, spanning a wide range of temperature and precipitation values (10° latitudinal range), represented a unique opportunity to calculate N content and C/N ratio of the different soil layers to a depth of 30 cm. Boosted Regression Tree (BRT) models were applied to investigate the main determinants of soil N distribution and C/N ratio. Forest category was shown to be the main explanatory factor of soil N variability in seven out of eight models, both for forest floor and mineral soil layers. Moreover latitude explained a larger share of variability than single climate variables. BRT models explained, on average, the 49% of the data variability, with the remaining fraction likely due to soil-related variables that were unaccounted for. Accurate estimations of N pools and their determinants in a climate change perspective are consequently required to predict the potential impact of their degradation on forest soil N pools.

European National Forest Inventories (NFIs) are well established in most of the European countries. In some NFIs, core samples are taken to estimate volume increments at annual resolution. However, the potential of the NFI for dendroecological investigations has not been evaluated so far. Therefore, the aim of this study was to test the potential of dendrochronological data collected during the Italian NFI in 2006. Silver fir was selected as a test species. Core samples were taken from eighty-one trees including one sample per inventory plot (IP). The 81 IPs were distributed across the Italian Alps and the northern Apennine thus well representing the site conditions of the study area. The effects of the detrending method and sample size on detecting middle- to long-term growth trends and high-frequency climate signals were tested. Further, cluster analysis was applied to find dissimilarities among tree-ring width (TRW) series. Results suggest the detrending method to be the most important factor for detecting growth trends, but not for identification of high-frequency climate signals. Sample size essentially influences the final mean chronology, but it does not bring new information when larger than 30 series. Two groups of trees were recognised with distinct middle-term TRW patterns, although being the same in terms of climate sensitivity. Results are mostly in line with recent findings of other authors, which suggest the potential of the NFI data for dendroecological investigations.

Interest in the accurate measurement of forest biomass has increased remarkably in the decades following the agreements made at the United Nations Framework Convention on Climate Change, particularly because of forest biomass’ role in carbon storage. Information on forest biomass over large areas is generally obtained from National Forest Inventory (NFI) statistics; however, these data are usually estimates of stem volumes and only rarely estimate tree biomass through direct measurements. Conversion from stem volumes to biomass is commonly achieved through factors known as biomass expansion factors (BEFs). BEFs may also expand the estimation of tree components and, in such cases, are also referred to as conversion and expansion factors (BCEFs). Experience has proven that biomass factors vary with stand characteristics such as composition, age, volume and others. For this reason, models that predict such factors as a function of stand variables have been developed. In Italy, the total above-ground tree biomass (AGB) of forests was estimated by the second Italian NFI through direct measurements and, on the basis of the stock estimates of tree volume and biomass, BCEFs representative of Italian broad-spectrum forest conditions (e.g., composition, density, silviculture treatment and others) for reference year 2005 were published. This paper presents a stand-level model developed to predict AGB density (Mg ha −1 ) from growing stock volume (GSV) density (m 3 ha −1 ) by forest category. The model was calibrated using NFI plot level data. The fitted model was tested to estimate total AGB from aggregated data, which was represented as the mean GSV per hectare at the plot level, and two broader aggregation scales using the mean GSV per hectare of forest category in each administrative region and at the national level. The accuracy of the estimates generally decreased with the level of data aggregation, but the upscaling exercises only overestimated the national overall AGB value by a maximum of 2.00%, an acceptable difference given the broad datum used as the independent variable. The upscaling exercises proved that the model is also suitable for predicting AGB by forest category, particularly given that the estimated values always fell within the 95% confidence interval of the NFI reference estimates.

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.

National forest inventories (NFIs) are a primary source of data for carbon stock reporting. In Italy the most comprehensive assessment of forest carbon has been carried out under the second NFI. The NFI was conducted between 2003 and 2007 with an additional field exercise in 2008 and 2009, which collectively aimed to exhaustively survey four out of five forest carbon pools recognized by IPCC. The final estimates were published in 2013 and confirmed the major role of soil as a carbon store. Italian forests contain 1.24 × 109 Mg organic carbon, 57.6% of which is in soil (715.7 × 106 Mg), 38.1% in the above-ground biomass (472.7 × 106 Mg), 2.3% in litter (28.3 × 106 Mg) and 2.0% in deadwood (24.9 × 106 Mg). This article analyzed the variability of total above-ground living biomass and soil organic carbon stocks along with environmental gradients related to elevation and biogeographic regions; examined the carbon storage capacity of the different forest categories; investigated the relationships, at plot level, between both litter carbon and soil organic carbon, and other site/stand features, as well as the relationships between the four carbon pools investigated at the level of forest categories. Forest category proved to be the main driver of variability in carbon stocks, especially for the total above-ground biomass (total-AGB). The observed variation in total-AGB with altitude was primarily due to the distribution of forest categories across altitude classes. There was less variation in SOC between altitude classes than in total-AGB, and soil generally remains the main carbon pool at any altitude. The environmental parameters related to the biogeographic regions did not have a clear effect on total-AGB or soil carbon stocks. Significant correlations between both litter carbon and soil organic carbon and different site/stand features were found in the plot level data, but the amount of variation explained by the multiple regression analysis was too small for use in predictions. More variation was explained when fitting data at the forest category level.

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 this article, we present equations derived for the prediction of the aboveground tree volume and phytomass for twenty-five of the most important forest species growing in Italy. These equations result from ongoing research aiming to fill a gap in the models available at the national scale. With regard to volume, the results are particularly important for thirteen species or groups of species that were once scaled with models, conventionally assumed as reference models, available for other species. In Italy, phytomass models had never been constructed at the national level before. For any single tree, specific equations allow estimations of the following tree components to be made: stem and large branches (for either volume or phytomass), small branches (phytomass), stump (phytomass) and the whole tree phytomass. The models have been constructed on the basis of nearly 1,300 sampling units (sample trees). Although these equations must be considered intermediate results of the ongoing research because only half the scheduled number of samples has been collected, they have already been used in the practice, for example in the estimates reported in the recently published second national forest inventory.

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.

National forest inventories represent a fundamental source of data and knowledge for forestry and environmental policy and allow for the production of national and regional level statistics on forests. The value of these statistics confirms the need for a sampling design that adequately delivers representation by reducing sampling error, but also for a data quality process that limits the non-sampling errors. The article summarizes the quality control procedures of the three sampling phases adopted in the Italian national forest inventory, carried out between 2003 and 2006. The development of an integrated system of actions and controls which are able to limit subjective interpretations, in order to guarantee harmonized information all over the country, was a considerable effort within the overall project. Critical points to be considered were the consistent number of measures and evaluations undertaken during the three inventory phases, the high variability of observed attributes, the consistent number of surveyors involved, and costs of quality control, especially those related to fieldwork. At the end, examples on the overall quality of the classification performed on land cover and vegetation are discussed, as well as the impact of classification errors on the total forest area estimates.

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/).