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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 âelementsâ 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 âlivingâ and âstandingâ, 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 nationsâ forests. Any comparison of the current status and trends of forest resources among nations presumes that the nationsâ 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 FAOâs 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/).

Forests provide a wide range of ecosystem services and information requirements on forests have grown considerably. Nevertheless, collecting information in the forest is expensive and for this reason assessment of forest resources strongly relies on statistical sampling. However, plot measurement remains essential even when remote-sensing data are used, and field assessments are still among the costliest components of forest inventories. Studies on the costs for plots survey are limited and usually based on expert evaluation rather than on data. This article analysed the relationship between the time needed for measuring forest plots and their site-related characteristics (slope, terrain roughness), stand features (number of trees, subsample trees, stumps, coarse woody debris, understorey vegetation) and protocol-related procedures, by means of univariate and multivariate analyses. Analyses showed that the time needed for measuring plots depends on the workload or the intensity of fieldwork. Especially, the number of variables surveyed matters, because the variables explained the measuring time variation by an additive effect, suggesting that within a complex field protocol the total number of measurements taken may not represent properly the overall intensity of work. Marking in an effective way permanent plots is recommendable because the retrieval success of the pins buried into the ground was the most important explanatory variable. Presence of understorey vegetation was more important than the number of individuals measured. The results obtained are consistent and logical, but the variance explained was limited, suggesting that predictability of the measuring time under complex field protocols might be intrinsically limited by the interactions among factors with opposite effects and especially by the adaptation of the surveyors to specific circumstances. For example, effects of physical tiredness were not detected in the days when two plots were measured; conversely, measuring a second plot reduced the measuring time of the first, an event most likely dependent on the surveyors’ behaviour. Under the conclusion that predictability is low by nature, the inference of studies based on simulation data and simplified protocols to practical applications was finally discussed. [ABSTRACT FROM AUTHOR]

Consistent knowledge about the increment in European forests gained amplified importance in European policies and decision processes related to forest-based bioeconomy, carbon sequestration, sustainable forest management and environmental changes. Until now, large-area increment information from European countries was lacking international comparability. In this study we present a harmonisation framework in accordance with the principles and the approach established for the harmonisation of National Forest Inventories (NFIs) in Europe. 11 European NFIs, representing a broad range of increment measurement and estimation methods, developed unified reference definitions and methods that were subsequently implemented to provide harmonised increment estimates by NUTS regions (Nomenclature of territorial units for statistics of the European Union), main forest types and tree species groups, and to rate the impact of harmonisation measures. The main emphasis was on gross annual increment (GAI), however, also annual natural losses (ANL) and net annual increment (NAI) were estimated. The data from the latest available NFI cycles were processed. The participating countries represent a forest area of about 130 million ha, and 82% of the European Unions' (EU) forest area, respectively. The increments were estimated in terms of volume (m³ year−1, m³ ha−1 year−1) and above-ground biomass (t year−1, t ha−1 year−1). The harmonised GAI volume estimates deviate in a range of +12.3% to −26.5% from the estimates according to the national definitions and estimation methods. Within the study area, the harmonised estimates show a considerable range over the NUTS regions for GAI, from 0.6 to 12.3 m³ ha−1 year−1, and 0.8–6.4 t ha−1 year−1, of volume and above-ground biomass, respectively. The largest increment estimates are found in Central Europe and gradually decrease towards the North, South, West and East. In most countries coniferous forests show larger increment estimates per hectare than broadleaved forests while mixed forests are at an intermediate level. However, in some instances, the differences were small or mixed forests revealed the largest increment estimates. The most important tree species groups in the study area are Pinus spp. and Picea spp., contributing 29% and 26% of the estimated total GAI volume, respectively. The shares of the prevalent broadleaved species are smaller with contributions of 9%, 7% and 6% by Quercus spp., Fagus sylvatica and Betula spp. The results underline the importance of harmonisation in international forest statistics. Looking ahead, harmonised large-area increment estimation is pivotal for accurate monitoring and evidence-based policy decisions in the changing context of future forest ecosystems dynamics, management strategies and wood availability. [Display omitted] • Increment estimation approaches differ among European NFIs. • Harmonisation framework developed for gross and net annual increment. • Common increment estimation method implemented by 10 European countries. • Harmonised increments estimated for NUTS regions, forest types and tree species. • Harmonised increments improve the comparability of international forest statistics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]