Patrizia Gasparini

Aboveground tree volume and phytomass prediction equations for forest species in Italy.

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.

Overview of methods and tools for evaluating future woody biomass availability in European countries

Key messageThis analysis of the tools and methods currently in use for reporting woody biomass availability in 21 European countries has shown that most countries use, or are developing, National Forest Inventory-oriented models whereas the others use standwise forest inventory--oriented methods.ContextKnowledge of realistic and sustainable wood availability in Europe is highly relevant to define climate change mitigation strategies at national and European level, to support the development of realistic targets for increased use of renewable energy sources and of industry wood. Future scenarios at European level highlight a deficit of domestic wood supply compared to wood consumption, and some European countries state they are harvesting above the increment.AimsSeveral country-level studies on wood availability have been performed for international reporting. However, it remains essential to improve the knowledge on the projection methods used across Europe to better evaluate forecasts.MethodsAnalysis was based on descriptions supplied by the national correspondentsinvolved in USEWOOD COST Action (FP1001), and further enriched with additionaldata from international reports that allowedcharacterisation of the forests in these countries for the same base year.ResultsMethods currently used for projecting wood availability were described for 21 European countries. Projection systems based on National Forest Inventory (NFI) data prevail over methods based on forest management plans. Only a few countries lack nationwide projection tools, still using tools developed for specific areas.ConclusionsA wide range of NFI-based systems for projecting wood availability exists, being under permanent improvement. The validation of projection forecasts and the inclusion of climate sensitive growth models into these tools are common aims for most countries. Cooperation among countries would result in higher efficiency when developing and improving projection tools and better comparability among them.

Quality control procedures in the Italian national forest inventory

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.

Potential Future Ranges of Tree Species in the Alps

Climate is one of the major drivers of plant and tree distribution, while soil variables or interspecific competition often are considered to primarily drive their local abundance. While the later is partly debated (Meier et al. 2010, 2011, 2012), the climate constraint to species ranges is generally accepted (Woodward 1987). The debate on climate change impacts on biodiversity and ecosystems is therefore specifically relevant to long-lived plants such as trees or shrubs, as these take many years to reach maturity and fecundity, and given their sessile growth strategy they are specifically vulnerable to rapid changes in climatic conditions. Also, forest management typically encompasses many decades, partly even reaching to the end of the 21st century, which highlights the challenge to manage such organisms successfully at such long planning horizons. This calls for careful and adaptive management strategies and for a good understanding of the uncertainties related to the expected changes and its impacts on trees and forest ecosystems.

Comparison of methods used in European National Forest Inventories for the estimation of volume increment: towards harmonisation

Key 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.

A stand-level model derived from National Forest Inventory data to predict periodic annual volume increment of forests in Italy

ABSTRACT A model was developed for predicting the periodic annual volume increment (PAI) of forests using variables commonly recorded through field surveys or the remote sensing. The model was developed using the Italian National Forest Inventory (INFC2005) data, publicly available at www.inventarioforestale.org. Data from 5707 plots were split into two groups. The first was used for fitting the model; the second was used for cross validation. Model reliability for applications at the local, in the Alpine and Mediterranean regions, and at the country level was tested. A sensitivity analysis was carried out to investigate the effects of entering inaccurate values of the number of trees per hectare, one of the predictors of the final model, that may occur in case of biased estimates from the remote sensing. During model calibration, the highest proportion of increment variation was captured using forest category (FC) as dummy variable and, in this respect, this study supports the classification of forests on ecological basis as a stratification criterion in environmental sampling. The model explained 72% of PAI and it predicted annual increment at plot level with no statistical difference to the observed value in any FC, at the country level.

Harmonisation of Data and Information on the Potential Supply of Wood Resources

The main objective of COST Action Usewood was to improve and harmonise data and information from National Forest Inventories (NFIs) on the potential supply of wood resources at European level considering the actual extent. This will improve the quality of data available to describe wood resources and possible wood uses. Such high quality information could be used for future scenario modelling and help inform political decision making for the renewable energy sector. The harmonisation of NFIs is based on reference definitions. Four different topics in terms of wood supply are covered and described in this chapter: forest available for wood supply, change estimation, stem quality, other wooded land and trees outside forest. These definitions will enable data comparison between countries and consequently, this will imply a transparent, comprehensive and robust information for decision-making.