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We examine the current status of greenhouse gas inventories of the sector Land Use, Land-Use Change and Forestry (LULUCF), in European countries, with specific focus on the utilization of National Forest Inventory (NFI) programs. LULUCF inventory is an integral part of the reporting obligations under the United Nations Framework Convention on Climate Change (UNFCCC) and its Kyoto Protocol. The analysis is based on two questionnaires prepared by the COST Action E43 “Harmonisation of National Forest Inventories in Europe”, which were answered by greenhouse gas reporting experts in European countries. The following major conclusions can be drawn from the analysis: 1) definitions used to obtain carbon pool change estimates vary widely among countries and are not directly comparable 2) NFIs play a key role for LULUCF greenhouse gas estimation and reporting under UNFCCC, and provide the fundamental data needed for the estimation of carbon stock changes covering not only living biomass, but increasingly also deadwood, litter and soil compartments. The study highlights the effects of adopting different definitions for two major reporting processes, namely UNFCCC and FAO, and exemplifies the effect of different tree diameter thresholds on carbon stock change estimates for Finland. The results demonstrate that more effort is needed to harmonize forest inventory estimates for the purpose of making the estimates of forest carbon pool changes comparable. This effort should lead to a better utilization of the data from the European NFI programs and improve the European greenhouse gas reporting.

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In Iceland, mountain birch dominates indigenous woodlands and scrub communities. For use in inventories of the natural birch population, we derived single parameter aboveground biomass functions from a stratified random sample encompassing the entire native birch population. We evaluated the accuracy of these models on independent data from the same population and used regressions of log-transformed predicted versus observed values and compared slope and intercept parameters against the 1:1 line. We propose that the level of accuracy of allometric models might be quantified by the size of Theil's random error component (U e) and the normality of residual variances might be a decisive test of acceptable functions. The commonly used allometric power function without intercept proved highly accurate for diameters at ground level but was biased for diameters measured at 0.5 m up the stem. We compared both non-linear regressions and log-transformed linear regression techniques. The latter produced more accurate models especially for applications to small diameter trees. Power functions with intercept and diameters measured 0.5 m above ground produced accurate estimates, except for trees with diameters less than 50 mm. We suggest allometric models for general use in Iceland for inventories of native birch woodlands and scrub.