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Life cycle assessment (LCA) has become a common methodology to analyze environmental impacts of forestry systems. Although LCA has been widely applied to forestry since the 90s, the LCAs are still often based on generic Life Cycle Inventory (LCI). With the purpose of improving LCA practices in the forestry sector, we developed a European Life Cycle Inventory of Forestry Operations (EFO-LCI) and analyzed the available information to check if within the European forestry sector national differences really exist. We classified the European forests on the basis of “Forest Units” (combinations of tree species and silvicultural practices). For each Forest Unit, we constructed the LCI of their forest management practices on the basis of a questionnaire filled out by national silvicultural experts. We analyzed the data reported to evaluate how they vary over Europe and how they affect LCA results and made freely available the inventory data collected for future use. The study shows important variability in rotation length, type of regeneration, amount and assortments of wood products harvested, and machinery used due to the differences in management practices. The existing variability on these activities sensibly affect LCA results of forestry practices and raw wood production. Although it is practically unfeasible to collect site-specific data for all the LCAs involving forest-based products, the use of less generic LCI data of forestry practice is desirable to improve the reliability of the studies. With the release of EFO-LCI we made a step toward the construction of regionalized LCI for the European forestry sector. [ABSTRACT FROM AUTHOR]

Tree mortality is an important process in forest ecosystems, frequently hypothesized to be highly climate sensitive. Yet, tree death remains one of the least understood processes of forest dynamics. Recently, changes in tree mortality have been observed in forests around the globe, which could profoundly affect ecosystem functioning and services provisioning to society. We describe continental-scale patterns of recent tree mortality from the only consistent pan-European forest monitoring network, identifying recent mortality hotspots in southern and northern Europe. Analyzing 925,462 annual observations of 235,895 trees between 2000 and 2012, we determine the influence of climate variability and tree age on interannual variation in tree mortality using Cox proportional hazard models. Warm summers as well as high seasonal variability in precipitation increased the likelihood of tree death. However, our data also suggest that reduced cold-induced mortality could compensate increased mortality related to peak temperatures in a warming climate. Besides climate variability, age was an important driver of tree mortality, with individual mortality probability decreasing with age over the first century of a trees life. A considerable portion of the observed variation in tree mortality could be explained by satellite-derived net primary productivity, suggesting that widely available remote sensing products can be used as an early warning indicator of widespread tree mortality. Our findings advance the understanding of patterns of large-scale tree mortality by demonstrating the influence of seasonal and diurnal climate variation, and highlight the potential of state-of-the-art remote sensing to anticipate an increased likelihood of tree mortality in space and time. [ABSTRACT FROM AUTHOR]

Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO. [ABSTRACT FROM AUTHOR]

Background: At the 15th Conference of Parties of the UN Framework Convention on Climate Change, Copenhagen, 2009, harvested wood products were identified as an additional carbon pool. This modification eliminates inconsistencies in greenhouse gas reporting by recognizing the role of the forest and timber sector in the global carbon cycle. Any additional CO2-effects related to wood usage are not considered by this modification. This results in a downward bias when the contribution of the forest and timber sector to climate change mitigation is assessed. The following article analyses the overall contribution to climate protection made by the forest management and wood utilization through CO2-emissions reduction using an example from the German state of North Rhine-Westphalia. Based on long term study periods (2011 to 2050 and 2100, respectively). Various alternative scenarios for forest management and wood usage are presented. Results: In the mid- to long-term (2050 and 2100, respectively) the net climate protection function of scenarios with varying levels of wood usage is higher than in scenarios without any wood usage. This is not observed for all scenarios on short and mid term evaluations. The advantages of wood usage are evident although the simulations resulted in high values for forest storage in the C pools. Even the carbon sink effect due to temporal accumulation of deadwood during the period from 2011 to 2100 is out balanced by the potential of wood usage effects. Conclusions: A full assessment of the CO2-effects of the forest management requires an assessment of the forest supplemented with an assessment of the effects of wood usage. CO2-emission reductions through both fuel and material substitution as well as CO2 sink in wood products need to be considered. An integrated assessment of the climate protection function based on the analysis of the study's scenarios provides decision parameters for a strategic approach to climate protection with regard to forest management and wood use at regional and national levels. The short-term evaluation of subsystems can be misleading, rendering long-term evaluations (until 2100, or even longer) more effective. This is also consistent with the inherently long-term perspective of forest management decisions and measures. [ABSTRACT FROM AUTHOR]

The paper is written jointly by two parties, computer scientists specializing in geovisualization and experts in forestry, who cooperated within a joint project. The authors tell a story about an attempt of the geovisualizers to introduce the foresters to the concept and principles of exploratory data analysis and to the use of visualization for systematic and comprehensive data exploration. This endeavor should be considered as an informal experiment rather than a rigorous scientific study. Unlike customary tests of the usability of specific tools and techniques, the geovisualizers did not give the forestry specialists a series of tasks to carry out by applying geovisualization tools and did not try to measure how well the foresters performed. The idea of the geovisualizers was to demonstrate the principles and power of exploratory data analysis to the foresters by example. For this purpose, the geovisualizers performed an exploration of a non‐trivial data set by themselves and reported the procedure, the principles, the techniques, and the findings to the foresters. The reaction of the foresters uncovered a range of fundamental issues that are relevant to geovisualization and information visualization research. The authors analyze these issues from their perspectives and formulate a set of questions which researchers in geovisualization should be asking. [ABSTRACT FROM AUTHOR]

Since 1987 tree crown condition is surveyed annually in large parts of Europe mainly in terms of defoliation. The plot-wise means of defoliation from 1994 to 2000 were evaluated by General Linear Models (GLM) in order to describe country-specific levels of defoliation and age-trends. Additional runs with estimates for influences of insects and fungi were performed. The amount of variance of defoliation explained by country, age and its interaction was between 35% and 59% for the main tree species, except forQuercus ilex. Additionally, up to 10% could be explained by the inclusion of estimates for infestations by insects and fungi. Residuals of the GLMs were taken as a measure of forest condition not biased by country or age effects and interpreted as ‘preliminarily adjusted defoliation’ (PAD). PAD values were analysed using geostatistical methods. The modelled spatial autocorrelations were used for kriging. The resulting maps give an overview on regions with elevated defoliation, which may pinpoint regional causes of defoliation. The elimination of methodologically caused variance is a precondition of any cause-effect oriented analyses. The combination of explorative modelling and geostatistics will promote the choice of further promising predictors. [ABSTRACT FROM AUTHOR]