Mathias Neumann

Climate variability drives recent tree mortality in Europe

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.

Creating a regional MODIS satellite-driven net primary production dataset for european forests

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.

Comparing MODIS Net Primary Production Estimates with Terrestrial National Forest Inventory Data in Austria

The mission of this study is to compare Net Primary Productivity (NPP) estimates using (i) forest inventory data and (ii) spatio-temporally continuous MODIS (MODerate resolution Imaging Spectroradiometer) remote sensing data for Austria. While forest inventories assess the change in forest growth based on repeated individual tree measurements (DBH, height etc.), the MODIS NPP estimates are based on ecophysiological processes such as photosynthesis, respiration and carbon allocation. We obtained repeated national forest inventory data from Austria, calculated a “ground-based” NPP estimate and compared the results with “space-based” MODIS NPP estimates using different daily climate data. The MODIS NPP estimates using local Austrian climate data exhibited better compliance with the forest inventory driven NPP estimates than the MODIS NPP predictions using global climate data sets. Stand density plays a key role in addressing the differences between MODIS driven NPP estimates versus terrestrial driven inventory NPP estimates. After addressing stand density, both results are comparable across different scales. As forest management changes stand density, these findings suggest that management issues are important in understanding the observed discrepancies between MODIS and terrestrial NPP.

Invasive alien pests threaten the carbon stored in Europe’s forests

Forests mitigate climate change by sequestering large amounts of carbon (C). However, forest C storage is not permanent, and large pulses of tree mortality can thwart climate mitigation efforts. Forest pests are increasingly redistributed around the globe. Yet, the potential future impact of invasive alien pests on the forest C cycle remains uncertain. Here we show that large parts of Europe could be invaded by five detrimental alien pests already under current climate. Climate change increases the potential range of alien pests particularly in Northern and Eastern Europe. We estimate the live C at risk from a potential future invasion as 1027 Tg C (10% of the European total), with a C recovery time of 34 years. We show that the impact of introduced pests could be as severe as the current natural disturbance regime in Europe, calling for increased efforts to halt the introduction and spread of invasive alien species.Invasive alien pests can cause large-scale forest mortality and release carbon stored in forests. Here the authors show that climate change increases the potential range of alien pests and that their impact on the carbon cycle could be as severe as the current natural disturbance regime in Europe’s forests.

Quantifying Carbon and Nutrient Input From Litterfall in European Forests Using Field Observations and Modeling

Litterfall is a major, yet poorly studied, process within forest ecosystems globally. It is important for carbon dynamics, edaphic communities, and maintaining site fertility. Reliable information on the carbon and nutrient input from litterfall, provided by litter traps, is relevant to a wide audience including policymakers and soil scientists. We used litterfall observations of 320 plots from the pan-European forest monitoring network of the “International Co-operative Programme on Assessment and Monitoring of AirPollution Effects on Forests” to quantify litterfallfluxes. Eight litterfall models were evaluated (four using climate information and four using biomass abundance). We scaled up our results to the total European forestarea and quantified the contribution of litterfall to the forest carbon cycle using net primary production aggregated by bioregions (north, central, and south) and by forest types (conifers and broadleaves). The 1,604 analyzed annual litterfall observations indicated an average carbon input of 224 g C · m2· year 1 (annual nutrient inputs 4.49 g N, 0.32 g P, and 1.05 g K · m2), representing a substantial percentage of net primary production from 36% in north Europe to 32% in central Europe. The annual turnover of carbon and nutrient in broadleaf canopies was larger than for conifers. The evaluated models provide large-scale litterfall predictions with a bias less than 10%. Each year litterfall in European forests transfers 351 Tg C, 8.2 Tg N,0.6 Tg P, and 1.9 Tg K to the forestfloor. The performance of litterfall models may be improved by including foliage biomass and proxies for forest management.

EFO-LCI: A New Life Cycle Inventory Database of Forestry Operations in Europe

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.

Assessment of MODIS NPP algorithm-based estimates using soil fertility and forest inventory data in mixed hemiboreal forests

Abstract Optical remote sensing data-based estimates of terrestrial net primary production (NPP) are released by different projects using light use efficiency-type models. Although spatial resolution of the NPP data sets is still too coarse (500–1000 m) for single forest stands, regional monitoring of forest management and growth with 25–100 ha sampling units is feasible if the NPPSAT estimates are sensitive to forest growth differences depending on soil fertility in the area of interest. In this study, NPP estimates for 2,914 mixed forest class pixels (according to the MODIS land cover map) located in Estonia were (1) obtained from three different NPPSAT products, (2) calculated using an empirical soil potential phytoproductivity (SPP) model applied to a 1:10,000 soil map (NPPSPP), and (3) calculated using stem volume increment estimates given in a forest management inventory data base (NPPFIDB). A linear multiple regression model was then used to explore the relationships of NPPSAT with the proportion of coniferous forests, the NPPSPP and distance of the pixels from the Baltic Sea coast – the variables that have been found informative in previous studies. We found a positive moderate correlation (0.57, p < 0.001) between NPPSPP and NPPFIDB. The local or downscaled meteorological data-based NPPSAT estimates were more consistent with the NPPSPP and NPPFIDB, but the correlation with NPPSAT was weak and sometimes even negative. The range of NPP estimates in NPPSAT data sets was much narrower than the range of NPPSPP or NPPFIDB. Errors in land cover maps and in estimates of absorbed photosynthetically active radiation were identified as the main reasons for NPPSAT inconsistencies.

Estimation of above-ground biomass in forest stands from regression on their basal area and height

Abstract A generic regression model for above-ground biomass of forest stands was constructed based on published data (R2 = 0.88, RSE = 32.8 t/ha). The model was used 1) to verify two allometric regression models of trees from Scandinavia applied to repeated measurements of 275 sample plots from database of Estonian Network of Forest Research (FGN) in Estonia, 2) to analyse impact of between-tree competition on biomass, and 3) compare biomass estimates made with different European biomass models applied on standardized forest structures. The model was verified with biomass measurements from hemiboreal and tropical forests. The analysis of two Scandinavian models showed that older allometric regression models may give biased estimates due to changed growth conditions. More biomass can be stored in forest stands where competition between trees is stronger. The tree biomass calculation methods used in different countries have also substantial influence on the estimates at stand-level. A common database of forest biomass measurements from Europe in similar to pan-tropical tree measurement data may be helpful to harmonise carbon accounting methods.