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.

Secondary succession that occurs on abandoned farmlands is an important source of biomass carbon stocks. Both direct and indirect tree biomass estimation methods are applied on forest lands. Using empirical data from 148 uprooted trees, we developed a seemingly unrelated mixed-effects models system for the young silver birch that grows on post agricultural lands in central Poland. Tree height, biomass of stem, branches, foliage, and roots are used as dependent variables; the diameter at breast height is used as the independent variable. During model elaboration we used restricted cubic spline: 5 knots at the quantiles (0.05, 0.275, 0.5, 0.725, and 0.95) of diameter at breast height provided sufficiently flexible curves for all biomass components. In this study, we demonstrate the use of the model system through cross-model calibration of the biomass component model using tree height measured from 0, 2, 3, and 4 available extreme trees feature in the plot in question. A different number of extreme trees were measured for final model system and our results indicated that for all analyzed components, random-effect predictions are characterized by higher accuracy than fixed-effects predictions.