## 5 Discussion
The stand density pattern is also consistent when plotting only the results for each of the major vegetation or biome types represented by the MODIS land cover types (Figure 4). In this study, regression analysis and exponential trend curves are used to quantify the stand density related trend similar to [PERSON] _et al._[3]. The resulting regression curves fitted using _SDI_ and _CCF_ in Equations (15) and (16) exhibit a consistent trend, no matter whether fitted by biome type or for all available plots. No clustering effect by biome type is evident, suggesting that one density-driven correction function adjusts for the bias in the NPP predictions across scales (Table 2, Figure 5). Both competition measures (_SDI_ and _CCF_) show a very similar behavior (see Figures 3-5). However, for this dataset, _SDI_ gives a slightly higher coefficient of determination (Table 2) and seemsto better address the recorded competition that exists within Austrian National Forest inventory plots. As shown in Figures 6 and 7, ignoring stand density effects will cause overestimation by MODIS NPP estimates by ecoregions and dominant tree species (see Figure 6), elevation and mean stand age (see Figure 7). Applying the correction function leads to a substantial improvement in the consistency of the different NPP results, and consistent and unbiased results across multiple scales can be expected. The fact that terrestrial NPP overestimates MODIS NPP at high stand densities (_SDI_\(>\) 1000 or _CCF_\(>\) 500) can be explained with the variable \"plot size\" of the inventory plots according to the breast height diameter of the trees on a given plot [20]. Thus, the terrestrial NPP estimates based on angle count sample data may have a large random variation and this differs substantially from the terrestrial plot data for deriving NPP as outlined in [PERSON] _et al._[3] who used fixed area research plots with an area of 2500 m\({}^{2}\). [PERSON] _et al._[2] also observed differences between MODIS and terrestrial NPP. They hypothesized that soil water constraints may be a limiting factor and proposed an \"available soil water index\" for adjusting MODIS NPP computations compared to terrestrial data. Our results of growth conditions in Austrian forests, however, suggest that stand density explains the observed discrepancy as the results grouped by water balance index show a systematic overestimation of terrestrial NPP by MODIS NPP (Figure 8). Correcting for stand density results in a better agreement of MODIS and terrestrial NPP across different \"available soil water indices\". One important concern of our study was if large-scale disturbances like bark beetle outbreaks or windthrow [53, 54] may have had an impact on our productivity results by method. Disturbances reduce the stand density of forests in a similar manner to management. In our available data from the Austrian Forest inventory, disturbances can be detected with a recorded variable explaining the reason for sample tree death or removal [17]. There are 484 plots (5% of all plots) where, between 2007 and 2009, at least one tree with \"random removal\" was recorded and, thus, were affected by the disturbances between 2000 and 2002 and between 2007 and 2009. These plots have a median NPP of 464.7 gC/m\({}^{2}\)/year, which is only slightly lower than the median NPP of all 8939 plots (486.3 gC/m\({}^{2}\)/year). While disturbances might have big impacts on small scales like forest stands or single inventory plots, they only have a limited and insignificant effect as compared to the forest productivity of Austria.