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.
•A forest growth model was calibrated for Scots pine, Norway spruce and Silver birch.•We assimilated into the model multiple source of data collected over decades.•Parametric and output uncertainties ...were reduced by means of Bayesian calibration.•Results showed how quality of data used for calibration affects model performance.•We identified model weaknesses and subroutines that need to be improved.
Policy-relevant forest models must be environment and management sensitive and provide unbiased estimates of predicted variables over their intended areas of application. While empirical models derive their structure and parameters from representative data sets, process-based model (PBM) parameters should be evaluated in ranges that have a biological meaning independently of output data. At the same time PBMs should be calibrated against observations in order to obtain unbiased estimates and an understanding of their predictive capability. By means of model data assimilation, we Bayesian calibrated a forest model (PREBAS) using an extensive dataset that covered a wide range of climatic conditions, species composition and management practices. PREBAS was calibrated for three species in Finland: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies L. H. Karst.) and Silver birch (Betula pendula L.). Data assimilation was strongly effective in reducing the uncertainty of PREBAS parameters and predictions. A country-generic calibration showed robust performances in predicting forest variables and the results were consistent with yield tables and national forest statistics. The posterior predictive uncertainty of the model was mainly influenced by the uncertainty of the structural and measurement error.
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•We compare 13 Tree Carbon estimation methods for 5 major Tree species in Europe.•We apply the methods on a consistent dataset to avoid biases due to input data.•Variation is large ...both for whole Europe as well as for European forest regions.•Possible reasons are Sample material, Biomass definition or Input variables.•These are important findings for reliable carbon stocks in Europe and worldwide.
National and international carbon reporting systems require information on carbon stocks of forests. For this purpose, terrestrial assessment systems such as forest inventory data in combination with carbon estimation methods are often used. In this study we analyze and compare terrestrial carbon estimation methods from 12 European countries. The country-specific methods are applied to five European tree species (Fagus sylvatica L., Quercus robur L., Betula pendula Roth, Picea abies (L.) Karst. and Pinus sylvestris L.), using a standardized theoretically-generated tree dataset. We avoid any bias due to data collection and/or sample design by using this approach. We are then able to demonstrate the conceptual differences in the resulting carbon estimates with regard to the applied country-specific method. In our study we analyze (i) allometric biomass functions, (ii) biomass expansion factors in combination with volume functions and (iii) a combination of both. The results of the analysis show discrepancies in the resulting estimates for total tree carbon and for single tree compartments across the countries analyzed of up to 140tcarbon/ha. After grouping the country-specific approaches by European Forest regions, the deviation within the results in each region is smaller but still remains. This indicates that part of the observed differences can be attributed to varying growing conditions and tree properties throughout Europe. However, the large remaining error is caused by differences in the conceptual approach, different tree allometry, the sample material used for developing the biomass estimation models and the definition of the tree compartments. These issues are currently not addressed and require consideration for reliable and consistent carbon estimates throughout Europe.
In this study we introduce and test a new simple approach for estimating annual stand-level gross primary production (GPP), net primary production (NPP) and stem biomass growth based on carbon ...acquisition and allocation, by combining existing summary models. The focus is on the variation of GPP and NPP across different parts of Finland caused by climate.
A summary model for estimating light-use efficiency is applied to calculate the annual GPP of a fully closed canopy (P0) for different sites as a function of climatic conditions. The site-specific P0s are first determined for five sites using long-term series of daily weather data from the Finnish Meteorological Institute (FMI), and these results are further generalised to all sites using effective temperature sum (ETS) as a predictor. Actual canopy GPP is calculated as a fraction of the estimated site-specific P0 using a summary model of the effect of shading on photosynthesis in non-homogeneous forests. Leaf area is estimated from measured tree data using the pipe model. NPP:GPP ratio is estimated on the stratum level from mean height. The allocation to stem growth is estimated from stand age and site type, using a summary equation based on results from earlier studies.
The method was tested against data from permanent sample plots of the Finnish National Forest Inventory (NFI) from years 1985 and 1995. The results indicate that the approach produces realistic short-term estimations for stem growth. At the stand level the model was nearly unbiased (2.1% underestimation), with RMSE of 34% and R2 of 0.52, and it provided a clearly better fit than a simple linear prediction of stem growth from the estimated GPP. More importantly, we showed in a model comparison that in the present data set our model provided results of similar accuracy as a well-established empirical tree-level growth model.
Leaf area index (LAI) is a key variable for many ecological models, but it is typically not available from basic forest inventories. In this study, we (1) construct a high-resolution LAI map using k ...nearest-neighbor (k-NN) imputation based on National Forest Inventory data and Landsat 5 TM images (Landsat-NFI LAI), and (2) examine a moderate-resolution LAI map produced based on reduced simple ratio derived from MODIS reflectances (MODIS-RSR LAI). The maps cover all the forested areas in Finland. Country-level averages of Landsat-NFI and MODIS-RSR LAI were at same level, but several geographical and land-use related differences between them were detected. Difference was the largest in the lake district of Finland and in northern Finland, and it increased with decreasing share of forests and increasing share of deciduous trees. As MODIS-RSR LAI does not take into account the sub-pixel variation in land use, Landsat-NFI LAI was found to produce more reliable estimates.
Tree-level and stand-level forest growth simulators and their combination were evaluated using data from a large network of permanent sample plots of the National Forest Inventory covering the whole ...of Southern Finland. The simulators were built up with the SIMO framework. The evaluation was carried out both at the stand-level and separately for Scots pine (Pinus sylvestris), Norway spruce (Picea abies), silver birch (Betula pendula) and white birch (Betula pubescens) strata within the plots. Effects of different factors, e.g. age, soil type, stand density and geographical location on the results were also analysed.
All the simulators provided relative RMSE of around 10% for stand-level tree diameter and height estimates, while the RMSE%s for basal area and the volume estimates were a little bit higher (22–25% and 24–30%, respectively). When estimating height and volume the least biased simulator was the tree-level simulator, while the diameter and basal area estimations were least biased with the combined simulator.
In general the examined simulators seem to work well in circumstances where natural mortality is low or does not exist, as happens to be the case in intensively managed commercial forests. Instead, in unmanaged, unhealthy or non-homogeneous forests the estimates are less reliable. Usually the models tend to underestimate the natural mortality, but with the combined simulator and the birch strata the mortality was also often highly overestimated. It seems necessary to make both the growth and mortality models more adaptable to varying conditions in the future. Also the models for the deciduous trees require improvement.
The effects of silviculture on wood and tracheid properties, and economic returns of Norway spruce (
Picea abies (L.) Karst.) were investigated in two case studies, one comparing different thinning ...intensity in southeastern Finland and the other considering the effects of optimal nutrient addition in northern Sweden. Models for predicting the wood and tracheid properties of Norway spruce were integrated into a distance-independent process-based growth and yield model. Increasing the thinning intensity resulted in a lower mean wood density, tracheid length, and latewood proportion in harvested wood. Wood density and tracheid length of harvested pulpwood slightly decreased in later thinnings and final cuts. Thinning regimes with high early growing stock and decreasing later growing stock were most profitable. Nutrient addition accelerated volume growth and increased the value growth. The increase in volume growth due to nutrient addition more than offset the economic influence of the loss in wood density and tracheid length.
Increasing use of airborne light detection and ranging (LiDAR) in forest inventories offers new possibilities to apply process-based forest models (PBM) in practice. We present a new approach, where ...a simplified PBM is run using inputs derived from the LiDAR data. The PBM was built by combining several existing models together, and it was tested with 52 Scots pine (Pinus sylvestris L.) dominated sample plots in Finland with the LiDAR (PBM_LIDAR) and field (PMB_FIELD) inputs. The results were compared with empirical growth predictions (EM_FIELD) and field reference growth. LiDAR-based stand variables (mean height of tree and crown base and leaf area index) were, on average, well in line with the field measurements. Basal area growth was slightly underestimated with the PBM_LIDAR (bias 4.1%, root mean square prediction error (RMSPE, 26.7%) and overestimated with the PBM_FIELD (bias â10.2%, RMSPE 33.3%), the EM_FIELD being the least biased (bias â1.9%, RMSPE of 24.6%). The bias varied with stand age and fertility. The dependence on field reference growth was highest with EM_FIELD and PBM_LIDAR (R² = 0.47 and 0.34, respectively), and lowest with PBM_FIELD (R² = 0.18). Despite several development needs, the approach is promising for easy incorporation of canopy and weather data into forest growth predictions.
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