Forest Research Institute Baden Wurttemberg

Freiburg, Germany

Forest Research Institute Baden Wurttemberg

Freiburg, Germany
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Makinen H.,Aalto University | Yue C.,Forest Research Institute Baden Wurttemberg | Kohnle U.,Forest Research Institute Baden Wurttemberg
Agricultural and Forest Meteorology | Year: 2017

The annual growth of the Finnish forests has doubled in less than a century but the reasons for the increase have been a subject of much debate. Site index aggregates the mid- to long-term impacts of variable edaphic and climatic factors on site productivity and is an important characteristic with regard to the ongoing environmental changes. We quantified changes of site index in Finland over time by applying a new approach developed in our previous study in Germany. A large data base for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and larch (Larix spp.) was available from the network of long-term growth and yield experiments in southern and central Finland. For all tree species, site indices fluctuated over time, i.e. they slightly decreased in the 1970s, followed by an increasing trend in the 1980s and a decline again in the 1990s. Apparently, the only significant change was a change point indicated for Scots pine around the year 1944; however, indication was weak due to notable limitations of the database during the respective period. Interestingly, the result of this study in Finland differs from central Europe, where Norway spruce site productivity has displayed a considerable increase since the 1950s. Potentially, the low nitrogen deposition in Finland may be of major importance accounting for the divergent changes in site productivity between Finland and central Europe. Thus, the results indicate that a large share of the growth increase in the Finnish forests might actually be due to altered management. © 2017 Elsevier B.V.


Latifi H.,Albert Ludwigs University of Freiburg | Nothdurft A.,Forest Research Institute Baden Wurttemberg | Koch B.,Albert Ludwigs University of Freiburg
Forestry | Year: 2010

In a mixed temperate forest landscape in southwestern Germany, multiple remote sensing variables from aerial orthoimages, Thematic Mapper data and small footprint light detection and ranging (LiDAR) were used for plot-level nonparametric predictions of the total volume and biomass using three distance measures of Euclidean, Mahalanobis and Most Similar Neighbour as well as a regression tree-based classifier (Random Forest). The performances of nearest neighbour (NN) approaches were examined by means of relative bias and root mean squared error. The original high-dimensional dataset was pruned using an evolutionary genetic algorithm search with a NN classification scenario, as well as by a stepwise selection. The genetic algorithm (GA)-selected variables showed improved performance when applying Euclidean and Mahalanobis distances for predictions, whereas the Most Similar Neighbour and Random Forests worked more precise with the full dataset. The GA search proved to be unstable in multiple runs because of intercorrelations among the high-dimensional predictors. The selected datasets are dominated by LiDAR height metrics. Furthermore, The LiDAR-based metrics showed major relevance in predicting both response variables examined here. The Random Forest proved to be superior to the other examined NN methods, which was eventually used for a wall-to-wall mapping of predictions on a grid of 20 × 20 m spatial resolution. © 2010 Institute of Chartered Foresters. All rights reserved.


Puhlmann H.,Albert Ludwigs University of Freiburg | von Wilpert K.,Forest Research Institute Baden Wurttemberg
Journal of Plant Nutrition and Soil Science | Year: 2012

The hydraulic properties of soils, i.e., their ability to store and conduct water, mainly govern the availability of soil water for plants. Information on the hydraulic properties is needed, e.g., for the quantification of drought risk at a given site. Furthermore, knowledge of the water transport is the precondition for the estimation of element fluxes in the soil, e.g., when predicting element leaching from the root zone to the groundwater. For forest soils, only few systematic investigations of their hydraulic properties exist. Within the 2nd forest-soil survey of Germany, soil samples were taken along a regular 8 km × 8 km grid in the forests of the State of Baden-Württemberg and the hydraulic properties were estimated in the laboratory by multistep outflow experiments. Besides the soil-hydraulic measurements, numerous additional soil chemical and physical analyses were carried out and comprehensive profile descriptions were compiled and integrated in a hydraulic database. Based on this database, multiple-linear-regression techniques were used to develop pedotransfer functions for the water-retention curve and the unsaturated-hydraulic-conductivity curve using the parametric models of Mualem/van-Genuchten. Our work fills a gap since to our knowledge, no pedotransfer functions for the unsaturated hydraulic conductivity for forest soils exist so far. The predictive accuracy of the established pedotransfer functions, both for the water-retention curve and the hydraulic-conductivity curve, is in the range of (and in some cases better than) other published pedotransfer functions that were mostly derived for agricultural soils. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Albrecht A.,Forest Research Institute Baden Wurttemberg | Hanewinkel M.,Forest Research Institute Baden Wurttemberg | Bauhus J.,Albert Ludwigs University of Freiburg | Kohnle U.,Forest Research Institute Baden Wurttemberg
European Journal of Forest Research | Year: 2012

Storms represent the most important disturbance factor in forests of Central Europe. Using data from long-term growth and yield experiments in Baden-Wuerttemberg (south-western Germany), which permit separation of storm damage from other causes of mortality for individual trees, we investigated the influence of soil, site, forest stand, and tree parameters on storm damage, especially focusing on the influence of silvicultural interventions. For this purpose, a four-step modeling approach was applied in order to extract the main risk factors for (1) the general stand-level occurrence of storm damage, (2) the occurrence of total stand damage, and (3) partial storm damage within stands. The estimated stand-level probability of storm damage obtained in step 3 was then offset in order to describe the damage potential for the individual trees within each partially damaged stand (4). Generalized linear mixed models were applied. Our results indicate that tree species and stand height are the most important storm risk factors, also for characterizing the long-term storm risk. Additionally, data on past timber removals and selective thinnings appear more important for explaining storm damage predisposition than for example stand density, soil and site conditions or topographic variables. When quantified with a weighting method (summarizing the relative weight of single predictors or groups of predictors), removals could explain up to 20% of storm risk. The stepwise modeling approach proved an important methodological feature of the analysis, since it enabled consideration of the large number of observations without damage ("zero inflation") in a statistically correct way. These results form a reliable basis for quantifying forest management's direct impact on the risk of storm damage. © 2010 Springer-Verlag.


Nothdurft A.,Forest Research Institute Baden Wurttemberg | Wolf T.,Forest Research Institute Baden Wurttemberg | Ringeler A.,University of Hamburg | Bohner J.,University of Hamburg | Saborowski J.,University of Gottingen
Forest Ecology and Management | Year: 2012

A methodological framework is provided for the quantification of climate change effects on site index. Spatio-temporal predictions of site index are derived for six major tree species in the German state of Baden-Württemberg using simplified universal kriging (UK) based on large data sets from forest inventories and a climate sensitive site-index model. It is shown by a simulation study that, with the underlying large sample size, residual kriging using ordinary least squares (OLS) estimates of the mean function leads to an approximately unbiased spatial predictor. Moreover, the simulated coverage probabilities of resulting prediction intervals are quite close to the required level. B-spline regression techniques are applied to model nonlinear cause-and-effect curves for estimating site indexes at existing inventory plots dependent on retrospective climate covariates. The spatially structured error is modeled by exponential covariance functions. The mean model is then applied to downscaled climate projection data to spatially predict the relative changes of site index under perturbed climate conditions.Applying climate projections of an existing regional climate model based on IPCC emission scenarios A1B and A2, it is found that site index of all tree species would be decreased in lowland areas, and may increase in mountainous regions. Silver fir and common oak stands would also show increased site indexes in mountainous regions, but further extended to lower elevation levels. Site conditions in the Alpine foothills may remain highly productive for growth of Norway spruce, Baden-Württemberg's most dominant tree species. Whereas site index of common beech and Douglas-fir may decrease to almost the same relative amount and on nearly the same sites as Norway spruce, site index of Scots pine may be less affected by future climate change. © 2012 Elsevier B.V.


Ritter T.,University of Gottingen | Nothdurft A.,Forest Research Institute Baden Wurttemberg | Saborowski J.,University of Gottingen
Canadian Journal of Forest Research | Year: 2013

The well-known angle count sampling (ACS) has proved to be an efficient sampling technique and has been applied in forest inventories for many decades. However, ACS assumes total visibility of objects; any violation of this assumption leads to a nondetection bias. We present a novel approach, in which the theory of distance sampling is adapted to traditional ACS to correct for the nondetection bias. Two new estimators were developed based on expanding design-based inclusion probabilities by model-based estimates of the detection probabilities. The new estimators were evaluated in a simulation study as well as in a real forest inventory. It is shown that the nondetection bias of the traditional estimator is up to -52.5%, whereas the new estimators are approximately unbiased.


Nothdurft A.,Forest Research Institute Baden Wurttemberg | Saborowski J.,University of Gottingen | Nuske R.S.,University of Gottingen | Stoyan D.,TU Bergakademie Freiberg
Canadian Journal of Forest Research | Year: 2010

In k-tree sampling, also referred to as point-to-tree distance sampling, the k nearest trees are measured. The problem associated with k-tree sampling is its lack of unbiased density estimators. The presented density estimator based on point pattern reconstruction remedies that shortcoming. It requires the coordinates of all k trees. These coordinates are translated into a simulation window where they remain unchanged. Empirical cumulative distribution functions of intertree and location-to-tree distances estimated from the sample plots are set as target characteristics. Using the idea of simulated annealing, an optimal new tree pattern is constructed in the simulation window outside the k-tree samples. The reconstruction of the point pattern minimizes the contrast between the empirical cumulative distribution functions and their analogs for the simulated pattern. The density estimator is simply the tree density of the optimum pattern in the simulation window. The performance of the reconstruction-based density estimator is assessed for k = 6 and k = 4 based on systematic sampling grids regarding its potential application in forest inventories. Simulations are carried out using real stem maps (covering different stand densities and different types of spatial point patterns, such as regular, clustered, and random) as well as completely random patterns. The new density estimator proves to be empirically superior in terms of bias and root mean squared error compared with commonly used estimators. The reconstruction-based density estimator has biases smaller than 2%.


Nothdurft A.,Forest Research Institute Baden Wurttemberg
Forest Ecology and Management | Year: 2013

An approach is presented to predict the effects climate change may have on mortality of forest trees. Mortality is modeled using long-term observations from the Pan-European Programme for Intensive and Continuous Monitoring of Forest Ecosystems plots, retrospective climate data and frailty models having a parametric baseline hazard function. The linear predictor is modeled by B-spline regression techniques to allow for nonlinear cause-and-effect curves. Spatio-temporal predictions of mortality of four major tree species in the German state of Baden-Württemberg were derived in terms of unconditional hazard ratios and based on climate projection data.According to the model, marginal risk of tree death for 100. year old Norway spruce trees will be doubled until 2100. Hazard rates of common beech will be halved in low elevation areas and will be reduced by 25% in higher elevations until 2100. Hazard rates of silver fir will be less affected by a changing climate and will increase by at least 25% and by a maximum of 100% in mountainous regions. Scots pines hazard rates will be halved on higher elevation sites and will increase on lower elevation sites. © 2012 Elsevier B.V.


Von Teuffel K.,Forest Research Institute Baden Wurttemberg
Folia Forestalia Polonica, Series A | Year: 2011

The presentation firstly describes the frame conditions in which forestry is presently acting in Central Europe. It is influenced by the experience of the author as director of the Baden-Württemberg Forest Research Institute (FVA) in Freiburg, Germany. In a second step emerging issues in forest research are listed, clearly dominated at present by the case of climate change. The core competences of forest research institutes as integral parts of public administration are described and special emphasis is put on the question how the agenda in defining research topics is set. Finally the most important present challenges in the management of forest research concerning cooperation with other institutions, personnel recruitment, funding and financing, organisation and quality management are discussed.


Bauerle H.,Forest Research Institute Baden Wurttemberg | Nothdurft A.,Forest Research Institute Baden Wurttemberg
Canadian Journal of Forest Research | Year: 2011

An approach is presented for the spatial modeling of rare habitat trees surveyed by line transect sampling (LTS) in a protected area of the European Natura 2000 network. The observed tree pattern is defined as a realization of a thinned point process where the thinning can be modeled by a parametric detection function. A complete pattern is reconstructed using an optimization algorithm. The start configuration contains detected tree locations and randomly generated tree positions. Empirical cumulative distribution functions (ECDFs) for intertree and location-to-tree distances estimated from the original LTS are set as target characteristics. The same ECDFs are estimated by means of virtual LTS in the reconstruction. Tree positions are relocated during the optimization. The sum of squared deviations between the ECDFs from the original LTS and the virtual LTS in the reconstruction is considered as a contrast measure. A new configuration is accepted if the contrast is lowered compared with the previous state. The nonparametrically reconstructed habitat tree patterns are described by a log Gaussian Cox process model. Evaluations by means of line transect resamplings in a complete habitat pattern show small deviations between the second-order functional characteristics obtained from the true pattern and their analogs derived from the reconstructions.

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