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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%. Source

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. Source

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. Source

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. Source

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. Source

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