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Annighofer P.,University of Gottingen | Ammer C.,University of Gottingen | Balandier P.,IRSTEA | Bartsch N.,University of Gottingen | And 32 more authors.
European Journal of Forest Research | Year: 2016

Biomass equations are a helpful tool to estimate the tree and stand biomass production and standing stock. Such estimations are of great interest for science but also of great importance for global reports on the carbon cycle and the global climate system. Even though there are various collections and generic meta-analyses available with biomass equations for mature trees, reports on biomass equations for juvenile trees (seedlings and saplings) are mainly missing. Against the background of an increasing amount of reforestation and afforestation projects and forests in young successional stages, such equations are required. In this study we have collected data from various studies on the aboveground woody biomass of 19 common tree species growing in Europe. The aim of this paper was to calculate species-specific biomass equations for the aboveground woody biomass of single trees in dependence of root-collar-diameter (RCD), height (H) and the combination of the two (RCD2 H). Next to calculating species-specific biomass equations for the species available in the dataset, we also calculated generic biomass equations for all broadleaved species and all conifer species. The biomass equations should be a contribution to the pool of published biomass equations, whereas the novelty is here that the equations were exclusively derived for young trees. © 2016 Springer-Verlag Berlin Heidelberg


Morgenstern Y.,FVA Baden Wurttemberg | Puhlmann H.,Albert Ludwigs University of Freiburg | von Wilpert K.,FVA Baden Wurttemberg
Waldokologie Online | Year: 2011

The measuring concept of randomised moving plots (RMP) is applied in four forest areas in Baden-Württemberg (southern Germany) to quantify the temporal and spatial variability of soil moisture and the parameters that influence it. Our investigations aim at (i) collecting data for the evaluation of a physically based, distributed water transport model, (ii) identifying influencing parameters and (iii) developing a transfer model which describes soil moisture as a function of time-constant but spatially variable site parameters on the one hand (e. g., terrain attributes, soil texture, forest stand structure) and time-dependent but spatially invariant global variables (e. g., air temperature, catchment runoff) on the other. The spatio-temporal variability of soil moisture (0-20 cm mineralsoil depth) was measured simultaneously at 31 positions over a period of 14 days. Measuring positions were changed randomly every 14 days. The global variables were continuously measured at one or two positions within each investigation area. The site parameters weremeasured or described for each of the measuring positions. Using multivariate statistics methods, such as cluster analysis or classification and regression trees, the site parameters which influence the soil moisture dynamics were identified. This paper presents first results and discusses one landscape section in more detail.


Many studies in soil science provide qualitative or (semi-) quantitative assessmentsof soil physical properties such as soil texture or percentage of soil skeleton (the >2 mm fraction). In this paper, we describe the process of upscaling soil physical properties measured during the second Forest Soil Monitoring Census (BZE II). In order to enhance the data basis for process-oriented hydrology models at the landscape level, the use ofupscaling techniques based on point-related monitoring data is essential. The statisticalmethods used in this work included ordinary least square regression (OLS) and geostatistics. One aim of this study was to evaluate how the different spatial scales used for stratifying statistical approaches affect the quality of spatial estimates. When applied tosoil physical properties, our evaluations showed that, by using a stratified modeling approach, the accuracy of the estimates could be improved compared to global modeling approaches. Thus the regression models displayed comparatively high coefficients of determination ranging from 0,59 to 0,7 (for soil skeleton), 0,52 to 0,65 (bulk density), 0,7 (depth of soil development) and 0,66 to 0,8 (soil texture). Only in the case of the response variable fine root density were the coefficients of determination markedly below 0,5 (0,2-0,4).One of the reasons for this could be the small-scale variation in silvicultural site conditions such as tree species distribution or stand density.


The hydraulic properties of soils, i. e., their capability to store and to conduct water, largely regulate the availability of soil water for plants and the risk of water shortage in forests. To date, only a few systematic surveys on the hydraulic properties of forest soils have been done. We conducted multi-step outflow experiments to derive data on soil water retention and unsaturated hydraulic conductivity for 1504 undisturbed samples from forest soils in Baden-Württemberg. Using complementary measurements (fractions of fine and coarse soil, bulk density, organic carbon content), pedotransfer functions were developed for the prediction of the Mualem/van Genuchten model parameters. The predictions of the new pedotransfer functions were compared with various pedotransfer functions from the literature. An advantage of the new pedotransfer functions is that they provide an unbiased estimate of the hydraulic properties of the forest soils in Baden-Württemberg, and their predictive ability is comparable to those of published pedotransfer functions.


Cullmann A.D.,FVA Baden Wurttemberg | Saborowski J.,University of Gottingen
Environmetrics | Year: 2010

For prediction in a Gaussian random field, we give an explicit formulation of the conditional mean-squared prediction error (cmspe). If the prediction method is ordinary kriging, we find that this error in most applications is likely to be very close to the ordinary kriging variance. This is additionally demonstrated based on a case study. Finally, we discuss the difference between these two errors compared to the error introduced by using estimated instead of true covariance parameters. © 2009 John Wiley & Sons, Ltd.

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