Saborowski, Joachim

[["dc.bibliographiccitation.firstpage","241"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","European Journal of Forest Research"],["dc.bibliographiccitation.lastpage","251"],["dc.bibliographiccitation.volume","128"],["dc.contributor.author","Nothdurft, Arne"],["dc.contributor.author","Saborowski, Joachim"],["dc.contributor.author","Breidenbach, Johannes"],["dc.date.accessioned","2018-11-07T08:30:14Z"],["dc.date.available","2018-11-07T08:30:14Z"],["dc.date.issued","2009"],["dc.description.abstract","This study aims at the development of a model to predict forest stand variables in management units (stands) from sample plot inventory data. For this purpose we apply a non-parametric most similar neighbour (MSN) approach. The study area is the municipal forest of Waldkirch, 13 km north-east of Freiburg, Germany, which comprises 328 forest stands and 834 sample plots. Low-resolution laser scanning data, classification variables as well rough estimations from the forest management planning serve as auxiliary variables. In order to avoid common problems of k-NN-approaches caused by asymmetry at the boundaries of the regression spaces and distorted distributions, forest stands are tessellated into subunits with an area approximately equivalent to an inventory sample plot. For each subunit only the one nearest neighbour is consulted. Predictions for target variables in stands are obtained by averaging the predictions for all subunits. After formulating a random parameter model with variance components, we calibrate the prior predictions by means of sample plot data within the forest stands via BLUPs (best linear unbiased predictors). Based on bootstrap simulations, prediction errors for most management units finally prove to be smaller than the design-based sampling error of the mean. The calibration approach shows superiority compared with pure non-parametric MSN predictions."],["dc.identifier.doi","10.1007/s10342-009-0260-z"],["dc.identifier.isi","000264945100004"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/3570"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/16844"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1612-4677"],["dc.relation.issn","1612-4669"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.gro","BLUP"],["dc.subject.gro","Calibration method"],["dc.subject.gro","Forest inventory"],["dc.subject.gro","Imputation"],["dc.subject.gro","Laser data"],["dc.subject.gro","Lidar"],["dc.subject.gro","k-NN"],["dc.title","Spatial prediction of forest stand variables"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","97"],["dc.bibliographiccitation.journal","Forest Ecology and Management"],["dc.bibliographiccitation.lastpage","111"],["dc.bibliographiccitation.volume","279"],["dc.contributor.author","Nothdurft, Arne"],["dc.contributor.author","Wolf, Thilo"],["dc.contributor.author","Ringeler, Andre"],["dc.contributor.author","Boehner, Juergen"],["dc.contributor.author","Saborowski, Joachim"],["dc.date.accessioned","2018-11-07T09:06:57Z"],["dc.date.available","2018-11-07T09:06:57Z"],["dc.date.issued","2012"],["dc.description.abstract","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-Wurttemberg 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-Wurttemberg'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. (C) 2012 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.foreco.2012.05.018"],["dc.identifier.isi","000307092900011"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25676"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","0378-1127"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.subject.gro","Climate change"],["dc.subject.gro","Forest inventory"],["dc.subject.gro","Site-index prediction"],["dc.title","Spatio-temporal prediction of site index based on forest inventories and climate change scenarios"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]