Saborowski, Joachim

[["dc.bibliographiccitation.journal","Tagung (Deutscher Verband Forstlicher Forschungsanstalten, Sektion Forstliche Biometrie und Informatik)"],["dc.contributor.author","Nothdurft, Arne"],["dc.contributor.author","Hradetzky, J."],["dc.contributor.author","Schöpfer, W."],["dc.contributor.author","Saborowski, Joachim"],["dc.contributor.author","Nuske, Robert S."],["dc.date.accessioned","2020-12-08T08:29:52Z"],["dc.date.available","2020-12-08T08:29:52Z"],["dc.date.issued","2009"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69472"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.title","Dichteschätzung für N-Baum-Stichproben durch Reproduktion von Baumartenverteilungsmustern"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","953"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Canadian Journal of Forest Research"],["dc.bibliographiccitation.lastpage","967"],["dc.bibliographiccitation.volume","40"],["dc.contributor.author","Nothdurft, Arne"],["dc.contributor.author","Saborowski, Joachim"],["dc.contributor.author","Nuske, Robert S."],["dc.contributor.author","Stoyan, Dietrich"],["dc.date.accessioned","2018-11-07T08:43:25Z"],["dc.date.available","2018-11-07T08:43:25Z"],["dc.date.issued","2010"],["dc.description.abstract","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%."],["dc.description.abstract","En chantillonnage de k arbres, aussi appel chantillonnage de k arbres selon leur distance, on mesure les k arbres les plus proches. Le problme li lchantillonnage de k arbres est son incapacit fournir des estimateurs de densit sans biais. Lestimateur de densit bas sur la reconstruction du patron des points comble cette lacune. Il requiert les coordonnes de tous les k arbres. Ces coordonnes sont traduites dans une fentre de simulation o elles demeurent inchanges. Les fonctions empiriques de distribution cumulative de distances entre les arbres et entre un point et les arbres estimes partir des placettes chantillons sont les caractristiques cibles. En utilisant le recuit simul, un nouveau patron optimal des arbres est construit dans la fentre de simulation en dehors des k arbres chantillons. La reconstruction du patron de points minimise le contraste entre les fonctions empiriques et leurs analogues drivs du patron simul. Lestimateur de densit est tout simplement la densit des arbres de la structure optimale dans la fentre de simulation. La performance de lestimateur de densit bas sur la reconstruction est value pour k = 6 et k = 4 sur la base des grilles dchantillonnage systmatique quant son application potentielle dans les inventaires forestiers. Des simulations sont effectues en utilisant les cartes relles des tiges (couvrant diffrentes densits de peuplement et diffrents types de patrons spatiaux de points, tels que rgulier, en grappe et alatoire) aussi bien que des patrons compltement alatoires. Le nouvel estimateur de densit savre empiriquement suprieur en termes de biais et derreur quadratique moyenne par rapport aux estimateurs frquemment utiliss. Son biais est infrieur 2%."],["dc.identifier.doi","10.1139/X10-046"],["dc.identifier.isi","000277602400011"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19960"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","0045-5067"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.title","Density estimation based on k-tree sampling and point pattern reconstruction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","344"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Canadian Journal of Forest Research"],["dc.bibliographiccitation.lastpage","354"],["dc.bibliographiccitation.volume","43"],["dc.contributor.author","Ritter, Tim"],["dc.contributor.author","Nothdurft, Arne"],["dc.contributor.author","Saborowski, Joachim"],["dc.date.accessioned","2018-11-07T09:26:26Z"],["dc.date.available","2018-11-07T09:26:26Z"],["dc.date.issued","2013"],["dc.description.abstract","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."],["dc.identifier.doi","10.1139/cjfr-2012-0408"],["dc.identifier.isi","000318024900004"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30296"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","0045-5067"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.title","Correcting the nondetection bias of angle count sampling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1-2"],["dc.bibliographiccitation.journal","ALLGEMEINE FORST UND JAGDZEITUNG"],["dc.bibliographiccitation.lastpage","13"],["dc.bibliographiccitation.volume","187"],["dc.contributor.author","Schoneberg, Sebastian"],["dc.contributor.author","Nothdurft, Arne"],["dc.contributor.author","Nuske, Robert S."],["dc.contributor.author","Ackermann, Joerg"],["dc.contributor.author","Saborowski, Joachim"],["dc.date.accessioned","2018-11-07T10:20:29Z"],["dc.date.available","2018-11-07T10:20:29Z"],["dc.date.issued","2016"],["dc.description.abstract","Practical forest management requires information on dendrometrical forest parameters in a high spatial resolution, particularly interesting is the timber volume. Nearest neighbor techniques and the random forest approach were employed in this study to predict timber volume per hectare (total stem volume and stem volume of large beech trees, DBH >= 60 cm) at forest stand level. The predictions were based on sample plot data from a regional forest inventory and selected sets of auxiliary variables derived from two different remote sensing data sources airborne laser scanning (ALS) data and aerial stereo images (ASI) to quantify and compare prediction precision. Existing studies conclude that ALS data provide more precise height information, but also that acquisition of ALS data is more expensive than of ASI data, which are often already available from other monitoring projects. Currently the cost of ASI data is about a half to a third of ALS data. To make spatial predictions we compared two frequently used methods for imputation: random forest and k-most similar neighbors. For both methods, the prediction precisions (RMSE) were similar. Most promising was the fact that the two different sources of auxiliary variables resulted in predictions of almost the same precision. The similarity between ASI and ALS predictions suggest that ASI may serve as a lower-cost alternative to ALS data for estimating many forest stand-level variables."],["dc.identifier.isi","000375351700001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/41899"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","J D Sauerlaenders Verlag"],["dc.relation.issn","0002-5852"],["dc.title","Comparison of stand volume predictions based on airborne laser scanning data versus aerial stereo images"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["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"]]