Saborowski, Joachim

[["dc.bibliographiccitation.firstpage","13"],["dc.bibliographiccitation.journal","Forstarchiv"],["dc.bibliographiccitation.lastpage","23"],["dc.bibliographiccitation.volume","84"],["dc.contributor.author","Bauling, S."],["dc.contributor.author","Saborowski, J."],["dc.contributor.author","Rühe, F."],["dc.date.accessioned","2020-12-14T12:09:20Z"],["dc.date.available","2020-12-14T12:09:20Z"],["dc.date.issued","2013"],["dc.description.abstract","Die Bestandesschätzung von Wildwiederkäuern in Waldgebieten ist oft deshalb so unsicher, weil wesentliche, den verschiedenen Berechnungsmethoden zugrunde liegende Prämissen bei ihrer Anwendung nicht ausreichend eingehalten werden. Im Solling, Niedersachsen, herrschten über 14 Jahre Rahmenbedingungen für eine Schätzung der Größe des Rotwildbestandes, die sich im Vergleich zu vielen anderen Rotwildgebieten Deutschlands als besonders günstig erwiesen. Die dortige Rotwildpopulation wurde über mehrere Jahrzehnte durch ein Gatter isoliert, sodass eine Verzerrung der Schätzwerte der Bestandesdichten durch Migrationseffekte vernachlässigt werden konnte. Wie groß der Rotwildbestand im Solling damals war, war unsicher und bislang nicht wissenschaftlich untersucht worden. Ziel der Untersuchung war es, die jährliche Rotwilddichte der Jahre 1981 bis 1994 zu schätzen. Um die Spannweite zu ermitteln, innerhalb der die geschätzte Größe des Rotwildbestandes lag, haben wir die Population in 2 Varianten auf Grundlage der Age-at-Harvest-Methode rekonstruiert: (A) mit Überlebensraten, die an frei, in relativ harscher Umwelt ohne Winterfütterung lebenden Populationen ermittelt wurden und als Mindestüberlebensraten der Sollingpopulation angenommen wurden, und (B) wegen günstigerer Umweltbedingungen im Solling mit deutlich höheren Werten, die als maximale Überlebensraten des dortigen Rotwildbestandes betrachtet werden. Die auf Basis der Variante A geschätzte Rotwilddichte (± SDF) sank von 14,54 ± 1,53 Stück pro 100 ha im Jahr 1981 um 56,05 % auf 6,39 ± 0,79 Stück pro 100 ha im Jahr 1994 herab. Im Mittel von 1981 bis 1994 betrug sie 8,32 ± 0,78 Stück pro 100 ha. Die Variante B schätzte die Bestandesdichte im selben Zeitraum auf durchschnittlich 5,11 ± 0,16 Stück pro 100 ha und damit um mehr als ein Drittel niedriger als Variante A. Die Spannweite zwischen den Schätzwerten beider Varianten innerhalb eines jeden der 14 Jahre zeigt, dass die damalige Zieldichte der Rotwildbewirtschaftung, 3 bis 4 Stück pro 100 ha, in 12 von 14 Jahren augenscheinlich verfehlt und in den übrigen 2 Jahren (1989 und 1994) nur unter Annahme extrem niedriger natürlicher Sterblichkeit erreicht wurde. Zur natürlichen Sterblichkeit von Rotwild in Mitteleuropa besteht dringender Forschungsbedarf."],["dc.description.abstract","Estimating the abundance of wild ruminants in woodland is often uncertain because essential assumptions of the applied methods are not kept sufficiently. For over 14 years, the conditions for assessing the population size of red deer in the Solling, a low mountain range in Lower Saxony, Germany, proved to be particularly favourable if compared to other red deer management areas in Germany. The red deer population of the Solling lived, for many decades, in a leak-proof enclosure 25,000 ha in size. Thus, a bias of the estimates of abundance of this isolated population through migration effects could be neglected. How big the red deer population of the Solling was at that time was uncertain and has to date not been studied scientifically. The aim of this study was to assess the annual red deer population density of the years 1981 to 1994. To determine the range of the estimated size of the red deer population, the population was reconstructed in 2 models, both on the basis of the age-at-harvest method: (A) using survival rates of red deer populations, which lived in a harsh environment without winter feeding, they are understood as the minimal survival rates of the Solling red deer population, and (B) using considerably higher survival rates due to the more favourable environmental conditions in the Solling, they are understood as the maximal survival rates of the Solling population. The red deer population density (± SE) estimated with model A decreased from 14.54 ± 1.53 head per 100 ha in 1981 by 56.05% to 6.39 ± 0.79 head per 100 ha in 1994. From 1981 to 1994 it averaged 8.32 ± 0.78 head per 100 ha. For the same time period model B estimated the red deer population density to be 5.11 ± 0.16 head per 100 ha on average and thus by over a third lower of what was estimated by using model A. The span between the 2 density values of both variants shows that the then aim-density of red deer management, 3 to 4 head per 100 ha, was missed in 12 of 14 years and was only achieved in the remaining 2 years (1989 and 1994) by assuming an extremely low natural mortality. Research on natural mortality of central European red deer populations is urgently needed."],["dc.identifier.doi","10.4432/0300-4112-84-13"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/79051"],["dc.language.iso","de"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.relation.orgunit","Abteilung Wildtierwissenschaften"],["dc.subject.gro","Rothirsch"],["dc.subject.gro","Populationsrekonstruktion"],["dc.subject.gro","Populationsdichte"],["dc.subject.gro","Kohortenanalyse"],["dc.title","Schätzung der Rotwilddichte (Cervus elaphus L.) im Solling mit der Age-at-Harvest-Methode"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1052"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Canadian Journal of Forest Research"],["dc.bibliographiccitation.lastpage","1059"],["dc.bibliographiccitation.volume","49"],["dc.contributor.author","Fischer, Christoph"],["dc.contributor.author","Saborowski, Joachim"],["dc.date.accessioned","2020-12-08T07:51:44Z"],["dc.date.available","2020-12-08T07:51:44Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1139/cjfr-2018-0520"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69452"],["dc.language.iso","en"],["dc.relation.issn","0045-5067"],["dc.relation.issn","1208-6037"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.relation.orgunit","Abteilung Ökoinformatik, Biometrie und Waldwachstum"],["dc.title","Estimating volume growth from successive double sampling for stratification"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","107"],["dc.bibliographiccitation.journal","Forstarchiv"],["dc.bibliographiccitation.lastpage","118"],["dc.bibliographiccitation.volume","84"],["dc.contributor.author","Husmann, K."],["dc.contributor.author","Saborowski, Joachim"],["dc.contributor.author","Hapla, F."],["dc.date.accessioned","2020-12-08T08:22:04Z"],["dc.date.available","2020-12-08T08:22:04Z"],["dc.date.issued","2013"],["dc.identifier.doi","10.4432/0300-4112-84-107"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69467"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.relation.orgunit","Abteilung Forstökonomie und nachhaltige Landnutzungsplanung"],["dc.title","Ursachenanalyse der Ringschäle bei Edelkastanie (Castanea sativa [Mill.]) in Rheinland-Pfalz"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","384"],["dc.bibliographiccitation.firstpage","201"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Silva Fennica"],["dc.bibliographiccitation.lastpage","216"],["dc.bibliographiccitation.volume","39"],["dc.contributor.author","Cancino, J."],["dc.contributor.author","Saborowski, Joachim"],["dc.date.accessioned","2018-11-07T08:47:59Z"],["dc.date.available","2018-11-07T08:47:59Z"],["dc.date.issued","2005"],["dc.description.abstract","Randomized Branch Sampling (RBS) is a multistage sampling procedure using natural branching in order to select samples for the estimation of tree characteristics. Usually, sampling units are selected with unequal probabilities. Conventional RBS uses sampling with replacement (SWR) for repeated sampling on the first stage, and the sample size equals 1 on all subsequent stages, thus resulting in n so-called sample paths. When the sampling fraction is large multiple selections of first stage units are likely. Sampling without replacement (SWOR) at the first stage is an alternative that is expected to increase efficiency of the procedure. In this case, the second stage sample size m must be larger than 1 to enable unbiased variance estimation. In the present study, a theoretical and an empirical comparison of the conventional RBS and the SWOR variant was accomplished. Requiring a certain precision of the RBS estimation, the conventional RBS method is mostly more time-consuming than the variant with SWOR at the first stage. Only if m = 1 is chosen as second stage sample size for the SWOR RBS, this is often more time-consuming. In those cases, conventional RBS is up to 5% cheaper. In general, the larger m is, the more expensive is conventional RBS compared with the variant with SWOR at the first stage. The smaller the ratio of the variance between the primary units to the total variance of the estimate, the larger is the advantage of the SWOR variant. Generally, it can be shown that the gain of efficiency by SWOR is larger in case of weak correlations between auxiliary and target variable."],["dc.identifier.doi","10.14214/sf.384"],["dc.identifier.isi","000230149400005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/21091"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Finnish Soc Forest Science-natural Resources Inst Finland"],["dc.relation.issn","2242-4075"],["dc.relation.issn","0037-5330"],["dc.title","Comparison of Randomized Branch Sampling with and without replacement at the first stage"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1589"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","International Journal of Geographical Information Science"],["dc.bibliographiccitation.lastpage","1602"],["dc.bibliographiccitation.volume","26"],["dc.contributor.author","Kwak, Hanbin"],["dc.contributor.author","Lee, Woo-Kyun"],["dc.contributor.author","Saborowski, Joachim"],["dc.contributor.author","Lee, Si-Young"],["dc.contributor.author","Won, Myoung-Soo"],["dc.contributor.author","Koo, Kyo-Sang"],["dc.contributor.author","Lee, Myung-Bo"],["dc.contributor.author","Kim, Su-Na"],["dc.date.accessioned","2018-11-07T09:14:57Z"],["dc.date.available","2018-11-07T09:14:57Z"],["dc.date.issued","2012"],["dc.description.abstract","Most forest fires in Korea are spatially concentrated in certain areas and are highly related to human activities. These site-specific characteristics of forest fires are analyzed by spatial regression analysis using the R-module generalized linear mixed model (GLMM), which can consider spatial autocorrelation. We examined the quantitative effect of topology, human accessibility, and forest cover without and with spatial autocorrelation. Under the assumption that slope, elevation, aspect, population density, distance from road, and forest cover are related to forest fire occurrence, the explanatory variables of each of these factors were prepared using a Geographic Information System-based process. First, we tried to test the influence of fixed effects on the occurrence of forest fires using a generalized linear model (GLM) with Poisson distribution. In addition, the overdispersion of the response data was also detected, and variogram analysis was performed using the standardized residuals of GLM. Second, GLMM was applied to consider the obvious residual autocorrelation structure. The fitted models were validated and compared using the multiple correlation and root mean square error (RMSE). Results showed that slope, elevation, aspect index, population density, and distance from road were significant factors capable of explaining the forest fire occurrence. Positive spatial autocorrelation was estimated up to a distance of 32 km. The kriging predictions based on GLMM were smoother than those of the GLM. Finally, a forest fire occurrence map was prepared using the results from both models. The fire risk decreases with increasing distance to areas with high population densities, and increasing elevation showed a suppressing effect on fire occurrence. Both variables are in accordance with the significance tests."],["dc.identifier.doi","10.1080/13658816.2011.642799"],["dc.identifier.isi","000309045700003"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27555"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1362-3087"],["dc.relation.issn","1365-8816"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.title","Estimating the spatial pattern of human-caused forest fires using a generalized linear mixed model with spatial autocorrelation in South Korea"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["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.journal","Allgemeine Forst- und Jagdzeitung"],["dc.bibliographiccitation.lastpage","13"],["dc.bibliographiccitation.volume","187"],["dc.contributor.author","Schoneberg, S."],["dc.contributor.author","Nothdurft, A."],["dc.contributor.author","Nuske, Robert S."],["dc.contributor.author","Ackermann, J."],["dc.contributor.author","Nagel, J."],["dc.contributor.author","Saborowski, Joachim"],["dc.date.accessioned","2020-12-08T08:44:34Z"],["dc.date.available","2020-12-08T08:44:34Z"],["dc.date.issued","2016"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69476"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.title","Comparison of stand volume predictions based on airborne laser scanning data versus digital color infrared images"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","89"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","European Journal of Forest Research"],["dc.bibliographiccitation.lastpage","100"],["dc.bibliographiccitation.volume","133"],["dc.contributor.author","von Luepke, Nikolas"],["dc.contributor.author","Saborowski, Joachim"],["dc.date.accessioned","2018-11-07T09:46:58Z"],["dc.date.available","2018-11-07T09:46:58Z"],["dc.date.issued","2014"],["dc.description.abstract","We extend the well-known double sampling for stratification sampling scheme by cluster subsampling to a three-level design and present corresponding estimators based on the infinite population approach in the first phase. After stratification of the sample points (phase I), a second-phase sample is drawn independently among the first-phase points within each stratum. On level III, clusters are formed of those phase II points and a sample of clusters is finally drawn without replacement. We used the forest planning units compartment and subdistrict as clusters and moreover formed clusters with a heuristic for the vehicle routing problem. The precision of the new estimator was compared to that achieved with classical double sampling for stratification in a case study. The results indicate that the expected increase in sampling errors caused by clustering cannot be compensated by the reduced inventory costs under the conditions given in the case study."],["dc.description.sponsorship","German Science Foundation (DFG) [Sachbeihilfe SA 415/5-1]"],["dc.identifier.doi","10.1007/s10342-013-0743-9"],["dc.identifier.isi","000329231900009"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35005"],["dc.language.iso","en"],["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.subject.gro","Cluster sampling"],["dc.subject.gro","Continuous forest inventory"],["dc.subject.gro","Double sampling for stratification"],["dc.subject.gro","Infinite population approach"],["dc.title","Combining double sampling for stratification and cluster sampling to a three-level sampling design for continuous forest inventories"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]