Magnussen, Steen

[["dc.bibliographiccitation.firstpage","293"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Forestry"],["dc.bibliographiccitation.lastpage","306"],["dc.bibliographiccitation.volume","83"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Smith, B."],["dc.contributor.author","Kleinn, Christoph"],["dc.contributor.author","Sun, I. F."],["dc.date.accessioned","2017-09-07T11:47:08Z"],["dc.date.available","2017-09-07T11:47:08Z"],["dc.date.issued","2010"],["dc.identifier.doi","10.1093/forestry/cpq012"],["dc.identifier.gro","3149259"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5916"],["dc.notes.intern","Kleinn Crossref Import"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Oxford University Press (OUP)"],["dc.relation.issn","0015-752X"],["dc.title","An urn model for species richness estimation in quadrat sampling from fixed-area populations"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","300"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Scandinavian Journal of Forest Research"],["dc.bibliographiccitation.lastpage","312"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Fehrmann, Lutz"],["dc.date.accessioned","2020-12-10T18:14:50Z"],["dc.date.available","2020-12-10T18:14:50Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1080/02827581.2019.1599063"],["dc.identifier.eissn","1651-1891"],["dc.identifier.issn","0282-7581"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74634"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","In search of a variance estimator for systematic sampling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","279"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Environmental and Ecological Statistics"],["dc.bibliographiccitation.lastpage","299"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Yang, Haijun"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Fehrmann, Lutz"],["dc.contributor.author","Mundhenk, Philip"],["dc.contributor.author","Kleinn, Christoph"],["dc.date.accessioned","2017-09-07T11:47:12Z"],["dc.date.available","2017-09-07T11:47:12Z"],["dc.date.issued","2016"],["dc.description.abstract","Adaptive cluster sampling (ACS) has the potential of being superior for sampling rare and geographically clustered populations. However, setting up an efficient ACS design is challenging. In this study, two adaptive plot designs are proposed as alternatives: one for fixed-area plot sampling and the other for relascope sampling (also known as variable radius plot sampling). Neither includes a neighborhood search which makes them much easier to execute. They do, however, include a conditional plot expansion: at a sample point where a predefined condition is satisfied, sampling is extended to a predefined larger cluster-plot or a larger relascope plot. Design-unbiased estimators of population total and its variance are derived for each proposed design, and they are applied to ten artificial and one real tree position maps to estimate density (number of trees per ha) and basal area (the cross-sectional area of a tree stem at breast height) per hectare. The performances—in terms of relative standard error (SE%)—of the proposed designs and their non-adaptive alternatives are compared. The adaptive plot designs were superior for the clustered populations in all cases of equal sample sizes and in some cases of equal area of sample plots. However, the improvement depends on: (1) the plot size factor; (2) the critical value (the minimum number of trees triggering an expansion); (3) the subplot distance for the adapted cluster-plots, and (4) the spatial arrangement of the sampled population. For some spatial arrangements, the improvement is relatively small. The adaptive designs may be particularly attractive for sampling in rare and compactly clustered populations with critical value of 1, subplot distance equal to the diameter of initial circular plots, or plot size factor of 2.5 for an initial basal area factor of 2."],["dc.identifier.doi","10.1007/s10651-015-0339-2"],["dc.identifier.gro","3149289"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5949"],["dc.language.iso","en"],["dc.notes.intern","Kleinn Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1352-8505"],["dc.title","Two neighborhood-free plot designs for adaptive sampling of forests"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","213"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","European Journal of Forest Research"],["dc.bibliographiccitation.lastpage","224"],["dc.bibliographiccitation.volume","127"],["dc.contributor.author","Magnussen, S."],["dc.contributor.author","Kleinn, C."],["dc.contributor.author","Picard, N."],["dc.date.accessioned","2017-09-07T11:47:11Z"],["dc.date.available","2017-09-07T11:47:11Z"],["dc.date.issued","2008"],["dc.description.abstract","Two new density estimators for k-tree distance sampling are proposed and their performance is assessed in simulated distance sampling from 22 stem maps representing a wide range of natural to semi-natural forest tree stands with random to irregular (clustered) spatial distribution of trees. The new estimators are model-based. The first (Orbit) computes density as the inverse of the average of the areas associated with each of the k-trees nearest to a sample location. The area of the k-th tree is obtained as a prediction from a linear regression model while the area of the first is obtained via a Poisson probability integral. The second (GamPoi) is based on the expected distribution of distance to the k nearest tree in a forest where the local distribution of trees is random but the stem density varies from sample location to sample location as a gamma distribution. In a comprehensive assessment with 17 promising reference estimators, a subset composed of Morisita’s, Persson’s, Byth’s, Kleinn’s, Orbit, and GamPoi was significantly better, in terms of relative root mean square error (RRMSE), than average. GamPoi emerged as the better estimator for sample sizes larger than or equal to 30. For smaller sample sizes, both Kleinn’s and Morisita’s appear attractive."],["dc.identifier.doi","10.1007/s10342-007-0197-z"],["dc.identifier.gro","3149290"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5950"],["dc.language.iso","en"],["dc.notes.intern","Kleinn Crossref Import"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1612-4669"],["dc.title","Two new density estimators for distance sampling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","429"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Forest Science"],["dc.bibliographiccitation.lastpage","441"],["dc.bibliographiccitation.volume","54"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Picard, N."],["dc.contributor.author","Kleinn, Christoph"],["dc.date.accessioned","2017-09-07T11:48:56Z"],["dc.date.available","2017-09-07T11:48:56Z"],["dc.date.issued","2008"],["dc.identifier.gro","3149560"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/6242"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.title","A gamma-Poisson distribution of the point to the k nearest event distance"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","429"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Forest Science"],["dc.bibliographiccitation.lastpage","441"],["dc.bibliographiccitation.volume","54"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Picard, N."],["dc.contributor.author","Kleinn, Christoph"],["dc.date.accessioned","2018-11-07T11:12:27Z"],["dc.date.available","2018-11-07T11:12:27Z"],["dc.date.issued","2008"],["dc.description.abstract","Distance sampling of events in natural or seminatural populations often indicates a larger variance in the distance to the kth nearest event than expected for events distributed completely at random. Overdispersion contributes to the well-known bias problem of distance sampling density estimators. Distance distribution models that accommodate overdispersion in the data should lead to more robust estimators of density. To this end we propose a gamma-Poisson distribution model for distances from a point to k nearest events. The model assumes a gamma distribution of local densities of randomly distributed events. Properties of the distribution and estimation of the parameters and event density are detailed for both constrained and unconstrained sampling. Four examples, one with simulated data from a known negative binomial distribution and three with simulated distance sampling in natural and seminatural stem-mapped tree stands, illustrate the promising performance of this new distribution, both as a model for distances and for density estimation. The modeling approach extends to other mixing distributions."],["dc.identifier.isi","000258282000005"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/53669"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.issn","1938-3738"],["dc.relation.issn","0015-749X"],["dc.title","A gamma-Poisson distribution of point to k nearest event distance"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Environmental Monitoring and Assessment"],["dc.bibliographiccitation.volume","191"],["dc.contributor.author","Fehrmann, Lutz"],["dc.contributor.author","Kukunda, Collins B."],["dc.contributor.author","Nölke, Nils"],["dc.contributor.author","Schnell, Sebastian"],["dc.contributor.author","Seidel, Dominik"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Kleinn, Christoph"],["dc.date.accessioned","2020-12-10T14:11:32Z"],["dc.date.available","2020-12-10T14:11:32Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1007/s10661-018-7152-y"],["dc.identifier.eissn","1573-2959"],["dc.identifier.issn","0167-6369"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71098"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","A unified framework for land cover monitoring based on a discrete global sampling grid (GSG)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","307"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","European Journal of Forest Research"],["dc.bibliographiccitation.lastpage","320"],["dc.bibliographiccitation.volume","131"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Fehrman, Lutz"],["dc.contributor.author","Platt, William J."],["dc.date.accessioned","2018-11-07T09:12:54Z"],["dc.date.available","2018-11-07T09:12:54Z"],["dc.date.issued","2012"],["dc.description.abstract","Density estimators for k-tree distance sampling are sensitive to the amount of extra Poisson variance in distances to the kth tree. To lessen this sensitivity, we propose an adaptive composite estimator (COM). In simulated sampling from 16 test populations, a three-component composite density estimator (COM)-with weights determined by a multinomial logistic function of four readily available ancillary variables-was identified as superior in terms of average relative absolute bias. Results from a different set of nine validation populations-with widely different stem densities and spatial patterns of tree locations-confirmed that relative root mean squared errors (RRMSE) of COM were, on average, considerably lower than those obtained with the three-component k-tree density estimators. The RRMSE performance of COM improved with increasing values of k. With k = 6 and sample sizes of 10, 20, and 30, the average relative bias of COM was between -5 and 5% in seven validation populations but in an open low-density savanna-like population bias reached -12% (1979 data) and 7% (1996 data). For k = 6 and n = 10, the RRMSE of COM was, in six of the nine validation populations, within 3.3 percentage points of the RRMSE for sampling with fixed-area plots. Jackknife estimates of the precision of COM estimates of density were negatively biased, leading to under-coverage (7%) of computed 95% confidence intervals."],["dc.identifier.doi","10.1007/s10342-011-0502-8"],["dc.identifier.isi","000301088000004"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27050"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1612-4669"],["dc.title","An adaptive composite density estimator for k-tree sampling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","169"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","European Journal of Forest Research"],["dc.bibliographiccitation.lastpage","178"],["dc.bibliographiccitation.volume","139"],["dc.contributor.author","Magnussen, S."],["dc.contributor.author","Kleinn, C."],["dc.contributor.author","Fehrmann, L."],["dc.date.accessioned","2020-12-10T14:11:18Z"],["dc.date.available","2020-12-10T14:11:18Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1007/s10342-020-01257-9"],["dc.identifier.eissn","1612-4677"],["dc.identifier.issn","1612-4669"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71035"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Wood volume errors from measured and predicted heights"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]