Magnussen, Steen

[["dc.bibliographiccitation.firstpage","223"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Environmental and Ecological Statistics"],["dc.bibliographiccitation.lastpage","237"],["dc.bibliographiccitation.volume","18"],["dc.contributor.author","Yang, Haijun"],["dc.contributor.author","Kleinn, Christoph"],["dc.contributor.author","Fehrmann, Lutz"],["dc.contributor.author","Tang, Shouzheng"],["dc.contributor.author","Magnussen, Steen"],["dc.date.accessioned","2017-09-07T11:47:09Z"],["dc.date.available","2017-09-07T11:47:09Z"],["dc.date.issued","2009"],["dc.description.abstract","Adaptive cluster sampling (ACS) is a sampling technique for sampling rare and geographically clustered populations. Aiming to enhance the practicability of ACS while maintaining some of its major characteristics, an adaptive sample plot design is introduced in this study which facilitates field work compared to “standard” ACS. The plot design is based on a conditional plot expansion: a larger plot (by a pre-defined plot size factor) is installed at a sample point instead of the smaller initial plot if a pre-defined condition is fulfilled. This study provides insight to the statistical performance of the proposed adaptive plot design. A design-unbiased estimator is presented and used on six artificial and one real tree position maps to estimate density (number of objects per ha). The performance in terms of coefficient of variation is compared to the non-adaptive alternative without a conditional expansion of plot size. The adaptive plot design was superior in all cases but the improvement depends on (1) the structure of the sampled population, (2) the plot size factor and (3) the critical value (the minimum number of objects triggering an expansion). For some spatial arrangements the improvement is relatively small. The adaptive design may be particularly attractive for sampling in rare and compactly clustered populations with an appropriately chosen plot size factor."],["dc.identifier.doi","10.1007/s10651-009-0129-9"],["dc.identifier.gro","3149274"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6653"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5933"],["dc.language.iso","en"],["dc.notes.intern","Kleinn Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1352-8505"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","A new design for sampling with adaptive sample plots"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1041"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Pérez-Cruzado, César"],["dc.contributor.author","Kleinn, Christoph"],["dc.contributor.author","Magdon, Paul"],["dc.contributor.author","Álvarez-González, Juan Gabriel"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Fehrmann, Lutz"],["dc.contributor.author","Nölke, Nils"],["dc.date.accessioned","2021-04-14T08:27:52Z"],["dc.date.available","2021-04-14T08:27:52Z"],["dc.date.issued","2021"],["dc.description.sponsorship","Forest Research Institute of the German Federal State of Rheinland-Pfalz (FAWF) in Trippstadt"],["dc.identifier.doi","10.3390/rs13051041"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82431"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","The Horizontal Distribution of Branch Biomass in European Beech: A Model Based on Measurements and TLS Based Proxies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["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.issue","1"],["dc.bibliographiccitation.journal","Forest Ecosystems"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","McRoberts, Ronald E."],["dc.contributor.author","Breidenbach, Johannes"],["dc.contributor.author","Nord-Larsen, Thomas"],["dc.contributor.author","Ståhl, Göran"],["dc.contributor.author","Fehrmann, Lutz"],["dc.contributor.author","Schnell, Sebastian"],["dc.date.accessioned","2020-12-10T18:41:26Z"],["dc.date.available","2020-12-10T18:41:26Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1186/s40663-020-00223-6"],["dc.identifier.eissn","2197-5620"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17215"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77581"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Forstwissenschaften und Waldökologie"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Comparison of estimators of variance for forest inventories with systematic sampling - results from artificial populations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["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","1"],["dc.bibliographiccitation.journal","Forest Ecosystems"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Kleinn, Christoph"],["dc.contributor.author","Magnussen, Steen"],["dc.contributor.author","Nölke, Nils"],["dc.contributor.author","Magdon, Paul"],["dc.contributor.author","Álvarez-González, Juan Gabriel"],["dc.contributor.author","Fehrmann, Lutz"],["dc.contributor.author","Pérez-Cruzado, César"],["dc.date.accessioned","2021-04-14T08:31:18Z"],["dc.date.available","2021-04-14T08:31:18Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1186/s40663-020-00268-7"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17621"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83552"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","2197-5620"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]