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dc.contributor.authorLong, Jed Andrew
dc.contributor.authorNelson, T.A.
dc.contributor.authorWulder, M.A.
dc.date.accessioned2014-09-17T09:31:03Z
dc.date.available2014-09-17T09:31:03Z
dc.date.issued2010
dc.identifier.citationLong , J A , Nelson , T A & Wulder , M A 2010 , ' Regionalization of landscape pattern indices using multivariate cluster analysis ' , Environmental Management , vol. 46 , no. 1 , pp. 134-142 . https://doi.org/10.1007/s00267-010-9510-6en
dc.identifier.issn0364-152X
dc.identifier.otherPURE: 69346470
dc.identifier.otherPURE UUID: 9ee3dc94-0d03-4ed6-ab82-6f5671c39daa
dc.identifier.otherScopus: 77955552851
dc.identifier.urihttp://hdl.handle.net/10023/5431
dc.descriptionThis project was funded by the Government of Canada through the Mountain Pine Beetle Program, a six-year, $40 million program administered by Natural Resources Canada, Canadian Forest Service. Additional information on the Mountain Pine Beetle Program may be found at: http://mpb.cfs.nrcan.gc.ca.en
dc.description.abstractRegionalization, or the grouping of objects in space, is a useful tool for organizing, visualizing, and synthesizing the information contained in multivariate spatial data. Landscape pattern indices can be used to quantify the spatial pattern (composition and configuration) of land cover features. Observable patterns can be linked to underlying processes affecting the generation of landscape patterns (e.g., forest harvesting). The objective of this research is to develop an approach for investigating the spatial distribution of forest pattern across a study area where forest harvesting, other anthropogenic activities, and topography, are all influencing forest pattern. We generate spatial pattern regions (SPR) that describe forest pattern with a regionalization approach. Analysis is performed using a 2006 land cover dataset covering the Prince George and Quesnel Forest Districts, 5.5 million ha of primarily forested land base situated within the interior plateau of British Columbia, Canada. Multivariate cluster analysis (with the CLARA algorithm) is used to group landscape objects containing forest pattern information into SPR. Of the six generated SPR, the second cluster (SPR2) is the most prevalent covering 22% of the study area. On average, landscapes in SPR2 are comprised of 55.5% forest cover, and contain the highest number of patches, and forest/non-forest joins, indicating highly fragmented landscapes. Regionalization of landscape pattern metrics provides a useful approach for examining the spatial distribution of forest pattern. Where forest patterns are associated with positive or negative environmental conditions, SPR can be used to identify similar regions for conservation or management activities.
dc.language.isoeng
dc.relation.ispartofEnvironmental Managementen
dc.rights© 2010. Springer Science+Business Media, LLC. The final publication is available at Springer via http://dx.doi.org/10.1007/s00267-010-9510-6en
dc.subjectRegionalizationen
dc.subjectLandscape pattern indicesen
dc.subjectMultivariate cluster analysisen
dc.subjectSpatial pattern regions (SPR)en
dc.subjectForest fragmentationen
dc.subjectG Geography (General)en
dc.subjectGE Environmental Sciencesen
dc.subject.lccG1en
dc.subject.lccGEen
dc.titleRegionalization of landscape pattern indices using multivariate cluster analysisen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.Geography & Sustainable Developmenten
dc.contributor.institutionUniversity of St Andrews.Bell-Edwards Geographic Data Instituteen
dc.identifier.doihttps://doi.org/10.1007/s00267-010-9510-6
dc.description.statusPeer revieweden


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