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Regionalization of Landscape Pattern Indices Using Multivariate Cluster Analysis

Environmental Management volume 46, pages 134–142 (2010)Cite this article

Abstract

Regionalization, 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.

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Acknowledgments

This project was funded by the Government of Canada through the Mountain Pine Beetle Program, a three-year, $100 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. Chris Butson and Xiaoping Yuan of the British Columbia Ministry of Forests and Range and Joanne White of the Canadian Forest Service are thanked for insight and access to data critical to the success of this research. Thanks to Dennis Jelinski and two anonymous reviewers, for helpful comments improving this manuscript.

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Affiliations

  1. Department of Geography, University of Victoria, P.O. Box 3060, Victoria, BC, V8W3R4, Canada

    Jed Long & Trisalyn Nelson

  2. Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre Victoria, Victoria, BC, Canada

    Michael Wulder

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  1. Jed Long
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  2. Trisalyn Nelson
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  3. Michael Wulder
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Correspondence to Jed Long.

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Long, J., Nelson, T. & Wulder, M. Regionalization of Landscape Pattern Indices Using Multivariate Cluster Analysis. Environmental Management 46, 134–142 (2010). https://doi.org/10.1007/s00267-010-9510-6

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Keywords

  • Regionalization
  • Landscape pattern indices
  • Multivariate cluster analysis
  • Spatial pattern regions (SPR)
  • Forest fragmentation