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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 134-142 |
| Number of pages | 9 |
| Journal | Environmental Management |
| Volume | 46 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jul 2010 |
| Externally published | Yes |
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Keywords
- Forest fragmentation
- Landscape pattern indices
- Multivariate cluster analysis
- Regionalization
- Spatial pattern regions (SPR)
ASJC Scopus subject areas
- Ecology
- Global and Planetary Change
- Pollution
Cite this
Regionalization of landscape pattern indices using multivariate cluster analysis. / Long, Jed; Nelson, Trisalyn; Wulder, Michael.
In: Environmental Management, Vol. 46, No. 1, 07.2010, p. 134-142.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Regionalization of landscape pattern indices using multivariate cluster analysis
AU - Long, Jed
AU - Nelson, Trisalyn
AU - Wulder, Michael
PY - 2010/7
Y1 - 2010/7
N2 - 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.
AB - 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.
KW - Forest fragmentation
KW - Landscape pattern indices
KW - Multivariate cluster analysis
KW - Regionalization
KW - Spatial pattern regions (SPR)
UR - http://www.scopus.com/inward/record.url?scp=77955552851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955552851&partnerID=8YFLogxK
U2 - 10.1007/s00267-010-9510-6
DO - 10.1007/s00267-010-9510-6
M3 - Article
VL - 46
SP - 134
EP - 142
JO - Environmental Management
JF - Environmental Management
SN - 0364-152X
IS - 1
ER -