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Related Experiment Video

Updated: Jun 12, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Regionalization of landscape pattern indices using multivariate cluster analysis.

Jed Long¹, Trisalyn Nelson, Michael Wulder

¹Department of Geography, University of Victoria, P.O. Box 3060, Victoria, BC, V8W3R4, Canada. jlong@uvic.ca

Environmental Management

|June 8, 2010

Summary

This summary is machine-generated.

Regionalization groups spatial data to reveal forest patterns influenced by harvesting and topography. This approach identifies fragmented forest regions (SPR2) for targeted conservation and management strategies.

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Area of Science:

Spatial analysis
Geographic Information Systems (GIS)
Landscape ecology

Background:

Regionalization organizes multivariate spatial data for better visualization and synthesis.
Landscape pattern indices quantify land cover composition and configuration.
Forest patterns are influenced by harvesting, human activities, and topography.

Purpose of the Study:

Develop an approach to investigate forest pattern distribution.
Identify regions influenced by anthropogenic activities and topography.
Utilize regionalization for landscape pattern analysis.

Main Methods:

Generated spatial pattern regions (SPR) using a regionalization approach.
Employed multivariate cluster analysis (CLARA algorithm) on land cover data.
Analyzed data from Prince George and Quesnel Forest Districts, British Columbia.

Main Results:

Six SPR were generated, with SPR2 being the most prevalent (22% of study area).
SPR2 landscapes averaged 55.5% forest cover.
SPR2 exhibited high fragmentation with numerous patches and forest/non-forest joins.

Conclusions:

Regionalization of landscape pattern metrics is effective for examining forest pattern distribution.
SPR can identify areas for conservation and management based on environmental conditions.
The approach aids in understanding the spatial impact of various factors on forest landscapes.