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Synergistic techniques for better understanding and classifying the environmental structure of landscapes
The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.
History
Journal
Environmental managementVolume
37Issue
1Pagination
126 - 140Publisher
SpringerLocation
New York, N.Y.Publisher DOI
ISSN
0364-152XeISSN
1432-1009Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2006, SpringerUsage metrics
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No categories selectedKeywords
landscapeGISecological land classificationenvironmental gradientsclustermultivariate analysisBayes TheoremClassificationEcologyEcosystemEnvironmental MonitoringGeographyModels, TheoreticalPrincipal Component AnalysisSouth AustraliaScience & TechnologyLife Sciences & BiomedicineEnvironmental SciencesEnvironmental Sciences & EcologyARTIFICIAL NEURAL-NETWORKSFUZZY K-MEANSRESERVE COVERAGESOUTH-CAROLINAFORESTMANAGEMENTSYSTEMTOOLREPRESENTATIVENESS
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