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Testing Tree-Classifier Variants and Alternate Modeling Methodologies in the East Great Basin Mapping Unit of the Southwest Regional Gap Analysis Project (Sw Regap)

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Abstract:We tested two methods for dataset generation and model construction, and three tree-classifier variants to identify the most parsimonious and thematically accurate mapping methodology for the SW ReGAP project. Competing methodologies were tested in the East Great Basin mapping unit comprising four mapzones in Nevada. Competing approaches to data set generation included the use of averaged digital data values within a training site polygon or use of randomly selected individual pixel values to create modeling data sets. Use of averaged values was faster but created smaller data sets. Use of individual pixels created larger data sets but was slower. Competing approaches to model construction included use of a single model for all vegetation types versus use of multiple presence/absence models for individual vegetation types. Use of the single model required minimal time but did not map all sampled communities, particularly rare types. Use of multiple models mapped all community types, typically with greater map accuracies, but was slower. Competing tree-classifier variants included the use of a simple CART algorithm and two iterative tree algorithms (See5 and Random Forests software). The simple CART algorithm used binary splits of dependent variables to classify data points into "pure" groups. The See5 algorithm used a subset of the data pool to construct a set of decision rules, and then iteratively reconstructed the decision rules based on inaccuracies. The third algorithm used subsets of the data pool and dependent variables to construct numerous decision trees. Each set of decision rules got a "vote" in the final outcome of each pixel in the classified vegetation map. Alternate methodologies were compared based on required time, internal model validation, and accuracy assessments. The alternate methodologies are discussed in terms of the competing interests of time required for completion, final thematic accuracies, and complexity .
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Citation:Sajwaj, T. D., W. G. Kepner, and D. F. Bradford. Testing Tree-Classifier Variants and Alternate Modeling Methodologies in the East Great Basin Mapping Unit of the Southwest Regional Gap Analysis Project (Sw Regap). Presented at 13th Annual National Gap Analysis Program Meeting, Fort Collins, CO, October 7-10, 2003.
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Contact: Chris Siebert - (702) 798-2234 or siebert.christopher@epa.gov
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Division: Environmental Sciences Division
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Branch: Landscape Ecology Branch
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Product Type: Abstrct/Oral
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Presented: 10/07/2003
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Related Entries:
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Bullet Item Development of Landscape Indicators for Use in Regional Ecological Risk Assessments
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