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• W272290413 abstract "Mountain pine beetles are a pest indigenous to the pine forests of the western United States. Capable of exponential population growth, mountain pine beetles can destroy thousands of acres of trees in a short period of time. Much research has been carried out on the mathematical modeling of mountain pine beetle phenology. The research reported here is part of a larger project to demonstrate the application of, and evaluate, differential equation models for mountain pine beetle progression through pine forests. The study area is the Sawtooth National Recreation area in Idaho. To provide input parameter to the mathematical models, and to measure the changes in the pine forest in the study area, IKONOS satellite imagery was used to classify the vegetation of the study area. Five classifiers—linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor discriminant analysis, classification trees and random forests—were applied to raw and transformed multispectral and panchromatic satellite imagery, with and without an elevation variable. Quadratic discriminant analysis and random forests proved to be the best classifiers as measured by cross-validated error estimates, with overall classification rates of about 12% without elevation, and about 5% when elevation was included. Redtops were relatively easy to identify, with misclassification rates of about 5%—6%, but green lodgepole pine and Douglas fir were relatively difficult to discriminate between and had much higher misclassification rates. 1 Research partially supported by NSF Grant No. DMS 0077663." @default.
• W272290413 created "2016-06-24" @default.
• W272290413 creator A5014768872 @default.
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• W272290413 date "2003-01-01" @default.
• W272290413 modified "2023-09-23" @default.
• W272290413 title "Identifying ’Redtops’: Classification ofSatellite Imagery for Tracking Mountain Pine Beetle Progression through a Pine Forest" @default.
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• W272290413 hasPublicationYear "2003" @default.
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