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Publication Information

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Title: Identifying "redtops": Classification of satellite imagery for tracking mountain pine beetle progression through a pine forest

Author: Cutler, Richard; Brown, Leslie; Powell, James; Bentz, Barbara; Cutler, Adele

Date: 2003

Source: In: Braverman, A.; Hesterberg, T.; Minnotte, M.; Symanzik, J.; Said, Y., eds. Computing Science and Statistics, Volume 35: Security and Infrastructure Protection. Proceedings of the 35th Symposium on the Interface; March 13-17; Salt Lake City, Utah. Fairfax Station, VA: Interface Foundation of North America, Inc. Online: http://www.interfacesymposia.org/I03/index.html

Publication Series: Paper (invited, offered, keynote)

Description: Mountain pine beetles (Dendroctonus ponderosae Hopkins) 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. 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 parameters to the mathematical models, and to measure the bark beetle impact (redtopped pines), IKONOS satellite imagery was used to classify the vegetation of the study area. Five classifiers - linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor discrminant analysis, classification trees and random forests - were applied to raw and transformed multispectral and panchromatic satellite imagery, with and without an elevation variable. Quadratic discrmininant analysis and random forests proved to be the best classifiers as measured by cross-validated error estimates, with overall misclassification rates were 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 discrminiate between and had much higher misclassification rates.

Keywords: mountain pine beetles, Dendroctonus ponderosae, IKONOS, satellite imagery

Publication Notes:

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  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
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Citation:


Cutler, Richard; Brown, Leslie; Powell, James; Bentz, Barbara; Cutler, Adele. 2003. Identifying "redtops": Classification of satellite imagery for tracking mountain pine beetle progression through a pine forest. In: Braverman, A.; Hesterberg, T.; Minnotte, M.; Symanzik, J.; Said, Y., eds. Computing Science and Statistics, Volume 35: Security and Infrastructure Protection. Proceedings of the 35th Symposium on the Interface; March 13-17; Salt Lake City, Utah. Fairfax Station, VA: Interface Foundation of North America, Inc. Online: http://www.interfacesymposia.org/I03/index.html

 


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