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Using landscape characteristics as prior information for Bayesian classification of yellow starthistle

Published online by Cambridge University Press:  20 January 2017

Bahman Shafii ,
William J. Price ,
Timothy S. Prather ,
Lawrence W. Lass  and
Donald C. Thill
William J. Price
Affiliation:
Statistical Programs, P.O. Box 442337, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844-2337
Timothy S. Prather
Affiliation:
Department of Plant, Soil, and Entomological Sciences, P.O. Box 442339, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844-2339
Lawrence W. Lass
Affiliation:
Department of Plant, Soil, and Entomological Sciences, P.O. Box 442339, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844-2339
Donald C. Thill
Affiliation:
Department of Plant, Soil, and Entomological Sciences, P.O. Box 442339, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844-2339
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Abstract

Yellow starthistle is an invasive plant of canyon grasslands in north-central Idaho. The distribution of yellow starthistle is associated with general landscape characteristics that include land use and specific terrain-related features such as elevation, slope, and aspect. Slope and aspect can be considered as indicators of plant community composition and distribution. Hence, these variables may be incorporated into prediction models to estimate the likelihood of yellow starthistle occurrence because plant communities differ in susceptibility to invasion. An empirically derived nonlinear model based on landscape characteristics has previously been developed to predict the likelihood of yellow starthistle occurrence in north-central Idaho. Although the model was used to predict the invasion potential of yellow starthistle into new areas, it could also be used as auxiliary data for classifying this weed species in remotely sensed imagery. To accomplish this, the predicted values from the model are regarded as prior information on the presence of yellow starthistle. A Bayesian image classification algorithm using this prior information is then applied to a corresponding set of remotely sensed data. This results in a map indicating the posterior probabilities of yellow starthistle occurrence given the landscape characteristics. This technique is demonstrated and is shown to reduce omissional error rates by 50% when the landscape characteristics are incorporated into the classification process.

Keywords

Yellow starthistle, Centaurea solstitialis L. CENSONonlinear modelimage classificationposterior probabilitieserror rates
Type
Weed Biology and Ecology
Information
Weed Science , Volume 52 , Issue 6 , December 2004 , pp. 948 - 953
Copyright
Copyright © Weed Science Society of America 

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