Author Information

William J. Price
Bahman Shafii

Abstract

Yellow starthistle is a dominant weed of north-central Idaho canyon grasslands. The distribution of yellow starthistle can be affected by general landscape characteristics, such as land use, as well as 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. An empirically derived nonlinear model based on landscape characteristics was developed to predict the likelihood of yellow starthistle occurrence in north central Idaho (Shafii, et al. 1999). While the model was employed 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 of 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. The end result is a map indicating the posterior probabilities of yellow starthistle occurrence given the landscape characteristics. This technique is demonstrated considering the presence and absence of prior information and is shown to result in lower omissional and commissional error rates when the landscape characteristics are utilized.

Keywords

nonlinear model, image classification, posterior probabilities, error rates

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Apr 28th, 2:30 PM

USING LANDSCAPE CHARACTERISTICS AS PRIOR INFORMATION FOR BAYESIAN CLASSIFICATION OF REMOTELY SENSED IMAGERY

Yellow starthistle is a dominant weed of north-central Idaho canyon grasslands. The distribution of yellow starthistle can be affected by general landscape characteristics, such as land use, as well as 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. An empirically derived nonlinear model based on landscape characteristics was developed to predict the likelihood of yellow starthistle occurrence in north central Idaho (Shafii, et al. 1999). While the model was employed 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 of 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. The end result is a map indicating the posterior probabilities of yellow starthistle occurrence given the landscape characteristics. This technique is demonstrated considering the presence and absence of prior information and is shown to result in lower omissional and commissional error rates when the landscape characteristics are utilized.