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Assessing Agreement in Multispectral Images of Yellow Starthistle (Centaurea solstitialis) with Ground Truth Data Using a Bayesian Methodology1

Published online by Cambridge University Press:  20 January 2017

Lawrence W. Lass ,
Bahman Shafii ,
William J. Price  and
Donald C. Thill
Lawrence W. Lass*
Affiliation:
Department of Plant, Soil, and Entomological Sciences, University of Idaho, Moscow, ID 83844-2339
Bahman Shafii
Affiliation:
College of Agriculture, University of Idaho, Moscow, ID 83844-2339
William J. Price
Affiliation:
College of Agriculture, University of Idaho, Moscow, ID 83844-2339
Donald C. Thill
Affiliation:
Department of Plant, Soil, and Entomological Sciences, University of Idaho, Moscow, ID 83844-2339
*
Corresponding author's E-mail: llass@uidaho.edu.
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Abstract

Digital imagery from satellites and airborne remote sensing offer an opportunity to accurately detect weed infestations. Image resolution and plant growth stage are critical factors for maximum weed detection with low errors. Data analysis in traditional image assessment has relied on agreement measures, such as Cohen's kappa and asymptotic procedures, that compare what is on the image but not on the ground and what is on the ground but not on the image. Statistical comparisons of multispectral images, however, require some knowledge of the variability of the image classification results to determine significant differences among agreement measures. Bayesian methods were used to develop probability distributions for an agreement measure, conditional kappa, and were then subsequently applied to assess and compare image resolutions and plant growth stages. Results showed that images of a study site known to have yellow starthistle populations could identify the noninfested areas with greater accuracy than infested areas at spatial resolutions of 0.5, 1.0, 2.0, and 4.0 m. The detection accuracy of yellow starthistle in the images taken either prebloom or at flowering with 4.0-m spatial resolution usually was equal to or better than spatial resolutions of 0.5, 1.0, and 2.0 m for the cover classes that were not, moderately (31 to 70%), and highly (71 to 100%) infested. The 0.5-m resolution was better than 4.0-m spatial resolution when detecting the moderate cover class, but both resolutions had high omissional and commissional errors. Contrasting the best detection resolution for finding yellow starthistle colonies across flight times indicated that flying at flowering stage with the 4.0-m spatial resolution provided the best detection of the yellow starthistle cover classes considered. In the cases where different spatial resolutions resulted in equal detection accuracy, the larger spatial resolution was selected because of lower costs of acquiring and processing the data.

Keywords

Yellow starthistleCentaurea solstitialis L. #3 CENSOAirborne charge-coupled devicesconditional kappaimage analysisremote sensingvideographyweed detectionconditional kappawhere i denotes the image classification category (high, moderate, and none)
Type
Research Article
Information
Weed Technology , Volume 14 , Issue 3 , September 2000 , pp. 539 - 544
Copyright
Copyright © Weed Science Society of America 

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