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The role of misclassification in estimating proportions and an estimator of misclassification probabilityAuthor(s): Patrick L. Zimmerman; Greg C. Liknes
Source: International Journal of Mathematical and Computational Forestry and Natural-Resource Sciences. 2(2): 78-85.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionDot grids are often used to estimate the proportion of land cover belonging to some class in an aerial photograph. Interpreter misclassification is an often-ignored source of error in dot-grid sampling that has the potential to significantly bias proportion estimates. For the case when the true class of items is unknown, we present a maximum-likelihood estimator of misclassification probability based on agreement between two interpreters. Two of the assumptions underlying the estimator are: (i) the probability that an interpreter makes a misclassification is constant, (ii) both interpreters have the same probability of misclassification. Simulation results suggest the estimator has acceptable performance when (ii) does not hold. This estimator can be used to investigate whether bias due to misclassification has exceeded a threshold, or to correct bias due misclassification.
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CitationZimmerman, Patrick L.; Liknes, Greg C. 2010. The role of misclassification in estimating proportions and an estimator of misclassification probability. International Journal of Mathematical and Computational Forestry and Natural-Resource Sciences. 2(2): 78-85.
Keywordsdot grid, remote sensing, image interpretation, misclassification
- Misclassification bias in areal estimates
- Calibration of remotely sensed proportion or area estimates for misclassification error
- Image-based change estimation (ICE): monitoring land use, land cover and agent of change information for all lands
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