Variance Estimates and Confidence Intervals for the Kappa Measure of Classification Accuracy

References

• Aickin, M. 1990. “Maximum likelihood estimation of agreement in the constant predictive probability model, and its relation to Cohen's Kappa”, Biometrics, 46:293–302.
   PubMed Web of Science ®Google Scholar
• Bishop, Y. M. M., S. E. Feinberg, and P. W. Hooland. 1975. “Discrete multivariate analysis theory and practice”, MIT Press, Cambridge, MA, 575 p.
   Google Scholar
• Cochran, W. G. 1977. “Sampling techniques”, 3rd ed. John Wiley and Sons, New York, 428 p.
   Google Scholar
• Cohen, J. 1960. “A coefficient of agreement of nominal scales”, Education Psychological Measurements, 20:37–46.
   Web of Science ®Google Scholar
• Congalton, R. G., and R. A. Mead. 1983. “A quantitative method to test for consistency and correctness in photointerpretation”, Photogrammtric Engineering and Remote Sensing, 49:69–74.
   Web of Science ®Google Scholar
• Congalton, R. G. 1991. “A review of assessing the accuracy of classification of remotely sensed data” Remote Sensing of Environment, 37:35–46.
   Web of Science ®Google Scholar
• Czaplewski, R. L., G. L. Catts, and P. W. Snook. 1987. “National land cover monitoring using large, permanent photo plots”, In: Land and Resource Evaluation for National Planning in the Tropics. An International Conference and Workshop, Chetumal Mexico, January 25–31, 1987, 524 p.
   Google Scholar
• Efron, B. 1979. “Bootstrap methods: Another look at the jackknife”, Annals of Statistic, 7:1–26.
   Web of Science ®Google Scholar
• Efron,B. And R. J.Tibshirani. 1993. “An introduction to the bootstrap”, Chapman & Hall, 431 p.
   Google Scholar
• ERDAS, Inc. 1992. 2081 Buford Highway, Suite 300, Atlanat, Georgia 30329, USA.
   Google Scholar
• Fitzgerald, R. W. and B. G. Lees. 1994. “Assessing the classification accuracy of multisource remote sensing data”, Remote Sensing of Enviroment, 47:362–368.
   Web of Science ®Google Scholar
• Kalkhan, M. A. 1994. “Statistical properties of six accuracy indices using simple random and stratified random sampling: An application in remote sensing”, Ph.D. Dissertation. Colorado State University, 134 p.
   Google Scholar
• Kuchler, A. W. 1985. “Potential natural vegetation”, National Atlas of the United States. U.S. Department of Interior, U.S. Geological Survey, (1 map sheet).
   Google Scholar
• Landis, J. R., and G. G. Kock. 1977. “The measurement of observer agreement for categorical data”, Biometrics, 33:159–174.
   PubMed Web of Science ®Google Scholar
• Monserud, R. A. and R. Leemans. 1992. “Comparing global vegetation maps with the Kappa statistic”, Ecological Modeling, 62:275–293.
   Web of Science ®Google Scholar
• Powell, D. S. 1981. “A solution for solving conflicts between land use and land cover inventories”, P. 665–668. In: Place Resource Inventories, Principles and Practices, National SAF Workshop, August 9–14, 1981.
   Google Scholar
• Rosenfield, G. H. and K. Fitzpatrick Lins. 1986. “A coefficient of agreement as a measure of thematic classification accuracy”, Photogrammtric Engineering and Remote Sensing, 52:223–227.
   Web of Science ®Google Scholar
• Schreuder, H. T. 1996. Rocky Mountain Forest and Range Experiment Station, USDA Forest Service, 240 West Prospect Street, Fort Collins, CO 80526.
   Google Scholar
• Schreuder, H. T., P. W. Snook, R. L. Czaplewski, and G. P. Catts. 1986. “A proposed periodic national inventory of land-use land cover”, National American Society of Photogrammetry Conference, September 1986, Anchorage, AK.
   Google Scholar
• Shapiro, S. S. And M. B.Wilk. 1965. “Analysis of variance test for normality (complete test)”, Biometrica, 52:591–611.
   Web of Science ®Google Scholar
• Skidmore, A., and B. Turner. 1989. “Assessing the accuracy of resource inventory maps”, p. 524–535 In: Proc. of Global Natural Resource Monitoring and Assessment: Preparing for the 21st Century, Venice, Italy.
   Google Scholar
• Stehman, S. V. 1992. “Comparison of systematic and random sampling for estimating the accuracy of maps generated from remotely sensed data”, Photogrammtric Engineering and Remote Sensing, 58:1343–1350.
   Web of Science ®Google Scholar