Environmental and Ecological Statistics

, Volume 10, Issue 3, pp 301–308 | Cite as

Introduction to special issue on map accuracy

  • Stephen V. Stehman
  • Raymond L. Czaplewski


Mathematical Biology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Agresti, A. (1989) An agreement model with kappa as parameter. Statistics and Probability Letters, 7, 271–3.Google Scholar
  2. Agresti, A., Ghosh, A., and Binia, M. (1995) Raking kappa: Describing potential impact of marginal distributions on measures of agreement. Biometrical Journal, 37, 811–20.Google Scholar
  3. Belward, A.S., Estes, J.E., and Kline, K.D. (1999) The IGBP-DIS global 1-km land-cover data set DISCover: A project overview. Photogrammetric Engineering and Remote Sensing, 65, 1013–20.Google Scholar
  4. Cihlar, J., Beaubien, J., Latifovic, R., and Simard, G. (1999) Land cover of Canada 1995 Version 1.1. Digital data set documentation, Ottawa: Natural Resources Canada.Google Scholar
  5. Clarke, K.C., Gaydos, L., and Hoppen, S. (1996) A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24, 247–61.Google Scholar
  6. Congalton, R.G. (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.Google Scholar
  7. Congalton, R.G. and Green, K. (1999) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Lewis Publishers, Boca Raton, Florida.Google Scholar
  8. Congalton, R.G., Oderwald, R.G., and Mead, R.A. (1983) Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing, 49, 1671–8.Google Scholar
  9. Czaplewski, R.L. (1994) Variance approximations for assessments of classification accuracy, Res. Pap. RM-316, U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO, p. 29.Google Scholar
  10. Czaplewski, R.L. (2000) Accuracy assessments and areal estimates using two-phase stratified random sampling, cluster plots, and the multivariate composite estimator. In Quantifying Spatial Uncertainty in Natural Resources, H.T. Mowrer and R.G. Congalton (eds), Ann Arbor Press, Chelsea, Michigan, pp. 79–100.Google Scholar
  11. Donoghue, D.N.M. (2002) Remote sensing: environmental change. Progress in Physical Geography, 26, 144–51.Google Scholar
  12. Edwards, T.C., Jr., Moisen, G.G., and Cutler, D.R. (1998) Assessing map accuracy in a remotely-sensed ecoregion-scale cover-map. Remote Sensing of Environment, 63, 73–83.Google Scholar
  13. Foody, G.M. (1999) The continuum of classification fuzziness in thematic mapping. Photogrammetric Engineering and Remote Sensing, 65, 443–51.Google Scholar
  14. Foody, G.M. (2002) Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185–201.Google Scholar
  15. Franklin, S.E. and Wulder, M.A. (2002) Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 26, 173–205.Google Scholar
  16. Fuller, R.M., Groom, G.B., and Jones, A.R. (1994) The landcover map of Great Britain: an automated classification of Landsat Thematic Mapper data. Photogrammetric Engineering and Remote Sensing, 60, 553–62.Google Scholar
  17. Gill, S.J., Milliken, J., Beardsley, D., and Warbington, R. (2000) Using a mensuration approach with FIA vegetation plot data to assess the accuracy of tree size and crown closure classes in a vegetation map of Northeastern California. Remote Sensing of Environment, 73, 298–306.Google Scholar
  18. Gopal, S. and Woodcock, C. (1994) Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing, 60, 181–8.Google Scholar
  19. Hayes, D.J. and Sader, S.A. (2001) Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series. Photogrammetric Engineering and Remote Sensing, 67, 1067–75.Google Scholar
  20. Janssen, L.L.F. and van der Wel, F.J.M. (1994) Accuracy assessment of satellite derived land-cover data: A review. Photogrammetric Engineering and Remote Sensing, 60, 419–26.Google Scholar
  21. Jensen, J.R., Rutchey, K., Koch, M., and Narumalani, S. (1995) Inland wetland change detection in the Everglades Water Conservation Area 2a using a time series of normalized remotely sensed data. Photogrammetric Engineering and Remote Sensing, 61, 199–209.Google Scholar
  22. Jones, K.B., Neale, A.C., Nash, M.S., Van Remortel, R.D., Wickham, J.D., Riitters, K.H., and O'Neill, R.V. (2001) Predicting nutrient and sediment loadings to streams from landscape metrics: a multiple watershed study from the United States Mid-Atlantic region. Landscape Ecology, 16, 301–12.Google Scholar
  23. Kyriakidis, P.C. and Dungan, J.L. (2001) A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions. Environmental and Ecological Statistics, 8, 311–30.Google Scholar
  24. Laba, M., Gregory, S.K., Braden, J., Ogurcak, D., Hill, E., Fegraus, E., Fiore, J., and DeGloria, S.D. (2002) Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover maps. Remote Sensing of Environment, 81, 443–55.Google Scholar
  25. Lawler, J.J. and Edwards, T.C. (2002) Landscape patterns as habitat predictors: building and testing models for cavity-nesting birds in the Uinta Mountains of Utah, USA. Landscape Ecology, 17, 233–45.Google Scholar
  26. Lunetta, R.S. and Elvidge, C.D. (1998) Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, Sleeping Bear Press, Inc., Chelsea, MI.Google Scholar
  27. Ma, Z. and Redmond, R.L. (1995) Tau coefficients for accuracy assessment of classification of remote sensing data. Photogrammetric Engineering and Remote Sensing, 61, 435–9.Google Scholar
  28. Mas, J.-F., Velázquez, A., Palacio-Prieto, J.L., Bocco, G., Peralta, A., and Prado, J. (2002) Assessing forest resources in Mexico: Wall-to-wall land use/cover mapping. Photogrammetric Enginering and Remote Sensing, 68(10), 966–8.Google Scholar
  29. McGwire, K.C. and Fisher, P. (2001) Spatially variable thematic accuracy: Beyond the confusion matrix. In Spatial Uncertainty in Ecology: Implications for Remote Sensing and GIS Applications, C.T. Hunsaker, M.F. Goodchild, M.A. Friedl, and T.J. Case (eds.), Springer, New York. pp. 308–29.Google Scholar
  30. Mücher, C.A., Steinnocher, K.T., Kressler, F.P., and Heunks, C. (2000) Land cover characterization and change detection for environmental monitoring of pan-Europe. International Journal of Remote Sensing, 21, 1159–81.Google Scholar
  31. Naesset, E. (1996) Use of the weighted Kappa coefficient in classification error assessment of thematic maps. International Journal of Geographic Information Systems, 10, 591–604.Google Scholar
  32. Riley, R.H., Phillips, D.L., Schuft, M.J., and Garcia, M.C. (1997) Resolution and error in measuring land-cover change: effects on estimating net carbon release from Mexican terrestrial ecosystems. International Journal of Remote Sensing, 18, 121–37.Google Scholar
  33. Scepan, J. (1999) Thematic validation of high-resolution global land-cover data sets. Photogrammetric Engineering and Remote Sensing, 65, 1051–60.Google Scholar
  34. Steele, B.M., Winne, J.C., and Redmond, R.L. (1998) Estimation and mapping of misclassification probabilities for thematic land cover maps. Remote Sensing of Environment, 66, 192–202.Google Scholar
  35. Stehman, S.V. (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62, 77–89.Google Scholar
  36. Stehman, S.V. (1999) Basic probability sampling designs for thematic map accuracy assessment. International Journal of Remote Sensing, 20, 2423–41.Google Scholar
  37. Stehman, S.V. and Czaplewski, R.L. (1998) Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment, 64, 331–44.Google Scholar
  38. Story, M. and Congalton, R.G. (1986) Accuracy assessment: a user's perspective. Photogrammetric Engineering and Remote Sensing, 52, 397–9.Google Scholar
  39. Tanner, R. and Young, M.A. (1985) modeling agreement among raters. Journal of the American Statistical Association, 80, 175–80.Google Scholar
  40. Uebersax, J.S. (1987) Diversity of decision-making models and the measurement of interrater agreement. Psychological Bulletin, 101, 140–6.Google Scholar
  41. Vogelmann, J.E., Howard, S.M., Yang, L., Larson, C.R., Wylie, B.K., and Van Driel, N. (2001) Completion of the 1990s National Land Cover Data set for the conterminous United States from Landsat Thematic mapper data and ancillary data sources. Photogrammetric Engineering and Remote Sensing, 67, 650–62.Google Scholar
  42. Wickham, J.D. and Norton, D.J. (1994) Mapping and analyzing landscape patterns. Landscape Ecology, 9, 7–23.Google Scholar
  43. Yang, L., Stehman, S.V., Smith, J.H., and Wickham, J.D. (2001) Thematic accuracy of MRLC land cover for the Eastern United States. Remote Sensing of Environment, 76, 418–22.Google Scholar
  44. Zhu, Z., Yang, L., Stehman, S.V., and Czaplewski, R.L. (2000) Accuracy assessment for the U.S. Geological Survey regional land-cover mapping Program: New York and New Jersey region. Photogrammetric Engineering and Remote Sensing, 66, 1425–35.Google Scholar
  45. Zwick, R. (1988) Another look at interrater agreement. Psychological Bulletin, 103, 374–8.PubMedGoogle Scholar

Copyright information

© Kluwer AcademicPublishers 2003

Authors and Affiliations

  • Stephen V. Stehman
    • 1
  • Raymond L. Czaplewski
    • 2
  1. 1.SUNY ESFSyracuse
  2. 2.U.S. Department of Agriculture, Forest Service, Rocky Mountain Research StationFort Collins

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