Geoscience Reference
In-Depth Information
the correct analysis techniques and use the proper sampling approaches. However, all these things
will do us no good if we forget about the map we are trying to assess. We must “put the map back
in the map assessment process.” We must do everything we can to ensure that the assessment is
valid for the map and not simply a statistical exercise. It is key that the reference data match the
map data, not only in classification scheme but also in sampling unit (i.e., minimum mapping unit)
as well. It is also important that we make every effort to collect accurate and timely reference data.
Finally, there is still much to do. Many maps generated from remotely sensed data still have no
validation or accuracy assessment. There are numerous steps that can be taken to evaluate how
good a map is. Now we must move past the age of “it looks good” and move toward the more
quantitative assessments outlined in this chapter.
REFERENCES
Bishop, Y., S. Fienberg, and P. Holland,
Discrete Multivariate Analysis: Theory and Practice
, MIT Press,
Cambridge, MA, 1975.
Campbell, J.,
Guilford Press, New York, 1987.
Campbell, J., Spatial autocorrelation effects upon the accuracy of supervised classification of land cover,
Introduction to Remote Sensing,
Photogram. Eng. Remote Sens.,
47, 355-363, 1981.
Cliff, A.D. and J.K. Ord,
Pion Limited, London, 1973.
Cohen, J., A coefficient of agreement for nominal scales,
Spatial Autocorrelation,
20, 37-46, 1960.
Congalton, R.G., A comparison of sampling schemes used in generating error matrices for assessing the
accuracy of maps generated from remotely sensed data,
Educ. Psychol. Meas.,
Photogram. Eng. Remote Sens.
, 54, 593-600,
1988a.
Congalton, R.G., Using spatial autocorrelation analysis to explore errors in maps generated from remotely
sensed data,
, 54, 587-592, 1988b.
Congalton R. and K. Green, A practical look at the sources of confusion in error matrix generation,
Photogram. Eng. Remote Sens.
Photogram.
59, 641-644, 1993.
Congalton, R. and K. Green,
Eng. Remote Sens.,
Assessing the Accuracy of Remotely Sensed Data: Principles and Practices
,
Lewis, Boca Raton, FL, 1999.
Congalton, R., R. Macleod, and F. Short, Developing Accuracy Assessment Procedures for Change Detection
Analysis, final report submitted to NOAA CoastWatch Change Analysis Program, Beaufort, NC, 1993.
Congalton, R.G. and R.A. Mead, A quantitative method to test for consistency and correctness in photo-
interpretation,
49, 69-74, 1983.
Congalton, R.G., R.G. Oderwald, and R.A. Mead, Assessing Landsat classification accuracy using discrete
multivariate statistical techniques,
Photogram. Eng. Remote Sens.,
49, 1671-1678, 1983.
Gopal, S. and C. Woodcock, Theory and methods for accuracy assessment of thematic maps using fuzzy sets,
Photogram. Eng. Remote Sens.,
60, 181-188, 1994.
Green, K. and R. Congalton, An error matrix approach to fuzzy accuracy assessment: The NIMA Geocover
project example, in Geospatial Data Accuracy Assessment, Lunetta, R. and J. Lyon, Eds., U.S.
Environmental Protection Agency, Report No. EPA/600/R03/064, 2003.
Hay, A.M., Sampling designs to test land-use map accuracy,
Photogram. Eng. Remote Sens.,
Photogram. Eng. Remote Sens.
, 45, 529-533, 1979.
Hord, R.M. and W. Brooner, Land use map accuracy criteria,
Photogram. Eng. Remote Sens.
, 42, 671-677,
1976.
Hudson, W., and C. Ramm, Correct formulation of the kappa coefficient of agreement,
Photogram. Eng.
, 53, 421-422, 1987.
Khorram, S., G. Biging, N. Chrisman, D. Colby, R. Congalton, J. Dobson, R. Ferguson, M. Goodchild, J.
Jensen, and T. Mace,
Remote Sens.
Accuracy Assessment of Remote Sensing-Derived Change Detection
, American
Society for Photogrammetry and Remote Sensing, Bethesda, MD, 1999.
Landis, J. and G. Koch, The measurement of observer agreement for categorical data,
Biometrics
, 33, 159-174,
1977.
Lillesand, T. and R. Kiefer,
Remote Sensing and Image Interpretation
,
3rd
ed., John Wiley & Sons, New York,
1994.
Search WWH ::




Custom Search