Abstract
The increasing availability of digital photographic materials has fueled efforts by agencies and organizations to generate land cover maps for states, regions, and the United States as a whole. Regardless of the information sources and classification methods used, land cover maps are subject to numerous sources of error. In order to understand the quality of the information contained in these maps, it is desirable to generate statistically valid estimates of accuracy rates describing misclassification errors. We explored a full sample survey framework for creating accuracy assessment study designs that balance statistical and operational considerations in relation to study objectives for a regional assessment of GAP land cover maps. We focused not only on appropriate sample designs and estimation approaches, but on aspects of the data collection process, such as gaining cooperation of land owners and using pixel clusters as an observation unit. The approach was tested in a pilot study to assess the accuracy of Iowa GAP land cover maps. A stratified two-stage cluster sampling design addressed sample size requirements for land covers and the need for geographic spread while minimizing operational effort. Recruitment methods used for private land owners yielded high response rates, minimizing a source of nonresponse error. Collecting data for a 9-pixel cluster centered on the sampled pixel was simple to implement, and provided better information on rarer vegetation classes as well as substantial gains in precision relative to observing data at a single-pixel.
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References
Cochran, W.G. (1977) Sampling techniques, John Wiley & Sons, New York.
Congalton, R.G. (1988) A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 54(5), 593–600.
Congalton, R. (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.
Congalton, R. and Green, K. (1993) A practical look at the sources of confusion in error matrix generation. Photogrammetric Engineering and Remote Sensing, 59(5), 641–44.
Crist, P. and Deitner, R. (1998) Assessing land cover map accuracy. National GAP Analysis Handbook. USGS/BRD, Idaho Coop. Fish and Wildlife Unit, Univ. of Idaho, Moscow.
Groves, R.M. (1989) Survey errors and survey costs, John Wiley & Sons, New York.
Jennings, M.D. (2000) Gap analysis: concepts, methods, and recent results. Landscape Ecology, 15, 5–20.
Lessler, J.T. and Kalsbeek, W.D. (1992) Nonsampling errors in surveys, John Wiley & Sons, New York.
Lohr, S.L. (1999) Sampling: design and analysis, Brooks/Cole Publishing Company, Pacific Grove, CA, p. 494.
Nusser, S.M. and Klaas, E.E. (2002) Final performance report to EPA Region 7, Part II: Gap accuracy assessment pilot study. Environmental Protection Agency Contract X997387–01 Final Report. Iowa Cooperative Fish and Wildlife Research Unit, Iowa State University, Ames, Iowa, p. 77.
Salant, P. and Dillman, D.A. (1994) How to conduct your own survey, Wiley, New York, NY, p. 232.
Särndal, C.-E., Swensson, B., and Wretman, J. (1992) Model assisted survey sampling, Springer-Verlag, New York, p. 694.
SAS (2000) SAS/STAT User's Guide, Version 8, SAS, Inc., Cary, NC, p. 3749.
Stehman, S.V. (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62, 77–89.
Stehman, S.V. (1999) Basic probability sampling designs for thematic map accuracy assessment. International Journal of Remote Sensing, 20, 2347–66.
Stehman, S.V. and Czaplewski, R.L. (1998) Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment, 64(3), 331–44.
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Nusser, S.M., Klaas, E.E. Survey methods for assessing land cover map accuracy. Environmental and Ecological Statistics 10, 309–331 (2003). https://doi.org/10.1023/A:1025107023980
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DOI: https://doi.org/10.1023/A:1025107023980