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Consequences of land-cover misclassification in models of impervious surface

Photogrammetric Engineering and Remote Sensing journal article
By Gerard McMahon

Full Journal Article (11 pages, 233K)


Abstract

Model estimates of impervious area as a function of landcover area may be biased and imprecise because of errors in the land-cover classification. This investigation of the effects of land-cover misclassification on impervious surface models that use National Land Cover Data (NLCD) evaluates the consequences of adjusting land-cover within a watershed to reflect uncertainty assessment information. Model validation results indicate that using error-matrix information to adjust land-cover values used in impervious surface models does not substantially improve impervious surface predictions. Validation results indicate that the resolution of the landcover data (Level I and Level II) is more important in predicting impervious surface accurately than whether the land-cover data have been adjusted using information in the error matrix. Level I NLCD, adjusted for land-cover misclassification, is preferable to the other land-cover options for use in models of impervious surface. This result is tied to the lower classification error rates for the Level I NLCD.


Citation:

McMahon, Gerard, 2007, Consequences of land-cover misclassification in models of impervious surface: Photogrammetric Engineering and Remote Sensing, v. 73, no. 12, p. 1343-1353.


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