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Consequences of land-cover misclassification in models of impervious surface

Photogrammetric Engineering and Remote Sensing
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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. ?? 2007 American Society for Photogrammetry and Remote Sensing.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Consequences of land-cover misclassification in models of impervious surface
Series title Photogrammetric Engineering and Remote Sensing
Volume 73
Issue 12
Year Published 2007
Language English
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Photogrammetric Engineering and Remote Sensing
First page 1343
Last page 1353