thumbnail

Consequences of land-cover misclassification in models of impervious surface

Photogrammetric Engineering and Remote Sensing

By:

Links

  • The Publications Warehouse does not have links to digital versions of this publication at this time

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
Number of Pages:
11