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.
| 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 |
| DOI | 10.14358/PERS.73.12.1343 |
| Volume | 73 |
| Issue | 12 |
| Year Published | 2007 |
| Language | English |
| Publisher | ASPRS |
| Contributing office(s) | Southeast Climate Science Center |
| Description | 11 p. |
| First page | 1343 |
| Last page | 1353 |
| Google Analytic Metrics | Metrics page |