Abstract
The National Land Cover Database (NLCD) contains three eras (2001, 2006, 2011) of percentage urban impervious cover (%IC) at the native pixel size (30 m-×-30 m) of the Landsat Thematic Mapper satellite. These data are potentially valuable to environmental managers and stakeholders because of the utility of %IC as an indicator of watershed and aquatic condition, but lack an accuracy assessment because of the absence of suitable reference data. Recently developed 1 m(2) land cover data for the Chesapeake Bay region makes it possible to assess NLCD %IC accuracy for a 262,000 km(2) region based on a census rather than a sample of reference data. We report agreement between the two %IC datasets for watersheds and the riparian zones within watersheds and four additional square units. The areas of the six assessment units were 40 ha cell, 433 ha (riparian mean), 2756 ha cell, 5626 ha cell, 8569 ha (watershed mean) and 22,500 ha cell. Mean Absolute Deviation (MAD) and Mean Deviation (MD) were about 1.5% and -1.5%, respectively, for each of the assessment units except for the riparian unit, for which MAD and MD were 0.88 and 0.62, respectively. NLCD reliably reproduced %IC from the 1 m(2) data with a small, consistent tendency for underestimation. Results were sensitive to assessment unit choice. The results for the four largest assessment units had very similar regression parameters, R(2) values, and bias patterns. Results for the riparian assessment were different from those for the watershed unit and the other three larger units. MAD was about 50% less for the riparian zones than it was for the watersheds, the direction of bias was less consistent, and NLCD %IC was uniformly higher than 1 m(2) %IC in urbanized riparian zones. For the smallest unit, bias patterns were more similar to the riparian unit and regression results were more similar to the four larger units. MAD and MD were also sensitive to the amount of urbanization, increasing as NLCD %IC increased. The low overall bias and positive relationship between bias and urbanization suggest that the benefits of obtaining 1 m(2) IC data outside of urban areas may not outweigh the costs of obtaining such data.