A Modeling Approach for Estimating Watershed Impervious Surface Area from National Land Cover Data 92

Abstract

We used National Land Cover Data 92 (NLCD 92), vector impervious surface data, and raster GIS overlay methods to derive impervious surface coefficients per NLCD 92 class in portions of the Mid-Atlantic physiographic region. Sample areas for the study were thirty-six subwatersheds ranging in size from 2 km 2 to 150 km 2 . A three-category rural-to-urban gradient design was utilized due to the changing sub-pixel relationship of impervious surface areas within developed/nondeveloped areas. A gradient rule based on the NLCD 92 DEVELOPED% defined the sample areas as “rural” (� 18 percent ‘developed’), “intermediate” (18 percent‐40 percent ‘developed’) and “dense suburban” (40.01 percent‐80 percent ‘developed’). The gradient scheme produced three separate sets of coefficients per NLCD 92 Level 1 and Level 2 class. Results show distinct per-class coefficient groupings across the rural-to-urban gradient with coefficients directly related to the increasing level of development in a subwatershed. We also developed a linear equation between the NLCD 92 DEVELOPED% and truth percent impervious area. Results show a relative accuracy of approximately 80 percent and a mean absolute TIA% estimate error of approximately 2.0 percent � 1.0 percent for both the Level 1 coefficients and the Level 2 coefficients. Results derived from the linear regression model show a relative accuracy of 70 percent with a mean absolute TIA% estimate error of approximately 2.0 percent � 1.0 percent. This suggests that a linear model can be used as a rapid assessment tool to approximate TIA% from NLCD 92 data. Results are based on a spatial aggregation of pixels to the subwatershed or “whole-area” scale and are most applicable to “pour-point” models utilizing a single percent impervious surface area parameter. The models reported here have been tested only in the Mid-Atlantic region (USEPA Federal Region 3).