Comparison of Spectral Analysis Techniques for Impervious Surface Estimation Using Landsat Imagery

Abstract

Various methodologies have been used to estimate and map percent impervious surface area (%ISA) using moderate resolution remote sensing imagery (e.g., Landsat Thematic Mapper). There is, however, a lack of comparative analyses among these methods. This study compares three major spectral analysis techniques (regression modeling, regression tree, and normalized spectral mixture analysis (NSMA)) for continuous %ISA estimation using Landsat imagery for 1986 and 2002 for the seven-county Twin Cities Metropolitan Area of Minnesota. Our study showed that all three techniques demonstrate the capability for estimating %ISA accurately, with RMSE ranging from 7.3 percent to 11 percent and R 2 of 0.90 to 0.96 for both years. Comparatively, regression modeling and regression tree methods produced similar results; however, both of them are highly dependent on accurate masks to differentiate urban impervious surfaces from bare soil. Within the urban mask, the regression tree-based estimates were the most accurate. In terms of time and cost, the NSMA approach is most efficient, but it tends to underestimate the percent imperviousness for highly developed areas. Findings from the study provide guidance for the selection of %ISA estimation techniques using moderate resolution remote sensing data, along with information for further methodological improvements.