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

Various methodologies have been used to estimate and map percent impervious surface area (%ISA) using moderate resolution remote sensing imagery. However, there is a lack of comparative analyses among these methods. This paper compares 3 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 7-county Twin Cities Metropolitan Area of Minnesota. This study showed that all 3 techniques demonstrate the capability for estimating %ISA accurately, with root mean squared errors ranging from 7.3-11% and R-squared of 0.90-0.96 for both years. Comparatively, regression modeling and regression tree methods produced similar results; however, both 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 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.

  • Availability:
  • Authors:
    • Yuan, Fei
    • Wu, Changshan
    • Bauer, Marvin E
  • Publication Date: 2008-8


  • English

Media Info

  • Media Type: Print
  • Features: Figures; References; Tables;
  • Pagination: pp 1045-1055
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01108797
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 12 2008 5:58PM