TY - JOUR
T1 - Comparison of spectral analysis techniques for impervious surface estimation using landsat imagery
AU - Yuan, Fei
AU - Wu, Changshan
AU - Bauer, Marvin E.
PY - 2008/8
Y1 - 2008/8
N2 - 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 R2 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.
AB - 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 R2 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.
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U2 - 10.14358/PERS.74.8.1045
DO - 10.14358/PERS.74.8.1045
M3 - Article
AN - SCOPUS:50249177593
SN - 0099-1112
VL - 74
SP - 1045
EP - 1055
JO - Photogrammetric Engineering and Remote Sensing
JF - Photogrammetric Engineering and Remote Sensing
IS - 8
ER -