Effectiveness of Subpixel Analysis in Detecting and Quantifying Urban Imperviousness from Landsat Thematic Mapper Imagery

Abstract

The quantification of urban imperviousness using remotely sensed spectral data is next to impossible unless a spectral fraction of impervious components in an urban pixel can be detected. In this research, a multiple‐signature subpixel analysis technique coupled with a layered classification approach was developed to map urban imperviousness of each pixel of an urban scene into eight 10‐percent levels. The subpixel analysis was based on the idea of removing background spectra from the total radiance of a pixel and testing the residual spectrum against the signature spectrum. This study demonstrated that although the subpixel analysis was able to quantify urban imperviousness from most of the urban pixels, it experienced some difficulty in handling the spectral heterogeneity of diverse urban features. The layered classification approach was used to identify the extreme cases. An experiment of classifying Landsat TM data into eight levels of urban imperviousness revealed that 83.0% (kappa = 0.787) of the impervious pixels agreed with reference data. A Spearman rank‐order correlation analysis yielded rho = 0.964, giving a much improved assessment of the urban imperviousness map at subpixel level. Linear regression analysis is suggested, if interval data are retained from the subpixel process, as an alternative for map error modeling and model calibration with ground truth.