High Resolution Impervious Surface Estimation: An Integration of Ikonos and Landsat-7 ETIVI+ Imagery

摘要

Recent studies have attempted to extract impervious surfaces from high-resolution satellite imagery such as Ikonos and QuickBird. These images, however, often lack necessary spectral information due to technological limitations. This study integratesspectral information (temperature and moisture) derived from Landsat-7 etm-- imagery with Ikonos imagery to derive high-resolution impervious surface information. Furthermore, three popular methods, including linear regression modeling, artificial neural network, and regression tree have been developed and compared using a paired t-test statistic. Analysis of results reveal that Tasseled Cap components particularly greenness and wetness of Ikonos imagery are most important in estimating sub-pixel imperviousness. Also, to some extent the brightness temperature derived from Landsat-7 etm+ image helps in better estimation of impervious surfaces. Moreover, a comparative analysis indicates that the non-linear approaches yielded statistically better results. Particularly, the regression tree model generated best results with highest Pearson's r (0.939) and lowest mean absolute error (8.307).