Extracting impervious surface by CART method according to different brightness values from remote sensing imagery

As a key indicator of the urbanization level, impervious surface has widely been used as an environmental indicator linking development to its impact on the environment. Many have for example suggested that there is a direct correlation between the amount of impervious surface and the environmental characteristics of an urban area. In recent years, researches and applications utilizing classification and regression tree (CART) technology have increased and been successfully applied in ecology and image processing or analysis. An approach to quantify urban impervious surfaces as a continuous variable by using QuickBird images as verification data and CBERS images as primary data source was developed. To evaluate model performance, extracted imperviousness from CBERS was compared against that derived from the QuickBird imagery of high resolution. The relative error of this method decreased to 2.9% compared with that of traditional CART method which was 5.8%.