Estimating impervious surface for the urban area expansion:Examples from changchun, northeast china

As a developing country, China is now undergoing a quick process of urbanization. Therefore, understanding and managing the urban environment is a prerequisite for addressing sustainability, which is an increasingly important issue need a range of discipline to cope with. This paper explored extraction of impervious surface information from Landsat ETM+ data with the integration of fraction images from linear spectral mixture analysis based upon Ridd’s vegetation-impervious surface-soil (V-I-S) model. A new approach for urban land-use classification, based on the combined use of impervious surface and spectral mixture analysis (SMA) were applied in this paper. The minimum noise fraction transform (MNF) procedure was applied to transform the six reflective bands into a new coordinate set to select the four endmebers, e.g. high-albedo surface, low-albedo surface, soil and vegetation.. Results showed that the integration of faction images improved urban impervious surface estimation. The impervious surface in the urban area were derived from high-albedo surface and low-albedo surface. Accuracy assessment indicated that the root-mean-square error is less than 10.2 % for the impervious surface image. The main factors that affect the accuracy are the reflectance variation caused by atmospheric factors, sun-sensor-target geometry. How to deal with these factors to minimize reflectance variation will be the future study topics. Also water body and shade were not addressed in this paper, which also need to be considered in the future study