Monitoring urban impervious surface area change using CBERS and HJ-1 remote sensing images

Abstract

Impervious surface plays an important role in monitoring urbanization and related environmental changes. CBERS and HJ-1 satellite images were employed to impervious surface extraction. Xuzhou City, located in the northwestern of Jiangsu Province, China, was chosen as the case study area. Using linear spectral mixture model (LSMM) and multi-layer perception (MLP) neural network, all pixels were decomposed to the four fraction images representing the abundance of four endmembers: vegetation, high-albedo objects, low-albedo objects and soil. Then, the impervious surface area was derived by the combination of high- and low-albedo fraction images after removing the influence of water body. Furthermore, some high spatial resolution images were selected to validate the impervious surface estimation results of the two methods. Experimental results indicate that the accuracy of MLP neural network is higher than LSMM. By comparing the urban impervious surface area based on the MLP neural network from three remote sensing images, the change pattern of impervious surface area was studied. In the past years, the impervious surface has increased rapidly in Xuzhou City, especially in the northeast and southeast regions.