Extraction of impervious surface based on the standardized ratio model

With rapid urban growth in recent years, it becomes an important research topic for us to understand urban biophysical composition and dynamics. Remote sensing technologies introduce a potentially scientific basis for examining urban composition and monitoring its changes over time. The vegetation-impervious surface-soil-water (V-I-S-W) model, in particular, provides a foundation for describing urban/suburban environments and a basis for further urban analyses including urban growth modeling, environmental impact analysis, and socioeconomic factor estimation. This paper develops a standardized ratio model (SRM) method to examine urban composition especially urban impervious surface in Haizhu District using Landsat ETM+ data. In particular, a brightness SRM method is applied to reduce brightness variation. Through this standardization, brightness variability within each V-I-S-W component is reduced or eliminated, thus allowing a single end-member representing each component. Further, with the standardized image, four endmembers, vegetation, impervious surface, soil, and water, are chosen to model heterogeneous urban composition using a constrained spectral mixture analysis (SMA) models. The accuracy of impervious surface estimation is assessed and compared with other existing model. Results indicate that the proposed model is a better alternative to existing models, with a root mean square error (RMSE) of 12.6% for impervious surface estimation in the study area.