Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery

With rapid urban growth in recent years, understanding urban biophysical composition and dynamics becomes an important research topic. Remote sensing technologies introduce a potentially scientific basis for examining urban composition and monitoring its changes over time. The vegetation–impervious surface–soil (V–I–S) 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 normalized spectral mixture analysis (NSMA) method to examine urban composition in Columbus Ohio using Landsat ETM+ data. In particular, a brightness normalization method is applied to reduce brightness variation. Through this normalization, brightness variability within each V–I–S component is reduced or eliminated, thus allowing a single endmember representing each component. Further, with the normalized image, three endmembers, vegetation, impervious surface, and soil, are chosen to model heterogeneous urban composition using a constrained spectral mixture analysis (SMA) model. The accuracy of impervious surface estimation is assessed and compared with two other existing models. Results indicate that the proposed model is a better alternative to existing models, with a root mean square error (RMSE) of 10.1% for impervious surface estimation in the study area.