greater consideration of the manner in which rural lands are developed to urban lands will become progressively more important.Removal of rural land cover types such as soil
water
and vegetation and their replacement with common urban impervious surface materials such as asphalt
concrete
and metal have significant environmental implications.Impervious surfaces are anthropogenic features through which water cannot infiltrate into the soil
existed in roads
driveways
sidewalks
parking lots
rooftops
and so on.To estimate urban impervious surface distribution
a major component of the vegetation-impervious surface-soil(V-I-S) model
is important in monitoring urban eco-environment
such as reduction in evapotranspiration
promotion of more rapid surface run-off
increased storage and transfer of sensible heat
and reduction of air and water quality.The conceptual V-I-S model may be implemented by using the technique of linear spectral mixture analysis(LSMA)
which decomposes the spectral reflectance of a pixel into different proportions.LSMA is regarded as a physically-based image processing tool that supports repeatable and accurate extraction of quantitative subpixel information.In this paper
impervious surface distribution
together with vegetation and soil cover
is estimated through a constrained linear spectral mixture model using Landsat ETM+ data within the metropolitan area of Shanghai city in China.Four endmembers
low albedo
high albedo
vegetion
and soil are selected to model complicated urban land cover.Impervious surface fraction is obtained by adding low and high albedo endmembers fraction.Estimation accuracy is assessed using root mean square(RMS) error and color aerial photography.The overall root mean square error is 0.71%.Results indicate that impervious surface distribution can be derived from remotely sensed imagery with promising accuracy.Then the spatial pattern of impervious surface fraction in central area of Shanghai is analyzed.The spatial pattern of impervious surface discloses urban framework and the characters of urban sprawling.