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Recent advances in digital airborne sensors and satellite platforms make spatially accurate, high-resolution multispectral imagery readily available. High-resolution imagery is particularly well suited to urban applications. This article provides an overview of a project in which one-meter digital imagery was used to produce a map of pervious and impervious surfaces to be used by the city of Scottsdale, Arizona for storm-water runoff estimation. The authors assess the accuracy of three different methods for extracting land-cover/land-use information from high-resolution imagery of urban environments: (1) combined supervised/ unsupervised spectral classification, (2) raster-based spatial modeling, and (3) image segmentation classification using classification tree analysis. The authors conclude that the image segmentation classification incorporating classification tree analysis as described in this study offers a significant time saving over the analyst-intensive spatial modeling technique by automatically integrating image segment measures.
Thomas et al. (Mon,) studied this question.