A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery
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.
Natural Resources and the Environment
Photogrammetric Engineering and Remote Sensing
American Society for Photogrammetry and Remote Sensing
Digital Object Identifier (DOI)
Thomas, N., C. Hendrix, and R. Congalton. 2003. A comparison of urban mapping methods using high-resolution digital imagery. Photogrammetric Engineering and Remote Sensing. Vol. 69, No. 9. pp. 963-972.
© 2003 American Society for Photogrammetry and Remote Sensing