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    High-spatial resolution digital color-infrared aerial imagery of Syracuse, NY was analyzed to test methods for developing land cover classifications for an urban area. Five cover types were mapped: tree/shrub, grass/herbaceous, bare soil, water and impervious surface. Challenges in high-spatial resolution imagery such as shadow effect and similarity in spectral response between classes were found. Classification confusion among objects with similar spectral responses occurred between water and dark impervious surfaces, concrete and baresoil, and grass/herbaceous and trees/shrub. Methods of incorporating texture, band ratios, masking of water objects, sieve functions, and majority filters were evaluated for their potential to improve the classification accuracy. After combining these various techniques, overall cover accuracy for the study area was 81.75%. Highest accuracies occurred for water (100%), tree/shrub (86.2%) and impervious surfaces (82.6%); lowest accuracy were for grass/herbaceous (69.3%) and bare soil (40.0%). Methods of improving cover map accuracy are discussed.

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    Myeong, Soojeong; Nowak, David J.; Hopkins, Paul F.; Brock, Robert H. 2003. Urban cover mapping using digital, high-resolution aerial imagery. Urban Ecosystems. 5: 243-256.


    remote sensing, image processing, NDVI, image texture, map accuracy assessment

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