|SUGG, Z. - University Of Arizona|
|FINKE, T. - University Of Arizona|
|Goodrich, David - Dave|
|YOOL, S. - University Of Arizona|
Submitted to: Photogrammetric Engineering and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/30/2013
Publication Date: 4/1/2014
Citation: Sugg, Z., Finke, T., Goodrich, D.C., Moran, M.S., Yool, S. 2014. Mapping impervious surfaces using object-oriented classification in a semiarid urban region. Photogrammetric Engineering and Remote Sensing. 80(4):343-352. https://doi.org/10.14358/PERS.80.4.343.
Interpretive Summary: Urban growth in the southwestern U.S. and elsewhere creates impervious surfaces that increase storm water runoff and flood potential. Here, we propose a cost-effective, automated approach for mapping imperviousness in semi-arid urban areas using images from orbiting Earth-observation satellites. The results showed that the automated method enabled mapping of impervious areas over large urbanized watersheds with consistent accuracy. This study contributes methodological information to the understanding of urbanization and its impacts on watershed hydrology.
Technical Abstract: Mapping the expansion of impervious surfaces in urbanizing areas is important for monitoring and understanding the hydrologic impacts of land development. The most common approach using spectral vegetation indices, however, is difficult in arid and semiarid environments where vegetation is sparse and often senescent. In this study object-oriented classification of high-resolution imagery was used to develop a cost-effective, semi-automated approach for mapping impervious surfaces in Sierra Vista, Arizona for an individual neighborhood and the larger sub-watershed. Results from the neighborhood-scale analysis show that object-oriented classification of QuickBird imagery produced repeatable results with good accuracy. Applying the approach to a 1,179 km2 region produced maps of impervious surfaces with a mean overall accuracy of 88.1 percent. This study demonstrates the value of employing object-oriented classification of high-resolution imagery to operationally monitor urban growth in arid lands at different spatial scales in order to fill knowledge gaps critical to effective watershed management.