Location: Southwest Watershed Research
Title: Mapping Impervious Surfaces Using Object-oriented Classification in a Semiarid Urban Region 2212 Authors
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 30, 2013
Publication Date: N/A
Interpretive Summary: Population in-migration related to environmental amenities in Arizona and many other western regions have been associated with high rates of urbanization. Impervious areas created by unbanization can produce significant changes in hydrological processes by altering runoff, increasing peak flows and degrading water quality. Mapping impervious surfaces with remote sensing techniques is an effective way to quantify impervious cover and thereby improve understanding of the impacts of urbanization on runoff processes. The most common approach using remotely derived vegetation indices, however, is problematic in arid and semiarid environments where vegetation is patchy and often senescent. This paper describes a method for mapping impervious surfaces using high-resolution imagery for an urbanizing semi-arid area. At the neighborhood scale we found that the automated method of image classification creates impervious area maps that are comparable in accuracy to manual delineation methods. The same method was then adapted to produce a map of impervious surfaces in the entire city of Sierra Vista, Arizona and the surrounding subwatershed with high accuracy. This method overcame a common limitation of high spatial resolution imagery, which is that broad spatial coverage can be difficult due to the high amount of data per image.
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 subwatershed. Results from the neighborhood-scale analysis show that object-oriented classification of QuickBird imagery produced results comparable to manual delineation methods. Applying the approach to a 1,179 km2 region produced maps of impervious surfaces with a mean overall accuracy of 88.1%. 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.