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Remote Sens. 2013, 5(10), 4942-4960; doi:10.3390/rs5104942

Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography

Department of Geography, Virginia Tech, 115 Major Williams Hall, Blacksburg, VA 24061, USA
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Received: 10 July 2013 / Revised: 14 September 2013 / Accepted: 25 September 2013 / Published: 10 October 2013
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Abstract

This paper evaluates accuracies of selected image classification strategies, as applied to Landsat imagery to assess urban impervious surfaces by comparing them to reference data manually delineated from high-resolution aerial photos. Our goal is to identify the most effective methods for delineating urban impervious surfaces using Landsat imagery, thereby guiding applications for selecting cost-effective delineation techniques. A high-resolution aerial photo was used to delineate impervious surfaces for selected census tracts for the City of Roanoke, Virginia. National Land Cover Database Impervious Surface data provided an overall accuracy benchmark at the city scale which was used to assess the Landsat classifications. Three different classification methods using three different band combinations provided overall accuracies in excess of 70% for the entire city. However, there were substantial variations in accuracy when the results were subdivided by census tract. No single classification method was found most effective across all census tracts; the best method for a specific tract depended on method, band combination, and physical characteristics of the area. These results highlight impacts of inherent local variability upon attempts to characterize physical structures of urban regions using a single metric, and the value of analysis at finer spatial scales. View Full-Text
Keywords: impervious surfaces; urban; Landsat; high-resolution imagery; landuse classification; Roanoke; Virginia impervious surfaces; urban; Landsat; high-resolution imagery; landuse classification; Roanoke; Virginia
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Parece, T.E.; Campbell, J.B. Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography. Remote Sens. 2013, 5, 4942-4960.

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