TECHNICAL PAPERS
Apr 1, 2009

Assessment of Impervious Surface Estimation Techniques

Publication: Journal of Hydrologic Engineering
Volume 14, Issue 4

Abstract

Impervious surfaces have been identified as a reliable indicator of the impacts of development on water resources. Research has found that imperviousness has a direct effect on local surface water as well as indirect effects on downstream receiving waters. Some of the affected characteristics of a given watershed due to the increase of impervious surfaces include hydrological impacts (the amount of runoff, peak discharge rates, and baseflow are altered), physical impacts (stream morphology and temperature are changed), water quality impacts (nutrient and pollutant loads increase), and biological impacts (stream biodiversity decreases). While attention has been focused on quantifying these relationships, little work has been done to assess the efficacy of various methods for estimating and mapping impervious surfaces. This paper assesses the results from six techniques for estimating the percent of impervious surface compared to photogrammetrically derived calibration and validation data from high spatial resolution digital planimetric datasets for 53 towns in Connecticut and New York. Impervious surface estimation layers and techniques examined include: (1) National Land Cover Dataset (NLCD) 2001 impervious surface layer, derived through regression tree classification of Landsat Enhanced Thermatic Mapper (ETM) data; (2) Connecticut’s Changing Landscape (CCL) 2002 impervious surface layer, derived through Landsat ETM subpixel classification and quantification of percent imperviousness; (3) and (4) land use-specific coefficients for two different land cover datasets, NLCD 2001 and CCL 2002, as modeled with the Impervious Surface Analysis Tool (ISAT); (5) and (6) a population density and a land cover-based regression model—Estimation Tool for Impervious Surfaces (ETIS)—using US Census Bureau population and CCL 2002 and NLCD 2001 land cover. In comparing results with the reference data, it has been found that estimates of census tract-wide imperviousness based on the direct Landsat-derived spectral methods tested yielded the lowest accuracy with an RMSE of 7.2% (NLCD) and 10.2% (CCL), respectively. The ISAT and ETIS, which leveraged readily available land cover information, and in the case of ETIS, population data, demonstrated the highest accuracy of the techniques examined with an RMSE of 4.4% (NLCD ISAT), 6.3% (CCL ISAT), 4.2% (NLCD ETIS), and 4.7% (CCL ETIS). An assessment of impervious surface estimates will provide decision makers and planners with useful information to guide them in selecting the optimal method of mapping imperviousness given their programmatic needs, available data, and technical resources.

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Acknowledgments

The research upon which this paper is based was supported by the project “Mapping and Monitoring Changes in Impervious Surfaces in the Long Island Sound Watershed,” sponsored by the US EPA Long Island Sound Study, by the University of Connecticut’s Institute of Water Resources under the project “Development of Regionally Calibrated Land Cover Impervious Surface Coefficients,” and, in part, by the project “Incorporating NASA’s Applied Sciences Data and Technologies into Local Government Decision Support in the National Application Areas of Coastal Management, Water Management, Ecologic Forecasting and Invasive Species,” sponsored by the National Aeronautics and Space Administration. The writers thank Jason Parent for his assistance in the Python programming used in developing ETIS. [CLEAR Publication Number 080131.1]

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Go to Journal of Hydrologic Engineering
Journal of Hydrologic Engineering
Volume 14Issue 4April 2009
Pages: 377 - 387

History

Received: Jan 30, 2008
Accepted: Nov 11, 2008
Published online: Apr 1, 2009
Published in print: Apr 2009

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Anna Chabaeva [email protected]
Research Assistant, Dept. of Natural Resources Management & Engineering, Univ. of Connecticut, U-4087, 1376 Storrs Rd., Storrs, CT 06269-4087 (corresponding author). E-mail: [email protected]
Daniel L. Civco
Professor, Dept. of Natural Resources Management & Engineering, Univ. of Connecticut, U-4087, 1376 Storrs Rd., Storrs, CT 06269-4087.
James D. Hurd
Research Associate, Dept. of Natural Resources Management & Engineering, Univ. of Connecticut, U-4087, 1376 Storrs Rd., Storrs, CT 06269-4087.

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