U.S. Department of the Interior

U.S. Geological Survey

Open File Report 2007-1008

Prepared for the U.S. Department of Transportation, Federal Highway Administration

Quantifying the Components of Impervious Surfaces 

By Janet S. Tilley and E. Terrence Slonecker

In collaboration with the 

U.S. Environmental Protection Agency

Office of Research and Assessment


Quantifying the Components of Impervious 

Surfaces

By Janet S. Tilley and E. Terrence Slonecker

U.S. Department of the Interior

U.S. Geological Survey

Open File Report 2007-1008

In collaboration with the 

U.S. Environmental Protection Agency

Office of Research and Assessment
ii

U.S. Department of the Interior

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2006

For additional information about this report write to:

Janet S. Tilley E. Terrence Slonecker

Physical Scientist Research Environmental Scientist

Eastern Geographic Science Center National Exposure Research Laboratory

U.S. Geological Survey U.S. Environmental Protection Agency

12201 Sunrise Valley Drive, MS-521 12201 Sunrise Valley Drive, MS-555

Reston, VA 20192 Reston, VA 20192

For more information on the USGS--the Federal source for science about the Earth, its natural and living resources, 

natural hazards, and the environment:

World Wide Web: http://www.usgs.gov

Telephone: 1-888-ASK-USGS

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Tilley, J.S., and Slonecker, E.T., 2006, Quantifying the Components of Impervious Surfaces: U.S. Geological Survey 

Open-File Report 2006-1008, 33 p.

Library of Congress Cataloging-in-Publication Data

Author1LastName, Author1FirstName Q.

Title of book/ Author List

p. cm. –(U.S. Geological Survey professional paper ; 0000)

Includes bibliographic references.

1. Subject heading. 2. Subject heading. 3. Subject heading. I. Main entries. 

II.Main entries. III. Main entries.

TN24.M9 M55 2002

553’.09786’6—dc21

2001051109

ISBN 0-607-97815-9 (Set)

ISBN 0-607-97815-7 (Volume 1)

ISBN 0-607-97815-5 (Volume 2)
iii

CONTENTS

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

• SIDEBAR: Why Impervious Surfaces Are Important . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

• Impervious Surfaces Defined . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

• A National Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

• Impervious Surfaces as an Environmental Indicator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

 • Correlations Between Impervious Surfaces and Aquatic Ecosystem Conditions . . . . . . . . . . . . . . . . . . . . . . 4

 • Impervious Surface Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

• Science and Policy Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

• The Emerging Science of Measuring Impervious Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

• Scope and Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 • Task 1 - Quantifying Components of Impervious Surfaces: Reference Data . . . . . . . . . . . . . . . . . . . . . . . . . 6

 • Task 2 - Intermediate Level Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

 • Task 3 - Synoptic Scale Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Study Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

• Watershed Selection Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

 • Local Data Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

• Watershed Locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

• Watershed Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

 • Black River, King County, Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

 • Difficult Run, Fairfax County, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

 • North Walnut Creek, Polk County, Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 • Oak Creek, Lancaster County, Nebraska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 • Tuscarora Creek, Loudoun County, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

• Wares Creek, Manatee County, Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

• Overall Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

 • Task 1 – Quantifying Components of Impervious Surfaces: Reference Data . . . . . . . . . . . . . . . . . . . . . . . . 14

 • Task 2 – Intermediate Scale Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

 • Task 3 – Synoptic Scale Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

• Task 1 – Quantifying Components of Impervious Surfaces: Reference Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

• Task 2 – Intermediate Scale Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

• Task 3 – Synoptic Scale Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

• Task 1 – Quantifying Components of Impervious Surfaces: Reference Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

• Task 2 – Intermediate Scale Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

• Task 3 – Synoptic Scale Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Additional Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Web Sites for Additional Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

References Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Appendix A: GIS Data Source Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Appendix B: High Spatial Resolution Imagery Source Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Appendix C: Small Scale Source Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
iv

FIGURES

1. Photo showing a typical suburban impervious surface cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2. Examples of data used in this research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3. Map showing watershed locations for this study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4. Locator map for Black River watershed, Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

5. Map and aerial photo of Black River watershed, Washington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

6. Locator map for Difficult Run watershed, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

7. Map and aerial photo of Difficult Run watershed, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

8. Area photo of Difficult Run watershed, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

9. Locator map for North Walnut Creek watershed, Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

10. Map and aerial photo of North Walnut Creek watershed, Iowa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

11. Locator map for Oak Creek watershed, Nebraska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

12. Map and aerial photo of Oak Creek watershed, Nebraska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

13. Locator map for Tuscarora Creek watershed, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

14. Map and aerial photo of Tuscarora Creek watershed, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

15. Area photos of Tuscarora Creek watershed, Virginia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

16. Locator map for Wares Creek watershed, Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

17. Map and aerial photo of Wares Creek watershed, Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

18. Before and after data collection for Task 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

19. Team member digitizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

20. RESAC subpixel product for Washington, D.C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

21. RESAC subpixel product with value attribute table file for Tuscarora Creek watershed, Virginia . . . . . . . . . . . . . . 16

22. Overall percent total impervious surfaces area by components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

23. Regression of the estimated impervious surfaces against the reference impervious surfaces . . . . . . . . . . . . . . . . . . 21

24. Road components contributions per watershed urbanization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

TABLES

1. Basic characteristics of the selected watersheds and the counties they reside in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2. The six principal components or features of impervious surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3. Comparison of land-use versus land-cover features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4. Land use codes and coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5. The 1992 National Land Cover Dataset coefficients for the Chesapeake Bay region . . . . . . . . . . . . . . . . . . . . . . . . 18

6. Center for Watershed Protection coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

7. Task 1 – Component quantification. The impervious surface area measurements per watershed with each

 component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

8. Task 1 – Truth data. The percent contribution of each component to the total 

 impervious surfaces for each watershed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

9. Task 2A – Intermediate scale analysis. Subpixel image processing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

10. Task 2B – Intermediate scale analysis. Land Use Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

11. Task 3 – Synoptic scale analysis. Comparison of total impervious area calculations from NLCD92 data . . . . . . . 24
Abbreviations

BMP Best Management Practices

CWA Clean Water Act

CWP Center for Watershed Protection

DOT U.S. Department of Transportation

DOQ Digital Orthophoto Quadrangle

EROS U.S. Department of the Interior, U.S. Geological Survey, EROS Data Center

ETM Enhanced Thematic Mapper

FHWA U.S. Department of Transportation, U.S. Federal Highway Administration

GAM U.S. Department of the Interior, U. S. Geological Survey, Geographic Analysis and Monitoring Program

GAO U.S. Government Accounting Office

GIRAS Geographic Information Retrieval and Analysis System for Handling Land Use and Land Cover Data 

GIS Geographic Information System

LC Land Cover

LU Land Use

MRLC Multi-Resolution Land Characteristics

NLCD National Land Cover Data

NLCD92 National Land Cover Data – 1992 product

NLCD2000 National Land Cover Data – 2000 product

NPS Nonpoint source pollution

ORD U.S. Environmental Protection Agency, Office of Research and Development

RESAC Mid-Atlantic Regional Earth Science Applications Center 

RIA Road Impervious Area

TIA Total Impervious Area

TM Thematic Mapper

USEPA U.S. Environmental protection Agency

USGS U.S. Geological Survey

VAT Value Attribute Table 

WSIA Watershed Impervious Area

WSTA Watershed Total Area


Executive Summary

Since the early 1970s our Nation has been experiencing a grow-

ing awareness of the complex relationships between the transportation infrastructure and environmental quality. One notable 

concern has been the potential for water quality degradation as a 

result of stormwater runoff over paved highway surfaces. Laws, 

executive orders, and government polices designed to minimize 

and mitigate the potential negative consequences of highway runoff have been enacted. These include the National Environmental 

Policy Act, the Intermodal Surface Transportation Efficiency 

Act, the Coastal Zone Reauthorization Amendment, the National 

Wild and Scenic Rivers Act, and the Clean Water Act of 1972, as 

amended, including the National Pollutant Discharge Elimination 

System and the Nonpoint Source Management Programs.

The Federal Highway Administration (FHWA) has designated 

environmental protection and enhancement are high-priority 

program areas that stress the evaluation of highway-related water 

quality impacts, as well as avoiding, mitigating, or managing 

such impacts, and coordinating with other agencies to ensure that 

Federal environmental policies are placed in perspective with other primary highway missions. The FHWA, the U.S. Geological 

Survey (USGS), and the U.S. Environmental Protection Agency 

(USEPA) are currently cooperating on research and development 

projects related to the minimization of water quality impacts from 

highway runoff. 

Past research sponsored by the FHWA identified and measured 

various pollution sources and developed techniques to lessen 

their impact on water resources. This research has been used by 

project development personnel to plan and implement highway 

improvements that minimized the impacts of pollution. The improved understanding of pollution sources and solutions to water 

quality problems has greatly increased the ability of States to plan 

and construct highways that have minimal effects on water qual-

ity (Bank, 1993).

The issues of highway stormwater runoff and its consequences 

were refocused in the early 1990s by the emergence of a new 

environmental water quality indicator called impervious surfaces. 

Impervious surfaces represent all materials and structures that 

inhibit the penetration of precipitation into the ground and divert 

Quantifying the Components of Impervious Surfaces 

By Janet S. Tilley and E. Terrence Slonecker

its flow over the land surface and eventually into surface waters. 

In general, impervious surfaces are manmade structures such as 

buildings, roads, parking lots, sidewalks, and other land covers. 

Research has shown that land development and the addition of 

impervious surfaces can increase streambank erosion, loss of 

aquatic habitat, and other changes, as the percentage of total impervious area increases in watershed (Arnold and Gibbons, 1996; 

Schueler, 1994).

As impervious surfaces emerged as an important indicator of 

water quality, researchers began to use numerous statistical, 

census-based, and land-use mapping methods to estimate the total 

impervious area (TIA) of a given area. None of these techniques, 

however, had been tested rigorously for the fundamental mapping 

accuracy of the measurement. Furthermore, in the literature and 

in discourse, road surfaces were often cited as the leading cause, 

the driving force, or the major component of the impervious surface problem. To better understand, plan, and control the effects 

of road surfaces to water quality, a quantitative assessment of the 

component makeup of total impervious cover is needed. Also, an 

assessment is needed of the accuracy of the current methods used 

by the scientific community to compute total impervious area.

The objectives of this study are to (1) determine the makeup of 

total impervious area and the relative contribution of individual 

components; and (2) assess the accuracy of various techniques in 

use for determining total impervious area.

Six urban and suburban watersheds were selected for study that 

represent a wide geographic distribution across the country. 

High-resolution orthoimagery (1 meter or better) was obtained 

for each watershed. Six classes of impervious cover were manually digitized as polygon features in a geographic information 

systems (GIS) environment. Relevant GIS data were obtained 

from County or City GIS departments. The six classes of cover 

were roads, buildings, parking lots, driveways, sidewalks, and 

other (such as sport courts). Quality control was provided by independent validation and mapping spot checks. The total area for 

each impervious surface class was totaled for the six watersheds 

and the percentage of each class was calculated against the total 

area of impervious cover. 

The largest area class of impervious cover was buildings at 29.1

percent, followed by roads (28.3 percent), and parking lots
(24.8 percent). Three minor classes of impervious surfaces were 

driveways (9.0 percent), sidewalks (3.2 percent), and other (1.8 

percent).

Three significant observations were apparent. First, roads were 

not the leading contributor to total impervious area. Second, 

parking lots represented a much larger than anticipated percentage of the area. Parking lots represent a different level of 

responsibility and a different set of environmental problems. 

Third, the three minor classes, driveways, sidewalks, and other, 

in total represented 14 percent of total impervious area. These 

three classes are rarely accounted for in impervious calculation 

methods, leading to the possibility of a statistically significant under-representation of minor class TIAs as compared to the impact 

of larger areas.

As part of the second objective of this study, to evaluate other 

TIA calculation methods, the high-resolution data from the six 

watersheds were used as control data to evaluate other methods. 

Three methods were tested. The first was, subpixel mapping 

of impervious area, land use coefficients and land cover coefficients. Subpixel estimates of impervious areas are derived from 

advanced image processing techniques using Landsat Thematic 

Mapper 30-meter multi-spectral and high-resolution digital orthophoto quarter quads. The result is a percentage of impervious 

area within each 30-meter pixel. Subpixel estimates of impervious area will be a nationally available, standard product of the 

USGS/USEPA National Land Cover Data (NLCD) set and could 

be a consistent base layer for a rapid method of TIA estimation. 

The results of testing this method showed a consistent underestimation of TIA, although usually within 10 percent. However, in 

one watershed, Difficult Run, Va., over 40 percent of TIA was not 

accounted for. This is likely due to coniferous tree cover in this 

area that masks the spectral reflectance of impervious materials 

underneath.

The second method, the land-use coefficient method, is widely 

used and is based on multiplying the area of each vector-based 

land use polygon by a predetermined coefficient that represents 

the average amount of impervious area for that particular land use 

class. Results from testing in four watersheds showed that the 

TIA derived from the land-use coefficient method were, on average, within 7 percent of the area derived from the high-resolution 

mapping.

The third method, the land cover method, involves generating an 

estimate of TIA from land cover data that is typically in a raster 

model generated from satellite-based image processing methods. 

Land cover data, like land use, is a thematic representation of 

surface phenomenon, but is based on the environmental cover 

or spectral reflectance properties of the surface instead of on an 

anthropogenic-based utilization of land. In general terms, land 

cover is generally less detailed than the type of land use data used 

in the second method and is generally mapped on a raster-based 

GIS model derived from the satellite imagery pixel size. It does, 

however, offer the advantage of a national, thematically consistent and regularly updated dataset from which many other spatial 

relationships, such as impervious surfaces, can be derived on a 

consistent, national basis.

Two methods were utilized to calculate TIA from National Land 

Cover Data (NLCD). Both methods use coefficients to calculate 

a percentage of impervious area per each pixel in the NLCD 

land cover classes. The results for both methods showed that the 

land-cover coefficient method delivered an average of 96 percent 

accuracy when compared to the high-resolution mapping data.

The high-resolution compilation of impervious surfaces begins to 

show us the component relationship of similar features that, as a 

whole, make up the functional impervious surface area. Roads, 

while often being the primary focus for impervious area, are 

only one of the three major components of TIA that also include 

buildings and parking lots. The contribution of Road Impervious Area to overall Total Impervious Area ranged from a low of 

20.8 percent in the 1992 Black River watershed to a high of 35.6 

percent in the 1998 Tuscarora Creek watershed. Driveways, 

often unaccounted for in impervious surfaces research, make up 

an average of 9 percent of the total impervious surface area in 

this study. Further, these data show that even very minor classes 

of impervious cover are important. Very few studies on impervious surface area have taken into account sidewalks, patios, and or 

other sport courts. Yet in this study, these are responsible for 5 

percent of TIA.

This study shows the component composition of impervious 

areas in six selected watersheds. While roads, buildings, and 

parking lots, make up the majority of impervious areas, driveways, sidewalks, and other covers make up 14 percent of TIA and 

should not be ignored in calculating TIA. Also, TIA is affected 

by regional or individual differences in land use in the watershed. 

Further research is needed to extend these findings to the entire 

nation.

Four of the common methods of computing impervious area 

show significant variability when measured against high-resolution truth data. Overall, the four methods tested here generally 

produced an estimate of TIA within 10 percent of the truth value. 

These methods could be refined further and result in an even better estimate of TIA. 
Introduction

Why Impervious Surfaces Are Important

Impervious surfaces are manmade objects that 

prohibit the infiltration of rainwater into the 

ground and cause increased surface flow. Impervious surfaces are a major concern for the quality 

of a community’s surface water and aquatic ecosystem resources. A number of well documented 

studies have shown the negative effects of 

increased imperviousness on stream morphology, 

water quality, and ecosystem health. However, 

beyond the basic science of the environmental 

effects of imperviousness, there is a growing 

and complex debate about the best practices for 

mitigating their negative effects.

At the core of this debate is the often cited belief 

that imperviousness is largely the result of roadway surfaces (or the overall transportation infrastructure). Roads are sometimes cited as making 

up as much as 80 percent of all imperviousness 

in a given area. This belief influence local and 

national transportation policy, including calls 

for more comprehensive environmental impact 

studies and new and expensive best management 

practices that are often required for new road 

construction (Center for Watershed Protection, 

1998).

At the local level, policies that establish stormwater utility fees are based on the area of 

impervious surface in a given property parcel. 

Even the basic land use policy and the nature 

of urban-suburban-exurban expansion are now 

being rethought with an eye towards reducing 

automobile-centric development patterns and 

increasing high-density communities. 

Yet throughout this debate is a lack of detailed 

studies and scientific quantification of what actually makes up the whole of impervious surfaces 

and how those components vary across the political and physical landscape. Further, the methods 

of measuring and computing impervious surface 

area are varied and complex and similarly have 

no established quality control associated with 

their use.

Any effort that helps to define the component 

nature of imperviousness and (or) helps to assess 

the accuracy of the various methods used to compute impervious area is critically needed. This 

study addresses some of these issues.

Impervious Surfaces Defined

A formal definition of an impervious surface can be found in the U.S. Environmental Protection Agency’s Draft Report on the Environment (2003a):

“Impervious surface: A hard surface area that either prevents or retards 

the entry of water into the soil mantle or causes water to run off the surface 

in greater quantities or at an increased rate of flow. Common impervious 

surfaces include, but are not limited to rooftops, walkways, patios, driveways, 

parking lots, storage areas, concrete or asphalt paving, and gravel roads.”

The increase in impervious surfaces is directly related to human activity 

through the construction of manmade structures. As precipitation is diverted 

from natural soil infiltration, the overland flow results in significant increases 

in surface water runoff as well as a rise in the acquisition of sediment and anthropogenic chemical contaminants. The subsequent surge in the inflow rate 

and volume in the receiving stream brings about an enlargement of bank-full 

and stream scour events and significantly influences a stream’s morphological structure. The instream and riparian ecology is thus altered because of 

changes in structural habitat and the related increases in sedimentation and 

pollution loadings (Arnold and Gibbons, 1996).

A National Problem

The USEPA now classifies urban runoff from impervious surfaces as a significant cause of impairment to water quality; local governments are required 

to address urban runoff through the National Pollutant Discharge Elimination System Storm Water Program (U.S. Environmental Protection Agency, 

2003a). According to the U.S. Department of Agriculture, between 1945 and 

1997 land devoted to urban areas in the United States increased by approximately 237 percent; paved road mileage increased by 278 percent (fig. 1).

Impervious Surfaces as an Environmental Indicator

The study of impervious surfaces has become one of the emerging areas of 

scientific interest in the control of Nonpoint Source Pollution (NSP), and as 

Figure 1. Photograph showing a typical suburban impervious surface cover in one of the study 

areas of this project: the Tuscarora Creek watershed located in Leesburg, Loudoun County, 

Virginia.
an indicator of terrestrial and aquatic ecosystem quality. NSP 

runoff from urban surfaces has been recognized as a leading 

threat to water quality, and the percentage of impervious surface within a particular watershed has been shown to correlate 

strongly with water quality, species diversity, and trophic status 

(Arnold and Gibbons, 1996; U.S. Environmental Protection 

Agency, 2001). The imperviousness issue has been suggested as 

one of the main unifying themes for overall study of watershed 

protection (Schueler, 1994) and as part of an urban ecosystems 

analytical model (Ridd, 1995). Problems with NSP include its 

sporadic and diffuse nature, a lack of monitoring capability, and 

the difficulty of assigning responsibility for the NPS pollution. 

Generally it is very difficult to identify the amount of discharge 

from individual or suspected pollution sources and to infer NPS 

levels from observable ambient pollutant levels (Wood and Bernknoph, 2003). 

In USEPA’s Draft Report on the Environment, impervious 

surfaces and the extent of urban and suburban development are 

mentioned prominently as potential key indicators of ecological 

condition of both water and terrestrial ecosystems (U.S. Environmental Protection Agency, 2003a and 2003b). The environmental protection of water quality has generally evolved away 

from end-of-pipe regulation to a more comprehensive watershed 

management approach in which the interface between human 

and ecological systems is better understood. Stormwater runoff 

has always been of concern to planners and civil engineers, but 

until recently the emphasis was on human safety, not ecological 

consequences. However, comprehensive watershed management 

techniques that seek to understand and balance ecological factors, 

classic stormwater management, and local land use policy are 

now being widely implemented. Several researchers have shown 

the direct effect of impervious surfaces on the water quality of 

receiving streams (Klein, 1979; Todd, 1989; Booth and Reinfelt, 

1993; Schueler, 1994). Numerous cities have adopted comprehensive water-quality planning efforts that integrate control of 

impervious surfaces as a central planning theme (Monday and 

others, 1994; Kienegger, 1992; Plunk, 1989). There are numerous other examples of impervious surfaces as a prime consideration in local planning processes.

Correlation Between Impervious Surfaces and 

Aquatic Ecosystem Conditions

Literature indicates that irreversible environmental degradation 

of an aquatic ecosystem occurs when a watershed contains more 

than 25 percent impervious surfaces. Schueler (1994) and Arnold 

and Gibbons (1996) both observe that research over the last 20 

years has consistently reported a correlation between watershed 

imperviousness and the health of the receiving stream ecosystem. 

Schueler proposes (and Arnold and Gibbons concur with) a threetiered threshold classification scheme of urban instream quality 

potential based on watershed imperviousness levels:

Stressed = 1 to 10 percent imperviousness 

Impacted = 11 to 25 percent imperviousness

Degraded = more than 25 percent imperviousness

These classes were described in further detail by the Center for 

Watershed Protection (1998), which also modified the names of 

the first level from stressed to sensitive, and the third level from 

degraded to non-supporting. 

A summary of the descriptions of the three classes follows:

Sensitive streams are of high quality and are typified by stable 

channels, have an excellent habitat structure, good to excellent 

water quality, and diverse communities of fish and aquatic insects. In addition, they do not experience frequent flooding. 

Impacted streams show clear signs of degradation due to watershed urbanization. Because of the greater storm flows due to the 

higher intensity of flooding and runoff from impervious surfaces, 

the stream geometry becomes wider due to more rapid erosion 

and also causes unstable banks. The physical habitat declines 

noticeably and the water quality transitions from good to fair. 

Biodiversity declines to fair levels with most sensitive fish and 

insects disappearing.

Non-supporting streams become a conduit for conveying stormwater flows and can no longer support a diverse stream community. The stream channels become highly unstable while the 

stream reaches experience severe widening, down cutting, and 

streambank erosion. Pool and riffle structures needed to sustain 

fish are diminished or eliminated, and the stream substrate can no 

longer provide habitat for aquatic insects or spawning areas for 

fish. Water quality is consistently fair to poor and water contact 

for people is no longer possible due to the high bacteria levels. 

Sub-watersheds in the non-supporting category will generally display increases in nutrient loads to downstream receiving waters, 

even if effective urban Best Management Practices are installed 

and maintained. Biological quality is generally considered poor 

and is dominated by pollution tolerant fish and insects.

Each of these classes has corresponding Best Management 

Practices associated with them. These categories could be used 

as a foundation for a watershed-based zoning approach, using 

impervious cover as the key measure and unifying theme in the 

municipal land-use zoning process (Schueler, 1994). 

Impervious Surface Impacts

Stream Morphology. The immediate and direct ecological 

consequence of watershed imperviousness is the effect on stream 

morphology. Increased water flow and volume destabilize 

streams through widening and incision, as well as streambank 

erosion and habitat degradation. Channel instability correlates 

with sub-bank full floods (Anderson, 1968; Leopold, 1968; Hammer, 1972; Hollis, 1975; Booth, 1993) and is characterized as loss 

of critical instream and riparian ecostructures such as pool and 

riffle networks and vegetative cover, in addition to an increase in 

the width of a channel during high flows (Schueler, 1994).

Conveying Urban Pollution. Impervious surfaces efficiently 

convey urban pollution to receiving streams and directly impact 

stream water quality. Prior to modern stormwater mitigation 
techniques, urban effluence transported sediments at a rate of an 

order of magnitude greater than comparably sized rural watersheds. For instance, sediment transport from a hectare of urban 

development and highway construction activity can yield 20,000 

to 40,000 times the sediment of a comparably sized agricultural 

or woodland area (Wolman and Schick, 1967; Burton and others, 

1977; Klein, 1979). Modern erosion and sediment controls now 

employed at highway construction projects significantly reduce 

sediment load from highway construction. Nevertheless, sediment load from other urban activities is still a significant obstacle 

to improving water quality. Monitoring studies in Wisconsin 

(Bannerman and others, 1993) revealed specific relationships 

between pollutants and types of impervious surfaces (for example, E. coli with residential streets, phosphorous with residential 

lawns, metals with industrial zones). Impervious surface runoff 

of hydrocarbons (Whipple and Hunter, 1979; Schueler, 1994), 

metals (Randall and others, 1978), and road salt (Crowther and 

Hynes, 1977) and their related effects on instream water quality 

have also been addressed. The hydrologic science community 

has consistently used the parameter of imperviousness both to 

model pollutant runoff and as a gauge to measure the relative 

level of instream water quality (Schueler, 1994).

Thermal Properties. Imperviousness has a two-fold effect on the 

thermal properties of a stream. First, impervious surfaces hold 

and retain more heat than the natural features they replace. Their 

heat is transferred downslope (via runoff) and warms the receiv-

ing stream. Schueler (1994) notes that impervious surfaces may 

be 10 to 12 degrees warmer than the fields and forests they replace. Galli (1991) compared urban related streams in Maryland 

with a forested reference stream and found a correlation between 

urban imperviousness and higher relative instream temperatures. 

Secondly, this instream warming reduces streamside vegetative 

cover, that shades the stream. This loss is often due to urban 

encroachment, as well as to erosion from flooding. Klein (1979) 

notes a 6- to 11-degree Celsius variation in shaded and unshaded 

areas on the same Maryland stream. 

Stream Biodiversity. Perhaps the strongest environmental 

indicator of instream health is stream biodiversity. A change of 

instream characteristics due to watershed development (for example, increased water flow volume, pollutant runoff, and change 

in thermal characteristics) plays a systematic role in altering an 

aquatic ecosystem. Schueler (1994) cites 18 studies associated 

with the effects of imperviousness on stream biodiversity. The 

focus of these studies is aquatic insect and fish surveys; results 

provide an overall characterization of ecosystem change as 

related to imperviousness of a watershed at the 10- to 15-percent 

level. These surveys reveal a consistent pattern of biodiversity decline as well as a concurrent relationship of a decline in 

pollution-sensitive species and an increase in pollution-tolerant 

species. Watershed impervious thresholds of 10- to 15-percent 

have also been shown to have a relationship to freshwater wetland 

health. Taylor (1993) and Hicks (1995) relate imperviousness 

around 10- to 15-percent to a decline in freshwater habitat quality 

and plant and amphibian diversity. 

Science and Policy Issues 

While the combined effect of imperviousness in a watershed 

and the direct relationships to measurable ecosystem and water 

quality parameters are generally accepted, the overall value of 

impervious surfaces as a key environmental indicator is currently 

being debated as researchers in this field have identified a number 

of science and policy issues. 

Pertinent science and policy issues include:

Natural Setting. If impervious surfaces are to be a valid indicator 

of watershed condition, some way of taking into account exist-

ing environmental conditions and watershed settings must be 

developed. Watersheds in relatively flat terrain with sandy soils 

will have a very different surface water runoff potential than a 

watershed with clay soils and significant topographic relief.

Mitigation Efforts. Surface runoff can be controlled by a number of engineering techniques (such as storm sewers, catchment 

basins, and retention ponds), all of which would alter the surface 

flow and ecological effects of the receiving stream. The relationship between impervious surfaces percentage and these mitigation efforts is not fully understood.

Land Use Policy. A greater understanding of the ecological 

consequences of urbanization should inevitably lead to changes 

in land use policy at all levels. However, a number of questions 

must be asked and answered quantitatively in order to realize urban/suburban development with a minimum of negative ecological effects. How might the understanding of impervious surfaces 

affect local land use policy? What steps might local planners 

take in order to minimize the ecological effects of urban/suburban development and at what cost? Of the various sub-classes of 

impervious surfaces, which ones are most responsible for overall 

imperviousness? And, of the various impervious surfaces, which 

have the most negative impact on the ecosystem?

These questions focus much of this research effort. While there 

is a significant and increasing level of scientific understanding 

about the effects of total imperviousness, there appears to be little 

information about the relative contribution of buildings, parking 

lots, roads, and other features. From a review of the literature, 

the transportation infrastructure appears to receive an inordinate 

amount of blame for the impervious surfaces problem. A Draft 

General Accounting Office Report (2001) cited roads as responsible for as much as 66 percent of the runoff in urban watersheds. 

Schueler (1994) attributes 63 to 70 percent of imperviousness 

to transportation land uses. Southworth and Ben-Joseph (1997) 

asserted a now infamous statistic that “...the automobile consumes close to half the land area in cities.” Shoup (1997) traces 

the origin of this statistic through several scientific works back to 

Sale (1980) and shows that it is not based on any scientific study, 

and most relevant data today puts the contribution of roads of 

around 25 percent. 
The Emerging Science of Measuring Impervious 

Surfaces

Perhaps the most fundamental issue in the debate over impervious surfaces is simply the method of measuring and quantifying 

impervious surfaces with defensible precision and accuracy. 

There exists no uniform methodology, and there are a variety 

of methods that range from statistical estimates from Census 

population data to laborious mapping of surface features from 

very high to moderately high spatial resolution aerial imagery 

(Slonecker and others, 2001). Given the potential of impervious 

surfaces to serve as an indicator of ecological condition, the and 

sensitivity of the metric to indicate impairment, greater understanding of the methods and accuracies of computing impervious 

surfaces values is needed.

Although the effects of land use, population, and impervious surfaces on water quality has been generally known for many years, 

a basic problem exists in quantifying the detailed spatial extents 

and distribution of various classes of impervious surface phenomena. Accurate and quantifiable measurements of impervious 

area remain elusive and expensive (Slonecker and others, 2001). 

Determining the area of imperviousness is primarily a mapping 

issue, and it is in mapping methods that the base data for determining the contribution of individual components for evaluating 

the other methods of imperviousness will be found. While there 

are a number of viable methods, such as statistical estimates, 

spectral reflectance methods, and GIS algorithms, there must 

necessarily be some set of ‘reference’ data in order to measure the 

accuracies of other methods. 

Objectives

One of the primary purposes of this study is to develop quantitative information about the spatial extent of various classes 

of imperviousness through detailed mapping from high-resolution imagery sources. Numerous studies differentiated between 

rooftops and transportation systems, some without supporting or 

additional data. In some of these studies buildings and roads are 

identified as the entire contribution of all impervious surfaces. 

If the components of impervious surfaces are broken down into 

more detailed components within watersheds, methodologies can 

be better evaluated to control and mitigate the impacts from these 

components. This report presents quantitative results that detail 

how much each impervious feature is contributing to the total 

imperviousness for six selected urbanized watersheds throughout 

the United States.

The objectives of this research were:

Objective 1 – to determine the relative contribution of the 

individual components that comprise the total area of impervious surfaces in six selected watersheds in the United States by 

locating or collecting impervious surfaces data from high spatial 

resolution aerial imagery. This objective is addressed by Task 

One (below).

Objective 2 - to demonstrate scale dependent and efficient methods for mapping impervious areas by using remote sensing and 

Land Use/Land Cover and other detailed GIS data. Potentially, 

reliable and efficient methodologies could be developed for use 

by State and local governments as well as Federal agencies to 

efficiently measure the imperviousness in any given watershed, 

thereby correlating road impact upon the quality of the environmental conditions. These objectives are addressed by Tasks Two 

and Three.

Scope and Approach

Six watersheds were selected, based on degree of urbanization 

and the availability of GIS data. Three primary tasks were completed:

Task 1 - Quantifying Components of Impervious 

Surfaces: Reference Data

The impervious surfaces for each watershed were mapped at 

highly detailed levels (see data source examples in fig. 2). Exist-

ing detailed GIS datasets were acquired from the local governments where available and spatially explicit classes of impervious 

surfaces were digitized from high spatial resolution orthoimagery 

to compile highly detailed GIS datasets for six classes of impervious surface cover. From this product, the relative contribution 

of each class of impervious cover was determined. This task 

addresses Objective One.

Figure 2. Examples of data used in this research: (1) high spatial resolution 

imagery at 1-foot resolution, Vargis, LLC Aerial Imagery ©2002 Commonwealth 

of Virginia; (2) digital orthophoto quadrangle image at 1-meter resolution, USGS; 

(3) local government GIS data, courtesy of Loudoun County Government, Virginia; 

(4) RESAC sub-pixel data, courtesy of the University of Maryland, Geography 

Department; (5) vector road GIS data, The National Map; and (6) 1992 National 

Land Characteristics data.
from which data were available from The National Map was used 

to choose the areas. Originally, six to eight study areas, each one 

square mile in size were to be selected. However, the value of 

working in watershed areas rather than the one-square-mile areas 

was compelling and very important in data sharing. Ultimately 

the watersheds ranged from very dense urban (four of the watersheds) to suburban (two of the watersheds). 

Local Data Availability

The team located contacts interested in this research who were 

willing to share their data. Use of existing datasets saved the 

project from the expensive, labor intensive, and time consuming 

process of collecting all reference data, and allowed datasets to be 

finished only slightly. The Appendix shows the sources and additional information about the various datasets used in this research. 

Watershed Locations

In cooperation with the U.S. Department of Transportation, 

Federal Highway Administration, the following watersheds were 

selected for study: Black River within King County, Washington; Difficult Run within Fairfax County, Virginia; North Walnut 

Creek within Polk County, Iowa; Oak Creek within Lancaster 

County, Nebraska; Tuscarora Creek within Loudoun County, Virginia; and Wares Creek within Manatee County, Florida (fig. 3).

Task 2 - Intermediate Scale Analysis

Total impervious surface area can be determined using various 

methods from remote sensing and Land Use data. These methods 

are both efficient and widely accepted for the quantification of the 

impervious surface area for many applications, including GIS/

Land Use coefficient techniques and subpixel satellite remote 

sensing techniques. Satellite remote sensing instruments, such 

as the Landsat Thematic Mapper, collect data in different bands 

of the electromagnetic spectrum, and this permits the identification of materials on the Earth’s surface based on their spectral 

reflectance characteristics. The identification of impervious 

areas lends itself well to this type of analysis because impervious 

surface materials are generally very different from most natural 

features. Further, new techniques in the field of digital image 

processing permit the derivation of information at the subpixel 

level. The standard 30-meter pixel size of the Landsat Thematic 

Mapper sensor has inherent limitations with respect to mapping 

small objects or areas. However, classification techniques such as 

spectral mixture modeling (Ji and Jensen, 1999; Ward and others, 

2000; Phinn and others, 2000) and neural network-based classification methods (Civco and Hurd, 1997) are capable of extracting 

subpixel information (RESAC, 2003). This task addresses Objective Two. 

Task 3 - Synoptic Scale Analysis

Because of the appeal of being able to quickly compute impervious cover from nationally consistent and regularly updated 

datasets, the National Land Cover Data (NLCD) and The National Map (U.S. Geological Survey, 2003) road data were used 

to compute impervious cover via coefficient methods and were 

tested for overall accuracy and efficiency. Although this is a 

regional view of the detail and statistics of the impervious surface 

extents, this task is designed to produce rapid results and quick 

assessments. This task addresses Objective Two.

Study Areas

The criteria used for selecting the six watersheds, the locations, 

and the relevant characteristics about each watershed are described below. Two low, two moderate, and two high intensity 

urban watersheds were selected for this study. 

Watershed Selection Criteria

A watershed is the area drained by a stream and its tributaries. 

Watersheds range in size from under a square mile to hundreds 

of thousands of square miles (for example, the Chesapeake Bay 

watershed, in which two of our watersheds are located, has an 

area of 66,388 square miles). Precipitation that falls within a watershed will eventually drain from the bottom of the basin through 

the main stream channel. 

The U.S. Department of Transportation, the agency this research 

was prepared for, requested that areas from each quadrant of the 

United States be represented in this study. A list of locations 

Figure 3. On the map of the United States are the watershed locations for this 

study, as represented by orange dots.

Watershed Characteristics

The following section describes each watershed in further detail. 

Size, population, and area characteristics are briefly summarized 

in table 1.

Black River Watershed, King County, Washington

The Black River watershed is located in King County, Wash., and 

is within the greater Seattle-Tacoma Metropolitan Region (fig. 4). 

It is part of the larger Duwamish-Green watershed and encom-
Table 1. Basic characteristics of the selected watersheds and the counties they reside in (U.S. Census Bureau, 2001). There are 3,141 counties in the United States. 

[CO=County; Hi-Res= High-Resolution; Pop=Population; est=estimated to be at x-percent built-up/year]

Watershed

Name

Watershed 

Size

(km2 / 

miles2 / 

acres)

CO Size

(miles2 / 

acres)

Greater

Watershed 

Name and

Hydrologic

Unit

CO Pop 

Percent 

Change 

from 1990-

2000*

CO Ranking 

in Pop for 

2000*

(out of 3,141 

COs)

CO Ranked by 

Numeric Pop 

Change from 

1990-2000*

(out of 3,141 COs)

Visual Assessment of the Watershed’s Urbanization as Observed on Hi-Res Imagery

(Percent Built-up/year)

Black River, Washington(King CO)

70 /

26.89 / 

17207.1

2,165 Duwamish

17110013

15.2 12 18 (increase of 

229,715 people)

Dense Urban - High/Low Density Residential and Commercial (est. 98%/1989)

Difficult Run, Virginia(Fairfax CO)

151 /

58.09 / 

37179.4

397 Middle 

PotomacCatoctin02070008

18.5 36 35 (increase of 

151,165 people)

Dense UrbanHigh/Low Density Residential and Commercial (est. 88%/1998)

North Walnut Creek, Iowa(Polk CO)

35 /

13.58 / 

8691.7

595 N.Racoon

07100006

14.5 156 159 (increase of 

47,461 people)

Dense UrbanHigh/Low Density Residential and Commercial (est. 55%/2001)

Oak Creek, Nebraska(Lincoln CO)

57 /

21.94 / 

14042.1

844 Salt

10200203

6.5 1,261 1456 (increase of 

2,124 people)

RuralResidential and some Commercial (est. 20%/2002)

Tuscarora,Virginia(Loudoun CO)

40 /

14.42 / 

9226.2

529 Middle 

PotomacCatoctin02070008

96.9 320 72 (increase of 

169,599 people)

RuralResidential and some Commercial (est. 25%/1998)

Wares Creek, Florida(Manatee CO)

21 /

8.00 / 

5091.4

734 Manatee

03100202

24.7 209 145 (increase of 

52,295 people)

Dense UrbanHigh Density Residential,Some Commercial (est. 99%)
Green River. Three main streams and rivers are within the watershed: Black River, Mill Creek, and Springbrook. Based on the 

NLCD92 (produced from 1992 source), the dominant Land Uses 

are (1) commercial, industrial, transportation, (2) low density 

residential, and, (3) deciduous forest, which has changed only 

slightly since this date. 

Difficult Run Watershed, Fairfax County, Virginia

Difficult Run (fig. 6) is located just west of the greater Washington, D.C., metropolitan region, which has undergone massive 

expansion, with many people commuting into D.C. for work. 

The watershed drains approximately 150.5 square kilometers and 

flows directly into the Potomac River. It is contained within Fairfax County, the most populous urban county in both Virginia and 

in the Washington, D.C., metropolitan area. In 1990 the population for Fairfax County was 818,584; by 2000 the population 

reached 969,749, an 18.5 percent increase over the 10-year period 

(U.S. Census Bureau, 2001). Within the Difficult Run watershed 

(fig. 7) are four large and major transportation corridors includ-

ing Interstate 66, U.S. Highway 50, and the Dulles Airport access 

and toll road (fig. 8). Other important roads in this watershed 

are State Highways 7 and 123. It is part of the larger Middle Potomac-Catoctin watershed and contains the following reservoirs: 

Lake Audubon, Lake Thoreau, Lake Anne, and Lake Fairfax. 

Many streams flow into Difficult Run, which in turn flows into 

the Potomac River, the main streams are: Colvin Run, Captain 

Hickory Run, Piney Run, Wolftrap Run, Piney Branch, Snakeden 

Branch, Little Difficult Run, South Fork, and Rocky Branch. 

Based on the 1990 and the 2000 NLCD, the primary Land Uses 

in Difficult Run are (1) deciduous forest, (2) pasture hay, (3) residential dispersed amongst the first two dominant features.

passes parts of the cities of Kent and Renton. The Black River 

watershed is approximately 69.6 square kilometers in area. The 

watershed is highly developed (fig. 5) with approximately 60 percent of the area in dense urban and commercial land uses (King 

County, 2001). The County’s population in 1990 was 1,507,319 

and in 2000 it was 1,737,034, resulting in a 15.2 percent increase 

(U.S. Census Bureau, 2001). The Black River watershed contains 

the following major roads: Interstate 405, State Highways 546, 

515, 167, 181, and 516, with no U.S. Highways. The only lake 

is Panther Lake. The west side of the watershed is bounded by 

Figure 4. The Black River watershed is located in King County, Washington, and 

is approximately 27 square miles. It is part of the Seattle-Tacoma metropolitan 

region. The locator map, from the National Atlas of the United States, left, shows 

the State of Washington with the red box giving the location of the watershed. 

The land cover data, 1992 National Land Characteristics Data, right, shows an 

overview of the characteristics of the study sites and characteristics of the State 

of Washington.

Figure 5. The nearly 28-square-mile Black River watershed in the top illustration shows a 1980, 1:100,000-scale map. Data and the imagery at 1-meter resolution, dated 

1989, are USGS DOQs. Much of the data, as well as more recent higher-resolution imagery, were courtesy of King County, Washington. Other sources of data for this 

area were Army Corp of Engineers and Walker Aerial Survey.
10

North Walnut Creek Watershed, Polk County, Iowa

The North Walnut Creek watershed (fig. 9) is situated on the 

north-western edge of the City of Des Moines, Iowa, in Polk 

County. It is approximately 35.17 square kilometers in area. 

Most of the south two-thirds of the watershed are developed 

as medium-intensity residential with growth trending further 

northwest (fig. 10). In 2000 the population of Polk County was 

374,601, compared to 1990 when it was 327,140, for a 14.5 

percent increase (U.S. Census Bureau, 2001). The population for 

the city of Des Moines in 2000 was 198,682 (ranked 92nd most 

populated out of 239 cities with a population over 100,000), and 

in 1990 was 193,333 (ranked 81st most populated out of 239 cit-

ies with populations over 100,000 people) (U.S. Census Bureau, 

2001). The major roads contained in the North Walnut Creek 

watershed are the following: overlapping Interstates 35/80, State 

Highway 141, and the smaller U.S. Highway 6. North Walnut 

Creek Watershed is contained within the larger North Raccoon 

Figure 6. The Difficult Run watershed, located in Fairfax County, Virginia, is 

approximately 58 square miles and is west of the Washington, D.C., metropolitan 

region. The top image is the locator map from the National Atlas of the United 

States. The lower image shows 1992 National Land Characteristics Data.

Figure 7. The top figure is of the 58-mile Difficult Run watershed shown on a 1983, 

1:100,000-scale map. The bottom image shows the road data draped on the 1998, 

1-foot resolution imagery, courtesy of Fairfax County, Virginia.

Figure 8. This photo was taken in early 2004 of the Dulles Toll Road, Route 267, a private highway, looking to the east from the Fairfax County Parkway in the western 

portion of the Difficult Run watershed, Virginia. The toll road is 14 lanes wide at this location. Buildings to the left are the growth centered around the Reston Town 

Center. This area’s growth trend is similar to that of the Tysons Corner area, a high retail and high density area also in Fairfax County. The Tysons Corner area is on the 

east side of the Difficult Run watershed.
11

6.5 percent population increase since 1990 (U.S. Census Bureau, 

2001). The Lincoln City population in 2000 was 225,581 (ranked 

76th most populated out of 239 cities with a population over 

100,000) and in 1990 it was 192,722 (ranked 82nd most populated out of 239 cities with a population over 100,000). The major 

roads in the watershed are the following: Interstates 180 and 80, 

U.S. Highways 6 and 34, and a small portion of U.S. Highway 

64. The main water features are Oak Creek, Oak Lake, and Salt 

Lake, with several intermittent streams and small reservoirs and 

is contained in the larger Salt watershed. The dominant Land Use 

is pasture/hay with a small, second use of herbaceous grass lands, 

followed by fallow lands (as determined with the NLCD92).

watershed. The watershed’s namesake, North Walnut Creek, 

flows toward the central part of Des Moines into Walnut Creek 

and after about 5 kilometers, it flows in turn, into the Raccoon 

River, which joins the Des Moines River after about another 5 

kilometers. The one lake in the watershed is Lake Halice with 

a limited number of small reservoirs and numerous intermittent 

streams. The dominant Land Use is low-intensity residential, 

followed closely by row crop and high-intensity residential (as 

determined by the NLCD92).

Figure 9. The North Walnut Creek watershed, almost 14 square miles, is located 

in Polk County, Iowa, and extends into the city of Des Moines. The locator map 

from the National Atlas of the United States (left) shows the watershed relative to 

the United States, while the 1992 National Land Characteristics Data shows the 

land cover characteristics surrounding the watershed and for the State of Iowa, 

predominantly agriculture.

Figure 10. The nearly 14 square mile North Walnut Creek watershed is shown on 

a 1983, 1:100,000-scale map. The 2001 bottom image has vector data draped on 

it and is better than 1-foot resolution. The 2001 imagery and building data were 

courtesy of Polk County as well as the buildings’ data layer. The research team 

collected information on roads, buildings, driveways, sidewalks/paths, parking 

lots, and all of the other features that do not fall into the categories listed.

Oak Creek Watershed, Lancaster County, 

Nebraska

The Oak Creek watershed (fig. 11) is located in the northwest 

area of Lincoln, Nebr., in Lancaster County and is approximately 

56.82 square kilometers in area. The Lincoln Municipal Airport 

is situated in the center of the watershed, with most of the commercial and residential areas in the eastern portion of the watershed (fig. 12). Newer residential areas are beginning to develop 

just west of the airport. In 2000, the Lancaster County population was 34,632, compared to 1990 when it was 32,508, for a 

Figure 11. The Oak Creek watershed, nearly 14 square miles, is located in Lancaster County, Nebraska, and extends into the city of Lincoln. The locator map 

from The National Atlas of the United States shows the watershed relative to the 

United States while the 1992 National Land Characteristics Data shows the land 

cover characteristics surrounding the watershed and for the State of Nebraska to 

be predominantly agriculture and pasture.

Figure 12. The top illustration shows the 22-square-mile Oak Creek watershed 

draped on a 1983, 1:100,000-scale map. Below it is draped on a 2002 National 

Map image that offers 1-foot resolution in natural color. All data layers needed to 

be collected for this watershed.

Tuscarora Creek Watershed, Loudoun County, 

Virginia

The Tuscarora Creek watershed (fig. 13), over 36 square kilometers in area, is located in Loudoun County, Va., about 40-kilometers west of the Washington, D.C., metropolitan area (fig. 14). 

Tuscarora Creek is 1.6 kilometers from the Potomac River and 
12

county is 1,919.2 square kilometers, while the small Wares Creek 

watershed is approximately 20.7 square kilometers. It is extremely flat terrain and is less than 16 kilometers from the Gulf of 

Mexico (fig. 17). The county’s population in 1990 was 211,707, 

and in 2000 it was 264,002, an increase of 24.7 percent over the 

10-year period (U.S. Census Bureau, 2001). The main roads 

contained within the watershed are the following: U.S. Routes 

301 and 41, State Routes 55, 45, and 684, where 684 is the road 

that takes tourists directly to the Gulf of Mexico beaches within 

12.9 kilometers. The Wares Creek watershed is 3.2 kilometers 

north of the Sarasota Bay and is directly off the Manatee River 

and drains into both by way of the Cedar Hammock Drainage 

Canal. The canal flows into north and south flowing segments, 

where the branch traveling south drains into the Sarasota Bay, 

while the branch traveling north drains into the Manatee River. 

The Manatee River flows directly into the Tampa Bay, which in 

turn flows into the Gulf of Mexico. The Wares Creek watershed 

contains most of the town of Leesburg. The U.S. Census Bureau 

has estimated Loudoun County to be the fastest growing county 

in the nation (U.S. Census Bureau, 2003). The population for 

Loudoun County in 1990 was 86,129; by the year 2000 the population grew to 169,599, for a 96.9 percent population increase 

over that 10-year period. By 2002, the estimated population was 

204,054 (U.S. Census Bureau, 2001), a 136-percent increase 

since 1990 and a 20-percent increase since 2000. The main roads 

in the watershed are private highway 267, State Highway 7, U.S. 

Highway 15, State Highway 621, and State Highway 9, which 

starts at the northern edge of this watershed. The Tuscarora 

Creek watershed is part of the larger Catoctin watershed and the 

still larger Potomac River Basin where the water flows into the 

largest estuary in the United States, the Chesapeake Bay. There 

are no lakes, but there are several small reservoirs that are less 

than 0.25 square kilometers. 

The main streams in the Tuscarora watershed are Town Branch, 

Dry Mill Branch, and its namesake the Tuscarora Creek, where 

the beginning is only 8 kilometers from Goose Creek, which 

when the Tuscarora Creek flows into it, is only 2.4 kilometers 

from the Potomac River. The Tuscarora Creek watershed is 

in the path of the urban growth that is rapidly occurring in the 

eastern portion of the county (fig. 15). Within the watershed 

the NLCD92 indicates the dominant covers are agricultural use, 

slightly leading the amount of forest. The community is a prime 

location for urbanization, especially with the availability of the 

convenient private highway 267. Maryland owns the water rights 

to the Potomac River, which is a challenge when planning for 

enough water to support the growing community.

Wares Creek Watershed, Manatee County, Florida

The Wares Creek watershed (fig. 16) is contained within the 

County of Manatee, Florida, and is approximately 9 kilometers 

north of Sarasota and contains part of the city of Bradenton. The 

Figure 13. The Tuscarora Creek watershed, over 14 square miles, located in Loudoun County, Virginia, is about 25 miles west of the Washington, D.C., metropolitan 

area. The locator map is from the National Atlas of the United States and the land 

cover data are 1992 National Land Characteristics Data (available free and nation 

wide by State at http://www.mrlc.gov/).

Figure 14. The 14-square-mile Tuscarora Creek watershed draped on the 1981, 

1:100,000-scale map. The vector data, courtesy of Loudoun County, are draped 

on a USGS 1994 color infrared digital orthophoto quarter quadrangle image at 

1-meter resolution.

Figure 15. Photographs showing views within the Tuscarora Creek watershed, 

Virginia, in the Leesburg area.
13

is located in the larger watershed of the Peace-Tampa Bay. There 

are approximately nine small man made lakes. Wares Creek is 

completely developed and has high-density residential areas as 

the predominant land use. The northern portion has more residential tree cover rather than the grasses and the occasional palm 

trees in the southern portion of the watershed. Any areas that are 

not residential are commercial areas serving the communities. 

The open lands are a golf course, lakes, and a few educational facilities. The NLCD92 shows the following three, respectively, as 

the primary features in the watershed: (1) low-intensity residential, (2) high-intensity residential, and (3) commercial, industrial, 

and transportation.

Methodology

Overall Process

In order to achieve objectives one and two, three tasks were designed to accomplish them:

Task 1 – Quantifying Components of Impervious Surfaces: Reference Data, was used to build and assemble GIS reference data, 

designed to fulfill the first objective; Task 2 - Intermediate Scale 

Analysis: Subpixel Image Processing Techniques and Land Use 

Coefficients Method addressed the needs of the second objective, 

as did Task 3 – Synoptic Scale Analysis, which allowed for the 

Figure 16. The Wares Creek watershed, approximately 8 square miles, is located 

25 to 30 miles south of the metropolitan area of Tampa on the Gulf coast of Florida, 

in Manatee County. The locator map is from the National Atlas of the United 

States and the land cover data are 1992 National Land Characteristics Data.

Figure 17. The Wares Creek watershed draped on the 1977, 1:100,000-scale map and on the 1-foot resolution image is from 2001, shown above courtesy of Manatee 

County. All data were available from the county except for the sidewalks and parking lots.
14

quick regional assessment of imperviousness and also to compare 

with the reference data from Task One. 

To complete the tasks, six phases were nested in the three tasks: 

(1) Watershed Selection, (2) GIS Data Acquisition, (3) High 

Spatial Resolution Imagery Acquisition, (4) Reference Data 

Match Process, (5) Components Summations, and (6) Accuracy 

Comparisons. 

Task 1 - Quantifying Components of Impervious 

Surfaces: Reference Data

Data modification and collection in assembling the reference 

datasets for this work involved the completion of one to three 

GIS datasets per watershed in order to obtain a match with both 

the NLCD92 (1992 product) and with the subpixel contemporary 

products, NLCD2000 (produced from the year 2000 source). By 

digitizing spatially explicit components (table 2) of impervious 

surfaces from high spatial resolution imagery and especially by 

utilizing existing detailed GIS datasets provided by local governments and The National Map, this task encompassed mapping 

and quantifying the impervious surfaces for each feature in the 

selected watersheds, at extremely detailed levels primarily for the 

years 1992 and 2000. The results showed temporally the relative 

contribution in area of each component’s impervious surface for 

all six watersheds. For each watershed, preliminary research was 

conducted to identify sources for both GIS data and high spatial 

resolution imagery from public or private sources (see appendix). 

In some cases, aerial photographs were acquired, scanned, georegistered, and orthorectified for use as the base sources. After all 

GIS data were acquired, local data were reviewed for consistency 

and accuracy, and were updated (heads-up digitizing/editing) to 

match the high spatial resolution imagery source for the years 

1992 and 2000. When no existing GIS data could be found, as in 

the Oak Creek watershed, the features were digitized from high 

spatial resolution imagery sources. Figure 18 shows an example 

of the end product with all the features delineated after the modiImpervious

Surface

Components

Component/Feature Descriptions

Buildings All roofed structures including storage sheds and trailers (with the assumption that trailer’s 

foundations were concrete pads). As often the base of structures are obscured by camera 

angle or vegetation, roof tops were digitized rather than the structure bases.

Roads Long, narrow areas of gravel, paved or other hard surfaces that are utilized primarily for public transportation by automobile and are maintained and regulated by Federal, State or local 

government.

Parking Lots Paved or hard surfaced areas that exist primarily for the temporary storage for 

automobiles and other vehicles, equipment, and materials. In commercial/industrial/business/institutions/apartment areas, entryways into these complexes were referred to parking 

lots to include the parking areas that often ran off the entryways.

Driveways Hard surface or gravel areas that connect a house, garage, or other structure to a road surface 

for the purpose of automobile access and storage. In residential areas entryways, into homes 

were put in this category.

Sidewalks Narrow hard surface areas that are generally found parallel to roadways and exist primarily 

for pedestrian traffic. Recreational trails, home and business entryways, park and golf course 

cart paths are included in this category.

Other Hard surface recreation areas, such as basketball or tennis courts, patios, swimming pools - to 

include surrounding patio, any other impervious surface that does not fit in any of the above 

categories.

Table 2. Table showing the six principal components features of impervious surfaces. See the appendix for sources of data.

Figure 18. The six types of impervious surfaces are digitized from rectified high 

spatial resolution imagery. These show buildings, roads, sidewalks, driveways, 

parking lots, and other covers (a sport court is shown).
15

fication/collection process. Figure 19 shows one of the researchers comparing the acquired data with the imagery. 

Once the base datasets were completed, the features were subtracted to construct the 1992 reference data for the NLCD92 with 

the appropriate dates from the metadata. For the match with the 

contemporary subpixel products, the team updated the base dataset to the appropriate date of those products as well. The areas of 

the different components were calculated for both 1992 and 2000 

spectral unmixing (a process enables the user to derive more 

information from a 30-meter pixel than before). The end result is 

a better resolution product than the original 30 meters (fig. 20).

This technique has shown to be successful when applied to high 

spectral resolution imaging data (hyperspectral offers more 

than 200 bands), where subtle diagnostic absorption features 

largely determine the spectral characteristics (Van der Meer, 

2002). Spectral unmixing techniques strive to find proportions 

of end-members, spectrally ‘pure’ pixels, within a pixel from the 

observed mixed pixel spectrum and a number of pure end-member spectra of known composition (Ji and Jensen, 1999; Ward and 

others, 2000). When used in conjunction with advanced image 

classification techniques such as neural networks or decision 

tree classifiers, subpixel techniques can return data that are more 

accurate and have more spatial detail, compared to traditional 

classification techniques, such as Task 1-Reference Data.

In determining the impervious surface percentage per pixel from 

subpixel products, the following two fundamental steps are involved. First, impervious surface products using subpixel image 

processing are generally released in a raster format in which the 

cell value is the percentage of imperviousness calculated for that 

pixel. The raster image is assigned color values by some percentage class of imperviousness. Second, after clipping the extent of 

the area of interest (in this case the Tuscarora Creek watershed) 

product (fig. 21) from the large raster the values from the value 

attribute table, which are the ‘value’ and ‘count,’ can be easily 

input to a spreadsheet for calculation of total imperviousness. 

The value represents the percentage of imperviousness and the 

count represents the total number of occurrences in these datasets. These steps are similar to those applied in Task 3-Synoptic 

Scale Analysis.

Figure 19. A team member is modifying the acquired data in the match process 

with the high resolution imagery.

reference datasets. The Task 1 results were compared with the 

NLCD92 products and the RESAC subpixel product (Smith and 

others, 2003). 

Task 2 - Intermediate Scale Analysis

Two separate techniques were used to calculate impervious 

surface area at an intermediate scale. These two techniques 

were selected for their efficiency and frequent mention in the 

literature. The first, Task 2A, subpixel processing, is a relatively 

recent advance in spectral analysis that utilizes a combination of 

spectral processing and statistical tools to derive information on 

the components makeup of an individual pixel. The second technique makes use of land use coefficients that are commonly used 

by hydrologists and watershed protection specialists working on 

impervious surfaces research.

Task 2A - Intermediate Scale Analysis: Subpixel Image 

Processing Techniques

For many years, satellite remote sensing and image process-

ing techniques were limited by the spatial resolution of these 

remotely sensed products. Typical systems such as the Landsat 

Thematic Mapper, with 30-meter pixel size, exhibited significant 

limitations on minimum mapping units, spatial accuracy, and 

general detail of derived products. However, a relatively new 

class of image processing techniques has been developed in the 

last decade that can be used to classify the pixel into relative 

abundance of materials within it based on statistical analysis and 

Figure 20. A subpixel impervious surface image of the Washington, D.C., area as 

derived from Landsat Thematic Mapper imagery by the University of Maryland 

RESAC. Red areas indicate pixels of high levels of impervious surfaces, yellow 

areas are less imperviousness than the red, while the black areas indicate no 

impervious cover.
16

The reflectance characteristics of impervious surfaces lend 

themselves well to delineation via spectral remote sensing. Concrete, macadam, and other substances that are utilized in human 

construction efforts generally have significantly different spectral profiles from natural surfaces. With the ability to potentially 

acquire information at the subpixel level, this method of classification can provide more accurate measures of composition. 

Subpixel processing of widely available satellite imagery could 

become an important new source of impervious surface data. 

Several organizations and institutions now regularly process 30-

meter subpixel impervious surface data, such as the Mid-Atlantic 

Regional Earth Science Applications Center (RESAC) at the 

University of Maryland, Geography Department (fig. 20 and 21). 

The USGS EROS Data Center (EDC) has completed national 

subpixel products, NLCD2000, for different ecoregions across 

the United States with some still in progress (U.S. Geological 

Survey, 2000). The potential accuracy and national availability of 

these data sources made them excellent candidates for testing in 

this study. This task used the RESAC and NLCD2000 products, 

which were only available for the Tuscarora Creek and the Difficult Run watersheds at the time of analysis.

Task 2B - Intermediate Scale Analysis: The Land Use Coefficient Methodology

The coefficient method can be used to derive impervious surface 

Figure 21. To the left, the subpixel impervious surface raster derived using subpixel digital image processing for the Tuscarora Creek watershed, Virginia. A geographic information system coverage of the watershed boundary was used to clip the larger raster, resulting in the impervious surface data for just the watershed. 

The value attribute table from the impervious surface raster, from which the total impervious area is easily calculated, is shown on the right.

estimates from land use (Task 2B) and land cover (Task 3) data 

separately from different sources. An important distinction must 

be made between land use and land cover data: land use data 

describes the anthropogenic utilization and purpose of various 

land parcels. It is usually derived from detailed maps and aerial 

photographs and is digitized in a vector GIS format. The work by 

Anderson and others (1976) has for many years served as the basis for land use classification and mapping from remotely sensed 

data. Land cover data are somewhat different, as they describe 

the natural surface and condition based on biophysical character-

istics. Table 3 shows examples of the differences between land 

use and land cover for the same parcels of land. 

The Land Use Coefficient method, applied in Task 2B, may be 

used to determine the impervious surface area of a watershed 

(Capiella and Brown, 2001). Land use mapping involves the 

delineation of similar land uses as interpreted from a digital image or cartographic base using moderately-high spatial resolution 

scale. Each specialized land use polygon is then multiplied by a 

specific, previously determined coefficient that represents the average fractional percentage of impervious cover for that particular 

land use. The sum of this area for all land use polygons is then 

the impervious amount for the watershed. For example, table 4 

lists the land use classes and coefficients that were developed for 

the Chesapeake Bay watershed (Capiella and Brown, 2001). 

Task 3 – Synoptic Scale Analysis: Calculation of 

Impervious Surface Area from Regional and National Synoptic Land Cover Data Sources

The NLCD92 product for which the coefficients were applied is 

typically generated from satellite-based image processing methods. Land cover data, like land use, is a thematic representation 

of surface phenomenon but is based on the biophysical cover and 

its spectral reflectance properties of the surface features instead of 

an anthropogenic-based utilization of land. The NLCD92 and the 

subpixel products are extremely important for the second objecLand Use

(NLCD92)

Land Cover

(NLCD2k)

Golf Course Herbacceous Grasses

Orchard Tree Cover

Retention Pond Open Water

Quarries/Strip Mines Barren

Table 3. Comparison of land-use versus land-cover features.

impervious surfaces. See the appendix for sources of data.
17

tive as it is anticipated that a combination of these methods and 

products could offer a more accurate quantification of imperviousness of a watershed. 

The final task in this research was the determination of impervious area from a regional or national synoptic data source, which 

incorporated the Land Use Coefficient methodology previously 

discussed. The Multi-Resolution Land Consortium’s National 

Land Cover Dataset (NLCD) was selected and was available for 

the 1990 timeframe (NLCD92), with some variation in scene date 

from 1988 to 1993. Unlike land use data, the big advantage of 

land cover in this application is that it can be derived spectrally 

from remotely sensed data with any one of several automated 

image classification techniques, which can be very efficient 

over large areas. The computation of impervious area from land 

cover offers two distinct advantages. First, land cover can be 

derived from spectral satellite data and computed mostly using 

automated digital classification routines. Second, the availability 

of a nationally consistent land cover data source allows for the 

rapid and efficient analysis of any watershed or area of the United 

States, conserving valuable resources that would be expended on 

Land Use Classes Land Use 

Code

 Impervious 

Coefficients

Open Urban land 10 0.086

Residential – 2 acre lot 11 0.106

Residential – 1 acre lot 12 0.143

Residential – 0.5 acre lot 13 0.212

Residential – 0.25 acre lot 14 0.278

Residential – 0.125 (1/8) acre lot 15 0.326

Townhome Residential 16 0.409

Multifamily Residential 17 0.444

Agriculture 20 0.019

Institutional 30 0.344

Churches 31 0.399

Schools 32 0.303

Municipal 33 0.354

Golf Courses 34 0.050

Cemeteries 35 0.083

Parks 36 0.125

Light Industrial 40 0.534

Commercial 50 0.722

Table 4. Land Use Codes and Coefficients (Capiella and Brown, 2001). the data acquisition and processing steps used in 

other methods. 

The computation of total impervious cover from 

this type of data source is similar to the subpixel 

processing discussed in Task 2A-Intermediate 

Scale Analysis: Subpixel Image Processing 

Analysis in the analysis of the UMD-RESAC 

and NLCD2000 product. Rather than using the 

UMD-RESAC or NLCD2000 data, the NLCD92 

was used and processed exactly the same way 

to extract the impervious surfaces. But before 

applying the coefficients to the different classes 

within the watershed, basic calculations were 

used to determine which type of urban intensity 

the watershed has, one of three categories based 

on the number of urban pixels. Once the values 

from the value attribute table have been put in 

the spreadsheet the total impervious area is calculated using the coefficients (table 5) for each 

of the class watershed. The coefficients are then 

multiplied times the total area for each class and 

summed, yielding an estimate of total impervious 

area.

The use of coefficients for impervious surface 

determination in this context implies that there 

are similarities in landscape structure that would 

make this method of computing impervious 

surfaces feasible. Developing the coefficients 

is accomplished by a sampling technique in the 

Mid-Atlantic region for these coefficients, where 

higher spatial resolution data are used to determine the average amount of imperviousness in 

each land cover category. This average is then 

used to compute a coefficient for a ‘per pixel’ 

amount of imperviousness for each class. In this 

task, two sets of coefficient methods were used 

for computing imperviousness from NLCD92. 

The details of the two techniques are found in Center for Watershed Protection (1998), as implemented in the Arcview Extension 

‘Attila’ (Ebert and Wade, 2003; Jennings and others, 2004) (table 

6). 

Results

All five methods in the three different tasks enabled the quantification of the different components of impervious surfaces – at 

different scales and accuracies with some interesting results.

Task 1 – Quantifying Components of Impervious 

Surfaces: Reference Data

After compiling all temporal datasets on the six watersheds, 

overall, buildings led all other categories at 29.2 percent of the 

total imperviousness, followed closely by roads at 28.3 percent, 

with parking lots in third place at 24.6 percent. These three cat-
1

8

Class Name Codes Type 1 Coefficient (Low Intensity) Type 2 Coefficient (Moderate Intensity) Type 3 Coefficient (High Intensity)

Open Water 11 0.0070 0.0440 0.0950

Low Intensity Residential 21 0.1820 0.2500 0.3060

 High Intensity Residential 22 0.6920 0.6200 0.4900

Commercial/Industrial/Transportation 23 0.3480 0.4500 0.6280

Bare Rock/Sand Clay 31

Quarries/Strip Mines/Gravel Pits 32 0.0620 0.1380 0.4470

Transitional 33 0.0580 0.2400 0.5000

Deciduous Forest 41 0.0130 0.0550 0.1190

Evergreen Forest 42 0.0100 0.0940 0.1530

Mixed Forest 43 0.0180 0.0870 0.1110

Shrubland 51

OrchardsVinards/Other 61

Grass Lands/Herbaceous 71

Pasture/Hay 81 0.0350 0.0580 0.1140

Row Crop 82 0.0450 0.1160 0.1400

Small Grains 83

Fallow 84

Urban / Recreational Grasses 85 0.0880 0.0900 0.2100

Woody Wetlands 91 0.0070 0.0130 0.0360Woody Wetlands 92 0.0080 0.0370 0.2110

Table 5. The 1992 National Land Cover Dataset coefficients for the Chesapeake Bay region (Jennings and others, 2004). The watershed can be a Type 1- rural/suburban, Type 2 - suburban, or a Type 3 - urban. Type is determined by the built-up intensity using the imperviousness/class for: Low Intensity Residential (21), High Intensity Residential (22), Commercial/Industrial/Transportation (23).
19

egories accounted for the majority of total impervious area for all 

the watersheds. Table 7 summarizes the area measurements for 

each category in acres and table 8 summarizes the percentages of 

total impervious surfaces area. Figure 22 shows the cumulative 

percentages of each category.

Task 2 - Intermediat Scale Analysis

Task 2A - Intermediate Scale Analysis: Subpixel 

Image Processing Techniques

The results are shown in table 9. In the Tuscarora Creek watershed, both the RESAC and the EDC data compiled total 

impervious area to within 10 percent of the Task 1-Reference 

Data values. For the Difficult Run watershed, however, both the 

RESAC and EDC datasets significantly under reported the total 

impervious area. The 1996 RESAC data were only 52 percent in 

agreement with the 1997 reference data, while the 200 EDC data 

were 59 percent in agreement with the 1997 reference data.

Task 2B - Intermediate Scale Analysis: Land Use 

Coefficient Methodology

The application of Land Use coefficients performed best in the 

Class Name Density (dwelling/acre) Coefficients

Forest -- 0.01

Agriculture -- 0.01

Urban Open Land -- -

Water/Wetland -- -

Low Density Residential <0.5 0.06

0.5 -

1 0.12

Medium Density Residential 2 0.18

3 0.20

4 0.25

High Density Residential 5 -7 0.35

Multifamily (Townhouse)

>7 0.35 - 0.50

(Highrise)

>20 0.60 - 0.75

Industrial 0.60 - 0.80

Commercial 0.90 - 0.95

Table 6. Center for Watershed Protection (1998) coefficients. Each land use area is measured and multiplied by the coefficient resulting in an estimate of imperviousness for that feature.

Figure 22. Task 1-Components Quantification - Truth Data. The percent of 

Total Impervious Area as calculated from eleven separate mappings of the six 

watersheds. Buildings, roads and parking lots account for the majority of total 

impervious surfaces area.

Black River watershed where the use was predominantly industrial and commercial, with some residential running from northto-south on the eastern side of the watershed (table 10). The Task 

1-Reference Data resulted in 35.2 percent impervious surfaces 

for the entire watershed. Visual observation (fig. 5) indicates that 

the watershed was nearly 100 percent built-up, with no open-
20

space lands, and the area being 

predominantly industrial and 

commercial.

In the Wares Creek watershed, 

where this method performed 

furthest from the reference 

data, the use was entirely 

residential. Task 1-Reference 

Data resulted in 45 percent im-

pervious surfaces for the entire 

watershed. The development 

covered the watershed homo-

geneously, visually appear-

ing to be nearly 100 percent 

built-up, with the area being 

predominantly residential.

The two remaining water-

sheds with percent agreements 

between Wares Creek and 

Black River had a combina-

tion of uses and undeveloped 

lands that covered at least 50 

percent of the watershed. Task 1-Reference Data resulted in, 

for both, less than 25 percent impervious surfaces for their 

entire watersheds. 

 

Task 3. Synoptic Scale 

Analysis

Task 3-Synoptic Scale Analy-

sis results for both methods 

are summarized in table 11. 

Both methods simply multiply 

a coefficient against the area 

of satellite image-derived land 

cover for each class. The coefficient represents the average impervious area for that land 

cover class. Both methods had 

overall percent agreements 

that fell within 5 percent of the 

reference data. The minimum 

percent agreement was 90.0 

percent and the maximum was 

99.9.

Conclusions

This study documented quanti-

ties of total impervious areas 

(table 2) as components in six 

watersheds (fig. 3). While 

roads, buildings, and parking 

Impervious Surface Area

(In Acres)

Watershed

Name

Image 

Source

Date

Total Area Watershed Roads Bldg DW SW Parking Lot Other Airport

Black River 1992 17207.1 6054.5 1257.1 1870.7 250.5 111.3 2508.9 55.9 N/A

Difficult Run 1997 37179.4 6516.1 2043.6 1955.1 786. 0 226.2 1295.2 210.1 N/ADifficult Run 1988 37179.4 5786.0 1877.4 1691.0 686.9 187.6 1150.7 192.5 N/A

N. Walnut Creek 2001 8691.7 2201.1 578.8 727.1 222.1 92.6 563.5 16.9 N/AN. Walnut Creek 1990 8691.7 1716.0 500.3 573.5 193.5 75.9 358.2 14.6 N/A

Oak Creek 2000 14042.1 2421.9 607.7 521.3 152.9 86.8 401.7 24.5 626.8Oak Creek 1990 14042.1 1873.6 525.8 307.2 109.1 56.9 236.3 11.6 626.8

Tuscarora Creek 2000 9226.2 1165.1 409.4 279.4 128.0 66.9 267.4 14.0 N/ATuscarora Creek 1998 9226.2 984.2 350.8 252.2 99.6 50.2 219.6 11.7 N/ATuscarora Creek 1988 9226.2 806.8 282.6 203.2 86.8 38.2 188.8 7.2 N/A

Wares Creek 2001 5091.4 2311.1 560.9 898.5 146.5 37.2 653.2 14.9 N/A

Totals N/A 169803.5 31836.4 8994.4 9279.1 2862.0 1029.9 7843.5 573.8 1253.6

Overall PCT per 

Component N/A N/A 18.75 28.25 29.15 8.99 3.23 24.64 1.80 3.94

Table 7. Task 1- Component Quantification - Truth Data. The Impervious Surface (IS) Area Measurements, in acres, for all the watersheds with dates used in this study. [Bldg=Buildings, DW=Driveways, SW=Sidewalks, PL=Parking Lots, and PCT=Percent]
21

lots make up the majority of impervious areas, driveways, sidewalks, and other covers make up 14 percent of total impervious 

area and should not be ignored in calculating the total impervious 

surface. Parking lots tended to be large connected, areas unlike 

the other components that tended to be fairly linear and fragmented.

Four of the common methods for computing impervious area 

showed significant variability when measured against the high 

spatial-resolution ground-reference information developed in 

Task 1. Overall, the four methods tested generally produced an 

estimate of total impervious area within 10 percent of the reference value. Figure 23 shows a simple regression of the derived, 

the various methods and years applied, versus the reference data 

for all four methods tested. There were 24 independent observations from all the methods and the different dates plotted against 

the Task 1-Reference Data percentages of imperviousness, result-

ing in a regression correlation R2 = 0.89. Ten-percent variability 

in the accuracy of the total impervious area value is important, 

considering that 10 to 25 percent and greater than 25 percent 

impervious area in a watershed was considered to result in an 

ecological condition of ‘potentially impacted’ or ‘non-supportive.’ 

The correlation from Schueler (1994) and Arnold and Gibbons 

(1996) has been supported by numerous other studies. Research 

over the last 20 years has consistently reported a correlation between imperviousness of a watershed and ecosystem quality. 

Task 1 – Quantifying Components of Impervious 

Surfaces: Reference Data

This method was supported by the abundance of geo-registered 

digital imagery at 1-meter resolution or better that is now widely 

available from the U.S. Geological Survey, other government 

sources, and educational institutions, as well as from private mapping vendors. The reference data provided excellent and reliable 

sources of detailed impervious surfaces in correlating them with 

the condition of the watershed, as well as the valuable and critical 

assessment of important new techniques.

The high spatial resolution compilation of impervious surfaces 

in this task revealed individual component features that made up 

functional impervious surface area. Roads, while often considered the main contributor to impervious area, are only one of the 

three major components of total impervious area that include 

buildings and parking lots. The contribution of road impervious area to overall total impervious area has been implied in the 

literature to be as high as 70 and 80 percent. In this research, the 

roads ranged from 20.8 percent, in 1992 Black River watershed 

(dense built-up commercial/industrial with some residential), to 

35.6 percent, in the 1998 Tuscarora Creek watershed (low-density 

residential with a significant amount of open space). Driveways, 

often unaccounted for in impervious surfaces research, made 

up an average of 9 percent of the total impervious surface area. 

Thus, these data show that even very minor classes of impervious 

cover are important. Very few studies on impervious surface area 

take into account sidewalks and other components. Yet in this 

study, these components were responsible for approximately 5 

percent of total impervious area (table 8). 

Figure 23. In this regression plot the estimated impervious surfaces percents 

were derived from all the methods and their 24 observations. These estimations 

were plotted against the actual impervious surfaces percent. Though the points 

are scattered along the ideal from low to high density, notice that in this research 

the techniques examined tended to overestimate the impervious surfaces in the 

less developed watersheds (for example, greater than 20 percent impervious 

surfaces).

Road Contribution/Watershed Urbanization

In the two watersheds in the suburban Washington, D.C., metropolitan area, roads were the leading components of impervious 

cover; even though one watershed was fairly rural and the other 

more built-up. In contrast, in the more rural Oak Creek watershed in Nebraska, the airport/air force base was the dominant 

component in both years of analysis, with roads being second. In 

the highly industrialized Black River watershed with Seattle to 

its north and Tacoma to its south, parking lots were the leading 

component of imperviousness (41.4 percent). In both the Wares 

Creek watershed, Fla., and the North Walnut Creek watershed, 

Iowa, extending into Des Moines, buildings were the largest component of impervious cover. Buildings were the lead impervious 

Figure 24. The relationship between road impervious area to watershed impervious area. As the watershed becomes more developed (resulting in increases in 

the percentage of impervious area) the contribution of roads to the total impervious area declines. WSIA = Watershed Impervious Surfaces Area, WSTA = Watershed Total Area, RIA = Road Impervious Area, and TIA = Total Impervious Area.
22

surface component of all these watersheds 

combined (table 8). 

A significant relationship can be found in 

the correlation of road impervious area 

to total impervious area (RIA/TIA). TIA 

correlates with the level of development 

of the watershed. Figure 24 shows the 

linear relationship between road imper-

vious area and total impervious area. 

Recognizing total impervious area as a 

general indicator of the level of human de-

velopment in a watershed, we can see that 

at lower percentages of imperviousness 

that the road impervious area is around 35 

percent. But as the watershed becomes 

more and more developed, the contribu-

tion of roads tends to decline. However, 

when examining the relationship of the 

percent impervious surface contribution 

of all of the other components, separately, 

to increasing urbanization, there was no 

apparent trend with the increase in urban-

ization.

Task 2 - Intermediate Scale 

Analysis

Task 2A - Intermediate Scale 

Analysis: Subpixel Image Pro-

cessing Techniques

Subpixel image processing produced 

mixed results. Although both methods, 

RESAC and EDC impervious surfaces 

results provided good estimates of total 

impervious area in Tuscarora Creek, both 

methods significantly under estimated to-

tal impervious area in Difficult Run. This 

may be due to the extensive tree cover 

and older, large-lot development in some 

parts of this watershed of Fairfax County, 

Va. As compared to the Tuscarora Creek 

watershed, it is much less built-up at this 

time, but has become one of the fastest 

growing counties in the United States. 

These methods may need to incorporate 

ancillary sources such as current vector 

road data. The availability of subpixel 

Land Use/Land Cover data, as derived 

from satellite imagery, is currently limited 

but will likely become widely available 

in the near future, and more accurate as 

techniques are improved.

Watershed and Components Impervious Surface Percentages

Watershed Image Source 

Date

Watershed Road Bldg DW SW PL Other IS/TIS Airport DW, SW, and Other Combined

Black River 1992 35.19 20.76 30.90 4.14 1.84 41.44 0.92 0.69

Difficult Run 1997 17.53 31.36 30.00 12.06 3.47 19.88 3.22 18.75Difficult Run 1988 15.56 32.45 29.22 11.87 3.24 19.89 3.33 18.44

N. Walnut Creek 2001 25.32 26.30 33.03 10.09 4.21 25.60 0.77 15.07N. Walnut Creek 1990 19.74 29.16 33.42 11.27 4.42 20.87 0.85 16.54

Oak Creek 2000 17.25 25.09 21.52 6.31 3.58 16.59 1.01 25.88 10.9Oak Creek 1990 13.34 28.06 16.40 5.82 3.04 12.61 0.62 33.46 9.48

Tuscarora Creek 2000 12.63 35.13 23.98 10.99 5.74 22.95 1.20 17.93Tuscarora Creek 1998 10.67 35.64 25.63 10.12 5.10 22.31 1.19 16.41Tuscarora Creek 1988 8.74 35.03 25.19 10.76 4.74 23.40 0.89 16.39

Wares Creek 2001 45.39 24.27 38.88 6.34 1.61 28.26 0.64 8.59

Overall PCT per 

Components N/A 18.75 28.25 29.15 8.99 3.23 24.64 1.80 3.94 14.0

Table 8. Task 1-Truth Data. The percent contribution of each component to the total impervious surface for each watershed/year. The highlighted percentage indicates these are the largest contributor of imperviousness in that watershed for that year.[IS=Impervious Surfaces, TIS=Total Impervious Surfaces, Bldg=Buildings, DW=Driveways, SW=Sidewalks, and PL=Parking Lots]
23

Watershed

Task 1 

Reference 

Data 

Image Source

Date

Task 2A Subpixel 

Data

Source/Date

Task 1

Reference 

Data TIA

Task 2A

Subpixel

TIA

Difference 

Btwn Task 1 

and Task 2A 

TIA

PCT AGR

with

Reference 

Tuscarora Creek 2000 RESAC / 2000 1165.1 1132.6 32.5 97.2

Tuscarora Creek 1998 RESAC / 1996 984.2 922.2 61.9 93.7

Tuscarora Creek 1988 RESAC / 1990 806.8 835.1 -28.3 96.5

Difficult Run 1997 RESAC / 1996 6516.1 3403.6 3112.5 52.2

Tuscarora Creek 2000 EDC / 2000 1165.1 1280.9 -115.8 90.1

Difficult Run 1997 EDC / 2000 6516.1 3866.5 2649.6 59.3

Average

Accuracy -- -- -- -- -- 81.5

Table 9. Task 2A-Intermediate Scale Analysis - Subpixel Image Processing Techniques. Subpixel image processing results above 

are in acres. Subpixel data are not yes available for the remaining watersheds of this study. [TIA=Total Impervious Surfaces Area, 

PCT = Percent, and AGR = Agreement]

Task 2B - Intermediate Scale Analysis: Land Use 

Coefficient Method 

Task 2B produced results generally within 10 percent of Task 

1 for total impervious area. If this technique used a remotely 

sensed product to derive the land use features using semi- or 

automated techniques, the 89 percent agreement for Wares Creek 

result from table 10 could be explained by the vegetation obscur-

ing the sensors view of the surface and structures. However, high 

spatial resolution images were used to collect these data. The 

average of the percent agreement for all of the watersheds, though 

only four watersheds in this comparison, for this method was 93.1

percent. Since the land use was derived from high spatial resolution imagery, the percent agreement was expected to be greater 

than the average agreement results, at 96 percent agreement, 

from Task 3-Synoptic Scale Analysis, which only used a product 

Watershed

Image 

Source

Date

Task 1-

Reference 

Data PCT IS 

of Watershed 

/ size (mi2)

Task 1-

Reference 

Data TIA

Task 2BLand Use 

Coefficients

TIA

Difference 

Btwn. 

Reference 

and Land Use 

Coefficients

Percent 

Agreement 

w/Reference 

Data

Black River 1992 35 / 26 6054.45 5866.37 188.08 96.89

North Walnut 

Creek 2001 25 / 13 2201.13 2045.37 55.76 92.92

Oak Creek 2000 17 / 2 421.87 2585.84 163.97 93.23

Wares Creek 2001 45 / 7 2311.09 2062.19 248.90 89.23

Average 

Accuracy -- -- -- -- -- 93.1

Table 10. Task 2B-Intermediate Scale Analysis: Land Use Coefficients (Capiella and Brown, 2001). [TIA=Total 

Impervious Surfaces Area, and LUC=Land Use Coefficients]

derived from 30-meter resolution satellite imagery. Only Black 

River met this expectation, although there were still good results 

from all tested watersheds. Clearly, further testing is needed as 

only four watersheds were examined in application of this process. This method is more time consuming, as the land use needs 

to be collected at a fairly detailed level (table 4). Some of the 

classes include the following: Residential 2-acre lots, Residential 

1-acre lots, Residential 0.5-acre lots, Residential 0.25-acre lots, 

Residential 0.125-acre lots, Townhomes Residential, Multifam-

ily Residential, and other very detailed classes based on lot size 

discrimination.

Task 3 - Synoptic Scale Analysis

In this technique in the application of land cover coefficients, 

basic calculations were used to determine the watershed type 

(urban, suburban, suburban/rural) based on the number of urban 
24

pixels in its watershed from the NLCD92. Once 

the type of watershed was determined, the coefficients were then multiplied against the total area 

for each class and summed yielding an estimate of 

total impervious area. One of the main drawbacks 

of any NLCD-based method is sensitivity to mis-

classification of these urban pixels in the dataset. 

The two land-cover coefficient methods resulted 

in some interesting patterns. The Center for Wa-

tershed Protection (CWP) method over estimated 

total impervious surface area in five of the six 

watersheds, while the Jennings and Jarnagin (J&J) 

method under estimated total impervious area 

in five of the six cases. Each method calculated 

total impervious area extremely accurately in one 

watershed; CWP did so in the smaller, more rural 

watershed at 9 percent impervious surfaces of the 

Tuscarora Creek watershed; the J&J method did 

so in the 35 percent impervious surfaces of the 

much larger Black River watershed. Overall, the 

average accuracy for the CWP was 95.6 percent 

and the J&J method was 96.3 percent. Given the 

small-scale source of 30-meter resolution, these 

results seemed impressive. Applying the coefficients to the NLCD product offers a tremendous 

advantage to all of the methods in that the NLCDbased product with its derivatives products is a 

fast method of analysis with results in less than 

an hour. Applying these methods does require the 

use of GIS software. Potentially, the CWP and 

the J&J methods could be combined, resulting in 

an even more accurate technique in determining 

the impervious area in any given watershed. 

Additional Research

This project has taken an initial look at two im-

portant issues: the component makeup of total im-

pervious area and the accuracy of various methods 

used to compute total impervious area. A logical 

progression of this initial research would entail 

construction of a larger, statistically significant 

sampling frame in order to establish defensible 

inferential relationships about the component 

structure and the relationship of road surfaces to 

the scientific discussion of impervious surfaces 

and water and ecosystem quality. A larger sample 

of the accuracy assessment of other methods 

would likely yield similar scientific results.

A larger sample size might be well-served by an 

initial research inventory of high-resolution data-

sets available from municipal and county govern-

ments. As noted in this report, the availability of 

local, high spatial resolution GIS datasets repre-

Task1

Reference 

Data Center for Watershed Protection Jennings and Jarnagin

Watershed

Image

Source

Date

NLCD

Scene

Date

Total

Area TIA PCT TIA TIA PCT IA Diff Btwn Reference PCT AGR TIA PCT IA Diff Btwn Reference PCT AGR

Black River 1992* 5/92 8/92 17207.1 6054.5 35.2 2735.0 45.2 10.0 90.0 6016.4 35.0 -0.2 99.8Difficult Run 1988 3/89 5/90 37179.4 5786.0 15.6 955.9 16.5 1.0 99.0 5072.0 13.6 -1.9 98.1N. Walnut Creek 1990 4/91 8/92 8691.7 1716.0 19.7 463.0 27.0 7.2 92.8 2310.8 26.6 6.8 93.2Oak Creek 1990 5/88 8/91 14042.1 1873.7 13.3 222.0 11.9 -1.5 98.5 1052.2 7.5 -5.8 94.2Tuscarora Creek 1988 3/92 9/93 9226.17 806.8 8.7 71.6 8.9 0.1 99.9 531.3 5.8 -3.0 97.0Wares Creek 2001 3/92 12/91 5091.4 2311.1 45.4 1198.8 51.9 6.5 93.5 2073.5 40.7 -4.7 95.3AverageAccuracy

-- -- -- -- -- -- -- -- 95.6 -- -- -- 96.3

Table 11. Task 3-Synoptic Scale Analysis. Comparison of total impervious area calculations from NLCD92 Data in acres with the other two methods in Task 3, Center for Watershed Protection (CWP) and Jennings & Jarnagin (J&J). [TIA=Total Impervious Surfaces Area, AGR=Agreement, and PCT = Percent. * There was no significant change since 1992 in the Black River watershed]
25

sents a major savings of research resources.

This research also indicates that the component structure of 

impervious area varied by region and differences appeared to be 

significant. Establishing a regional, economic and (or) ecoregionbased approach to impervious surface and water quality issues 

could be extremely useful in understanding the spatial context 

of impervious development in different parts of the country, but 

could also provide operational information on critical ecosystems 

and the potential benefit of mitigation efforts.

Further developing the concept presented in figure 24, under-

standing the changes and trends to total impervious area that road 

surfaces contribute over time would be a valuable contribution 

to transportation science. Impervious surfaces developed over a 

timeframe using historical imagery, correlated with population 

statistics and water quality parameters, could lead to a better un-

derstanding of the relationship between roads, mitigation efforts, 

best practices, and eventual water quality issues.

Finally, a comparative, time-series, watershed analysis of total 

impervious area in the United States and in similar watersheds in 

other parts of the world could provide an increased understanding 

of the efficiency of construction practices and best practices.

Acknowledgments

This study was supported by the U.S. Department of Transporta-

tion, Federal Highway Administration, under the direction of 

Patricia Cazenas. The following counties provided datasets for 

this study: Fairfax County, Va; King County, Wash.; Lancaster 

County, Nebr.; Loudoun County, Va.; and Polk County, Iowa. 

The following individuals gathered and modified the GIS data: 

Jacqueline D. Peyton and S. Gail Winters of the USGS; Ahira 

Sanchez-Lugo, Eliazar Del Toro, and Ingrid M. Pla, of the Uni-

versity of Puerto Rico; and most notably, Janet L. Parker of the 

USGS. 
26

Web Sites for Additional Information

Anderson Classification Level II: http://landcover.usgs.gov/pdf/anderson.pdf

American Planning Association: http://www.planning.org/

Bureau of Transportation Statistics: http://www.bts.gov/

Center for Watershed Studies: http://depts.washington.edu/cwws/

How to Find a Watershed Address: http://www.epa.gov/win/address.html

International Institute for Geo-Information

 Science and Earth Observation: http://www.itc.nl/research/policy/spearhead1/vdmeer.asp

Nonpoint Source Pollution: The Nations 

Largest Water Quality Problem: http://www.epa.gov/OWOW/NPS/facts/point1.htm

National Land Cover Data 1992: http://landcover.usgs.gov/natllandcover.html

Maryland’s Surf Your Watershed: http://www.dnr.state.md.us/watersheds/surf/index.html

Multi Resolution Land Consortium: http://www.epa.gov/mrlc/

National Land Cover Data 1992 http://www.mrlc.gov/

National Land Cover Data 21-classes: http://landcover.usgs.gov/classes.php

National Academies: http://www.nationalacademies.org/environment/

National Land Cover Data 2001: http://www.mrlc.gov/mrlc2k_nlcd.asp

The Brookings Institute: http://www.brook.edu/index/research.htm

University of Maryland, College Park

Department of Geography: http://www.geog.umd.edu/

University of Maryland, College Park, RESAC: http://www.geog.umd.edu/resac/index.html

People would walk if it wasn’t 

so far and dangerous: http://www.transact.org/report.asp?id=205

Science in Your Watersheds: http://water.usgs.gov/wsc/index.html

Soil and Water Conservation – Virginia 

Nonpoint Source Pollution Management

Program: Surface Transportation Policy: http://www.transact.org/

Soil and Water Conservation – Virginia 

Nonpoint Source Pollution Management 

Program Surface Transportation Policy

Program Update: http://www.dcr.state.va.us/sw/npsupdt.htm

Transportation Research Board: http://trb.org/

U.S. Department of Transportation: http://www.dot.gov/
27

U.S. Department of Transportation 

Federal High Administration: http://www.fhwa.dot.gov/

U.S. Environmental Protection Agency: http://www.epa.gov/

U.S. Geological Survey: http://www.usgs.gov

U.S. Geological Survey, Geography: http://geography.usgs.gov/

U.S. Geological Survey: 

Geographic Analysis & Monitoring http://gam.usgs.gov/

Water Quality Assessment Database

2000 305(b) Data: http://www.epa.gov/waters/305b/index.html

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30 Appendix A: GIS Data Source Information

GIS DATA SOURCE INFORMATION

Selected Watersheds

GIS Data Source 

(Date /

Resolution)

Themes Available in 

GIS Data

(metadata yes / no)

Modification To, or Creation of, Each Theme 

with Best Available Imagery

Provide Products 

& Results Back to 

Interested Local 

Government and Other 

Interested Contacts Modification Created New Themes

Black River, 

Washington

King County

(2000) Data covered city 

limits but not north 1/3 

portion of the watershed

Buildings – lines Buildings convert to polygons, closed, and add missing Top 1/3 ws

King Co.

Roads – lines Roads convert to polygons, closed, and add missing Top 1/3 wsParking Lots – None N/A Parking lotsDriveways - line Driveway convert to polygons, closed, and add missing Top 1/3 wsSidewalks – None N/A SidewalksOther – None N/A OtherWatershed N/A Top 1/3 ws

Difficult Run, Virginia Fairfax County (2000)

Buildings – polygon Adjusted and made some additions N/A

Fairfax Co.

Roads (major, minor) – polygon

Adjusted and made some additions N/A

Parking Lots – polygon Adjusted and made some additions N/ADriveways – None N/A DrivewaysSidewalks – lines Adjusted and made many additions, and buffered N/AOther (pools) - polygons

Adjusted and additions made (all recreation trails added, pool patios added)

N/A

Watershed N/A N/A

Continued.....
3

1

Appendix A: GIS Data Source Information (continued)

GIS DATA SOURCE INFORMATION (continued)

Selected Watersheds

GIS Data Source 

(Date /

Resolution)

Themes Available in 

GIS Data

(metadata yes / no)

Modification To, or Creation of, Each Theme 

with Best Available Imagery

Provide Products 

& Results Back to 

Interested Local 

Government and Other 

Interested Contacts Modification Created New Themes

North Walnut Creek, 

Iowa

Some AddedConverted to Polygons, Many Added and Others AdjustedN/AAdded and AdjustedN/APatios around pools added

N/A Manatee Co. N/A

Polk Co.

N/A

Combined, converted to polygon file closed polygons, added many more

N/A

Parking Lots N/A Parking LotsN/A Closed Polygons DrivewaysSidewalks N/A SidewalksN/A N/A OtherWatershed N/A N/A

Oak Creek, Nebraska The National Map (2002) None N/A

Roads, Parking Lots, Buildings, Driveways, Sidewalks, Other

None.

Continued....
32 Appendix A: GIS Data Source Information

GIS DATA SOURCE INFORMATION (continued)

Selected Watersheds

GIS Data Source 

(Date /

Resolution)

Themes Available in 

GIS Data

(metadata yes / no)

Modification To, or Creation of, Each Theme 

with Best Available Imagery

Provide Products 

& Results Back to 

Interested Local 

Government and Other 

Interested Contacts Modification Created New Themes

Tuscarora Creek, 

Virginia

Loudoun County

(1998)

Buildings N/A N/A

Larry Stepik

Roads N/A N/AParking Lots N/A N/ADriveways N/A N/ASidewalks N/A N/AOther N/A N/AWatershed N/A N/A

Wares Creek, Florida Manatee County (2001)

Buildings - polygon N/A N/A

Manatee Co.

Roads – lines N/A N/AParking Lots – None N/A N/ADriveways - polygon N/A N/ASidewalks - – None N/A N/AOther (pools present) N/A N/AWatershed N/A N/A
33

HIGH-SPATIAL RESOLUTION IMAGERY SOURCE INFORMATION

Selected 

Watersheds

Hi-Resolution

Imagery Source used for Base 

IS Completion (resolution/

date)

Imagery Source for NLCD92 

Match

 (resolution/date)

Hi-Resolution Imagery Source for 

Subpixel Data – RESAC and EDC 

Impervious Surfaces Data Match 

(Resolution/ Vintage)

Black River. 

Washington

King County

(1992)

USGS/DOQ (1-meter/1989)

Scanned Aerials (1.5’/1992)

No Significant Change Since 1992

EPA CIR Aerial Photographs

Difficult Run, 

Virginia

Fairfax County (1’/1997)

The National Map 2002 USGS/DOQ (1-meter/1988) The National Map

(1’/2002)

North Walnut 

Creek, Iowa

Polk County

(6”/2001/BW)

EPA/Scanned Aerial Photos 

(1.5’/1992/BW) 1990

Polk County

(6”/2001/BW)

Oak Creek, 

Nebraska

The National Map

(1’/2002/Natural Color)

EPA/Scanned Aerial Photos

(1.5’/1987/CIR)

USGS/ DOQs (1-meter/1990)

The National Map

(1’/2002/Color)

Tuscarora 

Creek, Virginia

Private Source (1’/1998/Color) 

and USGS DOQs 

(1-m/1998/CIR )

EyemapTM 1998; DOQs 1990, 

and 2000 Eyemap TM)

USGS/DOQ (1-meter/1988) EPA/Scanned Aerial Photos

(~1.5’/2000)

Wares Creek, 

Fllorida

Manatee County

(~2’/2001)

EPA Aerial Photos

1992/CIR No Significant Change Since 1992

Appendix B: High Spatial Resolution Imagery Source Information
34

SMALL SCALE SOURCE INFORMATION

Selected Watersheds NLCD Path/ Row NLCD92 Scene Date UMD RESAC 

Scene Date

EDC Impervious 

Surfaces Date

Black River, Washington 46/27 5/1992

8/1992 Unavailable Unavailable

Difficult Run, Virginia 15/33 3/1989

5/1990 2000 2000

North Walnut Creek, Iowa 26/31 4/1991

8/1992 Unavailable Unavailable

Oak Creek, Nebraska 28/32 5/19888/1991 Unavailable Unavailable

Tuscarora Creek, Virginia 15/33 3/19929/1993 2000 2000

Wares Creek, Florida 17/41 3/1992

12/1991 Unavailable Unavailable

Appendix C: Small Scale Source Information