Acevedo, others

Rates, Trends, Causes, and Consequences of Urban Land

Use Change in the United States

U.S. Geological Survey Professional Paper 1726

Printed on recycled paper
Rates, Trends, Causes, and Consequences of Urban 

Land-Use Change in the United States 

Edited by William Acevedo, Janis L. Taylor, Dave J. Hester, Carol S. Mladinich, and 

Sonya Glavac

Professional Paper 1726

U.S. Department of the Interior

U.S. Geological Survey 
U.S. Department of the Interior

DIRK KEMPTHORNE, Acting Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

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

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Produced in the Western Region, Menlo Park, California

Manuscript approved for publication, June 2, 2006

Text edited by James W. Hendley II

Layout and design by Stephen L. Scott

COVER—This aerial photograph of part of Santa Clara County, California, typifies the urbanization that has occurred 

throughout much of the United States since World War II. Before the war, this area was mostly agricultural land.
Contents

Introduction

The Historical Deveopment of the Nation’s Urban Areas ----------------------------------------- 1

 Roger F. Auch, William Acevedo, and Janis L.. Taylor

Factors Affecting Urban Land-Use Change

Cultural and Demographic Factors in Urban Decentralization in the United States—

A Historical Overview --------------------------------------------------------------------------------- 13

 Alexander von Hoffman and John Felkner

Demographic Drivers of Lakeshore Urbanization in the Ozarks--------------------------------- 25

 Sonya Glavac

Economic Aspects of Urban Decentralization in Historical Perspective---------------------- 33

	 	 Alexander von Hoffman and John Felkner

General Trends Regarding Aggregate Materials for Urban Development, 

With a Suggested Method for the Analysis of Aggregates Distribution----------------------- 45

 Dalia Varanka

Quantifying Landscape Change

Wrestling Sprawl to the Ground—Defining and Measuring an Elusive Concept ------------ 55

 George Galster, Royce Hanson, Michael R. Ratcliffe, Harold Wolman, 

 Stephen Coleman, and Jason Freihage

Measuring Urban Sprawl and Extent Through Multitemporal Imperviousness 

 Mapping-------------------------------------------------------------------------------------------- 59

 George Xian, Limin Yang, Jacqueline M. Klaver, and Nazmul Hossain

An Assessment of Impervious-Surface Measurement Methods ------------------------------ 65

 E. Terrence Slonecker and Janet S. Tilley

Comparing Impervious-Cover Densities on the Basis of Land-Use Types Between 

Sister Cities on the United States/Mexico Border ----------------------------------------------- 79 

 Jean Parcher

The Use of Spatial Analytical Techniques to Identify Potential Brownfields Sites----------- 87 

 Janet S. Tilley, Robin Paul, Terrance Slonecker, and Ellen Walkowiak

Road Densities for Urban Areas in the Conterminous United States -------------------------- 97

 Raymond D. Watts, John McCammon, Roger Compton, Carl Rich, and Tom Owens

Quantifying Urban Intensity in Drainage Basins for Assessing Stream Ecological 

Conditions--------------------------------------------------------------------------------------------101

 Gerard McMahon and Thomas F. Cuffney

Regional Trends in Urban Land-Use Change

Analyzing Albuquerque’s Landscape Evolution in the 20th and 21st Centuries--------------113

 David J. Hester

Change to Urban, Agricultural, and Forested Land in Central and Southern Maryland 

From 1850-1990 --------------------------------------------------------------------------------------129

 Janis L. Taylor and William Acevedo

Regional Landscape Change in the Northern Colorado Front Range-------------------------139

 Carol S. Mladinich

A 30-Year Study of Urban Dynamics for the Municipality of Anchorage, Alaska, 

1970-2000---------------------------------------------------------------------------------------------153

 Carl J. Markon

Consequences and Impacts of Urbanization 

The Effects of Urbanization on Discharge and Suspended-Sediment Concentrations 

in a Southern California River ---------------------------------------------------------------------163

 Jonathan A. Warrick and Kevin M. Orzech

III
Changes in Anthropogenic Impervious Surfaces, Precipitation, and Daily 

Streamflow Discharge—A Historical Perspective in a Mid-Atlantic Subwatershed-------171

David B. Jennings, and S. Taylor Jarnagin

Urban Growth and Seagrass Distribution Trends in Tampa Bay, Florida ---------------------185

Mike Crane and George Xian

An Overview of the Stressors and Ecological Impacts Associated with Regional 

and Global Patterns of Population, Land-Use, and Land-Cover Change ---------------------189

S. Taylor Jarnagin

IV


Introduction

Over the past 200 years, changes to the Nation’s urban 

areas have been dramatic. Changes that have occurred relate 

both to the location of urban centers, as well as to the spatial 

extent of land dedicated to urban uses. Urban areas at the beginning of the 19th century were located primarily along major 

rivers or bodies of water, as waterways provided the most 

efficient means for transporting goods and people. As railroads 

became prominent, urban areas were able to expand or develop 

away from the water’s edge.

Geographic features such as steep slopes, wetlands, and 

lack of freshwater impeded settlement. In 1902, the National 

Reclamation Act was passed and with it came funding for the 

construction of water storage and transportation systems. This 

encouraged urban expansion in the arid west. After World War 

II, the Nation’s urban areas continued to expand outward away 

from the city center as populations migrated to the margins of 

urban areas, where land was less expensive and the environment 

was less polluted. In 1956, the Federal Highway Act and the 

building of Interstate highways further facilitated urban expansion across the United States. Rural towns, small industrial centers, and farmland were engulfed by expanding urban centers. 

Over the past 200 years, numerous social, cultural, economic, and political incentives have encouraged urban expansion. In the 1800s, the industrial revolution influenced where 

people lived and worked. Many people shifted from agricultural 

production in rural areas to factory work in urban centers. 

Advances in transportation systems, such as rail transport in the 

19th and early 20th centuries, followed by the mass production 

of the automobile and convenient air travel, facilitated a mobile 

society and a national economy. Economic growth and a population boom after World War II spurred increased suburbanization—the shifting of residential areas to the outlying section of a 

city or to a separate municipality—on the fringe of urban areas. 

Other economic and political incentives that shaped the urban 

environment included Federally backed home loans, credit and 

tax mechanisms that encouraged new development, and less 

restrictive municipal ordinances regarding building codes, environmental laws, and zoning regulations.

Throughout the past two centuries land use changes 

associated with increasing urbanization have had impacts that 

resonate at local, regional, and even national scales. Landscape changes resulting from urbanization can be mapped and 

studied over time. Understanding these changes requires a 

study of the causes of change as related to social, economic, 

and political influences. Understanding these changes also 

requires analysis of how urbanization physically spreads 

across the landscape. The knowledge gained from studying 

urban land-use change can be helpful when it flows into local, 

regional, and national decisionmaking that relates to land-use 

decisions that impact the people, the economy, and the environment. Deriving a correlation between physical change and 

the explanations of the causes of change can help anticipate 

and mitigate the impacts of future change.

Throughout the past two centuries changes to the Nation’s 

urban areas are inextricably linked to population changes. The 

Nation’s population started growing slowly along the eastern 

seaboard during the 17th and 18th centuries, accelerated in the 

second half of the 19th century, and then continued steadily 

spreading westward throughout the next hundred years3 (fig. 

1). Currently, nearly 80 percent of the U.S. population resides 

in urban areas (fig. 2). Land area dedicated to urban use continues to expand, although differently than it has in the past. 

Most newly urbanized areas are much less densely populated 

and less intensively developed than they were 50 to 100 years 

ago (Lopez and Hynes, 2003; Platt, 1996).

Historical Perspective

American cities grew steadily throughout the first 75 years 

of nationhood but remained relatively small in geographic area 

The Historical Development of the Nation’s 

Urban Areas

By Roger F. Auch1, William Acevedo2,and Janis L. Taylor1

1

SAIC TSSC, work performed under U.S. Geological Survey contract 

03CRN001 at U.S. Geological Survey, Center for Earth Resources Observation 

and Science, Sioux Falls, SD 57198. 2

U.S. Geological Survey, Center for Earth Observations and Science, Sioux 

Falls, SD 57198.

3

We recognize that during the 17th and 18th centuries settled populations resided throughout the territory that became the United States, with concentrations 

in particular locations, some of which are pointed out elsewhere in the paper.
 The Historical Development of the Nation’s Urban Areas

Figure 1. Time-series of the Nation’s 50 largest cities (red triangles) in 1850, 1900, 1950, and 1990. In 1850, most of the 

largest cities in the United States were east of the Mississippi River. Transportation systems, such as railroads and 

major ports, helped push some of the largest cities into the western half of the Nation by 1900. The growth of cities in 

Florida, Texas, and Arizona can be seen on the 1950 map. The movement to the Southwest is even more pronounced by 

1990, as California, Nevada, Arizona, Colorado, and Texas have nearly 50 percent of the largest cities.

Urban Population as a Percentage of U.S. Total

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1790 1850 1910 1970 2000

Rural

Urban

Figure 2. Graph of percent U.S. population living in urban areas versus rural 

areas. (Sources: U.S. Bureau of the Census, 2003, American FactFinder, P2 

Urban and Rural-(6) Universe -Total Population ( http://factfinder.census.

gov]); U.S. Bureau of the Census, 2000, Statistical abstract of the United 

States—2000: Washington, D.C., U.S. Government Printing Office, p. 38; U.S. 

Bureau of the Census, 1976, Historical statistics of the United States—colonial times to 1970, part 1: Washington, D.C., U.S. Government Publishing 

Office, p. 11-12.)

Figure 3. Map showing the major rivers (blue lines) in the eastern 

United States and the locations of the 50 largest cities (yellow 

triangles) in 1850. American cities grew steadily throughout the first 

75 years of nationhood but remained relatively small in geographic 

area and population. Most were located along transportation 

access points—at seaports, along navigable tidal and major inland 

rivers, along canals, and around the Great Lakes.
and population. Most were located along transportation access 

points—at seaports, along navigable tidal and major inland 

rivers, along canals, and around the Great Lakes (fig. 3). At 

this time, the regional hinterlands beyond the cities produced 

raw commodities as a part of rural-based economies. Those 

commodities were then processed into durable and nondurable goods in urban centers and redistributed in the area or 

transported to other regions where they were refined or even 

exported (Borchert, 1967). 

By the eve of the Civil War, however, the nature of urban 

development had begun to change. Cities were becoming 

greater centers of industry, as access to local and regional natural resources improved and these resources were exploited. 

In addition, European social unrest led to a steady stream of 

immigrants arriving in the United States. These conditions 

were especially marked in New England and the Mid-Atlantic 

States. The South and Midwest, by contrast, remained predominantly rural, although regional economies were linked to 

urban markets (Goldfield and Brownell, 1990). A few western 

cities also appeared at this time, although few were significant 

on the national scene. San Francisco and Sacramento were the 

only western cities to place in the top 100 largest urban areas in 

1860, with San Francisco ranking in the top 25 cities (Gibson, 

1998). In general, the economies of these western cities tended 

to be based either on localized mineral wealth or agriculture. 

In the Southwest, a handful of these urban areas were Hispanic 

cultural centers (Starrs, 1995) (fig. 4).

The pace of urbanization quickened following the 

Civil War. Trains became the dominant transportation mode 

throughout the country, especially after 1870, when an economical way to mass produce steel was introduced, allowing for the construction of a vast rail network (fig 5). Many 

cities were created as a result of railroad expansion, whereas 

others grew in size after becoming rail hubs (fig. 6). By 

1890, industrialization gained prominence and a national 

economy was becoming established (Borchert, 1967). Natural resources from all parts of the country were used to fuel 

a rapidly expanding industrial nation. (Meyer, 1987, 321-

345). Immigration, mostly from Europe, continued to bolster 

the burgeoning population. Drawn by factory jobs, many 

immigrants settled in cities. At this time, the majority of the 

Nation’s urban population was located in the Northeast and the 

Midwest (Goldfield and Brownell, 1990, 185-212). Cities in 

the South and West continued to grow, but remained smaller 

in comparison. For example, in 1896, Miami had a mere 500-

registered voters (Proctor, 1996).

By 1900, changes in agricultural production increased the 

flow of people to the cities. Regional specialization of specific 

commodities as a result of environmental conditions gave agricultural producers in some places an advantage over producers 

elsewhere. Farmers in areas where land was of marginal quality many times had to abandon agriculture and seek out other 

sources of employment (Meyer, 1987; Borchert, 1987). In the 

South, many African Americans migrated to northern industrial cities to escape the growing failure of the post-slavery 

sharecropping system that relied on monoculture cotton production (Clark, 1984; Spinney, 2000). Hispanic workers came 

to California and other Western States in increasing numbers 

as new irrigation projects made it possible to raise high-value, 

but labor-intensive, fruits and vegetables. Migrant field workers and their families often ended up living in the small, but 

growing, urban areas throughout the region (Haverluk, 2000).

The old “downtown” city in America reached its zenith 

by the end of the World War I. The inner core of the city was 

the center of industrial management, production, and distribution. Such cities were predominantly densely populated, as 

most workers lived in multifamily dwellings near their sites 

of employment. Downtown landscapes were also being transformed as rising land prices and new technologies fostered the 

construction of high-rise buildings, or “skyscrapers.” Suburbanization was initially limited to neighboring communities 

linked by railroads and electric streetcars to the city centers. 

Paved, multilane highways were just making their first appearance (Muller, 1986). The U.S. Census of 1920 revealed that, 

for the first time, more Americans lived in urban, rather than 

rural settings4 (Platt, 1996). 

Between 1930 and 1945, the United States experienced 

both the Great Depression and the World War II. These events 

ultimately formed a watershed that marked a new era in urban 

development, one that has evolved over the past 50 years. 

Some of the seeds of this change had already been sown, however, during the previous few decades. These included the rapid 

increase in automobile ownership in the 1920s and the creation 

of the modern mortgage loan in the 1930s. The Depression and 

then rationing during World War II limited consumer spending, first due to hardship and then by forced austerity. This way 

of life eventually led to a build-up of personal savings that 

helped create the Nation’s booming post-war economy and into 

which the “baby boom” generation was born. Another factor 

that played a role in urban development was the involvement 

by the Federal Government, in both the national economy and 

those of local communities, in committing massive amounts of 

money to fight the Depression and later, the war. One noteworthy aspect of this involvement was longterm home mortgage 

loan insurance offered by several agencies, such as the Federal 

Housing Administration and then the Veterans Administration, 

following the war. Most of the housing units financed in this 

manner were built on the edge of the existing cities (Jackson, 

1985). In time, the selective funding of major public works and 

the creation of a “military-industrial complex” would come to 

favor some urban areas over others (Goldfield and Brownell, 

1990; Markusen and others, 1991). A third significant influence on urban growth in the post-war era was the exposure 

that millions of Americans gained to other parts of the country. 

During the 1930s, many people abandoned regions fraught 

with economic and environmental problems for new places that 

Historical Perspective

4 In 1910, the U.S. Census Bureau changed the definition of urban places so 

that the minimum population threshold for an urban place went from 4,000 to 

2,500. Had the threshold remained at 4,000, the United States would not have 

reached the 50 percent threshold, where more Americans lived in urban rather 

than rural settings, until 1940.
 The Historical Development of the Nation’s Urban Areas

Figure 4. Hispanic settlements (red stars) across the Southwest in the 1850s. Rivers are shown in blue.

Figure 5. Major railroad lines across the United States in 1869.

0

0 200 400 Kilometers

100 200 Miles

Metropolitan areas

Population 130,000 or more

other

Scheduled railroad service

Scheduled packet service: coastal and inland waterways
were thought to offer better opportunities and living conditions 

(Gregory, 1989; Lewis, 1987). Others who had been involved 

in military service were exposed to parts of the country that to 

many had greater potential appeal as future places of residence 

than their former communities (Mohl and Mormino, 1996; 

Lewis, 1987; Abbott, 1981). 

A new form of urban growth gained prominence after 

World War II—mass suburbanization around the older cities. 

Suburbanization had been occurring for more than a century, 

but the trend exploded during the post-war era. Several factors 

fostered the increased growth of suburbanization. First, the 

development of an improved highway system allowed people 

to use automobiles to commute to their jobs in the cities from 

outside municipal boundaries. This trend began in the 1920s, 

but accelerated greatly after the 1940s. The passage of the 

Federal Interstate Highway Act in the 1950s set the stage for 

large-scale, multi-lane roads that became a reality over the 

next 20 years. Other driving forces behind suburbanization 

were subtler. The Nation’s growing middle class now had 

financial resources and with the aid of Federal home loan 

programs could afford to buy single-family residences away 

from the high-density city center and maintain their suburban 

lifestyle by commuting (Jackson, 1985). Suburbs also represented a “refuge” for a growing number of white Americans 

who wished to remove themselves from the increasingly nonwhite population of the cities and the desegregation of schools 

(Bickford, 1997; Boustan, 2005). Many other Americans fled 

from the congestion, pollution, multifamily residences, and 

high land prices typically found in the heart of the city. Many 

of the Nation’s citizens were only a generation or two removed 

from farm or small-town living; the suburbs offered a means 

of bridging small-town and city life (Herbers, 1986, 91-101). 

The Cold War era spurred the development of technology aimed at keeping the United States ahead of its perceived 

adversaries. Federal funding was channeled into research 

centers and defense industries. Communities that were home 

to major universities, aerospace and electronics industries, or 

defense installations benefited and grew as a result of these 

expenditures. Examples include Boston, Los Angeles, Washington, D.C., and Colorado Springs (Markusen and others, 

1991). Civic leaders sometimes pressured state officials to 

restructure existing universities—or to establish new ones—in 

attempts to attract new industries to an area (Moehring, 2000). 

Technological advancements, such as the growth of civil aviation, increasingly inexpensive and plentiful electricity, and 

the widespread use of air conditioning encouraged the growth 

Historical Perspective

Figure 6. Railroad development in Saint Louis as seen on this U.S. Geological Survey topographic map of Saint Louis, Missouri, in 1912. Railroad 

lines are highlighted in red. On the east side of the Mississippi River there is a major railroad hub.
The Historical Development of the Nation’s Urban Areas

of cities in many parts of the country. Air conditioning, in 

particular, helped overcome environmental conditions in the 

Southeast and Southwest that were perceived drawbacks of 

year-round living in these regions (Mohl and Mormino, 1996; 

Moehring, 2000).

The 1970 census revealed that, by this time, more of the 

Nation’s urban population lived in the suburbs than in central 

cities. The suburbs had continued to grow during the 1960s, but 

by the end of the decade their relationship to the city centers 

had changed. The suburbs were no longer simply “bedroom 

communities” for commuting city workers. They had emerged 

as focal points for the retail and service sectors of the urban 

economy, a trend that had been growing during the previous 

decades (Abbott, 1981; von Hoffman and Felkner, 2001).

Through the 1970s, city centers remained strongholds of 

finance and corporate management. Manufacturing, on the 

other hand—one of the leading segments of urban employment—was beginning to suffer major losses in the central 

cities. This was especially true for manufacturing’s “heavy” 

industries. A number of factors contributed to this decline, but 

perhaps the most influential was the fact that the city centers 

had lost their real or perceived competitive advantage. 

Although economic restructuring had a detrimental effect, 

changes in transportation and changes in industrial architecture, as well as the need for more space, also led employers to 

move outside central cities to suburban locations. More space 

was needed as warehouses were now being built horizontally, rather than vertically, which required more land than in 

the past. A horizontal layout improved access by trucks that 

hauled the goods. Both truck access to the warehouse and convenient locations near highways were now a rising priority 

and led employers to move outside central cities. 

Deindustrialization of city centers accelerated throughout the next decade and continued into the 1980s. It left a 

changed urban landscape, both physically and functionally 

(Bluestone and Harrison, 1982; Jakle and Wilson, 1992). 

Cities such as Pittsburgh, Pennsylvania, and Youngstown, 

Ohio were prominent examples of this type of change.

The desire for new housing and lower costs also encouraged the spread of development around metropolitan regions. 

Land costs for new housing construction tended to be less 

expensive on the periphery of a metropolitan region. The 

increasing suburbanization of employment also allowed 

people who desired new housing to live further out from the 

urban core, while maintaining a similar commuting pattern 

to that so prevalent several decades earlier. From about 1970, 

land-use regulations became strict in many of the post- World 

War II suburban counties, which affected the cost of new 

housing by limiting the area available for development and 

driving individual unit costs higher. Adjacent counties that 

lacked such restrictive zoning in many instances absorbed the 

“spillover” of new development from more densely settled 

places (Morrill, 1992; Levinson, 1997a, 1997b).

Expansion of the elderly middle-class segment of society 

helped change the geographic patterns of urban areas in the 

post-World War II era. These “retirees” were living longer 

and enjoying greater geographic mobility because they could 

take “transfer payments”, such as Social Security, company 

pensions, and rental or investment money, away from their 

home communities and spend it elsewhere (Mohl and Mormino, 1996). Cities that grew because of inmigration of retirees include the Orlando, Tampa and St. Petersburg, Phoenix, 

and Las Vegas areas among others (fig. 7).

A second group, people with transferable skills primarily in the service sector of the economy, also enjoyed the 

freedom to move to regions that were perceived to be more 

desirable places to live. Areas with natural amenities, such 

as mild winters, scenic vistas, and opportunities for waterrelated recreational activities, attracted many people from 

both of these relatively mobile groups (“retirees” and people 

with transferable skills) (Herbers, 1986; Power, 1996). As a 

result, the “Sunbelt” of the South and West gained in population. Many small- and medium-sized cities in the former 

regions were transformed by the influx of people and grew 

into major urban areas of national significance.

The growth of many Sunbelt cities also benefited from 

laissez-faire attitudes of local governments in regard to urban 

planning. Civic boosterism had always been a means through 

which individual cities could gain status within regional and 

national urban hierarchies. But the explosive growth in the 

major Sunbelt metropolitan areas was greatly aided by “probusiness” and “pro-growth” policies that were characterized 

by few regulations. The “boom” atmosphere that ensued 

resulted in the perception of both positive and negative externalities that usually accompany rapid urbanization (Kirby 

and Lynch, 1987; Luckingham, 1989; Payne, 1994; Goodstein, 1999, 2000). Prominent examples include Houston, 

Phoenix, and Denver (fig. 8).

The spatial distribution of employment also changed 

over recent decades. As suburbs became retail and service 

centers over time, manufacturing—both in terms of production and management—had also become a growing presence 

in suburban and exurban areas (von Hoffman and Felkner, 

2001). Most industries in these areas are typically described 

as being “light” or ‘high-tech,” and are much less site specific than their older, “heavy” industrial counterparts. Light 

industries are often located near transportation or information-based access points, such as along interstate highway 

“corridors” or near research facilities and (or) major universities (Fulton, 1986) (fig. 9).

Many high-tech industries often cater to the needs and 

desires of their specialized workforce and are located in 

places with abundant natural amenities. To a certain extent, 

industry management has also left the city center in favor 

of “corporate campuses” located near interstate highway 

interchanges or major airports (Garreau, 1991). Even small 

or medium sized cities have seen an increase in industrial 

activities as the result of being within a day’s drive of larger 

manufacturing centers that can supply the new “just-intime,” inventory-limited production systems (Rubenstein, 

1990). An example is the Toyota car assembly plant, located 

near Lexington, Kentucky, that was built in the 1980s and is 
Historical Perspective 

Figure 7. Urban areas (red) in the Tampa Bay, Florida, region in 1970 and 1990. Retirees, many of 

them migrating from other places, have played a role in the urban growth of areas within the Tampa 

Bay region. Senior citizens (age 65 and over) constituted nearly 30 percent of the combined population of Pasco and Pinellas Counties in 1970. The percentage of residents of retirement age had 

dropped slightly by 1990, but an additional 134,000 seniors were living in these two counties (U.S. 

Census Bureau, 1973).

Figure 8. Counties in the United States gaining more than 100,000 in population between 1970 and 2000.
The Historical Development of the Nation’s Urban Areas

Figure 9. Images of Franklin, Massachusetts, approximately 30 miles southwest of downtown Boston, in 1985 and 1992. Modern manufacturing 

is often located near the edge of a city and with convenient interstate highway access. Interstate 495 is located in the right side of the photographs. In 1985, the area to the south and west of I-495 is mostly wooded. The scene dramatically changes by 1992 as an industrial park and 

other development replaces the forest land. (Images from U.S. Geological Survey aerial photography from the National High Altitude and the 

National Aerial Photography programs.)

1985 1992

Figure 10. The Toyota car assembly plant, located near Lexington, 

Kentucky, and built in the 1980s, is within a day’s drive of the traditional core region of U.S. automobile manufacturing. (Source: Toyota 

Motor Manufacturing, Kentucky, Inc.)

within a day’s drive of the traditional core region of U.S. automobile manufacturing (fig.10).

The retail and wholesale of goods has also followed the 

pattern of locating in areas outside of the city center. Big 

box stores, such as Walmart, are now frequently found more 

conveniently located near highway interchanges and at large 

intersections in suburban areas. Also found in convenient 

transportation locations are wholesale distribution warehouses 

that are built horizontally across large areas and have good 

ingress and egress for trucks that deliver the goods.

Suburbanization did not strictly take the form of everwidening concentric circles around a central city. Suburbs 

often developed along main highways in a linear fashion, linking pre-existing, outlying communities with the main city. In 

other cases, suburbanization progressed in leapfrog fashion, 

bypassing certain areas because of environmental conditions, 

ownership status, or zoning ordinances (fig. 11). 

Changes in land cover and land use that resulted from 

suburbanization differed across the Nation, depending on local 

conditions (Stansfield, 1998). In some places, agricultural land 

offered the best possibilities for development (Matlack, 1997) 

(fig. 12). Alternatively or in other areas, marginal or nonagricultural land was the preferred choice for development, especially as time passed and other options became limited (Abler, 

Adams and Borchert, 1976). Development around lakes and 

other natural amenities are examples of this type of growth. 

As a result, the periphery of most metropolitan areas became a 

jumbled mosaic of different land covers and uses (Hart, 1991).

The 1970s witnessed the spread of another form of urban 

growth, exurban living, although this trend had also been 
ongoing to a much lesser extent for decades (Gottmann, 1961, 

1969). In the minds of many, the suburbs had become havens 

of conformity, the site of nearly identical, ranch-style subdivisions and strip malls. The suburbs also started to manifest 

the same negative urban aspects of the older cities, especially 

in those closest to them. Those people who wanted some of 

the urban advantages sought something other than the typical suburb. Many moved to large lots in surrounding rural 

areas and built single-family residences. These “ranchettes” 

Figures 11. Different land 

ownership and its impact 

on urban growth are clearly 

seen in the Scottsdale/Mesa 

region of the Phoenix, 

Arizona, metropolitan area 

between 1970 and 1990. 

Urban land use shown in red. 

The urbanized land of Scottsdale stopped at the western 

and northwest boundaries of 

the Salt River Pima-Maricopa 

Indian Reservation. Mesa 

was also growing adjacent 

to the reservation’s southern 

boundary by 1990. 

1970 1990

Figure 12. Former cropland, now residential “blanks,” ready for new 

development, Sioux Falls, South Dakota.

Figure 13. Exurban residences: (top) a rural sub-division in 

Culpeper County, Virginia, and (bottom) a “hobby farm” in 

Chester County, Pennsylvania.

Historical Perspective
10 The Historical Development of the Nation’s Urban Areas

Los Angeles

0 10 MILES

0 10 KILOMETERS

N

Figure 14. Los Angeles, California, and vicinity seen from space, as viewed by the Landsat 7 satellite from 

an altitude of 437 miles on May 4, 2001. Topographic shading has been enhanced using an elevation data set 

acquired by the Space Shuttle Endeavour in February 2000. By the early 1990s, the development in the Los 

Angeles Basin extended from the edge of the ocean to the base of all the mountain ranges. This perspective 

view is Shuttle Radar Topography Mission (SRTM) combined with Landsat. Image is courtesy of Courtesy 

NASA/JPL-Caltech (http://photojournal.jpl.nasa.gov/jpeg/PIA03376.jpg).
11

or “acreages” typically ranged in size from 1 to 20 acres of 

land. The smaller ones were usually maintained as more traditional suburban house/yard/garden residence, while larger 

ones might have horses, ponds, woodlots, and even a few 

cattle (fig. 13). The latter did not function as farms, however, 

and actually fragmented surrounding land types, agricultural 

or otherwise. These “exurban” areas often had no recognized 

center, such as a major town, but instead were tied loosely 

to the region’s primary metropolitan center (Herbers, 1986; 

Theobald, Gossnell, and Riebsame, 1996).

Many of the processes described in the last few pages 

are still ongoing within urban areas. Some cities continue to 

grow significantly, whereas various factors have slowed the 

growth of others. One reason that some portions of metropolitan areas have appeared to stop growing is that they have 

reached certain physical limits of their geography. In the 

last several decades, many cities in the United States have 

experienced “infilling” of areas that were initially bypassed 

during past periods of urbanization. Many metropolitan 

regions, such as the Los Angeles basin or San Francisco Bay 

area, have little remaining land available for development 

within what has been perceived as their traditional urban 

base (fig. 14) (Fishman, 1987). 

The nonurbanized portions of these cities are preserved 

or restricted lands, such as regional parks, conservation or 

farmland trust areas, and military installations. Other nonurbanized lands either are prohibitively expensive or are 

unsuitable for development, such as those with steep terrain 

or extensive wetlands. Suprametropolitan regions continue to 

expand; for example, greater Los Angeles spills out onto the 

Mojave Desert beyond the San Gabriel Mountains and San 

Francisco Bay area urbanization pushes down numerous valleys in Contra Costa, Alameda, and Santa Clara Counties. 

Conclusion

There is little doubt that urban areas will continue to 

change and grow in size. Population growth, a driver of many 

of the changes, is not likely to cease or decrease in the near 

future. Associated with increasing population is an increase 

in the amount of land dedicated to urban uses. The fashion in 

which the land is to be used, including density and intensity of 

development, is yet to be determined. However, analyzing the 

dynamic land-use changes that have occurred up to the present 

time will lead to a better understanding of the impacts that can 

be anticipated from future urbanization.

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13

Introduction

Over the past three decades the phenomenon known as 

urban or suburban sprawl has become the subject of enormous 

controversy. Critics have charged that sprawl destroys the natural 

environment, causes traffic congestion, undermines a sense of 

community, and contributes to decay and concentration of poverty in innercity neighborhoods. 

Despite its notoriety, there is no commonly accepted definition of “sprawl.” Since it first appeared in the 1940s (Soanes, 

2001), the term sprawl has been a pejorative term used to describe 

forms of urban development in suburban or rural locales, deemed 

undesirable for a variety of reasons (Galster and others, 2001). 

Whatever definition of sprawl they adopt, all commentators on 

the subject implicitly or explicitly conceive of sprawl as a decentralized pattern of urban growth, the opposite of compact development, whether in a single urban core or in dense nodes.

The dispersion of settlements outside an urban center is 

hardly new—Geoffrey Chaucer wrote of “suburbs” in The Canterbury Tales—but it is in the United States that a decentralized 

arrangement of residences, businesses, commercial enterprises, 

and institutions emerged as the dominant form of urban development. Decentralized patterns of urban growth first appeared in the 

United States in the early 1800s, creating, as it were, early forms 

of sprawl. By 1970 the majority of Americans lived in suburbs 

rather than in cities or rural areas. In the United States suburban 

areas have become “the dominant American cultural landscape” 

(Hayden, 2000). American metropolitan areas are less dense 

and more “suburbanized” than metropolitan areas in other highincome countries (Mieszkowski and Mills, 1993).2

Cultural and Demographic Factors in Urban Decentralization 

in the United States—A Historical Overview

1

Joint Center for Housing Studies, Harvard University, Boston, MA.

2

A note on terminology: Whereas the term “suburb” in the United Kingdom 

and Australia generally refers to a defined neighborhood outside the central 

business district of a city, in the United States the term can mean either an outlying part of a city or a separate jurisdiction adjacent to or within commuting 

distance of a central city. Although its characteristics are not codified, most 

Americans would consider the characteristics of a suburb to be a significant 

proportion of resident wage-earners who commute to work out of the community (until relatively recently to the downtown area) and (or) housing built 

at lower densities than housing in the inner city. As historians such as Jackson 

(1987) and Binford (1985) have noted, however, the character and function of 

suburbs have evolved since the nineteenth century.

By Alexander von Hoffman1 and John Felkner1

Throughout the nineteenth and twentieth centuries, a 

complex of forces encouraged dispersion of urban land uses 

and population in the United States. For almost two centuries, 

among the most powerful forces encouraging urban decentralization have been demographic and cultural. On the demographic side, the dramatic increase in population, primarily 

through immigration, has led Americans to search for homes 

on the urban periphery, sometimes to place distance between 

themselves and other Americans. In the cultural sphere, 

reformers and policy makers have proclaimed the superiority 

of suburban living and the single-family home. 

Based on extensive review of the scholarly literature, the 

following essay traces the demographic and cultural causes 

of decentralized urban growth in the United States over the 

past two hundred years. It shows that the growth of population groups, including immigrants, racial minorities, and, 

more recently, the baby-boom generation, supplied the people 

to conquer the ever-expanding suburban realm. Deeply held 

cultural beliefs, the essay further demonstrates, provided an 

incentive for Americans to move to the outskirts of town. 

Demographic Factors in Urban 

Decentralization

An Urban, then Suburban People

Over the past two centuries, the number of inhabitants 

of the United States mushroomed, and most of that growth 

took place in metropolitan areas, whose territory in turn has 

expanded. In 1800, the Nation contained about 5 million 

people; by 1900 the number of inhabitants had grown to 76 

million. A hundred years later the United States was home to 

281 million. 

Most of the Nation’s population growth occurred in urban 

areas, first in central cities and later suburbs. During the three 

decades prior to the Civil War, urban population grew at rates 

never yet equaled in American history—the number of city 

dwellers grew by 64 percent from 1830 to 1840 and by 92 

percent from 1840 to 1850. After the war, the number of cities 

and the number of people living in them continued to grow 

dramatically. For example, between 1800 and 1850, New York 

City’s population ballooned from 61,000 to more than half a 
14 Cultural and Demographic Factors in Urban Decentralization in the United States—A Historical Overview

million, and by 1880 it had surpassed the million mark. During 

the first half of the nineteenth century, Baltimore’s population 

increased six-fold to 169,000, Boston’s population more than 

quintupled to 137,000, and Philadelphia’s tripled to 121,000 

(Gibson, 1998). Between 1860 and 1920, the population in 

cities of more than 8,000 residents skyrocketed from 6.2 to 

54.3 million (Chudacoff, 1975).

During the twentieth century, urban areas continued to 

attract people, but over time increasing numbers flowed to 

communities outside the old neighborhoods. The populations 

of most central cities grew until about 1950; thereafter, the 

numbers living in western cities such as Los Angeles and Phoenix continued to climb, while those in the large cities of East 

and Midwest began to decline3 (Gibson, 1998; U.S. Bureau 

of the Census, 1975; Teaford, 1986). Yet almost everywhere 

throughout the twentieth century, the growth in suburban populations accelerated. In 1910, 21 percent of all Americans lived 

in central cities, three times as many as lived in suburbs, but by 

1970, 38 percent of the Nation’s population lived in suburbs, 

more than those in either cities or rural areas, which held 31 

percent each. Outer areas have continued to gain people, so that 

by 2000, remarkably, half of the entire U.S. population lived in 

the suburbs (Hobbs and Stoops, 2002).

The Urban Migration of the Foreign-Born and 

Racial Minority Groups

Natural increase accounted for some of the dramatic 

growth in urban population during the nineteenth and early 

twentieth centuries, but massive migrations—especially from 

foreign lands—provided most of it (Chudacoff, 1975; Nash, 

1988; Palm, 1981; Ward, 1971). Great waves of immigrants 

came in the 1850s (when more than two million entered the 

United States), 1880s, 1900s, and 1920s, after which immigration subsided until the latter twentieth century. By 1910 the 

number of foreign-born in the United States reached 13.5 million and constituted 15 percent of the American population. 

During the nineteenth century, most immigrants hailed from 

western Europe—primarily Ireland and Germany—but by the 

early twentieth century, the newcomers came primarily from 

southern and eastern Europe, Asia, and other parts of the western hemisphere (Gibson and Lennon, 1999; Easterlin, 1980). 

Most immigrants poured into America’s growing cities 

to take advantage of the economic opportunities. When the 

Civil War broke out in 1861, half the inhabitants of Chicago, 

Milwaukee, and St. Louis were immigrants. In 1890, 61 percent of the foreign-born lived in cities where they made up a 

full quarter of the population. In some cities, the immigrants 

far exceeded that proportion. By the early twentieth century, 

for example, three-quarters of the populations of New York, 

Chicago, Cleveland, Detroit, and Boston were immigrants and 

their children (Gibson and Lennon, 1999; Higham, 1984).

After the Federal Government passed the rigorously restrictive Immigration Act of 1924—the climax to decades of antiimmigration campaigns—the number of immigrants declined 

sharply. For the rest of the twentieth century, European immigrants and their descendants assimilated into American society, 

living in ethnic neighborhoods or, increasingly, the suburbs. 

Instead of immigrants from across the oceans, migrants 

from the rural south and south of the border now flowed into 

the cities of the United States. Attracted by the industrial and 

defense jobs created during the World Wars, southern whites 

and blacks moved to the Nation’s urban regions. Mexicans, 

Puerto Ricans, Cubans, and other Hispanics also went north 

to the cities in significant numbers. An overwhelmingly large 

share of the African-Americans who fled the rural south to 

escape racial segregation and oppression went to northern and 

western cities, where they found better economic and political opportunities but continued to suffer discrimination, which 

declined following civil rights victories in the late twentieth 

century. The impact of black migrants on certain northern cities 

was enormous. By 1960, African Americans made up more than 

a quarter of the populations of Philadelphia, Detroit, Baltimore, 

Cleveland, Washington, and St. Louis (Thernstrom and Thernstrom, 1999), where, for the most part, they lived in ghettos. 

After a new Federal law passed in 1965 opened the 

doors of the United States to immigrants, foreign immigration resumed on a massive scale (Frey, 2001; Frey and Alden 

Speare, 1988). Approximately 10 million legal and illegal 

immigrants entered the United States between 1980 and 1990 

(Frey, 1995), perhaps the largest volume of any decade of the 

century, and more than 8 million more foreigners settled here 

in the 1990s (Mulder, and others, 2002). Most of the new wave 

of immigrants came from Central America—more than a third 

of the foreign-born population in 2000 hailed from Mexico or 

other Central American countries—Asia, and eastern Europe, 

and many, although not all, were unskilled workers. The overwhelming majority of newcomers settled in and around large 

cities. In 2000, the U.S. Census Bureau reported, 95 percent of 

the foreign-born lived in urban areas. Furthermore, the newcomers tended to concentrate in a handful of large metropolitan areas. More than half of the foreign-born lived in the nine 

largest metropolitan areas. The metropolitan regions of Los 

Angeles, New York, San Francisco, Miami, and Chicago each 

contained more than a million immigrants and together almost 

half of the total foreign-born population (Schmidley, 2001). 

The Impact of Immigrants and Racial Minority 

Groups on Suburbia 

The flow of immigrants and members of racial minority 

groups into urban areas encouraged decentralization of those 

3

 New York’s population, for example, more than doubled from 1900 to 

1950 from 3.4 million to 7.9 million, which was about the same population it 

had in 1970. Chicago’s population increased from 1.6 million in 1900 to 3.6 

million in 1950 and then decreased to 3.4 million in 1970. The population of 

Philadelphia, Detroit, Baltimore, and many other large cities followed similar 

patterns, increasing until 1950 and decreasing thereafter. 
15

regions indirectly and directly. They promoted decentralization indirectly by provoking better-off city dwellers to retreat 

to communities located further away from the city. When 

the first sizable waves of immigrants landed in the early 

nineteenth century, many native-born middle-class and wellto-do Protestant Americans disdained the Irish and Germans 

immigrants’ reputation for drunkenness and violence and 

adherence to the Roman Catholic faith. In the latter nineteenth century, immigrants’ alien languages and dress marked 

them apart from the native-born population. Many were poor 

and lived in scandalously crowded innercity slums, which 

the middle-class considered breeders of disease, crime, and 

potential revolution (Riis, 1890). Even as reformers urged 

that the slums be broken up and replaced, the specter of the 

slums and antiimmigrant feelings caused many middle- and 

upper-class urban residents to withdraw to quieter, greener, 

and more sparsely settled regions (Higham, 1984). Similarly, 

in the late twentieth century, white working- and middle-class 

families fled their neighborhoods rather than live next to 

minority households escaping the ghettos and barrios. Indeed, 

middle- and working-class African Americans also broke 

away from the inner city, where lower-class residents continued to struggle (Wilson, 1987). 

At the same time whites fled cities for the urban periphery, immigrants and racial minority groups also directly 

fostered urban decentralization by moving to suburbia themselves. During the nineteenth century, African-Americans and 

the foreign-born tended to concentrate mainly in innercity 

industrial areas near the waterfronts and rail lines, but blacks, 

particularly in the South, and first and second generation 

immigrants settled on the outskirts of cities as well (von Hoffman, 1994; Goldfield, 1989). 

Although innercity racial and ethnic ghettos grew during 

the twentieth century, suburban areas also contained a wide 

range of racial and ethnic groups. Douglass (1925) discovered 

suburbs with heavy concentrations of African-Americans and 

immigrants in the 1920s. In the early twentieth century, the 

best-known immigrant suburbs were the predominantly Polish 

community of Hamtramck, outside Detroit, Michigan, and the 

Italian immigrant town of Cicero, Illinois, which Al Capone 

made his base of operations (Nelli, 1970; Zunz, 1982). 

In the last decades of the twentieth century, the pace 

of African-American suburbanization increased markedly 

(Weise, 1992; Pendleton, 1973; Thernstrom and Thernstrom, 

1999; Frey, 2000). Aided by newfound prosperity and the 

lowering of discriminatory barriers, more than seven million African-Americans have relocated to the suburbs since 

1970, a movement of historic proportions (Thernstrom and 

Thernstrom, 1999). The black population of Prince George’s 

County, outside Washington, D.C., for example, jumped from 

14 to 63 percent between 1970 and 2000 (Frey, 2000). Suburban black settlements range from the classic enclaves for 

upwardly mobile city dwellers to extensions of segregated 

innercity ghettos, leading Muller (1981) to categorize them 

as either “colonized” or “ghettoized” (Massey and Denton, 

1993). 

Similarly, immigrants during the last 30 years have 

become a major feature of the contemporary suburban landscape. By 2000, half of the foreign-born lived outside central 

cities, slightly more than the share living within them. Asians 

were more likely to live in suburbs than in cities, and almost 

half of Hispanics lived in suburban areas. As a result, the 

recent wave of immigration produced, seemingly overnight, 

large foreign-born suburban communities, such as the Chinese 

suburb of Monterey Park, California (Frey, 2000; Frey, 2003; 

Fong, 1995). Indeed, by 2000, racial and ethnic minorities 

made up 27 percent of suburban populations, up from 19 percent in 1990 (Frey, 2000). 

As suburbia expanded, it became home to a range of 

income groups. The spread of factories to the suburbs encouraged the growth of communities of blue-collar workers. 

During the early twentieth century, thriving working-class 

communities grew on the outskirts of Toronto—where many 

built their own houses—and oil workers made communities in 

the industrial zones outside Los Angeles (Harris, 1996; Nicolaides, 2002). After World War II ended, according to Muller 

(1981) suburban blue-collar communities proliferated, both in 

newly built housing subdivisions and in the older inner-ring 

towns near central cities. In fact, by the end of the century, 

suburban areas had become home not only to the working 

class, but also increasingly to the poor, leading concerned policymakers and urban planners to seek solutions to the problem 

of suburban decline (Lucy and Phillips, 2000). 

Effects of the Baby Boom

The United States experienced another significant demographic event in the postwar period that is worth noting for 

its contribution to suburban growth. For most of the twentieth 

century, marriage and birth rates for native-born Americans 

declined, but for two decades starting in the mid-1940s, the 

rates surged, and the result became known as the “baby boom.” 

Spurred by the return of soldiers from World War II and the 

long-suppressed desires for a higher standard of living, unprecedented numbers of Americans formed new households, creating a pressing need for new housing. The resulting construction 

boom pushed suburban development as never before—housing 

starts between 1946 and 1955 doubled over the preceding 15 

years, and between 1950 and 1960 suburban areas grew by 46 

percent (Zukin, 1991). 

The coming of age of the baby-boom generation in the 

years after 1970 again spurred not only suburban but also exurban growth. Many baby-boomers purchased homes during the 

1970s and 1980s, fueling a home-buying surge, much of which 

further helped expand metropolitan areas (Dunphy, 1997). By 

the 1990s some baby-boomers had attained sufficient affluence 

to purchase second or retirement homes in exurbia. This was 

part of a related movement (described below in “To the Wilderness”), in which a growing number of affluent or elderly people 

moved in recent years to communities with amenities such as 

golf courses or pleasant views (Frey and Alden Speare, 1992). 

Demographic Factors in Urban Decentralization
16 Cultural and Demographic Factors in Urban Decentralization in the United States—A Historical Overview

Cultural Forces for Urban 

Decentralization

The dispersion of the urban population in the United 

States, however, was not simply the result of increasing numbers of people searching for comfortable living space. From 

the early days of the Republic, deep currents in American culture propelled its citizens out of cities into an ever-expanding 

suburban realm. Beliefs in an inexhaustible supply of land, the 

value of owning a single-family house, the uplifting role of 

nature in domestic life, the suburban community as an engine 

of reform, consumption as a way of life, romanticist notions 

of untamed nature, and the importance of rural recreation have 

all dug their way deep into the American psyche. Once lodged 

in the minds and hearts of Americans, these notions worked as 

powerful forces for the decentralization of urban settlements. 

Fundamental to the American attitude toward the land, 

perhaps, is the “prairie philosophy,” the perception that the 

land needs to be tamed and is available in unlimited supply 

for development—a” notion of boundless space” (Moe, 1995). 

Some observers linked the nineteenth-century belief in American’s “manifest destiny” to conquer the frontier to the sense 

that urban dwellers are entitled to settle where they wish in the 

hinterlands (Burchell and Listokin, 1995; Downs, Linneman, 

and Richmond, 1995). The link may be tenuous, but certainly 

a sense of the abundance of land and belief in the inalienable 

right to claim it has enabled cities to encroach on the countryside.

Social Change, Reform, and the Moral Home

Just as essential, if not more so, to the expansion of cities 

was the critical value placed on the independently owned 

single-family residence—a hallmark of the suburban realm. In 

the colonial period, most townsfolk, even wealthy merchants, 

both worked and lived in the same building, often sharing the 

space with servants and apprentices. Such domestic arrangements changed in the late eighteenth and early nineteenth 

centuries when city dwellers—including even artisans who 

could afford it—moved to homes apart from their workplaces 

(Blackmar, 1989). Until the luxury apartment building came 

into vogue, the shared dwelling, especially the boarding 

house, seemed declassé or a sign of immaturity. The question 

for many became not whether but how they could purchase a 

single-family house, and the search for an answer led to the 

creation of building and loan associations—known as building 

cooperatives in the late nineteenth century and, ultimately, the 

creation of the financial system that supports homeownership 

today. 

The dissemination of the cult of domesticity, which celebrated the home as the center of a moral middle-class life, 

also provided a powerful stimulus to settlement on the urban 

periphery. From the 1830s onwards, a flood of books, articles, 

and pamphlets insisted that family and home were key to a 

moral Christian life. In the domestic division of labor, nineteenth-century reformers argued, men should serve as public 

figures and breadwinners while women superintended domestic affairs (Cott, 1977; Ryan, 1981; Clark, 1986). In advice 

books to women, such as The American Woman’s Home, 

Catharine Beecher, a popular nineteenth-century writer, urged 

women to design and organize their houses and gardens in 

such a way as to nurture the spiritual life and moral character 

of their families (Beecher, 1841, Beecher and Stowe, 1869; 

Jackson, 1987; Hayden, 2000). 

The moral reformers argued that the American family 

should live with and amidst the beneficent influence of nature, 

in contrast to the baleful effects of crowded and unhealthy 

city life. Inevitably, some reformers, notably architect and 

writer Andrew Jackson Downing, urged Americans to take up 

residence in homes set in a properly domesticated landscape 

in the country or the suburbs. Expressing romanticist beliefs 

in the influence of the external environment on the individual, 

they asserted that pastoral settings were aesthetically, morally, 

and physically healthful places that would counteract the ills 

of urban existence, such as pollution, congestion, and psychological stress (Hayden, 2000; Schuyler, 1996; von Hoffman, 1994; Schuyler, 1986). The reformers felt that homes in 

natural environments would bring together the best of the city 

and the country, elevating the benefits of “aesthetic and moral 

nature” and linking the “life contemplative with the life practical” (Fishman, 1987). Such places, they concluded, were ideal 

for educated middle- and upper-class men and women to raise 

their families. Thus emerged in the mid-nineteenth century 

the belief in the superiority of suburban living that by the late 

twentieth century many Americans accepted without question 

(Lewis, 1996; Clark, 1986).

Suburban Design in American Culture

Innovations in the design of the moral suburb helped 

make decentralized urban settlements—with a particular physical form—popular in North America. Fishman (1987) traced 

the roots of modern American suburbia to late-eighteenth-century English suburban villas and their landscaped grounds. In 

their park and community plans, nineteenth-century American 

landscape architects drew heavily from the English pastoral 

or romantic school of landscape gardening, whose naturalistic 

elements contrasted with formal French and Italian garden 

designs. Inspired by suburban housing developments, such 

as Park Village in London and Victoria Park in Manchester, 

and urged on by Andrew Jackson Downing, Americans in 

the 1850s began to incorporate serpentine roads, parklands, 

and lawns into subdivisions and towns. Projects designed by 

Downing’s collaborator, Alexander Jackson Davis, and by 

Frederick Law Olmsted and Calvert Vaux—New Jersey’s 

Llewellyn Park in 1853 and Riverside, Illinois in 1869, respectively—became influential prototypes of the new suburb. From 

the mid-nineteenth century onwards, plantings and curvilinear 

roads in the English (and by now also American) landscape 
17

tradition appeared in railroad commuter towns—strung out, 

for example, along routes the Main Line outside Philadelphia 

and near the Chicago and Milwaukee line on Chicago’s North 

Shore—and in subdivisions sprouting up on the ever-expanding circumference of cities (Fishman, 1987; Jackson, 1987; 

Schuyler, 1986; Archer, 1983; von Hoffman, 1996; Ebner, 

1988).

European and American planners, designers, and reformers in the twentieth century continued to try to create new 

communities that improved upon crowded city neighborhoods 

and disorderly or unaesthetic areas outside cities. The Garden 

City movement, which originated in England at the turn of the 

century, aimed at combining the virtues of city and country—

such as urban economic opportunities and rural greenbelts. (At 

the first Garden City, Letchworth, and later Hampstead Garden 

Suburb, British architects Unwin and Parker experimented 

with grouping houses around communal greenspaces and 

shared walkways.) During the 1910s and 20s the Garden City 

movement in the United States inspired the design of industrial towns such as Kohler, Wisconsin, and suburbs such as 

Mariemont, Ohio (near Cincinnati). An offshoot of the movement, the Regional Planning Association of America (RPAA), 

evolved into diverse efforts to plan urban and rural areas in a 

comprehensive way. At the height of its influence, two RPAA 

architects, Clarence Stein and Henry Wright, designed Radburn, New Jersey, billed as a new kind of community for the 

motor age, with innovative features as superblocks free of 

automobile traffic, houses placed along cul-de-sacs, and separate circulation systems for pedestrians and vehicles, as well 

as parks and lawns throughout (Buder, 1990; Spann, 1996; 

Schaffer, 1982).

From the 1930s onwards, government and private developers of new communities adopted many of the signature 

planning features of the Garden City and regional planning 

movements. Several Federal Government agencies in the 

New Deal administration of Franklin D. Roosevelt developed 

housing and new towns in suburban and rural areas. Three 

Federally sponsored Greenbelt Towns in Wisconsin, Ohio, and 

Maryland, for example, were intended to offer both employment and residences were short-lived experiments that in any 

case evolved into suburbs. To carry out such projects, agency 

officials hired Garden City and RPAA designers as either 

advisers or planners. At the same time, the government planning agencies—including the Federal Housing Administration and the Regional Planning Commission of Los Angeles 

County— recommended that subdivision developers abandon 

straight grid plans in favor of such features as curvilinear 

streets, superblocks, cul-de-sacs, and greenbelts. As a result, 

the large-scale developers, such as Kaiser Community Homes, 

that built out the Los Angeles region in the 1940s produced an 

entire territory that reflected the traditions and innovations of 

suburban and new town design (Hise, 1997; Crawford, 1995; 

Rowe, 1991; Schaffer, 1982).

By bestowing recognizable forms on new development on 

the urban periphery, landscape and town planning efforts had a 

tremendous impact on suburban design in the post-World War 

II era. True, late twentieth-century suburban development took 

many shapes. It ranged from watered down versions—such 

as the first Levittown in Long Island—to the revival of the 

Garden City style in the plans of New Urbanism designers 

(Kelly, 1993; Calthorpe, 1993; Rowe, 1991). Nonetheless, the 

landscape and planning traditions produced the images that 

became the hallmark of modern American suburbia, and their 

popularity was yet one more cultural impetus to decentralized 

urban settlements. 

Some (Harris and Lewis, 2001; Wunsch, 1995) have 

criticized the work of historians of American suburbia for 

emphasizing the role of the elite and ignoring the workingclass and industrial areas in their work. Granted, as we have 

seen above, from early days America’s metropolitan areas 

contained a diversity of peoples, yet the elite suburban ideal 

and its realization on the ground stimulated the decentralization of American cities in two fundamental ways. First, it 

provided an incentive for the urban wealthy and upper-middle 

class to move to outlying areas, which in turn redefined these 

communities as “suburban.” Second, it set an example—even 

the mold—of suburban living that was copied and made available in one form or another to households across a spectrum of 

income groups (Sies, 2001). 

Strikingly, the idea and the form of suburban living 

spread to those lower on the economic ladder even though 

planned suburbs and subdivisions—including some inspired 

by the Garden City movement—usually prevented racial 

and religious minorities from living in them or relegated 

racial minorities to inferior living conditions. Such exclusive 

developments as Palos Verdes, California, the Country Club 

District, Missouri, and even Forest Hills Gardens—a progressively planned philanthropic development in Queens, New 

York—barred Jews and African Americans from purchasing 

homes. The planned industrial towns by definition usually 

included a range of workers, but as a rule separated residences 

by income group or by work status (management, skilled 

employees, and unskilled workers) and some segregated members of racial or ethnic minorities in houses significantly more 

humble than those for other residents. Yet as the persistent and 

increasing diversity of suburban areas shows, Americans of 

minority or low-income backgrounds have aspired to rather 

than reject suburban life. 

The Suburban Home as a Commodity 

Although the ideal of the suburban home as a place of 

uplift, first devised in the nineteenth century, persisted in one 

form or another through the twentieth century, Americans 

increasingly came to see—and lust for—the suburban house as 

a commodity that reflected social status and purchasing power.

Technological innovations helped bring the suburban 

home across the cultural bridge from a site of domestic reform 

to a good for domestic consumption. In the early twentieth 

century, popular magazines and newspapers—picking up 

where nineteenth-century reformers such as Beecher left 

Cultural Forces for Urban Decentralization
18 Cultural and Demographic Factors in Urban Decentralization in the United States—A Historical Overview

off—trumpeted progressive reform theories of sanitation 

and efficiency in domestic life. Periodicals—including Good 

Housekeeping, Cosmopolitan, and Hearth and Home—campaigned in the 1920s for healthful houses and championed 

the bungalow, preferably with its open-air sleeping porch, as 

the best choice for a new suburban house. The healthy house 

crusaders paid particular attention to kitchens and bathrooms, 

and applauded the combination of sanitation with the latest in 

household appliance technology (Teaford, 1986). In the same 

years, however, the novelist Sinclair Lewis would depict the 

healthy suburban house as a status symbol for the acquisitive 

upper-middle class (Lewis, 1922).

After World War II, sales of consumer goods and suburban houses surged. Even before the war ended, newspaper and 

magazine advertisements for all sorts of merchandise whetted 

the consumer appetite of Americans weary of depression conditions and wartime restrictions. After the war, suburban subdivisions proliferated—spurred by a short but severe housing 

shortage—and sales of domestic appliances and automobiles 

spiked. Real estate marketers emphasized efficient mass production techniques and the low-density, automobile friendly 

suburban plans, which included—in contrast to crowded city 

living—the detached single-family house, yard, and surrounding greenery. Nor did they neglect to point out the reasonable price for a suburban home—made more reasonable by 

Veterans Administration loans. In the process, a new vision of 

the ideal home—the “dream house of the future”—emerged 

that differed substantially from its bungalow and neocolonial 

predecessors. Magazines glowingly hailed the new “ranch” 

or “contemporary modern” homes that filled suburban tracts 

(Jackson, 1987; Clark, 1986). 

Despite intellectuals’ critiques of the Levittowns and Lake 

Forests as unaesthetic and conformist, industry thrived by 

focusing on the desires of the consumer family in the suburbs. 

Marketing campaigns for suburban homes proclaimed design 

features conducive to raising children, such as “family rooms,” 

“play rooms,” and large backyard patios with barbeques. 

Manufacturers of appliances and cleaning products appealed 

to women consumers by projecting images of housework 

as “easy and fun.” Technological innovation, they claimed, 

would free women from the hard work of the old “domestic 

economy” (Clark, 1986; Hayden, 2000). The suburban home 

became big business, something to market and sell on a massbasis. As a growing number of products became more available in postwar America, the single-family home became, 

more than ever, a reflection of personal taste, income, and 

status. Not surprisingly, the suburban shopping mall became 

the chief headquarters of the new consumer society (Cohen, 

1996).

To the Wilderness 

Americans’ long-standing affinity for nature in wilderness 

and rural areas—as opposed to the tamed landscapes of the 

suburbs—is yet one more important cultural ingredient in the 

persistent decentralization of their urban settlements. Americans’ attitudes toward nature are a large subject about which 

much has been written, far more than can be stated here. Suffice it to say that by the eighteenth century educated Europeans came to appreciate wilderness areas—once shunned as 

places of desolation—which they cataloged into types of landscapes, most notably the picturesque and sublime (Bate, 1961; 

Monk, 1960; Huth, 1957). 

By the nineteenth century, an ascendant romanticism 

fostered appreciation for nature, in both tame and wild forms, 

on both sides of the Atlantic. Under its influence, as we have 

seen, landscape architects such as Olmsted promoted pastoral 

park and suburban landscapes for their benign influences on 

individuals. Following similar romanticist principles, however, 

advocates of untamed nature argued that contemplation of picturesque and sublime landscapes also produced salutary moral, 

psychological, physical effects (von Hoffman, 1988). In such 

works as “Rip Van Winkle,” “The Scarlet Letter,” and “The 

Song of Hiawatha,” nineteenth-century writers Washington 

Irving, Nathaniel Hawthorne, and Henry Wadsworth Longfellow used the imagery of wild nature to produce a mythic 

history for America. Artists Frederic Church and Albert Bierstadt glorified the wilderness for its grandeur and sublimity 

(Novak, 1995; Crandell, 1993). Others followed in their wake 

and helped popularize romanticist thinking about America’s 

untamed natural environment and the people who inhabited it. 

Nothing demonstrates the power of these constructed myths 

better than the romantic image of the American West and its 

celebration in literature and popular culture—from Wild West 

shows to cowboy films (Smith, 1978; May, 1997; Cawelti, 

1984). 

Hence, as they conquered the frontier and ever since, 

Americans relished their continent’s natural areas as stirring scenery. During the nineteenth century, tourists began to 

explore the outback while well-to-do vacationers frequented 

resorts in the mountains and on the seashore (Sears, 1998; 

Sternglass, 2001). To exploit the enthusiasm for wild and 

picturesque scenery, the railroad companies published guides 

and offered special routes into the remote countryside. At the 

same time, naturalist John Muir among others led a successful 

movement to establish national wilderness parks and helped 

make the preservation of wilderness areas a national policy 

(Turner, 1985; Runte, 1997; Magoc, 1999). In the twentieth 

century, the automobile gave the public greater access to wilderness and resort areas. At the same time, automobile clubs 

championed car touring and lobbied for better roads on which 

to get to the countryside (Flink, 1988; Jakle, 1985), and recent 

superhighway construction has made it even easier to travel to 

rural America.

Americans also have a long tradition of active recreation 

in natural settings—from urban sports such as baseball to 

spelunking. From the mid-nineteenth century leaders of the 

Muscular Christianity movement to today’s physical fitness 

advocates, active recreation has been promoted as virtuous to 

mind and body alike—often in similar terms as the arguments 

for passive contemplation of natural scenery (von Hoffman, 
19

1988). From the early twentieth century, outdoorsmen and 

women took to wilderness and rural areas to camp, fish, hike, 

and ski. In recent decades masses of Americans have flocked 

enthusiastically to the outback to ski, hike, climb mountains, 

ride horses, and raft on white-water rivers, to name a few of 

the popular recreational activities. In the process, the rustic 

lodges of yore have given way to large recreational developments, and in some places the entire countryside economy has 

turned from traditional pursuits, such as agriculture, to become 

primarily places of recreation (Braden, 1988). 

The popularity of natural environments—as scenery or 

setting for active sports and recreation—directly contributed to 

the dispersion of urban dwellers into an ever-expanding area 

away from cities. Inevitably, journeys into the outback lead 

some to settle there permanently—to enjoy activities in nature 

all year, to service those who come to such places, or both. 

At the same time, some urban dwellers—more frequently 

suburban dwellers—have sought to escape the expanding and 

increasingly populated suburbs by moving to rural environments where they can bask in the glory of the countryside. 

Urbanites trickled into the countryside for much of the 

twentieth century, but the settlement of what became known 

as “exurbia” began in earnest about mid-century. Since the 

1970s, affluence and the coming-of-age of the baby boom 

generation brought an influx of hobby farmers, second-home 

owners, retirees, and long-distance commuters into rural 

America. After slowing down in the 1980s, the movement of 

people into the backcountry accelerated in the 1990s. In the 

last decade, counties that offered natural amenities and outdoor recreational activities attracted disproportionate numbers 

of people, some of whom were of retirement age but others of 

whom were 30 years of age or older and with children (Johnson, 2002). 

In some places, the arrival of city folk augurs the arrival 

of the city itself—in the form of subdivisions and shopping 

centers (Spectorsky, 1955; Audirac, 1999; Hart, 1995). There 

along the rural-urban fringe, once-wild nature—championed 

for its wholesome effects on its beholders—is transformed into 

tamed suburban nature, whose apologists also argued for its 

uplifting influence.

Political Localism Versus Coordination 

of Suburban Interests

The outward movement of peoples of diverse racial, 

ethnic, and income groups and the cultural imperatives of 

moral reform, modern suburban design, the house as a commodity, and romance of rural and wild landscapes created a 

patchwork quilt of population groups and types of communities on the urban periphery. The political autonomy of local 

jurisdictions in the United States aided and abetted not only 

this geographic expansion of urban development, but also the 

expression of the diverse interests of the diverse population 

groups. As a result, political localism has hindered—if not 

prevented—coordinated planning in metropolitan areas that 

would centralize or at least concentrate new growth.

The independence of local political entities took root 

during the colonial period and grew stronger thereafter. Provincial assemblies originally and state legislatures after the 

American Revolution granted corporate charters to local jurisdictions. Although occasionally legislatures abrogated some 

measure of home rule from large cities as part of reform movements, they have usually not interfered with the prerogatives 

of towns, and today there is widespread support for small, 

local governments as opposed to regional government entities 

(Baldassare, 1992). 

Whether their leaders liked it or not, however, towns 

located near cities generally were incorporated into the economic and social networks of the growing metropolis. As the 

towns grew and changed, conflicts emerged over whether to 

limit services and taxes or to spend to provide new schools, 

sewers, utilities, and police and fire assistance to the growing populace (von Hoffman, 1994; Binford, 1985; Warner Jr., 

1972). At the same time, urban business and political leaders 

wanted to extend the domain of their cities to increase the tax 

base and enhance their sense of local prestige. 

There were four different possible approaches to these 

problems: (1) cities could expand their boundaries by annexing adjacent communities into the municipal corporation; (2) 

increased powers of old communities or create new municipalities within the suburban ring; (3) special taxing districts could 

be established to provide for one or more important functions; 

and (4) county governments could expand their powers by 

becoming more like cities themselves (Jackson, 1987).

From the nineteenth century onwards, powerful fastgrowing cities adopted the first alternative, annexing neighboring territory. Between 1850 and 1910 a dozen major U.S. 

cities expanded their boundaries by more than 500 percent 

through the addition of more than 800 square miles of land 

(Jackson, 1987). The largest cities annexed the most land. In 

1898, for example, New York City annexed Brooklyn, Queens, 

Staten Island, and the Bronx to increase its size from 44 to 300 

square miles (and take in almost two million more residents), 

and between 1890 and 1920, Los Angeles added more than 

330 square miles. Large booming cities continued to annex 

territory in the twentieth century. Houston, Texas, captured 

the most land between 1948 and 1967 when it ballooned from 

76 to 446 square miles, but numerous other Sunbelt cities—

including San Antonio, Texas, Phoenix, Arizona, and San 

Jose, California—added more than 100 square miles to their 

domains between 1950 and 1975. 

As peripheral communities grew wealthier and stronger, 

however, they were able to assert their independence from 

central cities. An early turning point came in 1874 when, 6 

years after Boston had annexed five neighboring communities, the town of Brookline spurned Boston. Thereafter, a 

number of suburban communities, including Oak Park and 

Evanston outside Chicago and parts of Alameda County adjacent to Oakland, California, rebuffed efforts at consolidation 

(Palm, 1981). In the 1920s, movie stars rallied to preserve 

Political Localism Versus Coordination of Suburban Interests
20 Cultural and Demographic Factors in Urban Decentralization in the United States—A Historical Overview

the posh suburb of Beverly Hills from becoming part of Los 

Angeles (Teaford, 1986). In the postwar era, even dynamic 

cities encountered fierce resistance to annexation from outlying towns. In the 1950s and 1960s suburban cities, Tempe and 

Scottsdale, Arizona, fought off Phoenix’s plans for aggrandizement, and in the 1970s Aurora, Colorado, held back Denver 

(Abbott, 1981). 

The leaders of the resistance to joining central cities were 

usually upper-middle class suburbanites. In general, they 

feared low-income immigrants and, in the twentieth century, 

blacks, were hostile to urban political machines, which they 

saw as corrupt and dependent on lower-class voters, or wanted 

to preserve political prerogatives for people like themselves. 

Local leaders who held these kinds of sentiments found a 

powerful tool in zoning, a land use planning device introduced 

in 1916 in New York where it was used to protect residential 

areas from the encroachments of the garment district. A 1926 

U.S. Supreme Court decision ensured the right of local governments to pass zoning laws in order to separate land uses. 

In the following decades suburban towns used zoning rules 

to exclude multifamily housing from single-family neighborhoods, establish minimum lot sizes for areas of new residential 

development, and prohibit particular kinds of commercial or 

industrial development. In many suburban communities, these 

sorts of regulations have been used to enforce or reinforce the 

exclusion of low-income groups from the towns (Burchell, 

1998; Cervero, 1991; Downs, 1999; GAO, 1999; Orfield, 

1997; Sierra Club, 1999). By stipulating that single-family 

houses be on large lots and in effect forcing moderate- and 

low-income families to live elsewhere, zoning and like tools 

encourage extensive low-density development of urban areas 

(Baldassare, 1992; Doyle, 2001). 

The autonomy of local jurisdictions also allowed them 

to pursue diverse agendas, including enabling individuals to 

develop properties freely, without coordinating with other 

communities or working under metropolitan plans that might 

channel growth to use land efficiently. Numerous authors have 

blamed the fragmented political authority of metropolitan 

areas for a host of ills. These include not only scattered and 

disorganized land uses and uneven aesthetic qualities, but also 

traffic congestion, lack of affordable housing in suburbs, inefficient local service delivery, and racial and income segregation (Rusk, 1995; Baldassare, 1992; Bollens, 1988; Kasarda, 

1980; Schneider, 1980; Schneider and Logan, 1981; Black 

and Henderson, 1999; Burchell, 1998; Calthorpe, 2000a; Calthorpe, 2000b; Downs, 1999; Lessard, 2001; Moskowitz and 

Lindbloom, 1993; Orfield, 1997). The chief problem, the critics complain, is a lack of a clear regional planning vision—or 

at least one coordinated vision shared by multiple municipalities (Sanchez, 2000; Chen, 2000). 

In fact, some urban regions have tried to adopt various 

metropolitan solutions to solve their problems. County governments in some places—Dade County, Florida, for example—

expanded their powers to rule over a large territory—although 

this was often a way of achieving political control over the 

central city. In counties such as Montgomery County, Maryland, however, county-wide planning has managed growth 

and saved land (von Hoffman and Harrigan, 2002). Several 

metropolitan areas established agencies to provide for one 

or more important functions. For example, both Denver and 

Atlanta established regional transportation agencies to develop 

mass transit systems in the 1970s, and the latter area set up the 

Atlanta Regional Commission to coordinate planning (Abbott, 

1981). Perhaps the greatest exception to the lack of regional 

planning is the Portland Metropolitan Council, a board that 

controls the urban growth boundary outside the city of Portland. Also, States such as Florida, New Jersey, Georgia, and 

Oregon have established statewide agencies to supervise urban 

growth or conserve forests, fields, and wetlands (DeGrove and 

Miness, 1992; Cullingworth, 1993).

Yet all in all, such regional solutions were temporary 

or partial, or in the case of Portland, exceptional. The chief 

reason regional planning has been extremely limited in the 

United States is that most Americans approve of fragmented 

political authority. Voters have turned down regional government proposals frequently (Campbell and Dollenmeyer, 1975) 

and, when surveyed, strongly opposed intracounty regional 

government and single purpose agencies to address metropolitan-wide problems, such as environmental pollution (Baldassare, 1989). These suburban attitudes express distrust of large, 

urban government bureaucracies, a preference for decentralized public services, and a strong desire for local rule of 

homogeneous communities (Baldassare, 1989; Fischer, 1984; 

Popenoe, 1985).

Altshuler (1999) described the conflict between the idea 

of a coordinated political approach across communities and 

the beliefs in individual property rights and local prerogatives 

as the expression of competing “ideo-logics.” On one hand, 

an American communal instinct highlights values such as 

equality, integration, and democracy and provides a rationale 

for government intervention to prevent market failures; on 

the other, the primacy of individual rights stresses personal 

property ownership, the freedom to choose one’s associates, 

the efficiency of market allocation, and the perverse effects 

of big government (Altshuler, 1999). The result is that within 

any given metropolitan area, efforts at coordination of metropolitan development are only partially successful. In general, 

some communities exclude new development while others 

encourage it or fail to resist it, and the urbanized area extends 

ever-outwards in piecemeal fashion.

Conclusion

Urban decentralization has been a powerful trend in 

American history over the past two hundred years. The United 

States today boasts great metropolitan areas whose number 

and size continue to grow. The outer parts of these urban 

regions are, contrary to the image of the homogeneous suburb, 

diverse and ever-changing patchworks of land uses and neighborhoods—like their nineteenth-century predecessors but ever 

more vast. 
21

The growth of the national population, including immigrants, racial minorities, and the baby-boom generation, supplied the people who pressed inexorably outwards from the 

urban core. Yet beneath the reasons that Americans today 

choose to live in suburban and exurban locales—such as 

consumer taste, status anxieties, and desire for high-quality 

schools—lies a moral imperative inherited from the past. Deep 

in the American consciousness is a belief that seminatural suburban or rural environments are better for us. The autonomy 

and power of local government in the United States has compounded the problem of coordinating the diverse interests of 

the communities within metropolitan regions and managing 

future growth in a rational way. 

This overview, however brief, demonstrates the deepseated hindrances that face those who would control urban 

development in order to balance the needs of the natural environment with the desires of the humans who inhabit it. 

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25

Introduction

The rapid expansion of recreation and retirement at the 

many manmade lakes in the Ozarks has created many large 

and small communities along the shorelines of these lakes. In 

many cases these are equivalent to small towns and in some 

cases, such as the Lake of the Ozarks, the lakeshore communities are equivalent to medium sized cities, at least in the 

summertime with 300,000 persons on or near the lake. The 

transient nature of the “dwellers” of these communities, which 

vary by seasons, days of the week and even time of the day 

change the nature of the urbanization process. The problems 

of these communities may be classified as social, economic 

or environmental, but in fact all are intricately related. Most 

of these communities are consumption rather than production based economies and thus their existence and futures are 

closely tied to maintenance of those environmental qualities 

that enticed people there in the first place. 

In this paper the urbanization around the lakeshores in 

the Ozarks is examined. More specifically the paper explores 

how urbanization has evolved around the lakes and examines 

what is causing this rapid growth in urban land uses around 

these once small rural communities. These urban communities are quite distinguishable from other urban forms due to 

their demographic compositions and their temporal occupancy. 

Between 10 and 85 percent of the homes in these communities are seasonal holiday homes, often only occupied for a few 

months of the year. Therefore the physical structure of these 

urban areas is very different as a result of the temporary nature 

of these populations. The seasonal nature of the population 

also makes traditional measures of urbanization, such as population density, inappropriate because they are based on census 

data that is only collected at one point each decade and does 

not represent the changing seasonal nature of these populations. In this paper the change in the extent of urbanization is 

calculated through the use of remote sensing. This data is then 

used in a regression model along with 2000 and 1990 census 

data to test what is driving urbanization around the shores of 

Ozark lakes.

Missouri Lakeshore Communities 

Throughout mid‐America there are numerous large lakes 

that have been created by dams constructed by the U.S. Army 

Corps of Engineers (USACE) and other governmental and 

private organizations. The primary purposes for the construction of these dams have been flood control, power generation 

and navigation. However, many of the lakes have become 

major recreation areas for nearby populations and in some 

cases the lakes draw visitors and potential residents from great 

distances. The summer vacation homes on the lakes of southern Missouri are an immediately recognizable element of the 

rural‐recreational landscape. Vacation homes have become the 

dominant land use along the lakes and waterways. Intensification of lakefront development can be seen across the region, 

but is mostly clustered adjacent to the lakes. Accessibility 

to the water is the key attractive feature of these properties. 

Another important feature of the urban patterns around the 

lakes is the roadway structure, which now provides year-round 

access, whereas along more rural roads access during winter is 

more limited. Many of the townships surrounding the lakes are 

a result of residential-development spillover. The growth pressure has resulted in cottages turning into year-round homes. 

This paper will concentrate on two reservoirs in the Ozark 

mountain region—Table Rock Lake and Pomme de Terre 

Lake. These two lakes were chosen because both are USACE 

lakes and therefore must abide by similar controls, but also 

because these two lakes are very different in their location, 

their size, the populations they attract, and the form of urbanization developing around the lakes.

 Table Rock Lake is located on the main stem of the 

White River at river mile 528.8 adjacent to the city limits 

of Branson, Missouri. However, Branson is located on Lake 

Taneycomo and therefore is not included in this study. Construction of Table Rock was completed in 1958 and is one 

of the largest lakes in Missouri with a surface area of 52,300 

acres. Although the lake was originally designed for flood 

control and hydroelectric power generation, over the years 

the lake has been used as much for recreation as for its original purposes. This initially led to small resorts and second 

home communities being built around the lake that have more 

recently turned into a combination of permanent retirement 

communities and resort communities.

 The second study area is Pomme de Terre Lake, which 

is located in the Missouri River Basin on the Pomme de Terre 

River. Pomme de Terre Lake was completed in 1961 and 

Demographic Drivers of Lakeshore Urbanization 

in the Ozarks

1

NRC Post Doctoral Associate, United States Geological Survey, 1400 

Independence Road, MS 706, Rolla, MO 65401‐2602.

By Sonya M. Glavac1
26 Demographic Drivers of Lakeshore Urbanization in the Ozarks

covers 7,820 acres and can expand to as much as 16,100 acres 

during periods of heavy rain as excess runoff is impounded 

to prevent downstream flooding. Many recreational and 

retirement communities now reside adjacent to this lake. 

The Pomme de Terre area gives residents a sense of “sought 

isolation”—the isolation of the lake itself, as well as how the 

individual properties have developed around the lake. The 

area is low density and rural residential, unlike the development around Table Rock Lake. The physical form of the urban 

property is also very different in the two study areas. Along 

Pomme de Terre Lake are all single row cottage properties 

along the margins of the USACE lake, giving the area a more 

rural atmosphere. This is unlike the properties around Table 

Rock Lake, where there is primarily medium-density developments or clusters. These areas are also somewhat unique 

in that there is no planning in the rural counties of Missouri; 

building permits are not even required. This results in unregulated development around Pomme de Terre Lake and limited 

zoning around Table Rock Lake.

A Conceptual Model of Urbanization of 

Lakeshore Communities

Urban theories and models examining urbanization 

abound, but most are not valid in the study of lakeshore 

communities in their current state as these theories typically 

model metropolitan areas and only address a rather narrow 

spectrum of issues. The particular problems of modeling 

lakeshore recreation-retirement communities are exacerbated 

by the extreme “seasonality” of the populations of these 

areas. This study emerges from the fundamental contention 

that the dynamics of population change over time are intimately linked to changing urban structure of lakeshore communities. That is, a changing urban structure is reflected by 

the characteristics of the population that evolves as the population changes over time. In general, the importance of the 

lake within tourist areas has been responsible for a common 

pattern of linear development along the lake frontage itself, 

with pronounced gradients of decline in land values and associated changes in land uses with increasing distance from the 

lake.

The migration literature often alludes to tourism leading 

to permanent migration and urban change. However, there is 

some contradiction in the findings. For example, Stallman and 

Espinoza (1996) found that tourism in earlier life is not a sufficient causal factor for migration. This is similar to the findings of the earlier study by Wiseman and Rosemont (1979) 

who found that although some retirees considered vacation 

areas as potential retirement destinations the majority did not. 

Martine and others (1987) found that of the seasonal residents 

they interviewed only 23 percent were considering moving 

to their vacation home. Contradicting these findings are the 

studies by Halseth (1998), who found that there is strong link 

between owning a vacation home and permanent migration 

and Marcooiler and others (1996), who found that tourism 

led to buying second homes and later seasonal residence after 

retirement.

In addition, there are many other factors that influence the 

act of migration and in the development of an urban community. Tourism can act as a pull factor by providing information 

about the existence of amenities elsewhere, as well as providing social networks. Many persons considering retirement 

consider only those locations where they have vacationed for 

many years and to which they have developed strong attachments (Wiseman and Roseman, 1979). Other factors quoted 

in the literature (Wiseman and Roseman, 1979) that influence destination choices include amenities, ecological conditions, warmer climates, similar cultures, health care, scenic 

importance, recreational opportunities, low cost of living, and 

modest tax rates. More specifically, features that attract retirees to communities include business/employment opportunities, mild climates, low cost of living, being close to family, 

being close to friends, low cost of housing, adult educational 

opportunities, low medical costs, retirement communities, recreational opportunities, and modest tax rates.

The role of the second home development often associated with early stages of resort or tourism development is 

common in the lake districts in middle America. These second 

homes often attract retired migrants who reside there during 

the summer months only. These people adopt different uses of 

the landscape and require somewhat different amenities than 

the community as a whole, thereby having a different impact 

on the urban morphology and how it changes over time. 

In figure 1 the process of lakeshore development is summarized. The rural residential countryside surrounding the 

lakes in Missouri is a geographically and socially divided 

landscape. The conversion of vacation homes from seasonal to year-round homes is a dynamic responsible for new 

in-migration. Initially these Missouri lakeshore communities 

had limited tourist and recreational activities, along the lakeshores themselves and the rivers. There were resort hotels that 

also catered for the more elite who could afford the time and 

expense of hunting and fishing in these newly accessible areas. 

Increased accessibility over time through better quality road 

networks has opened up these areas to a much wider public. 

 The original population was drawn to the lakes for their 

natural and recreational amenities. Visitors were more likely to 

be from local urban regions within driving distance. Over time 

these visitors purchased second homes around the lake. Due 

to the lack of development land was inexpensive and there 

was little or no planning regulations (which is still the case in 

most rural areas today). This led to low-density development 

with little infrastructure and limited commercial and industrial 

activities. As time progresses the age of the second homeowners has increased. Many of the second homeowners retire to 

these areas, converting their vacation homes into permanent 

homes. This resulted in the need for more government and 

community infrastructure, such as all season roads, hospitals, 

restaurants and shops. This additional infrastructure combined 

with the natural amenities of the lakes attracted a new popu-
27

lation to the lakes. As the population grew so did the cost of 

housing as well as the socioeconomic status of the community, 

leading to a segmented population and distinct urban structure 

around the lake.

Three population types can be found to live in these communities—original inhabitants, converters (people who turn 

their vacation home into a permanent residence), and migrants 

(people who move to the area without previously owning a 

home there). When comparing original inhabitants, seasonal 

inhabitants, and permanent migrants Halseth (1998) found that 

54 percent of converters had at least a university education and 

55 percent of original inhabitants had at least a high school 

education, as compared to 73 percent of seasonal migrants 

having at least a university education.

The costs of purchase, mortgage, insurance, maintenance, 

travel to and from the home, and property taxes situate vacation home ownership within a particular economic framework, 

making ownership selective. This causes separation in geographic and social space of the original permanent residents of 

the small towns and rural countryside and the vacation homeowners. The costs of owning cottage property may not only 

act to limit access to ownership but may also work to generate 

a significant push factor behind the conversion of cottages into 

year-round or retirement homes. 

Around the lakes of southern Missouri three types of communities can be seen:

(1) Small communities located at principal crossroads or historic transshipment areas.

(2) Individual residences dispersed along the local road networks. Some farms and rural residences associated with 

local employment, local retirees, and more recently exurban hobby farmers.

(3) Settlements along vacation home areas. These areas contain 

seasonal homes, permanent residences, and recreational and 

commercial businesses, which form a distinctive linear pattern along the margins of the lakes and waterways. There 

is also an element of dispersion as the pattern is broken 

due to roads and other natural features. The most heavily used area is the area directly on or around the lake in 

what is labeled the consumer consumption district core and 

periphery areas in figure 1. The core and periphery is not 

unlike the core and periphery in traditional central business 

districts, with the primary difference being that nothing is 

being produced, but rather it is the area itself that is being 

consumed or used as a recreational district. Development 

closest to the lake consists of resorts and high-density housing due to the high cost of land. As value decreased with 

distance from the lake so does housing density.

Origin (Visitor Population)

 • Local Origins

 • Families

 • Varied Socioeconomic

Status

Permanent and Seasonal Development

 • Greater Proportion Retired Persons

 • Higher SES

 • More Seasonal Visitors

 • More Expensive Housing

 Diversified Housing Types (Condos)

 • More Rental Properties

 • Higher Densities

 • Greater Regulation and Planning

More Gov’t Services

 • All-Season Roads

 • Sewerage Controls

 • Parking (Major Problem)

 • More Community Infrastructure

(Restaurants, Shopping etc.)

 • Organized Water Sports

 • Community Docks

Destination Factors

 • Amenities

 • Inexpensive Accommodation

 • Close to other Urban Centers

 • Limited Housing Stock

 • Low Housing Prices

 • Availability of Short-term housing

 • Attracted by Natural Amenities

Early Urban Development

• Second Homes

• Cheap Land

• Primary Development Bordering Lake

• Low Density Housing (Sprawl)

• Private Ownership

• Little Planning and Regulation

• Uncontrolled Docks

• Substantial Green Space

• Poor Infrastructure (Roads, Sewerage,

 Hospitals, Schools, Parking)

• Poor Community Facilities

• Limited Commercial and Industrial

 Activities

Current Urban System Moving Outward from the Lake

1. Consumer Consumption District Core (The Lake Itself and Immediately Adjacent to the Lake used for Recreation)

2. Consumer Consumption District Periphery (Primarily used for Recreation)

3. Resorts and High Density HSE Housing

4. Middle Density, MSE Housing, Some Commercial

5. Low Density, SE Housing (Permanent and Mobile) Intermixed With Commercial and Light Industrial

6. Rural-Residential, MSE and LSE Housing

7. Commercial and Community Infrastructure Located Along Major Road Corridors

Figure 1. Conceptual model of lakeshore urbanization. SES, socioeconomic status; SE, socioeconomic; HSE, high socioeconomic; MSE, middle 

socioeconomic; LSE, low socioeconomic.

A Conceptual Model of Urbanization of Lakeshore Communities
28 Demographic Drivers of Lakeshore Urbanization in the Ozarks

Realization of the Conceptual Model

In the sections that follow the model developed in figure 

1 will be tested through the use of remote sensing to uncover 

land-use change and by a linear regression model for each 

lake. The first stage of the analysis involved uncovering 

land-use change from 1990 to 2000. Using population densities and similar measures to uncover urban change is not 

appropriate when examining seasonal communities as the 

population from the summer season to other seasons varies 

dramatically. Typically census data is used. As the census is 

typically taken in April each ten years, it does not give an 

appropriate picture of the true urban extent. To calculate the 

urban change around the lakes Landsat Thematic Mapper 

(TM) imagery was used. Four Landsat TM winter scenes 

(from Path 25, Row 34, and Path 25, Row 35) for the years 

1990 and 2000 were used (see figs. 2 through 5). Imagery 

from 1990 was used as the reference year as census data was 

also available for that year. Although higher‐density urban 

areas were readily identifiable, low density housing outside the 

towns located in agricultural landscapes were problematic due 

to the large amounts of tree cover. There are limitations when 

examining small communities due to the large amount of 

vegetation cover, thereby limiting the amount of detail that is 

available from Landsat data. Aerial photography, such as digital orthoquads (DOQs) and other aerial photography were used 

to test the accuracy of the classification. Ground truthing for 

the 2000 image was also performed to verify that the images 

were correctly classified. The red in figures 2 through 5 show 

urban areas. As can be seen on the images of both lakes, there 

has been a tremendous amount of urban growth over the past 

decade. Image differencing in ARCView was then performed 

to identify the change from nonurban pixels to urban pixels. 

Census block boundaries were then applied to the 30-meter by 

30-meter pixels. 

Remotely sensed data can only provide information on 

changes in the physical environment, rather than the dynamics behind the urban growth. Data on the dynamics of urban 

change came from Census data. It should be noted that there 

are also problems with using census data as it does not include 

data on the human occupancy but collects information on permanent residence only and at only one point in time. As much 

of the population in this region is likely to be seasonal, either 

retirees or persons using the area for recreational purposes, 

Figure 2. Classified Landsat image of Pomme de Terre Lake, 1990. Figure 3. Classified Landsat image of Pomme de Terre Lake, 2000.

Classified Landsat image of Pomme de Terre Lake, 2000 Classified Landsat image of Pomme de Terre Lake, 1990
29

Variable Mean Std.

Deviation

NEW URBAN (acres) 5.191 4.046

ORIG URBAN (acres) 2.2456 7.3494

DISTANCE (km) 4.00 3.94

POPULATION 1990 13.14 177.20

POPULATION CHANGE 2.33 20.42

AGE<18 YEARS 1.59 3.16

RETIRED 2.36 5.09

HOUSEHOLDS 3.81 5.98

HOUSING UNITS 6.33 9.93

MEDIAN NO. ROOMS 4.98 .16

MEDIAN YEAR MOVED 1993 .60

MEDIAN VALUE ($) 64,827 83,770

RENTER OCCUPIED .53 1.70

SEASONAL HOUSING 90 69.10 34.61

CHANGE IN SEASONAL

PROPORTION/ TOTAL

HOUSING

.13 .29

N 860

Table 1. Descriptive statistics for Pomme de Terre Lake.

Variable Mean Std.

Deviation

NEW URBAN (acres) 5.810 16.813

ORIG URBAN (acres) 9.600 22.012

DISTANCE (km) 1.98 1.83

POPULATION 1990 8.06 23.40

POPULATION CHANGE 4.37 21.97

AGE<18 YEARS 2.35 6.80

RETIRED 2.54 7.18

HOUSEHOLDS 5.29 13.24

HOUSING UNITS 7.64 19.62

MEDIAN NO. ROOMS 5.07 .381

MEDIAN YEAR MOVED 1995 1.62

MEDIAN VALUE ($) 95,475 22,684

RENTER OCCUPIED 1.14 5.02

SEASONAL HOUSING 90 128.74 79.98

CHANGE IN SEASONAL

PROPORTION/ TOTAL HOUSING

-.13 .24

N 2,806

Table 2. Descriptive statistics for Table Rock Lake.

Realization of the Conceptual Model

Classified Landsat image of Table Rock Lake, 1990

Figure 4. Classified Landsat image of Table Rock Lake, 1990.

Classified Landsat image of Table Rock Lake, 2000

Figure 5. Classified Landsat image of Table Rock Lake, 2000.

they are missed in the census taken in April every ten years. 

However, to gather that information purpose-built surveys 

must be undertaken, which is cost prohibitive. However, as the 

aim of this paper is to examine urban change and not permanent migration the use of census data is appropriate.

The drivers of urban change are calculated through the 

use of a linear regression model where the dependent variable is the number of acres in a block that were urban in 2000 

that were not urban in 1990. The conversion from nonurban 

to urban is seen as a function of several factors identified in 

figure 1, including the amount of block that is already urban, 

distance from the lake and demographic and socioeconomic 

variables such population in earlier period (1990), population change, age of the population, retired population, number 

of households, number of housing units, median number of 

rooms, median value of housing units, housing that is occupied by renters, and the amount of seasonal housing in 1990 

and the change in the proportion of seasonal housing of all 

housing between 1990 and 2000. Although multicollinearity 

would be an expected problem between several of these variables, the correlations between them were not either very high 

or very low and therefore do not violate the assumptions of 

regression analysis. One reason for this is because the housing 

stock does not truly represent the population, as much of the 
30 Demographic Drivers of Lakeshore Urbanization in the Ozarks

housing is for seasonal use only. The means and standard deviations for these variables can be seen in tables 1 and 2 below. 

Results

The first aim of this study was in investigate the spatial 

patterns of urban development around Pomme de Terre Lake 

and Table Rock Lake. Figures 2 through 5 reveal that there are 

in fact three types of urban communities surrounding the lakes 

in Missouri. The first type includes those located at crossroads, 

such as in the case of Hermitage and Wheatland. The amount 

of land dedicated to urban purposes is expanding outward in 

these communities, along with some infill of land within the 

already urbanized areas. These communities continue to serve 

the communities located closer to the lakes, where there is still 

a lack of services, especially in the case of Pomme de Terre 

Lake. 

It is also evident that people are continuing to locate along 

major local roads as well as along major transportation routes. 

However, the largest amount of change occurred within three 

kilometers from the shoreline of the lakes. Again, by examining figures 2 through 5 it can see that urbanization is not only 

moving outward from the lakes as people settle for properties 

without direct lake access but that there is a significant amount 

of redevelopment occurring around the lakes in addition to 

more infill, resulting in higher-density development, especially 

around Table Rock Lake. Overall the amount of land that has 

become urban in the Pomme de Terre area rose from 0.57 percent to 2.91 percent, which is not a huge increase in percentage terms but in total numbers increased by 8,959 acres, as 

seen in table 3. This indicates that it is a case of urban sprawl 

rather than redevelopment or infill. Conversely, the amount 

of land classified as urban around Table Rock Lake grew by 

16,309 acres, but this is much smaller as the amount of land 

that was already urban abound Table Rock Lake was much 

larger proportionally (7.57 percent to 12.6 percent). 

The population at Pomme de Terre Lake grew by nearly 

25 percent, compared to that of Table Rock Lake, which grew 

by nearly 60 percent. Pomme de Terre Lake has a higher proportion of retired migrants, and a smaller proportion of persons less than 18 years compared to Table Rock Lake. Table 

Rock Lake, on the other hand, is of higher socioeconomic 

status, as indicated by higher median housing prices and 

larger houses (median number of rooms). This is indicative 

of residents converting their vacation cottages into permanent 

residences. There are also differences between the two lakes 

in the change in the proportion of housing that is seasonally 

occupied of all housing. In the case of Table Rock Lake, the 

proportion of vacation/seasonal homes rose from only 12.17 

percent to 22.10 percent of all housing over the ten-year 

period, whereas for Pomme de Terre there was a decline in the 

proportion of housing that is seasonally occupied.

To investigate the influence of these variables on urban 

development two multiple regressions (r) were performed at 

the census-block level. Land cover change to urban was the 

dependent variable (number of acres that was not urban in 

1990 but was 2000). The results of these regression models 

can be seen in tables 4 and 5. Although the adjusted r2 values 

are less than expected for both models, the models are of a 

reasonable fit and statistically significant, with an Adjusted r2

of 0.497 for Pomme de Terre Lake and 0.423 for Table Rock 

Lake. An examination of the blocks with large residuals did 

not reveal any obvious spatial pattern to the residuals. 

Although there are some similarities between the variables that are significant in the two lake models there are also 

several differences. The variable that accounted for the most 

variation in both models is the amount of land that was already 

urban in 1990. The more urban a block was in 1990 the more 

likely there was additional urban development between 1990 

and 2000. 

The variables, Retired and Population under 18 Years, 

are both statistically significant and negative in both lake 

models. For the variable Retired, the coefficients are not of 

the expected sign (+) as it was expected that the greater the 

number of retirees the greater the urban development. The 

variable of Number of Persons under 18 Years is also negative. This suggests that the increase in population and urbanPomme de

Terre

Table Rock

1990 2000 1990 2000

URBAN (Acres) 2180 11139 26,938 43,247

% URBAN/TOTAL 0.57% 2.91% 7.57% 12.6%

DISTANCE 4.00 km 1.83 km

TOT POPULATION 7,752 9,689 22,050 34,387

RETIRED 2,033 7,121

<18 YEARS 1,366 6,599

MOVED 1,993 1,994

HOUSEHOLDS 4,232 14,837

OCCUPIED RENTING 453 3,186

HOUSING UNITS 5,454 21,448

MEDIAN ROOMS 4.98 5.07

MEDIAN VALUE $31,543 $85,924 $50,538 $95,474

SEASONAL VAC 1,768 4,739

PROPORTION

SEASONAL/TOTAL

UNITS

39.51% 32.42% 12.17% 22.10%

Table 3. Overview of lake statistics.

Variable Coefficient Std. Error

(Constant) 2,126.860 1,980.390

ORIG URBAN .353 .057***

DISTANCE -.107 .126

POP1990 -8.5E-04 .005

POP CHANGE 1.8E-02 .046

RETIRED -.219 .110**

AGE<18 -.750 .174***

HOUSEHOLDS .384 .246

HOUSING UNITS .917 .119***

MEDIAN ROOMS -6.029 3.951

MEDIAN YEAR MOVED -1.052 .988

MEDIAN VALUE 3.473E-05 .000

RENTER OCC -.919 .249***

SEASONAL HOUSING 90 -3.639E-02 .015**

CHANGE IN SEASONAL PROPORTION -5.418 1.524***

Adjusted R2 .497***

*** Significant .01; ** Significant .05; * Significant .1

Table 4. Regression results for Pomme de Terre Lake.
31

ized land around the lakes is by persons of working age, most 

likely of preretirement age.

The Renter Occupier variable is also negative and statistically significant in both models, indicating that the growth 

in urban development is negatively related to an increase in 

rental properties, or in other words, the more rental occupiers 

the less likely there is to be urban growth.

The only other variable that is statistical significant in 

both models is the variable Seasonal Housing Units in 1990. 

In both models the coefficient is negative but very small. This 

is not surprising given the size of the unit of observation. For 

each additional seasonal housing unit in 1990 there is a decline 

in the number of acres of new urban land. This is the opposition of what was expected. What is also surprising is that the 

variable Change in the Proportion of Seasonal Housing Units 

to All Housing Units from 1990 to 2000 is not statistically significant in both models. This variable is only statistically significant in the Pomme de Terre model and of the expected sign 

(-). The decline in the proportion of seasonal housing between 

1990 and 2000 led to an increase in the number of acres that is 

new urban. This would imply that a greater proportion of land 

is being occupied by permanent residents, rather than just for 

seasonal use, as suggested in figure 1.

Two variables that are not significant in either model are 

the Number of Households and Population Change. This is 

surprising as it was expected that an increase in permanent 

population would result in an increase in the number of acres 

that are urban. However, this could lead to the conclusion that 

urban growth is due to seasonal rather than permanent migration to the lakeshore.

The only other variable that is statistically significant in 

the Pomme de Terre model is the Number of Housing Units. 

This coefficient is of the expected sign (+). For each additional 

housing unit there is an additional 0.917 acres of urban land. 

This would indicate urban sprawl rather than infill or redevelopment.

There are several other variables that are statistically 

significant and of the correct sign for Table Rock Lake. These 

include Distance from the Lake, Median Number of Rooms, 

Median Value of the Housing Units and Median Year of 

Migration to the area. The coefficient of the variable Distance 

is negative, suggesting that urban expansion is occurring close 

to the lakeshore rather than in a pattern of sprawl away from 

the lake. This pattern can also be seen in figure 5. 

Also as expected, an increase in the Median Number of 

Rooms leads to an increase in the number of additional acres 

of urban land. The coefficient for Median Value of Housing 

Units is also positive—the higher the housing price the more 

land that has become urban. The results of these two variables 

indicate that there is an increase in socioeconomic status 

around Table Rock Lake, as indicated in figure 1. 

The only other statistically significant variable is Year of 

Migration. As expect the later people moved to Table Rock 

Lake the greater the urban expansion.

Conclusions

The objectives of this paper were to first analyze recent 

patterns of urban expansion around the two lake areas of Missouri and second to explain what has been driving growth 

around the lakes over the past decade. The first objective was 

accomplished through the use of remotely sensed data. Urbanization around both Pomme de Terre Lake and Table Rock 

Lake has been extensive over the past decade. The most active 

urban growth areas are located on the northern sides of both 

lakes. The majority of the urban development is taking place 

adjacent to the lakes and along major arterial roads. However, 

in the case of Pomme de Terre Lake there is also a significant 

amount of urban sprawl. 

Multiple regression models were then used to test how 

great an influence the factors identified in figure 1 had on 

urbanization around the two lakes. The overall model performances are adequate, but not as high as expected. The 

variables that have the greatest influence on the increase on 

Original Urban (the amount of urban land that was originally 

classified as being urban) are age structure of the population 

(it is not the retired population that is causing growth) and 

Seasonal Housing. For Pomme de Terre Lake the Number of 

Housing Units and the Change in the Proportion of Seasonal 

Housing Units to All Housing Units from 1990 to 2000 are 

also significant, indicating that owning a vacation home can 

lead to permanent migration to the amenity rich lakeshore 

communities. Socioeconomic variables in the Table Rock 

model also indicate that there is positive relationship with 

urban expansion.

There are a number of implications of the rapid urban 

growth around these communities, which is why it is important to understand how and why these lakeshore communities 

are growing. Population and urban growth can bring new jobs, 

economic prosperity, add new industries, improve regional 

infrastructure, enhance educational opportunities, and increase 

Variable Coefficient Std. Error

(Constant) -1,373.418 368.645***

ORIG URBAN .338 .016***

DISTANCE -.503 .154***

POP1990 .410 .089***

POP CHANGE .415 .089

RETIRED -.239 .086***

AGE<18 -.575 .158***

HOUSEHOLDS -.206 .172

HOUSING UNITS 2.605E-02 .051

MEDIAN ROOMS 1.835 .770**

MEDIAN YEAR MOVED .682 .184***

MEDIAN VALUE 5.658E-05 .000***

RENTER OCC -.426 .093***

SEASONAL HOUSING 90 -2.19E-02 .005***

CHANGE IN SEASONAL PROPORTION -.467 1.134

Adjusted R2 .423***

*** Significant .01; ** Significant .05; * Significant .1

Table 5. Regression results for Table Rock Lake.

Conclusions
32 Demographic Drivers of Lakeshore Urbanization in the Ozarks

tax revenue in the form of sales tax, occupancy taxes, and 

property taxes. Services that would other wise not be possible in such small communities are becoming more common. 

Seasonal migrants, tourists and local residents, especially 

more recent ones expect a high quality of life that the natural 

amenities of these lakeshore communities can provide along 

with the benefits of larger urban areas. However, there are 

number negative impacts of this growth that should also be 

mentioned. These include the increase in solid-waste production, higher volumes of urban nonpoint runoff, loss of green 

space, decline in water quality, and traffic. There is also the 

problem of increased economic costs for local governments, 

such as providing all weather roads and hospitals. Whether or 

not growth of lakeshore communities is a good or a bad thing, 

depends upon one’s viewpoint, but either way it warrants further investigation.

References

Beale, Calvin L, 1988, Americans heading for the cities, once 

again: Rural Development Perspectives, v. 4, no. 3, p. 2‐6.

Espinoza, Maria C., and Stallmann, Judith I., 1996, Seasonal 

migration of retirees—a review of the literature: Department 

of Agricultural Economics, Texas A&M University, College 

Station, Texas, Faculty Paper Series FP97‐2.

Halseth, Greg, 1998, Cottage country in transition—A social 

geography of change and contention in the rural-recreation 

countryside: Montreal, Canada, McGill-Queens University 

Press, 304 p.

Marcouiller, David W., Green, Gary P., Deller, Steven C., 

Sumathi, N.R. and Erkkila, Daniel L., 1996, Recreational 

homes and regional development—a case study from 

the Upper Great Lakes States: University of Wisconsin 

Extension, Madison, WI.

Martin, Harry W., Hoope, Sue K., Larson, C. Lynn, and Leon, 

Robert L., 1987, Texas snowbirds—seasonal migration to 

the Rio Grande Valley: Research on Aging, v. 9, no. 1, p. 

134‐147.

McHugh, Kevin E., 1990, Seasonal migration as a substitute 

for, or precursor to, permanent migration: Research on 

Aging, v. 12, no. 2, p. 229‐ 245.

Stallman, Judith I., and Espinoza, Maria C., 1996, Tourism 

and retirement migration: 

Department of Agricultural Economics, Texas A&M 

University, College Station, Texas, Faculty Paper Series 

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as an economic development strategy—looking into the 

future: Economic Development Quarterly, v. 9, no. 4, p. 

372-383.

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process: Economic Geography, v. 55, no. 4. p. 324-337.
33

Introduction 

Economic forces have been crucial to the geographic 

expansion of metropolitan areas of American cities. The 

increasing production of goods and services across a metropolitan territory, the demand for labor at workplaces within 

the metropolitan area, and the creation of new land devoted 

to urban uses are among the many economic factors that have 

contributed to the territorial expansion of the metropolis. 

Indeed, throughout the history of the United States, numerous 

economic factors have helped decentralize urban places. In the 

limited space available here, however, we are able to consider 

only a few. 

The following pages consider the growth of national 

economy, decentralization of economic activity, transportation 

and communications, the process of land development, factors 

influencing development on the rural fringe, and the Federal 

Government’s role in financing suburban expansion. They 

demonstrate that the economic dimensions of urban decentralization are complex and deeply rooted in American history, 

and will not be easily reversed.

The Growth of the National Economy

Like the growth of the national population, the growth of 

the national economy enabled and contributed to the expansion of urban areas. A nation with small and stagnant production of goods and services would have little reason or ability 

to de-concentrate its few economic activities. In contrast, 

the United States over the past two centuries experienced 

tremendous economic growth. The commercial expansion of 

the eighteenth and early nineteenth centuries, the industrial 

revolution of the nineteenth and twentieth centuries, and the 

technological advances of the twentieth century, along with 

the creation of national communications and transportation 

networks, made the United States into a global economic 

superpower and gave its people an unprecedented standard of 

living. 

Over the Nation’s history, America’s cities acted as 

control centers and switchboards for the Nation’s economic 

growth. As regional economies exploded in the nineteenth 

century, urban capital and entrepreneurs pushed increasing 

production of hinterland farms, mines, and eventually factories, and the number of commercial transactions in city business districts multiplied exponentially. American cities became 

immense clearinghouses of wholesale and retail goods for 

regional, national, and international markets; headquarters for 

the national and international corporate businesses and banks; 

and great manufacturing centers in their own right as well. 

When the American economy shifted to producing services 

rather than goods in the late twentieth century, cities led in the 

provision of business-related services, such as legal work, and 

administrative and health services.

Transportation and Communication 

Innovations 

Many students of urban development attributed the decentralized growth pattern of American cities to improvements 

in transportation and communications (Lewis, 1996; Warner, 

1987; Borchert, 1967). According to economic theory, the 

dense clusters of people and goods found in traditional cities 

facilitated economic transactions through the “agglomeration 

economies” achieved by efficient transfer of goods and information in close physical proximity. Technological innovations 

in the means of transport and communication, however, reduce 

the costs and increase the speed and efficiency of transporting 

goods and people (LeRoy and Sonstelie, 1983; Mieszkowski 

and Mills, 1993). These gains in ability to move merchandise 

and information mitigate the loss of agglomeration economies 

and therefore encourage urban decentralization (Beeson, 1992; 

Burchell and Listokin, 1995; Duranton, 1999). 

Technological improvements in urban transportation and 

communications, however, were as much a result as a cause of 

urban expansion. Improvements in transportation often arose 

in response to demands from individuals or groups who had 

an interest in those improvements. Entrepreneurs, suburban 

residents, and automobile clubs, for example, at one time or 

another called for and received commuting services, rail connections, and better roads. Once efficient methods of transportation and communications were created, however, they acted 

as powerful stimuli to further development and expansion of 

the urban periphery.

Economic Aspects of Urban Decentralization in 

Historical Perspective 

By Alexander von Hoffman1 and John Felkner1

1

Joint Center for Housing Studies, Harvard University, Boston University, 

Boston, Mass.
34 Economic Aspects of Urban Decentralization in Historical Perspective

Early Urban Transportation 

Although a fundamental condition of the United States has 

been an abundance of land2, until the 1850s the settled areas 

of even the largest cities—including New York, NY,, Boston 

and Philadelphia—rarely extended beyond 2 miles from the 

city-center, the average distance a person could walk in half an 

hour. Thus, historians labeled this early configuration “the walking city” because of its size and mode of conveyance (Binford, 

1985; Chudacoff, 1975; Jackson, 1987; Vance, 1991). The limitations of walking largely contributed to the compactness of the 

early-nineteenth-century cities. The fastest method of travel or 

freight was by water, but in the years following the American 

Revolution, a nation intent on exploiting the hinterland and 

expanding its commerce worked assiduously to improve transportation by building new roads—especially the straight-line 

turnpikes—bridges, and canals. 

During the nineteenth century, the growth of the population of cities and consequent pressure for expansion rendered 

the walking city obsolete and led to a series of systems of urban 

transportation based on different technologies (Jackson, 1987). 

The first successful means of mechanized transportation for 

urban residents was the steam-powered ferry. The introduction 

of regular ferry service between Brooklyn and New York, NY, 

in 1814 attracted to Brooklyn large numbers of Manhattan commuters who sought refuge from the congestion and high housing 

costs of Manhattan (Jackson, 1987), as they still do today. 

In the following decades, American cities adopted various forms of mass transit that made access to peripheral communities possible for commuters. First came the omnibus (a 

horse-drawn coach), then steam commuter railroads, the horse 

railway, the cable car, and finally in the early twentieth century, 

the electric powered subway and elevated rail cars. The lines of 

transportation systems generally followed extant major roads 

and encouraged further building along them. As a result, spokes 

of relatively dense development radiated from the inner city to 

clusters of residences and stores, which had served previously 

as inland village centers. As these networks enhanced property 

values along these networks, they encouraged further growth 

(Jackson, 1987; Adams, 1970; Lewis, 1996; von Hoffman, 

1994; Warner, 1973, 1987, Vance, 1991). 

Meanwhile, starting in the 1830s, railroads began offering 

short-distance passenger service in response to requests from 

businessmen living on the urban fringe. As the new system of 

discounted or “commutation” fares became popular, train companies catered to the growing number of commuters to travel 

to work. In time the railroad became an essential part of life for 

prosperous suburban commuters living in elite railroad suburbs, 

such as those built up along the Main Line outside Philadelphia 

and through Westchester County outside New York, NY, City 

(von Hoffman, 1994; Fishman, 1987). 

Industrial Decentralization Begins

At the same time that urban people were moving to the 

outlying neighborhoods and suburbs, the rapidly increasing urban economic activities were spilling out of the inner 

cities as well. In the first decades of nineteenth century, 

entrepreneurs on the urban periphery processed hinterland 

materials for city buyers in cattle yards, slaughterhouses, 

and grain mills or, taking advantage of inexpensive land for 

large-scale operations, operated factories, such as foundries 

and brickyards, to fabricate goods for the urban economy 

(Binford, 1985; von Hoffman, 1994). The railroad contributed to further industrial deconcentration by carrying freight 

to and from factories located in the suburban borderlands and 

connected to trunk lines by short spurs. These factories in 

turn attracted workers who moved to nearby neighborhoods. 

During the late nineteenth century the number and size of 

suburban manufacturing complexes grew. By the early twentieth century, sociologists made note of industrial suburban 

towns, such as the “satellite cities,” devoted to railcar and 

steel manufacturing that appeared outside Chicago and St. 

Louis (Taylor, 1915). 

As railroad and streetcar service became more frequent 

in the late nineteenth century, increasing numbers of people 

traveled in and out of outer city and suburban neighborhoods 

for work, recreation, and even worship. As the urban areas 

expanded, secondary business centers developed at the larger 

intersections, and some even grew to resemble small downtowns. In addition to factories and workshops, suburban 

areas sprouted retail stores, tradesmen, personal services—

such as barbershops—professional services of doctors and 

lawyers, local newspapers, real estate firms, and insurance 

companies. As a result, from at least as early as the 1860s 

(long before the automobile highway era), residents began 

to practice cross-town commuting, traveling between homes 

and jobs in outer neighborhoods and suburbs, rather than to 

and from the central business district (von Hoffman, 1994). 

The centers of cities became crowded with residents and 

businesses during the late nineteenth century, and the urban 

periphery as a result also increased population and economic 

activities. The peripheral areas developed diverse communities, ranging from satellite cities to bedroom communities 

that provided jobs as well as homes. A study of 10 major 

cities and 57 suburbs in their metropolitan areas showed that 

in 1920 the percentage of industrial wage earners in industrial and mixed residential and industrial suburbs frequently 

equaled or exceeded the percentage of industrial wage earners in central cities (Douglass, 1925, p. 87-89) (table 1). 

An Automotive Society

In the twentieth century, the creation of a transport system 

based on the automobile and the truck further expanded the 

territory and the scope of economic activities in outlying areas. 

2

Much of this land, of course, was expropriated from the indigenous 

peoples of North America so that citizens of the United States could exploit it.
35

The invention and mass-production of the automobile was a 

breakthrough in the search for a flexible and convenient means 

of urban transportation. In the early twentieth century, American automobile manufacturing was subject to the upheavals of 

a rapid growing industry, but it produced a font of innovations, 

among the most important of which was assembly-line production. Entrepreneurs such as Henry Ford brought the price 

of automobiles within reach of the average family, and as a 

result car ownership in the United States skyrocketed. In 1920, 

there were 13 Americans for every registered car; by 1960, 

this number had dropped to three (Mitchell, 2001).

The automobile provided Americans with unprecedented 

mobility. As automobile clubs lobbied successfully for better 

roads, real-estate developers began to take heed of the car’s 

potential. Among the first was Jesse Clyde Nichols who developed the exclusive Country Club District in Kansas City with 

prosperous automobile owners in mind. Other developers built 

up hitherto overlooked suburban areas. No longer restricted to 

areas next to fixed rail transport and the cable-car tracks, realestate developers filled in interstices between rail routes and 

extended the boundaries of the urban region outwards (Flink, 

1988; Worley, 1990; Jackson, 1987).

Since the automobile became readily available, urban 

Americans have embraced it. This embrace could be deadly 

to the traditional city with its central business district and a 

densely settled inner core. In Los Angeles, for example, voters 

in the 1920s and 1930s encouraged unrestricted use of cars on 

city streets, embraced general fees for freeway construction, 

and resisted subsidies for mass transit, which contributed to 

the demise of one of the most complete regional streetcar systems in the country (Bottles, 1987). 

In addition, the automobile and the highways that served 

it contributed to suburban competition for downtown retail 

and office activities, which took new design forms. The introduction of the automobile-oriented shopping center in Kansas 

City’s exclusive Country Club District in the 1920s heralded 

a new physical plan for retail shopping which would in time 

overtake the old ribbon-shaped commercial strip (Worley, 

1990) and steal business from downtown department stores. 

From the shopping center eventually evolved the indoor 

regional mall (Rowe, 1991), which in some places supplanted 

early shopping centers. Meanwhile, elegant corporate headquarters and clusters of office buildings—also once the exclusive province of downtowns—appeared far from the city near 

major highway exchanges, further undermining the importance of traditional central business districts (Rowe, 1991; 

Garreau, 1991).

In the late twentieth century, the love affair with the automobile continued unabated. Since 1970, the number of cars 

and trucks in the United States has grown twice as fast as the 

population. The number of vehicle miles traveled jumped by 

an astounding 41 percent, while the population grew by only 

four percent. By 1990, the number of vehicles per household 

exceeded the number of eligible drivers per household, and yet 

the total number of cars and the number of cars per person and 

per eligible driver continued to climb in the 1990s. Low fuel 

costs encourage greater use of the car and in turn undermined 

the use of public transportation or carpooling, which might 

encourage more concentrated development patterns (Dunphy, 

1997). 

Trucking

Concurrent with the growth of automobile ownership was 

the increase in the use of trucks for the movement of supplies 

and goods. The ratio of trucks to cars grew from 1 to 55 in 

1905, to 1 to 6 by 1935 (U.S. Bureau of the Census, 1975). 

The availability of trucks gave executives of companies the 

choice of locating in areas outside central and industrial cities. 

By the 1940s, enough firms were relocating that the National 

Industrial Conference Board sponsored a survey of 148 companies to understand the trend toward “decentralization in 

industry.” Although many had decentralized by acquiring 

geographically dispersed companies and war plants, about one 

in four had adopted a policy of decentralization. Interestingly, 

none of the reasons that executives gave for decentralizing 

their plant facilities had to do with technology or efficient 

factory design. The most frequently mentioned reasons were 

proximity to important new markets, the ability to tap new 

reservoirs of labor, and a preference for a small city or town 

location—in other words, the companies were pursuing the 

residents of suburbs and country towns as either consumers or 

workers (Dickson, 1948). 

Table 1. Distribution of Industrial Wage Earners by Location, 1920.

Types of Communities Number Median Percentage of

Industrial Wage Earners

Central Cities 10 12.4

Mixed Suburbs 10 11.9

Industrial Suburbs 28 25.5

Residential Suburbs 17 2.6

Trucking

Data from the 1920 census. Source: Douglass, (1925 p. 88).
36 Economic Aspects of Urban Decentralization in Historical Perspective

With the construction of the interstate highways in the 

postwar years, trucking increased to a massive scale. The 

ability to move goods enabled the establishment of wholesale 

distribution centers and manufacturing plants near highway 

interchanges deep in the hinterland of metropolitan areas 

(Baldassare, 1992; Warner, 1987). In the 1930s, for example, 

rail and truck transport helped turn the Los Angeles region 

into an industrial giant, with such industries as oil drilling, 

airplane, automobile, and rubber in suburban towns such as 

El Segundo, San Pedro and Long Beach, California. After the 

war, the aerospace industry boosted industrial suburbs in Los 

Angeles and Orange Counties. In the latter twentieth century, 

manufacturing in metropolitan areas increasingly located in 

suburbs and exurban or nonmetropolitan areas beyond the suburbs, even as the manufacturing sector declined (Stanback and 

Knight, 1976; Stanback, 1991; Till, 1981).

Having taken up an automobile driving way of life, 

Americans helped to create sprawling urban regions by seeking to reduce their time commuting in their cars. The cycle 

began when an employer moves an industrial, service, or other 

employment center to a suburban location, perhaps to obtain 

a more pleasant environment for directors or employees or 

(as mentioned above) to be close to a large base of customers, workers, distributors, or suppliers. The employees who 

lived close to city centers then followed their employer to the 

suburbs in order to shorten the length and time of their journeys to work (Pivo, 1990). Such decisions caused commuting 

from suburbs to central cites to decline noticeably as early as 

the 1960s (Berry, 1977), and their full decentralizing effect 

became apparent in the 1980s when journeys to work from 

one suburb to another became more common than either central-city-to-central-city or suburb-to-central-city commuting 

(Pisarki, 1987).

The Stimuli of Highway Construction 

and Airports

The explosion of automobile and truck use would not 

have been possible without a simultaneous enormous investment in road construction. Early in the century, special interest groups lobbied for new streets, as the automobile quickly 

became an integral part of the entire economy. Soon most 

major cities built “parkways” and “expressways,” new circulation routes dedicated to motor vehicular traffic, to avoid 

congestion of the city streets (Jackson, 1987). The Federal 

Interstate Highway Act of 1956 created a new generation of 

high-speed automobile roads, many of which connected the 

central cities with far-flung suburban locales. The newly built 

Federal interstate highways encouraged the development of 

new suburban homes and businesses within easy access of the 

roads—a situation which stranded businesses on the old routes 

without customers, as Alfred Hitchcock humorously depicted 

the Bates Motel in his 1960 movie, Psycho. Federal and state 

governments continue to pay out large sums to build, expand, 

and maintain the nation’s highways not only for automobiles 

but also for increasingly large and heavy freight trucks.

In the late twentieth century, air travel emerged as another 

principal form of transportation, especially for many kinds 

of business interactions, which interlocks with the systems 

of automobile and truck conveyance of people and freight. 

As economic transactions became increasingly national and 

global, they involved an ever-expanding network of clients 

and customers (Lessard, 2001). According to one theory, as 

more and global economic trade is carried out by air, airports 

are replacing seaports as primary economic hubs (Kotkin and 

DeVol, 2001). Certainly airports have become new nodes of 

development. The areas around them now have developed not 

only ancillary services to the airline industry, but also urban 

complexes of offices, hotels, and other businesses (Muller, 

1981). The success of these airport nodes of development rests 

not only on the airline traffic, but also on their locations near 

major highways and convenient connections to the metropolitan road system.

Improvements in Communications

Improvements in communication allowed people within 

the widening business and social networks to coordinate 

their activities and thus reinforced the effects of advances in 

transportation. In the nineteenth and twentieth centuries, the 

introduction of mass production techniques to publishing and 

applied to trade and popular publications vastly expanded the 

information available to business people and to their customers. At the same time, the creation of first telegraph and then 

telephone networks facilitated the expansion of metropolitan 

areas even as they made national and international communication possible. The telegraph allowed the national railroad 

companies to synchronize the movement of trains and at the 

same time enabled businessmen to place orders and ship goods 

across the country and the world. By the early twentieth century, a rapidly expanding telephone network “gave stock traders such as Edward Harriman, managers of mammoth retail 

stores such as Macy’s and the city’s burgeoning population the 

ability to communicate instantaneously by voice from one end 

of the metropolis to the other” (Levitt, 2002, p. 29-30). 

The widespread dissemination of radio and television 

communication connected a disparate population by providing 

instant access to national news and entertainment. In the first 

half of the twentieth century, residents of metropolitan areas, 

sharing in mass culture and connecting to one another through 

electronic telephony, were able to spread out further than they 

had in the past.

In recent years, telecommunications systems—especially 

the growth of computer networks and now the Internet—have 

allowed firms to do business at greater geographic distances 

than previously possible. Telecommunications allow access to 

remote customers, suppliers, and subsidiary offices or employees. Although businesses and activities still tend to cluster 

in certain locations or regions, telecommunications systems 
37

reduce the cost and inconvenience of distance and thus those 

clusters occur in ever-more decentralized nodes in evergrowing urban realms (Berry, 1981; Clark, 2000; Duranton, 

1999; Kain, 1975; Kotkin and DeVol, 2001; McArdle, 1999; 

Townsend, 2001). 

The Decentralization of Economic 

Activity

By the middle decades of the twentieth century, if not 

before, business firms in many metropolitan areas in the 

United States were creating more new jobs in the suburbs 

than in the cities. Moreover, as Stanback and Knight showed, 

by the 1940s the suburbs were generating employment more 

rapidly than they were attracting people, which foreshadowed 

further future population growth (Stanback and Knight, 1976) 

(table 2).

The increase in suburban employment ran across the 

board of job categories. A study of 101 metropolitan areas 

found that between 1960 and 1970 suburbs gained blue-collar 

employment by 29 percent as opposed to a 13 percent loss in 

central cities and suburbs increased white-collar jobs by 67 

percent while central cities gained only by 7 percent (Berry 

and Kasarda, 1977, p. 236). The suburbs were usurping the 

traditional role of the big city downtown as an employment 

center. 

Hence, by the late twentieth century, the entire range 

of economic activities could be found as easily outside the 

cities as within. In 1970, suburbs were the principle places of 

employment in nine of the 15 largest metropolitan areas (Jackson, 1987; Warner, 1987). In the previous 20 years, manufacturing especially dispersed, with many enterprises relocating to 

suburban industrial parks or from the Northeast and Midwest to 

the South and West. Yet at the same time, insurance companies, 

branch banks, regional sales staffs, and doctors offices reduced 

their costs and increased their accessibility by moving to suburban locations (Baldassare, 1992). Suburban areas became 

as economically diverse as the cities and in time duplicated or 

supplanted the core’s economic activities—such as the shift 

of large-scale retail trade carried on in department stores from 

downtown to shopping malls.

The long-term increase in the share of total employment 

located outside of central cities has persisted in recent decades. 

Five of every six jobs added to the economy since 1960 were 

located beyond central city limits. Of the 18.5 million people 

added to the work force during the 1980s, 7.2 million (39 percent) found work in suburbs, 6.5 million (35 percent) found 

work in central cities, while the remainder found work in 

nearby metropolitan regions or outside metropolitan boundaries. As jobs in industry, retail, services, and offices increasingly 

moved outside cities in the late twentieth century (Dunphy, 

1997), scholars discovered, the sort of agglomeration economies that had given rise to the central cities now accrued to the 

office, mixed-use, and retail nodes of the outer parts of metropolitan areas (Dunphy, 1997; Stanback, 1991). 

Furthermore, scholars argue, the shift in employment from 

manufacturing to information and producer services pushed 

the decentralization of employment centers (Baldassare, 

1992; Frey and Alden Speare, 1988; Riebsame, Gosnell, and 

Theobald, 1996). Services in such areas as finance, insurance, 

real estate, and accounting are said to be less dependent than 

manufacturing on access to water and inland shipping routes 

and thus have a greater freedom of mobility. The freedom of 

mobility combined with dramatic improvements in telecommunication networks has brought, according to some analysts, has 

had a dramatic impact on the distribution of jobs in metropolitan areas by dramatically increasing office space in suburban 

locales (Armstrong, 1979; Clark, 1982; Fulton, 1986; Glaeser, 

2000; Pivo, 1990; Sassen, 1990). 

By the end of the twentieth century, the phenomenon of 

“job sprawl” had become as conspicuous as population sprawl. A 

survey of jobs in the 100 largest primary metropolitan statistical 

areas (which for the larger metropolitan areas are smaller entities 

than the Consolidated Metropolitan Statistical Areas) found that 

in 1996 on average only 22 percent of the employed workers had 

jobs located within three miles of the central city center while 35 

percent worked at sites more than 10 miles from the city center. 

Metropolitan areas varied in their distribution of employment, 

Decade

Central

Cities

Population

Manufacturing

employment

Retail

employment

Suburbs

Population

Manufacturing

employment

Retail

employment

1940s 0.8 10.4 4.7 4.2 12.2 8.4

1950s -0.2 -1.1 -0.5 6.8 5.0 7.7

1960s -0.5 -1.3 -1.3 3.4 4.2 7.5

Table 2. Average annual rates of growth of employment in retailing and manufacturing in the central cities and 

suburbs of eight large metropolitan areas, 1940-1970.

Notes: Metropolitan areas are Baltimore, Boston, Cleveland, Denver, New Orleans, New York, NY,, Philadelphia, and St. 

Louis. Unweighted modified average of average annual rates of employment in the eight metropolitan areas. Decades 

for retail figures run from 1939 to 1948, 1948 to 1958, and 1958 to 1967; for manufacturing, years decades run from 

1939 to 1947, 1947 to 1958, and 1958 to 1967.

Source: Stanback, and Knight (1976 p. 97).

The Decentralization of Economic Activity
38 Economic Aspects of Urban Decentralization in Historical Perspective

of course. About two-thirds of the metropolitan areas had relatively concentrated employment—either 25 percent or more jobs 

within 3 miles of the central business district or 60 percent or 

more jobs within 10 miles of the center. Yet a third of the metropolitan areas were highly decentralized in employment, with 40 

percent or more of the jobs outside the ten-mile ring (Glaeser, 

Kahn, and Chu, 2001). A number of researcher have found that 

decentralized employment has been increasing, with the number 

of urban-type jobs growing not only in inner-ring suburbs, but 

also in outer-ring suburbs and exurbia (Muller, 1981; Harris, 

1991; Lang and LeFurgy, 2003, Roepke, 1983; von Hoffman and 

DeNormandie, 2004). This trend has its critics, who assert that 

“job sprawl” is inefficient and penalizes low-income workers 

and central city residents (Persky and Wiewel, 2000). 

The Land Development Process 

Versus Planned Growth

The outward expansion of the urban complex—its people 

and activities—into the territory surrounding cities ultimately 

relies on the transferal and development of real estate for 

urban uses. Active land markets and professional real-estate 

development operations emerged gradually in American cities, 

but they did so in a way that hindered metropolitan planning or managed growth. The following discussion concerns 

the development of urban residential land in the previously 

nonurban places, but similar processes occur in the development of urban commercial and industrial areas.

In the Nation’s early years, the market in land outside 

the urban core was slow. Except for areas near fast-growing 

New York, NY,, farmers and country estate owners in the early 

nineteenth century held most exurban land in large tracts for 

their own purposes, seldom thinking of their properties as 

a speculative investment. When the possibility of suburban 

development came to outer city neighborhoods, landholders 

thought of their property in terms of longterm investment, 

selling off a few lots when their values rose. So slow was the 

process and so modest the pay-off that people who developed 

the exurban lands did it as a part-time occupation (Blackmar, 

1989; von Hoffman, 1996).

 From about the 1860s onward, a long-term boom in the 

population in America’s large cities transformed the outerareas land market, which became increasingly organized and 

specialized. Land subdividers targeted specific segments of 

the housing market more precisely by lot size and landscape. 

Full-time neighborhood real-estate agents appeared, acquiring capital and credit for real estate projects became easier, 

and occasionally developers sometimes built houses instead 

of passing the job on to others. Some developers increased 

the scale of their developments and worked in more than one 

town (von Hoffman, 1996; Keating, 1988). By the late nineteenth century it was clear that suburban land markets could 

be profitable, and, as a study of Chicago land values showed, 

upswings in real estate cycles lured ordinary citizens and 

small operators to speculate in neighborhood properties (Hoyt, 

1933).

The trends toward organizing the market for and development of suburban lands continued in the twentieth century. 

Professional developers subdivided large tracts of land, tapped 

banks and insurance companies for financing, and sometimes 

built and marketed their own structures. Real estate developers 

targeted their projects to different income groups. For upperand middle-class consumers, who were concerned about 

maintaining prestige and property values, developers—such as 

Kansas City’s J.C. Nichols—used deed restrictions and other 

techniques to exclude unwanted income and ethnic groups. 

Other developers such as B.E. Taylor in outer Detroit laid out 

subdivisions with modestly built and priced houses for the 

working class (Worley, 1990; Loeb, 2001; Jackson, 1987). 

Since World War II, large land developers known as 

community builders have attracted much attention by acquiring large tracts of land, designing comprehensive plans, and 

overseeing the entire development process, even including 

the building of homes. From the first Levittown to the giant 

Irvine Ranch, the community builders achieved economies of 

scale much greater than their competitors and produced dramatic results: creating entire towns and cities where there had 

been nothing. The developers of Kaiser Community Homes in 

southern California or Sharpsville in Texas—not to mention 

new towns such as Reston, Virginia, and Columbia, Maryland, implemented sophisticated community plans based on 

the Garden City and community planning tradition. Even the 

Levitt company, although ambivalent about community planning, incorporated some of its principles in their Levittown 

developments (Hise, 1997; Rowe, 1991; Kelly, 1993; Weiss, 

1987; Gans 1967). If left to the community builders, then, the 

expansion of urban areas would likely take place in a more 

orderly and planned fashion than it would in other hands.

For the most part, however, the economics of suburban real estate have brought about a fragmented, parochial 

approach to land development, with little regard for metropolitan planning. In the nineteenth century, small-scale local actors 

developed real estate in suburban neighborhoods and towns 

because only they knew what land was available and the likelihood of attracting purchasers (von Hoffman, 1996). Despite 

the increasing organization of the industry, most suburban 

real-estate development in the twentieth century continued to 

be a local affair (Loeb, 2001). Even the postwar developers 

who developed new subdivisions for the towns outside Boston 

and in Chicago’s “technoburb”3 of Naperville, Illinois, resided 

in the general area in which they worked, had long-standing 

ties to their communities and, importantly, local bankers and 

political leaders (Charles and Keating, 1993; Rowe, 1991). 

True, some homebuilding and shopping center developers 

began operating on a national scale from the 1960s, and simi3

Robert Fishman coined the term, “technoburb,” in his history of suburbia 

(Fishman, 1978).
39

lar national office developers, such as Trammell Crow, came 

to prominence starting in the 1980s, but most builders still 

worked at a relatively small scale. As before, however, successful suburban real estate development depended on knowledge of local conditions, both economic and political. In 

General, developers specialized in a particular territory and 

local officials imposed any planning regulations. As a result, 

development projects tended to be unrelated to other developments or regional growth schemes and contributed to the kind 

of scattered, leap-frog development often labeled sprawl. 

Land Markets and Urban Development

The conversion of rural or vacant lands for urban uses 

became a widespread phenomenon during the twentieth century. Until relatively recently, most students of land markets 

reported that the value of urban land—as well as population 

density—fell as distance from the city center increased (Clark, 

1968; Hoyt, 1933). If all factors were equal, Clawson (1971) 

noted, this generalization would hold for suburbs and the rural 

fringe, but all factors are not equal in such areas. The growth 

of employment and shopping nodes in the urban periphery has 

created new locational preferences that exert an influence on 

land values and density. And, as always, the presence of a particular social class of resident or of amenities—such as lakes 

or mountains that might provide views or recreation—raises or 

lowers the prices buyers are willing to pay. 

The expectation of intensive development, moreover, 

boosts land prices in areas of potential new urban development. Studies of the development of suburban areas outside 

Sacramento, California and Philadelphia, Pennsylvania, in 

the 1950s and 1960s showed that nearby construction of a 

freeway, sewers, and shopping centers increased the value 

of developable land, and that tracts zoned for dense housing 

types such as row houses—potentially more profitable—were 

priced twice as high as single-family housing (Clawson, 

1971). In case studies of six communities scattered about the 

San Francisco Bay Area in the 1970s, Dowall (1984) discovered that perceived conditions in surrounding land markets 

played a large part in determining land and housing prices. 

Even in a town that had little developable land, prices did 

not soar because of the supply of vacant land in nearby communities, which gave developers the choice to go elsewhere 

rather than pay dearly for property. In contrast, expectations of 

future growth in towns that were near communities with high 

demand or in the path of housing development encouraged by 

the county’s urban corridor raised land and housing values.

Conversion of the Rural Fringe to 

Urban Outskirts

Beyond the areas of potentially intense suburban development is the rural fringe or exurbia, where the seeds of development may have been planted even though the transition may 

lie far in the future. The conventional perception is that such 

areas are arenas for conflict between farming and urban land 

uses, in which urban land uses always prevail (Nelson, 1999). 

Such views rarely take into account the longterm restructuring 

of agriculture that in effect reduced profitability in farming, 

especially small-scale farming, in many parts of North America (Hart, 1995). Increased productivity and specialization 

in agriculture—based on among other factors technological 

improvements in equipment and increased reliance on chemical fertilizers—over the course of the twentieth century has led 

to geographic specialization of production, large-scale farming 

and raising of animal stock, and high capital costs. Many agricultural areas and small-scale family farms became marginally profitable at best, causing young farmers and the youth of 

farm regions to have increasingly turned away from farming to 

other pursuits. 

The decline of agriculture invited the development of the 

rural fringe. The fall in agricultural profits and the number 

of people in agriculture living on the land led inevitably to a 

decline in farmland prices in rural areas in many regions. As 

old farmhouses become vacant and fields went unplanted, city 

folk found it relatively inexpensive to set up either part-time or 

full-time residence in the country. The well-to-do city dweller 

who finds his dream house in an old abandoned farmhouse 

situated deep in the countryside became something of a stock 

figure in American twentieth-century literature (Kaufman and 

Hart, 1940; Hodgins, 1946; Spectorsky, 1955). In an empirical 

study, Clawson (1971) reported abandoned farms provided a 

significant portion of rural land developed for urban purposes 

outside Wilmington, Delaware, between 1937 and 1964. 

More frequently, however, farmland passed out of production and the hands of farmers well before it became urban. In 

an interim stage, which can last decades depending on the pace 

and direction of suburban development, other owners—including hobby farmers, vacation homeowners, large and small capital investors, and wealthy syndicates—retain the land. Only 

when approaching development raises land prices and taxes 

sufficiently will individual owners—if they wish to—decide to 

sell. The diversity and number of owners of rural lands causes 

land to become available for development in relatively small 

tracts and at different times (Clawson, 1971; Brown, Phillips, 

and Roberts, 1981; Brown and Roberts, 1978). Hence, the patterns of land ownership on the rural fringe—like the patterns 

of urban real-estate development—allow the decentralization 

of urban land uses and activities but at the same time create 

obstacles to planned growth. 

The Economic Role of the Federal 

Government in Suburban Development

In the twentieth century, the United States Government 

helped to shape, encourage, and—most importantly—finance 

The Economic Role of the Federal Goverment in Suburban Development
40 Economic Aspects of Urban Decentralization in Historical Perspective

urban decentralization, especially through single-family home 

ownership. Prior to the Great Depression of the 1930s, the 

Federal Government was reluctant to intervene in the production or purchase of homes. Although some housing reformers 

felt differently, the prevailing ideology of the time held that 

the purchase of a home was an individual’s responsibility, 

thrift was important, and subsidizing the rent or purchase of 

homes smacked of socialism (Chudacoff, 1975). However, the 

Great Depression brought soaring mortgage foreclosures at the 

rate of one thousand per day, a collapse in the housing industry, and attitudes about government intervention in the housing 

field soon changed. The government now experimented with 

directly providing housing—such as in the greenbelt towns 

and the public housing program enacted in 1937. However, the 

government actions that had the most profound effect on the 

housing industry and consequently the building out of suburbia were taken to shore up the private housing industry.

The important measures the New Deal took on behalf of 

the housing industry were aimed at its financiers. To save the 

financially stressed savings and loan associations, the Home 

Owners Loan Corporation (HOLC) was established in 1933 

with the power to exchange government bonds for defaulted 

home mortgages. The HOLC refinanced tens of thousands 

of mortgages in danger of foreclosure and even granted lowinterest loans so owners could recover homes lost through 

forced sales. The Federal Housing Administration (FHA) 

established by the Housing Act of 1934, helped mortgage 

lenders by insuring home mortgages. As a result, mortgage 

lenders gained the confidence to lower down-payments and 

extend loan repayment periods to twenty-five or thirty years, 

thus making home purchase affordable to a wider group of 

potential buyers. 

The new Federal programs, HOLC and FHA, helped 

housing production almost immediately. The amount of new 

housing climbed rapidly from 93,000 in 1933 to 332,000 in 

1937. By the end of 1972, FHA had helped nearly 11 million 

families to buy houses (Jackson, 1987). 

After World War II, new programs helped to raise housing numbers even more dramatically. The Veterans’ Administration offered to guarantee longterm, low-interest loans to 

returning soldiers, bringing the price of a home in new developments such as Levittown well within reach of the middleclass buyers. It was not until the postwar period that the 1934 

act’s provisions took full effect by allowing the establishment 

of entities for conducting a national secondary market for 

mortgages. Once established, however, the agencies—Federal National Mortgage Association (known as Fannie Mae) 

started in 1938, the Government National Mortgage Association (Ginnie Mae) in 1968, and the Federal Home Loan Mortgage Corporation (Freddie Mac) in 1970—attracted a flood 

of investment money into the real-estate market and further 

fueled the market in houses. They also helped made it possible 

to funnel funds to the fast-growing areas of the country in the 

South and West.

The Federal programs that aided the market for singlefamily houses contributed heavily to the expansion of metropolitan areas. From the beginning, the FHA—through its 

standards for the structure and style of the houses whose 

mortgages it would insure—primarily aided the purchase (and 

therefore production) of single-family, rather than multipleunit structures, which were instead produced by the chronically under-funded public housing program. Furthermore, the 

FHA and HOLC shied away from insuring sales in innercity 

and racial-minority neighborhoods, which both helped undermine property values in those places and boosted sales in 

suburban locales. It is a remarkable fact that Federal mortgage 

instruments financed more than 50 percent of suburban houses 

constructed after World War II (Kleinberg, 1997). 

Other Federal policies also promoted extensive decentralization of urban areas. Since World War II, U.S. tax codes 

allowed homeowners a deduction for their mortgage payments 

and provided favorable conditions for the treatment of capital 

gains from home sales, which provided incentives for both 

home ownership and purchase of new homes (Bereuter, 2000; 

Doyle, 2001; Hanchett, 2001; Hayden, 2000). Also, Federal 

subsidies for infrastructure and transportation networks—

especially the landmark Interstate Highway Act of 1956 and 

later highway appropriation legislation—alleviated the cost of 

suburban development for local governments, as well as the 

individuals who used them and facilitated rapid urban decentralization (Cashin, 2000; Government Accounting Office, 

1999; Katz, 2000).

In the past decade, the finance policies of the Federal 

Government and government-sponsored enterprises further 

encouraged urban decentralization. Through its policies, the 

Federal Reserve Board delivered historically low mortgage 

interest rates, while Fannie Mae and Freddie Mac pursued the 

aggressive mortgage purchase policies. These policies have 

brought single-family homeownership rates up to an unprecedented 68 percent of American households (Joint Center for 

Housing Studies, 2003). The promotion of construction and 

purchase of single-family homes, which are disproportionately located in suburban and exurban areas, thus contributes 

directly to the sprawling expansion of metropolitan areas. 

Finally, the Federal Government has played a direct role 

in decentralizing urban development within metropolitan 

areas. The decisions to build and expand Federal facilities, 

such as military bases, promote employment and therefore 

increase settlement in the areas where they are located. In the 

Washington, D.C., metropolitan area, the Federal Government’s long-standing policy to move government agencies 

to Virginia and Maryland has helped create one of the most 

sprawling urban regions in the Nation (von Hoffman and 

Harrigan, 2002; von Hoffman and Harrigan, 2003).

Conclusion

However brief and limited it is, this survey of the economic history of urban decentralization in the United States 

underscores how complex and deeply rooted is the tendency to 

expand urban activities into ever-increasing metropolitan areas.
41

The growth of the American economy, propelled by technological innovations not only in industry but also in transportation and communications, set the stage for two related 

developments. As business and industry expanded in volume, 

some urban workers began moving to homes further away from 

the crowded centers of the early towns. At the same time, entrepreneurs also moved their enterprises to the urban periphery. 

The movement of people and firms to the outskirts encouraged 

improvements in transportation, which in turn encouraged 

more people and firms to move to the growing suburbs. After 

a series of different technologies of intra- and interurban transport, the invention of the automobile and the truck ushered in 

the era of the modern sprawling metropolitan area. 

To incorporate heretofore rural or idle lands for urban 

uses, specialists in real-estate development worked out planning and development techniques, as well as devising new 

suburban building types such as the shopping mall and the 

office park. The fragmented nature of holdings and investment 

on the urban-rural fringe facilitated urban development but 

hindered rational planning. 

Finally, the Federal Government has played a large role 

in the ongoing development of suburbia, particularly through 

financing mechanisms that make the purchase of single-family 

homes relatively inexpensive and low risk.

In conclusion, any successful management of sprawling 

urban growth will have to involve many economic aspects of 

urban development. Efforts to succeed in managing growth 

may depend upon incentives for workers and entrepreneurs to 

centralize their activities, new transportation guidelines or even 

technologies, policies to encourage real-estate developers to 

use land efficiently, and new Federal transportation and home 

financing programs. The strategies may have to be diverse and 

complex, but, as this review suggests, so too are the economic 

factors in the ongoing expansion of metropolitan areas.

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References

45

Introduction

Urban infrastructure development is highly dependent 

on aggregates industrial minerals—sand, gravel, and crushed 

stone—as the primary materials for concrete and other types 

of construction. The trend for inland urban areas is, however, 

that obtaining adequate amounts of aggregates is becoming 

an increasing challenge. Aggregates are a mineral commodity that because of its low unit value and the large quantities 

used is prohibitively expensive to transport overland for long 

distances. As these urbanized areas expand, the availability of 

aggregates becomes increasingly limited, primarily because 

communities being built over the mineral deposits are in conflict with the industry about land use priorities. The impacts 

caused by quarries have led to the establishment of environmental standards in the permitting process for resource extraction that set terms for the aggregates industry to meet. This 

paper is written in two parts; the first provides the background 

to problems involved in aggregates resource extraction for 

construction where the operations come in conflict with quality of life standards of suburban residential communities. The 

second part presents a method for analyzing the flow of aggregates between quarries and urban infrastructure job sites that 

can show potential impacts of expanding the infrastructure or 

permitting new quarrying near communities. 

Background

The rapid decentralization of cities, industry, and population in the United States has brought to the forefront a 

national dialogue on the balancing of the expanding urban 

and industrial infrastructure and the consumption of economic and natural resources. Concurrently, the extensive 

urban and regional infrastructure built during the twentieth 

century is in great need of repair or replacement. One of the 

key resources central to these issues is aggregates—crushed 

stone, sand, and gravel—for both new construction and to 

maintain or replace earlier structures. The extraction of aggregates, however, requires setting priorities between beneficial 

and detrimental effects. A problem foreseen by the aggregates 

industry is whether or not the availability of these resources 

will be sufficient to meet future urbanization demands and the 

expansion of infrastructure. Suburban development on top of 

geologic deposits of aggregates restricts the development of 

new quarries. Suburban municipalities have enacted land-use 

restrictions on subsurface resource extraction to mitigate the 

environmental effects of these intensive industries. 

The objective of this paper is to provide background on 

national trends on the material flow of aggregates in terms of 

the relation between industrial production of aggregates, the 

urban built environment, and social concerns of municipal 

residents. This paper will outline the spatial patterns in the 

diffusion of these mineral resources, and will clarify the geographical context of their production and use. The availability 

of aggregates is central to transportation and urban development, yet is under represented in both traditional land use 

and emerging sustainable development discussions. The main 

sources of information are government reports and industry 

publications. One objective of this paper is to help link these 

two spheres of the study of aggregates resources. Specific 

questions that this paper is intended to address include:

(1) What are the factors presently affecting the mining 

and consumption of aggregates?

(2) What is the general pattern of proximity and accessibility of aggregates for urban development?

(3) What volume of supply and demand of aggregates 

may be expected in the future?

(4) How may the expected urban growth cause the pattern 

of aggregates distribution to shift?

(5) What is the capacity of the transportation network to 

 affordably transport aggregates, given the expected 

patterns?

(6) What impact would opening new quarries have on 

competitive market supply, or the impact of new 

 development—residential, commercial, or 

industrial—on aggregates demand?

Other regional studies on this topic used analytical 

approaches, such as the correlation of population and aggregates within proximity to each other, comparison of populaGeneral Trends Regarding Aggregate Materials for 

Urban Development, With a Suggested Method for 

the Analysis of Aggregates Distribution

By Dalia Varanka1

1

U.S. Geological Survey, 1400 Independence Road, Rolla, MO 65401, 

573-308-3897.
46 General Trends Regarding Aggregate Materials for Urban Development

tion and urban size relative to aggregate consumption, and the 

analysis of counties producing or not producing aggregates 

(Robinson, 2002; Wilburn and Langer, 2004). This study 

focuses on the analytical potential of applying a quantitative 

method called spatial accessibility and interaction modeling 

to the problem of aggregates distribution in urban areas. The 

quantitative patterns of past aggregates production and consumption are analyzed using a historical database and mapping 

technology. However, the environmental and social contexts of 

supply and consumption of aggregates in the past are equally 

important in understanding how land-use policies and future 

decisions will affect resource and urban development.

Chicago was chosen as a study site because of its tremendous demand for aggregates and its prominent position as a 

global harbor city, yet one linked to most of the mid-western 

region of the United States. Situated on the Great Lakes, Chicago is a starting and ending point in international trade shipping and serves as an intermediary point for expanding cities 

on the Mississippi River Basin network (fig. 1). In addition to 

its position on the river network (of key importance because 

waterborne transport is the cheapest way to move the massive 

weight of aggregates), Chicago is a locus on the continental 

railway network, enabling the movement of the aggregates to 

and from inland points of consumption. A study of Chicago 

potentially could yield regional, national, and international 

implications. 

This paper will present some background on the use of 

aggregates in urban areas, including the problems involved in 

the extraction, use, and disposal. That section will be followed 

by a presentation on the geographic concepts of spatial accessibility and interaction, and the urban Chicago context. The paper 

ends with a report on the status of a geographic research project 

on the quantitative study of aggregates flow in the Chicago area.

The Context of the Aggregates Industry

Aggregate Products and Industrial Production

Aggregates in general may consist of limestone and 

dolomite, with varying amounts of shale, and some types of 

chert. The proper rock is necessary for the specific surface 

conditions and for the preferred chemical characteristics, but 

product characteristics do not differ significantly between residential or industrial use. Rock absorption (permeability) is a 

key factor in some products, such as asphalt. Techniques have 

been researched for producing an acceptable product with 

aggregates that previously were not useable. 

The main aggregate products (sand, gravel, and crushed 

stone) constitute as much as 60 to 70 percent of the volume 

of concrete and are also in high demand for materials such as 

asphaltic concrete (“asphalt”) and road-base fill. The processing of aggregates for concrete production and road construction begins with crushing the rock into gravel, which causes 

extensive dust. Sizing and washing the stone follows the 

crushing. These three steps are the extent of aggregates processing prior to their use in construction. Crushed stone is also 

the principle raw material for portland cement, but undergoes 

extensive processing in the making of cement.

Recycling to help replace shortages of aggregates is not 

considered to be economical. Recycled aggregates are not as 

strong as virgin aggregate products so cannot be used in all 

applications. Various additives are being tested to improve 

the recycled product, but results are poor. Recycled products 

fill only a small percentage of the total demand for aggregates, except for asphalt paving (Wilburn and Goonan, 1998). 

Recycling is most likely in large urban areas where there is 

a shortage of natural aggregates, and disposal of deteriorated 

structures is expensive or not permitted; however, the potential 

benefits of recycling are substantial. Recycling can slow the 

rate and depletion of natural aggregates, which will slow the 

rate of surface disturbance.

The economy of aggregates is closely tied to the construction sector of the national economy and especially to 

the public spending on roads and bridges. The profitability 

of specific companies also depends on two other factors. The 

first is having a large relative share of the market and a low 

relative share of the costs. The lowest delivered cost includes 

the combined costs of service, equipment, freight, and products. Second is the extraction of a high-quality product, that is 

materials that meet users’ needs. This factor depends at least in 

part on the geological exploration of aggregates deposits (new 

technology, for example microgravity tests, are making exploration cheaper and more effective). The viability of aggregate 

extraction is constrained mostly by the thickness of the geologic overburden and the thickness of aggregate deposits of 

particular qualities.

Consumption

The demand for aggregates products is huge. In addition 

to the demand for new construction, existing infrastructure 

needs to be replaced cyclically. Complete replacement of 

asphalt roads is needed within about 7 years, though potholes 

appear much sooner than that. Concrete is estimated to last 

about 30 years or less. Federally funded highway construction requires the use of SUPERPAVE (Superior Performing 

Asphalt Pavements, a rigorous standard for asphalt concrete 

developed in the 1980s) that is estimated to last approximately 

20 years. 

The demand for aggregates for new construction is difficult to quantify. Measures of estimated consumption often are 

offered in per capita averages, but this method does not allow 

the spatial analysis of consumption. Urban growth history can 

be estimated from various statistics, for example housing starts 

and other building permits, such as for overpasses, high rises, 

parking garages, and airport terminals. Development, used as a 

variable or measure of urban growth can be monitored but not 

projected quantitatively (Northeastern Illinois Planning Commission, 2004).
47

Transportation

Aggregates are a high-volume/low-unit-value material, 

entailing high transportation costs. Part of the valuation of 

aggregates is considered to be “place” value; supplies closer 

to the delivery site are more desirable than supplies further 

away. Historically, most aggregates were consumed locally, 

but this is less true today. Because fewer quarries are opening, the existing quarries are serving larger surrounding areas. 

Extremely extensive mining operations are serving areas as 

much as 80 miles away. However, most concrete is manufactured close to the consumer. The path of aggregates shipping 

network is pretty much direct between the quarry, the concrete 

plant, and the job site. 

Transportation costs can double for distances more than 

50 miles away. Trucking costs are increasing because of congested traffic—charged not by distance, but by the hour. Since 

most construction is done from late spring through early fall, 

inventories of aggregates often are built during the winter and 

shipped through the summer, just when traffic is most congested. Rail may also help ease costs, but heavy regulation and 

limited tracks and terminal facilities limit this. Each transport 

mode has economic and physical risks involved in terms of 

flexibility of movement, timeliness, labor intensity, and cost. 

Risk is inversely proportional to cost (for example, trucking 

is most convenient but most expensive.) Each transport mode 

has its required infrastructure and schedule, capacity, and distance constraints. 

Anticipated future transportation trends in the aggregates 

industry include greater deregulation, using transportation 

brokers, and more product distribution centers, including 

appropriate terminals for increased rail, barge, and ship use. 

Challenges include a growing emphasis on just-in-time delivery practices that are best served by trucking. A movement 

away from sand and gravel and toward crushed stone is occurring, as sand and gravel become less available. Future trends 

in the industry may include importing crushed stone by barge 

from distant locations, though fewer ports can accommodate 

Figure 1. Map showing Chicago as the hub of transportation for the Midwest. 

The Context of the Aggregates Industry
48 General Trends Regarding Aggregate Materials for Urban Development

the larger ships. Imports travel by barge to specific harbors, 

or often directly to specific construction projects. Sometimes 

shipping costs can be reduced if the carrier can back haul 

another commodity. 

Land Use and Environmental Impact

Issues Surrounding Land Use and Impacts

Land-use policy and planning rarely include a mineral 

component. If aggregates resources are not considered in 

current land-use plans, they will, either be restricted from 

development despite the community’s need for them or inefficiently considered in growth management. Obtaining permits 

to extract aggregates requires time, money, and work and 

often involves negotiations for approval. Proposed sites for 

quarries may fall within more than one regulatory jurisdiction 

or specially designated areas. The specific impacts of quarrying include problems related to water-table fluctuation, risks 

related to dangerous land surfaces (sharp edges and drop offs), 

and postmining land use. Production impacts such as dust, 

ground vibration, risks related to industrial machinery, falling 

debris, noise, the affected viewscape, and heavy truck traffic 

are all concerns. In mixed land-use areas, blasting is blamed 

when structural deterioration appears in nearby buildings and 

other structures. A problem at the end of the aggregates life 

cycle is that deteriorated products must be disposed of. Suitable land-use planning can minimize or mitigate these anticipated scenarios. 

Quarries may affect the local water table. By removing 

the upper layers of the surface, ground water infiltrates faster 

and the water table drops, most of which would be due to 

dewatering the quarry. Shallow ground water may be vulnerable to depletion if a water supply is nearby and pumping the 

resource along side a sand or gravel pit that is dewatering. 

Quarrying without dewatering or near a large water source 

such as a river would probably have minimal real impact on 

the water table. This impact would probably be restricted 

spatially; flooded quarries would help recharge deeper aquifer 

systems, which are more in need of recharge. Sand and gravel 

operations, however, are typically in unconfined aquifers, and 

often are within recharge areas for confined aquifers. They are 

also quite extensive in some places (Warner, Kelly, oral communication, 2002). The interaction of quarries, groundwater, 

and aquifers comes into focus again when considering landuse plans for spent quarries as water holding facilities. Lately, 

ground-water contamination from past mining operations is a 

topic for increased public concern when using these quarries 

for wastewater holding and disposal. 

Environmental regulation, particularly of air quality, can 

also be an issue where limestone quarries are located close to 

cement manufacturing plants. Crushed stone for cement production use the most common elements and compounds found 

in Earth’s crust—calcium, alumina, silica, and iron—and 

are processed at extremely high temperatures, which creates 

both very high energy demands and results in carbon dioxide 

deposition into the atmosphere. Wastes from the intermediate 

product (called clinker) may also have to be deposited in the 

environment. These pollutants are sometimes the reason for 

limits on the permitting of new production, and thus perhaps 

the available commodity as well. Alternative fuels and materials sources are being investigated and developed.

The public may be even more greatly opposed to aggregates operations than other urban infrastructure development, 

because people are generally unaware of the cost of the aggregates they use each month. Land-use plans are highly influenced by economic considerations that are applicable to the 

permitting of quarries as well. Like many industries, aggregates companies require an experienced labor force. Economically depressed communities need infusions of jobs. Though 

quarries do not generally create a lot of employment, employment does rise in the construction and transportation economic 

sectors, and the local economy benefits indirectly. Communities may chose to sell their aggregates resources; however, the 

likely costs of mitigation and remediation measures must be 

considered as well. A variety of postmining land uses may be 

considered, but some, like waste storage or nuclear shelters, 

may be considered undesirable. When possible, the conversion 

of abandoned quarries to recreational lakes and parks can be a 

positive reclamation land use. 

Pushing mining further away causes the unit price of 

delivered aggregates to increase. By maintaining adequate 

local supplies, the cost in local demand markets could be 

controlled. Local planners should be encouraged to inventory 

the aggregates supplies and create protection zones so that 

they can be mined under appropriate permitting processes. 

Underground mining may be an option in developed areas; 

in addition to extracting materials, mining leaves space for 

underground land use. Good community planning can minimize short and long-term environmental impacts of quarrying 

in multiple land use areas.

Principles of Spatial Accessibility and 

Interaction Modeling

A technique to effectively organize and study the problems of the aggregates industry and market as discussed in 

the first section of this paper, as well as analyzing and testing 

specific elements or influences in their supply and demand, 

is a quantitative technique called spatial accessibility and 

interaction modeling. Spatial interaction modeling analyzes 

trends, as a result of decisionmaking, in the flow of goods 

between origins and destinations, and over various types of 

impediments in the spatial separation between them. Certain 

conditions must be in place for the modeling of interaction to 

occur: 

(1) There must be a market of product supply and consumer demand that complement each other; there 
49

 must be mutual interests in the commodities to trans- 

 port,

(2) There are no intervening opportunities along the net 

work to interfere with the measure of the flow of 

 commodities between origins and destinations, and 

(3) The product must be transferable along a network.

Three fundamental assumptions of spatial interaction 

theory include:

•	 Favorable conditions for the suppliers at the origins 

 to generate shipments and market conditions at the 

destination to attract products. The factors involved 

 may be simple, such as aggregate volume, or 

complex factors, such as economic feasibility. 

• Spatial interaction theory assumes a positive relation 

between volume of flow and population size of origin 

and destination areas. 

•	 Between the origins and destinations, impedance along 

a network diminishes the volume and rate of flow. 

Appropriate measure of impedance may be distance, 

 cost, availability of transport, or time and may be 

calculated with an equation. The theoretical 

assumption is that interaction decreases with or is 

inversely proportional to increasing distance; this is 

sometimes called distance decay. 

These concepts are summarized in the mathematical 

expression:

production of origin × attractiveness of destination

 (distance)2

The exponent of the distance represents the average trip 

length.

A destination point has many attributes that attract the flow 

of materials or create consumption demand. For aggregates, 

some of these would be market value, land use, or low risk 

shipping. An effective way to express destination attractiveness 

is as an index value that combines all of the factors that make 

the destination attractive. Shipments result from the properties 

of the origin to generate the transport of a particular product. 

Commercial production is sometimes controlled by the expected 

demand at the market (the destination); the production and flow 

of aggregates is assumed to be the ability of the industry to produce an affordable product to satisfy consumer demand.

For conventional retailing studies, where consumers travel 

to make purchases, origins are the points where transportation 

starts and destinations are the market place where a trip ends, 

but for aggregates, the quarry will be considered the origin. 

Demand is the need for a particular type of service or goods; 

in the case of delivered goods, as for aggregates, it would 

be calculated at the destination. In this study of aggregates, 

the point of consumption is the concrete plant or the job site. 

Supply is the availability of services or goods. For aggregates, 

this will be calculated at the place of production (fig. 2).

The analysis of the flow of materials takes the form of a 

network of points of aggregate origin, transportation linkages 

and nodes connecting to points of development locations. 

Our perception of the distance of a destination may not match 

Euclidean distance, because humans tend to view distance 

with deterrents to movement. Deterrents can be used as a 

parameter to exaggerate the geometric distance to destinations. 

That parameter is called the distance decay factor. The impedance of transport is calculated in this study in terms of distance 

and volume of flow. The chemical or geological qualities of a 

product can be considered an intervening factor from the distance decay curve of transport cost. Another intervening variable along the relatively simple curve is recycling, although 

the amount of recycled materials is only a fraction of the total 

demand. 

The equivalency between geographical features, spatial 

interaction theory, and the Geographical Information System 

(GIS) data model (ESRI, 2004) is described in table 1.

The selected factors explain much of the variability in 

the data and could serve as predictive factors. Other variables 

have an impact on this process, but their incorporation, though 

important for explanatory purposes, wasn’t taken into account 

for reasons of generalization. This study of aggregates in 

Chicago is a macro spatial interaction model focusing not on 

the choices of individuals, but on general trends. The model, 

as it is outlined in this paper, provides the major measurable, 

explanatory factors, but not any predictive functions.

Data About Aggregate Diffusion Processes

Specific attributes of origin attributes for geographical 

analysis include the prioritization of resources for development according to physical and chemical qualities of 

aggregates or the desirability for extraction (due to depth or 

thickness, for example), the economical feasibility of extraction and expected amount of production of aggregates, and 

societal regulation of extraction activity. The data needed to 

reflect attributes of origins in the spatial interaction of the 

aggregates and urbanization include annual production figures, 

quarry locations, landuse, and geological maps.

The volume of natural aggregates shipping is relative to 

the required timing, risk factors, cost of transport, labor needs, 

and transfer points. The assumption of this study is that the 

Chicago area quarries ship their entire output to the Chicago 

area (Bhagwat, 2000; Mikulic, Donald, Oral Communication). Concrete plants (and to a lesser degree, cement plants) 

are considered intervening factors of the diffusion pattern, 

but ultimately, all aggregates are delivered to the construction 

site. Local sites that can economically consider recycling of 

aggregates near the building location are intervening variables 

as well. 

For aggregates, the transportation network in the Chicago 

area is composed of three modes of transportation—ship/

barge, railroad, and truck. A single mode of transportation, 

truck, is assumed for the short distances found in the Chicago 

area. The impedance of transport is calculated in this study 

simply in terms of distance and volume of flow. The chemical or geological qualities of a product can be considered an 

Principles of Spatial Accessibility and Interaction Modeling
50 General Trends Regarding Aggregate Materials for Urban Development

Figure 2. Locations of quarries, pit mines, and centers of urbanization in the Chicago area examined in this study.

GEOGRAPHY FEATURE THEORETICAL CONCEPT GIS DATA MODEL

Quarries Origin Node

Delivery point Destination Centers

Construction Demand Demand

Aggregate volume Supply Supply

Profit Flow impulse or generation Production index

Development expansion Flow attraction Attractiveness index

Transportation costs Distance decay Impedance

Table 1. Equivalencies of features in theoretical, geographical, and representational concepts of 

aggregates studies.
51

intervening factor from the distance decay curve of transport 

cost. However, to our advantage, few intervening variables 

are known that would interrupt the simple curve.

Deciding on data to adequately reflect aggregate demand, 

or destination points, is more complicated. 

Destination attributes include:

• Projection of amounts required for new development 

 and infrastructure maintenance,

• Quantity and type of material demanded, 

• Accessibility—past development and future develop

 ment plans may be curtailing availability by removing 

 surface production, and 

• Acceptable economic cost of product.

Destination points were selected to represent urban centers as defined by the U.S. Census Bureau (2004). According 

to the Census Bureau, an area is urbanized if the population is 

greater than 50,000 or the population density is greater than 

1,000 per square mile. These destination points within the 

Chicago urban-land-cover polygons were calculated by overlaying population distribution by county on urban-land-cover 

polygons (U.S. Geological Survey, 2000) and calculating percent population density. The resulting set of points represents 

a combination of areas of new urban expansion and older 

areas of the city that would require maintenance of earlier 

built urban structures. Demographic data can be further refined 

by incorporating urban social data, including growth plans, 

employment and travel to work data, household analyses, and 

building permits, at multiple scales. Measures such as development databases or accounting of Federal funding of urbaninfrastructure projects are strongly related to both demand or 

consumption quantities and impervious surface. The advantage of using urban-land-cover polygons is that it ties a third 

variable, area of impervious surface, to urban analysis, which 

fits better in systematic environmental studies. 

The Supply and Demand of Aggregates 

in the Chicago Area

The local stone industry has affected the extent and quality of building in Chicago since the inception of the city’s 

settlement, first as building stone and later as crushed stone for 

concrete in the twentieth century. The historical development 

of the Chicago infrastructure system can be traced to the transportation of rock outcrops along the Chicago River and the 

Illinois and Michigan Canal, to supply building materials and 

the opportunities for employment (Mikulic, 1989). 

As the third largest city in the United States, Chicago 

has massive demand for aggregates. In addition to new construction, freeze/thaw conditions make concrete and asphalt 

infrastructure short-lived. The distribution of growth for population and employment is estimated by the Northeastern Illinois Planning Commission by requesting growth plans from 

local municipalities and shows growth to be most likely in 

Chicago’s inner core and older suburbs and in the outer fringe 

of concentric rings around Chicago. The middle belt, where 

growth occurred from 1980 to 2000, is expected to have minimal expansion, but growth occurring in the room left between 

the axial lines of the railroad system (Northeastern Illinois 

Planning Commission, 1997). 

There are two main geologic sources for aggregates in the 

Chicago area. One is the bedrock that underlies most of the 

entire Chicago metropolitan region except for the very western 

edge. Less prominently, the unconsolidated Quaternary glacial 

overburden produces aggregates as well. The average overburden is about 100 feet thick, but is thickest at the terminal 

moraines that the glaciers pushed to the western and southern 

edge of the region. Outcrops of bedrock occur in areas of deep 

erosion and in places associated with reef deposits. Many quarries are located on these outcrops (Mikulic and Goodwin, 1985). 

The fringe area of expected metropolitan expansion and 

that of the terminal moraines suitable for aggregates extraction 

would roughly coincide within the time period of the regional 

growth plan. Existing quarries in the Chicago area have either 

exhausted their reserves or are limited in potential expansion by 

urban development. Two possibilities for alleviating this problem have been proposed. First is the recognition that most of 

the aggregates will have to be imported from further away at a 

higher cost than was the case in the past. Second, is the potential 

to exploit existing mines further by extending the mining underground. Additional rock suitable for aggregate exists below 

geologic layers of other surface land use. Underground mining 

has proven successful at some quarries and other companies are 

presently considering this option (Mikulic, 1990).

Interaction Modeling of Aggregates for the Year 

1990

To date, a version of the spatial interaction model has 

been written as an ESRI-ArcInfo script and used successfully 

with data about aggregates production volumes in six Chicago 

area metropolitan counties. The U.S. Bureau of Mines collected aggregates production data until those data functions 

were transferred to the U.S. Geological Survey (USGS) at the 

end of 1995 (U.S. Geological Survey, 2004). Yearly production figures and quarry locations data are matched to points of 

aggregate origin on a transportation network of distribution 

linkages that connect to centroid points of urbanized areas 

identified by city name (fig. 3.; particular quarries remain 

identified by number only). The destination values that are 

used in computing the potential interactions associated with 

each of those destinations are derived by creating the population density values of the urban area they represent.

The output from spatial interaction modeling gives us an 

index of all possible potential interactions between known 

pairs of origins and destinations, but the numbers are more 

meaningful if constrained to the known production and destination values. Making the model “production constrained” 

means that predicted values of interaction starting at the 

origins equals the production volume (in tons) at the origins. 

The Supply and Demand of Aggregates in the Chicago Area
52 General Trends Regarding Aggregate Materials for Urban Development

Urbanized Areas

 1 2 3 4 5 6 7

Quarry 1 11571, 4960, 2986, 387189, 47968, 4271, 3720

Quarry 2 701, 496, 712, 196827, 39375, 2866, 78066

Quarry 3 12000, 8774, 51806, 1583220, 298262,33634, 17888

Quarry 4 4311, 3511, 4610, 315763, 42435, 4477, 2686

Quarry 5 128, 91, 122, 36088, 7248, 481, 15182

Quarry 6 184, 133, 240, 60075, 15211, 1115, 5887

Quarry 7 160, 114, 198, 49837, 12058, 879, 4148

Quarry 8 51052, 32445, 18138, 2015909, 251436,22563, 16985

Quarry 9 22479, 14286, 7986, 887625, 110710, 9935, 7479

Quarry 10 16631, 8824, 6426, 669166, 87178, 8230, 6387

Quarry 11 811, 615, 1343, 75290, 10805, 1177, 679

Quarry 12 17667, 13931, 24254, 1433937, 205806,22412, 12935

Quarry 13 52034, 17936, 6314, 949670, 104477, 8892, 7595

Quarry 14 11759, 3825, 941, 176537, 18772, 1335, 1152

Quarry 15 475, 320, 1691, 103144, 34189,24535, 3989

Table 2. Spatial interaction output for quarries and urban subcenters in the Chicago 

metropolitan area.

[City numbers correspond to the urban polygons on the map in figure 2: (1) Waukegan, (2) Highland, (3) 

Geneva, (4) Chicago, (5) Westmont, (6) Aurora, (7) Crest Hill]

Figure 3. Map showing Chicago as an interaction network of aggregates producers and urban consumption in the 20th century.
53

“Demand constrained” means that the predicted index of 

interactions ending at the destination equals the demand at the 

destinations, in this case, the demand represented by population density. Either one alone will produce unacceptable 

outcomes for the other, so we try to balance the interactions 

to match production and demand values or to be doubly constrained. “Doubly constrained” means that separate variables 

are selected for both origin and destination, providing they 

match the same number of flows as observed. When doubly 

constrained, the volume of interactions will equal the relative 

volume of production and demand. The spatial interaction 

model was run, doubly constrained, on total annual production 

data for 15 quarries and population densities of 7 urban centers 

(table 2).

The table represents the relative amount of potential or 

expected interactions in the commercial relationship between 

each quarry and city pairs. The numbers in the table are a 

combination of production (origins), population density (destinations), and distance (impedance, or cost). They are not raw 

values for any geographical feature, but are available for further mathematical calculations, such as averages, percentages, 

or regression analysis. The table would alter if a new quarry 

and its expected production were added to the area, increasing 

the supply of aggregates, or if new urban development and its 

expected population were considered, making demands for 

more aggregates that must be supplied from the local market.

Conclusion

Nationally, the suppliers of aggregates for urban expansion face economic and environmental complexities in 

extracting and moving the aggregates to the urban market. 

The aggregates industry and the people involved in urban 

expansion have mutual interests in planning for the supply of 

aggregates resources in the future. The problems facing the 

supply of aggregates for urban expansion can be organized 

and analyzed using the spatial interaction concept. Tools based 

on this concept provide both industry and local communities 

with a quantitative measure for selecting sites for the most 

effective use of natural aggregates. The USGS has produced 

such a tool for ESRI ArcInfo software users (Miller, 2004; 

Tiefelsdorf, 2003). Findings and visualization (adequate tools 

for the visual presentation of findings) will be completed in 

future stages of the project.

Acknowledgments

The technical organization of this research owes a particular debt to the USGS Minerals Information Team, particularly 

Lucy McCartan, Hendrik van Oss, and V.V. Tepordei. Byron 

Stone and James O’Neal of the Great Lakes Geological Mapping Coalition offered valuable comments and research support. Thomas Beard, Scott Whittaker, and George C. Miller 

compiled and analyzed data for mapping and geographic 

analysis, with data from George Xian and comments from 

E. Lynn Usery and Jeffrey D. Spooner, all researchers of the 

Geography Discipline, USGS. This project also benefited from 

my attendance at the Center for Spatially Integrated Social 

Science (CSISS), Spatial Accessibility and Interaction Workshop at the Geography Department of Ohio State University in 

2003. 

References

Bhagwat, Subhash, 2000, Industrial minerals in illinois—A 

key to growth: Illinois State Geological Survey, Champaign, 

Ill. 

Drew, Lawrence, 1999, The Megaquarry, A Conversation on 

the state of the aggregate industry: Geotimes, August 1999, 

p. 17-22.

ESRI, 2004, Interaction; Arc/Info Help: Redlands, Calif.

Fotheringham, A.S., and O’Kelly, M.E., 1989, Spatial 

interaction models—Formulations and applications: 

Amsterdam, Kluwer Academic, 244 p. 

Martin, Nathan, Worrell, Ernst, and Price, Lynn, 1999, 

Energy efficiency and carbon dioxide emissions reduction 

opportunities in the U.S. cement industry: Ernest Orlando 

Lawrence Berkeley National Laboratory, University of 

California, LBNL-44182. 

Mikulic, Donald G., 1989, The Chicago stone industry—A 

historical perspective, in R.E. Hughes and J.C. Bradbury, 

eds., Proceedings of the 23rd Forum on the Geology of 

Industrial Minerals: Illinois State Geological Survey Illinois 

Mineral Note 102, p. 93-99. 

Mikulic, Donald G., 1990, Cross section of the Paleozoic 

rocks of northeastern Illinois— Implications for Subsurface 

Aggregate Mining: Illinois State Geological Survey, Illinois 

Minerals 106. 

Mikulic, Donald G., The future of urban underground 

aggregate mining—Examples from Chicago, Indianapolis, 

and Milwaukee [abs.]: 40th Forum on the Geology of 

Industrial Minerals Program with Abstracts, Bloomington, 

Ind., May 2-7, 2004, p. 55-56.

Mikulic, Donald G., and Goodwin, Jonathan H., 1985, Urban 

encroachment on dolomite resources of the Chicago 

area, Illinois: Illinois Department of Energy and Natural 

Resources, State Geological Survey Division Reprint 

1986L, reprinted from Glaser, J.D., and Edwards J., eds., 

Proceedings of the Twentieth Forum on the Geology of 

Industrial Minerals—Industrial Minerals of the MidAtlantic States. (May 15-18, 1984): 1985, Maryland 

Geological Survey Special Publication No. 2. p. 125-131.

Northeastern Illinois Planning Commission, 2004, Research 

and forecast group development database: [http://www.nipc.

cog.il.us/dataserv.htm].

Northeastern Illinois Planning Commission, 1997, Population, 

household and employment forecasts for northeastern 

Illinios 1990-2020: Chicago, Ill. [http://www.nipc.cog.il.us].

Robinson, Jr., Gilpin R., and Brown, William M., 2002, 

References
54 General Trends Regarding Aggregate Materials for Urban Development

Sociocultural dimensions of supply and demand for natural 

aggregates—examples from the Mid-Atlantic region, United 

States: U.S. Geological Survey Open File Report 02-350 

[http://pubs.usgs.gov/of/2002/of02-350/].

U.S. Bureau of the Census, 2004, Urban and Rural Definitions: 

[http://www.census.gov/population/censusdata/urdef.txt].

U.S. Geological Survey, 2004, Minerals resources on-line 

spatial data: [http://mrdata.usgs.gov/].

U.S. Geological Survey, 2002, Regional assessment of land 

use change—Land use change in the Chicago/Milwaukee 

region: [http://landcover.usgs.gov/urban/chicago/intro.php].

Wilburn, David R., and Goonan, Thomas G., 1998, Aggregates 

from natural and recycled resources, economic assessments 

for construction applications—A materials flow analysis: 

U.S. Geological Survey Circular 1176, p. 2 [http://pubs.

usgs.gov/circ/1998/c1176/c1176.html].

Wilburn, David R., and Langer, William H., 2000, Preliminary 

report on aggregate use and permitting along the Colorado 

Front Range: U.S. Geological Survey Open File Report 00-

258 [http://pubs.usgs.gov/of/2000/ofr-00-0258/].
55

Introduction

The literature on urban sprawl confuses causes, consequences, and conditions. This article presents a conceptual 

definition of sprawl based on eight distinct dimensions of 

land use patterns: density, continuity, concentration, clustering, centrality, nuclearity, mixed uses, and proximity. 

Sprawl is defined as a condition of land use that is represented by low values on one or more of these dimensions. 

A Metaphor Rich in Ambiguity 

Urban sprawl is one name for many conditions. It has 

been attached to patterns of residential and nonresidential 

land use, to the process of ex­tending the reach of urbanized 

areas (UAs), to the causes of particular practices of land 

use, and to the consequences of those practices. Sprawl 

has been denounced on aesthetic, efficiency, equity, and 

environmental grounds and defended on choice, equality, 

and economic grounds. Sprawl has become the metaphor of 

choice for the shortcomings of the suburbs and the frustrations of central cities. It explains everything and nothing. 

Much of the confusion about sprawl stems from the 

conflation of ideolo­gy, experience, and effects. A term so 

widely used cannot be easily dis­missed as too vague for 

serious discussion. Many policy makers claim to know 

it when they see it and make important policy judgments 

based on what they see or think they see. As a first step 

toward developing policies to deal with the causes or consequences of sprawl, it would help both critics and apologists if agreement could be reached on what sprawl is and 

how to measure it empirically and compare its occurrence 

across a large number of urban areas. 

Sprawling Literature: Lost in a Semantic 

Wilderness 

Consistent with the findings of others (Burchell and 

others, 1998), our survey of the literature yielded no common 

definition of sprawl and relatively few attempts to operationally define it in a manner that would lead to useful comparisons of areas to determine which had experienced greater or 

less degrees of sprawl. There are two notable recent exceptions: First, Torrens and Alberti (2000) offer sophisticated 

indices for measuring multiple aspects of sprawl, such as density, scatter, leapfrogging, inter­spersion, and accessibility, but 

provide no empirical prototypes. Second, Malpezzi (1999) has 

undertaken an ambitious effort to develop some precise definitions of several dimensions of sprawl such as density and lack 

of continuity. Moreover, he has quantified them for a sample 

of metropolitan areas and related them statistically to determinants of sprawl such as the length of average daily commutes. 

Our analysis of the social science and planning literature 

suggests that definitions of sprawl can be grouped into six 

general categories: 

(1) Sprawl is defined by an example, which is seen to 

embody the char­acteristics of sprawl, such as Los Angeles. 

(2) Sprawl is used as an aesthetic judgment about a general urban de­velopment pattern. 

(3) Sprawl is a cause of an externality, such as high 

dependence on the automobile, isolation of the poor in the 

inner city, the spatial mis­match between jobs and housing, or 

loss of environmental qualities. 

(4) Sprawl is the consequence or effect of some independent variable, such as fragmented local government, poor 

planning, or exclusionary zoning. 

(5) Sprawl is defined as one or more existing patterns of 

development. Those most frequently mentioned are low density, leapfrogging, dis­tance to central facilities, dispersion of 

employment and residential development, and continuous strip 

development. 

Wrestling Sprawl to the Ground—Defining and 

Measuring an Elusive Concept1

1

Reprinted from George Galster, Royce Hanson, Michael R. Ratcliffe, 

Harold Wolman, Stephen Coleman, and Jason Freihage, “Wrestling Sprawl 

to the Ground: Defining and Measuring an Elusive Concept,” Housing Policy 

Debate v. 12, n. 4, p. 681–717. ©2001 Fannie Mae Foundation, Washington, 

D.C. Used with permission. The remainder of the original article elaborated 

on the conceptual definitions and was able to operationalize six of the eight 

dimensions for 13 urbanized areas. 2

Wayne State University, Detroit, Mich.

3

University of Maryland, Baltimore County, Baltimore, Md.

4

U.S. Bureau of the Census.

5

George Washington University, Washington, D.C.

6

University of Maryland, Baltimore County, Baltimore, Md.

By George Galster2, Royce Hanson3, Michael R. Ratcliffe4, Harold Wolman5, Stephen Coleman6, and 

Jason Freihage6
56 Wrestling Sprawl to the Ground—Defining and Measuring an Elusive Concept

(6) Sprawl is defined as a process of development that 

occurs over some period of time as an urban area expands. 

Definition by Example 

In both the popular and scholarly literature, sprawl is 

frequently de­fined by one or more examples of scattered or 

low-density patterns of urban development. Los Angeles is 

often given a place of honor in ex­emplary definitions. Robert 

Geddes (1997) asserted, “Its key words are fragmented, 

incomplete, ad hoc, uncentered” (p. 3). Unfortunately, while 

it is big and spread over a large area, Los Angeles is more 

densely settled than most large areas in the United States. 

Atlanta has come to replace it as an example of sprawl, but 

with the possible exception of Portland, OR, any area is a 

potential nominee. The flexibility of definition by ex­ample 

makes it possible to include all sorts of development patterns, from planned communities with clustered housing 

and mixed uses to exurban rural estates. A Wells Fargo 

Bank report, Preserving the American Dream, concluded 

that sprawl “receives blame for seemingly every bad aspect 

of contemporary urban life” (quoted in Myers and Kitsuse 

1999, p. 6). 

Aesthetic Definition 

Ad hoc examples at least imply and often express an aesthetic judgment: Sprawl is ugly development. In The Language of Cities (1971) Charles Abrams defined sprawl as 

follows: “The awkward spreading out of limbs of either a 

man or a community. The first is a product of bad manners, 

the second of bad planning” (p. 293–94). Even so careful a 

land economist as Marion Clawson (1962) could not resist 

judgmental adjectives and adverbs in his definition: “[the] 

rapid spread of suburbs across the pre­viously rural landscape, tendency to discontinuity, large closely settled areas 

intermingled haphazardly with unused areas” (p. 94). 

The Cause of an Unwanted Externality 

Traffic congestion (Black, 1996; Downs, 1999; Vermont Forum on Sprawl, 1999), environmental contamination (Sierra Club, 1998), income and ra­cial segregation of 

neighborhoods (Downs, 1998), the mismatch between jobs 

and housing (Orfield, 1997), local fiscal disparities (Burchell 

and others, 1998), conversion of farmland to urban uses 

(U.S. General Accounting Office, 1999), and civic alienation 

(Popenoe, 1979), among other maladies of contemporary 

urban life, have been attributed to sprawl. Here the definitions segue from judgments about the appearance of sprawl 

to assertions of causal links between sprawling land use 

patterns and environmental, social, and economic costs. But 

these definitions basically describe what sprawl does (or is 

supposed to do) rather than what it is. 

A Consequence 

Sprawl is also frequently defined as the consequence of 

something else. Downs (1998), among others (Black, 1996; 

Burchell and others, 1998; Mosko­witz and Lindbloom, 1993; 

Orfield, 1997), argues that sprawl occurs as a consequence 

of the fragmentation of control over land use in metro­politan 

areas. It is unclear whether sprawl is an intentional, necessary, or inadvertent consequence of fragmented governance of 

growth. In light of development patterns in areas with unified 

governments, such as Houston and Lexington, KY, it seems a 

doubtful proposition. In any event, understanding the policies 

that induce specific development pat­terns could lead to their 

correction if there were a clearer specification of what those 

patterns are. 

Selected Patterns of Land Development 

Altshuler and Gomez-Ibanez (1993) edge toward a clearer 

definition of sprawl by identifying the types of development 

patterns associated with it: Continuous low density residential 

development on the metropolitan fringe, ribbon low density 

development along major suburban high­ways, and development that leapfrogs past undeveloped land to leave a patchwork of developed and undeveloped tracts. (p. 67)

The inconsistency of continuous and leapfrog development aside, this definition at least characterizes land use conditions, and it is conceiv­able that continuous development, 

ribbon development along corridors, and leapfrog development are different forms of sprawl (Harvey and Clark, 

1965). Other development patterns frequently characterized 

as sprawl include low density (Lockwood, 1999), random 

(GAO, 1999), large-lot single-family residential (Popenoe, 

1979), radial discontinuity (Mills, 1980), single land use 

or physical separation of land uses (Burchell and others, 

1998; Cervero, 1991), widespread commercial development 

(Downs, 1998), strip commercial (Black, 1996; Burchell and 

others, 1998), and non-compact (Gordon and Richardson, 

1997). 

A Process of Development 

Finally, some commentators (Ewing, 1997; Harvey and 

Clark, 1965) sug­gest that sprawl represents a stage in the 

development process rather than a static condition. This definition suggests that some parts of an urban area may pass 

through a sprawl stage before eventually thicken­ing and diversifying so they can no longer be characterized as sprawl, at 

least by those authors. When used to signify a stage or process 

of devel­opment, sprawl is a verb, rather than a noun connoting 

a condition. But little is gained by changing the part of speech, 

for there is little in the literature to indicate when sprawl 

metamorphoses into nonsprawl. It does, however, suggest that 

sprawl may represent some range on a development pattern 

continuum. 
57

These descriptions of sprawl leave one grasping for something more solid. How far does the development frog have to 

leap and how light and broad must its footprint be for sprawl 

to be present? When do land uses thin sufficiently from being 

compact, centered, or concentrated before they degrade into 

sprawl? An empirical definition is needed if the dis­cussion is 

to move from polemics to a common understanding and useful analysis of urban form. Once that is achieved, it should be 

possible to conduct a better-informed discussion of the forces 

and factors that cause certain patterns of development and to 

address the consequences that flow from certain urban forms 

for different population groups, such as a region’s poor. 

Conceptually, a thing cannot simultaneously be what it is 

and what causes it or what it causes. If sprawl is to be a useful 

concept for describ­ing something important that occurs in 

urban areas, it must first be re­duced to some objective conditions or traits. Some of the characteriza­tions drawn from the 

literature are too impressionistic for empirical measurement. 

Others identify conditions, dimensions, or attributes of sprawl 

that can be operationally defined, among them discontinuous, 

widespread, or random development; low-density residential 

or non­residential development; continuous low-density or strip 

development; spatially segregated land uses; and dispersed 

employment centers. 

A Conceptual Definition 

As a noun, sprawl implies a condition characterizing an 

urban area, or part of it, at a particular time. If sprawl is to be 

measured as a condition of land use, it must be distinguished 

from other conditions that may well be related to it. On the 

basis of the descriptions of conditions char­acterizing it in the 

literature and amplified by observation and experience, the following conceptual definition is suggested: 

Sprawl (n.) is a pattern of land use in a UA that exhibits 

low levels of some combination of eight distinct dimensions: 

density, continu­ity, concentration, clustering, centrality, nuclearity, mixed uses, and proximity. 

This definition suggests the possibility that there can be 

different types of sprawl, consisting of different combinations of these dimensions7

.

 

It also suggests the possibility of 

defining sprawl as a process of develop­ment by looking at 

changes in patterns of land use over time, particularly at the 

periphery. 

Emphasizing the pattern of land use distinguishes the condition from its causes or effects. The pattern of land use refers 

to the arrangement of the built environment for residential and 

employment activities. Other uses, such as passive or active 

open space, agriculture, and pub­lic facilities and networks, 

will affect that pattern along some of its dimensions.

The UA is a more appropriate unit of analysis than the 

metropolitan area (MA), which is composed of contiguous 

counties, some of which may contain large outlying rural areas 

with population densities far below the minimum UA threshold of 1,000 people per square mile. Including such rural areas 

can result in exaggeration of some dimensions of sprawl, such 

as density. Using the census-defined UA alone, however, may 

exclude semirural development at the urban fringe that some 

con­sider the epitome of sprawl. Whatever its limitations, the 

UA is a well-established construct that captures settlements 

averaging as few as 2.4 units per acre and the vast majority of 

all development of the MA. It may ultimately be useful to add 

to the UA subdivisions that are inte­grally associated with it, as 

distinct from established rural homesites and outlying communities located in metropolitan counties. Using the UA alone, 

however, should provide a reasonable test of the ability of the 

eight dimensions to characterize the extent of sprawl within it. 

And if they can, they can also be applied to a carefully delineated larger area.

Despite its disagreements and contradictions, the literature agrees that all development is not sprawl; simply 

because a UA is larger does not mean that it is more 

sprawled. Moreover, all sprawl does not have the same characteristics or dimensions. Excluding terms that cannot be 

easily quantified, such as ugly or excessive, there appear to 

be several objective dimensions of land use, that, if present 

at low levels in a UA, can fairly be called sprawl. Therefore, 

if each dimension of land use pattern is placed on a continuum, the lower the level, the greater the extent of sprawl 

on that dimension. It would seem fair to characterize UAs 

with development patterns that score low on all dimensions 

as ex­periencing more sprawl than others. Further, the dimensions may be present in different degrees and combinations 

across many urban areas, making it possible to distinguish 

different types of sprawl.

Note: Following the publication of this article in 2001, 

the research team, with assistance from the Urban Dynamics 

Program of the U.S. Geologic Survey, has engaged in a multiphased project to: 

(1) Determine the relative accuracy and economy of using 

Census and National Land Cover Data Base data in measuring the dimensions of sprawl that were initially operationalized using census data alone,

(2) Improve the definition of the area over which sprawl 

should be measured, 

(3) Develop a typology of sprawl and estimate the effects of 

each type on environmental, economic and social outcomes such as environmental pollution, traffic congestion, 

housing prices, income disparities, and racial segregation. 

Results of this later research are reported in the following 

three publications:

Acevedo, W.; Galster, G.; Hanson, R.; Ratcliffe, M.R.; and 

Wolman, H., 2002, Patterns and process of sprawl—Phase 

1, Final Report to U.S. Geological Survey.

7

In our conceptualization that sprawl is a multidimensional phenomenon, we 

are con­sistent with the work of Malpezzi (1999) and Torrens and Alberti (2000).

A Conceptual Definition
58 Wrestling Sprawl to the Ground—Defining and Measuring an Elusive Concept

Wolman, H.; Galster, G.; Hanson, R.; Ratcliffe, M.R.; Furdell, 

K.; and Sarzynski, A., 2005, The Fundamental Challenge 

in Measuring Sprawl—Which Land Should be Considered? 

Professional Geographer.

Cutsinger, J.; Galster, G.; Hanson, R.; and Wolman, H., 2004, 

Developing Typologies of Urban Sprawl, Unpublished 

paper delivered at Urban Affairs Association Annual 

Conference, April 2004.

References 

Abrams, C., 1971, The Language of Cities: N.Y., Viking. 

Altshuler, A., and Gomez-Ibanez, J.A., 1993, Regulation for 

Revenue: The Political Economy of Land Use Exactions, 

Washington, D.C., Brookings Institution. 

Black, T.J., 1996, The Economics of Sprawl, Urban Land, v. 

55, no. 3, p. 6–52. 

Burchell, R.W., Shad, N.A., Listokin, D., Phillips, H., Downs, 

A., Siskin, S., Davis, J.S., Moore, T., Helton, D., Gall, M., 

and ECONorthwest, 1998, Costs of Sprawl—Revisited: 

Washington, D.C., National Academy Press. 

Cervero, R., 1991, Land use and travel at suburban activity 

centers: Transportation Quarterly, v. 45, n. 4, p. 479–91. 

Clawson, M., 1962, urban sprawl and speculation in suburban 

land: Land Economics, v. 38, n. 2, p. 94–111. 

Downs, A., 1998, How America’s cities are growing—The big 

picture: Brookings Review, v. 16, n. 4, p. 8–12. 

Downs, A., 1999, Some realities about sprawl and urban 

decline: Housing Policy Debate, v. 10, n. 4, p. 955–74. 

Ewing, R., 1997, Is Los Angeles-style sprawl desirable?: 

Journal of the American Planning Association, v. 63, n. 1, p. 

107–26. 

Geddes, R., 1997, Metropolis unbound—The sprawling 

American city and the search for alternatives: American 

Prospect, v. 8, n. 35, p. 40 [accessed September 2001, 

http://www. prospect.org/v8/35/geddes-r.html].

Gordon, P., and Richardson, H.W., 1997, Are compact cities a 

desirable planning goal?, Journal of the American Planning 

Association, v. 63, n. 1, p. 89–106. 

Harvey, R.O. and Clark, W.A.V., 1965, The nature and 

economics of sprawl: Land Economics, v. 41,n. 1, p. 1–9. 

Lockwood, C., 1999, Sprawl: Hemispheres, September, p. 

82–91. 

Malpezzi, S., 1999, Estimates of the measurement and 

determinants of urban sprawl in U.S. metropolitan areas: 

University of Wisconsin, Madison, WI, Center for Urban 

Land Economics Research, unpub. paper.

Mills, D.E., 1980, Growth, speculation, and sprawl in a 

monocentric city, Journal of Urban Economics, v. 10, p. 

201–26. 

Moskowitz, H.S., and Lindbloom, C.G., 1993, the new 

illustrated book of development definitions: New 

Brunswick, NJ, Rutgers University, Center for Urban Policy 

Research. 

Myers, D, and Kitsuse, A., 1999, The Debate over the Future 

Density of Development: Lincoln, NE, Lincoln Institute of 

Land Policy, working paper.

Orfield, M., 1997, Metropolitics—A regional agenda for 

community and stability: Washington, DC, Brookings 

Institution, and Cambridge, MA, Lincoln Institute of Land 

Policy. 

Popenoe, D., 1979, Urban sprawl—Some neglected sociology: 

Sociology and Social Research, v. 31, n. 2, p. 181–88. 

Sierra Club, 1998, Sprawl—The dark Side of the American 

dream: [accessed September 2001, http://www.sierraclub.

org/sprawl/report98/].

Torrens, P.M., and Alberti, M., 2000, Measuring sprawl: 

University College, London, Center for Advanced Spatial 

Analysis, unpub. Paper No. 27.

U.S. General Accounting Office, 1999, Community 

development—Extent of Federal influence on “urban 

sprawl” is unclear: Letter Report, April 30, GAO/RCED 

99–87. Washington, DC. 

Vermont Forum on Sprawl, 1999, Sprawl defined: [accessed 

September 2001, http://www. vtsprawl.org/sprawldef.htm].
59

Introduction 

Urbanization and urban sprawl continues to spread through 

the United States. This land consumptive pattern of suburban 

growth has dominated the American landscape since Word War 

II. Urban land cover and land use (LCLU) have many significant impacts on the urban environment (Kirtland and others, 

1994). Accurate and updated information on the status and 

trends of urban ecosystems is needed to develop strategies for 

sustainable development and to improve the livebility of cities. 

The ability to monitor urban LCLU changes is highly desirable 

by local communities and by policy decision makers alike. With 

increased availability and improved quality of multispatial and 

multitemporal remote sensing data, as well as new analytical 

techniques, it is now possible to monitor urban LCLU changes 

and urban sprawl in a timely and cost-effective fashion. 

Many efforts have been made to develop of change detection methods using remote sensing data (see for example, 

Singh 1989; Kam, 1995; Ridd and Liu 1998; Sohl, 1999; 

Jensen, 1996; Seto and Liu, 2003). The most commonly used 

change detection methods are spectrally based (image to 

image) and classification based (map to map) (see for example, Green and others, 1994: Yang and Lo, 2002; Loveland 

and others, 2002). Urban change studies using Landsat Multispectral Scanner (MSS) or Landsat Thematic Mapper (TM) 

data have been conducted either at regional scales encompassing several urban areas (Todd, 1977; Royer and others, 1988) 

or a single metropolitan area (Gomarasca and others, 1993; 

Johnston and Watters, 1996). Recently, long term urban LCLU 

changes (over two decades or longer) have been studied using 

the methodology of post-classification comparison using the 

Landsat archive as a baseline data source (Chen and others, 

2002; Yang and Lo, 2002; Loveland and others, 2002). 

Detection of land-cover change in urban and suburban 

areas using remote sensing technique faces great challenges. 

Urban areas are heterogeneous and most pixels of urban 

images consist of a mixture of different surfaces. For instance, 

pixels that were classified as residential might vary between 

20 percent and 100 percent in their impervious surface, and 

they might vary between 0 percent and 60 percent in their 

tree canopy coverage (Clapham, 2003). Only recently, urban 

change studies using remotely sensed data have begun to 

explicitly account for non homogeneity at the sub-pixel level, 

providing quantifiable measurements for detecting changes (Ji 

and others, 1999; Ward and others, 2000; Flanagan and Civco, 

2001, Yang and others., 2003a; Goetz and others, 2004) 

We have recently developed a change detection method—

Sub-pixel Imperviousness Change Detection (SICD) (Yang 

and others, 2003b)—to spatially quantify urban LCLU 

changes using remote sensing data in an objective and repeatable way. The method uses high-spatial-resolution imagery as 

training data in representing urban land-cover heterogeneity 

and medium resolution Landsat images to extrapolate imperviousness over large spatial areas. The research work presented in this paper describes further applications of the SICD 

method in urban change detection of two metropolitan areas in 

the United States. 

Study Areas and Landsat Data 

Urban land-cover change of two study areas were tested 

using the SICD method. The first area covers the western 

portion of the state of Georgia, extending southwest from 

the Atlanta metropolitan area to the cities of Columbus and 

Albany. The other area is in northern and central Florida. 

Major cities in this area include Tampa and St. Petersburg. 

For each study area, Landsat-5 TM and Landsat-7 Enhanced 

Thematic Mapper Plus (ETM+) data of two path/rows (~ 180 

km from east to west and ~350 km from north to south) were 

obtained, including two dates acquired for Atlanta and four 

dates for Florida during the 1990 to 2002 time period (table 1). 

All images were preprocessed by the U.S. Geological 

Survey (USGS), Center for Earth Resources Observation 

and Science (EROS) using standard methods (Irish, 2000), 

including (1) radiometric and geometric calibration and terrain 

correction, (2) conversion from digital number to at-satellite 

reflectance in order to correct for changing illumination geometry (for six reflective bands), (3) reprojection to Albers Equal 

Area, and (4) resampling to 30-meter resolution using cubic 

convolution. After initial preprocessing, tasseled-cap brightness, greenness, and wetness were derived using at-satellite 

reflectance-based coefficients according to Huang and others 

(2002).

Another important preprocessing step for change detection analysis is the radiometric normalization among images 

Measuring Urban Sprawl and Extent through 

Multitemporal Imperviousness Mapping

By George Xian1, Limin Yang2, Jacqueline M. Klaver1, and Nazmul Hossain1

1

SAIC, U.S. Geological Survey, Center for EROS, Sioux Falls, SD 

57198. 2

Joint Laboratory for GeoInformation Science, The Chinese University 

of Hong Kong, Shatin, N.T. Hong Kong.
60 Measuring Urban Sprawl and Extent through Multitemporal Imperviousness Mapping

acquired from different dates. Although we were able to obtain 

cloud-free images near an anniversary date, which greatly 

reduces the phenology differences and changes related to sunsensor-target geometry, some of the changes between each 

image pair could arise from changes in atmospheric conditions 

and slightly different sensor characteristics between TM and 

ETM+ arising from longterm degrading of the TM sensor. 

To correct the potential bias caused by the sensor differences 

and their calibration, we first converted the digital number of 

Landsat 5 TM (DN5) to a pseudo Landsat 7 ETM+ DN (DN7) 

using calibration coefficients developed by Vogelmann and 

others (2001). The coefficients were derived using an analysis 

of tandem data sets of the Landsat 7 and Landsat 5 collected 

over the central United States. This conversion was made to 

take advantage of the superior radiometric calibration of the 

ETM+. The conversion equation is DN7 = DN5 * slope + 

intercept. The slope and intercept values are shown in table 2 

for all six spectral bands. 

After Landsat-5 data were converted to pseudo ETM+ DN 

(DN7), the remaining processing steps were the same as those 

applied to ETM+ data. Correction of atmospheric effects using 

absolute radiometric normalization was not possible for this 

study due to the lack of in-situ measurements needed for characterizing the atmospheric conditions of optical depth, water 

vapor content, and aerosols. 

Method

A physical parameter, “anthropogenic impervious surface” defined as the impenetrable surfaces, such as rooftops, 

roads, and parking lots, was used as an indicator to identify the 

spatial extent and intensity of urban development. This parameter was chosen because the amount and change of impervious 

surfaces are likely linked to the major urban land-cover types 

and their changes. Indeed, imperviousness can be used as a 

key environmental indicator for urbanization and urban sprawl 

(see for example, Arnold and Gibbons, 1996; Clapham, 2003). 

The spatial extent and changes of impervious surfaces impact 

urban climate by altering sensible and latent heat fluxes within 

the urban surface and boundary layers. Development of impervious surfaces within a drainage basin increases the frequency 

and intensity of downstream runoff and decreases water quality (Schueler, 1994). 

The SICD is an objective and simple method to quantify 

urban LCLU changes through mapping imperviousness at different points in time. The methodology as it is illustrated in 

figure 1 includes several components: (1) training/validation 

data development, (2) predictive variable selection, and initial 

regression tree modeling and assessment, (3) spatial modeling 

and mapping, and (4) imperviousness change detection and 

interpretations.

Reference Data Development for Sub-Pixel 

Imperviousness Mapping 

It is important to select a reference data source in order to 

derive an accurate training dataset for tree models. The Digital 

Orthrophoto Quarter Quadrangles (DOQQs) of the late 1990s 

produced by the USGS were utilized to derive training data 

and testing for imperviousness mapping. The DOQQs were 

scanned from the color infrared photographs acquired from the 

National Aerial Photography Program. Each DOQQ has three 

bands—green, red and near infrared—with a nominal spatial 

resolution of 1-meter. 

For the western Georgia study area, three DOQQ images 

were selected for deriving the training/reference data, one 

within southern Atlanta, Georgia and two within Columbus, 

Georgia. Four DOQQs—central St. Petersburg, northern St. 

Petersburg, Tampa, and southern part of Tampa—were used 

for the Tampa Bay, Florida, area. These DOQQs were selected 

to capture the spectral and spatial variability of impermeable 

surfaces due to differences in building materials, ages, surface 

colors, and spatial orientation in and around the urban areas. 

Areas where observed land-cover changes occurred between 

the acquisition of the DOQQs and the TM/ETM+ images were 

excluded from the training dataset. All DOQQ images were 

reprojected to Albers Equal Area projection to register to the 

Landsat imagery. Visual inspection showed that the co-registration uncertainty between the DOQQ and the TM/ETM+ 

images were within 10 meters. 

Six broad land cover classes—impervious surface, trees, 

grass, water, barren, and shadow—were differentiated on all 

DOQQ images. The classification was made through an unsuLocation Path Row Date 1 Date 2 Date 3 Date 4

Atlanta, Ga. 19 36 3/9/1993 3/11/2001

19 37 3/9/1993 3/11/2001

Tampa, Fla. 17 40 10/2/1990 5/22/1994 10/21/2000 5/4/2002

17 41 10/2/1990 5/22/1994 10/21/2000 5/4/2002

Table 1. Study areas and selected Landsat TM/ETM+ images used for 

this study.

Band Slope Intercept

1 0.9398 4.2934

2 1.7731 4.7289

3 1.5348 3.9796

4 1.4239 7.032

5 0.9828 7.0185

7 1.3017 7.6568

Table 2. Coefficients for conversion of Landsat 5 TM DN to Landsat 7 

ETM+ DN.
61

pervised method. The preliminary classification was further 

refined by screen digitizing and recoding to achieve specified 

classification accuracy (greater than 85 percent). The shadow 

class was excluded from the following calculation of imperviousness because the class could not be unambiguously merged 

with any single land-cover class.

Pixels classified as impervious surface in 1-meter grids 

from the DOQQ were utilized to calculate the impervious surface percentage for each Landsat pixel. The result was a 30-m 

resolution raster image of imperviousness. 

Regression-Tree Modeling

A regression tree algorithm was utilized to model subpixel percent imperviousness. The regression tree conducts a 

binary recursive partitioning and produces a set of rules and 

regression models to predict a target variable (percent imperviousness) based on training data. Each rule set defines the 

conditions under which a multivariate linear regression model 

is established for prediction (Breiman and others, 1984). In the 

partitioning process, each split is made such that the model’s 

combined residual error for the two subsets is significantly 

lower than the residual error of the single best model (Huang 

and Townshend, 2003). The main advantage of the regression 

tree algorithm is that it can account for a non-linear relationship between predictive and target variables, and allows both 

continuous and discrete variables to be used as input (predictive) data. Cubist (http://www.rulequest.com) is the regression tree program used in this study. This program has some 

advanced features, including pruning, committee models, and 

instance models.

The predicted percent impervious surface attributed to 

each pixel was evaluated using independent test data. Ideally, 

the test data should be collected from fieldwork based on a 

statistical sampling design. Due to time and resource constraints, 1-meter spatial resolution DOQQ was selected as the 

best option for providing the spatial resolution necessary to 

accurately determine impervious surfaces in our study areas. 

Impervious estimates derived from the 1-meter DOQQ were 

used for model evaluation. To ensure the validity of the assessment, all test data (pixels) were selected randomly and independently from the training dataset.

Results

Final products generated using the SICD include (1) spatial estimates of subpixel percent imperviousness at 30-meter 

resolution for western Georgia and Tampa Bay, as well as the 

impervious surface change over several points in time, and 

(2) error estimates of regression tree predicted imperviousness through validation. Over western Georgia, the average 

absolute error of the 1993 impervious prediction (by comparing model prediction against “true” values from the test data 

derived from 1m DOQQs) was 16.4 per cent, and the correlation coefficient (r) was 0.73. For the 2001 impervious prediction, the average absolute error was 15.3 percent and r was 

0.78. For Florida implementation, the average absolute error 

of the impervious prediction ranges from 10 to 12 percent, 

and the correlation coefficient (r) ranges from 0.70 – 0.80 for 

1991 – 2002 prediction. The absolute accuracy of the change 

products can be affected by several factors, including relative 

image normalization among all images of different years, presence of small cloud/cloud shadow patches, use of a single date 

image for predicting imperviousness, and the uncertainty/error 

in the training data. The accuracy estimates reported here were 

based on evaluation of imperviousness prediction for each 

individual time period. No direct assessment was made to 

evaluate the quality of the change product due to the lack of 

ground-truth data for each time period from 1990 to 2002. 

Western Georgia

The spatial patterns of the predicted percentage of imperviousness over western Georgia were reasonable. As was 

expected, all urban centers and the immediate surroundings 

located in and around the Atlanta metropolitan area were 

identified as the medium-to-high imperviousness. The rate of 

High-resolution image (DOQ)

Classification

Impervious surface estimation (1 m)

Overlay on 30 m TM/ETM+ image

grid

Impervious surface estimation (30 m)

Sub-pixel impervious surface modeling

using Regression Tree algorithm

(Training and Landsat data)

Mapping impervious surface for two

time periods

Change of impervious surface over

time interpretation

Training data

Modeling/

Prediction

Change

Detection

Figure 1. The procedure for impervious-surface mapping and change 

detection using remote-sensing data and a regression-tree model.

Results
62 Measuring Urban Sprawl and Extent through Multitemporal Imperviousness Mapping

urban land cover change over metropolitan Atlanta (areas with 

an increase of percent imperviousness greater than 10 per cent 

from 1993 to 2001) was 7.5 percent. This estimate may be a 

little aggressive by including all areas with 10 per cent increase 

or higher, considering that the uncertainty of the regressiontree prediction was within the same order of magnitude (~10 

percent). By restricting changed areas to at least a 20 per cent 

increase, the change rate became 4.0 percent. This is similar to 

a 3.5 percent increase reported by Yang and Lo (2002) based on 

area of increased low and high-intensity urban land use derived 

from 1992 and 1997/98 Landsat data within an area that is 

slightly different from our study coverage. 

Another area of interest is in the Fort Benning military 

training base and the adjacent city, Columbus, Georgia. The 

military installation is located in the lower Piedmont Region 

in western-central Georgia. Its immediate neighbors include 

Columbus, Georgia, and Phoenix City, and Alabama. Over 

time, urban development in those areas has influenced the 

military community’s ability to maintain their mission focus. 

Conversely, the military installations affected the local community by alteration of land cover/use and other training activities. The SICD method identified LCLU changes in 1990s due 

to continued urban development, as well as land cover changes 

within and around the military installation. 

Figure 2 presents the difference of sub-pixel imperviousness between 1993 and 2001 with the background of urban 

extent obtained from the 1992 USGS National Land Cover 

Dataset. The map illustrates many LCLU changes in this area 

including some residential growth near the fringe of Columbus. 

Most of the noticeable change (change greater than 30 percent) 

occurred in the eastern and northern edges of the city. Several 

change patches on the eastern side showed greater than 70 

percent change. New urban developments after 1992 were also 

detected. The spatial extent and the magnitude of changes of 

the impervious surface provide useful information to assess the 

interactions between the military installation and nearby cities. 

North and Central Florida

Florida has experienced a rapid rate of population growth 

since 1945. Its eastern and western coasts have attracted many 

people to live in the area. More than 2 million people now live 

within the Tampa Bay watershed, and the population continues 

to grow. The urban extent indicated by zoning information in 

the Tampa Bay watershed area increased about 117 percent 

from 19,617 to 42,623 hectares (ha) from the 1940s to 1960s. 

Most of the growth occurred in Pinellas and Hillsborough 

Counties where population increased about 148 percent and 

158 percent, respectively, from 1960 to 2001. Recent census 

data show that population starts to increase in the north Florida area between 1990 and 2000. Increased development has 

resulted in high pressure for demand of more fresh water and 

better air quality. The continued anthropogenic alterations in 

the area have changed land cover and land use over the past 50 

years. The mapped imperviousness over several points in time 

provides a unique dimension showing changes of land cover 

and land use from 1990 to early 2000 in the Tampa Bay and 

its surrounding areas. An interesting example of such change 

is seen in Hillsborough County and City of Tampa, Florida. 

Significant development of residential and recreational areas 

along major roads from 1991 to 2002 was reflected by the 

detected increase of imperviousness on the northern side of 

Tampa (fig. 3). The significant growth has received much 

attention by the local communities. According to the Hillsborough County Planning Agency, Hillsborough County is facing 

pressures from accelerated growth that threatens established 

areas, constrains public facilities and services and impacts natural resources. Overall, this impervious surface change highlights new urban developments in the area. The urban sprawl 

is highly aggregated around many cities.

Discussion and Conclusions

Studies presented in this paper demonstrate that use of 

the SICD can detect urban land-cover changes by quantifying 

Figure 2. Imperviousness variations from 1993 to 2001 near Columbus, 

Georgia. The urban extent in 1992 was extracted from U.S. Geological 

Survey National Land Cover Dataset 1992. Most new development 

occurred on the fringe of the city.
63

subpixel percent imperviousness over time using Landsat and 

high-resolution imagery. The SICD provides necessary spatial 

details to detect changes of urban land cover. Compared to conventional urban change detection methods (band differencing, 

ratioing, change vector, post-classification and others), change 

detection through sub-pixel percent imperviousness mapping 

provides more information on the spatial extent and the intensity 

of urban LCLU change. This approach is more flexible and captures heterogeneity of urban land cover characteristics. In addition, the SICD is repeatable once training data are developed. 

The information on intensity of urban land-cover change 

provided by percent imperviousness may help to infer the type 

of land use changes. For instance, where the imperviousness 

change over two time periods exceeds 80 percent, it is likely 

that the area has undergone a significant land-use change from 

rural land-use (for example, forest, agricultural, grassland) 

to a high-intensity urban land use (for example, commercial 

and industrial development), whereas a much lower amount 

of change (less than 20 percent) near an urban rural fringe 

is likely related to newly developed residential land use. A 

conceptual framework presented in Yang and others. (2003b) 

illustrates that the magnitude of change in percent imperviousness over time could be correlated to type and intensity of 

urban rural LCLU changes. In addition, the change product 

generated by this approach allows users to define and interpret 

land cover/use changes based on their own definition (e.g. 

change above certain percentage). It should be noted that the 

change product generated by this method has some limitations. Due to the uncertainty of the predicted imperviousness 

(around 10 to 15 percent), a 15 percent or less increase in 

imperviousness may or may not reflect actual changes within 

the study areas. This uncertainty poses a detail limit to the 

product generated using this method. With regard to potential 

applications of the impervious surface products other than 

urban land-use change detection, timely and spatially explicit 

imperviousness change data can help urban-land managers 

in their decision making and implementation of strategies for 

today’s complex urban growth issues. Another application 

is in field of urban ecological modeling. Geospatial data of 

impervious surfaces and its change can be used not only as an 

indicator for water quality but also as a critical input data set 

for urban hydrological modeling. The data can also be used to 

quantify the land atmosphere interaction within urban climate 

system. Finally, quantification of urban development through 

mapping change of impervious surface provides useful data 

for urban dynamic simulation through model calibration and 

validation (Jantz and others, 2003). 

Acknowledgments

This research was made possible in part by SAIC under 

USGS contract 03CRCN0001. Part of the work was funded 

by the Strategic Environmental Research and Development 

Program (SERDP) through project number 1257. We wish to 

thank Zheng Zhang, Joyce Fry, and Mark Driscoll for preprocessing Landsat data used in this study. 

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Figure 3. Comparison of Landsat imagery and mapped impervious surface change near Tampa, Florida (2002 prediction subtracts 1991 prediction). Landsat images in 1991 and 2001 are on the left and middle, respectively. The right image is the imperviousness change from 1991 to 2002.

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65

Introduction

Over the past decade, impervious-surface coverage has 

emerged as one of the most important factors in the assessment of environmental condition. The amount of impervious-surface cover in a watershed has been established as an 

important indicator of water quality and aquatic ecosystem 

health (Arnold and Gibbons, 1996; Schueler, 1994; and Center 

for Watershed Protection, 2003). The study of impervious 

surfaces has become one of the growing areas of scientific 

interest in the characterization and control of non-point source 

(NPS) pollution, and the imperviousness issue has even been 

suggested as a unifying theme for overall study of watershed 

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

analytical model (Ridd, 1995). The effects of impervious surfaces have been tied to global carbon sequestration, and the 

total impervious surface area of the United States has recently 

been shown to be approaching the size of the State of Ohio 

(Elvidge and others, 2004). 

However, even with the increased attention to impervious 

cover (IC), there is no predominant or widely accepted method 

for determining impervious area. Further, there are a wide 

variety of techniques that include statistical models, roadway 

and population correlations, cartographic products, digital 

image processing, photographic interpretation, and other methods that are all commonly utilized to determine one factor; 

the amount of impervious cover in a particular geographical 

area at a particular point in time (Slonecker and others, 2001). 

Unfortunately, very little research has been performed to 

determine the accuracy of the various methods, even though 

the total impervious area (TIA) indicator is strongly correlated 

with negative aquatic conditions in only a very small range (10 

to 15 percent) of imperviousness of the total watershed area 

(Schueler, 1994).

Although our understanding of the effects of impervious 

cover is maturing, our understanding of effective planning 

and mitigation efforts for reducing the ecological effects of 

anthropogenic activity is still in its infancy. A logical first step 

in this process is to determine the components, or classes, of 

man-made structures that as a whole make up TIA. In this, it 

is critical to understand the accuracy and variability of other 

commonly used methods of deriving impervious-area measurement.

With the relatively recent availability of very high spatial 

resolution, georeferenced digital imagery, we now have the 

data necessary to address some of these issues of the components of imperviousness and the accuracy of some of the 

methods that we use to compute this parameter. In cooperation 

with the United States Geological Survey (USGS), Geographic 

Analysis and Monitoring (GAM) Program and The National 

Map, and the United States Environmental Protection Agency’s (USEPA) Landscape Ecology Program, this collaborative 

research seeks to utilize very high spatial resolution imagery 

to map the individual components of impervious surfaces in 

six different watersheds in the United States in order to (1) 

identify the relative contribution on the individual components 

of IC and (2) utilize these high-accuracy GIS databases as 

“truth” to assess the accuracy other common methods of determining impervious cover. 

Background

In the 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 (USEPA, 2003a; USEPA, 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. 

Storm water runoff has always been of concern to planners 

and civil engineers, but until recently, the emphasis was on 

human safety as opposed to ecological consequences. However, comprehensive watershed management techniques are 

now being widely implemented that seek to understand and 

balance ecological factors, classic storm water management, 

and local land-use policy. 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; and 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; 

and Plunk and others, 1990). There are numerous examples 

An Assessment of Impervious-Surface Measurement 

Methods

By E. Terrence Slonecker1 and Janet S. Tilley2

1

U.S. Environmental Protection Agency, 12201 Sunrise Valley Drive, Mail 

Stop 521, Reston, VA 20192. 2

U.S. Geological Survey, 12201 Sunrise Valley Drive, Mail Stop 521, 

Reston, VA 20192.
66 An Assessment of Impervious-Surface Measurement Methods

of impervious surfaces as a prime consideration in local 

planning processes.

In watershed planning and engineering, the relationship 

between impervious cover and runoff is viewed as a direct and 

quantitative relationship in which impervious cover systematically changes an aquatic ecosystem (Schueler, 1994; and 

Arnold and Gibbons, 1996). This process assumes many forms 

in both the in-stream and riparian zones. Schueler (1994) 

showed the relatively direct relationship between the percentage of watershed imperviousness and runoff coefficients 

and also showed that discharge rate and runoff volume can 

increase by orders of magnitude between natural and impervious areas. Several researchers have shown that a threshold for 

urban stream stability and habitat quality exists at approximately 10 to 15 percent watershed imperviousness (Schueler, 

1994; Klein, 1979; Booth and Reinelt, 1993; Arnold and Gibbons, 1996; and May and others, 1997). Studies observing 

greater than 15 percent imperviousness have invariably registered the urban stream habitat as poor. 

The immediate and direct ecological consequence of 

imperviousness of a watershed is the effect on stream morphology. With increased flow rate and volume comes destabilization of streams due to widening and incision accompanied 

by the onset of stream bank erosion and related habitat 

degradation. Channel instability is correlated with sub-bank 

full floods (Anderson, 1968; Leopold, 1968; Hammer, 1972; 

Hollis, 1975; and Booth, 1990) and is characterized as a loss 

of critical, in-stream and riparian ecostructures such as pool 

and riffle networks and vegetative cover as well as an increase 

in the cross-sectional zone of a channel during rates of high 

flow (Schueler, 1994).

Impervious surfaces have proven very efficient at conveying urban pollution to receiving streams and directly impacting stream water quality. Urban effluent transports sediment 

an order of magnitude greater than the sediment transported 

by comparably sized rural watersheds. 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; and Klein, 1979). 

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 and so forth). The runoff from impervious surfaces of hydrocarbons (Whipple and Hunter, 1979), metals and 

road salt runoff (Crowther and Hynes, 1977), and their related 

effects on stream water quality has also been addressed. The 

hydrologic community has consistently utilized the parameter 

of imperviousness to model pollutant runoff and as a general 

measure of stream water quality (Schueler, 1994).

The effect of imperviousness on thermal properties of a 

stream is two-fold. Impervious surfaces hold and retain more 

heat than the natural features they replace. The heat from these 

surfaces is transferred down slope (via runoff) and warms 

the receiving stream. Schueler (1994) notes that impervious 

surface areas may be 10 to 12 degrees warmer than the fields 

and forests that 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 stream temperatures. The other trending factor 

for in-stream warming is related to the reduction of streamside 

vegetative cover that shades the stream. This loss of riparian 

stream buffer is often due to urban encroachment as well as to 

erosion of stream banks from flooding. This is pointed out by 

Klein (1979), who notes a 6°C to 11°C variation in shaded and 

nonshaded areas on the same Maryland stream. 

Perhaps the strongest environmental indicator of in-stream 

health is stream biodiversity. A change of in-stream characteristics due to watershed development (that is, increased flow 

volume, pollutant runoff, 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, and results provide 

an overall characterization of ecosystem change as related to 

imperviousness of a watershed at the 10 to 15 percent level. 

Schueler (1994) and Arnold and Gibbons (1996) both observe 

that research over the past 20 years has consistently reported 

a correlation between watershed imperviousness and health of 

the receiving stream ecosystem. Schueler proposes and Arnold 

and Gibbons concur on a three-tiered threshold classification 

scheme of urban stream-quality potential based on watershed 

imperviousness levels:

• Stressed=1 to 10 percent imperviousness

• Impacted=11 to 25 percent imperviousness

• Degraded=>26 percent imperviousness

Study Areas

Six urban/suburban watersheds were chosen for this study 

based on their geographic distribution, urban setting, and 

imagery and data availability. The six watersheds are listed 

below and shown geographically on figure 1 (a more complete 

description of the test watersheds, methods, and procedures 

can be found in Tilley and Slonecker, 2005):

(1) Black River, Seattle, Washington.

(2) Difficult Run, Fairfax, Virginia.

(3) North Walnut Creek, Des Moines, Iowa.

(4) Oak Creek, Lincoln, Nebraska.

(5) Tuscarora Creek, Leesburg, Virginia.

(6) Wares Creek, Bradenton, Florida.

Task 1—Obtain Detailed Impervious-Surface 

Data to Serve as Ground Reference Information

In the first phase of this research, the primary task 

involved developing very high (sub-meter) spatial resolution 

GIS databases of the major component classes of impervious 
67

Figure 1. Locations of the six watersheds used in the study.

Study Areas

cover. Six classes were selected based on functional characteristics including:

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.

Driveways—Hard paved surface or gravel areas that connect 

a house, garage, or other structure to a road surface for the 

purpose of automobile access and storage.

Buildings—All roofed structures including storage sheds 

and trailers (with the assumption that trailer’s foundations 

were concrete pads). 

Sidewalks—Very narrow hard surface areas that are generally found parallel to roadways and exist primary for pedestrian traffic. Recreational trails, such as bike trails, park and 

golf course cart paths are included in this category.

Parking lots—Hard surfaced areas that exist primarily for 

the temporary storage of automobiles, other vehicles, equipment and materials.

Other—Hard surface recreation areas such as basketball or 

tennis courts, patios, swimming pools-to include surrounding patio, any other impervious surfaces that do not fit in 

any of the above categories.

Preliminary research was performed for each watershed 

to identify sources of both high-resolution imagery and GIS 

data from public or private sources. In some cases, aerial 

photographs were acquired, scanned, georegistered and (or) 

orthorectified using ENVI 3.6 (RSI Boulder, Colorado) image 

processing software. After all GIS data were acquired, local data 

were reviewed for consistency and accuracy, and were updated 

(by heads-up digitizing/editing) to temporally match the highresolution imagery source using ARCVIEW 3.x software 

(ESRI, Redlands, California). When no existing GIS data could 

be found, themes were digitized as polygons from a high spatial 

resolution imagery source. Figure 2 shows a graphic example 

of the data collection process. All datasets were subjected to a 

three-phase review and quality assurance process. This process 

is described in more detail in Tilley and Slonecker (2003).

Task 2—Perform Intermediate Scale Calculation of Impervious Surface Using Subpixel and 

Land-Use Coefficient Processing 

Two separate techniques were used to calculate impervious area at an intermediate scale. These two techniques were 

selected for their efficiency and common utilization in the 

literature. The first, subpixel processing, is a relatively recent 
68 An Assessment of Impervious-Surface Measurement Methods

advance in spectral analysis that utilizes a combination of 

spectral processing and statistical tools to derive information 

on the component 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 in the impervious surfaces research area.

Task 2a—Subpixel Processing Techniques

For many years, satellite remote sensing and image processing techniques were limited by the spatial resolution, or 

ground sample distance of the picture element (pixel). 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 decompose the pixel into relative abundance of materials within it based on statistical analysis and spectral unmixing.

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 endmember spectra of known composition (Ji and Jensen, 1999; 

and Ward and others, 2000). The unmixing approach has been 

shown to be very successful when applied to high spectral 

resolution imaging or nonimaging data where subtle diagnostic 

absorption features largely determine the spectral characteristics of the data. These techniques have been also been extended 

to the mapping of impervious surfaces from Landsat imagery 

by utilizing samples of very high resolution imagery in conjunction with classification and regression tree (CART) statistical analyses. These subpixel mapping techniques can return 

data that is more accurate and at a finer spatial detail, than 

traditional classification techniques (Yang and others, 2003; 

Huang and Townshend, 2003).

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 much different 

spectral profiles than do natural surfaces. With the ability to 

potentially acquire and provide classification data that are 

more accurate measures of the composition of the basic pixel, 

subpixel processing of widely available satellite imagery could 

become an important new source of impervious-surface data.

Further, several organizations now regularly process 

subpixel impervious-surface data, such as the Mid Atlantic 

Regional Earth Science Applications Center (RESAC) at the 

University of Maryland. The USGS Eros Data Center (EDC) 

plans to release a national subpixel data set as part of the year 

2000 National Land Cover Dataset (NLCD) product. The 

potential accuracy and national availability of this data source 

made it an excellent candidate for testing in this study.

Subpixel image processing for impervious-surfaces is generally released in the form of an Arc/Info GRID file in which 

the cell value is the percentage of imperviousness calculated 

for that pixel. The GRID file is assigned color values by some 

percentage class of imperviousness.

The computation of total impervious cover from this type 

of data source is simple. After clipping the extent of the area 

Roads

Buildings

Parking Lots

Driveways

Sidewalks

Other

Figure 2. An example of heads-up digitizing of six classes 

of impervious-surface from rectified high-spatial-resolution 

imagery.
69

of interest, such as a watershed, from the large grid, the values 

from the value attribute table (.vat), which are the “value” and 

“count”, can be easily input into a spreadsheet for calculation 

of total imperviousness. Value represents the percentage of 

imperviousness and count represents the total number of occurrences in the data set. For example, the watershed boundary of 

Tuscarora Creek was used to clip out its extent from the larger 

impervious surface grid. The .vat table of the grid file lists the 

impervious percentage and the number of pixels that contain 

that percentage in the grid. The .vat can be directly input into a 

spreadsheet where TIA can easily be calculated using standard 

spreadsheet functions (see fig. 3).

Study Areas

Figure 3. An impervious-surface map of Tuscarora Creek, Virginia, derived using subpixel digital-image processing. The value attribute table from the impervious-surface grid is input directly into a spreadsheet where the total 

impervious area can be calculated.
70 An Assessment of Impervious-Surface Measurement Methods

Task 2b—The Land-Use Coefficient 

Methodology

The Land-Use Coefficient method may be used to determine the impervious surface area of a watershed (Capiella and 

Brown, 2001). Land-use generally differs from land-cover in 

two important aspects. First, land-use classification is based on 

the anthropogenic utilization of the land, not the ecological or 

biological function. Second, land-use is generally mapped in a 

vector, polygon form, as opposed to the raster-based model of 

most land-cover mapping. Determining land-use from remote 

sensing data has been a popular form of geographic analysis for 

many years, especially since the seminal paper by Anderson and 

others in 1976. This form of mapping involves the extraction 

of polygons of similar land-use from an image or cartographic 

base map. Impervious area of each land-use polygon is determined by multiplying the area of the polygon by a specific, 

previously determined coefficient that represents the average 

fractional percentage of impervious cover for a particular landuse (that is, industrial, low density residential). The sum of these 

areas for all the different land-use polygons is the impervious 

cover for the watershed. For example, table 1 lists the land-use 

classes and coefficients that were developed for the Chesapeake 

Bay Watershed (Capiella and Brown, 2001). 

Task 3—Calculation of Impervious Surface 

Area From Regional/National Synoptic 

Land-Cover Data Sources

The third task in this research is the determination of impervious area from a synoptic data source that incorporated the 

land-use coefficient methodology previously discussed. In this 

case, the National Land Cover Database (NLCD) was selected. 

Synoptic, national NLCD data is currently available for the 1990 

time frame and is being produced for the 2000 time frame. A 

complete description of the NLCD process and dataset can be 

found in Vogelmann and others (2001) and at the NLCD Web 

site. The computation of impervious area from land-cover offers 

two distinct advantages. First, land-cover can be developed from 

spectral satellite data and computed mostly from 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 country, conserving 

valuable resources that would be expended on data acquisition 

and processing steps if the other methods were used.

The use of coefficients for impervious-surfaces in this 

context implies that there are similarities in landscape structure that would make this method of computing impervious 

surfaces feasible. Deriving the coefficients is accomplished 

by some type of sampling technique, where samples of higher 

resolution data are used to determine an 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. Two different methods of computing TIA from NLCD land-cover by using the coefficient 

method were used. The details of these two techniques can be 

found in Center for Watershed Protection (1998), as implemented in the Arcview Extension ‘Attila’ (Ebert and Wade, 

2003 and Jennings and Jarnagin, 2003).

Results 

Task 1—Detailed Impervious-Surface Data 

Compilation

Task 1 assessed the individual contribution (percent) of 

roads and other classes of imperviousness to the watershed 

TIA. In all, six watersheds were mapped at the detailed, 

sub-meter resolution level and were temporally updated for 

five additional dates in time (to correlate with tasks 2 and 3) 

resulting in a total of 11 detailed GIS datasets of impervious 

cover. In each, six classes of impervious cover were mapped, 

including roads, buildings, parking lots, driveways, sidewalks, and other.

Overall, buildings led all other categories at 29.1 percent 

followed closely by roads at 28.3 percent. Parking lots were 

third at 24.6 percent. These three categories accounted for the 

great majority of TIA. Table 2 summarizes the area measurements for each category in acres, and table 3 summarizes the 

respective percentages of TIA. Figure 4 depicts graphically 

the cumulative percentages of each category.

Task 2a—Subpixel Processing

The availability of subpixel land-use and land-cover data 

is currently limited but will likely be widely available in the 

near future, as part of the NLCD year 2000 dataset. For this 

research, two separate subpixel datasets were acquired for the 

greater Washington, D.C., area. The first was developed by the 

Regional Science Applications Center (RESAC) at the University of Maryland and included 1990, 1996, and 2000 datasets. 

The second was developed by the USGS at the EROS Data 

Center for 2001 (NLCD, 2001).

The results are shown in table 4. In the Tuscarora Creek 

Watershed, both the RESAC and the EDC data compiled TIA 

to within 10 percent of the task 1 ground-reference value. For 

the Difficult Run Watershed however, both the RESAC and 

EDC datasets significantly under-reported the TIA.

Task 2b—Processing Using Land-Use 

Coefficients

Land-use maps and coefficients were utilized on the other 

four watersheds in task 2. Vector land-use maps were created 

and coefficients were applied. Results are shown in table 5.
71

Task 3—Calculation of Impervious-Surface 

Area From Regional/National Synoptic 

Land-Cover Data Sources

Task 3 of this process involved generating an estimate of 

TIA from land-cover data typically generated from satellite 

remote sensing data. Land-cover data, like land-use data, is a 

thematic representation of surface phenomenon but is based on 

the environmental cover or spectral-reflectance properties of the 

surface instead of an anthropogenic based utilization of land. 

In general terms, land-cover is generally less detailed than the 

type of land-use data used in task 2 and is generally mapped on 

a raster-based GIS model derived from the satellite imagery. 

It offers 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 NLCD 

data. Both methods were derived from the literature and both do 

essentially the same thing, which is to multiply a derived coefficient times the pixel area for that land-cover class. The difference 

lie in the methods used to determine the coefficients from the 

NLCD land-cover data. The method used by CWP uses a simple 

average, whereas the Jennings and Jarnagin (2003) method uses 

three sets of coefficients based on the level of development in 

the watershed. The two different methods are described in CWP 

(1998), as implemented in the ATTilA, Arcview extension (Ebert 

and Wade, 2003; and Jennings and Jarnagin, 2003). The results 

for both methods are summarized in table 6. 

Analysis and Discussion

Task 1—Detailed Impervious-Surface Data 

Compilation

The high-resolution compilation of impervious surfaces 

revealed the component features that make up functional 

Land-Use Category Land-Use

Code

Impervious

Cover Coefficients

Open Urban land 10 0.086

Residential – 2 acre l ot 11 0.106

Residential – 1 acre l ot 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 0.05

Cemeteries 35 0.083

Parks 36 0.125

Light Industrial 40 0.534

Commercial 50 0.722

Table 1. Land-use codes and coefficients for the Chesapeake Bay Watershed (Capiella and Brown, 

2001).

0.00

OVERALL PERCENT TOTA L BY

IMPERVIOUS AREA BY CLASS

29.1

9.0

28.3

3.2 1.8

24.6

50.00

45.00

40.00

35.00

30.00

25.00

20.00

15.00

10.00

5.00

0.00 Roads

Buildings

Driveways

Sidewalks

Parking Lots

Other

Figure 4. The percent of total impervious area as calculated from 11 

separate mappings of 6 watersheds. Buildings, roads, and parking 

lots account for the majority of total impervious area.

Analysis and Discussion
72 An Assessment of Impervious-Surface Measurement Methods

impervious-surface area. Roads, often getting the bulk of the 

“blame” for impervious area, are only one of the 3 major components of TIA that also include buildings and parking lots. 

The contribution of road impervious area (RIA) to overall TIA 

ranged from a low of 20.8 percent in 1992 for the Black River 

Watershed to a high of 35.6 percent in the 1998 Tuscarora 

Creek Watershed. Driveways, often unaccounted for in impervious-surface research, made up an average of 9 percent of 

the total impervious-surface area. Further, this data shows that 

even very minor classes of impervious cover are important. 

Very few studies on impervious-surface area take into account 

sidewalks, patios and tennis courts. Yet we can clearly see that 

in this study, these are responsible for 5 percent of TIA. 

In five datasets, in the two watersheds in the suburban 

Washington, D.C., area, roads were the leading class of 

impervious cover. In the Oak Creek Watershed, the large airport/Air Force base was the dominant feature in both years of 

analysis. In the highly industrialized Black River Watershed, 

the parking lots were the leading class of imperviousness 

(41.4 percent). In Wares Creek, Florida, and North Walnut 

Creek, Iowa, buildings were the largest class of impervious 

cover, as well as the leading IS component overall. 

Further, the relationship of RIA to TIA appears to correlate with the level of development of the watershed. 

Figure 5 shows the linear relationship between RIA and 

TIA. Recognizing TIA as a general indicator of the level of 

Impervious-surface area

(In acres)

Watershe d

Image

Source

Date Total

Ar ea

WatershedRoads Build -

ings

DrivewaysSidewalks

Parking

Lots

Other Airport

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

Difficult Ru n 1997 37179.4 6516.1 2043.6 1955.1 786. 0 226.2 1295.2 210.1 N/A

Difficult Ru n 1988 37179.4 5786.0 1877.4 1691.0 686.9 187.6 1150.7 192.5 N/A

N. W alnut

Creek 2001 8691.7 2201.1 578.8 727.1 222.1 92.6 563.5 16.9 N/A

N. W alnut

Creek 1990 8691.7 1716.0 500.3 573.5 193.5 75.9 358.2 14.6 N/A

Oak Cree k 2000 14042.1 2421.9 607.7 521.3 152.9 86.8 401.7 24.5 626.8

Oak Cree k 1990 14042.1 1873.6 525.8 307.2 109.1 56.9 236.3 11.6 626.8

2000 9226.2 1165.1 409.4 279.4 128.0 66.9 267.4 14.0 N/A

Tuscarora

Creek 1998 9226.2 984.2 350.8 252.2 99.6 50.2 219.6 11.7 N/A

1988 9226.2 806.8 282.6 203.2 86.8 38.2 188.8 7.2 N/A

Wares

Creek 2001 5091.4 2311.1 560.9 898.5 146.4 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

Tuscarora

Creek

Tuscarora

Creek

Table 2. The impervious-surface area measurements for all watersheds/dates used in this study.
73

Watershed and Component Impervious Surface Percentages

Watershed

Image

Source

Date

Watershed Road Bldg Drive-way Side-walk Parking

Lots

Other Airport

DW, SW,

and Other

Combined

Black

River 1992 35.19 20.76 30.90 4.14 1.84 41.44 0.92 6.9

Difficult

Run 1997 17.53 31.36 30.00 12.06 3.47 19.88 3.22 18.75

Difficult

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 15.07

N. 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 10.9

Oak 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 17.93

Tuscarora

Creek 1998 10.67 35.64 25.63 10.12 5.10 22.31 1.19 16.41

Tuscarora

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

Component

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

(DW) (SW)

Table 3. Percent (PCT) contribution of each component to the total impervious area for each watershed per year.

Analysis and Discussion

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

Urbanization Increase

Watershed

Impervious Surface Area

per

Watershed Total Area

Road Impervious Area

per

Total Impervious Area

Percent Impervious Surface

ROAD CONTRIBUTION CHANGE AS 

WATERSHED URBANIZATION INCREASES

Figure 5. The relationship between road impervious area to watershed impervious area. As the watershed becomes more developed 

(that is, increases in the percentage of impervious area), the contribution of roads to the total impervious area declines.

human development in a watershed, we can see that at lower 

percentages and TIA, RIA is around 35 percent. But as the 

watershed becomes more and more developed, the contribution of roads consistently declines, showing that there a 

potential issue in understanding the age and life-cycle of 

developed areas. 

Task 2—Subpixel and Land-Use Coefficient 

Processing

The subpixel image processing produced mixed results. 

Although both methods provided good estimates of TIA in 

Tuscarora Creek, both methods significantly underestimated 

TIA in Difficult Run. This may be due to the extensive tree 

cover and older-large lot development in some parts of this 

watershed. 

The Land-use coefficient method produced results generally within 10 percent of task 1 value for TIA. 
74 An Assessment of Impervious-Surface Measurement Methods

Watershed

Task 1

Reference

Data Ima ge

Source D ate

Task 2 a

Subpixel Data

Source / Date

Task 1

Reference

Data

TIA

Task 2 a

Subpixel

TIA

Difference

Betwe en

Task 1 and

Task 2 a

TIA

Percent

Agreement

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

1997 EDC / 2000 6516.1 3866.5 2649.6 59.3

Difficult

Run

Watershed

Image

Source

Date

Task 1

Reference

Data TIA

Task 2b

Land-Use

Coefficients

TIA

Difference Btwn.

Reference and

Land-Use

Coefficients

Percent

Agreement

w/Reference

Data

Black

River

1992 6054.45 5866.37 188.08 96.89

North

Walnut

Creek

2001 2201.13 2045.37 55.76 92.92

Oak Creek 2000 421.87 2585.84 163.97 93.23

Wares

Creek

2001 2311.09 2062.19 248.90 89.23

Task

Reference

Data

Center for Watershed Protection

Method

Jennings and Jamagin Method

Watershed Image

Source

Date

Total

Area

TIA PCT

TIA

TIA PCT

IA

DIFF

Between

Reference

Data

PCT

AGR

TIA PCT

IA

Black

River 1992 17207.1 605 4 .5 35.2 2735.0 45.2 10.0 90.0 601 6 .4 35.0 -0.2 99.8

Difficult

Run 1988 37179.4 578 6 .0 15.6 955.9 16.5 1.0 99.0 507 2 .0 13.6 -1.9 98.1

N. Walnut

Creek 1990 8691.7 1716.0 19.7 463.0 27.0 7.2 92.8 231 0 .8 26.6 6.8 93.2

Oak

Creek 1990 14042.1 187 3 .7 13.3 222.0 11.9 -1.5 98.5 105 2 .2 7.5 -5.8 94.2

Tuscarora

Creek 1988 9226.17 806.8 8.7 71.6 8.9 0.1 99.9 531.3 5.8 -3.0 97.0

Wares

Creek 2001 509 1 .4 231 1.1 45.4 1198.8 51.9 6.5 93.5 207 3 .5 40.7 -4.7 95.3

DIFF

Between

Reference

Data

PCT

AGR

Table 4. Subpixel image-processing results.

[TIA, total impervious area]

Table 5. Land-use coefficients (Capiella and Brown, 2001) results.

[TIA, total impervious area]

Table 6. Comparison of total impervious area percentage calculations from NLCD92 data (in acres) (Center for Watershed Protection, 1998; and 

Jennings and Jarnagin, 2003).

[TIA, total impervious area; IA, impervious area; PCT, percent; DIFF, difference; AGR, agriculture]
75

Task 3—Calculation of Impervious-Surface 

Area From Regional/National Synoptic LandCover Data Sources

The two land-cover coefficients methods resulted in 

some interesting patterns. The CWP method over-estimated 

TIA in 5 of 6 cases. The Jennings and Jarnagin method 

under-estimated TIA in 5 of 6 cases. Each method calculated 

TIA very accurately in one watershed. In general, the CWP 

method tends to overestimate, whereas the Jennings and 

Jarnagin method tends to under-estimate. One of the main 

drawbacks of any NLCD-based method will be sensitivity to 

misclassification of the urban-class pixels in the dataset.

Conclusions

This study documented the component composition of 

impervious area in six selected watersheds. Although roads, 

buildings, and parking lots, make up the majority of impervious area, driveways, sidewalks, and other areas still make 

up a significant 14 percent of TIA and should not be ignored 

in calculating TIA. We also found that four of the common 

methods of computing impervious area show significant 

variability when measured against high-resolution ground 

reference information. Overall, the four methods tested 

generally produced an estimate of TIA within 10 percent of 

the ground-reference information. Figure 6 shows a simple 

References

Estimated Impervious Surface %

y = 0.8461x + 3.6213

r2 = 0.8894

Ground Reference Impervious Surface %

Figure 6. A regression of the estimated impervious-surface percentage 

against the ground-reference impervious-surface percentage. There 

were 24 observations. The ground-reference impervious surfaces data 

was derived by detailed digitizing from high-spatial-resolution imagery. 

The estimated impervious-surface percentage was derived from four 

common methods of estimating impervious-surface area.

regression of the derived versus reference impervious area 

values for all 4 methods tested. There were 24 independent 

observations and an r2 value of 0.89. Unfortunately 10 percent variability in the accuracy of this statistic is nontrivial 

when 25 percent impervious area is the absolute upper end 

of a stream’s ability to support aquatic life. It is clear that 

there exists a need for further research in this area.

Additional high spatial resolution imagery research is 

needed in the area of impervious surfaces to:

(1) Quantify the component makeup of impervious surfaces, 

especially with respect to regional or morphological 

characteristics of the watershed, and

(2) Refine other commonly used estimation methods to known 

and quantifiable levels of accuracy and variability.

Acknowledgment

The United States Environmental Protection Agency 

through its Office of Research and Development partially 

funded and collaborated in the research described here under 

Interagency Agreement DW14939389-01-0 to the United 

States Geological Survey. 

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78 An Assessment of Impervious-Surface Measurement Methods
79

Comparing Impervious-Cover Densities on the Basis 

of Land-Use Types Between Sister Cities on the 

United States/Mexico Border

By Jean Parcher1

1

U.S. Geological Survey, 8027 Exchange Dr., Austin, TX 78754.

Introduction

As the world’s population moves from a rural to a more 

urban environment (14 percent urban in 1900, almost 50 percent urban in 1990), man’s impact on the hydrologic cycle 

becomes more pronounced (Douglas, 1994). The hydrologic 

impacts of urban growth include increased surface runoff, 

higher flood peaks, shorter runoff lag time, declined base 

flows, and impaired water quality (Savini and Kammerer, 

1961; Shaw, 1988; Harbor, 1994; Arnold and Gibbons, 1996; 

Leopold, 1997). Increased impervious-cover due to urbanization impedes through-flow infiltration rates, escalates the 

probability of flooding, and accelerates the transport of contaminants into both ground and surface water flows (Leopold, 

1968; McCutcheon and others, 1992; Bhaduri and others, 

2000). Rapid urban growth during the 20th century has shifted 

much of the world’s population from rural farms to integrated 

population centers. Reliable methods are needed both to monitor current anthropogenic modifications to landscapes related 

to urban growth and to assess their hydrologic effects on the 

natural environment. Remotely sensed satellite data provide 

the capacity to analyze the relationship of these land-use and 

land-cover changes in a timely manner at both the regional and 

local level by depicting landscape data at various spatial and 

temporal scales (Ridd, 1995).

Few hydrologic studies have compared Mexican and 

American urban morphologies and their effects on surface 

runoff and flood management. This research examined the 

differences in urban morphology of cities in developed and 

developing countries by comparing the density of imperviouscover for different urban land-uses classes within the sister 

cities of Brownsville, Texas, and Matamoros, Tamaulipas, 

Mexico. The selection of an area spanning the U.S./Mexican 

border provides an opportunity to examine the differences 

in first and third world urbanization, while holding constant 

similar physical environments such as topography, vegetation, 

soils, and climate characteristics. 

Cities in developing countries tend to be overcrowded, 

densely concentrated in structure, and characterized by a proliferation of substandard housing located in marginal areas, 

increasing their vulnerability to natural disasters (Chardon, 

2000; Lopez and others, 2001). In contrast, cities in the United 

States tend to sprawl in leapfrog, low-density developments 

(Johnson, 2001). The spatial diffusion of urban sprawl contributes to inefficient use of land resources for human mobility, 

larger amount of ecosystem modifications, greater dispersal of 

pollution, increased storm-water runoff and greater flood risks 

(Forman, 1995; Johnson, 2001; Yeh and Li, 2001). The shape 

of urban areas, their density, and their growth process significantly affect key hydrologic processes (Forman, 1995). 

Surface runoff parameters used in hydrologic modeling applications are typically estimated from the density of 

impervious-cover and hydrologic soil properties in a watershed. Common values for these parameters are based on curve 

numbers developed by the U.S. Department of Agriculture, 

Natural Resource Conservation Service (NRCS)2 (Maidment, 

1993; Haan and others, 1994). Parameters used to calculate 

curve-number values include hydrologic drainage properties of the soil group (retention), land-use or land-cover type 

(initial abstraction), and average percent of impervious-cover 

(abstraction). Impervious-cover estimates are calculated from 

each urban land-use based on national estimates. 

 Various methods have been used to estimate percentages of impervious-cover in a watershed basin. Since most 

impervious-cover is characteristic of human environments, 

one method is to map population densities derived from U.S. 

Census data to estimate impervious-cover for the temporal 

time period of census data collection (Stankowski, 1972; Bird, 

2002b). Combining multiple datasets, such as population density from block-level census data, National Land-cover Data 

classes for commercial, manufacturing, and mining, and major 

U.S. highway road networks provided greater accuracy (Bird 

2002a). Remotely sensed methods include classifying the fundamental physical features in urban areas that can be derived 

from imagery, based on the conceptual vegetation, impervious-cover, and soil (V-I-S) model for ecosystem analysis 

simplicity (Ridd, 1995, Hung, 2002). The simplicity of the triangle based V-I-S model facilitates quantifying the changes to 

2

The U.S. Department of Agriculture, Natural Resources Conservation 

Service was formally called the Soil Conservation Service. Curve numbers are 

defined in the Soil Conservation Service publication TR-55. 
80 Comparing Impervious-Cover Densities on the Basis of Land-Use Types Between Sister Cities

surface materials and their relationship to urban morphology 

due to urban growth (Ward, 2000). Yet, classification of impervious surfaces with medium-resolution imagery rarely isolates 

pure pixel response due to the linear patterns of many impervious features. For the National Land-Cover Dataset (NLCD) 

2002, Yang (2003) mapped subpixel percent imperviousness 

of the medium-resolution Landsat imagery by developing 

a regression- tree algorithm from manually classifying data 

using high-resolution imagery over a sample area. The rule 

based classifier was applied to the entire study area to classify 

subpixel imperviousness per 30 meter pixel. 

In rapidly growing areas, such as near the U.S./Mexico 

border, urban growth rates quickly increase the amount of 

impervious-cover, demanding more frequent updates and(or) 

current sources of geospatial data. Satellite imagery, with 

repeat cycles from daily to monthly, provide a timely data 

source to derive impervious-cover estimates to aid city flood 

mitigation and infrastructure planning. In Brownsville and 

Matamoros, infrequent but intense tropical storms and hurricanes cause major water damage from severe ponding in the 

flat topography and from overbank runoff in the agricultural 

canals and natural drainage areas.

The main objective of this study was to test NRCS curve 

number (CN) assumptions of percent impervious surface 

and in doing so develop improved estimates of imperviouscover for urban land-use types for cities in both a developed 

country (United States) and a developing country (Mexico). 

This research compares the impervious features in the different urban environments using land-use and land-cover data 

collected from medium-resolution Landsat 7 ETM+ satellite 

imagery and impervious surface data collected from high-resolution Quickbird 2 imagery for the U.S./Mexico Border sister 

cities of Brownsville and Matamoros. For each city, the density of impervious-cover by land-use type was quantified and 

compared to the conventional infiltration parameters used for 

hydrologic modeling. Results can be used for flood planning 

in developed and developing countries. The comparison of 

NRCS curve numbers with impervious-cover data provides an 

insight into the degree of correlation between the typical urban 

structure of both first and third world cities.

Study Area

The Brownsville/Matamoros sister city metropolitan area 

is located in the lower Rio Grande Valley near the terminus of 

the Rio Grande River which separates Cameron County, Texas 

from Tamaulipas, Mexico. The landscape is characterized by 

a wide deltaic floodplain, interspersed with abandoned river 

channel meanders. The historical meandering pattern of the 

Rio Grande in its delta caused alternating patterns of erosion 

and deposition leaving abandoned channel remnants called 

“resacas” in Brownsville and “meanderos” in Matamoros 

(Brown and others, 1980: White and others, 1986). As a wetland, resacas or meanderos play an important role in collecting 

and storing local flood runoff (Federal Emergency Management Agency, 1999). On the U.S. side of the border, Brownsville, has left many resacas in their natural form to provide 

multiple benefits, such as collection and storage of local storm 

runoff, conveyance channels for Rio Grande waters, irrigation 

and drinking water sources, wildlife habitat, and recreational 

attractions. In contrast, the rapid urban growth experienced 

by Matamoros has resulted in paving over all but two of their 

natural resacas (Mendoza, 2001). Therefore, flood control in 

Matamoros is limited to agricultural and sewage canals that 

lack proper maintenance for effective surface runoff. 

During the past 30 years, the population of Matamoros 

tripled, while the population of Brownsville doubled (Instituto 

Nacional de Estadística, Geografía, e Informática, 2001; U.S. 

Census Bureau, 2001). The two cities exhibit major differences in their population density, complexity of road networks, 

and utility infrastructure. These three variables are interrelated. 

As public spending increases to construct efficient road networks and expand water, sewer, and electrical infrastructure, it 

becomes easier for the population to decentralize. A complex 

network of roads in Brownsville provides an easy commute 

between residences and businesses. In contrast, basic infrastructure in Matamoros is concentrated near the older, most 

central part of the city due to a lack of public spending. Thus, 

it is more convenient for higher income residents to remain 

centralized and reduces incentives for lower density urban 

growth and sprawl. Differences in land-use and urban-spatial 

patterns for the two cities are highly related to the availability of efficient transportation networks and funding for basic 

infrastructure (Alacron, 2000). 

These differences in urban patterns and surface hydrologic flows result in more serious flood problems for Matamoros than for Brownsville. Since the construction of the 

Falcon and Amistad dams upstream from these sister cities, 

the Rio Grande River that separates them is no longer the 

primary flood hazard. Infrequent but intense tropical storms 

and hurricanes are more likely to cause major water damage 

from severe ponding in the flat topography and from overbank 

runoff in the agricultural canals and natural drainage areas. 

Unchecked urban sprawl, rapid growth, and inadequate landuse planning combine to increase the human vulnerability to 

flood hazards. 

Methods 

LULC Classification Procedures

To derive the general-purpose land-use and land-cover, 

supervised classification was performed on the Landsat 7 

ETM+ scene using the National Land-cover Classification 

(NLCD) schema. The NLCD schema is a national land-use 

and land-cover (LULC) standard that also contains estimates 

for impervious-cover for each urban land-use class. The geometrically corrected Landsat 7 ETM+ scene was subsetted 

into a 141 square kilometer Matamoros urban area and a 310 

square kilometer Brownsville urban area to reduce spectral 
81

confusion between training data signatures when applying 

classification procedures to both urban and rural areas. For 

the classification step, the two cities were separated because 

the residential patterns were distinct between Matamoros and 

Brownsville.

The Landsat 7 ETM+ scene was classified into fifteen 

LULC classes using the maximum likelihood supervised classification method. On the basis of field work and photo interpretation, 21 separate training classes were collected for the 

U.S. urban area and 20 training classes for the Mexican urban 

area, using the Rio Grande/Rio Bravo as the International 

Border. To compensate for similar spectral response patterns 

between certain classes, probability thresholds for inclusion 

into each class were set. After the classification was run, 

manual editing was performed to modify large misclassified 

areas that had similar spectral responses and needed ancillary 

information to determine the correct land-use class. To reduce 

the spectral output, a post classification clumping process was 

run on selected classes using a three by three pixel window. 

Finally the classes were combined into the final classification 

scheme, with both the Brownsville and Matamoros merged 

into one classification image (fig. 1). The overall classification 

accuracy was 78.11 percent derived from an accuracy assessment of 466 randomly selected points referenced from two 

higher resolution images, SPOT 5 and Digital Globe Quickbird imagery. 

Impervious-Cover Classification 

Procedures

The impervious surface classification was extracted from 

a high-resolution Digital Globe Quickbird-2 satellite image 

date April 29, 2002, using the vegetation, impervious-cover, 

and soil V-I-S model derived by Ridd, 1995. The image was 

subsetted into distinct Brownsville and Matamoros urban 

areas, with separate training data defined for each country. 

Because impervious-cover was the significant feature to map, 

impervious-cover training classes consisted of light and dark 

paved roads, various types of roofs, cars, and trucks. The vegetation and soils training classes were aggregated according to 

their light and dark spectral responses.

A limiting factor in the supervised classification was the 

small number of bands of data (4), all clustered in the visible 

and near infrared spectral areas, which made it difficult to 

distinguish between similar spectral responses of different features. Water features were masked off using ancillary vector 

and polygon data because they were difficult to classify due to 

sun glint and growths of hydrilla and hyacinth in the river. To 

provide more spectral response information at a high-resolution, LIDAR (light detection and ranging) intensity data collected from a January 2003 LIDAR survey for the Brownsville 

area was resampled from 1 meter to 2.4 meters and merged 

with the Quickbird imagery to perform the supervised classification. This LIDAR intensity data greatly reduced confusion 

between fallow areas and dark roofs, and dirt roads and white 

roofs. Unfortunately, the LIDAR data did not cover Matamoros. The Matamoros impervious classification required extensive hand editing to correct for the confusion between fallow 

and impervious roads, fallow and dark roofs, and dirt roads 

and white roofs. Finally, both the Brownsville and Matamoros 

results were merged together into one classification. Overall 

classification accuracy resulted in 93.3 percent for Brownsville 

and 89.7 percent for Matamoros, using a randomly selected 

sample of 325 points referenced by the panchromatic Quickbird image. 

Calculating Impervious-cover for Each LULC 

Class

Calculating the percentage of impervious surfaces per 

land-use class in each country was achieved by spatially intersecting the impervious classification layer with each of the 

Figure 1. Land-use and land-cover classification results for Brownsville and Matamoros urban areas. High Res., high density residential; 

Med Res., medium density residential; Low Res., low density residential; Sh, shrub.

Methods
82 Comparing Impervious-Cover Densities on the Basis of Land-Use Types Between Sister Cities

LULC classes. Using a raster calculator process, the percent 

of impervious pixels was obtained by dividing the number 

of impervious pixels by the total number of pixels from that 

LULC class. Data were collected separately for each city.

Results

The final results are examined in several ways. First a 

comparison of percent impervious surfaces and impervious 

aggregated with bare soil between the two cities, and then a 

comparison with the NRCS CN and NLCD impervious surface estimates.

The final results demonstrate a higher percentage of 

impervious surfaces for commercial, low-density residential, 

and open space in Brownsville than Matamoros (table 1). For 

these two cities, impervious-cover estimates are very similar 

for high- density residential. Matamoros has higher rates of 

impervious-cover for industrial and medium-density residential and exhibits comparatively very high amounts of bare soil 

or dirt roads for all classes. For medium- and low-density residential and open space, the amount of bare soil or dirt roads in 

Matamoros almost equals or exceeds the amount of impervious surfaces. This result may be an outcome of city planning 

in Matamoros, where streets are paved only when all residents 

along that street block contribute an equal amount to match the 

city funds to pave the street. 

The high percentage of bare soils in Matamoros requires 

further analysis of their effects on the urban structure. Normally, urban areas with a high composition of dirt surfaces 

are undergoing transition to a different land-use category 

(Ridd, 1995). Field observations in Brownsville supported 

this theory. During fieldwork in Matamoros, evidence showed 

that many urban streets were not paved due to a lack of infrastructure funding. The compacted soil on these unpaved urban 

streets resulted in muddy quagmires after precipitation, due to 

their extremely poor drainage. Compacted bare soils have very 

low infiltration rates, which could result in runoff rates near 

precipitation rates (Haan and others, 1994). For Matamoros, 

aggregating this variable into the impervious surface cover 

may be justified for hydrologic applications. 

For the commercial land-use class, the density of impervious surfaces in Brownsville is higher (74 percent) than Matamoros (63 percent), yet, when impervious and bare soil classes 

are aggregated, the totals are similar at 78 percent for Brownsville and 76 percent for Matamoros. In Brownsville there are 

several nuclei of commercial areas that include Sunrise Valley 

shopping mall and strip malls with large parking areas. The 

commercial areas in Matamoros are principally concentrated 

near the central business district, with older, narrow streets. 

For the industrial class, the density of impervious-cover 

was much lower than the NRCS curve number and NLCD 

estimates. Matamoros was slightly higher at 40 percent, but 

when aggregated with bare soil, the density at 68 percent 

still did not reach the NLCD estimate of 72 percent. The 

LULC classification for industry is very broad and does not 

distinguish between types of industry, such as a manufacturing plant versus a software development company, yet the 

landscapes around the two types of industry are very distinct. 

Figure 2. Vegetation, impervious-cover, and soil classification results 

for Brownsville and Matamoros.

Brownsville Impervious Soil Vegetation

Commercial 74% 4.65% 19.03%

Industrial 37.90% 10.10% 45.65%

High Density 46.05% 9.22% 44.03%

Medium Density 28.70% 7.18% 63%

Low Density 15.94% 6.15% 76.40%

Open Space 5.30% 1.89% 90.80%

Matamoros Impervious Soil Vegetation

Commercial 63.04% 12.98% 23.50%

Industrial 40.31% 25.35% 32.07%

High Density 45.89% 25.71% 28.32%

Medium Density 29.98% 28.28% 41.55%

Low Density 8.60% 18.41% 72.87%

Open Space 4.22% 26% 69.51%

Table 1. Comparison between urban land-use types 

in Brownsville and Matamoros and their surface 

components.
83

For Brownsville, the low 38 percent density of impervious 

surfaces show the changing landscape of industrial areas 

from a 1950s style manufacturing plant to a mid-1990s style 

computer-manufacturing campus with athletic fields and landscaped areas. In Mexico, the lower impervious values may 

reflect the amount of unpaved roads and parking areas.

As expected the residential urban classes in Brownsville 

and Matamoros demonstrated decreased amounts of impervious surfaces as the density of housing decreased. The majority of residential areas in Matamoros were classified as high 

density with an average of 46 percent impervious-cover. When 

bare soil surfaces were aggregated with impervious-cover to 

reach 78 percent in Matamoros, this value exceeded the NRCS 

CN for high-density residential. In Matamoros, high-density 

residential is located in both the newer and older areas. The 

large amount of bare soils in the newer areas may be due 

to areas in transition, whereas in the established neighborhoods the bare soil is due to lack of infrastructure. In contrast, 

Brownsville’s high-density residential, including apartment 

buildings, exhibited almost equal amounts of vegetation (44 

percent) and impervious surfaces (46 percent). The use of 

Quickbird imagery taken during the leaf on season may have 

skewed the density of impervious surfaces due to tree canopies 

obscuring pavement in urban areas.

The majority of residential areas in Brownsville were 

classified as medium density areas, reflecting a suburban landscape. In Brownsville, the percentage of vegetation for this 

class was high at 69 percent whereas impervious surfaces were 

only 29 percent. In Matamoros, a large amount of medium 

density residential areas are located in the older, wealthier portions of the city, near the river and central business district. 

The biophysical landscape for this class was broken down to 

30 percent impervious, 42 percent vegetation, and 28 percent 

dirt surfaces exhibiting wider transportation infrastructure than 

the high-density residential.

Next, the results for the two cities are compared with the 

average amount of impervious-cover for similar classes from 

the NRCS curve number tables and the NLCD. For the NRCS 

curve number table, the medium-density residential impervious percentage was averaged out from the middle ranges of 

land lot sizes. The NLCD impervious levels are based on 

high-density (including commercial, industrial, and residential) and medium and low density residential areas. Therefore, 

impervious-cover percentages were roughly estimated for the 

residential land-use classes for the NLCD for comparison purposes. Figure 3 shows the relationships between percentages 

of impervious levels:

As a final comparison (fig. 4) examines impervious 

features aggregated with bare soils with the percentage of 

impervious features for the LULC classification schemes. This 

method of comparison significantly changes the density of 

impervious surfaces for Matamoros. When bare-soil density 

is added to impervious density, Matamoros exhibits a greater 

density of impervious surfaces than Brownville in all classes 

except commercial. This aggregation greatly modifies the 

comparison with the NLCD and NRCS impervious estimates. 

Now Matamoros exceeds the NLCD and NRCS CN estimates 

for all classes except commercial and industrial. Brownsville 

exhibits similar results as Matamoros, except does not exceed 

the NRCS CN for high density residential. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Commercial Industrial High Density Medium

Density

Low Density Open Space

Brownsville

Matamoros

CN

NLCD

Percentage of Impervious Cover

Land Use

Figure 3. Comparison of impervious surfaces in Brownsville and Matamoros with Natural Resource Conservation 

Service curve number (CN) and National Land-Cover Dataset (NLCD) impervious-cover estimates.

Results
84 Comparing Impervious-Cover Densities on the Basis of Land-Use Types Between Sister Cities

Discussion

The differences in urban structure between Mexican and 

American cities may require different scales of satellite imagery for similar classification schemas (Parcher, 2003). Due 

to the dense urban form and smaller spatial patterns in Matamoros, medium-resolution 30-meter pixel satellite imagery 

may not be able to distinguish the same level of hierarchical 

LULC features as sufficiently as it would in Brownsville. The 

selection of separate training data for each country attempted 

to compensate for some of these distinctions. Variations in predominate types of residential areas between the two countries, 

with high- density most prevalent in Matamoros and mediumdensity in Brownsville, reflect the unique spatial patterns 

between the two cities. 

The variations in the physical composition of urban features between the two cities was reflected in the vegetation, 

impervious, and bare-soil classification results. For hydrologic 

modeling applications, the reliability of aggregating bare soil 

with impervious surface data would depend on the soil type 

of the dirt areas, their compaction, and relative moisture. If 

the dirt areas are well compacted, and the hydrological soil 

type is of poor drainage, then these areas may be very similar 

to impervious features. In developing countries with large 

unpaved urban areas, the V-I-S classification model provides 

critical biophysical data needed for both hydrologic models 

and urban ecosystem analysis. In more affluent countries with 

enforced zoning laws, estimates of impervious-cover, vegetation, and bare-soils from land-use and land-cover data may be 

sufficient for urban hydrology models. 

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available data: EPA peer reviewed publication.

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problems—estimating impervious area accurately and 

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the global vulnerability in urban area—Case of Manizales, 

Colombian Andes: GeoJournal, v. 49, p. 197-212. 

Douglas, Ian, 1994, Human settlements, in Changes in landFigure 4. Comparison of impervious-cover aggregated with bare soil for Brownsville and Matamoros with Natural 

Resource Conservation Service curve number (CN) and National Land-Cover Dataset (NLCD) estimates.

0%

10%

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30%

40%

50%

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90%

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Density

Low Density Open Space

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Matamoros

CN

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Land Use
85

use and land-cover—A global perspective: Press Syndicate 

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Flood insurance study—Cameron County, Texas, 

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Emilio, 2001, predicting land-cover and land-use change in 

the urban fringe—A case in Morelia, Mexico: Landscape 

and Urban Planning, v. 55, p. 271-285.

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Environmental modeling with GIS: Oxford University 

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1992. p. 11.1-11.73.

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Matamoros.

Parcher, J.W., 2003, remote sensing methods for estimating 

impervious cover for hydrologic modeling applications—a 

case study of the Brownsville/Matamoros urban area: 

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surface-soil) model for urban ecosystem analysis through 

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2185.

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growth in rapidly urbanizing areas using remotely sensed 

data: Professional Geographer, v. 52. no. 3, p. 371-386.

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Littleton, T.G., McGowen, J.H., Nance, H.S., Shemedes, 

K.E., 1986, Submerged lands of Texas, BrownsvilleHarlingen area—Sediments, Geochemistry, Benthic 

Macroinvertebrates, and Associated Wetlands: Bureau of 

Economic Geology, University of Texas, Austin, 138 p.

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and monitoring of urban sprawl in a rapidly growing 

region using entropy: Photogrammetric Engineering 

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87

Introduction

Brownfields are abandoned, idled, or underutilized properties where expansion or redevelopment is complicated by real 

or perceived environmental contamination (United States Environmental Protection Agency, 2004). Most brownfields sites are 

located in urban, commercial or industrial areas and are often 

prime locations for development. Because “perceptions” of a 

pollution problem can be extremely difficult to identify, GIS 

technology offers a potentially significant tool in the inventory 

of potential brownfields sites. In the field assessment process, it 

is visually difficult to determine whether a site is contaminated 

or not. However, with the application of particular data in the 

geospatial analysis, this technology can identify sites that have a 

history or association with a past pollution problem that may be 

appropriately defined as brownfields. In addition to freeing up 

resources, more efficient identification and inventory of possible 

brownfields sites will help any community pinpoint other properties that have a potential for economic redevelopment. 

This study represents geographic information systems 

(GIS) based spatial analysis that can provide a reasonable inventory of potential brownfields properties over a city-wide area. 

In cooperation with United States Environmental Protection 

Agency (USEPA) Region 7, Polk County, Drake University, 

and the City of Des Moines, Iowa, we utilized field data, maps, 

existing environmental information, remotely sensed data, and 

tax records in a GIS overlay and analysis process to identify 

potential brownfields redevelopment properties. The results can 

save funds that can be applied in the later steps of redeveloping 

the brownfields or in other urban revitalization processes.

Background

The Small Business Liability Relief and Brownfields 

Revitalization Act (Public Law 107-118, http://www.epa.

gov/brownfields/sblrbra.htm) was signed into law on January 

11, 2002. It defines brownfields sites as “...real property, the 

expansion, redevelopment, or reuse of which may be complicated by the presence or potential presence of a hazardous 

substance, pollutant, or contaminant.” Many once thriving 

industrial and commercial properties throughout the United 

States have been abandoned. Some are contaminated from 

their former uses. Uncertainties over liability issues have 

caused lenders, investors, and developers to ignore the redevelopment potential of these areas in favor of pristine areas, 

or “greenfields.” The result has been blighted areas with abandoned industrial and commercial facilities that create safety 

and health risks for residents, precipitate disinvestments, and 

create areas of “urban blight.” 

USEPA’s brownfields program is designed to empower 

States, communities, and other stakeholders in economic 

development to work together in a timely manner to assess, 

safely clean up, and return abandoned or underutilized properties to productive use. It provides funding in the form of 

grants to (1) assess brownfields sites and to test cleanup and 

redevelopment models; (2) fund job training pilot programs 

that provide training for residents of communities affected 

by brownfields to facilitate cleanup of brownfields sites and 

prepare trainees for future employment in the environmental 

field; (3) capitalize cleanup revolving loan-fund programs to 

make loans for the environmental cleanup of brownfields; and 

(4) for direct cleanup of eligible brownfields sites (USEPA, 

2003b). This will assist the return of vacant/underutilized 

properties to productive use, helping to curb sprawl by 

enabling reuse of urban resources (Ellman, 1997; Gernstein, 

2002).

One of the key information requirements in the successful application of a regional brownfields program is the ability to identify and inventory all potential brownfields sites in 

a given metropolitan area. Current methods are often expensive, time consuming, and unreliable. One example of identifying brownfields sites, applied by the City of Des Moines, 

was by selecting large areas in the city that were more industrialized/commercialized or located in the heart of the city 

then driving these selected areas and conducting windshield 

surveys. During these surveys such information as the following were documented: company name, presence of for 

sale sign, what type of business it was, and whether it appears 

to be abandoned or not, as well as two to three photographs 

(Drake, 2001). However, not only is driving a car around for 

The Use of Spatial Analytical Techniques

to Identify Potential Brownfields Sites

1 U.S. Geological Survey, Reston, Virginia, jtilley@usgs.gov.

2

U.S. Environmental Protection Agency, Des Moines, Iowa, paul.robin@

epa.gov. 3

U.S. Environmental Protection Agency, Reston, Virginia, slonecker.t@epa.

gov. 4

City of Des Moines, Des Moines, Iowa, eawalkowiak@dmgov.org.

By Janet S. Tilley1, Robin Paul2, Terrence Slonecker3, and Ellen Walkowiak4
88 The Use of Spatial Analytical Techniques to Identify Potential Brownfields Sites

such surveys time consuming and expensive, but this method 

is potentially dangerous to the individuals conducting the surveys. Another example of a process of identifying brownfields 

sites is the designation of targeted redevelopment areas, or 

“enterprise zones,” as is the case with the City of Des Moines 

(Des Moines, 2004; USEPA, 2003a). Although this approach 

benefits the near-term goals of the city’s redevelopment 

plan, properties outside of the selected area, with excellent 

brownfields potential, may be overlooked. An objective and 

repeatable method for an area-wide inventory of potential 

brownfields sites could be an important part of a long term 

brownfields-program redevelopment strategy.

Research Partners

The Role of the U.S. Geological Survey

Geographic Analysis and Monitoring (GAM) Program 

scientists with United States Geological Survey (USGS) 

conduct geographic assessments of land surface change to 

improve our understanding of the rates, causes, and consequences of natural events, as well as human-induced processes, that shape and change the Nation’s landscape over 

time. Studies are conducted within a geographic context and 

at a range of spatial and temporal scales so that investigations 

provide comprehensive information needed to understand the 

environmental, resource, and economic consequences of landscape change. Integrating natural-resource and socio-economic 

information with data themes from The National Map allows 

GAM scientists to quantify rates, identify key driving forces, 

and forecast future trends of landscape change (USGS, 2005).

The GAM program addresses three fundamental scientific issues concerning changes at the Earth’s land surface. 

Specifically, GAM strives to achieve the following:

• Understand what changes are occurring on the land surface and why;

• Understand the impacts of these land surface changes 

 on ecosystem health, climate variability, biogeochemi 

cal cycles, hydrology, and human health; and to 

• Understand what the best methods are to incorporate 

scientific findings into the decision making process.

The ultimate goal of GAM is to aid people in understanding and adapting to change, now and in the future, and in 

making wise land-management decisions that will enhance 

their quality of life.

The Role of U.S. Environmental Protection 

Agency

The USEPA relies on sound science to safeguard both 

human health and the environment (USEPA, 2004). The 

Office of Research and Development (ORD) is the scientific 

research arm of USEPA. ORD’s leading-edge research helps 

provide the solid underpinning of science and technology for 

the Agency. ORD conducts research on ways to prevent pollution, protect human health, and reduce risk. The work at ORD 

laboratories, research centers, and offices across the country 

helps improve the quality of air, water, soil, and the way we 

use resources. Applied science at ORD builds our understanding of how to protect and enhance the relationship between 

humans and the ecosystems of the earth. Within ORD, the 

National Exposure Research Laboratory’s Environmental Science Division develops methods for characterizing chemical 

and physical stressors, and landscape and regional assessment 

capabilities through the use of remote-sensing and advanced 

spatial-analysis techniques.

The Landscape Ecology Branch focuses on the science of 

Landscape Ecology as an interdisciplinary science framework 

that studies the relationship between spatial patterns of landscape characteristics and conditions of and risks to ecological 

resources, including forests, rangelands, wetlands, rivers, 

streams, lakes, and urban environmental settings. Part of the 

mission of USEPA research is to integrate our efforts with 

that of its science partners, such as the USGS, to help solve 

the nation’s environmental problems. To this end, in 2001 

the USEPA Environmental Sciences Division entered into a 

cooperative interagency agreement with the USGS in order to 

jointly study urban ecological issues. This report is one of the 

first results of that collaboration.

Objectives

There is no commonly accepted methodology to identify 

potential brownfields sites over a city or even smaller areas 

within a city. It is hypothesized that geographic analysis and 

technologies will offer more efficient brownfields site identification than current methods being applied. To that end, the 

study will examine properties that returned multiple indicators from various GIS datasets. Multiple hits on suspect sites 

should allow for the refinement of a set of indicators from 

each data layer that can be used to establish a logical set of 

criteria for brownfields identification. The result set will be 

verified against a current brownfields inventory that was 

compiled using a series of traditional identification methods. 

Additionally, the study will begin to identify and recommend certain datasets, where further research will reveal 

other excellent types of data for rapid brownfields site identification.

Study Area

The selected study area was the City of Des Moines, Iowa, 

located in the Mid-west, which is situated in the Great Plains. It 

was selected because of the enthusiasm of the USEPA Brownfields Coordinator, Robin Paul, and the Brownfields Showcase 

Community Initiative Coordinator, Ellen Walkowiak, who were 
Data and Methods 89

in search of a better technique than what they were currently 

using to identify brownfields sites. The city is divided into 

three sections by the two rivers flowing through it. The Raccoon River flows from west-to-east and splits off the southern 

portion of the city where it connects to the Des Moines River. 

The Des Moines River flows from north to south and splits the 

northern part of the city in half. The population of the city in 

the year 1990 was 392,928 and 10 years later increased by 12.9 

percent to 443,496 (U.S. Census Bureau, 2000) (fig. 1). This 

City represents an excellent choice for testing our methods, as 

there is significant interest and activity in brownfields redevelopment. 

Brownfields Showcase Communities are distributed across 

the country and vary by size, resources, and community type. 

Along with a total of 28 other cities across the United States, 

the USEPA selected Des Moines to be a Brownfields Showcase 

Community in 2000. The designation of Showcase Community 

allows Des Moines to serve as a national model for brownfields redevelopment. The City of Des Moines has created an 

impressive coalition of public and private entities focusing 

its brownfields redevelopment efforts on creating residential, 

commercial, and recreational developments in the Riverpoint 

West area and industrial development in the Agrimergent Technology Park projects. Des Moines is developing an area-wide 

redevelopment strategy to assemble private property and has 

made significant progress in identifying the extent of contamination in these areas. The City has identified approximately 250 

brownfields sites throughout Des Moines, located primarily in 

the following targeted redevelopment areas: (1) the 1,100-acre 

Agrimergent Technology Park, (2) the 300-acre Riverpoint 

West area, and (3) the City’s federally designated Enterprise 

Community. 

Data and Methods

The potential brownfields inventory process described 

here is a basic GIS overlay/intersection operation based on 

property parcels with multiple data layers, for reference, that 

individually and in total represent some potential for brownfields redevelopment activity. Various GIS datasets, each 

with a unique contribution to brownfields site potential, were 

acquired for the purpose of determining a geographic-based 

brownfields identification strategy. These layers were roads, 

historical topographic maps, historical aerial photographs, 

underground storage tank (UST) sites, CERCLIS (Superfund) 

records, tax assessments, buildings, schools, and property 

parcels. The datasets that were most critical for our analysis 

are discussed further below. 

Field Surveys

Two separate field surveys were conducted. Students 

from Drake University performed the first field assessment. 

The Drake students performed a “windshield survey” of 

potentially abandoned properties in the commercial corridors 

of Des Moines. They provided a database containing potential brownfields site addresses, description of the property’s 

condition, business function, photographs, and the building 

and land parcel property value (Drake University, 2001). 

The second field survey was conducted by Federal and City 

staff. They concentrated on the industrial corridors of the city 

trying to similarly identify abandoned properties. The same 

data collected for the commercial sites were collected for 

the industrial sites. Both datasets were entered into the GIS 

parcel database.

Aerial Photographs and Historical Topographic 

Maps

Urban landscapes are dynamic, constantly changing 

regions. In the period of one or two decades, land use can 

change dramatically, often leaving no visible or obvious indication of the previous activity. Historical aerial photographs 

and historical topographic maps have proven to be extremely 

valuable in identifying past hazardous waste activity as well 

as in developing historical profiles of landscape characteristics. A search of commercial and government sources of 

aerial photographs was conducted to develop a profile of 

available historical photography. From this profile, aerial 

photographs were obtained to represent a series of decadal 

snapshots of the City of Des Moines. Historical aerial photographs were obtained for 1938, 1945, 1956, 1961, 1967, and 

1985. Numerous maps were obtained from USGS archives, 

the Library of Congress, and the Iowa Historical Society (fig. 

2). 

Aerial photographs and maps were analyzed for any 

anthropogenic activity that might result in a historical pollution profile. Among these are landfills/dumps, mining 

areas/tailings, ponds/quarries/pits, sewage disposal sites, oil 

tanks, sludge pits, electrical substations, industrial activity, 

and buildings in disrepair or obviously abandoned. These 

features, interpreted from historical aerial photographs and 

maps were digitized onto orthophoto base maps as a separate 

polygon theme. All sites identified in the Des Moines study 

were entered by property location and number in the parcel 

database. 

Underground Storage Tanks

Information on existing USTs was acquired from the 

Iowa Department of Natural Resources (2002). These tanks 

are buried in commercial and industrial locations and generally contain petroleum. They have the potential to leak 

and cause groundwater contamination. These data were 

correlated and entered by property location in the parcel 

database. 
Figure 1. The Des Moines, Iowa, study area, displayed using the National Land Characteristics Dataset. Pink, peach, and red-orange areas (where rivers mepoint) were built-up areas in 1992.

11 – Open Water21 – Low Intensity Residential22 – High Intensity Residential23 – Commercial Industrial Transportation31 – Bare rock/sand/clay32 – Quarries/Strip Mines, Gravel Pit33 – Transitional41 – Deciduous Forest42 – Evergreen forest43 – Mixed Forest51 – Shrubland61 – Orchard, Vineyards71 – Grasslands/Herbaceous81 – Pasture/Hay82 – Row Crop83 – Small Grains 84 – Fallowa85 – Urban Recreational Grasses91 – Woody Wetlands92 – Open Water

National Land Characterization

Data Classification

N
91

Comprehensive Environmental Response, Compensation Liability Information Systems

Comprehensive Environmental Response, Compensation Liability Information Systems (CERCLIS) is the official 

Superfund hazardous waste site information system. CERCLIS contains information on potential hazardous waste sites 

and remedial activities across the nation, including sites that 

are on the National Priority List (NPL) or are being considered for the NPL. Those being considered often include sites 

where there is a low level of contamination or sites where 

emergency cleanups have been conducted. Either of these 

situations could contribute towards a perception of contamination as defined by the Brownfields Program. All CERCLIS 

identified sites in Des Moines were extracted from the database and correlated and entered by property location in the 

parcel database.

Tax Assessor Data

One of the most interesting and useful datasets was that 

containing the property values provided by the Polk County 

Assessors Office (fig. 3). The data used in the study was for 

the 1995 and 2002 tax years and contained information on 

both the land and building values for all taxable commercial 

and industrial properties in the city. Analyses of these datasets led to several derived data layers that proved to be good 

indicator datasets for brownfields identification potential. The 

primary goal was to reveal unusual trends in the tax records 

that could have been indicative of abandonment or underutilization.

Five different queries were possible through the analysis. 

We examined two variables--land value and building value-

-and attempted to identify properties where these values 

declined rather than increased or showed unusual trends over 

the seven-year period. 

These queries were as follows: 

(1) Land value decreasing | Building value decreasing

(2) Land value decreasing | Building value unchanged

(3) Land value unchanged | Building value decreasing

(4) Land value increasing | Building value decreasing

(5) Land value decreasing | Building value increasing

Each of these was extracted by a Boolean reselect operation 

resulting in the individual data layer. 

Underground Storage Tanks

1907 1956 1990

1938 1950 1967

Figure 2. Examples of historical maps and aerial photographs used to derive land-use patterns that could indicate potential brownfields.
92 The Use of Spatial Analytical Techniques to Identify Potential Brownfields Sites

Figure 3. Tax and assessment records for a 7-year period from the Polk County Assessor’s Office were invaluable in defining trends in property 

values possibly indicative of a brownfields profile.

Analysis and Preliminary Results

Our initial analytical strategy involved simple overlay/intersection operations of all the datasets (fig. 4). The information 

from all the data layers was combined with the property parcel 

file obtained from the Polk County GIS Department. Information 

from field surveys, historical maps and aerial photographs, UST 

and CERLIS data were identified on a property parcel basis by 

creating a “flag” or specific attribute in the property table. Data 

layers were overlaid with the property layers and when an intersection occurred, the attribute value was calculated. For example, 

when an UST point fell into a specific property parcel, the “UST” 

attribute for that parcel was then set to 1. All of the non-taxassessors’ data layers were combined with the property data and 

attributes were flagged. The property data, with all attributes from 

the above data identified, was then intersected with the data layers 

extracted from each of the five tax queries. When any particular 

property parcel contained two or more flags from any of the data 

layers, it was then identified as a “potential brownfields site” and 

extracted into a separate layer.

The results from the overlay/intersection method then were 

compared separately for each of the five analyses and resulted in 

subsets of between 29 and 250 potential brownfields sites. With 

this many potential sites derived from the datasets compared 

against the tax query results, the determination of brownfields 

sites knows only the spatial limits of the data extent. The tax 

assessor data proved to be a powerful tool in the geographic 

analysis of potential brownfields sites. For example, the project 

is demonstrating that declining tax valuations on properties in 

industrialized areas is a strong indicator of possible abandonment 

or general under utilization, and good brownfields potential.

Despite a limited ability to perform field verification and 

database inquiries, the initial results of this research are promising. 

Figure 5 shows a property with multiple hits from the different 

databases: tax value data, USTs, and CERCLIS; and shows the 

field photo of another property that was identified via GIS and verified in the field, that visually suggests it has brownfields potential. 
Analysis and Preliminary Results 93

Figure 4. Example of methodology used in this study, showing various data sources except for underground storage 

tanks, Comprhensive Environmental Response, Compensation, and Liability Information System, and others. Each 

dataset offers numerous potential brownfields sites, with some sites showing up consistently from one dataset to the 

next. Sites present in two or more sources are referred to as “coincident” and could be brownfields. Coincident sites 

are put into a final potential brownfields dataset ready for field verification. In some cases, information accompanying 

parcel in tax-assessment data was enough to categorize it as a potential brownfields site.
94 The Use of Spatial Analytical Techniques to Identify Potential Brownfields Sites

Further Research

The intent of this study was to demonstrate the potential 

geospatial analysis techniques have with regards to the identification of potential brownfields sites and that such analysis 

could be an excellent planning tool. One of the fundamental 

problems in this research is determining an accuracy measure 

against which the potential sites derived from GIS analysis 

could be evaluated. Since brownfields can involve perceptual 

distinctions, they are not easily quantified by field or analytical methods. One obvious method would be to measure GISderived properties against actual brownfields sites. However, 

this could take a considerable amount of time to determine 

overall effectiveness.

Another method that could be useful might be the use 

of existing brownfields sites as calibration measures for 

methods refinement. However, this could also be problematic 

because many cities, such as Des Moines, target specific city 

sections as redevelopment areas and encourage brownfields 

redevelopment in these areas. While this is a viable strategy 

for urban governments, it creates a bias in terms of spatial 

distribution. There are likely many viable commercial and 

industrial properties that exist outside of the redevelopment 

zone.

Research should be conducted to analyze the trend of 

both building and land values relative to previously identified 

brownfields sites. This information might identify market 

activity that is not readily observed. Market activity could 

include both private and public investment, and such data 

could be used to ascertain which brownfields sites are more 

viable redevelopment opportunities. Brownfields redevelopment assistance programs could then be targeted to expedite 

improvements in areas where the economic trend demonstrates growth, however subtle. This approach could specifically assist in serving to catalyze economic revitalization in a 

more strategic way. 

In summary, GIS and spatial analysis of commonly 

available data sets appears to be a promising method for 

area-wide inventory of properties that have strong potential 

for brownfields redevelopment. Further research is needed 

Figure 5. Screen capture showing different datasets being used in this study. Purple-tinted parcel is one that had more than one indicator from 

both underground storage tanks and Comprhensive Environmental Response, Compensation, and Liability Information System data.
95

in the following areas: (1) development of a validation/verification method for area-wide brownfields assessment, (2) 

development of a statistical indicator that would measure 

the contribution of various different datasets and serve as a 

“weighting factor” of various levels of contribution to potential brownfields identification, and (3) extension of these 

techniques to other metropolitan areas.

References

Des Moines, 2004, City of Des Moines, Iowa, Office of 

Economic Development: [http://www.dmoed.org/sites/index.

html, last accessed July 5, 2004].

Drake University, 2001, An inventory of potentially abandoned 

commercial properties in Des Moines, Iowa, in support of the 

USEPA Brownfields Program: Unpublished Report.

Ellman, T., 2004, Infill—The cure for sprawl?: Arizona Issue 

Analysis [http://www.urbanfutures.org/abstract.cfm?ID=28, 

last accessed July 5, 2004], last accessed July 5, 2004].

Gernstein, S., 2002, Brownfields revitalization cuts urban blight, 

suburban sprawl: National Policy Analysis 

[http://nationalcenter.org/NPA385.html, last accessed July 5, 

2004].

U.S. Census Bureau, 2004, American fact finder: [http://

factfinder.census.gov/home/saff/main.html?_lang=en, last 

accessed February 5, 2004].

U.S. Census Bureau, 2004, Population estimates: [http://www.

census.gov/popest/cities/, last accessed February 5, 2004].

U.S. Geological Survey, 2005, Geographic Analysis and 

Monitoring Program: [http://gam.usgs.gov/, last accessed 

July 5, 2005].

U.S. Environmental Protection Agency, 2003a, Brownfields 

2003 grant fact sheet, Des Moines, Iowa: [http://www.epa.

gov/swerosps/bf/03grants/desmoines.htm, last accessed July 

5, 2004].

U.S. Environmental Protection Agency, 2003b, The Brownfields 

Program—Setting change in motion: Washington, D.C., 

Office of Solid Waste and Emergency Response, USEPA 

Report USEPA-500-F-03-248, [http://www.epa.gov/

brownfields/pubs/03brochure.pdf, last accessed July 5, 2004].

U.S. Environmental Protection Agency, 2004, Landscape 

Ecology Program: [http://epa.gov/nerlesd1/land-sci/intro.htm, 

last accessed February 5, 2004].

97

Road Densities for Urban Areas in the Conterminous 

United States

By Raymond D. Watts1, John McCammon2, Roger Compton2, Carl Rich2, and Tom Owens2

The character of an urben landscape depends, in part, on 

the gaps between the roads and streets of the urban area. The 

statistics of these gaps vary widely across the conterminous 

48 states, as illustrated on figure 1. 

Regional road density patterns are unmistakable.The 

Northeast, the Midwest, the Pacific Northwest,and a string 

of cities along the Appalachian Mountains generally have 

low average distance toroad (high road density) and low 

maximum distance to road (minimal roadless area). A group 

of cities, primarily in the lower Mississippi River Valley, has 

somewhat greater mean distance to road (lower road density), 

but again no large roadless areas. The southern tier of States 

contains many cities with the lowest road densities, with 

an additional low-density cluster extending north along the 

Rocky Mountains and northwestern Great Plains.

A variety of historical and contemporary factors influences urban road density: topography, changes in utilities 

infrastructure, evolution of transportatation technologies, 

availability and cost of land, and changing cultural perceptions and legal practices of land-use managment. There is an 

apparent correlation between early development of cities in 

the East versus later development of cities in the West, and 

generally higher road densities (lower mean distance to road) 

in the East and lower road densities (higher mean distance to 

road) in the West.

The United States Geological Survey Geographic Analysis and Monitoring (GAM) program has developed a landscape metric called NORM-ED (National Overview Road 

Metrics-Euclidean Distance). For each 30 meter square covering the conterminous United States, the value of NORM-ED 

is the Euclidean distance to the nearest road (DTR). Figure 1 

characterizes the road density of 437 urbanized areas based 

on distance to road data from the NORM-ED data set. The 

five inset maps show the actual distance to nearest road 

values for five selected urban areas. The urban areas on the 

map of the conterminous United States are color-coded based 

on a combination of the mean DTR value within an urban 

area, and the maximum DTR value within the urban area. 

The urban areas were generated using 1990 Census 

Tracts from the U.S. Census Bureau, and Rural-Urban Commuting Area codes (RUCA’s) from the U.S. Department of 

Agriculture Economic Research Service (ERS). The RUCA 

codes classify census tracts according to whether they are 

part of a metropolitan-area core or areas in which certain percentages of the population commute to a metropolitan-area 

core (for details on RUCA codes, see http://www.ers.usda.

gov/emphases/rural/data/index.htm). The urban area polygons 

shown on this figure were generated by merging contiguous 

census tracts whose RUCA codes (1.0, 1.1, 4.1, or 7.1) indicate they are part of a metropolitan-area core or have greater 

than 30 percent of their population commuting to a metropolitan-area core. 

The NORM-ED data were derived from the 

Dynamap/1000 street data of Geographic Data Technology; 

Dynamap/1000 is based on TIGER transportation files compiled by the Census Bureau for the 2000 Census.

1

U.S. Geological Survey, Rocky Mountain Geographic Science Center, 

Fort Collins, CO 80526. 2

 U.S. Geological Survey, Rocky Mountain Geographic Science Center, 

P.O. Box 25046, MS 516, Lakewood, CO 80225.
Figure 1. Map of road densities for urban areas in the conterminous United States.

By

Raymond D. Watts, John McCummon, Roger Compton,

Carl Rich, and Tom Owens
Explanation for Inset Maps Explanation for Conterminous U.S.

14039 - 1864610354 - 14038

Maximum DTR (m)

4865 - 10353

218 - 4864

>277

120 - 153 205 - 277

154 - 204

Mean distance to nearest road (DTR) (m)

Distance to nearest road (km)

403020141210

8543210

Figure 1. Map of road densities for urban areas in the conterminous United States—Continued.

101

Introduction

The deleterious effects of urbanization on water-quality 

are evident across the United States. In locations as geographically diverse as Washington State (Nelson, 1999), Portland, 

Oregon (Abrams and Prescott, 1999), the Edwards Aquifer 

region of Texas (Kennedy, 1999), and south Florida (Williams, 1999) urbanization is reported to adversely affect the 

physical (for example sedimentation), chemical (for example 

eutrophication), and biological (for example endangered salmonid species) characteristics of water quality. Concern about 

the effects of urbanization has motivated efforts to understand 

and manage urban development on the part of governmental 

organizations at the national (the Smart Growth Program of 

the U.S. Environmental Protection Agency (2000)), regional 

(Smart Growth Networks, 2000), and local (Pelley, 1999; 

American Planning Association, 2000) levels, as well as 

by research efforts such as the Baltimore Ecosystem Study, 

funded by the National Science Foundation (Foresman and 

others, 1999) and the Urban Dynamics Research Program 

(U.S. Geological Survey, 2000a).

As part of the National Water Quality Assessment 

(NAWQA) Program (U.S. Geological Survey, 2000b) an 

investigation is underway to determine the relation between 

varying intensities of drainage-basin urbanization and water 

quality in three contrasting environmental settings: (1) the 

humid Northeast (Boston, MA metropolitan area), (2) the 

humid Southeast (Birmingham, Ala. metropolitan area), and 

(3) the arid West (Salt Lake City, Utah metropolitan area). For 

budgetary reasons, each study has approximately 30 study 

drainage basins, with the basins in each study area having 

similar natural (for example climate, elevation, soils) characteristics and a gradient of urban development intensity. 

This paper is an extract from a paper (McMahon and 

Cuffney, 2000) describing the activities that occurred during 

the planning phase of these studies. The paper covers the 

characterization of the anthropogenic landscape and the development of an urban intensity index, a measurement of the 

intensity of urbanization. One of the major objectives of the 

planning phase was to rank the population of potential study 

basins based on their urban intensity and choose a set of study 

basins with similar natural characteristics that represent a gradient of urban intensity. The urban intensity of each basin is 

measured using an index that integrates information about the 

multiple dimensions of human influence on the urban landscape at the drainage basin scale. The index, which includes 

information about land cover, infrastructure, population, and 

socioeconomic characteristics, provides an a priori ranking of 

potential study sites that is used to ensure that sites chosen for 

the investigation represent a cross-section of urban intensity. 

Some of the variables used in the index may also be important 

factors for explaining variations in water quality; this cannot 

be determined, however, until water quality data are collected 

and analyzed. 

These methods were applied at three locations across the 

country. In order to maximize the possibility for comparing 

and contrasting the eventual study findings, characterization 

of the drainage basins used in each of the three investigations were accomplished using land cover, infrastructure, 

population, and socioeconomic data sets developed using 

similar protocols (table 1). Typically these were national data 

sets available in all NAWQA study areas. Superior data (for 

example,more recent or better resolution) may have been 

available for some variables in each of the study locations but 

were not used at the planning stage. Basin characterization 

and site-selection efforts associated with the Boston study are 

described here for illustrative purposes.

An Evolving Perspective on 

Water-Quality

The gradient design used in the NAWQA Program to 

investigate the effects of urbanization is premised on two 

perspectives about water quality. First, in-stream water quality is an ecological construct. Water quality is a composite of 

physical, chemical, and biological characteristics that vary in 

space and time and are influenced by natural factors and by the 

human activities and values (Karr, 1991; Fairweather, 1999; 

fig. 1). Traditional measures of water-quality have relied on 

Quantifying Urban Intensity in Drainage Basins for 

Assessing Stream Ecological Conditions1

1

Modified from Journal of the American Water Resources Association, Dec. 

2000, v.36, no. 6, p. 1247-1261; used with permission. 2

U.S. Geological Survey, 3916 Sunset Ridge Road, Raleigh, NC, 27607, 

gmcmahon@usgs.gov and tcuffney@usgs.gov.

By Gerard McMahon2 and Thomas F. Cuffney2
102 Quantifying Urban Intensity in Drainage Basins for Assessing Stream Ecological Conditions

Natural landscape characteristics

Landscape theme Available maps Scale

Ecological regions: U.S. Environmental Protection

Agency (USEPA) level III ecoregions 7,000,000

USEPA level IV ecoregions 250,000

U.S. Forest Service subsections

(level IV resolution) 250,000

Soil drainage

characteristics:

U.S. Department of Agriculture

State Soil Data Base (STATSGO)

soil hydrologic groups 250,000

Watershed boundaries: Developed from digital elevation models varies

(U.S. Geological Survey, 2000c)

Lithochemical zones: Bedrock litho-chemical zones approx.

250,000

(Robinson, 1997)

Anthropogenic landscape characteristics

Land-cover data: Multi-Resolution Land Characteristics 100,000

(MRLC) (Loveland and Shaw, 1996)

Derived from Land

Cover:

Impervious surface

Urban Sprawl

Riparian Land Cover

Infrastructure: Roads 100,000

Point Source Dischargers Point

USEPA Toxic Release Inventory Point

Dams Point

Census Block Group

derived (examples):

Population 100,000

Housing Unit Density 100,000

Per Capital Income 100,000

Socioeconomic indices 100,000

physical and chemical characteristics as surrogate indicators 

for potential achievement of biological integrity—a goal of the 

Clean Water Act (Yoder, 1995). Because biological communities are subject to an array of physical, chemical, and biological influences, the condition of these communities reflect the 

integration of these influences as they occur over time and 

space (Yoder, 1995). Advancements in the understanding and 

measurement of in-stream biological communities over the 

past 15 years makes it possible and practical to consider water 

quality, and the factors that influence it, from an ecological 

perspective (Davis, 1995).

Second, landscape and stream characteristics within a 

watershed reflect not only the physical and geographic context 

of a watershed but the choices associated with human values 

and priorities. These choices are reflected in the mix of land 

uses in a watershed and in the diverse activities associated 

with even the same land uses. The character of the urban residential landscape, for example, can vary dramatically as socioeconomic factors—income, age of housing stock, levels of 

education—change among neighborhoods. Variations in both 

natural and built characteristics in a watershed affect stream 

flow variability (Hammer 1972, Bevin, 1986; Poff and others, 

Table 1. Potential sources of digitally mapped information for use in characterizing natural 

and urban landscape patterns in drainage basins.
103

1997; U.S. Environmental Protection Agency, 1997b), eutrophication (Mueller and Helsel, 1996; U.S. Geological Survey, 

1999), the distribution of fish (Steedman, 1988; Whittier and 

others, 1988; Poff and Ward, 1989; Taylor and others, 1993; 

Poff and Allan, 1995; Angermeier and Winston, 1998), invertebrates (Corkum and Ciborowski, 1988; Corkum, 1989, 1990, 

1992; Quinn and Hickey,1990; Tate and Heiny, 1995), as well 

as combined measures of biological, chemical, and physical 

water-quality measures (Cuffney and others, in press). There 

also is a spatio-temporal scale to these relationships—that is, 

water-quality responses are influenced by both regional and 

local factors, and they vary across space and time (Angermeier 

and Winston, 1998). 

This conceptual framework has shaped the tasks associated with the study planning effort in two important ways. 

First, a drainage-basin-oriented investigation of the effects of 

Physical, chemical, evolutionary, and biogeographic processes influence watershed

landscape and in-stream conditions

Physical and Geographic C ontext Biological Integrity

Location Taxa richness

Geological substrate Species composition

Climate, Elevation Tolerance, Intolerance

Stream size, Gradient Adaptive strategies

 Landscape and stream conditions are influenced by human activities

Social values and priorities

Land use (agriculture, cities)

Effluent discharge

Water withdrawal

Reservoir discharge

Sport and c omm ercial fisheries

Aesthetics

These social values and associated choices alter biogeochemical process to influence

one or more in-stream characteristics

Thereby altering stream health, as measured by indicators of

Geophysical condition Biological conditions

Riparian land cover, Erosion rates Taxa richness

Slope stability, Evapotranspiration Taxonomic

 Composition

Surface permeability

Individual health

Runoff amount and timing

Ecological processes

Groundwater recharge

Evolutionary processes

Water chemistry

Five key environmental factors 

 Flow regime

 Physical habitat structure

 Chemical water quality

 Energy sources

 Biological interactions

Figure 1. Conceptual framework of an ecological construct for in-stream water quality (after Karr and Chu, 1997).

An Evolving Perspective on Water-Quality
104 Quantifying Urban Intensity in Drainage Basins for Assessing Stream Ecological Conditions

urbanization on water quality must control for the effects of 

natural factors, while allowing the degree of urbanization to 

vary in known ways between study basins. Large variations 

in the natural characteristics of study drainage basins such as 

drainage basin size, climate, elevation, soils, and geology will 

obscure the nature of any relation between urbanization and 

water quality. An a priori knowledge of the degree of urbanization in each candidate study basin allows the choice of a 

set of basins that together reflect a gradient of urbanization 

representative of conditions across a population of potential 

sites. Second, the degree of urbanization cannot adequately be 

described using only land-cover data, such as the amount of 

developed land within a drainage basin. In this study, spatial 

patterns in infrastructure and socioeconomic characteristics are 

also used to determine the degree of urbanization. Although 

not discussed in this paper, the response data collected at 

streams in each study will include nutrients, pesticides, and 

ions in stream water, trace elements in bed sediments, geomorphic and habitat characteristics, hydrologic stage, water 

temperature, alkalinity, pH, dissolved oxygen, specific conductance, chlorophyll a, benthic algae and invertebrate communities, and fish communities. 

Characterizing the Anthropogenic 

Landscape

Efforts to understand the functioning of urban ecosystems have emphasized the linkages in the spatial patterns and 

processes of both sociocultural and biophysical resources 

(Grove and Burch, 1997). These investigations suggest that it 

is impossible to understand the differentiation that occurs in 

the spatial pattern and processes associated with natural ecological systems without an understanding of the spatial pattern 

and processes of human ecological systems, including socioeconomic factors. Anthropogenic landscape characteristics 

used in this study to describe the population of potential gradient-study basins include (1) biophysical measures of the urban 

landscape, such as land cover and impervious surface (Wang 

and others, 1997), (2) measures of the infrastructure that 

supports urban development patterns (Center for Watershed 

Protection, 1998), and (3) socioeconomic measures describing 

factors such as population, housing, and income (Anson, 1991; 

Grove and Burch, 1997). A GIS was used to overlay thematic 

maps of these characteristics with drainage basin boundaries. 

These data were compiled in a basin characteristics spreadsheet, with a single row for each potential basin and multiple 

columns for the land cover, infrastructure, and socioeconomic 

variables, and used to develop the urban intensity index.

The primary source of land-cover/land-use information 

was data developed as part of the Multi-Resolution Land 

Characteristics (MRLC) consortium, a Federal interagency 

project to develop mapped land-cover data for the contiguous 

United States based on Landsat Thematic Mapper satellite 

images (Loveland and Shaw, 1996). These hierarchically organized data provide both general land cover information (for 

example, developed versus forest) and more specific land-usetype information. For instance, land classified as developed in 

the general land-cover classification also has a land-use designation as either low- or high-intensity residential or commercial/industrial/transportation. MRLC data used in this study 

were collected in the early 1990s and processing of the data 

for the conterminous United States was nearly completed by 

the summer of 2000. Accuracy assessments have been completed for the areas within the Boston and Birmingham studies 

(Limin Yang, written commun., U.S. Geological Survey, July 

2000). Classification errors for the developed land classes 

in these areas ranged between 15 and 50 percent, with better 

accuracies in the New England classification. Residential land 

areas were generally classified more accurately than commercial/industrial/transportation areas. 

The MRLC land-cover data were also used to derive 

estimates of another important characteristic of urban intensity—impervious surface area. Several steps were followed to 

estimate impervious surface. A range of impervious-surface 

percentages associated with the detailed land-cover classes 

used in the MRLC classification were compiled from existing literature (Stankowski, 1972; Bedient and Huber, 1988; 

Arnold and Gibbons, 1996; Booth and Jackson, 1997; Center 

for Watershed Protection, 1998). Simple distributions (for 

example, uniform or triangular) were developed from these 

literature values and used with each MRLC land-cover type 

in the basin characteristics spreadsheet as multipliers in a formula that estimated impervious-surface area for all land-cover 

types and summed these individual estimates into a single 

impervious surface estimate for each potential basin. For each 

watershed, the spreadsheet was calculated repeatedly—1,000 

iterations were used in this simulation. The software kept track 

of the outcomes of each iteration, created a distribution of 

all possible output values, and reported the mean impervious 

value for each watershed 

Several infrastructure measures associated with anthropogenic activities were used to characterize urban intensity. 

These included road density, the number of point-source dischargers (U.S. Environmental Protection Agency, 1999), the 

number of dams (U.S. Army Corps of Engineers, 1996), and 

the number of Toxic Release Inventory sites (U.S. Environmental Protection Agency, 1997a; Price and Clawges, 1999). 

Socioeconomic conditions shape perceptions of both 

the degree (for example, amount of density) and character 

(for example, affluence) of development within a drainage 

basin; they may also exert an important effect on factors that 

can influence water quality (Grove and Burch, 1997; Ryznar, 

1998). For example, population and housing density provide 

a direct measure of development intensity and are likely to 

be correlated with impervious surface (Stankowski, 1972). 

Residential use of fertilizers and pesticides may vary according to the income levels of neighborhoods within a watershed, 

although the very limited data on the use of these chemicals 

in urban areas makes assessment of this intuitively appealing 

claim difficult. Census counts (1990 data), estimates (1997), 
105

Land-use characteristics

LU21_AB Proportion of watershed with low intensity residential land on welldrained soils

LU21_CD Proportion of watershed with low intensity residential land on poordrained soils

LU22_AB Proportion of watershed with high intensity residential land on welldrained soils

LU22_CD Proportion of watershed with high intensity residential land on poordrained soils

LU23_AB Proportion of watershed with commercial/Industrial/Transportation on

well-drained soils

LU23_CD Proportion of watershed with commercial/Industrial/Transportation on

poor-drained soils

IMPERV Proportion of watershed with impervious land surface (not used in index

calculation)

URBAN_MI Total urban land area in watershed (square miles)

COMRESIN Commercial/residential urban land index (ratio of commercial to

residential land cover)

FOR_MI Total forested land area in watershed (square miles)

WET_MI Total wetland area in watershed (square miles)

MRLC_11 Watershed area in open water (square miles)

MRLC_12 Watershed area in Perennial Ice/Snow (square miles)

MRLC_21 Watershed area in Low Intensity Residential (square miles)

MRLC_22 Watershed area in High Intensity Residential (square miles)

MRLC_23 Watershed area in Commercial/Industrial/Transportation (square miles)

MRLC_31 Watershed area in Bare Rock/Sand/Clay (square miles)

MRLC_32 Watershed area in Quarries/Strip Mines/Gravel Pits (square miles)

MRLC_33 Watershed area in Transitional cover (square miles)

MRLC_41 Watershed area in Deciduous Forest (square miles)

MRLC_42 Watershed area in Evergreen Forest (square miles)

MRLC_43 Watershed area in Mixed Forest (square miles)

MRLC_51 Watershed area in Deciduous Shrubland (square miles)

MRLC_52 Watershed area in Evergreen Shrubland (square miles)

MRLC_53 Watershed area in Mixed Shrubland (square miles)

MRLC_61 Watershed area in Orchards/Vineyards/Other (square miles)

MRLC_71 Watershed area in Grasslands/Herbaceous (square miles)

MRLC_81 Watershed area in Pasture/Hay (square miles)

MRLC_82 Watershed area in Row Crops (square miles)

MRLC_83 Watershed area in Small Grains (square miles)

MRLC_84 Watershed area in Fallow (square miles)

MRLC_85 Watershed area in Urban/Recreational Grasses (square miles)

MRLC_91 Watershed area in Woody Wetlands (square miles)

MRLC_92 Watershed area in Emergent Herbaceous Wetlands (square miles)

BUF_AREA Total area (square miles) within 240 meter wide buffer (120 m. on each

side of stream) in watershed

Table 2. Variables used in the analysis of New England urban watershed gradient.

[Variables in bold type were correlated at a level >= 0.50 with 1997 population density and were used in the construction of the urban 

intensity index. Bold italics indicate significant negative correlation. Variables marked with an asterisk are redundant with other variables listed in parentheses and were not used in index development]

An Evolving Perspective on Water-Quality
106 Quantifying Urban Intensity in Drainage Basins for Assessing Stream Ecological Conditions

BUF_11 Total area (square miles) of MRLC 11 within buffer

BUF_21 Total area (square miles) of MRLC 21 within buffer

BUF_22 Total area (square miles) of MRLC 22 within buffer

BUF_23 Total area (square miles) of MRLC 23 within buffer

BUF_31 Total area (square miles) of MRLC 31 within buffer

BUF_32 Total area (square miles) of MRLC 32 within buffer

BUF_33 Total area (square miles) of MRLC 33 within buffer

BUF_41 Total area (square miles) of MRLC 41 within buffer

BUF_42 Total area (square miles) of MRLC 42 within buffer

BUF_43 Total area (square miles) of MRLC 43 within buffer

BUF_51 Total area (square miles) of MRLC 51 within buffer

BUF_61 Total area (square miles) of MRLC 61 within buffer

BUF_81 Total area (square miles) of MRLC 81 within buffer

BUF_82 Total area (square miles) of MRLC 82 within buffer

BUF_85 Total area (square miles) of MRLC 85 within buffer

BUF_91 Total area (square miles) of MRLC 91 within buffer

BUF_92 Total area (square miles) of MRLC 92 within buffer

URB_BUF Percent of watershed buffer area in urban land cover

FOR_BUF Percent of watershed buffer area in forested land cover

WET_BUF Percent of watershed buffer area in wetland land cover

Infrastructure characteristics

ROAD_KM* road length in watershed (kilometers) (ROAD_DEN)

ROAD_DEN Road density in watershed [road length(km)/watershed area(km2)]

PSCOUNT Number of points source dischargers in watershed (EPA database)

DAMCOUNT Number of dams in watershed

TRICOUNT Number of Toxics Release Inventory sites in watershed

Population characteristics

AGESTR97 Age structure of population (population under 18/population over 18)

POP90* 1990 population (P97DENMI)

POP97* 1997 population (P97DENMI)

P90DENMI* 1990 population density (people/square mile of watershed area)

(P97DEN)

P97DENMI 1997 population density (people/square mile of watershed area)

POP9097* Population change 1990-97 (proportion) (P97DEN)

URBSPRWL urban sprawl index [(urban land area/1997 population)*10,000]

Socioeconmic characteristics

PCINC97 1997 per capital income (dollars)

HOUSEAGE Age of residential housing stock (years)

AVGBEDRM Average number of bedrooms in residences

SEI-1 Socio-economic index 1: high levels of income and owner occupied

housing; low population density

SEI-2 Socio-economic index 2: high levels of population, housing units,

households, and rental units

SEI-3 Socio-economic index 3: high levels of income, population density, per

capita income, rental units

SEI-4 Socio-economic index 4: high levels of length of tenure in house and

occupancy rates, low per capital income and 90-97 pop change

SEI-5 Socio-economic index 5: High levels of children, persons below poverty

level, houisng units on septic systems

Table 2. Variables used in the analysis of New England urban watershed gradient—Continued.
107

Social Income Housing

SEI-1 + high school grad + housefold and family Income + Own occ housing units

+ 2 & 3+ vehicle households - % poverty, fam in poverty, chil<18 + OOHU with mortgage

- population density 90 and 97 + 97 household income + average number ofbedrooms

- % HH income less than 15K + % own occ housing units

- % rental occupied housing units

SEI-2 + 1990 population + number of households + number of housing units

+ 1 vehicle households + number of families + number of occupied housing units

+ 97 and 02 population + number of rental and % rental units

+ persons 16+ + 97 hu - total and occupied

+ 02 hu - total and occupied

- average number of bedrooms

SEI-3 + % Bachelors degree or higher + 90, 97, 02 per capital income + housing unit density

- children/1,000 women 25-34 yo + % household income > $100,000 + age of housing unit

+ pop density 90 and 97 + % housing units on public sewer

- 90-97 & 90-02 pop change - % housing units on septic

- proportion pop under 18 + % housing units on utility gas

+ % using public transportation + proportion hu occupied, 90, 97 & 02

+ % rental occupied housing units

SEI-4 - % Bachelors degree or higher - 90 per capita income + % housing units using fuel oil

+ % born in state of residence + length of tenure in house

+ proportion pop under 18 + proportion hu occupied, 90, 97 & 02

- 90-97 & 90-02 pop change - % condo housing units

SEI-5 + children/1,000 women 25-44 yo + household income - 90-97 & 90-02 population change

- % born in state of residence + % persons below poverty + average number of bedrooms

+ proportion pop under 18 + % poverty, fam in poverty, chil<18 - % housing units on public sewer

+ % females > 16 unemployed + % household income> $100,000 + % housing units on septic

+ % female headed household - 90-97 & 90-02 change in # of hu

+ proportion population under 18

Table 3. New England census block group socioeconomic indices (SEI) and primary associated variables. 

[Indices were derived from principal components analysis of 80 social, income, and housing characteristics associated with 1990 (counts), 1997 (estimates), and 2002 

(projections) census block group data (Geolytics, 1998). Variables in these socioeconomic classes with relatively high (+) and low (-) loadings on these indices are 

listed. HS - high school; pop - population; yo - years old; fam - families; chil - children; HH - households; K thousands; own occ - owner occupied; HU - housing unit]

and projections (2002) for population, labor, income, and housing characteristics, based on census block group areas, were 

used to characterize the socioeconomic aspects of the urban 

landscape (table 2; Geolytics, 1998). Socioeconomic indices 

(SEI) also were derived for each basin by using ordination of 

population, labor, income, and housing census variables (Anson, 

1991; table 3). For the Boston study, the indices were based 

on 80 census variables computed over the 11,500 census block 

groups within the New England States. The indices contrast 

areas with differing combinations of social, income, housing, 

and labor characteristics. For example, high scores for SEI1 (which explains 23 percent of the variation between block 

groups in the 80 dimensional space) are associated with areas 

with a high number of high school graduates, relatively high 

household incomes, and a high level of owner occupied housing. Scores for SEI-3 are positively associated with households 

with very high income, small numbers of children, high percentages of college graduates, and high proportion of rental housing. 

An Evolving Perspective on Water-Quality
108 Quantifying Urban Intensity in Drainage Basins for Assessing Stream Ecological Conditions

Developing an Index of Urban Intensity

The planning phase of an urban gradient study uses an a 

priori measure of the intensity or degree to which a basin is 

urbanized in order to rank potential study basins. This ranking, 

along with information about each basin’s natural characteristics, is necessary to choose a final set of study basins that have 

similar natural characteristics and a desired distribution of 

urban intensity.

Neighborhoods within a city or communities within a 

single metropolitan area have distinct urban intensities that 

are a function of not only the amount of developed land but of 

differences in infrastructure, population, social, income, and 

housing characteristics. A multimetric approach was used in 

the planning phase of the gradient studies to characterize the 

relative urban intensity of each potential study basin. Multimetric indices are used to describe the overall condition of 

complex systems (Ward, 1996; Karr and Chu, 1997). Used in 

disciplinary settings as distinct as Wall Street (for example, 

index of leading economic indicators (Mitchell and Burns, 

1938)) and stream ecology (for example, index of biological integrity (Yoder and Rankin, 1995)), indices combine a 

number of generally accepted individual condition measures 

of the system being assessed (for example, net business formation and new manufacturing orders for assessing economic 

health; amount of developed land and population density for 

assessing the intensity of urban development) that may be correlated but which provide distinct information about different 

dimensions of often complex systems. This approach allows 

the integration of multiple, commonly used sources of information about the urban landscape, such as urban land area, 

amount of impervious surface, road density, population density, and socioeconomic indices into a single measure of urban 

intensity (Cuffney and others, 2000; Karr and Chu, 1997). 

In the Boston area study, a basin attribute table with 73 

land-use, infrastructure, population, and socioeconomic variables was developed for each of the 208 potential study basins 

within the level-III ecoregion. Principal component analysis 

of the variance structure of the 73 variables across the 208 

candidate basins indicated that 1997 population density was 

the most important characteristic in explaining the variation 

among the basins. The urban index was developed based on 43 

landscape and socioeconomic characteristics that had an absolute Pearson correlation value of greater than or equal to 0.50 

with 1997 population density (table 2).

The urban index was calculated by using a five-step procedure, with the resulting index values ranging from 0 to 100 

over the set of candidate basins. An example of the calculation 

of the urban intensity index for one watershed using representative variables is given in table 4 and a graphical representation of the index results for the candidate New England study 

basins is presented in figures 2 and 3. Calculation of the index 

proceeds as follows:

1. Adjust raw data for basin size and measurement units 

(see variables in table 2):

Proportions into percentages

Areas into percent of basin area

Toxic Release Inventory, point-source dischargers, and 

dam counts into densities (count per unit area)

SEI= SEI - minimum(SEI) to maintain absolute differences and make all values positive

Negative correlation with population = 100% - percentage

2. Transform original data so value of variables rangefrom 0-100 for candidate sites:

 where: X is the value of variable X for the site, 

 Y is the transformed value of variable X for 

the site,

 Xmin is the minimum value of variable X over 

all sites, and

 Xmax is the maximum value of variable X over 

all sites.

3. Variables that were negatively correlated with population density are adjusted so that all variables increase 

as population density increases:

Y = 100-Yneg corr.

4. Urban intensity is calculated as the average value of 

the transformed variables:

where: Yi is the adjusted value of variable i, and

 n is the number of variables in the index.

5. Transform index URBI so the range of intensity is 0-

100 for candidate sites:

where: X is the value of the URBI index for the site, 

 Xadj is the transformed value of the URBI index for 

the site,

 Xmin is the minimum value of the URBI index over 

all sites, and

 Xmax is the maximum value of the URBI index over 

all sites.

Conclusions

The most important lesson from implementing the methods described in this paper is that it is possible to identify 

and analyze the natural and anthropogenic characteristics of a 

population of potential-study drainage basins for a large spatial area in a relatively short period of time using straightforward GIS techniques and nationally available data sets. These 

techniques are scaleable, in the sense that they can be easily 

implemented in larger or smaller areas.

Another lesson related to the methodology of identifying and selecting basin for an urban gradient study relates to 

Y X = − ( ) X X min ÷ − ( ) max Xmin

URBI Y n i

n

= ∑

  1

/

X X adj( ) − ÷ X X min ( ) max − Xmin *100
109

the importance of field reconnaissance. Basin reconnaissance 

was judged by project staff to be a centrally important in 

selecting study basin that not only made sense based on the 

GIS analysis but from a standpoint of large scale factors that 

cannot be considered by the GIS, especially related to suitability of the site for sampling activities and the local habitat 

features. Reconnaissance is costly in terms of staff time. The 

Boston study staff visited approximately 150 potential drainage basins. Approximately three weeks of time for one staff 

person were spent preparing for the reconnaissance effort. 

Three staff people took four weeks to complete these visits, 

and another week of one persons time were spent compiling 

the information into a useable format. Ideally, the timing of 

reconnaissance should correspond to a low-flow period; seasonally unusual wet weather conditions hampered the ability 

of Boston staff to visit and assess sites.

The value of the index is potentially constrained by several factors. The first is the age of the land-cover data. Sampling activities in the Birmingham study during the summer of 

2000 suggest that large changes in land cover have occurred 

since the MRLC data were collected 8 years earlier. This is 

likely to be the case in any rapidly urbanizing area of the 

country. A significant challenge in developing an a priori

urban intensity index lies in gaining access to contemporary 

land-cover data. This challenge is amplified if there is a need 

to implement these studies simultaneously in many areas 

across the country. The ability to compare and contrast results 

from multiple studies relies to a great extent on the availability 

of explanatory and response data collected using common protocols. The need for regularly updated land-cover information 

in urbanizing areas, based on comparable data and classification protocols, far exceeds the availability of such data.

The urban intensity index did not include any measure 

or estimate of impervious surface; the impervious surface 

estimate for each basin was instead used to assist in site selection. Imperviousness is a particularly important indicator of 

urban intensity (fig. 3) because it can be readily measured at 

a variety of scales (that is, from the parcel level to the watershed) and because it has been consistently shown to affect 

stream hydrology and water quality (Schueler, 1994). The 

measurement of impervious surface for large spatial areas, 

whether from interpretation of large-scale aerial photography 

WSHED Units Adjustment Maximum Minimum Adjusted

imperv1 0.03 3.12 45.85 1.66 3.30

LU21_CD1 0.01 0.63 48.38 0.00 1.31

LU22_CD1 0.00 0.00 11.47 0.00 0.00

LU23_CD1 0.00 0.08 12.76 0.00 0.66

urban_mi2 1.02 2.99 70.36 0.25 3.91

for_mi2 24.92 72.99 88.18 17.65 21.54

MRLC_212 0.90 2.64 53.99 0.16 4.60

MRLC_222 0.00 0.00 11.56 0.00 0.01

MRLC_232 0.12 0.35 15.02 0.08 1.77

MRLC_432 8.62 25.25 48.11 4.61 52.54

BUF_212 0.12 0.36 11.37 0.00 3.15

BUF_222 0.00 0.00 2.57 0.00 0.00

BUF_232 0.01 0.03 3.99 0.00 0.79

URB_BUF 2.71 2.71 69.15 0.00 3.91

FOR_BUF 66.57 66.57 93.83 13.99 34.14

TRICOUNT3 1.00 2.93 179.39 0.00 1.63

SEI_34 -3.84 0.74 8.88 -4.69 8.36

AGESTR971 0.42 41.52 48.57 22.83 27.39

ROAD-DEN 1.68 1.68 9.56 1.14 6.45

Average value of all metrics: 9.24

Maximum average for all sites: 80.54

Minimum average for all sites: 4.04

Index adjusted to 0-100 range: 6.80

Table 4. Example calculation of the urban intensity index for one 

watershed, using representative variable types.

1

 Proportion of basin area converted to percentage of basin area

2

 Area converted to percentage of basin area

3

 Total counts converted to counts per 100 mi2 of basin area 4

 SEI adjusted = SEI- minimum SEI


43


42


41


73 
72 
71 
70

Figure 2. Urban intensity index scores for New England land-use 

gradient study.

y = 2.1326x + 0.3428

R2 = 0.9558

0

20

40

60

80

100

120

0 10 20 30 40 50

Urban land-use intensity index

Percent impervious surface area 

Figure 3. The relation between impervious area and the urban intensity index in the Boston study.

Conclusions
110 Quantifying Urban Intensity in Drainage Basins for Assessing Stream Ecological Conditions

or through classification of remotely sensed data, was not 

practical for this project. As is the case with land-cover data, 

the need for impervious surface data far exceed the resources 

typically available to generate the data, particularly to support 

investigations with a large spatial extent. Research is needed 

on techniques to measure or model impervious-surface data 

at the scale needed to support regional investigations of the 

impacts of urbanization.

The value of the urban intensity index in explaining water 

quality response will be uncertain until water quality data have 

been collected and analyzed. The index was intended primarily 

to be used to provide an a priori basis for ranking the relative 

intensity of urban development. The conceptual understanding 

of factors that are associated with urban intensity was purposefully inclusive and included factors not usually considered in 

explaining variations in water quality. An important result of 

the analysis stage of the urban gradient studies will be to shed 

light on the explanatory value of the individual variables that 

comprise the index.

Acknowledgments

The authors gratefully acknowledge the assistance provided by colleagues participating in the urban gradient studies—Brian Atkins, Rob Baskin, Wade Bryant, Jim Coles, 

Carol Couch, Elise Giddings, Steve Goodbred, Marty Gurtz, 

Doug Harned, John Harvill, Evelyn Hopkins, Craig Johnston, 

Keith Robinson, Terry Short, Steve Smith, Kidd Waddell, and 

Humbert Zappia.

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113

Introduction

The Middle Rio Grande Basin (MRGB) Study was a 

6-year effort (1995 to 2001) by the U.S. Geological Survey 

(USGS) and other agencies to improve the understanding of 

the hydrologic, geologic, and landscape characteristics of the 

MRGB in order to provide the scientific information needed 

for water-resources management. The Albuquerque metropolitan area located within the MRGB is faced with rapid population and economic growth and relies on the Santa Fe Group 

aquifer as the main source of municipal water. The New 

Mexico Office of the State Engineer administers the appropriation and use of New Mexico water resources and has declared 

the basin a “critical basin”; that is, a ground-water basin faced 

with rapid economic and population growth where there is less 

than adequate technical information regarding the available 

water supply (Bartolino, 2002). 

To investigate how humans are altering the landscape, the 

USGS, as part of the MRGB study, researched the anthropogenic impacts of Albuquerque’s urban growth on central New 

Mexico’s land surface. To monitor land-use transitions, temporal land-use and land-cover data for the timeframes covering 

the 1930s, 1950s, 1970s, and 1990s were mapped to analyze 

patterns, rates, and trends in Albuquerque’s landscape change.

Study Area Description

As part of the MRGB Study, USGS researched the anthropogenic impacts of Albuquerque’s urban growth on central 

New Mexico’s land surface. For the landscape change investigation, the USGS used the Albuquerque Metropolitan Statistical Area (MSA: consisting of Bernalillo, Sandoval, Torrance, 

and Valencia Counties) as the geographic extent for regional 

land-use history and socioeconomic analysis. This area 

includes twenty-eight 1:24,000-scale (24K) quadrangles for 

temporal land-use trend analysis and sixteen 24K quadrangles 

centered on Albuquerque’s urban core as the study area for 

predictive landscape change modeling (fig. 1).

Socioeconomic Setting

As of 1990, the Albuquerque MSA accounted for about 40 

percent (590,000) of New Mexico’s residents. From 1900 to 

1990, the population of New Mexico increased from 195,000 

inhabitants to 1,515,000 (U.S. Bureau of the Census, 1995). 

The Albuquerque MSA contributed approximately 40 percent 

(550,000) to New Mexico’s population growth during this 

time.

Historically the employment sectors that drove urban 

growth and landscape change in the Albuquerque region were 

transportation (the Atchison, Topeka, and Santa Fe Railroad), 

education (University of New Mexico), healthcare (U.S. Veterans Administration hospital), and military/Federal (Kirtland 

Air Force Base and Sandia National Laboratories). These same 

employment sectors in 1995 accounted for about 45 percent 

of employers in the Albuquerque region and the Albuquerque 

MSA employed 325,000 or about 50 percent of New Mexico’s 

workforce (Middle Rio Grande Council of Governments, 

1997a).

The location and geographic extent of Albuquerque’s 

urbanized areas on the landscape is influenced by land-surface 

property rights such as land ownership. Public and NativeAmerican land ownership, such as Cibola National Forest, 

Petroglyph National Monument, Isleta Pueblo, Sandia Pueblo, 

and Santa Ana Pueblo create land areas within the Albuquerque metropolitan area that are exempt from future urbanization. Approximately 60 percent (2,304,000 acres) of the 

Albuquerque MSA is excluded from future urban growth due 

to land ownership (Middle Rio Grande Council of Governments, 1999).

Physical Environment Setting

Located in central New Mexico along the Rio Grande, 

the Albuquerque urban area at an elevation of approximately 

5,300 feet is the highest metropolitan region in the United 

States (Albuquerque Economic Development Team, 2004). 

Albuquerque’s physiographic setting is comprised of mountain, mesa, and river valley landforms. Starting in the Rio 

Grande Inner Valley and traveling east of central Albuquerque 

in Bernallilo County we cross alluvial fans on the East Mesa 

bounded by the Sandia Mountains, which rise to an elevation of almost 11,000 feet. Intersecting the Sandia Mountains 

to the south is Tijeras Canyon through which runs Federal 

Analyzing Albuquerque’s Landscape Evolution in the 

20th and 21st Centuries

By David J. Hester1

1

U.S. Geological Survey, Rocky Mountain Geographic Science Center, Fort 

Collins, CO 80526, (303) 202-4318, dhester@usgs.gov.
114 Analyzing Albuquerque’s Landscape Evolution in the 20th and 21st Centuries

Figure 1. Study area overview map showing geographic extents for Albuquerque land-use history, landscape-change 

analysis, and land-use modeling research.
115

Interstate Highway 40; formerly U.S. Route 66, and is the 

northern border for the Manzanita Mountains to the southeast 

of Albuquerque. Further south of the Manzanita Mountains 

is the Manzano mountain range which is primarily located 

within the Torrance County portion of the Albuquerque MSA. 

Traveling west across the high plateau mesa, we traverse back 

through Valencia County to the Rio Grande Inner Valley and 

riparian woodland area known as the Bosque. To the west of 

the Rio Grande floodplain and Bosque riparian corridor, the 

Albuquerque metropolitan area is framed on the western side 

by a volcanic escarpment located within Sandoval County 

known as the West Mesa.

Physical terrain characteristics, such as slope, can influence which portions of the land surface will typically transition from the natural landscape into urban lands. As of 1991, 

approximately 98 percent of Albuquerque’s land-use development (83,600 acres) had occurred predominantly on flat land 

with slopes less than 10 percent (table 1). Because about 90 

percent of land available for development (240,800 acres) 

has slopes less than 10 percent, Albuquerque’s future urban 

growth does not appear to be constrained by steeper slopes on 

the land surface.

Prior to the actual settlement of Albuquerque in 1706, the 

Spaniards evaluated the area to determine the most suitable 

site for a village. The location selected for Albuquerque was 

based on the necessary natural resource, water, as required by 

Spanish law (Stier, 1998). Today, the Santa Fe Group Aquifer 

is the main source of municipal water for sustaining urban 

growth in the Albuquerque region. Because approximately 

600,000 people live in the region, water shortfalls could have 

serious economic consequences for the State. Albuquerque’s 

annual ground-water withdrawal from the Santa Fe Group 

Aquifer has increased from about 2,000 acre‐feet in 1933 to 

about 113,000 acre‐feet in 1998 (Kernodle, 1995), with more 

than 55 percent (2,080,000 acre-feet) of the total withdrawal 

occurring between 1980 and 1998. New high-technology 

industries, for example Intel’s Rio Rancho microchip manufacturing plant, place a high demand on existing ground-water 

resources. To avoid continued sole reliance on ground-water 

for domestic water supplies, the City of Albuquerque has 

acquired 48,000 acre‐feet of surface water rights known as 

the San Juan‐Chama project (Albuquerque Journal, 1999) to 

sustain Albuquerque’s continued economic and population 

growth.

Ecological Setting

The Albuquerque urban area is a semiarid metropolis 

that is an intersection of the Great Basin and Great Plains 

grasslands and upper Chihuahuan Desert ecological regions 

(Middle Rio Grande Council of Governments, 1999). Annually, the Albuquerque metropolitan area averages approximately 9 inches precipitation, with the surrounding higher 

elevation mountainous areas receiving as much as 20 inches 

per year. Most of the total annual precipitation comes during 

Albuquerque’s monsoon season which runs from June through 

September (Albuquerque Economic Development Team, 

2004).

Elevation variance from approximately 5,000 feet up to 

almost 11,000 feet and localized differences in the amount of 

annual precipitation result in the Albuquerque metropolitan 

area being covered ecologically by six distinct vegetative 

zones (Rosner, 1996a). The Rio Grande Inner Valley and 

Bosque riparian areas fall within the river’s floodplain and the 

Grasslands vegetative zone (less than 5,500 feet). Even though 

Albuquerque’s urban growth through time has decreased the 

irrigated acreage and number of farms within the Rio Grande 

Table 1. Terrain characteristics, such as percent slope, for the Albuquerque metropolitan area 

and 1991 developed land.

Study Area Description
116 Analyzing Albuquerque’s Landscape Evolution in the 20th and 21st Centuries

Valley, there still are fertile agricultural lands used for pasture 

and growing crops such as alfalfa. The riparian woodland area 

known as the Bosque, which is adjacent to the Rio Grande, 

benefits ecologically from available ground water and is dominated by native species such as cottonwood as well as invasive vegetative species such as salt cedar and Russian olive 

(Rosner, 1996b). Rising above 5,500 feet and the Rio Grande 

Inner Valley, we enter the Upper Sonoran vegetative zone that 

covers the terraces known as the East and West Mesas. These 

terraces, formed by debris flows from the Sandia Mountains as 

well as windblown and volcanic processes, have historically 

been prairie grasslands. Today, the natural vegetative cover 

for the East Mesa remains short grasses, such as blue and 

black gramma, as well as shrubs like saltbush. In comparison, 

the West Mesa is transitioning from short prairie grasses to 

vegetative cover indicative of a desert grassland ecosystem 

(Rosner, 1996d). Climbing into the Sandia and Manzano 

Mountains east and southeast of Albuquerque we encounter 

the montane and subalpine forest vegetative zones. These 

zones are the Upper Sonoran (6,100 to 7,600 feet) in the foothills, the Transition (7,600 to 8,200 feet), and Canadian (8,200 

to 10,000 feet) on the mountain slopes and Hudsonian (above 

10,000 feet) on the mountain peaks. Albuquerque’s foothill 

communities on the western slope of the Sandia Mountains, as 

well as towns such as Cedar Crest and Sandia Park on the eastern mountain slopes, are covered by these vegetative zones. 

Due to higher elevation of the mountainous terrain, greater 

precipitation and runoff in these zones results in denser, taller 

vegetative cover. The aspect or direction of sunlight creates 

different vegetation on each side of the canyons within the 

Sandia and Manzano Mountains depending on whether the 

mountain slope has a southern or northern exposure. Vegetative species that are indicative of the Upper Sonoran, Transition, Canadian, and Hudsonian zones for a northern-facing 

mountain slope are pinyon pine, ponderosa pine, Gambel oak, 

and blue spruce (Rosner, 1996c). In contrast, a southern-facing 

slope in the mountains surrounding Albuquerque would typically have prickly pear cactus, Utah juniper, mountain mahogany, and white fir.

Historic Urban Growth

Analyzing the evolution of Albuquerque’s landscape 

required developing an understanding of the regional land-use 

history and the historical events that helped form the city’s 

land use patterns. For example, evaluating the “Bird’s Eye 

View of Albuquerque” map (fig. 2) (Fitzpatrick, 1976) drawn 

by Augustus Koch in 1886 reveals that during the late 1880s 

several characteristics affected land-use patterns. These were 

(1) the Albuquerque Foundry (city’s 2nd largest employer), (2) 

the Atchison, Topeka, and Santa Fe Railroad (railroad transportation improved access to the region), (3) East Mesa “sandhills,” east of Walter Avenue (terrain inhibited development), 

(4) “new” Albuquerque’s rectangular network of city streets 

(surveyed and platted roads shaped future urban form), and (5) 

the Rio Grande (water resources sustained irrigated agriculture 

adjacent to Old Town).

Human-induced land transformations were characterized by temporally mapping the shape and spatial extent of 

Albuquerque’s urban form. The urban form at any given time 

represents the land that has been consumed by the built environment, including residential, retail, industrial, communications and utilities, transportation, and recreational facilities 

and institutions. Understanding Albuquerque’s regional landuse history required relating the mapped temporal land-use 

patterns to significant social, political, and environmental 

events that contributed to the region’s urbanized area.

Albuquerque’s ability to conduct commerce was 

enabled with the arrival of the Atchison, Topeka, and Santa 

Fe (AT&SF) railroad in 1880. The improved transportation allowed Albuquerque’s agricultural, textile, and mining 

commodities to be sold in markets throughout the United 

States (Stier, 1998). The AT&SF railroad also brought an 

influx of new residents seeking employment to the region. 

Road‐influenced growth began to affect the landscape with the 

introduction of Albuquerque’s first automobile in 1897. The 

introduction of the electric trolley in 1904 permitted access to 

Albuquerque’s perimeter and created urban growth corridors 

extending outward from the established urban center (Johnson, 1981). Albuquerque’s entrance into the age of air travel 

in 1928 enhanced urban growth as the region became New 

Mexico’s major transportation hub. By 1935, the spatial extent 

of the road network had grown to about 245 miles of principal 

transportation routes (fig. 3), and Albuquerque’s urbanized 

area covered approximately 4,400 acres of the land surface 

(table 2 and fig. 4). Developed land was predominantly east 

of the Rio Grande and within Bernalillo County. The majority of the urban center was concentrated in the Central Albuquerque region. Development began to expand onto the East 

Mesa (Near Heights), as influenced by the University of New 

Mexico, U.S. Veterans Administration Hospital, and Albuquerque Municipal Airport (Fitzpatrick, 1976). Locations of small 

urban cores such as Isleta and Bernalillo (North Valley) were 

found adjacent to the AT&SF railroad. The only urban centers 

west of the Rio Grande were Atrisco and Los Padillas in the 

South Valley.

Between 1940 and 1950 Albuquerque’s urban footprint 

doubled, and by 1951 the built environment had grown to 

cover approximately 15,400 acres of the land surface (table 2 

and fig. 4). Driving urban growth during this period was the 

development of major military installations (U.S. Air Force 

bases) such as Kirtland, Sandia, and Manzano (Taylor, 1996) 

on the East Mesa. The establishment of Los Alamos Laboratory in 1943 initiated New Mexico’s involvement in atomic 

research during World War II (WWII). The development 

of nuclear weapons by the Los Alamos Manhattan Project 

required extensive testing facilities and flight support and 

in 1945 the Z Division Unit was moved to Sandia Base in 

Albuquerque (Collins, 1996). In 1949, the Sandia Corporation was formed from the Z Division Unit to focus on development and testing of special weapons. Today, the Sandia 
117

National Laboratories (SNL) continues conducting research 

and development of energy sources as the largest tenant on 

Kirtland Air Force Base (AFB). During the post-WWII years, 

Albuquerque’s urban development occurred primarily in the 

North Valley (Los Ranchos de Albuquerque and Los Griegos), 

in close proximity to Kirtland AFB and SNL on the East Mesa 

(Mid-Heights area), and spread west of the Rio Grande (South 

Valley‐Armijo and Pajarito). The introduction of commercial 

jet aircraft in 1958 and subsequent opening of Albuquerque’s 

new airport terminal in 1963 shifted one of the primary entry 

points for newly arriving residents from the railroad depot to 

the airport (Gerckens, 1998). Air transport provided by the 

Albuquerque International Sunport has assumed a similar 

enabling role for Albuquerque’s future landscape change as 

did the AT&SF railroad in the 1880s.

As part of the 1956 Federal Interstate Highway System 

Act implementation, the completion of Interstate 40 in 1973 

and Interstate 25 in 1975 stimulated the peripheral expansion 

of Albuquerque’s metropolitan area. The Albuquerque region 

continued to boom during the post‐World War II years, with the 

highest annual rate of urban landscape change peaking at 7.19 

percent during the 1960s (table 2). The Winrock and Coronado 

Shopping Centers were constructed then, ending downtown 

Albuquerque’s retail dominance (Taylor, 1996) and stimulated 

urbanization in the East Mesa (Mid‐Heights) area. Urban 

development in the East Mesa (Foothills) area, including the 

Figure 2. 1886 “Bird’s Eye View of Albuquerque” drawn by Augustus Koch from Albuquerque—100 Years in Pictures (Fitzpatrick and Caplin, 

1976).

Historic Urban Growth
118 Analyzing Albuquerque’s Landscape Evolution in the 20th and 21st Centuries

1966 Sandia Peak Tramway opening, brought an increasing 

demand for urban city services. The completion of I‐40 and 

I-25 improved the east-west and north-south access through 

the region (Stier, 1998) and increased road‐influenced corridor 

development. The region experienced approximately 60 percent growth in primary roads between 1951 and 1973 (fig. 3), 

which influenced urban growth in the North Valley (Alameda 

and Corrales) and the South Valley (Mountainview). Interstate 

40 became the eastern gateway to Albuquerque, contributing 

to tremendous urban growth and land development between 

1951 and 1973 on the East Mesa area (North Albuquerque and 

Mid-Heights). During the early 1960s, urban growth northwest 

of Albuquerque was initiated when the American Real Estate 

and Petroleum (AMREP) corporation purchased the Koontz 

Ranch and began a nationwide land sales marketing program 

to develop 90,000 acres on the Northwest Mesa. AMREP sold 

more than 75,000 lots between 1961 and 1977 with the first 

homes for the Rio Rancho community west of the Rio Grande 

being constructed in 1963 (Ryan, 1996). By 1973, the Albuquerque region’s developed land had grown to 49,700 acres 

(table 2 and fig. 4) and the Rio Rancho community was well 

on its way to becoming an incorporated city.

By 1991, Albuquerque’s human‐induced land transformations had consumed 84,900 acres of the region’s landscape as 

urban land (table 2 and fig. 4). The dominant land-use trend 

was urban expansion west of the Rio Grande and along Coors 

Boulevard (New Mexico State Highway 45) onto the Northwest Mesa (Rio Rancho) and the Southwest Mesa. Rapid 

development west of the Rio Grande, that is the West Side, 

was due to lower land prices and close commuting proximity 

to Albuquerque. Rio Rancho’s urban growth on the Northwest 

Mesa by 1995 had resulted in the fourth largest New Mexico 

city (Ryan, 1996). Economic growth driving West Side urbanization included the construction of Intel’s Rio Rancho microchip manufacturing plant (Albuquerque Journal, 1999) and 

the State’s largest regional shopping mall (Cottonwood). Intel 

selected Rio Rancho in 1980 as the site for constructing its 

new plant based on criteria such as the presence of intellectual 

capital (Sandia National Laboratories), proximity to an international airport (Sunport) and major highways (Interstate 25 

and 40), a metropolitan area population greater than 500,000, 

local access to a major academic research institution (University of New Mexico), housing and living costs below the 

national average and the abundance of ground-water resources 

beneath Albuquerque (Starner, 2001). As urban growth 

expanded on the Rio Grande’s west side, land-use development encroached upon the Petroglyph National Monument. A 

proposed 1998 Federal bill suggested withdrawing 8.5 acres 

from the National Monument to extend the Paseo del Norte 

transportation corridor through the historical area. The objective in extending Paseo del Norte is to service planned urban 

developments on the western edge of the monument (Robbins, 

1999). Urbanization continued to spread in the North Valley 

(Corrales) while South Valley land-use development increased 

concerns regarding preservation of existing agricultural land. 

Albuquerque’s urban footprint on the East Mesa expanded 

eastward with increased growth in the East Gateway area and 

Tijeras Canyon (Carnuel). Even though Albuquerque’s metropolitan urban form exhibited in‐fill development and satellite 

0

100

200

300

400

500

600

1935 1951 1973 1991 Year

Miles

Figure 3. Primary road mileage (1935 to 1991) for the Albuquerque 

metropolitan area.

Table 2. Albuquerque Metropolitan Statistical Area (MSA) and City of Albuquerque urbanization and population growth metrics for the 

20th century.
119

urban centers growing together through time to become urban 

cores, the long-term land-use trend from 1935 to 1991, based 

on the basis of a visual assessment, was greater dispersion of 

urban lands on the region’s landscape (fig. 4).

Albuquerque’s rank as New Mexico’s largest city according to the 2000 Population and Housing Census is attributable 

to a variety of socioeconomic factors from which it benefited 

throughout the 20th century. Although the city’s historic urban 

growth was enabled by being a regional hub of the national 

transportation infrastructure (that is, AT&SF Railroad, Interstate Highways 25 and 40, and Sunport International Airport), 

it was the diversity of its historic economic development that 

drove its urban expansion and distinguished it from other 

cities in New Mexico. The amenities of higher education and 

health-care systems that attracted people to Albuquerque early 

in the 20th century were augmented by the lure of employment 

provided by significant local investment by the Federal government during the Great Depression and WWII. The post-WWII 

period saw the emergence of Albuquerque’s manufacturing 

base as industries ranging from electronics to clothing began 

locating their operations in the area. Presently, Albuquerque’s 

solid base of labor and intellectual capital coupled with its low 

business tax rates and various incentives (Albuquerque Economic Development Team, 2004) position the city to maintain 

its appeal to new industries and residents in the 21st century.

Regional Land-Use Trend Analysis

The dominant land-use trend is the transition of undeveloped land to human settlements, workplaces, and urban infrastructure. To monitor regional land-use transitions, temporal 

land-use and land-cover (LULC) data were mapped to analyze 

patterns, rates, and trends in Albuquerque’s landscape change.

Temporal Land-Use and Land-Cover Mapping

As part of the MRGB Study, the USGS temporally 

mapped LULC data for the timeframes covering the 1930s, 

1950s, 1970s, and 1990s (Stier, 1999). The contemporary 

and historical LULC were collected for an area consisting 

of twenty-eight 24K quadrangles covering the Albuquerque 

urban area (fig. 1).

Figure 4. Three-dimensional perspective view of urban land in the vicinity of Albuquerque (1935 to 1991). Urban land is shown in red and 

perspective view is to the northeast.

Regional Land-Use Trend Analysis
120 Analyzing Albuquerque’s Landscape Evolution in the 20th and 21st Centuries

The first step in compiling the temporal LULC data was 

to acquire historical aerial photographs, USGS digital orthophoto quadrangle (DOQ) imagery, and ancillary data for collecting, interpreting, and classifying the land-surface features. 

As part of the MRGB Study, historical aerial photographs 

were acquired from the Department of Agriculture’s Natural 

Resources Conservation Service, U.S. Forest Service, and 

USGS archives. For the 1930 and 1950 time periods, only 

black-and-white aerial photographs were available. Acquisition of ancillary source material, such as the U.S. Fish & Wildlife Service National Wetlands Inventory data, USGS 24K 

topographic maps, and the Bureau of Reclamation’s land-use 

trend analysis data, helped enhance the interpretation and classification of Albuquerque’s historical LULC data. 

The historical aerial photographs acquired were scanned, 

geo-referenced, and mosaiced using image processing software. During the geo-referencing process, a USGS 24K 

digital-line-graph (DLG) file was used to locate well-defined 

features, such as road intersections, and the DLG coordinates 

for each feature were transferred to the same feature on the 

scanned historical image. The geo-referenced portion of each 

photograph was clipped and mosaiced to adjacent aerial photographs in order to create a historical image covering each 

USGS 24K quadrangle for the Albuquerque urban area.

Temporal LULC mapping for Albuquerque used a minimum mapping unit of 2.5 acres and a minimum polygon 

width of 125 feet for delineating land-surface features. The 

interpretation of LULC categories followed a modified Anderson Level IV classification scheme developed by the USGS 

Rocky Mountain Geographic Science Center. Contemporary 

LULC features were mapped first using the 1990s DOQ as the 

primary data collection source. Upon completion, the 1990s 

LULC data set was used as the foundation for collecting the 

1970s LULC data. The completed 1990s LULC data set was 

modified using the 1970s imagery as a reference for delineating 1970s land-use patterns. The 1950s and 1930s LULC data 

sets were collected using the next most recent time period as 

the land-surface foundation and subsequently modified using 

the older historical images to reflect the respective temporal 

land-use patterns. Using the more recent LULC data set as 

the foundation for collecting earlier time periods avoided the 

collection of unchanged LULC features. The historical LULC 

data compiled for each 24K quadrangle was then merged into 

one seamless data set per vintage for subsequent land-use 

trend analysis.

Land-Use Trends

Albuquerque is a post-WW II American city that experienced more than half of its population growth since 1940. 

Subsequently, the 1930s LULC data are considered the base 

land-surface condition for analyzing landscape composition 

and categorical land-use trends. In the 1930s, the principal 

LULC categories dominating the landscape were rangeland 

(shrubs, natural grasses, and other herbaceous vegetation) 

and forested areas (fig. 5 and table 3). During the early half of 

the 20th century, surface water from the Rio Grande was the 

source of life for agrarian communities and irrigated agriculture was centered within the Inner Valley due to easy access 

to water. However, existing irrigation systems for agriculture 

were insufficient and primitive. Consequently, the Middle Rio 

Grande Conservancy District (MRGCD) was created during 

the 1920s to improve the irrigation system, drain and reclaim 

saturated land, and make the Inner Valley a more profitable 

place to farm (Hansen, 1997). The irrigation system improvement by the MRGCD is exhibited by the fact that agricultural 

land occupied 5.5 percent of the landscape by 1935 (table 3) 

and was the primary land use along the Rio Grande. Albuquerque was still the dominant urbanized area, however, communities such as Bernalillo, Placitas, and Isleta are evident as 

satellite urban centers in figure 5.

Between the 1930s and 1950s, the urban land area nearly 

tripled to approximately 24,000 acres (table 3). The increase 

in the region’s urban footprint was primarily the result of agricultural land and rangeland being converted to urban land use. 

Conversion of a land parcel to another land use is dependent 

on an area’s accessibility and proximity to the transportation 

infrastructure. Expansion of the transportation network by 

approximately 30 percent resulted in urbanization extending 

linearly in all directions outward from Albuquerque’s urban 

core (fig. 5). The decline in water acreage and the corresponding increase in barren land (table 3) are most likely a result of 

the 1950s drought experienced by the region.

The distance and speed that humans can commute 

between their residence and workplace is one of the factors influencing dispersion of land use development on the 

landscape. Transportation infrastructure improvements, such 

as Interstate 40, resulted in leapfrog development of urbanized areas during the 1970s occurring primarily east of the 

Sandia Mountains and Cibola National Forest (fig. 5). The 

spontaneous and diffusive growth of bedroom communities 

east of Cibola National Forest, such as Sandia Park and Cedar 

Crest, resulted in the first occurrence of forested land actually 

declining on the Middle Rio Grande landscape (table 3). Even 

though the agricultural land acreage decreased from the 1950s 

to the 1970s (table 3), there is a definite growth in agriculture 

north and south of Moriarty (fig. 5). Torrance County in which 

the community of Moriarty is located, historically had agricultural land devoted to pinto bean production until the drought 

of the 1950s affected the agricultural land-use management 

practice of dry crop land farming (Middle Rio Grande Council 

of Governments, 1997a). Declaration of the Estancia Underground Water Basin by the New Mexico State Engineer in 

1950 (Shoemaker, 1996) led to the development of Estancia 

Basin ground-water resources in Torrance County and circular 

land-use patterns evident on the 1970s landscape surrounding 

Moriarty demonstrate the use of center-pivot technology for 

production of irrigated agricultural crops (fig. 5). The increase 

in water acreage (table 3) between the 1950s and 1970s is 

due to dredging and channelizing the Rio Grande by the U.S. 

Bureau of Reclamation and Army Corp of Engineers to control 
121

the river’s flow and reduce Albuquerque’s susceptibility to 

flash flooding.

By the 1990s, Albuquerque’s urban footprint approximately doubled in comparison to the 1970s landscape (table 

3). The continued decline in agricultural land (table 3) illustrates the shift from an extractive economy to a service-oriented economy. The increase in barren land from the 1970s 

to the 1990s (table 3) is most likely not a result of climatic 

change, but rather land that is in a state of transition. The 

barren land may be either “brownfields” awaiting redevelopment or land where the natural land cover has been removed 

in preparation for new urban development. Emergence of Rio 

Figure 5. Temporal (1935 to 1991) 1:24,000-scale land-use and land-cover (LULC) data generalized to Anderson Level I LULC categories for the 

Albuquerque area.

Table 3. Temporal land-use and land-cover categorical composition for the Albuquerque area from the 1930s to the 1990s.

Regional Land-Use Trend Analysis
122 Analyzing Albuquerque’s Landscape Evolution in the 20th and 21st Centuries

Rancho and the associated Intel microchip processing plant 

west of the Rio Grande began the westward shift of Albuquerque’s population and urban growth. Transition of Rio Rancho 

from a smaller local community to a larger regional city is 

exhibited by the creation of the Double Eagle airport west of 

the Rio Grande (fig. 5). From 1980 to 1990, continued urban 

land transformations around the communities of Edgewood in 

southern Santa Fe County and Moriarty in Torrance County 

(fig. 5) contributed to Torrance County becoming the fastest-growing nonmetropolitan county in New Mexico during 

the 1990s. Since the 1970s, Torrance County has experienced 

inmigration population growth from residents who work in 

the Albuquerque urban area but desire the amenities of living 

in a rural landscape (Middle Rio Grande Council of Governments, 1997b). Communities such as Moriarty and Edgewood 

on Albuquerque’s eastern urban fringe began to grow between 

the 1970s and 1990s with low-density land-use development 

and became part of the metropolitan area’s commuting region. 

Because Moriarty and Edgewood are approximately 35 miles 

from Albuquerque on Interstate 40, the fact that 35 percent of 

Torrance County residents who did not work at home in 2000 

had commute times between 45 to 90 minutes demonstrates 

the employment opportunities offered by the Albuquerque 

metropolitan area (New Mexico Economic Development 

Department, 2004).

Urban Growth Rates

Spatial statistical analysis of Albuquerque’s temporal 

urban growth and human footprint can improve the understanding of how the land surface has transitioned through time. 

During the process of modeling Albuquerque’s landscape 

change, an extract of the urbanized area from the temporal 

land use and land cover data is used for calibrating the University of California-Santa Barbara (UCSB) SLEUTH (Slope, 

Land use, Exclusions, Urban, Transportation, and Hillshade) 

land-use model. During model calibration, Albuquerque urban 

form metrics, such as number of urbanized pixels, number of 

urban edges, and number of clusters, are statistically calculated for each temporal period.

Figure 6A represents the number of urbanized pixels, or 

portion of the landscape converted to urban land use. Each 

pixel equates to a 240-meter cellsize resolution, or 57,600 

square meters (~14.2 acres) in area. Figure 6A illustrates that 

the period 1973 through 1991 represented the greatest total 

transition to urban land (2,470 pixels), whereas the period 

1935 to 1951 represented the highest percent urban growth 

rate (table 5) due to Albuquerque’s post-WW II economic 

boom. From 1951 to 1973, the conversion rate of 200.8 pixels 

per year to an urban use was a direct result of the densification 

and extension of the road network.

The interface between urban land (developed) and wildland (undeveloped land) is what is classified as “edges” or 

the urban fringe. For example, any pixel classified as urban 

land that shares at least one side with land that is undeveloped is counted as an edge. The increasing number of pixels 

from 1935 to 1991 with an urban land/wildland edge is an 

indicator of Albuquerque’s dispersed land-use development 

on the Middle Rio Grande landscape. Examination of Figure 

6B shows that from 1951 to 1973, the greatest total number 

of pixels with an urban land and wildland interface occurred. 

This urban fringe metric correlates with the implementation of 

the 1956 Federal Interstate Highway Act and the construction 

of I-25 and I-40 during the 1951 to 1973 timeframe.

Clusters are the number of pixels classified as developed land that are spatially contiguous. For example, a single 

isolated urban pixel surrounded by undeveloped land would 

count as one cluster. Quantifying Albuquerque’s landscape 

change using a cluster metric provides insight regarding the 

type of urban growth occurring on the landscape and the phase 

of land-use development. Initially, a single pixel can be a 

cluster or urban area driving landscape change. The increase 

in the number of clusters from 1935 to 1973 is an example 

of spontaneous and diffusive growth as urbanization centers 

expanded across the regional landscape. An increase in the 

number of clusters indicates new development or the transition 

of undeveloped land cover to an urban land use. The greatest 

total increase in the number of clusters (66) and the highest 

annual rate for cluster development during the period from 

1951 to 1973 (fig. 6C), illustrate the effects of road-influenced 

growth and the spread of urban centers on the landscape. Since 

1973, the total number, percent growth, and annual rate of 

urbanized area clusters have been declining. The downward 

trend indicates that Albuquerque’s landscape is maturing as 

satellite urban centers grow together into urban cores along 

transportation corridors as the result of in-fill development.

Human Migration Trends

Analysis of Albuquerque’s human migration trends allows 

researchers to investigate the social, economic, and political 

causes influencing temporal changes in the direction, distance, 

and migration rates in population and urbanized area centers 

of mass. Comparative analysis between the Albuquerque 

MSA historical population center and Albuquerque’s temporal urbanized area center migratory paths was conducted as a 

technique to study the different patterns of movement between 

these two centers as a quantitative metric of population density loss (distribution) and urban sprawl (landscape change). 

According to a Brookings Institution Center on Urban and 

Metropolitan Policy study, Albuquerque was ranked as having 

the fourth largest population density loss between 1982 and 

1997 for MSA’s in the Census Bureau’s Western Region 

(Fulton, 2001).

To coincide as closely as possible to the historical vintages and time periods that were used for mapping Albuquerque’s urban lands, temporal population centers for the 

Albuquerque MSA were calculated for the 1930, 1950, 1970, 

and 1990 decades. A population center represents the point at 

which an imaginary, weightless, rigid, and flat land surface 
123

would balance if each individual person had an equal weight. 

The historical population centers for the Albuquerque MSA 

were calculated as the spatial mean of the four counties (Bernalillo, Sandoval, Torrance, and Valencia) comprising the 

MSA weighted by each county’s population for the selected 

decades. An urban center is defined as the geographic center of 

all urbanized areas at which point the urban or developed land 

surface would balance if it were a plane with uniform weights 

for each urban pixel. During the process of calibrating the 

SLEUTH land use model for simulating Albuquerque’s future 

growth, an urban area center was calculated for each historical thematic urban land input. For each historical population 

and urban area center, migration distance, direction and annual 

migration rate metrics were derived (table 4).

Analysis of table 4 and annual rates of urban change and 

population change from table 2 illustrate that the population 

center shows greater sensitivity to changes in growth rates 

than does the urban center. Although the annual rate of urban 

change was consistently greater through time than the annual 

rate of population change (table 2), the annual MSA population-center migration rate changed more dramatically than did 

the urban-area-center migration rate. For instance, the MSA 

population center migration rate in table 4 decreased by 49 

percent from 1950 to 1970 and increased 212 percent from 

1970 to 1990, whereas in comparison the urban area center 

migration rate respectively increased 18 percent and decreased 

23 percent for these same time periods. From 1970 to 1990, 

the Albuquerque MSA population center migrated the farthest 

distance and at the fastest rate and coincides with the Albuquerque MSA experiencing the greatest change in population 

(table 2) during this same timeframe. In comparison, the urban 

area center migrated the greatest distance and at the highest annual rate from 1951 to 1973 and can be predominantly 

attributed to dispersed land-use development and road-influenced urban growth during this period as a result of the completion of Interstate 25 and Interstate 40 through the region.

Visualization of the population and urban area centers 

through time (fig. 7) is a technique that can be used to understand the relation between the temporal migratory path and 

historical land-use and demographic trends. From 1935 to 

1973, the urban area center migrated in an eastern or southeastern direction as a result of the majority of Albuquerque’s 

land-use development occurring primarily on the East Mesa 

(Mid-Heights), as well as around Kirtland Air Force Base and 

Sandia National Laboratories. Since the 1970s, the migration 

direction for Albuquerque’s urban area and population cenNumber of Pixels Number of Pixels Number of Pixels

A

B

C

Figure 6. Urban area (A), urban land/wildland edges (B), and urban 

clusters (C) metrics monitoring Albuquerque’s urban landscape 

change from 1935 to 1991. Each pixel equates to a 240-meter cell size 

resolution or 57,600 square meters (~14.2 acres).

Figure 7. Visualization of Albuquerque Metropolitan Statistical Area 

(MSA) population center and urban area center of gravity migratory 

paths from the 1930s to the 1990s.

Regional Land-Use Trend Analysis
124 Analyzing Albuquerque’s Landscape Evolution in the 20th and 21st Centuries

ters have been pulled westward across the Rio Grande due to 

lower land prices and 60 percent of the Albuquerque MSA’s 

population growth between 1980 and 1995 occurring west of 

the Rio Grande (Middle Rio Grande Council of Governments, 

1997a). Figure 7 illustrates that from 1970 to 1990 the population center for the Albuquerque MSA has been migrating 

in a northerly direction due to population growth occurring 

in Sandoval County (Rio Rancho). The significant northern 

movement of the Albuquerque MSA population center during 

this period is influenced by Rio Rancho’s population growing 

to account for approximately 5 percent of the MSA total population by 1990, whereas the City of Albuquerque’s population 

contribution to the MSA has remained constant at approximately 65 percent since the 1970s (table 2). Analysis of the 

county-level population data for the Albuquerque MSA from 

1970 to 1990 indicates that Valencia County has been experiencing a 3 percent per year population loss since 1980, which 

would have created an additional northern push to the MSA 

population center migration.

Landscape-Change Modeling

The goal of landscape change modeling is to provide 

accurate scientific information such as land-use model projections to allow the formation of sound policies for guiding 

future urban growth. Because the Albuquerque area is surrounded by numerous boundaries with Federal and Pueblo 

lands, and because the availability of water may ultimately be 

limited, decisions on urban growth only can be improved by a 

firm foundation of scientific knowledge. Temporal urban retrospective and LULC databases not only provide the scientific 

foundation for analyzing how urbanization has affected the 

landscape but also are used to calibrate and simulate landscape 

change by modeling future land-use development patterns 

(USGS, 1999).

Albuquerque’s future landscape change was simulated 

using the SLEUTH land-use model (USGS, 2001). The 

SLEUTH model incorporates four types of urban growth—

spontaneous, diffusive, organic, and road-influenced, which 

are regulated by growth-control coefficients. The growth rate 

of an urban area is the summation of all four types of growth 

for each model year. If the growth rate is unusually high or 

low, the growth-control coefficients are altered by the model’s 

self-modification growth constants, that is boom and bust multipliers. Self modification of the SLEUTH model’s growthcontrol coefficients is essential to simulate the typical S-curve 

growth rate of urban expansion (USGS, 2001). Generating 

a landscape prediction using the SLEUTH model does not 

take into consideration factors such as socioeconomic projections for population, employment, or housing. Therefore, the 

SLEUTH model only can predict the probability of an area 

becoming urbanized based on land-use trends (for example, 

existing urban areas) and landscape characteristics (for example, transportation infrastructure, slope, and areas excluded 

from land-use development).

Model Calibration

To predict future urbanization patterns in the Albuquerque area, the SLEUTH model was calibrated using historical 

urbanized areas, a historical transportation network, lands 

that restricted urban development and land-surface slope. The 

urban land-use footprint of 1935 was used as the urbanization 

seed for model calibration. An initial set of model parameters (growth control coefficients) were used to calibrate the 

behavior of the model from 1935 through 1991. Collected 

observations of Albuquerque’s urbanized areas from 1951, 

1973, and 1991 were used as control years for self modification of the model’s calibration parameters depending on 

whether the region’s historical annual urban growth rate for 

Years Mapped

Mean

center

west

longitude

(D-M-S)

Mean

center

west

longitude

(D-M-S)

Mean

center

north

latitude

(D-M-S)

Mean

center

north

latitude

(D-M-S)

Migration

distance

from

previous

center

(meters)

Migration

bearing

direction

from

previous

center

(D-M-S)

Annual

center

migration

rate

(meters)

Migration

distance

from

previous

center

(meters)

Migration

bearing

direction

from

previous

center

(D-M-S)

Annual

center

migration

rate

(meters)

Urban

Area

Center

MSA

Population

Center

Urban Area Center MSA Population

Center

Urban Area Center MSA Population

Center

1935

1951

1973

1991

1930

1950

1970

1990

106-38-35

106-37-28

106-36-21

106-36-58

106-37-52

106-39-53

106-40-57

106-41-13

35-09-38

35-09-39

35-08-30

36-07-43

35-01-33

35-02-11

35-02-22

36-05-12

No Data

1,680

2,736

1,731

No Data

N90-00-00E

S37-52-30E

S33-41-24W

No Data

105.00

124.38

96.15

No Data

3,292

1,680

5,240

No Data

N68-47-15W

N77-28-32W

N03-59-14W

No Data

164.60

84.00

262.00

Table 4. Albuquerque temporal urban-area mean center of gravity and Metropolitan Statistical Area (MSA) population-center migration analysis metrics.
125

each model year was classified as no growth, slow growth, 

or normal growth. In addition, urbanization “boom or bust” 

multipliers were applied during Monte Carlo simulations 

to adjust the SLEUTH model calibration parameters if the 

region was experiencing rapid urban growth or urban decay. 

During the model calibration, statistical metrics were calculated to obtain the best fit solution of the growth control 

coefficients between Albuquerque’s observed and predicted 

urbanization patterns.

Model Prediction

Once calibrated, the SLEUTH land-use model can be used 

stochastically to forecast land-use development (Clarke, 2002). 

The contemporary thematic inputs for urban lands, transportation, and lands excluded from development can be modified 

to take into consideration future planned urban developments, 

transportation infrastructure improvements, and open-space 

acquisitions to simulate land-use development scenarios for 

planning purposes. However, one limitation of the SLEUTH 

model’s predictive capability for simulating landscape change 

is that the model does not incorporate future events that are 

entirely random drivers facilitating urban growth.

For the Albuquerque area, SLEUTH model simulations 

were run to predict the urbanized area in 2050 (fig. 8). This 

50-year projection was chosen to correspond with the FOCUS 

2050 Regional Land Use Plan (Middle Rio Grande Council 

of Governments, 2000). Using a “best-fit” model calibration 

composite score, a combination of model parameters was 

used to simulate land-use patterns covering the timeframe 

between 1991 and 2050. Figure 8 illustrates the land surface 

for the Albuquerque area that has a 100 percent probability of 

becoming urbanized by the year 2050. Planned urban developments, such as Mesa del Sol on the Southeast Mesa, were not 

included as urbanized areas for the landscape change modeling, because the contemporary landscape (1991) did not have 

any existing population base for the planned community. 

Because the land on which Mesa del Sol will be developed 

was not excluded from the urban growth process, urbanization actually did occur in that area, however, development did 

Year Urban area

(acres)

Percent growth,

cumulative from

1935

Percent growth

from

 previous period

1935 4,372.2 No data No data

1951 15,397.9 252 252

1973 49,746.1 1,038 223

1991 84,890.5 1,842 71

2050 124,608.5 2,750 47

Table 5. Albuquerque’s urban growth, 1935–2050; figures for 2050 are projections.

not occur for each model run (100-percent probability) during 

SLEUTH execution. Subsequently, only those lands that 

became urbanized for every SLEUTH model run (100-percent 

probability) were retained as the 2050 land-use prediction or 

human footprint for the Albuquerque metropolitan area.

Conclusions

The long-term land-use trend for the Middle Rio Grande 

landscape is that Albuquerque’s developed land or human 

environment is replacing the natural environment. Decline 

and displacement of agricultural land has increased concerns 

for preserving agrarian land-management practices and lifestyles for future generations. Continued land-use development 

and expansion of urban land has resulted in the decrease of 

land conducive to ground-water recharge. The conversion of 

agricultural, range, and forested lands into urbanized areas, 

which are predominantly impermeable, is resulting in more 

efficient drainage systems that increase urban runoff, decrease 

the amount of water available for replenishing the aquifer on 

which Albuquerque depends for its municipal water supply, 

and increase the area’s susceptibility to flooding.

Even though Albuquerque’s historical urban growth has 

occurred on both vacant land within developed areas (in‐fill 

development) and on the fringe of previously developed areas 

(new development), the long-term land-use trend was greater 

dispersed development leading to an increase in the human 

footprint or size of Albuquerque’s urbanized area (table 5). 

Assuming that redevelopment did not contribute to the total 

urban area, from 1940 to 1990 the amount of urban land in 

the Albuquerque area consumed per person approximately 

doubled (fig. 9).

Albuquerque’s economy ranked 22nd highest of 316 MSAs 

nationwide in 1999 (Middle Rio Grande Council of Governments, 1999), and population projections from approximately 

713,000 in 2000 to 1,536,000 in 2050 (Middle Rio Grande 

Council of Governments, 2000) are indicators of economic 

and population growth driving potential 21st century landscape change. The existence of Rio Rancho’s Intel microchip 

manufacturing facility and continued expansion of tourism 

Conclusions
126 Analyzing Albuquerque’s Landscape Evolution in the 20th and 21st Centuries

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

1930 1950 1970 1990 2010 2030 2050

 Non-Urban to

Urban Annual

Conversion Rate

 Acres of Urban

Land per Capita

X 10

 Number of

Persons per

Acre of Urban

Land

Figure 9. Metrics analyzing Albuquerque’s landscape change from 

1940 to 2050—annual rate (percent) of non-urban to urban land conversion, acres of urban land per capita, and number of persons per 

acre of urban land. Figures for 2050 are projections.

Figure 8. Contemporary urban land and land exempt from urbanization (as of about 1991) 

and simulated 2050 urbanized area for the Albuquerque vicinity. The 2050 urban land 

shown in red was predicted using the SLEUTH land-use model. Perspective view is to the 

northeast.
127

through Native American gaming casinos, golf courses, and 

resort hotels, as well as the International Balloon Fiesta, may 

continue to stimulate Albuquerque’s future urban growth. If 

land-use development in the Albuquerque area continues until 

2050, the SLEUTH land-use model forecasts that approximately 125,000 acres of the Middle Rio Grande landscape 

will be urbanized (table 5), with a population density of 11.8 

persons per urban acre (fig. 9). The land-use trend from the 

contemporary (1991) to the simulated (2050) landscape, as 

illustrated by the downward trend in “acres of urban land per 

capita” (fig. 9), indicates a major shift in historical land-use 

development patterns as the Albuquerque landscape matures 

and evolves into a more compact urban form through infill 

development occurring on the East Mesa and existing urban 

centers on the Northwest and Southwest Mesas growing 

together into a contiguous urban core.

Note on Metrics

Metrics quantifying historical urban landscape change for 

figures 3, 6 and 9 and tables 1, 2, 4 and 5 were calculated for 

the sixteen 24K quadrangle area centered on Albuquerque’s 

urban core. This is the same study area that was used for the 

predictive landscape change modeling. Table 3 temporal landuse and land-cover categorical composition was computed for 

the twenty-eight 24K quadrangle study area.

Acknowledgments

The Middle Rio Grande Council of Governments of New 

Mexico contributed FOCUS 2050 Regional Plan information 

that fostered Albuquerque landscape-change research. In addition, this publication would not have been possible without 

the USGS research assistance provided by Kathy Casey, Mark 

Feller, Maria McCormick, Steve Methven, Ben Sherrouse, 

Mike Stier, and Stewart Wright.

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129

Introduction

European colonization of North America began in the 

seventeenth century and many of the landscape changes that 

have occurred since that time are the result of human activities such as agricultural production and urbanization. On 

the west coast of the Chesapeake Bay, landscape changes in 

central and southern Maryland have been dramatic over the 

past 300 years due to a long history of intensive agriculture 

followed by high population-growth rates. Today, this area 

includes parts of the Baltimore-Towson and WashingtonArlington-Alexandria metropolitan statistical areas—two of 

the Nation’s largest metropolitan statistical areas. Over the 

years, as populations increased, human-induced changes to 

the landscape in this region have resulted in serious consequences to natural resources such as waterways and native 

species (Chesapeake Bay Program 2002). There are currently numerous efforts underway to restore the bay’s natural 

resources. Most of the restoration strategies are developed 

from a scientific understanding of both the landscape change 

and the associated environmental consequences of human 

activities.

Landscape change is the result of both natural and 

human-induced alterations to land cover. Natural forces that 

shape the landscape include climate, hydrology, and geology. 

Landslides, hurricanes, and flooding are the result of natural forces. Human-induced forces that shape the landscape 

include building towns and cities, planting and harvesting 

crops, and logging forests. As human populations grow, 

human activities continue to increase in frequency and the 

rate and extent of land-cover change also increases. Negative 

consequences of land-cover change include erosion, declining water quality and quantity, species endangerment, and 

loss of native vegetation. 

Studying land-cover and associated land-use change is 

viewed as a means to understand and possibly reduce the 

negative impacts of human-induced change. Studying and 

analyzing the links between the physical changes to the landscape and the social influences associated with those changes 

can help land managers to understand and plan for future 

land-use change. 

The goal of this paper is to demonstrate how urban 

land-cover changes between 1850 and 1992 were associated with changes in agricultural and forested land in central 

and southern Maryland. This is achieved through a spatial and temporal reconstruction of three land-cover types 

(urban, agricultural, and forested land). The changes to the 

three land-cover types are illustrated and associated with 

historical events. This work builds upon an earlier U.S. 

Geological Survey (USGS) study that mapped the urban and 

transportation changes in the Baltimore/Washington, D.C. 

region between 1790 and 1990 (Acevedo and others, 1996; 

Crawford-Tilley and others, 1996; Clarke and others, 1996). 

The temporal urban data from this earlier work provided the 

baseline for land use and land cover change in this study.

Study Area Description

By 1861, all of the land in southern and central Maryland was divided into seven counties, Anne Arundel, 

Calvert, Charles, Howard, Montgomery, Prince George’s, 

and Saint Mary’s. This study focuses on the land-use and 

land-cover changes in these seven counties. This portion 

of southern and central Maryland is also divided into two 

distinct physiographic regions, the Coastal Plain and the 

Piedmont. The division between the Coastal Plain and the 

Piedmont is the Fall Line. The Fall Line is the zone on the 

landscape in which the rivers fall due to a change in elevation resulting from a change in geology. Montgomery and 

Howard counties are mostly in the Piedmont region with 

the Fall Line running through the eastern portions of these 

two counties. The other counties, Prince George’s, Anne 

Arundel, Charles, Calvert, and St. Mary’s lie mostly in the 

Coastal Plain (fig. 1).

These seven counties are located on the western shore 

of the Chesapeake Bay. The Chesapeake Bay is the largest 

estuary in the United States. It extends nearly 200 miles 

north to south and is 30 miles wide at the mouth. More 

than 100 rivers and tributaries flow into the bay, sending 

fresh water to mix with the salty Atlantic Ocean currents. 

Flowing in and around the seven counties are the Patuxent, 

Potomoc, and Patapsco Rivers. The Patuxent and Potomoc 

are two of the nine largest rivers that flow into the Chesapeake Bay.

Change to Urban, Agricultural, and Forested Land in 

Central and Southern Maryland From 1850-1990

By Janis L. Taylor1 and William Acevedo2

1

 SAIC TSSC, work performed under U.S. Geological Survey contract 

03CRN001 at U.S. Geological Survey, Center for Earth Resources Observation and Science, Sioux Falls, SD 57198.

2

 U.S. Geological Survey, Center for Earth Observations and Science, Sioux 

Falls, SD 57198.
130 Change to Urban, Agricultural, and Forested Land in Central and Southern Maryland From 1850-1990

Methods

The temporal land-use and land-cover (LULC) data sets for 

this region of southern and central Maryland were developed 

using remote sensing and geographic information systems (GIS) 

technology. Information from a variety of sources, both modern 

and historical, were integrated to create LULC data for six time 

periods 1992, 1982, 1972, 1953, 1900, and 1850.

The land-use and land-cover categories of urban, agricultural, and forested land were mapped. For all time-periods, the 

extent and location of urban land came from a previous study, 

the Baltimore-Washington Regional Collaboratory (Acevedo 

and others, 1996; Crawford-Tilley and others, 1996; Clarke and 

others, 1996). The Baltimore-Washington Collaboratory data 

were derived from a wide variety of information sources ranging in scale and resolution including historical maps, USGS topographic maps, aerial photographs, USGS Digital Line Graphs 

Figure 1. The seven southern and central Maryland counties in the study area. The Fall Line delineates the physiographic regions of the Coastal 

Plain and the Piedmont and it runs across the southeastern boundaries of Montgomery and Howard Counties. The Potomac River borders the 

counties on the western side. The Patuxent River forms the border between counties, running north to south, with three counties on the west 

shore and four counties on the east shore.

Peidmont

Coastal Plain

0 5 10 15 Kilometers

N
131

(DLG) of hydrography and transportation routes, USGS Geographic Information and Analysis System (GIRAS) Land-use 

and Land-cover (LULC) data, Landsat Multispectral Scanner 

(MSS) satellite data, and Landsat Thematic Mapper (TM) data. 

In the Baltimore-Washington Regional Collaboratory study, 

urban extent was characterized based on the source, either by 

color tint on historical maps, road density on DLGs, or spectral 

reflectance in the satellite data used. Similar methods were used 

to map urban extent in a study of the San Francisco-Sacramento 

region (Acevedo and Bell, 1994; Kirtland and others, 1994; Bell 

and others, 1995).

In this study of southern and central Maryland the landuse and land-cover categories of agricultural and forested land 

for the six time periods were compiled from a wide variety 

of information sources using methods similar to compilation 

methods used in the Baltimore-Washington and San FranciscoSacramento studies. For the time periods 1850, 1900, and 1950 

statistical information from the Census of Agriculture (U.S. 

Census) was used to model the location of agricultural land. 

Historical topographic maps yielded the locations of forested 

lands for 1950. Remotely sensed data and national land-cover 

data sets were used to develop both the agricultural land cover 

and the forested land cover for the contemporary time periods 

(the 1970s, 80s and 90s). 

The 1992 dataset of agricultural and forested land came 

directly from the USGS National Land-cover Dataset (NLCD) 

(Vogelmann and others, 1998). The NLCD land-cover categories for agricultural and forested land were aggregated to level 

1 classes in the Anderson classification system (Anderson and 

others, 1976). The 1982 dataset for agricultural and forested 

land was derived from the National Oceanic and Atmospheric 

Administration’s Coastal and Shoreline Change Analysis Program (C-CAP) (NOAA 1995) data. The 1972 dataset for agricultural and forested land was derived from the USGS Land-use 

Land-cover (LULC) Geographic Information Retrieval and 

Analysis System (GIRAS) (Fegeas and others, 1983; Loelkes 

1977) data. For all three time-periods, corresponding Landsat 

multispectral imagery was used to verify proper classification. 

For the 1992 and the 1982 data, Landsat Themattic Mapper 

(TM) images were used. For the 1972 data, a Landsat Multispectral Scanner (MSS) image was used. Classification crosswalks were established to mitigate differences between the data 

sources. In some cases, differences between the land-cover data 

sets were resolved through additional interpretation and adjustment of the previously mapped pixels.

Land-cover data representing agriculture for the historical time periods 1950, 1900, and 1850, were determined using 

digital elevation data, agricultural statistics, and GIS modeling 

techniques. Statistical data from the Census of Agriculture (U.S. 

Census Bureau 1995) was essential to the spatial reconstruction. 

The location of the land dedicated to farming for these historical time periods were determined by a spatial allocation model 

that used image processing software. The model identified areas 

most likely to have been in agriculture using a simple allocation 

table defined by a predetermined set of rules. The rules included 

spatial information about location of urban lands, location of 

water, topographic, and geologic features, including slope and 

soil quality, along with acreage information from the Census of 

Agriculture. 

The spatial extent of forested land for the 1950 time period 

was interpreted directly from topographic maps. The green tint 

on the topographic map offered a reasonable representation of 

forest cover for that time period. The spatial extent of forested 

land for the 1900 and 1850 time periods was not determined 

through analytical methods. Land that was not determined to be 

urban, water, wetland, or agriculture was considered forested in 

both time periods. More information about the development of 

this time series of urban, agricultural, and forested land can be 

found on the USGS web site http://landcover.usgs.gov/urban/

cbay/ag/intro.asp.

Regional Land-Use History

Land-Use and Land-Cover Changes: Before

1850 

Prior to European colonization, the landscape surrounding the Chesapeake Bay consisted of a large amount of forested land (American Forests, 1999; Grumet, 2000). Native 

Americans lived in this forested landscape and all around the 

Chesapeake Bay for thousands of years before the European 

colonists arrived in the seventeenth century (Grumet, 2000; 

Virta, 1991). These early inhabitants were hunters and gatherers and slowly evolved into farmers raising beans, squash, 

tobacco, and corn (Mountford, 1999). English Captain John 

Smith’s 1608 exploration of the Patuxent River encouraged 

future colonization of the region by Europeans. Early colonists 

inhabited the land on the western shore of the Chesapeake Bay 

and along the Patuxent, Potomoc, and the Patapsco Rivers. 

Colonists came from England, Scotland, Wales, Ireland, 

and France. To the colonists that were able to pay for their 

voyage, generous grants of land were offered. Those who 

could not pay their way spent several years working as indentured servants (Grumet, 2000). What brought many of the 

colonists to Maryland was the promise of land and freedom. 

Ironically, Africans also became a large part of the growing 

population, brought here to work as slaves. 

The soil and climate of southern and central Maryland 

made agriculture the way of life for many colonists. Colonial 

agriculture was primitive but exceedingly profitable. Soon 

after colonists became established, they found tobacco to be 

the highest paying crop per acre, due to high demand for it in 

Europe (Middleton 1994). Even small farmers could support 

their families by planting tobacco. Family farms grew into 

plantations as tobacco production developed into a prosperous 

agricultural economy throughout southern and central Maryland (Virta, 1991). 

Agriculturally induced soil erosion increased in the late 

1700s, after use of the plow became widespread throughout 

the Chesapeake Bay Region and the field rotation system was 

discarded in favor of crop rotation and increased use of fertilRegional Land-Use History
132 Change to Urban, Agricultural, and Forested Land in Central and Southern Maryland From 1850-1990

izers. Increased soil erosion in the late 1700s caused increased 

build up of sedimentation in many of the tobacco ports along 

the rivers, leading to their demise and depopulation. 

Land-Use and Land-Cover Changes: 1850 to 1900

During this 50-year time period all three land cover 

types increased in acreage with respective decreases in water, 

wetlands, and bare soil. Agriculture was the dominant land 

use and tobacco was the dominant crop. Land continued to 

be cleared or filled to create new agricultural land. Land in 

agriculture increased by 4,500 acres between 1850 and 1900 

from 1.49 million acres to 1.5 million acres (fig. 2). The spatial extent of urban land grew from only 500 acres in 1850 to 

4,000 acres in 1900 (fig. 3). This relatively small amount of 

land that became urban was found mainly around Baltimore 

and Washington, D.C. 

Forested land increased by 50,000 acres, mainly due to 

agricultural lands that had been exhausted and left fallow. 

Just prior to this time period, the amount of forested land had 

reached an all time low throughout the seven county region 

due to clearing of forests for agriculture, urban settlement, 

harvesting for wood products, and for fuel to fire furnaces (fig. 

4). Even though coal replaced wood as fuel for the furnaces, 

Acres of Land in Agriculture

0.0

50,000.0

100,000.0

150,000.0

200,000.0

250,000.0

300,000.0

350,000.0

Anne Arundel 236,060.1 290,023.2 140,383.3 55,177.0 54,239.5 53,171.0

Calvert 112,661.4 104,037.6 68,624.9 29,429.7 34,811.8 31,696.3

Charles 244,656.2 245,961.1 134,072.7 63,926.0 63,433.8 58,788.0

Howard 145,657.1 134,521.3 117,850.7 84,465.7 68,270.1 67,605.4

Montgomery 278,936.3 253,677.0 242,030.2 159,098.5 125,637.3 119,568.8

Prince George 276,602.2 264,305.4 181,653.9 61,924.5 66,290.5 60,909.0

St. Marys 201,463.9 208,036.4 124,044.1 64,574.0 71,191.6 61,952.4

1850 1900 1953 1972 1984 1992

Acres

Figure 2. Graph and table of changes in the number of acres of land 

in agriculture for six time periods. It includes values for each county 

and totals for the seven-county region.

Acres of Urban Land

0.0

10,000.0

20,000.0

30,000.0

40,000.0

50,000.0

60,000.0

70,000.0

80,000.0

90,000.0

100,000.0

Anne Arundel 107.7 578.1 20041.9 51013.2 62281.4 67933.3

Calvert 5.3 131.4 1,690.5 4,016.2 6,071.0 7,240.3

Charles 28.0 183.4 2,697.7 8,745.0 13,628.1 14,612.5

Howard 68.2 578.5 2,488.8 17,917.2 21,798.4 28,079.4

Montgomery 105.9 646.2 29,174.6 62,725.4 72,384.2 83,627.6

Prince George 53.9 1,482.1 30,628.7 74,259.0 85,199.8 92,452.1

St. Marys 26.0 376.7 4,726.4 11,419.9 14,840.7 19,799.5

1850 1900 1953 1972 1984 1992

Acres

Figure 3. Graph and table of changes in the number of acres of urban 

land, for the six time periods. It includes values for each county and 

totals for the seven-county region.

Population Change

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

Anne Arundel 32,393 39,620 117,392 297,539 370,775 440,055

Calvert 9,646 10,223 12,100 20,682 34,638 57,015

Charles 16,162 17,662 23,415 47,678 72,751 105,654

Howard 0 16,715 23,119 61,911 118,572 199,348

Montgomery 15,860 30,451 164,401 522,809 579,053 783,399

Prince George's 21,549 29,898 194,182 660,567 665,071 741,730

Saint Mary's 13,698 17,182 29,111 47,388 59,895 79,487

1850 1900 1950 1970 1980 1992

Number of people

Figure 5. Population change over time in all seven counties. 

Population statistics for each county from the Census Bureau (U.S. 

Census Bureau, 1995; University of Virginia Geospatial and Statistical 

Data Center, 2004).

Acres of Forested Land

0.0

50,000.0

100,000.0

150,000.0

200,000.0

250,000.0

Anne Arundel 28,323.4 33,830.4 104,900.3 148,538.9 144,547.8 123,738.8

Calvert 23,479.4 31,977.1 67,197.6 96,281.6 87,290.8 87,549.0

Charles 47,738.4 46,278.1 158,124.2 212,759.9 202,399.6 189,748.1

Howard 15,991.3 26,616.7 41,380.1 54,247.0 62,464.1 62,059.2

Montgomery 38,961.0 63,680.0 46,798.5 86,024.6 102,306.7 100,115.6

Prince George 32,297.4 43,165.0 98,610.4 153,543.6 146,346.8 132,701.2

St. Marys 30,055.7 23,132.4 103,303.9 149,696.2 133,909.7 134,097.1

1850 1900 1953 1972 1984 1992

Acres

Figure 4. Graph and table of changes in the number of acres of 

forested land for the six time periods. It includes values for each 

county and totals for the seven-county region.
133

factories, and foundries as early as 1860, this conversion did 

not stop the consumption of forested land. 

The corresponding population change during this time 

was slow but steady growth, increasing from a population of 

about 110,000 in 1850 to a population of more than 160,000 in 

1900 (fig. 5). The growing population in southern and central 

Maryland slowly shifted from scattered farms on the waterways, to river-towns in the Coastal Plain, to Fall Line towns in 

the Piedmont, and finally to the urban centers of Baltimore and 

Washington, D.C. 

The growing population was made up of a large number 

of Africans and indentured servants, because up until 1865 

farmers relied on slave labor for the cultivation of tobacco. In 

some areas, such as the rich tobacco plantation neighborhoods 

near Upper Marlboro, in Prince George’s County, the population 

consisted of 60 to 70 percent slaves (Virta, 1991). 

Tobacco production continued to dominate the agricultural 

industry even though the tobacco market went through a series 

of depressions. It was the highest paying crop per acre (Calvert 

County Living, 2003b) in the region. After slaves were freed, 

a big change was seen as larger plantations broke into smaller 

land holdings for more efficient farming without slave labor. 

For example, between the end of the Civil War and the turn of 

the century, the number of farms in Prince George’s County 

doubled, while the average farm size decreased dramatically 

(Virta, 1991). In Calvert County, many of the large plantations 

disappeared altogether and the economy shifted to commercial 

fishing, boat building, and fish processing (Calvert County 

Living, 2003a). 

During this 50-year time period, agricultural practices intensified. Intensive tillage methods improved production in the short 

term, however they destroyed soil retention in the long term, 

resulting in increased erosion. Massive soil erosion due to agricultural practices and the clearing of forests directly effected surrounding waterways (Friends of Jug Bay, 1999; Grumet, 2000; 

Mountford, 1999). Rivers became choked with sediments, water 

quality declined, and fisheries were lost. Many water-based 

transportation routes used for shipping goods and moving people 

throughout the region were lost. 

As agricultural activities increased and agricultural practices 

intensified, fertile soils became nutrient deficient and the quality and quantity of the harvests declined. Farmers began to rely 

more and more on fertilizers such as bird guano, lime, marl, river 

mud, gypsum, manure, ashes, and fish and vegetable matter to 

revitalize the depleted soil (Grumet, 2000). As the available rich 

soils of the Coastal Plain were completely exhausted, the low 

rolling landscapes and fertile valleys in the Piedmont became the 

replacement. As soils became increasingly depleted, new land 

continued to be opened up to agriculture. 

Improvements to local and regional transportation routes 

stimulated other economic development throughout southern 

and central Maryland. The Washington branch of the Baltimore 

and Ohio Railroad (B&O) provided a link between the towns 

in the Coastal Plain and the urban centers of Baltimore and 

Washington, D.C. Railroad access encouraged other industries, 

such as truck farming, associated food processing plants, and 

recreational centers, to grow in this region, toward the end of this 

50-year period.

Agricultural production shifted from primarily tobacco to 

corn, wheat, hay, and fruit. Market gardens supplied fresh fruit 

and vegetables to the growing populations in urban areas in and 

around Baltimore and Washington, D.C. Canning and fertilizer 

plants that supported the truck farming industry opened in northern Anne Arundel County. Initially, Baltimore and Washington, 

D.C. were the primary markets for local farmers. As transportation systems improved and diversified, local products such 

as peas, beans, strawberries and cantaloupes from the region 

became available across the eastern seaboard (Anne Arundel 

County, 2003).

Land-Use Changes: 1900 to 1950

Dramatic land-use and land-cover changes continued 

across southern and central Maryland during the first half 

of the twentieth century (fig. 6). Agriculture remained the 

dominant land use; however, the amount of land in agriculture decreased steadily from 1.5 million acres in 1900 to only 

1.0 million acres in 1950. During this time, forested land 

increased by 350,000 acres (fig. 4) from 269,000 acres in 1900 

to 620,000 acres in 1950. Active reforestation efforts were 

underway, in addition to forests that were reemerging on abandoned agricultural fields. Forest advocates encouraged landowners to replant and protect forests in an effort to improve 

water quality (Grumet, 2000; Mountford, 1999). 

The spatial extent of urban land increased by more than 

87,000 acres from close to 4,000 acres in 1900 to more than 

90,000 acres in 1950 (fig. 3). The corresponding population 

change in these seven Maryland counties during this time 

period began to pick up speed. The population of more than 

160,000 in 1900 tripled to 560,000 people in this half century 

(fig. 5). A large portion of the population along the Patuxent 

0.0

200,000.0

400,000.0

600,000.0

800,000.0

1,000,000.0

1,200,000.0

1,400,000.0

1,600,000.0

urban 394.9 3,976.5 91,448.7 230,095.9 276,203.7 313,744.8

agriculture 1,496,037.4 1,500,562.2 1,008,659.7 518,595.3 483874.752 453,691.0

forest 216,846.7 268,679.7 620,315.1 901,091.8 879,265.6 830,008.9

1850 1900 1950 1972 1984 1992

Acres

Land Use Change: 1850 - 1992

Figure 6. Graph and table of land-use change for each of the land 

covers—urban, agricultural, and forested land—in the study area.

Regional Land-Use History
134 Change to Urban, Agricultural, and Forested Land in Central and Southern Maryland From 1850-1990

River in southern and central Maryland was now located in 

and around Baltimore and Washington, D.C. urban centers. 

The land along the Patuxent River in southern Maryland was 

largely rural in the 1900 to 1950 time period. 

During the 1930s and 1940s, wartime mobilization generated jobs and housing in and around Baltimore and Washington, D.C. These jobs drew in people at a record pace. Many of 

the individuals drawn to the region for the war effort remained 

in the area after the war was over, working in peacetime production industries (Grumet, 2000). Suburbs of the large cities 

were growing out into the surrounding counties. All along 

the borders of Baltimore and Washington, D.C., towns were 

built as bedroom communities to the larger metropolitan area. 

Suburbanization also encompassed previously existing towns 

that had their own economies, like the mill towns of Laurel, 

Savage, and Ellicott City or the market towns of Rockville and 

Reisterstown. During this time, the percentage of the population that earned their livelihood through agriculture was in 

decline (Virta, 1991). 

Land-Use Changes: 1950 to 1970

During this 20-year time period the population and urban 

extent grew considerably (figs. 3 and 5). There was a 138,000 

acre increase in the spatial extent of the urban footprint. The 

population more than tripled in both Montgomery and Prince 

George’s Counties to 522,00 and 660,000 respectively. In Anne 

Arundel County, population more than doubled to 297,000. The 

spatial extent of urban land in these three counties more than 

doubled (fig. 3). Jobs in and around Baltimore and Washington, 

D.C., continued to draw people at a record pace. 

The amount of land in agriculture continued to decline 

from just more than 1 million acres in 1950 to just 518,000 in 

1970. Forested land continued to increase from 620,000 acres 

in 1950 to more than 901,000 acres in 1970. Even with less 

agricultural land, the use of chemical fertilizers and pesticides 

increased six-fold (Environment and Energy Study Institute, 

2001). This quickly took its toll on regional water quality. 

Waterways previously polluted with sediments now flowed 

with vast amounts of nitrogen, phosphorus, and synthetic 

chemicals found in fertilizers and pesticides that were washing off farm fields. Comprehensive reforestation efforts were 

established as a means to improve water quality.

Land-Use Changes: 1970 to 1990

The amount of land in agriculture continued to decrease, 

losing almost 65,000 acres in this 20-year time period (fig. 

2). At the same time, however, the amount of urban land 

increased from 230,000 acres in 1970 to more than 313,000 

acres. Much of the urban growth occurred in Montgomery, Prince George’s, and Anne Arundel Counties along the 

Potomac and Patapsco rivers. Urbanization along the Patuxent 

River in St. Mary’s and Calvert Counties occurred late in the 

20th century. This increase in urban land came at the expense 

of forested land that was no longer on the increase and in some 

counties was in decline (fig. 4). Throughout the seven counties, forested land decreased from a high of 901,000 acres to 

830,000 acres.

Locally, as well as nationally, there was heightened concern 

for the quality of water. Nationally, the Federal Clean Water Act 

was passed early in the 1970s, regulating the flow of chemicals 

and other pollutants into bodies of water. Water-quality issues 

continued to plague the counties in southern and central Maryland. For example, pollution from chemicals, waste, and erosion 

began to cause significant decline in water clarity and quality in 

the Patuxent River. In 1977, the Tri-County Council for Southern Maryland (TCCSMD) coordinated a campaign to reduce the 

flow of nutrients into the river from point and non-point sources 

in the seven counties along the river (Tri-County Council for 

Southern Maryland, 2002). The use of chemical fertilizer finally 

began to taper off in the 1980s. 

In 1983, the Chesapeake Bay Agreement was signed 

into action and through this agreement the Chesapeake Bay 

Program began a regionally coordinated effort to reverse the 

declining natural resources of the bay. In a separate effort, the 

Patuxent River Policy Plan was established to protect the river 

and its watershed by restoring surface and ground-water quality, habitat, and quality of life along the river and its tributaries 

(Patuxent River Commission, 2000). The original Patuxent 

River Policy Plan was adopted in 1984. Amendments for the 

plan have been considered every 5 years as new information 

and ideas are developed. Because agriculture was considered an important part of the Southern Maryland economy, 

TCCSMD organized the Southern Maryland Agricultural 

Commission to support and encourage local agriculture in 

1988. The 1990s also brought more nutrient-management programs and riparian-forest protection (Alliance for the Chesapeake Bay, 1996). 

Conclusion

Landscape changes that have occurred in Anne Arundel, 

Calvert, Charles, Howard, Montgomery, Prince George’s, and 

Saint Mary’s Counties in southern and central Maryland over 

the past 300 years are mainly the result of human activities. 

Agriculture, logging, and urban expansion have each played 

a role in shaping the evolving landscape. Prior to European 

colonization, forests covered the landscape like a blanket 

from the Atlantic to the Mississippi River (American Forests, 

1999). In the seven counties of southern and central Maryland 

a similar forest pattern existed. After European colonization, 

the blanket of forest began to unravel as agricultural land uses 

took over and population in the region began to increase. 

Between 1850 and 1900, the decrease of forested land 

mirrored the increase in agricultural land. Colonists cleared 

forests to create agricultural fields, as well as urban areas. Forests were cut to export wood and wood products to Europe, as 

well as to provide fuel for homes and industry. By 1850, there 
135

were only 216,000 acres of forested land in the seven counties. 

In 1972, forests covered more than 900,000 acres. After this 

high point, forested land began to decrease again and was at 

830,000 acres in 1992 (fig. 7) (American Forests, 1999). 

Over the years, agriculture, specifically tobacco production, played a large role in shaping the landscape. When colonists first settled the land of southern and central Maryland, 

the physical condition of the land was a key factor in the 

location of agricultural land. The land that was cultivated first 

included areas with good soil quality with a moderate slope, 

areas not inundated with water, and areas that had good access 

to waterways for transportation. However, modernization of 

agricultural methods and improved transportation routes made 

all the lands within the region susceptible to cultivation. In 

1900, there were more than 1.5 million acres of agricultural 

land throughout these seven counties. The counties with the 

greatest amount of agricultural land at this time included Anne 

Arundel, Prince George’s, and Montgomery. By 1992, the 

amount of land in agriculture for all seven counties was only 

454,000 acres (fig. 8). 

At the end of the twentieth century, the three counties 

with the largest population and greatest amount of urbanized 

land were Montgomery, Prince George’s, and Anne Arundel. 

In Montgomery County, the largest of the seven counties in 

size and population, the urban footprint grew from 646 acres 

in 1900 to more than 83,000 acres in 1990. In 1990, Prince 

George’s County had the largest urban extent with more than 

92,000 acres of urban land (fig. 9).

The other four counties, Howard, Charles, Calvert, and 

St. Mary’s, had much smaller populations and resulting urban 

extent. The urban extent of Howard County in 1992 was just 

more than 28,000 acres with a population of almost 200,000. 

In 1992, the urban footprint of Charles was only 14,612 acres 

with a population of 105,000, making it one of the more rural 

counties. Saint Mary’s, the second smallest county, had less 

then 20,000 acres of urban land in 1992 with a population of 

79,487. Calvert, the smallest of the seven counties, had 7,240 

acres of urban land with a population of 57,000 in 1992.

During the 1990s, agriculture continued to be the major 

land use, while forested land remained second. However, 

Figure 7. Spatial time series of changes in forested land in the study area. The green tint identifies the location of 

forested land.

Figure 8. Spatial time series of changes in agriculture land in the study area. The gold tint identifies the location of 

agricultural land.

Figure 9. Spatial time series of changes in urban land in the study area. The red tint identifies the location of urban land.

Conclusion
136 Change to Urban, Agricultural, and Forested Land in Central and Southern Maryland From 1850-1990

spreading subdivisions and other urban uses continued to 

whittle away the landscape. Of the seven counties, the four 

counties in the Coastal Plain remained more rural, primarily 

due to their distance from the large urban centers of Baltimore 

and Washington, D.C. Within this region, local and regional 

zoning along with conservation easements, maintain agriculture as an important part of the southern Maryland economy. 

However, agricultural trends in the region have changed as 

farmers shift away from the 300-year-old tradition of tobacco 

production. In 2000, the State of Maryland instituted a voluntary tobacco buy-out program to transition farmers out of 

tobacco production (Tri-County Council for Southern Maryland, 2002). 

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139

Introduction

Forty percent of future urban growth in the United States 

is expected in the eight Rocky Mountain West (Arizona, 

Colorado, Idaho, Montana, New Mexico, Nevada, Utah, and 

Wyoming) States. Over the past 150 years, cultivation, heavy 

livestock grazing, mining, and urban/suburban development 

have modified the northern Colorado Front Range. This region 

is becoming more urban as it becomes the center of growth in 

the Rocky Mountain West. 

A commonly held perception sees urban development 

as occupying all the Nation’s open spaces. In reality, only a 

small fraction of the land in the United States has been developed; one third of the land is not available for development 

because it is Federal land. Urban development often occurs 

in areas valued for other uses, including prime agricultural 

land, natural resource development such as mining for minerals or aggregate, and physical environment amenities such 

as wildlife habitats and scenic environments (Shumway and 

Otterstrom, 2001; Rasker, 1993, 1994; Power, 1996). As 

populations move and grow in a region, there is an overall 

shift in land use from open spaces (forests and grasslands) and 

agriculture to more urban and residential uses, each with their 

own environmental and economic implications. A more pressing issue is the multiple demands for the land and the need 

to understand and convey the temporal evolution of regional 

landscape change by human populations. Over the past 150 

years cultivation, heavy livestock grazing, mining, and urban/

suburban development have modified the northern Colorado 

Front Range. 

The northern Colorado Front Range urban corridor 

between Denver and Fort Collins is an example of a region 

whose natural resources and environment have been greatly 

modified by human modifications. The region has evolved 

through a series of economic booms and busts related to 

beaver furs, gold and silver, cattle, coal, oil and gas, agriculture, manufacturing, and now perhaps its most precious commodity, the “Colorado landscape,” as typified by its beauty 

and natural environment. The value of the Colorado landscape 

was recognized early in the state’s formative years but was far 

overshadowed by the extraction and exploitation of its mineral 

resources. Today, it is this landscape value that draws people 

to Colorado and in particular the Front Range urban corridor 

every year (Governor’s Commission on Saving Open Spaces, 

Farms and Ranches, 2000). 

Landscape studies are integral to any assessment of 

human-induced impacts on the environment. These studies 

look at arrangements of environmental elements and incorporate humans and their activities. Historical perspectives of 

urbanization give insight into the development and growth of 

urban ecosystems that assess the degree of man’s influence as 

it affects both the biotic and physical environment. The loss of 

natural land covers may result in the loss of native species and 

habitats, increasing threats to ground water and coastal water 

quality, as well as a loss of open space and view sheds. To 

understand and possibly reduce negative impacts of landscape 

change, urban growth modeling can be used to simulate future 

shifts and patterns on the landscape.

Population growth is a driving factor for landscape 

change (Matheny and Wear, 2000; Hobbs and others, 1997; 

Shumway and Otterstrom, 2001; Vias, 1999). As population 

transforms the landscape, increased demands are placed on 

natural resources. Forty percent of the future growth in the 

Nation is expected to occur in the eight Rocky Mountain West 

(West) States of Colorado, Utah, Wyoming, New Mexico, 

Idaho, Montana, Arizona, and Nevada (as defined by the U.S. 

Department of Commerce, Rasker 1994; Clark, 2000; Vias, 

1999). These states are now more urban (86 percent) than 

the Mid-Atlantic region (75 percent). Population growth is 

intensifying around existing urban centers, such as Phoenix, 

Denver, and Salt Lake City. The seven counties of the northern 

Colorado Front Range—Adams, Arapahoe, Boulder, Denver, 

Jefferson, Larimer, and Weld—have long been a leading area 

of urban growth and development in Colorado. The City and 

County of Broomfield was officially created in 2000, after the 

time-period used for this study. This region is already home to 

80 percent of Colorado’s population (Clark, 2000). The State’s 

population grew by more than 30 percent, from 3,294,394 to 

4,301,261 from 1990 to 2000, and the Front Range accounted 

for more than 61 percent of that growth. In the past, worries 

about population growth had never been an issue, there was 

plenty of land, and growth was encouraged and sought after. 

From 1990 to 1995, Colorado alone had ten of the fifty fastest growing counties with Douglas County (south of Denver) 

being one of the fastest growing counties in the country. Ft. 

Collins’ (north of the Denver) population took almost 100 

years to reach 50,000, but took only 20 years to double to 

Regional Landscape Change in Northern Colorado 

Front Range

By Carol S. Mladinich1

1

 U.S. Geological Survey, Rocky Mountain Geographic Science Center, P.O. 

Box 25046, MS 516, Lakewood, CO 80225, 303-202-4313, csmladinich@

usgs.gov.
140 Regional Landscape Change in Northern Colorado Front Range

100,000. It is estimated that it will only take another 20 years 

to double that again to 200,000 (Cornelius, 1999).

The landscape assessment of the Northern Colorado Front 

Range described in this paper was completed in 2001 as part 

of a 5-year U.S. Geological Survey (USGS) study to develop 

methods for assessing infrastructure resources and to characterize the location, distribution, and quality of the infrastructure resources along the Northern Colorado Front Range urban 

corridor between Denver and Fort Collins (USGS Front Range 

Infrastructure Resources Project, 2002).

Study Area Description

The seven counties of the Northern Front Range, including all or portions of Adams, Arapahoe, Boulder, Denver, 

Jefferson, Larimer, and Weld Counties, have long been a leading area of urban growth and development in Colorado. This 

urban corridor area was selected by the USGS Front Range 

Infrastructure Resources (FRIR) project because of its abundance of natural resources–aggregate, coal, oil and gas, and 

ground water—that are all needed to support growing urban 

regions yet are put at risk for development by these same 

urban areas (USGS Front Range Infrastructure Resources 

Project, 2002). The northern Front Range has been comprised 

of two distinct yet intricately linked regions since the region’s 

early development in the 1850’s as described by Wyckoff 

(1999). The regions include the Denver metropolitan area of 

Denver, Golden, and Boulder and the northern agricultural 

area, north of the urban core, in the Fort Collins, Loveland, 

and Greeley area. The urban core developed as the financial 

and manufacturing core for the mining coming from the 

mountains to the west and the agricultural area to the north 

provided the food stuffs needed by the urban area and western 

mining. I will use these same regional distinctions to provide 

the historical perspective of the spatial patterns of land use 

change in this area that marks the western limits of the High 

Plains and the eastern most range of the Rocky Mountains. 

The region is roughly 2,500 square miles in area and is a 

mixture of open rolling country and stream valleys that gradually rise westward to the Front Range foothills and the higher 

ranges of the Rocky Mountains (fig. 1).

The Front Range’s semiarid climate plays an important 

role in the development of the region. Precipitation averages 

only 15 inches per year. The amount of precipitation can vary 

greatly from year to year and drought is an ever-present possibility. The climate is characterized by wide difference in 

temperature between summer and winter and is subject to 

sudden and extreme changes on a daily basis. The presence 

and availability of water (or lack of water) continues to play 

a major role in the urban development along the Front Range. 

During Zebulon Pike’s exploration of the Southwest in 1806, 

he declared the region to be the “Great American Desert, never 

to be settled.” Pike recalled that the deserts were like those of 

Africa and that there were “tracts of many leagues where the 

wind had thrown up the sand in all the fanciful form of the 

ocean’s rolling wave; and on which not a speck of vegetable 

matter existed” (U.S. National Park Service, 2003). 

The average elevation of the region is 6,800 feet above 

sea level. Steeply dipping sedimentary rocks mark the boundary between the Rocky Mountains and the High Plains. The 

major streams in the region flow northeasterly to the South 

Platte River. The major tributaries to the South Platte River, 

including Clear and Boulder Creeks and the St. Vrain, Big 

Thompson, and Cache la Poudre Rivers, originate at an elevation of nearly 14,000 feet in the Front Range, west of the study 

area near the Continental Divide. The most fertile soils in 

Colorado are found in the irrigated alluvial soils of the river 

valleys that cross the plains. Prior to European settlement, 

the High Plains region was almost treeless, except for certain 

areas that contained peach leaf willow and cottonwood trees. 

Natural grasslands once covered most of Colorado’s plains but 

Figure 1. Map of the northern Colorado Front Range study area. The urban 

core is circled in green and the northern agricultural area is circled in red.
141

are now limited to those areas that are not cultivated. Buffalo 

grass and blue grama are the most common native grasses and 

large areas of bare ground are common and natural. Examples 

of the native grasslands are preserved in the Pawnee National 

Grasslands, northeast of Greeley, Colorado (Veblen and 

Lorenz, 1991).

Regional Land-Use History

The Front Range is a prime example of a region that has 

been shaped by its proximity to natural resources and agriculture that typifies the West. This region is also an example of 

the paradigm shift that began after WWII, described by Shumway and Otterstrom as the change of the Old West to the New 

West. The economy is no longer based on the exploitation of 

raw mineral resources and agriculture but rather the exploitation of the natural environment, the place, and the servicebased economies that thrive on it (Rasker, 1993; Power, 1996; 

Rudzitis, 1999; McGranahan, 1999; Nelson, 1999). 

After the discovery of gold and silver in the late 1850s, 

this region has continued to be the financial and economic 

Gateway for not only the rest of Colorado but also the entire 

West. The Front Range area landscape was transformed by a 

boom and bust export-based economy that began with the fur 

trade and progressed through the extraction of various mineral 

resources, such as gold, silver, uranium, molybdenum, coal, 

and oil and gas (Bird, 1993; Kendall, 2002). The Front Range 

grew and flourished as a support base, providing commerce, 

agriculture, transportation, and communication to supply the 

various industries that were key drivers to landscape change. 

Today, the ambiance of the “Colorado landscape,” the natural 

environment, and its amenities now draw high technology and 

other service-based industries to the region. Using Wyckoff’s 

designation, the Front Range area can be subdivided into 

two zones—an urban core zone and an agricultural zone to 

describe the land use history of the region. The urban core 

is concentrated in a triangle made up of the cities of Denver, 

Boulder, and Golden. The agricultural zone is north of this 

urban core (1999).

Early European History to 1900

Prior to the 1850s, the Front Range was largely unsettled 

by of European descent, with the exception of the fur trading 

and military outposts of Fort Lupton, Jackson, Vasquez, and 

St. Vrain along the South Platte River. The westward migration 

was to the California gold fields and travel was either north 

along the Oregon Trail or south along the Santa Fe Trail. The 

discovery of gold in 1858 was the principle driving factor that 

transformed the Front Range area from a landscape characterized by native grasses, labeled in the East as the Great American Desert (United States National Park Service), into booming 

settlements and thriving agricultural lands. It is estimated that 

as many as 100,000 people came to Colorado as “59ers.”

As the gateway to the West, transportation has been the 

key enabler shaping the region’s landscape. Simple foot trails 

were transformed to more substantial trails and roads as their 

use intensified with the discovery of gold after 1858. The railroad, which originally bypassed the Front Range, arrived after 

business owners and politicians financed the linking of Denver 

north to Cheyenne and east to Kansas City. Multiple railroad 

lines soon connected the mining regions in Gilpin, Clear 

Creek, and Boulder Counties to the growing urban centers of 

Golden, Denver, and Boulder and the agricultural communities 

to the north.

The three towns of Denver, Boulder, and Golden, making 

up the urban core, developed their own individual traits and 

marks on the landscape. Denver, a rugged outpost in 1860, 

was by 1900, one of the 25 largest cities in the Nation. Now 

the state capitol, Denver was beginning to see the boom and 

bust trends that are still present today. It maintained its gateway status for the rest of the State by continuing to provide a 

steady transportation infrastructure, a reliable water supply, 

and a regional trade center. Boulder was transformed from a 

collection of miners’ cabins to the home of the University of 

Colorado and an economically diverse urban center. Golden, 

because of its unique foothill setting at the base of the Rockies and its nearness to mineral resources, became home to the 

Colorado School of Mines and many different types of manufacturing. As Denver grew, an extensive streetcar network 

enabled the development of residential suburbs outside of the 

city. This allowed people to move into the country where the 

land was cheaper and where they could still be connected to 

their jobs and other amenities of the city center. Yesteryear this 

was a modern marvel; today this is commonly referred to as 

urban sprawl.

Even in the early years, the region’s scenic physical 

environment attracted people to the area. The rise in tourism 

helped shape the economy, with the development of dude 

ranches and hotels in the scenic foothills and nearby Rocky 

Mountains. By 1940, tourism was the third-largest source of 

income in the State after agriculture and manufacturing (Williamson, 1999). In the late 1800s, the reported fresh clean 

air also became a mecca for those Easterners suffering from 

respiratory ailments. It is estimated that one third of the population in the region were either present or former victims of 

respiratory ailments. More people came to the region for the 

“climate cure” than for gold and silver (Bird, 1993). Many 

hospitals and sanatoriums opened and flourished in the region. 

The National Jewish Medical and Research Center, founded in 

1899 as a hospital for tuberculosis (TB) patients, is still today 

the only facility in the world dedicated exclusively to respiratory, immune, and allergic disorders. This migration included 

a large number of wealthy business owners resulting in a 

buildup of many new and service related industries.

The agricultural zone produced crops of an amount that 

was not typical of a semiarid region. The agricultural development was made possible with the creation of a vast network 

of ditches and reservoirs transforming the grasslands into productive farmlands and thriving agricultural towns. The intenRegional Land-Use History
142 Regional Landscape Change in Northern Colorado Front Range

sive agriculture was also made possible by the fertility of the 

piedmont soils, the availability of water, and the proximity to 

local markets in the urban core and mining districts. The agricultural zone had regional centers in Fort Collins, Loveland, 

and Greeley. Though agriculture was established in the region 

by the 1860s, the output was small until the establishment of 

several agricultural colonies and rail lines in the late 1870s. 

Water became and remains the lifeblood for the entire 

region, acting as not only an enabler but also constrainer of 

landscape change. The West’s prior appropriation water doctrine was codified for the first time as competing farmers and 

then the courts determined water claims (Wyckoff, 1999). The 

new water laws gave preference to the “first user in time”; this 

resulted in agriculture having preference over manufacturing. Interbasin projects or the transferring of water from one 

basin to another and the protection of forestland to protect the 

watersheds for agricultural purposes was also pioneered in the 

region. 

In just fifty years, the landscape of the region had dramatically changed from the rest of the Arid West. By 1900, more 

than 1 million acres and 90 percent of the agricultural land 

were irrigated along the Front Range. The yield and intensity 

of the irrigated operations, the high price of the farmland, and 

the nearby commercial markets kept the farm sizes small in 

comparison to the rest of the High Plains to the east and the 

rest of Colorado. The resulting landscape had farmhouses 

closer together and had produced a very lush developed landscape that was different from many other regions in the arid 

West. The transportation infrastructure also changed the landscape. Unlike most of the rural West, multiple rail lines and 

heavily used roads connected the region to markets in Cheyenne, Denver, and nearby mining districts (Wyckoff, 1999).

1900 to 1940

After the turn of the century, farming and ranching dominated the region’s economy. The introduction of sugar beets 

on the eastern slope had a very large impact on the agricultural 

economy, giving rise to facilities to process the raw materials 

into food products. The network of railroads also helped provide easy access for the ranchers to bring their cattle and sheep 

to market. The Denver stockyards became one of the Nation’s 

largest with most of the largest brand-name packers of the 

time period having plants along with many smaller local operators. The period following World War I and leading up to the 

1930’s depression saw the region’s chief economic industries 

of agriculture and mining becoming stagnate and in decline.

Transportation continued as a key enabler during the automobile age that increased the demand for more roads, enabling 

city-dwellers to spread out even further on the landscape. The 

extent of paved roads increased from 1,200 miles in 1914 to 

more than 8,500 miles by the mid-1920s. The transportation 

infrastructure also grew with the start of the commercial airline industry and the opening of Denver Municipal Airport in 

1929. Denver became the region’s key aviation point in the 

western United States, because of its large size, good weather, 

and strategic location.

The Federal Government’s New Deal Program began a 

dramatic transformation on the landscape by adding to the 

region’s basic infrastructure that included roads, agriculture, 

and municipal water works, telephone and electrical lines, 

public buildings, dams and reservoirs, and parks. This building effort stabilized the economies of many urban and rural 

communities. A larger presence for the Federal Government, 

as reflected in the growth of number of government offices, 

also began in the region. The region would soon grow to have 

the second largest concentration of Federal agencies outside 

of Washington D.C. This governmental presence would help 

soften the impact of economic downturns caused not only by 

the Great Depression and Dust Bowl, but continue through 

future economic downturns. 

Water was now recognized as a potential constraining 

factor to any kind of development and in particular to agriculture. It was becoming a large regional issue not only for the 

Front Range but also for the rest of Colorado and West. State 

and the Federal governments were forced to develop strategies 

to insure water availability. Agreements were made to secure 

the legal basis for long term water supply in Colorado and 

other western states. New transmountain water diversion projects brought water from the Western Slope to the Front Range. 

The Big Thompson Project, the largest effort during this time, 

was started in 1930 and completed in 1954. This project demonstrated the lengths that western agriculturists would go to 

get a steady supply of water to maintain and (or) increase their 

productivity.

1940 to Present

The population growth in Colorado slowed between 

WWI and WWII to only 10 percent. By 1940, over half of 

Colorado’s population lived in cities, mainly along the Front 

Range. In terms of the landscape, the urban West was setting 

the stage for what was to become the dominant urban feature after WWII, with population growth as the driving force 

behind landscape change (Vias, 1999, Shumway and Otterstrom, 2001). The growing populations were characterized by 

the growth of decentralized populations in suburbs, the formation of multicentered nodes of commercial activity, and the 

institutionalization of commuting. It was during these years 

that the combined population of the seven county area grew 

to include more than 50 percent of the population of the entire 

State (Colorado Dept. of Local Affairs, 2002) (fig. 2). Denver 

dominated not only the Front Range but also the surrounding 

Mountain West and western High Plains. The city was in the 

middle of the Colorado Mineral Belt and large agricultural 

areas. It became the regional trade center of industry and 

finance for the interior West. Development in the Front Range 

urban centers paralleled southern California, but rather than 

the key centers growing larger with annexations, the surrounding smaller cities grew larger and became more integrated with 
143

Denver. Smaller farm communities declined as the improved 

roads made travel to larger; more diversified service centers 

easier (Colorado Dept. of Local Affairs, 2002). Agricultural 

mechanization enabled large-scale farm operations resulting in 

fewer farm laborers and larger, fewer farms.

During WWII, agricultural production was at its highest 

and Colorado military installations greatly expanded. The war 

brought many people to the Denver area; afterwards many 

decided to make Denver home, and new developments continued. After WWII, Denver became the business and financial 

center of the Intermountain West. The region had shifted from 

a heavy reliance on mining and agriculture to a more diversified economy with a strong emphasis on the presence of the 

Federal Government and high-technology and space/telecommunication industries. The biomedical sector became an 

important high-tech sector; built on the historical migration of 

people to the area with TB and other respiratory ailments and 

the resulting pioneering medical advances. Today, the Denver/

Boulder area is a leader in genetic research and home to one 

of largest concentrations of biotechnology firms in the Nation 

(Williamson, 1999).

Suburbs were spreading to the north, west (though constrained somewhat by the mountains), and east of Denver, 

enabled by the construction of several highways in the 1950s. 

The three major highways were Interstate 25, running north/

south through Denver, Interstate 70 running east/west through 

Denver, and the Boulder Turnpike connecting Boulder and 

Denver. In the mid- to late- 1970s and early 1980s, a booming oil and gas industry helped foster another large influx of 

out-of-state residents into the seven county area. After an economic slowdown in the mid-1980s, urban population growth 

again sped up, this time fueled by the influx of many high-tech 

companies and a very strong state economy that attracted 

many skilled workers. Once more the region has become the 

crossroads for another type of transportation, in the form of 

satellite communication and imagery.

Methodology

This study selected four time-periods, the mid to late 

1930s (1937), the mid to late 1950s (1957), the mid to late 

1970s (1977), and the mid to late 1990s (1997), to represent the 

major growth periods in the region (fig. 3). In the late 1930s 

were just beginning to recover from the Dust Bowl and Great 

Depression. The post-WWII patterns, represented by 1957, 

found along the Front Range were indicative of the great shifts 

taking place throughout the country’s population and economic 

activity (Power, 1996). This significant growth along the Front 

Range set a pattern that continues today. The late 1990s saw a 

diversification in Colorado’s economy, which no longer relied 

on one or two major industries; it was a time for growth after 

the economic stagnation of the 1980s.

Aerial photography, obtained from the U.S. Department of 

Agriculture (USDA) and the USGS, from these time-periods 

were scanned, rectified, and interpreted into land use and land 

cover (LULC) classes using a modified Anderson classification. 

For more details on the methodology and classification system 

see the metadata file for each time period (USGS, 2001a,b,c,d). 

The LULC vector polygons were manually collected using an 

ESRI Arc Info Workstation with the imagery as a backdrop. A 

minimum mapping unit of 2.5 acres was used. Work was begun 

with the 1997 imagery and then the interpreters worked backwards through each time-period. Stereo pairs (two overlapping 

photos that can be viewed with stereoscopic glasses) were used 

when possible to help identify features. There was a team of 

two to three operators working on the project, so consistency 

in interpretation was emphasized over individual conclusion. 

When there were disagreements, the group was to come to a 

consensus and then hold to that interpretation. A rigorous accuracy assessment of the temporal LULC data was not possible 

but a validation of the collection was performed to ensure consistency in the interpretation of the LULC classes for each time 

period. Unfortunately, the imagery was not coregistered which 

is crucial when doing temporal studies. The slight misregistration between the images made collection that much harder 

because the interpreter had to manually shift the polygons to 

make it match the new image he or she was working on. The 

change detection program used in this study required raster 

data. The polygons were rasterized to a 50-meter cell size. The 

cell size was selected to be compatible with other modeling and 

analysis on going in the project.

There are many methods of change detection techniques 

that were summarized in Lu and others (2004), that include 

image differencing, image ratioing, tasseled cap, and post classification comparison. This project selected to use the thematic 

change approach to change detection provided by the Erdas 

ArcView Image v.3.2 Analysis Extension software module. 

This module creates a new output image documenting every 

combination of each pixel change from the first image to the 

second image. For example, if the pixel in the first image 

(1937) was water, class 1, changed in the second image (1957) 

to natural vegetation, class 6, a new class would be generated in the output image, in this case class 15, representing 

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

7 C ounty A rea 483,253 723,186 1,409,063 2,105,885 2,657,234

C olorado 1,035,791 1,325,089 2,209,596 3,294,394 4,301,261

1930 1950 1970 1990 2000

Figure 2. A comparison of the population of the seven-county northern 

Colorado Front Range region and the entire State of Colorado from 

1930 to 2000.

Methodology
144 Regional Landscape Change in Northern Colorado Front Range

the combined change from class 1 to class 6. The Thematic 

Change module also generates a summarization of areas based 

on either input theme or the output theme in acres, hectares, 

or square miles. Combining the three change images (1937 to 

1957, 1957 to 1977, and 1977 to 1997), interactions between 

the time-periods can be shown. This ability made it very easy 

to take this output into a spreadsheet to analyze the data further and generate charts and graphs (ERDAS, Inc., 1998). One 

of the advantages of this type of change detection when using 

four time-periods or three time changes is the ability to track 

how pixels or regions have changed. Has a region stayed the 

same over the 60-year period of the study, how has it changed, 

or how many times has it changed? 

Some strange class transitions were found in the residential and nonresidential classes. Although they accounted for 

only small percentages, it was unusual to see urban classes 

switch to either another urban class in the early years and 

especially to see the switch to the natural vegetation and agricultural classes (table 1). It would be expected to see classes 

such as natural vegetation and agriculture change to residential or nonresidential but not the other way around.When one 

encounters figures such as these, it shows that one cannot rely 

on numeric data alone. One must return to the spatial distribution of the data and ask questions, such as are these anomalies 

happening in just one area during all time periods or are all 

the anomalies uniformly distributed? Other questions may be 

raised by the data itself. We know the imagery was not coregistered, so are we seeing misregistration errors. We also know 

there was no accuracy assessment done on the data, which 

we admit would be difficult to achieve with the dates of the 

imagery, so misclassification could be a cause of the errors. 

A 50-meter cell size was used for the change detection algorithm. Perhaps a smaller cell size might have provided better 

results, but these values were relatively small and uniform 

and could be account for by any of the above suggestions and 

demonstrate the spatial relationships have an important role in 

analyzing the landscape.

Land-Surface Analysis

Four time-periods were used to analyze not only the 

rates of landscape change over time but also to understand 

Urban-Residential

Urban-NonResidential

Natural Vegetation

Agriculture

Wetlands

Bare

Bare-Quarries

Water

1937 1957

1977 1997

5 5 0 10 15 20 Miles

Scale 1:1,250,000

Figure 3. A, Temporal (1937 to 1997) views of landscape cover 

for the northern Colorado Front Range using Modified-Anderson 

categories. B, Chart showing the distribution of the four major 

land-cover classes in the northern Colorado Front Range from 

1937 to 1997. Natural Vegetation includes the forest, shrub, 

grassland, and wetland land-cover classes.
145

the dynamics that caused the changes (Marceau and others, 

2001). By combining the three change images (1937 to 1957, 

1957 to 1977, and 1977 to 1997), interactions between the 

time-periods can be shown. The resulting map shown in figure 

4 highlights those areas with one LULC class change in light 

yellow, those areas that changed twice in bright yellow, and 

those areas that did not change in the standard land cover 

class colors. Table 2 demonstrates how six random pixels 

changed in each time period starting in 1937 and tracks 

those pixels through 1997.

The areas closer to the cities demonstrated two transitions 

during the three time-change periods, with the exception of 

the area slightly north east of Denver that is now the location 

of Denver International Airport. The 1937 LULC data correFigure 4. This image shows those areas of the northern Colorado 

Front Range that have undergone either one or two transitions in 

the three time change periods (1937-1957, 1957-1977, and 1977-

1997). Transitions are shown in shades of yellow. The areas that 

have not changed remain in their representative colors shown 

in the legend, essentially representing the 1937 time period. Res, 

residential; Nat, natural vegetation; Ag, agriculture.

LULC Class 1937-1957 1957-1977 1977-1997

Acres Percent Acres Percent Acres Percent

Total Residential 47,148.97 100% 76,709.53 100% 159,860.64 100%

Residential to

Nonresidential 1,908.27 4.05% 5,470.30 7.13% 2,117.08 1.32%

Residential to

Natural Vegetation 1,608.66 3.41% 2,577.31 3.36% 2,572.99 1.161%

Residential to

Agriculture 2,865.80 6.08% 3,706.58 4.83% 2,918.93 1.83%

Total Nonresidential 27,319.39 100% 43717.84 100% 97,059.28 100%

Nonresidential to

Residential 1,391.20 5.09% 1,051.43 2.24% 835.22 0.86%

Nonresidential to

Natural Vegetation 1,033.52 3.78% 3,101.79 7.10% 3,279.09 3.38%

Nonresidential to

Agriculture 2,397.54 8.78% 1,052.67 2.41% 753.05 2.41%

Natural Vegetation

– no change

503,013.00 79.73% 470,916.00 77.12% 454,466.00 81.12%

Natural Vegetation

to Residential

11,297.00 1.79% 32,157.70 5.27% 28,168.80 5.03%

Natural Vegetation

to Nonresidential

7,891.31 2.57% 22,896.80 6.93% 19,063.60 4.20%

Agriculture – no

change 766,834.00 85.07% 692,112.00 79.46% 631,805.00 89.90%

Agriculture to

Residential 23,202.60 2.57% 60,382.70 6.93% 31,976.10 4.20%

Agriculture to

Nonresidential 10,975.80 1.22% 28,564.10 3.28% 23,277.30 3.05%

Table 1. Acres of residential and nonresidential land-use and 

land-cover (LULC) classes and the largest amount of acreage lost 

for each time-period as compared to amount of acreage lost from 

natural-vegetation and agriculture LULC classes to residential and 

nonresidential LULC classes.

spond to the relatively stable landscape conditions that had not 

significantly changed since the turn of the century. The 1957 

LULC data correspond to the initial growth resulting primarily from wartime and immediate post wartime activities. The 

1977 LULC data represent the beginning of the transition from 

an extractive natural resource- and agricultural-based economy 

to the high-tech service-based economy driven by the “ColoLand-Surface Analysis

0 5 10 15 Miles
146 Regional Landscape Change in Northern Colorado Front Range

rado landscape.” The last time-period used in this study correlates to the huge boom in high-tech service-based economy 

that resulted after the last extractive resource (the oil and gas/

energy) economic bust that took place in the early 1980s. As 

shown in table 2, pixels either do not change their land class 

value or change their land class values over time. When computing the total number of acres in a class, acres either remain 

static or are subtracted or added to the land class value. This is 

demonstrated in table 3.

By the 1930s, the predominant LULC categories were 

agriculture (55 percent of the landscape) and natural vegetation (39 percent of the landscape). Urban lands only occupied 

4.5 percent of the landscape (fig. 5). As the largest city in the 

region, Denver is the most significant feature on the landscape. 

Boulder, Fort Collins, and Greeley established themselves as 

the largest satellite cities in the region (refer back to fig. 4). 

Surrounding Denver and the larger urban centers are predominantly small, farming and ranching rural communities.

The post-WWII boom was reflected in the significant 

growth of the suburbs around Denver. Growing urban populations increased the demand to convert forest, grass, and 

agricultural land to residential, commercial, and industrial 

uses. The amount of urban land grew by 61 percent to approximately 120,500 acres. The farm and ranch lands in and close 

to the suburbs continued to change to residential, commercial, 

and industrial land uses over time (fig. 6). 

This transition was enabled by the growing transportation 

infrastructure demanded by an automobile-centered society 

and cheaper land prices. The road network expanded by 123 

percent with the construction of major highways (Primary 

routes/Class 1) and residential streets in the new subdivisions 

(Roads/Class 3) (fig. 7). The majority of the converted urban 

land radiated out from Denver to the west, south, and east. 

Urban development north of Denver, in spite of the presence 

of Interstate 70, an east to west bounding highway north of 

downtown Denver, was slow due to the traditional manufacturing/industrial emphasis of the area that included the railroad 

yards, the Denver Stockyards, Stapleton International Airport, 

and the Rocky Mountain Arsenal. The growth of individual 

towns closest to Denver on the west was constrained because 

of their proximity to the mountains. To the east, the presence of Lowry Air Force, Fitzsimons Army, and Buckley Air 

National Guard bases enabled Aurora to expand, converting 

large tracts of grass and ranch lands to subdivisions and associated services. Individual towns merged to form a continuous 

Denver metropolitan mass. 

LULC Class 1937-1957

Acres

1957-1977

Acres

1977-1997

Acres

Residential Total 47148.97 76709.53 159860.64

Residential Gained 76641.59 159601.69 219216.64

Residential Lost -6516.17 -11992.03 -8034.64

Nonresidential Total 27319.39 43717.84 97059.28

Nonresidential Gained 43612.28 96718.84 138501.34

Nonresidential Lost -4945.19 -5611.14 -5949.68

Bare Total 6162.19 10424.14 15586.17

Bare Gained 8290.38 14034.97 19734.46

Bare Lost -3825.81 -8915.56 -12740.13

Quarries Total 1444.95 3288.97 6912.77

Quarries Gained 3288.97 6841.11 8735.79

Quarries Lost -817.30 -2189.97 -4504.73

Natural Vegetation

Total 630886.32 610625.12 560271.00

Natural Vegetation

Gained 607159.93 559250.05 526640.59

Natural Vegetation Lost -127873.32 -139709.12 -105805.00

Agriculture Total 901411.97 871012.02 762109.30

Agriculture Gained 870549.72 761750.66 681111.14

Agriculture Lost -134577.97 -178900.02 -130304.30

Wetland Total 5546.91 4714.77 5845.89

Wetlands Gained 2314.76 3586.73 1897.16

Wetlands Lost -3261.18 -2710.13 -2934.99

Water 23274.25 22778.79 36242.92

Water Gained 6534.70 16793.28 9029.85

Water Lost -7030.15 -3355.69 -4253.92

Table 3. Comparison of total acreage, acreage gained, and acreage lost 

for each time change period (1937-1957, 1957-1977, 1977-1997).

[The acreage lost is from each change period and class, the acreage gained is from the other 

land-use and land-cover (LULC) classes from each change period.]

Year Pixel LULC Class Values

No Change 1 Change 2 Changes 3 Changes

1937 7 6 1 7 7 7

1957 7 6 6 7 7 6

1977 7 3 1 6 4 7

1997 7 3 1 3 2 3

Class 1 – Water Class 2 – Residential Class 3 – Non Residential

Class 4 – Bare Class 5 – Quarries Class 6 – Natural Vegetation

Class 7 – Agriculture Class 8 –Wetland

Table 2. Demonstration of how six random pixel land-use and landcover (LULC) class values for the northern Colorado Front Range have 

changed through time and how many changes, if any, the pixels made 

over time.
147

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

Acres

1937

Land-Cover Class

Water

Residential

Non-Residential

Bare

Quarries

Natural Vegetation

Agriculture

Wetland

Figure 5. This chart shows the distribution of the landuse and land-cover classes for the northern Colorado 

Front Range in 1937. It is quite evident that agriculture 

and natural vegetation (used for grazing) were still the 

major classes.

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

Acres

1957

Land-Cover Classes

Water

Residential

Non–Residential

Bare

Quarries

Natural Vegetation

Agriculture

Wetland

Figure 6. This chart shows the distribution of the land-use and landcover classes for the northern Colorado Front Range in 1957. It is quite 

evident that agriculture and natural vegetation (used for grazing) were 

still the major classes.

0

2000

4000

6000

8000

10000

12000

1937 1957 1977 1997

Year

Miles

Primary Route, Class 1 Secondary Route, Class 2

Road, Class 3 Road, Class 4 Trail, Class 5

Figure 7. The total road mileage by road class for the northern Colorado 

Front Range for each time period studied (1937-57, 1957-77, 1977-97). 

The road class in green (road, class 3) represents residential and other 

similar size streets.

Figure 8. Chart showing the distribution of the land-use and land-cover 

classes for the northern Colorado Front Range in 1977. It is quite evident 

that agriculture and natural vegetation (used for grazing) were still the 

major classes, but the urban class areas are beginning to show an 

increase.

In the 1970s, the amount of urban land more than doubled 

to 104,008 ha (257,000 acres). The land continued to change 

from agricultural (loss of 6.6 percent) and grass (loss of 3.3 

percent) lands to urban land uses (fig. 8). To the north, the 

towns of Boulder, Fort Collins, Greeley, Longmont, and Loveland were expanding, the results of the improved transportation 

networks and the arrival of high-technology industries. The 

area around Boulder is unique in the Front Range. Boulder 

enacted growth control measures in the 1970s that slowed 

Land-Surface Analysis

0

100000

200000

300000

400000

500000

600000

700000

800000

Acres

1977

Land Cover Classes

Water

Residential

Non–Residential

Bare

Quarries

Natural Vegetation

Agriculture

Wetland
148 Regional Landscape Change in Northern Colorado Front Range

down the rate of growth, while the bordering towns of Lafayette and Louisville began to grow (fig. 9A). The slowdown in 

the traditional agricultural- and mining-based economies was 

reflected in the trend of static population growth in the rural 

towns, such as Berthoud, Erie, Frederick, and Timnath. By 

1977, the metropolitan area started to expand northward, due 

to the decline and replacement of the traditional manufacturing and agricultural base. The populations of these towns show 

a slow rise in the 1980s with dramatic increases by 2000. The 

population increases are due to the rise of service-base industries, cheaper land values, and small-town atmospheres of safety 

and low living costs (fig. 9B) (Clark, 2000, Kendall, 2002).

Urban development to the southwest and south of Denver 

also started in the 1980s, though hampered by a lack of 

major highways. Cheaper land prices were becoming a major 

enabling factor of urban growth. As the northward migration 

began, the smaller communities of Brighton and Fort Lupton, 

to the northeast between Denver and Greeley, were seeing 

some growth. This pattern is an example of the leapfrog effect 

that takes place along transportation corridors.

There are environmental and social implications for the 

change of highly valued “open spaces” to urban development (Governor’s Commission on Saving Open Spaces, 

2000. Theobald and Hobbs 2002. Theobald and Hobbs, 2000. 

Cornelius, 1999). These open spaces include areas of natural 

vegetation (forests, grasslands, and wetlands) and agricultural 

lands that offer rich habitats for many different species as well 

as the amenities valued by the people. Though few in acreage when compared with the entire study area, wetlands are 

a good example of the effect that other land covers had on 

open spaces over the study time-period. The overall acreage 

total has remained relatively stable but the distribution on the 

landscape has changed (fig. 10A). In addition, there are other 

transformations resulting from urban growth with environmental implications (Matheny and Wear, 2000). An example 

of a consequence of urban growth in the semiarid Front Range 

region is demonstrated by the growing trend of changing 

wetland LULC class and associated with the wetlands is the 

increase in area in the water and quarry LULC classes. Though 

only comprising a small percentage of the overall landscape, 

each of these features together portrays a land demanding the 

raw resources required to support urban growth (especially in 

semiarid to arid regions)—raw building materials (aggregate) 

and water (fig. 10B). 

Of the original 5,500 acres of wetlands mapped in 

1937, approximately 41 percent (2286 acres) of the area has 

remained wetlands while the other 59 percent (3,261 acres) 

has changed to other LULC classes in 1957 (primarily natural 

vegetation, agriculture, and water). At the same time, wetlands 

gained approximately 42 percent (2,315 acres) of the area 

from other LULC classes, primarily from natural vegetation, 

agriculture, and water (fig. 10C). In the later years, 1977 on, 

a possible reason for this change is the legal requirement 

under section 401 of the Clean Water Act to replace wetlands 

that had been converted to other uses. Between 1937 and 

1997, approximately 17,000 acres of land were converted to 

water, almost doubling the amount of acres in that class. The 

movement of the aggregate industry and subsequent reservoir 

development can be tracked with the temporal data. The large 

numbers of these reservoirs are the result of a lack of other 

quarry reclamation efforts and now meet the demand for water 

by agriculture and the urban centers (Arbogast, 2001). Other 

larger reservoirs such as Chatfield, Cherry Creek, and Bear 

Creek Reservoirs were built for flood control along the South 

Platte River Valley or as part of western slope diversion projects (Horsetooth Reservoir) for irrigation, drinking water, and 

hydropower generation. The largest land-cover contributors to 

this transition were natural vegetation, agriculture, and bare 

0

20000

40000

60000

80000

100000

120000

140000

1930 1940 1950 1960 1970 1980 1990 2000

Year

Number

Boulder

Lafayette

Louisville

Fort Collins

Greeley

Longmont

Loveland

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

1930 1940 1950 1960 1970 1980 1990 2000

Year

Number

Elizabeth

Erie

Frederick

Timnath

Louisville

Superior

Wellington Windsor

A

B

Figure 9. A, Graph showing the nonmetropolitan Denver area cities 

populations, 1930-2000. B, Graph showing selected rural town 

populations in the northern Colorado Front Range from 1930-2000.
149

5546.91

2314.76

-3261.18

4714.77

3586.73

-2710.13

5845.89

1897.16

-2934.99

-4000.00

-3000.00

-2000.00

-1000.00

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Acres

1937 to 1957 1957 to 1977 1977 to 1997

A Existing Wetlands Wetlands gained Wetlands lost

0.00

5,000.00

10,000.00

15,000.00

20,000.00

25,000.00

30,000.00

35,000.00

40,000.00

45,000.00

1937 1957 1977 1997

Year

Acres

0

1000

2000

3000

4000

5000

6000

Acres

Water Quarries Wetlands

0.00

500.00

1000.00

1500.00

2000.00

2500.00

1937 to 1957 1957 to 1977 1977 to 1997

Year

Acres

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Acres

Wetland to

Water

Wetland to

Forest/Grass

Wetlands to

Agriculture

Total

Wetlands

B

C

Figure 10. A, Chart showing total existing acres of wetlands, acres lost to 

other land covers, and acres gained from other land-use covers for the three 

time periods studied for the northern Colorado Front Range. B, Comparison of 

transitions of wetlands, water, and quarry land-cover classes because of urban 

growth. C, Comparisons of key land-cover conversions of wetland cover class.

Land-Surface Analysis
150 Regional Landscape Change in Northern Colorado Front Range

classes. Besides the urban LULC class, the water and bare 

classes were the only classes in the region to actually increase 

in size rather then decrease. This trend contributes to the trend 

of the region more and more urban.

The 1990s typified the changing social and economic 

drivers behind the contemporary landscape dominating the 

landscape on urban fringes. While the landscape patterns are 

easy to characterize (fig. 11), integrating socioeconomic data, 

such as land ownership behavior, transportation-related characteristics, and population dynamics, will be of most value 

to planners and land managers as they deal with the multiple 

demands placed on the landscape. In the case of the Front 

Range, the Colorado lifestyle and emphasis on open spaces 

and high-tech industries are the principal enablers behind the 

changing landscape.

This combination of the physical and human environment 

demonstrates the need to integrate both the socioeconomic 

and environmental data with the spatiotemporal description of 

the landscape. The region has moved from an economy reliant on natural resources (extractive minerals and agriculture) 

to an economy strongly influenced by the high tech and other 

service-based industries (Bird, 1993. Kendall, 2002). Today, 

these industries are not driven by the proximity to a natural 

resource or transportation hub but rather the proximity to the 

amenities that will entice and retain employees, such as those 

found along the Front Range and the West (Shumway and 

Otterstrom, 2001, McGranahan, 1999, Nelson, 1999, Rudzitis, 

1999, Vias, 1999).

An interesting characteristic of the urban growth in the 

region can be seen in the migration patterns exhibited on 

the landscape. The desire to be in or close to open space has 

become a driving factor for the current and future landscape 

transformations. James Weskott, Colorado State demographer, 

has suggested in a Denver Post study, that employment considerations and Western attitudes have combined to produce the 

growth patterns (Olinger, 1999). People from out of state tend 

to locate in established areas where jobs are more plentiful, 

creating a demand for housing and driving up housing costs. 

As these areas become denser and housing more unaffordable, many current Colorado residents move out into the edge 

suburbs and into the countryside where the housing costs are 

typically lower. As more out-of-state people move in, current 

residents move out and the amount of urban acres grows. 

Another growing migration trend that became apparent 

in the late 1970s and has greatly increased in the 1990s is the 

move to the mountains. Bountiful land on the plains has kept 

urban growth concentrated in lands under 10 percent slopes. 

Once again, the growing desire to be closer to nature and in 

open space is resulting in a move into the mountain foothills 

west of the urban centers. The mountain communities west 

of Denver and Boulder are all experiencing growth. Areas of 

summer homes have given way to year-round dwellers. The 

mountains constrained urban development in the past, but no 

more, today they act as a driving factor for attracting people 

(fig. 12). 

Conclusions

The landscape of the United States is constantly changing. The rates, causes, types, impacts, and extents of change 

0

100000

200000

300000

400000

500000

600000

700000

Acres

1997

Land-Cover Classes

Water

Residential

Non Residential

Bare

Quarries

Natural Vegetation

Agriculture

Wetland

Figure 11. Chart showing the distribution of the land-use and land-cover 

classes for the northern Colorado Front Range in 1997. Agriculture and 

natural vegetation are still the major land classes, but those acreages are 

falling and the urban and water classes acreages continue to grow.

0

30000

60000

90000

120000

150000

180000

210000

240000

270000

300000

Acres

1937 1957 1977 1997

Urban Class on Percent Slope

urban >30% urban 21-30% urban 11-20% urban 6-10%

urban 1-5%

Figure 12. Chart combining the area of urban land cover with percentage 

of slope shows the movement of people into the mountains to the west in 

the northern Colorado Front Range. The trend of mainly summer homes 

in the 1930s and 1950s was changing into year-round residences starting 

in the 1970s and continuing through the present.
151

vary from place to place. Local changes in land use can have a 

ripple effect throughout a larger region. Expanding urban areas 

are placing multiple and often competing demands on the 

land. Understanding and analyzing the rates, trends and drivers 

behind landscape change are essential to help answer fundamental questions asked by not only scientists but also planners 

and land-management decisionmakers. Change detection, 

the understanding of both the time and space, remains at the 

heart of many of these questions (Marceau and others, 2001). 

Temporal, geospatial databases coupled with socioeconomic 

data in models can provide an understanding to the dynamics occurring through time that causes landscape change. By 

understanding the dynamics of landscape change, urban and 

regional planners, policymakers, and Earth scientists can 

effectively measure trends in urban expansion, analyze patterns of water pollution and sedimentation, understand the 

impacts of development on ecosystems, or develop predictive 

modeling techniques to better simulate the effects of future 

urban growth. The analysis of the changing landscape of the 

Front Range will not only help local and regional decisionmakers but may also be used to simulate the future landscape 

of the West. The dynamics that shaped the Front Range have 

already been put into motion and could herald similar changes 

in regions in the West that are just beginning to experience 

growth.

Improved decisionmaking can be aided by understanding 

not only the rates and trends of landscape change but also the 

causes behind the change and the consequences of land-development decisions. Forty percent of the future growth in the 

United States is expected in the eight Rocky Mountain West 

states. The Denver metropolitan area is expected to experience 

the bulk of this population growth (Clark, 2000). This study 

begins to look at the rates, trends, and general causal factors that shape the Colorado Front Range landscape. A better 

understanding and integration of quantitative socioeconomic 

influences is needed. This study has shown that human actions, 

such as population change, are the primary cause in landscape 

change along the Front Range. 

Growing populations will continue to see the transition 

from agricultural, grass, and forest lands to a more human 

dominated landscape. The cycle of people wanting to live on 

the urban fringe continues to shape the urban landscape. As 

the region grows, there is an outmigration of those who feel 

crowded and this in turn causes urban growth to occur at the 

urban fringe. The checkerboard development pattern and subsequent fragmentation of farm and ranch land are soon filled 

in with other homes and the desired amenities of the urban 

environment. Low-density ranchette developments become the 

new suburban town, and what were isolated farm communities 

expand and join to become part of the growing metropolitan 

area. The northern Colorado Front Range is becoming more 

urban as the region is becoming the center of growth in the 

Rocky Mountain West. 

This study sets the stage for further development into 

techniques and metrics for assessing landscape change (Mladinich and others, 2003). Future urban growth simulations 

can be derived and assessments made with the integration of 

socioeconomic and environmental information in a spatiotemporal context. This understanding can help planners and land 

managers make informed decisions regarding the balancing of 

the multiple demands being placed on the landscape and natural resources. Scientists may also have a better understanding 

of the effects of such issues as climate change and air and 

water quality. 

Acknowledgments

Stacy Welding, Orkand Corp., Stewart Wright and Rick 

Poss, Rocky Mountain Geographic Science Center, and Chad 

Owens, USGS Volunteer for Science, University of Colorado, 

Boulder.

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Bird, M., 1993, The economic development of Colorado 

in historical perspective: Golden, Colo., Independence 

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Clark, T.A., 2000, Auguring the Future of Rural Communities 

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153

Introduction

For most urban areas, development of local economies 

is driven by a number of factors, including (1) transportation 

networks to and from other urban, non-urban (natural lands), 

or industrial areas, (2) population increases (both in terms 

of local birth rates and influx from other areas), and (3) the 

amount of available land for development. In northern, more 

boreal or remote communities, however, development is often 

driven by boom and bust economic cycles, such as gold discovery (Yukon and Nome gold rush), mining of rare elements 

or metals (diamonds near Yellowknife, Northwest Territories, 

Canada), or fossil fuels (oil discovery at Prudhoe Bay, Alaska). 

To a lesser extent, albeit becoming more important, the easy 

access to areas for recreation or tourism (Denali National 

Park) is also significant. In many of the larger metropolitan 

areas, urban expansion is currently facilitated by easily accessible land, that is, relatively level forested, agricultural, or arid 

areas (for example Chicago, Phoenix, and Los Angeles). The 

ultimate result of urbanization is the loss of natural vegetation, 

open space, and agricultural lands (Wear and others 1998), a 

decline in wetlands and available wildlife habitat, encroachment of recreational areas, a decline in ground water availability and surface water quality (Booth and Jackson, 1997; 

Ourso, 2001), and additional impacts on human health and 

psychological well being (Horowitz, 2002). With increased 

urban expansion to the park and forest boundaries, wildlifehuman interactions are becoming increasingly common, most 

often to the detriment of the wildlife.

The Municipality of Anchorage is a northern latitude 

urban area that has experienced a number of boom and bust 

cycles, each serving to provide periods of growth and quiescence. Its political extent covers approximately 4,370 km2

 of 

land; however, its border includes areas of water to the west 

and south (684 km2), and rugged mountains to the east (fig. 

1). Although Anchorage issurrounded by vast areas of undeveloped land, the Alaska Department of Natural Resources, 

Division of Parks Recreation, Bureau of Land Management 

(BLM), and the U.S. Forest Service administer the vast majority of these lands within the municipal boundary to the east, 

and the presence of two military bases further limit the usable 

space for growth and development. Expansion of the municipality, therefore, is beginning to reach a critical stage were 

development will become more restricted in some areas (that 

is, small lot size and more dense housing) and expanding into 

other areas that are less conducive to building (on steeper 

slopes or in areas that have a further commuting distance). 

To document the expansion of the Municipality of 

Anchorage from its core urban area (areas prone to greatest 

development) to less developed surrounding lands (areas of 

steeper terrain at the edges of recreational and multi-use lands) 

and speculate on the future of this northern boreal urban area, 

a study to document the recent historical development and 

subsequent expansion of the urban environment was made, 

spanning 30 years. This was accomplished by mapping 

increases in urban sprawl over the Municipality using aerial 

photography and satellite data at roughly 10-year intervals 

from the early 1970s to 2000. From this data, a composite 

record can be made about the effects urban sprawl had on the 

local ecosystem and what future effects may result if the current growth trends continue in the area.

Historical Background

The initiation of Anchorage as a hub of urban activity 

began in 1915 when President Woodrow Wilson authorized 

funds for the construction of the Alaska Railroad. The engineering headquarters were set up at the mouth of Ship Creek, 

creating an ocean- accessible locus for construction-related 

activities. By the late 1920’s, the population reached 2,700, 

and by 1940 it had nearly doubled with most activities occurring along the creek and on the southern bluff. With the 

United States pending involvement in World War II, the population of Anchorage saw growth resulting primarily from the 

arrival of military personnel and an increase in the civilian 

population for construction activities on the newly activated 

Elmendorf and Fort Richardson bases. The influx of defense 

dollars continued into the 1950s, and with it, the population, 

increasing fivefold to more than 30,000.

By the start of the 1960s, Anchorage was experiencing 

a steady increase in population that continued into the 1970s 

(fig. 2; Municipality of Anchorage, 2000) due to a Cold 

War military build up and the presence of an over-the-pole 

refueling airport (Anchorage International Airport). In the 

early 1970s, the speculation of oil development created jobs 

and the immigration of people, especially those associated 

A 30-Year Study of Urban Dynamics for the Municipality 

of Anchorage, Alaska, 1970-2000

By Carl J. Markon1

1

U.S. Geological Survey, Alaska Science Center, 4230 University Drive, 

Anchorage, AK 99508-4664, email: markon@usgs.gov.
154 A 30-Year Study of Urban Dynamics for the Municipality of Anchorage, Alaska, 1970-2000

with the development of the Prudhoe Bay oil fields and the 

Trans-Alaska Oil Pipeline. Anchorage, being the largest 

metropolitan area in the State, and the center of both railroad, 

airline, and shipping facilities, saw a steady and sometimes 

dramatic increase in population, office space, and housing. 

At about the same time, a major influx of government personnel occurred because of the Alaska National Interest Lands 

Conservation Act, when vast areas of land were turned over 

for management by different Federal land management agencies. This precipitated an increase in Federal employees into 

the state, followed by a commercial and residential building 

boom to house them. Between 1973 and 1977, new housing 

units (single family, duplex, and multifamily) authorized for 

construction increased by more than 2,800 units (Municipality of Anchorage, 1989). This did not last long, however, as 

a slight economic slowdown followed the completion of the 

Trans-Alaska Pipeline in 1976.

During the 1980s, the economy picked up again because 

of a number of infrastructure development activities that arose 

from a relatively dramatic increase in statewide funds due to 

Figure 1. Location and extent of the Municipality of Anchorage study area (red outline).
155

taxes on the new export oil commodity. During this time, the 

Municipality of Anchorage initiated its Project 80’s program, 

a multiyear period that resulted in a new convention center, 

performing arts complex, and a sports arena. In addition, the 

prospects of new grain exports (barely), the construction of 

a coal loading dock in Seward, 90 miles south of Anchorage, 

and new oil field developments on the Arctic Coastal Plain 

fueled urban expansion throughout the city. Between 1980 

and 1987, nearly a billion dollars worth of capital projects 

were initiated or completed in Anchorage. These events created another brief economic boom in 1983 that increased the 

number of commercial businesses in the city and the authorization of over 9,000 new housing starts (Municipality of 

Anchorage, 1989). As the major construction projects came to 

an end and oil prices dropped, the economy started to collapse 

by 1987, resulting in a slight efflux of people from the area. 

This was also reflected in the number of new housing starts, as 

indicated by the number of approved permits for new houses, 

ultimately falling to a low of 208 by 1988. 

Toward the end of the 1980s, a local economic recession resulting from declining oil prices and the end of major 

infrastructure construction brought a significant slowdown to 

both commercial and residential development. During this 

time, many businesses went defunct and numerous parcels 

cleared for development remained barren or were revegetated 

by shrubs, although a small amount of urban development did 

occur, resulting in a four percent increase in urban sprawl. 

The depressed economy remained in effect until the end of the 

1980s when the economy slowly returned and new development (primarily residential) increased.

 From the late 1980s and into the 1990s, the economy 

slowly began to grow, with tourism becoming a multimillion 

dollar industry. By the mid-1990s, the economy had started 

to stabilize. The core job producers, oil, coal, timber, and 

the Federal Government, continued to employ the bulk of the 

work force. Tourism continued to be a multimillion dollar 

industry, and many of the “Big Box” stores (Fred Meyer, 

Wal-Mart, Costco, Kmart) became prevalent in Anchorage 

and Fairbanks. During the 1990s and early 2000 period, the 

number of landscape patches (areas of distinct, identifiable 

land use types) increased (fig. 3), primarily by the spread of 

new neighborhoods along the hillside in southeast Anchorage, 

the construction of new schools and other institutions, and 

the clearing of land for development. Today, Anchorage is 

the largest municipality in Alaska with a population of a little 

under 260,000 (2000 Census); it is still the center for commerce in the State, and is on the verge of another population 

increase due to a stable economy, prospects of new oil and gas 

discoveries, and a new gas pipeline. With these developments, 

increasing demands for space for housing and commercial 

enterprises will probably also occur.

Geographical Scope

Developable land for urban expansion in Anchorage is 

primarily limited to what is commonly called the Greater 

Anchorage Bowl Area. It is delimited on the north by two 

military bases, with the demarcation line being Ship Creek that 

provides a substantial source of drinking water upstream and 

an urban salmon fishery; in the past it was also used for hydroelectrical power. Two large bodies of water, Cook Inlet and 

Turnagain Arm, demarcate the west and southern extent, and 

the eastern boundary roughly follows the tree line (approximately 850 m) along the west range of the Chugach Mountains 

(fig. 1). The area can be roughly demarcated into two general 

geographic regions. The larger, western portion consists of 

level undulating outwash plains interspersed with lakes, poorly 

drained peatlands and low hills. The dominant soils are well 

drained, sandy and gravely deposits of glacial outwash and 

drift. These areas have provided the municipality with a rich 

source of construction-grade gravel material, and still do to a 

limited extent today. Soils in the area provide few or moderate 

limitations for residential or commercial buildings (Rieger and 

0

50,000

100,000

150,000

200,000

250,000

300,000

19291950196119631965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

Figure 2. Population of Anchorage, Alaska, 1929-1999.

Geographical Scope
156 A 30-Year Study of Urban Dynamics for the Municipality of Anchorage, Alaska, 1970-2000

others, 1979; Moore, 2001) and are vegetated primarily with 

paper birch and white spruce. Large areas of poorly drained, 

shrub and Sphagnum-dominated peatlands occur in the west 

central portion of the lowlands, and shrub and black sprucedominated peatlands occur along some stream floodplains and 

in scattered pockets between moraines. Although originally 

considered substandard for housing or business construction, 

some of these areas have been drained, filled, and in the case 

of single and multiple unit housing, placed on pilings 20 or 

more meters deep. 

On the eastern side of the area, elevation contours begin 

as loess bluffs roughly 20 to 30 m above Cook Inlet and gradually increase in elevation inland to 150 m above sea level. 

The eastern (smaller portion) of the study area is dominated 

by silty loess over gravely to stony glacial drift on moderate 

to steep foothills of the Chugach Mountains (Moore, 2001). 

Most of these slopes have slight or moderate limitations for 

construction and are generally good for residential buildings. 

The Bureau of Land Management and the Department of 

Defense manage a large portion of this area, providing relatively undisturbed areas for recreation. Houses in this area 

usually have high market value because of their somewhat 

rural nature and scenic views of the Alaska Range to the west, 

and Mount McKinley and the Talkeetna Mountains to the 

north. Paper birch, aspen, and white spruce, or mixtures of 

each, dominate these areas. 

The foothills region begins at about 150 m elevation in 

the north and at water’s edge in the south, rising to about 

300 m elevation before the more rugged mountainous terrain 

begins. Paper birch and white spruce dominate the lower to 

mid-slopes, gradually giving way to open white spruce mixed 

with alder. On the upper and steeper slopes, alder and low 

shrubs predominate. In this area, Chugach State Park and 

Chugach National Forest dominate the land holdings, and little 

development occurs here except in small inholdings. During 

the mid to late 1990’s, a spruce bark beetle epidemic infected 

the spruce, resulting in a large loss of spruce stumpage and 

making the area a substantial fire hazard, so much so that the 

Municipality of Anchorage developed a special fire Web site, 

indicating the importance of urban development along the hillside areas of the Municipality. 

Methods

Land-Use Patterns and Trends

Land-use/land-cover (LULC) types were mapped using 

a general classification scheme (table 1), similar to level one 

of Anderson and others (1972). The LULC scheme used was 

slightly different from the Anderson scheme in that it is nonhierarchical, but would easily fit into the Anderson hierarchy. 

Image-Data Interpretation

Aerial photography and satellite imagery used to analyze urbanization in the Municipality of Anchorage roughly 

represented the decadal periods 1970, 1980, 1990, and 2000 

(table 2). The temporal intervals used are based primarily on the availability of historical photography and satellite 

imagery, but also on major developmental periods for the 

area, availability of census data, and temporal time frames 

used in other national environmental studies (for example, 

National Water Quality Assessment [NAWQA] program). The 

aerial photographs were acquired from the U.S. Geological 

Survey (USGS), Earth Science Information Center; the satellite image (false-color infrared IKONOS) was acquired from 

Space Imaging. LULC features were delineated and identified on the aerial photos using standard stereo photo-interpretation techniques using a minimum spatial mapping unit 

of 5 hectares for polygonal features (except for ponds at 2.5 

hectares) and a 50-m width for linear corridors (for example, 

major roads). The delineated features for each period were 

digitized and georeferenced as a composite to baseline USGS 

1:63,360-scale topographic series maps (circa 1960-1974). 

Each data layer for each year was superimposed on each other 

and manually checked on a display screen to ensure common 

boundaries matched for each temporal period. Data layers for 

the 2000 period were produced by heads-up digitizing, using 

the satellite image and 1990 polygon data as a backcoverage. Any changes from 1990 to 2000 were then identified and 

delineated.

750

800

850

900

950

1,000

1970 1980 1990 2000

Figure 3. Number of landscape patches (areas of distinct, identifiable 

land-use types) mapped in the Anchorage study area for each of the 

temporal periods).

Residential

Commercial

Urban Vegetation

Natural Vegetation (forest and shrub)

Urban Barren

Natural Barren

Lake/Pond

Wetlands

Table 1. Land-use/land-cover classes used to map the Municipality of 

Anchorage.
157

Although numerous site visits were made throughout 

the study area to assess the type and extent of development 

throughout the Anchorage bowl in 2002, no formal accuracy 

assessment was produced.

The resulting database was then used to analyze changes in 

LULC activities through time, taking note of the patterns, locations, and trends. This was accomplished by summarizing the 

type and amount (size) of changes between each LULC over 

any given polygon and in the number of patches (individual 

polygons) of each of the LULC classes (Elkie and others, 1999). 

Results and Discussion

The time period of most noteworthy change in urban 

development occurred between the early 1970s and mid1980s, with less dramatic changes thereafter (fig.e 4). In 

1970, approximately 7,356 ha of the 31,117 ha study area was 

mapped as urban developed, increasing to 12,263 ha by the 

mid-1980s. Most residential development occurred between 

1970 and 1980 in the forested areas (1,654 ha; fig. 1) with 600 

ha being converted from a low-density type to a higher density 

type, the bulk of which occurred directly east and south of the 

core downtown area. Residential areas outside the core downtown area were relatively sparse in the early 1970’s, with an 

average patch size of 22 ha and occurring primarily in forested 

areas and shrub and graminoid communities along the lower 

to upper hillsides in the southeast portion of the city (fig. 1). 

Between 1980 and 1990, relatively little new development 

occurred in this area. Those areas that were developed, however, occurred predominantly as extensions of existing sites 

in forested sites. By 2000, development had again increased, 

extending over the hillside areas, at times in more remote 

forest and shrub land areas.

The development of commercial sites increased dramatically between 1970 and 2000. Most development occurred in 

the downtown area to the north on previously denuded sites 

through the conversion of forest and wetland areas for airport 

development to the west and within a north-south oriented 

“commercial corridor” that occupied the center portion of the 

municipality between two main transportation arteries. 

To support the development of commercial, institutional, 

and residential areas in Anchorage, large quantities of fill were 

needed. Most of this material was extracted from within the 

municipality from a few sites that were intensely quarried 

over the years. In 1970, approximately 373 ha were identified 

as being used for the extraction of sand and gravel. Between 

1970 and 1980, 227 ha of newly excavated area were mapped, 

with more than one-half (168 ha) coming from natural vegetated sites (forest, and shrub/graminoid-dominated areas). 

From 1980 to 1990, 73 ha were newly excavated, coming from 

previous commercial and urban barren sites, but by 2000 little 

new excavation was performed (about 16 ha), coming primarily from forested sites.

One of the more interesting classes that showed change 

throughout the 30-year period was that of urban barren. This 

type was used for those areas that were barren due to the 

activities of man and normally involved areas such as recently 

stripped ground as preparation for construction, temporary 

storage of equipment and supplies, and those areas that had 

been recently cleared of vegetation on speculation of development; it also included areas that were cleared because of 

the demolition of buildings. Of the urban barren mapped in 

the 1970 data, the majority was used primarily for residential 

(129 ha) and commercial (112 ha) development. From 1970 

to 1980 there was a dramatic increase in land clearing (about 

a 22 percent increase); however, only about one-third was 

used for housing or commercial development and the majority 

of the land (44 percent) reverted back to a vegetated state by 

1990. The reduction in area was probably due to a recession 

that occurred in the mid 1980s, and areas that were cleared for 

anticipated construction were never developed. Of the 177 ha 

mapped as urban barren in 1990, about 99 ha were new (that 

is, not previously mapped as urban barren), occurring over 

areas of previous commercial activity, forest and shrub land, 

and wetlands. Approximately 5 ha were mapped as previously 

being institutional, occupied by an old native hospital and a 

small (less than 5 ha) high-density residential area, both of 

which were demolished (the native hospital site was ultimately 

reseeded and the residential site became a cemetery). By 2000, 

additional land cleared for development occurred primarily on 

forestlands, wetlands, and areas previously mapped as urban 

barren in 1990.

Lakes and ponds remained relatively stable throughout 

the entire period although a fare amount of wetlands were 

lost to drainage ditches and development. Ponds in the area 

Date Nominal

Reference

Date

Nominal Scale Data Type Format

2000 2000 1:20,000 Satellite Color Infrared

1988/1990 1990 1:40,000 Photo Black and White

1984 1980 1:60,000 Photo Color Infrared

1972/1973 1970 1:60,000 Photo Black & White

Table 2. Photography and satellite imagery used for mapping the temporal urban development and 

associated landscape of Anchorage, Alaska (scale is nominal for most data sets).

Results and Discussion
158 A 30-Year Study of Urban Dynamics for the Municipality of Anchorage, Alaska, 1970-2000

increased by 19 percent between 1970 and 1980, primarily due 

to the accumulation of melt water in gravel pits, with a subsequent loss of ponds between 1980 and 1990 due to the filling/conversion of the gravel pits for other uses (predominantly 

commercial development) and expansion of the Ted Stevens 

Anchorage International Airport and associated complex.

More than half of the wetlands mapped in 1970 were 

identified as a different LULC class in 1980. The largest loss 

was due to the construction of residential housing and commercial development, and from 1980 through to 2000 an additional 84 ha were lost to road construction. Throughout the 

mapping period, approximately 180 ha of wetlands remained 

as wetlands but changed in type and form. In 1970, shrubby 

wetlands were dominant, with many small areas of graminoid and strang/moore. With the draining of wetlands that 

began in the late 1970’s and continued through 2000, some 

of the graminoid wetlands became shrubby wetlands, and 

the strang/moore type had all but disappeared. Strang/moore 

wetlands were present in a large wetland system located in 

a northwest/southeast-oriented swath from north of the Ted 

Stevens Anchorage International Airport to south of Campbell 

Lake. In some areas of intense draining, many of the remaining shrubby wetlands became forested wetlands (primarily 

codominates of spruce and shrubs). 

Trends

Like most urban areas, Anchorage has recently experienced a steady increase in growth, both in terms of urban 

1972-1973 1984

1988/1990 2000

Wetlands

Lake and Pond

Natural Vegetation

Natural Barren

Urban Vegetation

Commercial

Residential

Urban Barren

Figure 4. Land-use/land-cover map of Anchorage, Alaska, for the four temporal periods: 1970 (1972-1973), 1980 (1984), 1990 

(1988/1990), and 2000.
159

development and population. Population growth has increased 

in a linear manner since 1950 at about 4,400 people per year 

(fig. 5A), with a slight increase to 5,600 people between 2000 

and 2020 (Municipality of Anchorage, 2001). Urban sprawl 

is also increasing but is approaching a limit imposed by the 

shrinking amount of usable land, and thus urban growth in the 

area is beginning to level off (fig. 5B), with most new singlefamily homes being built in communities 10 to 50 miles north 

of the Greater Anchorage Bowl (Eagle River, Chugiak, Matanuska-Susitna Borough; Municipality of Anchorage, 2001). 

With Anchorage being surrounded on three sides by water 

(Cook Inlet), and on the east by the Chugach Mountains, the 

amount of readily available land is reducing and is approaching it limits to urban growth. A 1998 study (Municipality of 

Anchorage, 2001) indicated that there were 6,100 ha of land 

available for development, but only 4,700 ha were deemed 

suitable or marginal for use. This could change however, 

if some of the lands currently being used as state parkland, 

natural areas, or military lands are opened up for development; 

however, prospects of parkland and military land being opened 

to new development are slim. The parkland is administered by 

the State of Alaska and occurs primarily in rugged mountainous terrain that is unsuitable for development. The military 

land may be made available in the distant future, but an extensive and thorough search for unexploded ordinances would 

have to be conducted first. The only large natural area within 

the study area that may become available for development is 

a large tract of mostly undeveloped land called the Campbell 

Creek Track. This area is currently administered by BLM for 

recreational and educational use and not available for development except for multiple-use trails (for example, walking, running, biking, horseback riding, and sled-dog mushing). 

Conclusions

This project provides one of the first historical overviews 

of land-use/land-cover changes for the Greater Anchorage 

Bowl area. Results from this study indicate that the process 

of urban development closely followed the boom and bust of 

economic conditions until the late 1980s, after which most 

development proceeded at a slower pace as the local economy 

gradually became more stable, relying less on short term, 

high impact events. In other studies of urban growth (Bell 

and others, 1995; Crawford and others, 1996; Pijanowski 

and others, 2002) it was noted that existing transportation 

corridors have a profound effect on where urban growth 

occurs (Buchanan and Acevedo, 1996), although Paquette 

and Domon (2001) found other landscape contexts such as 

woodlots and panoramic views to be equally important in 

southern Quebec, Canada. For the Anchorage area, urban 

growth appeared to be more influenced by the availability 

of nearly level land near the city center, then expanding to 

other areas that provided views of the Alaska Range and less 

developed woodlots. Between the early 1970s and mid-1980s, 

most urban development occurred in vacant areas and open 

space alongside existing development in the areas of low 

topographic relief close to and encircled by the major transportation network. Beginning in the late 1980s, development 

started to expand into the southeast portion of the Anchorage 

Bowl along the predominantly undeveloped and more rugged 

hillside, requiring a totally new network of roads and utilities. 

It is not clear, however, if this was due to the desirability of 

having panoramic views, a “leapfrog” effect where developers skip over properties to obtain land at a lower price (Heim, 

2001), the recent availability of land from existing homesteads, or a combination of one or more of these events. As 

urban growth continues in the Greater Anchorage Bowl area, 

the amount of commercial lands will continue to decrease 

as suitable land decreases (generally flat and close to major 

transportation corridors) and the need for residential property 

increases. Because requirements for residential property are 

less stringent, there is still a sizable area that can be developed 

in the southeast portion of the municipality, although these 

lands are also limited to some extent by State park, military, 

and other public land boundaries. 

The urbanization of this enclosed area also contributes 

to local hydrologic problems. The increase in impermeable 

structures and surfaces (buildings, paved lots, and roads), 

channeling of watercourses, and draining and filling of wety = 49174x - 25644

R2 = 0.9904

0

50,000

100,000

150,000

200,000

250,000

300,000

1950 1960 1970 1980 1990 2000

Year

y = 4673Ln(x) + 7877.5

R2 = 0.9297

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1970 1980 1990 2000

Population

Population Growth

Urban Growth

A

B

Figure 5. (A) Population growth (1950-2000) and (B) urban growth 

(1970-2000) for Anchorage, Alaska.

Conclusions
160 A 30-Year Study of Urban Dynamics for the Municipality of Anchorage, Alaska, 1970-2000

lands has promoted a number of environmental problems. 

Runoff from impermeable surfaces has increased the potential 

of hazardous substances being washed into the water system 

and elevated bacteria in streams (Frenzel and Couvillion, 

2002). Increased runoff and the loss of wetlands reduce water 

percolation into the ground, thereby lessening the natural purification process of water to surface and subsurface water supplies. The channeling of small streams reduces the amount of 

habitat for fish and water birds and allows increased flows of 

runoff into nearby Cook Inlet. 

The data used for this study were obtained, in part, to 

correlate as closely as possible to a ten-year interval that is 

used with the NAWQA program based on the even decade 

(for example, 1970, 1980, 1990, 2000). Intervals such as this, 

however, may not be appropriate for studying urban change 

through time (Marceau and others, 2001), being more dependent on the area that is being observed and the social and 

economic events that have or are about to take place. For 

example, the authorization of new housing units from 1973-

1999 for Anchorage (fig. 6), indicate that land development 

was more abundant during the subdecadal periods and were 

minimally captured by the 1980s data. On the basis of this 

information, a more appropriate time interval to study urban 

dynamics in the Anchorage area would be on the order of 5 

years. This would better correspond to some major economic 

events that happened in Alaska, such as the 1964 earthquake, 

an oil lease sale in 1969, oil pipeline construction from 1974-

77, an economic depression in 1979-80, an oil price boom 

from 1981-1984, and subsequent recession in the late 1980s. 

Dramatic economic swings have not occurred in Anchorage 

recently, and are reflected in the amount of change during 

the last 10 years (fig. 4). This may change, however, during 

the next decade with the possibility of oil development in 

the Arctic National Wildlife Refuge and National Petroleum 

Reserve-Alaska, and the construction of a natural gas pipeline 

from Alaska to the conterminous United States; these events 

may again trigger increased economic development in the 

area. The prospects of continuing this study into the future 

is made somewhat easier by the IKONOS and Digital Globe 

high resolution satellites in orbit that can readily supply highresolution imagery on a repeatable interval, thereby allowing 

the quick updating of the current maps to track developments 

in a more timely manner.

Acknowledgments

This work originally produced for the USGS, by Raytheon STX Corp. staff, Contract Number 1434-CR-N97-CN40274, with funding support by the USGS, Alaska Geographic 

Science Office and Water Resources Office.

References

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mapping of urban regions—Growth of the San Francisco/

Sacramento region: URISA Annual Convention, San 

Antonio, Texas., p. 723-734. 

Booth, D.B., and Jackson, C.R., 1997, Urbanization of aquatic 

systems—degradation thresholds, storm water detection, 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Year

Number of Units

Figure 6. New housing units authorized for construction in Anchorage, Alaska, from 1973 to1999 (Municipality of Anchorage, 2000).
161

and the limits of mitigation: Journal of the American Water 

Resources Association, v. 33, no. 5, p. 1077-1090.

Buchanan, J.T., and Acevedo, W., 1996, Studying urban 

sprawl using a temporal database: Geo Info Systems, v. 6, 

p. 42-47. 

Crawford, J.S., Acevedo, W., Foresman, T.W., and Prince, 

W., 1996, Developing a temporal database of urban 

development for the Baltimore-Washington region: 

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Baltimore, Md, p, 101-110. 

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Resources, Northwest Science and Technology, Thunder 

Bay, Ontario, 16 p.

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bacteria in streams along a gradient of residential 

development: Journal of the American Water Resources 

Association, v. 38, p. 265-273. 

Heim, C.E., 2001, Leapfrogging, urban sprawl, and growth 

management: American Journal of Economics and 

Sociology, v. 60, no. 1, p. 245-283.

Horowitz, S., 2002, The adverse effects of urban sprawl: 

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Anchorage, Alaska.

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indicators: Community planning and Development 

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163

Introduction

The purpose of this paper is to explore the effects 

of urbanization on the hydrologic systems of the southern California region with a primary focus on the largest 

and longest monitored river, the Santa Ana. Urbanization 

in the United States occurred rapidly in the 20th century, particularly along the East and West Coasts (Beach, 

2002). Exceptionally high rates of urbanization occurred 

in southern California (fig. 1), where the population grew 

from ~250,000 to almost 20 million between 1900 and 

2000 (U.S. Census Bureau, 2003). Much of this population 

growth has resulted in the building of single-family home 

communities, creating widespread, sprawling suburbanization over much of the coastal and inland lowlands (Gumprecht, 1999).

As the southern California landscape has changed, the 

rivers of this region (fig. 1A) have also been fundamentally 

altered. Most rivers have been dammed for flood control 

and (or) water supply (~54 percent of the coastal southern 

California watershed area is dammed; Willis and Griggs, 

2003), whereas many of the urban rivers have been channelized to prevent channel avulsion and increase flood 

discharge capacity. Damming is estimated to have caused 

~40 percent reduction in sand and gravel discharge to the 

southern California beaches, which may be influencing 

coastal erosion patterns (Willis and Griggs, 2003). Population growth has also put an increasing demand on ground 

water that has led to a reversal of subsurface pressure gradients and intrusion of ocean water into aquifers (Edwards 

and Evans, 2002). This problem has promoted the diversion 

of surface water and treated municipal wastewater into the 

ground-water aquifers of the region.

In southern California the predominant land-use alteration during the past 50 years was from cultivated land to 

urban (State of California Dept. of Water, 1993). Urbanization increases impervious surfaces, which in turn are known 

to increase runoff rates during precipitation (Leopold, 

1968). Generally, runoff rates over paved areas are about 

two to ten times larger than non-paved surfaces, such as 

cultivated ground or grassland, with the variability associated with variables such as slope, infiltration rates, interception, and soil type (Haan and others, 1994). 

Below, analyses of the time history of river discharge for southern California rivers show that substantial 

increases in runoff have occurred in the urbanized rivers 

and that the increases coincided with the history of development. Expanding upon this work, we detail hydrologic 

and sedimentological impacts of urbanization in southern 

California’s largest river basin, the Santa Ana. These analyses reveal that peak floods and annual average discharges 

have increased over the past 40 years concurrent with a dramatic ten-fold decrease in river suspended sediment rating 

curves.

Southern California Rivers

Southern California rivers have highly pulsed discharge 

(dominantly occurring during frontal precipitation events 

of the winter) and are highly variable annually, largely due 

to El Niño climatic forcing (Brownlie and Taylor, 1981; 

Inman and Jenkins, 1999; Warrick and Milliman, 2003). 

River discharge in southern California responds differently 

during dry and wet years. Discharge during years of low 

precipitation is generally very low to negligible due to the 

high infiltration capacity of the dry soils. During years with 

high precipitation the earthen ground can become saturated 

promoting exceptional runoff rates and volumes.

Increases in total annual discharge were observed in 

many southern California rivers during the past century 

(fig. 2). For large watersheds (Santa Ana and Los Angeles 

Rivers shown in figs. 2 A and B), average annual discharge 

(here normalized by precipitation) show the effects of major 

dams, which trap water for municipal uses and thus reduce 

river discharge. Since this period of dam construction, however, increases in discharge are observed in both the Santa 

Ana and Los Angeles Rivers. In the smaller creeks (Trabuco 

and San Diego Creeks shown in figs. 2 C and D), discharge 

increases over the past 50 years appear to be more dramatic 

than for the large rivers. This is largely due to the lack of 

damming and flow regulation in these watersheds. All of 

the observed discharge increases (for both large and small 

rivers) can be accounted for during winter stormflow (as 

shown for the Santa Ana River below) and have occurred 

during periods of rapid urbanization. 

The Effects of Urbanization on Discharge and SuspendedSediment Concentrations in a Southern California River

By Jonathan A. Warrick1 and Kevin M. Orzech1

1

Coastal and Marine Geology Program, U.S. Geological Survey, 345 Middlefield Rd., MS 999, Menlo Park, CA 94025-3591.
164 The Effects of Urbanization on Discharge and Suspended-Sediment Concentrations in a Southern California River

1920 1940 1960 1980 2000

0

0.1

0.2

0.3

0.4

1920 1940 1960 1980 2000

0

0.2

0.4

0.6

1920 1940 1960 1980 2000

0

0.01

0.02

1920 1940 1960 1980 2000

0

0.01

0.02

0.03

(A) Santa Ana River (B) Los Angeles River

(C)San Diego Creek (D) Trabuco Creek

Prado

Dam

Water Year

Ratio of annual average discharge to precipitation

(m3/sec/cm)

PostDam

Dam

Construction

Figure 2. The time history of the relationship between river discharge and precipitation for southern 

California rivers showing increases in discharge with respect to precipitation. River discharge data 

obtained from the following U.S. Geological Survey (USGS) stream gauges: A, Santa Ana River at 

Santa Ana (USGS 11078000); B, Los Angeles River at Long Beach (USGS 11103000); C, San Diego 

Creek at Culver Drive near Irvine (USGS 11048500); D, Arroyo Trabuco at San Juan Capistrano (USGS 

11047300). All records have been normalized by the annual precipitation measured at Santa Ana, 

California. Solid lines show 10-year means, shadings are one standard deviation about the means. 

A B

o o o

Santa Ana River

Santa Clara River Basin

33°

34°

35°

N

120 W 118 116

San

Diego

Creek

USGS gauge

Trabuco

Creek

Pacific

Ocean

Los

Angeles

River

Pacific

Ocean 2000

1957

Urban Area

Upper Basin

Lower

Basin

Santa Ana

River

Basin

USGS gauge

Prado Dam

N

10 km

Basin

Basin

Figure 1. A, The southern California watersheds; watersheds identified in the text are highlighted in blue. B, The Santa Ana River watershed with extent of urban land cover during 1957 and 2000. Land-cover information digitized from California Department of Water Resources 

(1957, 1964 and 2000).

Santa Ana River 

The Santa Ana River (fig. 1B) is the conduit for the largest watershed in southern California (4,381 km2

) and drains 

portions of the San Gabriel, San Bernardino, and Santa Ana 

Mountains. [The San Jacinto tributary of the Santa Ana watershed rarely discharges into the Santa Ana River (Brownlie and 

Taylor, 1981) and is therefore not considered here in computations of land-use and drainage areas.] A majority of the watershed (3,911 km2

 or 89.3 percent) is upstream of Prado Dam, 
165

which was built in 1941 to retain winter stormflow for subsequent ground-water recharge in infiltration basins downstream 

of the dam and to provide flood control for the lower watershed. 

For the purpose of this paper, the “upper basin” is defined as the 

drainage upstream of Prado Dam, whereas the “lower basin” is 

below the dam (fig. 1B). 

Time evolution of Santa Ana River water and sediment 

discharges were evaluated using discharge and suspended sediment data from two of the long-standing United States Geological Survey (USGS) gauges in the watershed: USGS 11074500 

(Santa Ana River below Prado Dam) and USGS 11078000 

(Santa Ana River at Santa Ana; fig. 1B). These sites represent 

discharge immediately downstream of the dam (upper basin) 

and just upstream of the river mouth (combined upper and lower 

basins), respectively. These data represent some of the longest 

and most complete discharge and sediment-sampling records for 

southern California rivers.

Water Discharge 

To help evaluate how discharge has changed during the 

20th century, figure 3 summarizes annual precipitation and 

discharge information from the Santa Ana River along with corresponding urbanization and population statistics. The most dramatic rise in urbanization and population occurred in the later 

part of the 20th century (figs. 3A and B). Urban areas increased 

from ~4 percent in 1942 to ~35 percent in 2000, with the most 

prominent growth occurring in the 1960s and 1970s. The human 

population in the region of the Santa Ana River has grown at an 

average rate of 60 percent per decade during the 20th century 

(fig. 3B). 

Annual peak discharges at the lower gauging station (fig. 

3D) have increased dramatically since the building of Prado 

Dam (1941) and especially since 1967, the period for which 

USGS suspended sediment sampling record exists. The 7-

year running median annual peak discharges (plotted in fig. 

3D) increased by a factor of 4 between 1967 and 1998. These 

increases were greatest for the driest years, however, as dry-year 

peaks (years with precipitation less than the lower quartile of 

231 mm) increased by more than 6 times during the sampling 

period. 

Total annual discharge from the river (fig. 3E) is related 

to precipitation (fig. 3C), although the discharge response to 

rain has increased dramatically over the period of record. For 

example, since Prado Dam was built, 12 of the 15 (80 percent) 

highest discharge years were recorded after 1977, even though 

only 8 of the 15 (53 percent) highest precipitation years were 

recorded during this period. Increases in total discharge were 

again greater for dry years than for the wet years of 1967-2001. 

Total annual discharge increased by a factor of 5 for the driest 9 

years during 1967-2001, compared to doubling of total annual 

flow for the 9 wettest years. 

These increases in discharge volume were not associated 

with runoff during the dry summer and fall, but instead occurred 

during winter storms. Discharge-duration relationships (fig. 3F) 

Santa Ana River

0%

20%

40%

0

2

4

6

0

500

1,000

0

200

400

600

0

500

1,000

0%

50%

100%

1920 1940 1960 1980 2000

0%

50%

100%

Urban land use (% of subwatershed)

Percent annual discharge from lower basin (%)

Percent of annual river discharge (%)

Annual river discharge (m3 x 106)

Annual peak river discharge (m3/sec)

Annual precipitation (mm)

Population (millions)

upper basin

lower basin

OC

(A)

(G)

(F)

(E)

(D)

(C)

(B)

top 100

days

top 3

days

upper

basin

lower

basin

Year

SBC

RC

1310 m3/sec

Prado Dam

USGS sampling

mean

Figure 3. Temporal trends in the Santa Ana River characteristics. Grey shade represents the period before Prado Dam 

(pre-1940), whereas the yellow shade represents the period of 

U.S. Geological Survey (USGS) suspended-sediment sampling 

(1967-2001). Thick red lines represent 7-year weighted means 

for C, E, F, and G and 7-year median for D. A, Land use in 

the Santa Ana River watershed from California Department 

of Water Resources (1957, 1964, 1993 and 2000). B, Population of counties with land in the Santa Ana drainage (San 

Bernardino (SBC), Riverside (RC) and Orange (OC) Counties) 

from U.S. Census (2003; note that portions of the populations 

live outside of the watershed). C, Annual precipitation at 

Santa Ana from California Department of Water Resources. D

and E, Annual Santa Ana River discharge and peak flow rate 

at the lower stream gauge (USGS 11078000). F, Annual flow 

duration relationship for the Santa Ana River at Santa Ana. 

G, Percent of annual discharge derived from the lower basin 

as measured at Santa Ana station (see text for computational 

methods).
166 The Effects of Urbanization on Discharge and Suspended-Sediment Concentrations in a Southern California River

show that since the building of Prado Dam, the top three days 

of runoff contributed on average slightly less than 50 percent 

of the annual discharge. This three-day percentage is inversely 

related to annual precipitation, largely due to an increase in the 

total number of storms during wet years. The top 100 days of 

discharge (roughly the winter months each year) almost always 

contribute 100 percent of the total discharge. Thus, baseflow 

and urban runoff during the dry months contribute very little to 

the total annual discharge from the river. 

The primary region of runoff generation has changed, 

however, coming less from the lower basin (downstream of the 

dam) and more from the upper basin (upstream of the dam). 

A mass balance of water from the upper and lower basins 

was created with average daily discharge from the two USGS 

gauges and diversions and infiltration between the gauges for 

groundwater recharge (always less than or equal to 15 m3/s; 

Izbicki and others, 1998). The mass balance reveals that for 

most years since Prado Dam was built, the majority of discharge at the Santa Ana River mouth was generated in the 

lower portion of the watershed (even though it only represents 

10.7 percent of the total watershed area; fig. 3G). This is not 

unexpected because Prado reservoir was designed to store 

most of the upper watershed runoff except during wet winters 

when flows are released at rates in excess of the groundwater 

recharge capacity (Brownlie and Taylor, 1981). However, the 

upper basin provided a greater proportion of the total discharge 

with time due to higher upper basin runoff and therefore more 

frequent and larger dam releases. More frequent and larger 

releases from the dam are also due to a slight decrease in reservoir storage capacity from the accumulation of river sediment. 

Storage capacity measurements in 1941 and 1975 showed a 

decrease in reservoir volume of 11 percent (from 275 to 244 

million m3) due to sediment accumulation (Brownlie and 

Taylor, 1981). Projecting this number forward, the reservoir 

would have lost about 25 percent of its storage capacity in 2001 

due to sedimentation. However, this storage loss will be more 

than accounted for in dam expansion plans of the U.S. Army 

Corps of Engineers to be completed in 2008. 

In summary, large increases are observed in peak and 

total discharge of the Santa Ana River, an important source of 

which is stormflow generated in the upper basin and released 

from Prado Dam during high flow events. Below we look at 

the implications of these changes on suspended sediment discharge.

Suspended-Sediment Discharge

Suspended sediment concentration records at the two 

gauging stations (fig. 1B) are available from 1967 to 2001. 

The data from each site differ greatly. For example, suspended sediment concentrations immediately downstream 

of the dam (mean = 480±290 mg/l, median = 130 mg/l, n = 

454) are about an order of magnitude less than concentrations near the mouth (mean = 3200±5800 mg/l, median = 

1150 mg/l, n = 206). The suspended sediment concentrations immediately below the dam do not correlate with river 

discharge (power law, R2 = 0.07), and do not show temporal 

trends over the period of record (1967-2001). The concentrations near the mouth, on the contrary, were positively correlated with discharge (power law, R2

 = 0.53), and exhibited 

decreasing trends with time (fig. 4). Thus, much of the scatter in the complete lower gauge suspended sediment data can 

be attributed to consistent decreases in suspended sediment 

concentrations per unit flow. Average concentrations at this 

site decreased by 10 to 20 times for equivalent discharge 

rates during the sampling record (1967-2001; fig. 4). These 

decreases are significant for all discharge values using nonparametric, Kendall-tau techniques of Dinehart (1997). Further, the 5- to 7-year intervals chosen in figure 4 enable the 

incorporation of short term climactic variations within each 

of the five periods, which makes the comparisons relatively 

free of bias due to climactic shifts. 

Order of magnitude drops in the sediment concentration-discharge relationships, such as observed at the 

lower Santa Ana River site (fig. 4), typically coincide with 

decreasing river sediment load due to reduction in sediment 

supply (Van Sickle and Beschta, 1983; Reid and Dunne, 

1996; Dinehart, 1997). However, this is not the case with 

the Santa Ana. To test whether the sediment loads have 

declined, cumulative sediment loads from the Santa Ana 

were compared with cumulative loads from the Santa Clara 

River (USGS 11114000) for the period 1967-2001. This 

comparison is useful because the Santa Clara River does 

not have time-varying suspended sediment concentrations 

(Brownlie and Taylor, 1981; Warrick, 2002). Using the five 

time-dependent rating curves for the Santa Ana River (fitted 

power functions shown in fig. 4), cumulative sediment discharge does not show a decreasing trend with time when 

compared to the Santa Clara River (fig. 5). In fact, since the 

1969 water year the Santa Ana River suspended sediment 

discharge reveals a slight increase with respect to the Santa 

Clara River, which is counter to the rating curve implications described above. 

Thus, the strong decreases in suspended sediment 

concentrations per unit flow in the Santa Ana River are not 

associated with decreases in sediment loads. It is important 

to note that suspended sand loads do not decrease with time 

using the same methods and rating curves of suspended 

sand (>63 μm) concentrations, which similarly drop 10 to 

20 times over the period of record. 

Discussion and Conclusion

Data presented here suggest that southern California river 

discharges have increased in conjunction with urbanization. 

As shown for the Santa Ana River, increases in discharge were 

inversely related to the event magnitude (that is, greatest for 

low magnitude events and least for high magnitude events), 

which has long been known to be a characteristic of urbanizing basins (for example, Hollis, 1975). Further, discharge 

to the river mouth is overwhelmingly from winter stormflow, 
167

whereas summer discharge is negligible. Lastly, with time, 

stormflow at the mouth has originated more and more from the 

upper basin due to storm releases from Prado Dam. 

We hypothesize that the increases in discharge (fig. 3) 

have caused the decreases in the suspended sediment rating 

curve (fig. 4) largely through dilution. This “dilution hypothesis” suggests that the additional sources of stormflow did not 

include considerable amounts of suspended sediment or erode 

sediment within the river channel. Additional stormflow from 

the upper basin does have comparatively low concentrations 

of suspended sediment due to settling behind Prado Dam (data 

summarized above). For example, if the average suspended 

sediment concentrations from the upper gauging station and 

the time varying rating curves are used to estimate sediment 

flux past each USGS station, only ~7 percent of the suspended 

flux past the lower station can be accounted for from the upper 

station. Thus, the majority of river suspended-sediment discharged at the mouth originates from the lower basin (Brownlie and Taylor, 1981), and additional discharge from the upper 

basin would serve to dilute suspended sediment generated 

from erosion in the lower basin. 

The dilution hypothesis, if true, should also be consistent with sediment production patterns in the lower basin. 

Sediment production within the lower basin can generally be 

attributed to erosion in the uplands, lowlands and river channel. Below we focus on sediment production in each of these 

three regions to evaluate whether changes may have occurred 

over time and influenced the sediment rating curve results presented above.

Sediment production rates in the uplands have likely 

remained relatively steady during the period of USGS sampling, since these areas have remained undeveloped (and 

largely in state and county park hands). The uplands have, 

however, been subject to periodic wildfire, which is known 

to increase landscape erosion in southern California (for 

example, Taylor, 1981; Rice, 1982; Florsheim and others, 

1997). These post-fire erosion responses are known to last for 

a period of approximately 2 to 4 years (Florsheim and others, 

1997). The recent history of fire in the uplands of the lower 

Santa Ana River basin is shown in figure 6. The large Paseo 

Grande fire of 1967 ended a nearly two-decade period without 

large wildfires and occurred at the beginning of the USGS suspended sediment sampling. Another period of wildfire occurs 

in the late 1970s and early 1980s and includes the Owl and 

Gypsum fires. The history of fire in the Santa Ana River Basin 

would suggest that sediment production would peak following 

the 1967, 1980, and 1982 events and, perhaps, be lower during 

the period between these fires and during the most recent 

period. However, these patterns are not observed in either the 

suspended sediment concentration or sediment discharge data 

(figs. 4 and 5). In fact, sediment production during the lowfire 1990s appears to be equal or higher with respect to the 

Santa Clara River than during periods of fire. Thus, although 

sediment production in the lower watershed has likely been 

0

50

100

150

200

250

300

0 5 10 15 20 25

Cumulative Santa Ana River Sediment Discharge (metric 

tonnes)

1970

1980

1990

2000

Cumulative Santa Clara River Sediment Discharge

(metric tonnes)

Increasing

Decreasing

Figure 5. Cumulative sediment loads for the Santa Ana River (USGS 

11078000) and Santa Clara River (USGS 11140000) during 1965-2000. 

Dashed line represents a constant relationship in sediment loads; 

gray shade is the accumulated standard error of the cumulative loads. 

Inserts show the patterns for increasing and decreasing loads from 

the Santa Ana River. Santa Ana River loads calculated with daily 

discharge records and time-dependent power law rating curves 

shown in figure 4. Santa Clara River sediment loads calculated with 

daily discharge records and a stationary rating curve (data tabulated 

in Warrick, 2002).

Discussion and Conclusion

1

10

100

1.000

10,000

100,00

0.001 0.01 0.1 1 10 100 1,000

1974-'79

1967-'73

1995-'01

1980-'87

1988-'94

River discharge (m3/s)

Suspended-sediment concentration (mg/L)

r2 = 0.59-0.84

Figure 4. Suspended-sediment concentration relationships for the 

Santa Ana River at Santa Ana (USGS 11078000). Data have been 

binned into consecutive 5- to 7-year periods (the length of periods 

were not uniform so that high and low discharges could be included 

in each) to show temporal trends using power law rating curves (r2 = 

0.59, 0.64, 0.75, 0.84 and 0.70, respectively).
168 The Effects of Urbanization on Discharge and Suspended-Sediment Concentrations in a Southern California River

influenced by fire in the uplands, wildfire does not appear to 

be the dominant control in the total flux out of this watershed 

or to have caused the order of magnitude changes in suspended 

sediment concentrations. 

Sediment production in the lowlands has likely changed 

dramatically, largely due to the widespread land-use changes 

from agriculture to urban (fig. 1B). It is not clear how these land 

use changes affected sediment loads, since many have shown 

that sediment production is complex during and following 

urbanization (Ebisemiju, 1989; Booth, 1990; Nelson and Booth, 

2002). For example, paving over the adjacent San Diego Creek 

(fig. 1A) imparted increases in sediment fluxes, due to tributary 

channel degradation (Trimble, 1997). Once urbanized, increased 

rates of runoff eroded these natural, small channels at rates 

that surpassed denudation from the former agricultural lands 

(Trimble, 1997). However, it is not clear whether these results 

are applicable to the Santa Ana River, largely because rates 

and styles of development (especially tributary channelization) 

have been different. Thus, without a detailed sediment budget 

of the lower Santa Ana River Basin lowlands (for which limited 

data exist), we cannot determine how steady lowland erosion 

has been. Since the overall flux of sediment from the Santa 

Ana River with respect to other regional rivers has remained 

relatively steady (fig. 5), we will assume that either the lowland 

contributions to the sediment budget have remained somewhat 

steady or that they are substantially lower than contributions 

from the steep uplands. 

Finally, some sediment has been available for erosion in 

the mainstem channel. The channel is not a dominant source 

of suspended sediment, however, since bank stabilization has 

largely limited this potential source. The channel bed, where it 

is natural, has experienced and is projected to experience significant degradation (Brownlie and Taylor, 1981; Nelson, 1982; 

Chang, 2003). However, little of this material is represented 

in the suspended sediment, because on average 96 percent (±9 

percent) and 88 percent (±16 percent) of the suspended sediment mass is finer than the bed material D50 (0.45 mm) and 

D15 (0.25 mm), respectively. Further, the size distributions of 

the bed and suspended sediment have not changed with time, 

which do not suggest that the decreasing sediment rating 

curves are due to coarsening of the bed (for example, Rubin 

and Topping, 2001). Lastly, the rates of channel degradation 

(~0.12 Mt/yr; Chang, 2003) are less than 15 percent of the 

average suspended-sediment budget of the lower basin. Thus, 

the relatively steady suspended-sediment flux from the Santa 

Ana River with respect to other regional rivers during a 30+ 

year period of urbanization (fig. 5) can be attributed to steady 

sediment production in the uplands, altered, but perhaps steady 

production in the lowlands, and limited production in the mainstem channel.

Because we have focused only on suspended-sediment 

discharge up to this point, the relationship between our suspended-sediment results and bedload sediment transport 

in the Santa Ana River deserves comment. Unfortunately, 

bedload is generally not measured in southern California 

rivers. Many have tried to understand bedload transport in 

the region; however, because it is likely an important source 

of sand to the southern California beaches. Brownlie and 

Taylor (1981) used a “modified Einstein” technique for a 

limited number of basins and calculated average bedload to 

suspended load ratios of 5 to 14 percent. Using more recent 

results of similar techniques, Inman and Jenkins (1999) 

assumed that bedload was ~10 percent and ~15 percent of 

total sediment load for large (>500 km2) and small (<500 

km2) southern California rivers, respectively. Due to a general lack of data, Willis and Griggs (2003) assumed that 

10 percent of the total sediment load was bedload for their 

inventory of sand and gravel for coastal California rivers. 

Thus, researchers have generally agreed that roughly a tenth 

of the total sediment transport from southern California 

rivers is due to bedload. For the Santa Ana River, increases 

in peak and total discharge, along with dilution of suspended 

sediment, should increase the importance of bedload transport. However, due to the lack of data, this has not been 

quantified. Lastly, due to the importance of this information 

for littoral sand budgets, we strongly encourage more attention and investigation into these topics.

One important ramification of the altered river discharge 

conditions we report here is the dispersal and fate of this 

material in the coastal ocean. First, the higher rates of river 

discharge will increase the total and peak momentum of river 

water entering the ocean during runoff events, forcing river 

plumes of water and sediment farther offshore (for example, 

Warrick and others, 2004). Second, river water that is heavily laden with sediment (>40 g/L) will exhibit negative 

buoyancy (that is, hyperpycnal plumes) once it is expelled 

into coastal waters (Mulder and Syvitski, 1995; Warrick and 

Milliman, 2003) and form gravity driven turbidity currents of 

1950 1960 1970 1980 1990 2000

0

100

200

300

400

500

600

Year

Cumulative burned area since 1950 (km2

)

Paseo Grande (1967)

Owl (1980)

Gypsum (1982)

USGS Sampling

Figure 6. Cumulative area of wildfire in the lower Santa Ana River 

basin during 1950-2002. Some regions of the basin have burned repetitively during the period of record. Data from California Department of 

Forestry and Fire Protection (2004).
169

sediment along the seabed. Due to the changes in sediment 

concentrations with time, the Santa Ana River was much 

more likely to exhibit hyperpycnal behavior (>40 g/L) in 

the 1960s and 70s than in the 1980s and 90s (fig. 4; also see 

instantaneous data from 1969 in Warrick and Milliman, 2003, 

with concentrations in excess of 150 g/L). Because sediment 

concentrations were diluted by runoff from landscape urbanization, it follows that land-use change may be responsible 

for altering the dispersal mechanisms and fate of sediment on 

the Continental Shelf offshore and along the coast.

In conclusion, maintaining and expanding the long-term 

data collection networks in watersheds is essential for understanding the long-term trends and impacts of urbanization on 

the river and coastal regions. As shown here, these long-term 

records clearly detail the time history of urban impacts on 

river discharge in southern California. Unfortunately, other 

southern California basins do not have long sediment sampling records such as those for the Santa Ana River so the 

applicability of our sedimentological results cannot be shown 

at this time. Continued monitoring is needed to further characterize the hydrologic and sedimentological impacts of the 

widespread urbanization of the southern California lowlands.

Acknowledgments

Reviews provided by Jerad Bales and Doug Johnson 

greatly improved this paper. We are also grateful to David 

Rubin, Noah Snyder, Jodi Harney, and Eric Grossman 

for insightful discussions early in our investigations. G.I. 

Bergquist at California Department of Water Resoucres supplied land-use data and Lori Hibbeler, Beth Feingold, and 

Florence Wong assisted with data management and interpretation. Jonathan Warrick was funded by a USGS Mendenhall Postdoctoral Fellowship. 

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data: accessed June 26, 2003 [http://www.census.gov].

Van Sickle, J., and Beschta, R.L., 1983, Supply-based models 

of sediment transport in streams: Water Resources Research. 

v. 19, n. 3, p. 768-778.

Warrick, J.A., 2002, Short-term (1997–2000) and long-term 

(1928–2000) observations of river water and sediment 

discharge to the Santa Barbara Channel: Santa Barbara, 

Calif., University of California at Santa Barbara, Ph.D. 

dissertation, 337 p.

Warrick, J.A., and Milliman, J.D., 2003, Hyperpycnal 

sediment discharge from semiarid southern California 

rivers—Implications for coastal sediment budgets: Geology, 

v. 31, p. 781-784.

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2004, Dispersal forcing of southern California river plumes, 

based on field and remote sensing observations: GeoMarine Letters, v. 24, n. 1, p. 46-52. 

Willis, C.M., and Griggs, G.B., 2003, Reductions in fluvial 

sediment discharge by coastal dams in California and 

implications for beach sustainability: Journal of Geology, v. 

111, p. 167-182.
171

Introduction

Environmental protection of water quality is evolving 

from the detection of end-of-pipe sources of water pollution, 

such as sewage and industrial waste, towards a watershed 

management approach based primarily upon detection of 

non-point-source (NPS) pollution (USEPA, 1994). In generally urbanized watersheds, NPS pollution is related to human 

habitation and the subsequent build-up of anthropogenic 

impervious surfaces on the landscape. In this study, these 

features specifically include roads, rooftops, parking lots, 

driveways, and sidewalks. The amount of impervious surface 

in a watershed is a landscape indicator integrating a number of 

concurrent interactions that influence a watershed’s hydrology 

(Schueler, 1994). The direct hydrologic effect of impervious 

surfaces occurs as a change in the magnitude and variability 

of velocity and volume of surface flow. Within landscapes 

under the influence of impervious surfaces, precipitation that 

would normally be part of natural infiltration instead falls on 

and flows over impervious surfaces. The runoff is then channeled and released via storm sewers directly into the receiving stream. This alteration of the natural hydrologic process 

reduces runoff lag time, increases the peak rate of streamflow 

discharge, increases the resulting number of bankfull/subbankfull values and brings about subsequent increases in the 

scouring and incision of the stream channel (Leopold, 1973; 

Booth, 1990). In this way, the channeled runoff from anthropogenic impervious surfaces influences the morphological 

structure of the stream and thereby alters the in-stream and 

riparian ecology.

The impact of impervious surfaces on hydraulic change 

and stream stability has been discussed in Hammer (1972), 

Leopold (1973), Booth (1990), and Henshaw and Booth 

(2000). Klein (1979) and Galli (1991) related increases in 

impervious surfaces to increases in stream temperature. Jones 

and Clark (1987) and Kennen (1999) discussed the lack of 

benthic diversity associated with urban environments. Studies by Limburg and Schmidt (1990), Weaver and Garman 

(1994), May and others (1997) and Wang and others (2001) 

related changes in fish communities to increases in urbanization. Anderson (1968), Hollis (1975), and Rose and Peters 

(2001) discussed the impacts of urbanization and impervious 

surfaces on streamflow, whereas Biggs (1995) and Poff and 

Allen (1995) specifically related disturbance from streamflow 

variability to significant changes in aquatic habitat structure. 

Schueler (1994) observed that imperviousness of a watershed 

is the primary gauge of urban stream ecosystem health and 

hypothesized that a threshold for urban stream stability and 

habitat quality exists at approximately 10 percent to 20 percent watershed impervious surface area. While the biological 

effects of altered hydrologic regime have been quantified, few 

empirical studies have attempted to discriminate and relate 

the relative historical impacts of precipitation and landscape 

change on streamflow rate at the daily temporal scale. In addition, although continuous hydrologic modeling efforts such as 

Dinicola (1989), James (1994), and Leitch and Harbor (1999) 

are numerous, there have been no empirical studies that specifically utilized daily precipitation as an input for determining 

changes in long-term streamflow response. In light of current 

climate change investigations (Karl and Riebsame, 1989; Karl 

and others, 1995; Karl and Knight, 1998), historical precipitation data is necessary for discrimination of long-term causes of 

streamflow rate change in urbanized basins.

In their review investigation into the uses of remote sensing for the detection of impervious surfaces, Slonecker and 

others (2001) reported on such uses as aerial photography, 

satellite multi-spectral imagery, and airborne multi-spectral 

imagery to map impervious surfaces. In general, they found 

that most uses of remote sensing have emphasized general 

land use mapping and single-date conditions as inputs into 

hydrologic models or tax assessment structures. Of note are 

Hammer (1972), Graf (1975), and Moscript and Montgomery 

(1997). These studies employ historical approaches but rely 

upon secondary geo-spatial sources and/or generalized land 

cover mapping to determine the area and extent of impervious 

surfaces. Concerning historical landscape change and streamflow investigations, there has been little scientific effort placed 

on the accuracy, resolution, and consistency of explicit impervious surface area calculation across time.

In this study, we related the impact of anthropogenic 

impervious surfaces upon daily streamflow discharge, indeChanges in Anthropogenic Impervious Surfaces, Precipitation, 

and Daily Streamflow Discharge—A Historical Perspective in a 

Mid-Atlantic Subwatershed1

By David B. Jennings2 and S. Taylor Jarnagin3

1

Originally published in Landscape Ecology, 2002, v. 17, no. 5, p. 471-489.

2

U.S. Environmental Protection Agency, 12201 Sunrise Valley Dr., Reston, 

VA. 20192, jennings.david@epa.gov.

3

U.S. Environmental Protection Agency, USGS National Center, MS 555, 

12201 Sunrise Valley Dr., Reston, VA 20192, jarnagin.taylor@epa.gov.
172 Changes in Anthropogenic Impervious Surfaces, Precipitation, and Daily Streamflow Discharge

pendent of precipitation variables, over a 51-year period 

(October 1947 through September 1998) in the upper Accotink 

Creek subwatershed of Fairfax, Virginia. Six dates of rectified 

aerial photography were utilized to map historical changes in 

total impervious surface area (TIA). Impervious surface area 

data were then compared with coincident mean daily streamflow discharge data and daily precipitation records to determine historical trends between impervious surface growth and 

changes in the amount and pattern of mean daily streamflow 

discharge.

Study Area

The area of interest for this study is the upper Accotink 

Creek subwatershed. This sub-watershed is bisected from the 

larger Accotink Creek watershed at the pour point location of 

the Virginia Department of Environmental Quality (VDEQ) 

Stream Gage 01654000, Accotink Creek, near Annandale, 

Virginia. (fig. 1). Located 15 km west of Washington D.C., 

within Fairfax County, Virginia, Accotink Creek flows in a 

generally southeasterly direction from its source in Fairfax 

City and empties directly into the Potomac River at Gunston 

Cove. The upper Accotink Creek subwatershed drains 61 km2

with a stream length from headwater to gauging station of 18 

km and an average slope of 3.6 m km-1 (Anderson, 1968). The 

basin lies in the Piedmont region of Northern Virginia and has 

a geologic composition of crystalline rock and saprolite with 

an overburden primarily of clay and silt (Froelich and Zennone, 1985). The instantaneous peak discharge for stream gage 

01654000 was initially set by VDEQ as 11.34 m3 s-1 in 1947, 

and modified to 22.67 m3 s-1 in 1961 and to 39.67 m3 s-1 in 

1966. The changes in the reporting of instantaneous peak discharge are related to the consistent increases in bankfull discharge as observed by the stream gage (fig. 2). This is further 

borne out by the historical increases in the mean frequency per 

year (6.0 to 20.6) and mean streamflow rate per year (23.16 m3

s-1 to 27.40 m3 s-1) of instantaneous discharge values ≥ 11.34 

m3 s-1 observed between the first and last decade of record for 

the gage.

Methods

Calculation of Impervious Surface Area

This study makes use of hardcopy film and digital rectified historical aerial photographs to map impervious surfaces from six dates ranging from 1949 to 1994 (table 1). 

U.S.Geological Survey (USGS) digital orthophoto quarter 

quads (DOQQ, USGS, 1996) derived from 1994 National 

Aerial Photography Program (NAPP) data (Light, 1993) were 

utilized as a base to rectify all historical aerial photographs 

not commercially available in digital rectified format (years 

1979 and earlier). The unrectified historical photographs were 

scanned and converted to digital format at 1 m2 pixel resoluCanada

United

States

Virginia

Atlantic Ocean

Upper Accotink

Creek subwatershed

Fairfax County,

Virginia

NWS Monitoring Station,

Vienna/Dunn Loring

Washington

D.C.

VDEQ Stream Gage, 01654000

Accotink Creek near Annandale, VA.

0 5 10 Kilometers

0 500 1000 Kilometers N

Figure 1. Location of the upper Accotink Creek subwatershed study area.
173

tion. These data were subsequently rectified to the 1994 orthoimagery utilizing image-to-image registration techniques and 

second order polynomial transformation algorithms (Jensen, 

1996). The use of rectified digital imagery allowed impervious surfaces to be compiled in a digital mapping environment. 

This enabled the compiled impervious surface data to have 

“real world” areal extent that could be concisely tabulated as 

TIA.

Impervious surface areas were analyzed and compiled 

through the composite means of analog stereoscopic visual 

analysis and “heads-up” digital collection procedures. Impervious surface boundaries were delineated using standard 

photographic interpretation techniques as put forth in Jensen 

(1983) and Haack (1997)). The five categories chosen for 

compilation (roads, parking lots, sidewalks, rooftops, and 

driveways) were representative of the predominant anthropogenic cover types in the basin. The minimum mapping 

unit set for this project was 1 pixel or 1 m2. This resolution 

corresponds to the spatial resolution necessary to discern 

and compile sidewalks, the smallest spatial unit of the five 

impervious surface categories. Where features were not clearly 

discernable on the photograph and the scanned image, they 

were not compiled. Photographic resolution (Table 1) limited 

our ability to discriminate the degree of compaction of soil 

in order to determine infiltration and runoff capability. Based 

on the compacted nature of bare soil transportation networks 

(“dirt roads”), a decision rule was employed to compile bare 

soil areas related to road features only. If a “dirt road” pattern 

could be distinguished then it was compiled as an impervious 

surface.

The map compilation of the various impervious surface components was broken down into two categories: (1) 

direct compilation (fig. 3) in which a unique area feature was 

mapped with a unique polygon, thus allowing an individual 

area assessment for each unique polygon; and (2) indirect

compilation (fig. 4) in which sampling procedures were introduced to assign probable area assessments to point features. 

Direct compilation methods were utilized for the analysis 

and mapping of roads, parking lots, sidewalks, multifamily rooftops as well as commercial rooftops and institutional 

rooftops (schools, churches, hospitals). Indirect compilation of 

Methods

10

20

30

40

50

1 10

Return Period in Years

10/1/87 - 9/30/98 10/1/77 - 9/30/87 10/1/67 - 9/30/77

10/1/57 - 9/30/67 10/1/47 - 9/30/57

Bankfull

Discharge

Figure 2. Bankfull discharge per decade for the upper Accotink Creek subwatershed using yearly peak mean 

daily streamflow data. Return Period derived as per Leopold (1968).

Date Photo Scale Film Type Source

3/94* 1:40,000 Color InfraRed USGS

4/88* 1:40,000 Color InfraRed USGS

10/79 1:40,000 Black and white ASCS

4/71 1:24,000 Black and white VDOT

4/63 1:24,000 Black and white USGS

4/49 1:24,000 Black and white USGS

Table 1. Aerial photographic data for the upper Accotink 

Creek subwatershed.

[USGS, U.S. Geological Survey; ASCS, Agricultural Soil Conservation Service; VDOT, Virginia Department of Transportation; *, also 

acquired in USGS DOQQ format]
174 Changes in Anthropogenic Impervious Surfaces, Precipitation, and Daily Streamflow Discharge

single-family detached rooftops employed a sampling strategy 

based on the number of residential parcels/rooftops contained 

within 2 km2 grid areas. For each given grid, a point theme 

denoting the total number of single-family dwellings was created. Area assessments were then derived for a sample set of 

5 percent of these single-family dwellings for each respective 

grid. Rooftops, driveways, and walkways from the singlefamily parcel sample set were delineated with polygons, and 

a resultant sampled mean for the impervious surface area per 

parcel was derived. The sample set mean was then multiplied 

by the number of total rooftops, per respective grid, such that 

impervious surface area related to single family houses could 

be calculated. Derived impervious surface area data from the 

two approaches (direct and indirect) were combined and a TIA 

for the watershed was calculated. The TIA was then divided 

by the total watershed area to compute an impervious surface 

percentage for each separate year of imagery. No efforts were 

made to distinguish between the TIA and the Effective Impervious Area (EIA) as defined in Alley and Veenhuis (1983).

Precipitation/Streamflow Data

Historical mean daily streamflow data from the VDEQ 

stream gage 0165400, Accotink Creek near Annandale, VA 

(USGS, 2001), were obtained for the water-year period of 

1948 through 1998. Water year is defined as October 1st of 

the previous year through September 30th of the current year 

that is water year 1950 = 10/1/49 – 9/30/50). Historical daily 

precipitation records coincident with the streamflow records 

were acquired from the National Weather Service (NWS) 

monitoring station Vienna/Dunn Loring (NCDC, 1998). The 

NWS station is located approximately 10 kilometers north 

of the VDEQ streamflow gage (fig. 1) and provided the best 

historical estimate of overall precipitation amount and distribution in the upper Accotink Creek basin. The streamflow 

dataset was 100 percent complete while the precipitation 

dataset was missing 434 total records - primarily from the 

first two years of data. No attempt was made to interpolate 

values for the missing precipitation records.

Coincident daily precipitation/streamflow data pairs 

were formed from the individual data sets and grouped into 

categories based on measurable daily precipitation cutoff 

values at (1) > 0 mm, (2) ≥ 6.0 mm, and (3) ≥ 35.0 mm. The 

Figure 3. Direct impervious-surface compilation method. Delineated 

roads, parking lots, and commercial/institutional rooftop areas are 

mapped in red. Accotink Creek is mapped in blue.

A B C

Figure 4. Indirect compilation based on (A) 2 km2 sampling grid network overlaid on the basin (the stippled area in the center 

of the sampling grid is the identical 2 km2 area referenced in figure 3 above); (B) the compiled point theme representative of 

parcel/rooftops; (C) an area sample displaying 5 percent of the parcel/rooftop point theme compiled as polygons from rooftop, driveway, and walkway impervious-surface features associated with each parcel. The mean of the polygonal areas then 

became the mean area for all residential rooftops in that particular grid.
175

precipitation cutoff # > 0.0 mm included all daily occurrences of measurable precipitation and allowed the general 

relationship of precipitation and streamflow to be explored. 

The precipitation cutoff # ≥ 6.0 mm included approximately 

45 percent of the data pairs with measurable precipitation 

(6.0 mm was viewed as a “normal” precipitation occurrence). 

The precipitation cutoff # ≥ 35.0 mm included approximately 

5 percent of the data pairs with measurable precipitation 

(35.0 mm was viewed as “extreme” precipitation occurrence).

The precipitation/streamflow data pairs were further 

binned into temporal categories as: (1) 10-year bins to examine the overall historical precipitation/streamflow discharge 

trend; and (2) 3-year bins, around each date of photographic 

coverage, to examine the relationship of a specific percent 

impervious surface area on streamflow discharge. Due to 

missing precipitation data and a single “decade” that consisted of more than 10 years (10/1/87–9/30/98), we used a 

sample year (365 days) and sample decade (3,650 days) to 

standardize the number of data pairs per year for 10-year and 

3-year bins.

Streamflow Frequency Pattern

We grouped the counts of mean daily streamflow discharge rates ranging in volume from 0.25 m3

 s-1 to 14.0 m3 s-1

into 10-year bins to develop a frequency analysis that would 

characterize historical variability in given levels of streamflow discharge.

Calculation of a Streamflow Response Value

For the streamflow-precipitation data pairs related to the 

precipitation cutoff groupings of ≥ 6 mm and ≥ 35 mm, a 

daily streamflow response value (per meter of precipitation) 

was produced. The streamflow response (m3 s-1 m-1) metric is 

the product: [mean daily streamflow (m3 s-1) / daily precipitation (in meters)]. “Streamflow response” provided a unifying 

term for the data pair whereby historical changes in streamflow response to per unit of precipitation could be tracked 

and reported over time. 

The streamflow response value is similar to a daily 

runoff coefficient (RC) in that the variables of streamflow 

and precipitation are utilized to derive a runoff ratio. The 

terms of area and time are constants in this study, so that the 

relative change in the daily relationship of the streamflow 

response value and the RC are identical. With respect to 

change over time in single watershed studies, there is no difference between the streamflow response value and the RC. 

However, we feel that as a metric, streamflow response provided a more intuitive stream ecology relationship than the 

RC and better captured the changing magnitude of streamflow as an ecological disturbance event.

Historical Precipitation Pattern

As part of the data pair approach, precipitation data was 

tested for differences in frequency and amount per 10-year and 

3-year bins for the categories > 0mm, ≥ 6mm, and ≥ 35mm.

Additionally, we tested for changes in historical precipitation pattern that could influence historical streamflow discharge patterns. Precipitation data for the categories > 0mm 

and ≥ 6mm were grouped into 10-year bins based on occurrences of 1-day, 2-day, 3-day, and > 3-day precipitation duration by decade. The categories > 0mm and ≥ 6mm were also 

grouped into 10-year bins based on the seasonal categories 

of winter, spring, summer and fall to test for changes in seasonal patterns. The “continuous daily precipitation duration” 

and “daily precipitation by season” groupings were tested for 

changes in the frequency and mean amount of daily precipitation per decade.

Statistical Analysis

Normality of data was tested utilizing KolmogorovSmirnov One Sample Tests using a Normal (0.00, 1.00) 

distribution and 2-tail Lilliefors probabilities. All statistical 

tests were run with SAS (Science Analysis System) 7.0 and 

SYSTAT 9.0 on a Windows NT platform. 

Results

Both the streamflow and precipitation data were not normally distributed and this condition was not correctable using 

data transformations. Therefore, we used the nonparametric 

Kruskal-Wallis One-Way Analysis of Variance for the tests of 

differences in amount of the streamflow and precipitation variables. One-Way Crosstabs using Pearson chi-square were used 

to test for differences in the frequency of variable values.

Mapping of Impervious Surfaces

Figure 5 is a set of image mosaics, produced from rectified 

digital photographs, overlaid with compiled impervious street 

networks and commercial/institutional areas. These images 

reveal the changing nature of the landscape in the basin over 

the study period. The percentage of impervious cover in the 

basin grew from approximately 3 percent in 1949 to 33 percent 

in 1994. Root Mean Square (RMS) error for the image-toimage registration was < 2.5 pixels for each coverage, while 

post-rectification check points showed that registration errors 

were in the 2 to 4 pixel range. Individual map accuracy assessments were achieved via simple random sampling procedures 

as set forth by Congalton and Green (1999). Total map accuracy for the six impervious surface maps ranged from 97 percent in 1949 to 91 percent in 1994 (table 2).

Results
176 Changes in Anthropogenic Impervious Surfaces, Precipitation, and Daily Streamflow Discharge

Ten-Year Bins (General Streamflow and Precipitation Trends)

Figure 6 is a graph of the regression lines from the scatterplots of the streamflow-precipitation data pairs related to 

precipitation values > 0mm, grouped in 10-year bins. The 

graph represents a general historical characterization of 

streamflow-precipitation relationship. A rising slope over 

time is observed indicating an increasingly direct relationship 

between precipitation and runoff. The data appear to form two 

distinct groupings of slopes over time, with the precipitation/

streamflow response curves for the first two decades showing 

a lower streamflow response to increasing precipitation than 

the last three decades.

B

1949

A

1994

Figure 5. Upper Accotink Creek subwatershed image mosaics for 

(A) 1949 and (B) 1994 where roads and commercial/institutional 

areas are mapped in red. Anthropogenic impervious-surface area 

has increased from 3 percent in 1949 to 33 percent in 1994.

Year of

Imagery

Registration Error

(in Pixel Units)

Approximate

TIA %

Total Map

Accuracy %

1949 X = 2.1 Y = 3.4 3 97

1963 X = 1.1 Y = 2.7 13 95

1971 X = 2.9 Y = 2.9 21 96

1979 X = 1.9 Y = 1.5 27 93

1988 N/A 33 92

1994 N/A 33 91

Table 2. Registration error, percent impervious-surface area, and 

total map accuracy assessment per photographic coverage for the 

upper Accotink Creek subwatershed.

[Registration error is based on the mean of four post-rectification check points 

per coverage]

0

20

40

60

80

100

0 25 50 75 100 125 150 175 200

Daily Total Precipitation (mm)

Mean Daily Streamflow (m3 s-1) 

Oct 1947 - Sep 1957

Oct 1957 - Sep 1967

Oct 1967 - Sep 1977

Oct 1977 - Sep 1987

Oct 1987 - Sep 1998

Figure 6. Streamflow versus precipitation for days of measurable 

precipitation (1947-1998) for the upper Accotink Creek subwatershed. 

A decade-by-decade series of regressions illustrating the general 

historical curvilinear relationships of daily precipitation and streamflow. Figure includes all streamflow-precipitation data pairs where 

precipitation values > 0, N = 5865. The regression lines for the first 

two decades follow a similar path, whereas the curves for the last 

three decades form a second group. 

Regression equations and sample n per decade: 

1st decade (10/47 - 9/57): y = 0.001x2 + 0.0678x + 0.3595, R2 = 0.4609, n = 1051;

 2nd decade (10/57 - 9/67): y = 0.0005x2 + 0.1259x + 0.1366, R2 = 0.5429, n = 

1076; 

3rd decade (10/67 - 9/77): y = 0.0022x2 + 0.0811x + 0.4803, R2 = 0.7139, n = 

1159; 

4th decade (10/77 - 9/87): y = 0.0013x2 + 0.1372x + 0.1425, R2 = 0.5406, n = 

1199; 

5th decade (10/87 - 9/98): y = 0.0014x2 + 0.1297x + 0.4052, R2 = 0.4849, n = 

1380.
177

Table 3 is a set of historical streamflow discharge frequencies, in 10-year bins, ranging in volume from 0.25 m3

 s-1 to 14.0 

m3 s-1. The table reflects streamflow data only and does not refer 

to coincident precipitation values. The frequency of flows for 

the three cutoff values above the historical mean (0.80 m3 s-1) 

have significantly increased over time, while the frequency of 

flows for the three cutoff values at or below the mean have significantly decreased over time. Figure 7 compares the streamflow response value and precipitation for precipitation values ≥

6.0 mm by 10-year bin. Two trends emerge from the data: (1) 

mean precipitation volume shows no significant variation over 

time, whereas (2) the streamflow response value shows a significant change over the same period.

Table 4 is a compilation of 10-year bin statistics of precipitation and streamflow for data pairs associated with the 

daily precipitation cutoff values of ≥ 6.0 mm (fig. 7) and ≥ 35.0 

mm. There was no significant difference among bins for the 

frequency of precipitation occurrences or in the mean amounts 

of precipitation per value at either cutoff value. There was a 

significant difference among decades found for the streamflow 

response value at both cutoff values.

Three-Year Bins (Impervious Surface and 

Streamflow Relationship)

Figure 8 compares the streamflow response value and 

precipitation for precipitation values ≥ 6.0 mm by 3-year bins 

centered on aerial photography acquisition dates. As in figure 7, 

Decades

Number of

days with mean

daily stream

flow

≥ 0.25 m3 s

-1

per sample

decade

Number of

days with mean

daily stream

flow

≥ 0.5 m3 s

-1 per

sample decade

Number of

days with mean

daily stream

flow

≥ 0.8 m3 s

-1 per

sample decade

Number of

days with mean

daily stream

flow

≥ 1.5 m3 s

-1 per

sample decade

Number of

days with mean

daily stream

flow

≥ 9.0 m3 s

-1 per

sample decade

Number of

days with mean

daily stream

flow

≥ 14.0 m3 s

-1

per sample

decade

10/1/47 -

9/30/57 2450.0 1525.7 816.3 281.8 22.0 6.0

10/1/57 -

9/30/67 2360.7 1265.3 722.6 305.8 23.0 13.0

10/1/67 -

9/30/77 2691.8 1397.9 738.4 360.7 46.0 19.0

10/1/77 -

9/30/87 2102.8 1125.4 684.6 362.8 38.0 19.0

10/1/87 -

9/30/98 2204.7 1143.7 672.2 447.8 49.1 25.4

Results (90.051, 4,

< 0.001)

(88.231, 4,

< 0.001)

(17.81, 4,

0.001)

(46.74, 4,

< 0.001)

(17.94, 4,

0.001)

(13.04, 4,

0.011)

Table 3. Differences in streamflow frequencies by decade for the upper Accotink Creek subwatershed.

[The darker middle column denotes historical mean (0.8 m3 s-1). Significant differences were found among decades at α = 0.05 for all 

mean daily stream-flow occurrence cutoff levels. One-way cross-tabs (χ2) statistical tests used were for the number of values per sample 

decade, results reported as (test statistic value, df, p-value)]

10

20

30

10/1/47 -

9/30/57

10/1/57 -

9/30/67

10/1/67 -

9/30/77

10/1/77 -

9/30/87

10/1/87 -

9/30/98

Decade

Precipitation (mm)

75

125

175

225

10/1/47 -

9/30/57

10/1/57 -

9/30/67

10/1/67 -

9/30/77

10/1/77 -

9/30/87

10/1/87 -

9/30/98

Streamflow Response (m3

s–1m–1) Figure 7. Comparison of decade averages of daily precipitation (mm) 

and daily streamflow response value (m3 s-1 m-1) for the upper Accotink Creek subwatershed. Daily streamflow response values were 

calculated for days where daily precipitation value ≥ 6.0 mm. Error 

bars show the ± 95 percent confidence interval for the means. N = 

2,681 streamflow-precipitation pairs. Data related to Hurricane Agnes 

(June 21-22, 1972) were excluded from the analysis as outliers.

Results
178 Changes in Anthropogenic Impervious Surfaces, Precipitation, and Daily Streamflow Discharge

(Kuskal-Wallis test statistic = 3.24, df = 4, p = 0.518; χ

2

 = 

1.45, df = 4, p = 0.835), ≥ 6mm (statistics in table 4) and ≥

35 mm (statistics in table 4).

The pattern of daily precipitation ≥ 6mm revealed a statistically significant increase for the frequency of precipitation occurrences > 3-days in duration (χ2 = 27.47, df = 12, p 

= 0.007) by 10-year bin. We tested the frequency of >3-day 

duration occurrences and found that for the first 4 decades, 

when the predominant increase in streamflow response value 

occurred, no significant change in the frequency of > 3-day 

occurrences was observed (χ

2

= 5.09, df = 6, p = 0.532). It is 

not until we include the final decade of the > 3-day duration 

data that a significant difference in frequency is observed. 

The frequency of all other duration periods related to the 

cutoff values > 0 mm and ≥ 6 mm were not statistically significant.

We did factor analysis of streamflow and related precipitation duration values. These results showed that streamflow significantly increased with an increase in the duration 

of precipitation (four levels: one-day, two-day, three-day, 

and ≥ three-day; ANOVA F-Ratio = 35.05, df = 12, p < 

0.001) and the decade of occurrence (ANOVA F-Ratio = 

15.95, df = 12, p < 0.001). However, the interaction of 

streamflow to precipitation duration among 10-year bins was 

not significantly different (ANOVA F-Ratio = 1.05, df = 12, 

p = 0.402). This means that the increase in streamflow with 

Decades

Number of

daily

precipitation

values ≥ 6

mm per

sample year

Mean

precipitation

amount

(mm) per

value ≥ 6

mm ± 95%

C.I.

Stream-flow

response

(m3 s

-1 m-1)

per

precipitation

value ≥ 6

mm

± 95% C.I.

∗

Number of

daily

precipitation

values ≥ 35

mm per

sample year

Mean

precipitation

amount

(mm) per

value

≥ 35 mm

± 95% C.I.

Stream-flow

response

(m3 s

-1 m-1)

per

precipitation

value ≥ 35

mm

± 95% C.I.

∗

10/1/47 -

9/30/57 54.7 18.1 ± 1.3 108.9 ± 10.9 5.3 53.7 ± 5.3 119.5 ± 28.3

10/1/57 -

9/30/67 49.9 18.0 ± 1.3 129.6 ± 11.3 4.9 50.6 ± 6.7 172.3 ± 34.0

10/1/67 -

9/30/77 53.6 18.8 ± 1.3 161.8 ± 11.8 5.8 51.7 ± 5.0 191.7 ± 27.3

10/1/77 -

9/30/87 56.2 17.7 ± 1.0 158.3 ± 11.2 4.6 49.2 ± 3.6 224.1 ± 39.3

10/1/87 -

9/30/98 54.6 18.9 ± 1.1 178.4 ± 14.5 5.4 51.5 ± 4.9 223.1 ± 38.5

Results (0.42, 4,

0.981)

4.08, 4,

0.395)

(176.07, 4,

< 0.001)

(0.16, 4,

0.997)

(1.53, 4,

0.821)

(24.17, 4,

< 0.001)

Table 4. Statistics for October 1947 through September 1998 precipitation and stream-flow response value for the upper 

Accotink Creek subwatershed.

[The data are binned by decade. ∗ = significant difference found among decades at α = 0.05. Statistical tests used for number of 

values = one-way cross-tabs (χ2), tests for amounts = Kruskal-Wallis one-way analysis of variance, results reported as (test statistic 

value, df, p-value). N = 2,681 stream-flow-precipitation pairs for precipitation values ≥ 6.0 mm, N = 259 stream-flow-precipitation pairs 

for precipitation values ≥ 35.0 mm. Data related to Hurricane Agnes (June 21-22, 1972) were excluded from the analysis as outliers]

two trends emerge from the data: (1) mean precipitation volume 

show no significant variation over time, whereas (2) the streamflow response value shows a significant difference over the same 

period. The transition in streamflow, previously observed in 

figures 7 and 8 is now generally present between the 3 percent to 

21 percent impervious surface levels (years 1949, 1963 and 1971 

respectively). Impervious surface percent for the years 1988 and 

1994 were both mapped at 33 percent. Pair-wise testing revealed 

no significant difference in their respective streamflow response 

values; therefore, these years were combined into a single category.

Table 5 is a compilation of 3-year bin statistics of precipitation and streamflow for data pairs associated with the daily precipitation cutoff values of ≥ 6.0 mm (fig. 8) and ≥ 35.0 mm. There 

was no significant difference among bins for the frequency of 

precipitation occurrences or in the mean amounts of precipitation 

per value at either cutoff value. There was a significant difference 

among decades found for the streamflow response value at the 

cutoff value ≥ 6.0 mm but not at the cutoff value ≥ 35.0 mm.

Precipitation Frequency, Magnitude, and Pattern

No statistically significant differences were found in 

the amounts or frequency of the daily precipitation data, per 

10-year bins, associated with the cutoff values of # > 0 mm 
179

Impervious

surface %

related to

3-year bin

Number of

daily

precipitation

values ≥ 6

mm per year

Mean

precipitation

amount (mm)

per value

≥ 6 mm

± 95% C.I.

Stream-flow

response value

(m3 s

-1 m-1) per

precipitation

value

≥ 6 mm

± 95% C.I.

∗

Number of

daily

precipitation

values ≥ 35

mm per year

Mean

precipitation

amount (mm)

per value

≥ 35 mm

± 95% C.I.

Stream-flow

response value

(m3 s

-1 m-1) per

precipitation

value

≥ 35 mm

± 95% C.I.

3 62.2 17.0 ± 2.0 113.4 ± 15.4 5.5 51.2 ± 7.9 130.3 ± 41.1

13 49.3 17.8 ± 2.0 139.2 ± 21.5 4.3 49.3 ± 6.0 162.3 ± 42.9

21 56.3 17.8 ± 2.0 168.4 ± 20.1 5.0 52.0 ± 6.8 205.2 ± 63.6

27 60.6 18.1 ± 1.9 181.9 ± 25.5 4.6 52.5 ± 4.9 238.1 ± 62.5

33 53.7 18.1 ± 1.4 167.1 ± 12.7 4.6 48.3 ± 6.2 227.2 ± 53.4

Results (1.93, 4,

0.748)

4.18, 4,

0.383)

(54.51, 4,

> 0.001)

(0.51, 4,

0.973)

(5.00, 4,

0.292)

(8.38, 4,

0.079)

Table 5. Statistics for precipitation and standardized stream-flow per percentage of impervious surface found over time 

in the upper Accotink Creek subwatershed.

[Three-year bins centered on the aerial photography acquisition dates group the data. ∗ = a significant difference found among decades at α = 

0.05. Statistical tests used for number of values = one-way crosstabs (χ2), tests for amounts = Kruskal-Wallis one-way analysis of variance, results 

reported as (test statistic value, df, p-value). N = 990 streamflow-precipitation pairs for precipitation values ≥ 6.0 mm, 85 streamflow-precipitation 

pairs for precipitation values ≥ 35.0 mm. Data related to Hurricane Agnes (June 21-22, 1972) were excluded from the analysis as outliers]

Discussion

10

20

30

0 5 10 15 20 25 30 35

Percent Impervious Surface

Precipitation (mm)

75

125

175

225

0 5 10 15 20 25 30 35

Streamflow Response (m3

s–1m–1)

Figure 8. Comparison of averages 

of daily precipitation (mm) and daily 

stream-flow response value (m3 s-1

m-1) for the percentages of impervious surface found over time in the 

upper Accotink Creek subwatershed. The 3-year bin periods are 

centered on the acquisition date 

years of aerial photography used to 

determine the percentage of impervious surface. Daily stream-flow 

response values were calculated 

for days where total precipitation 

≥ 6.0 mm. Error bars show the ± 

95 percent confidence interval for 

the means. N = 990 stream-flowprecipitation pairs. Data related to 

Hurricane Agnes (June 21-22, 1972) 

were excluded from the analysis as 

outliers.
180 Changes in Anthropogenic Impervious Surfaces, Precipitation, and Daily Streamflow Discharge

increasing duration of precipitation was the same across 

decades.

The pattern of daily precipitation ≥ 6mm also showed a 

statistically significant change in the mean amount of 3-day 

precipitation duration occurrences (Kruskal-Wallis test statistic 

= 10.65, df = 4, p = 0.031) by 10-year bin. This, however, is 

due to a declining trend in > 3-day duration amounts. A declining precipitation amount trend should have no positive impact 

on an increasing streamflow trend. The amounts of all other 

duration periods related to the cutoff values > 0 mm and ≥ 6 

mm were not statistically significant.

We observed no significant change in seasonal data, by 

10-year bin, in mean amount or frequency related to the daily 

precipitation cutoff values of > 0mm and ≥ 6 mm.

Discussion

We feel that historical climate data analysis is a required 

component for any analysis of change in historical streamflow discharge rates, particularly at the daily temporal scale 

where changes in streamflow pattern would have the greatest observable impact on stream ecology. Climate change 

research by Karl (1998) reported historical precipitation 

increases for the frequency of days with precipitation as well 

as the frequency of extreme precipitation values (50 mm) 

across the United States. We did not find this result for the 

precipitation station in our study area. Our results suggest 

that the historical frequency, magnitude, and temporal pattern of precipitation values at multiple cutoff levels were 

generally unchanged for the period of the study. Where 

we did observe a statistically significant change in pattern 

(frequency of precipitation values > 3-days in duration, 

amount of precipitation per 3-day duration value), we found 

no concurrent increases in historical streamflow response. 

It is understood that wet and dry climate patterns may have 

short-term impacts upon streamflow rate; however, we found 

no long-term precipitation trend that would alter streamflow 

discharge over time.

Three historical trends emerge from our data: (1) mean 

precipitation volume and the frequency of precipitation 

show no significant variation (2) both the daily streamflow 

response per unit precipitation and the daily streamflow 

frequency pattern show significant change, and (3) the 

landscape trend is characterized by increasing impervious 

surface area. These results suggest that hydrologic changes 

in daily streamflow have occurred due to landscape changes, 

specifically, the buildup of impervious surfaces. From the 

historical trend, several possible inter-relationships and transitions emerge related to landscape pattern and streamflow 

discharge.

We found a statistically significant shift in mean daily 

streamflow rate in the 10-year bins associated with daily precipitation values ≥ 6.0 mm and ≥ 35.0 mm and in the 3-year 

bins associated with daily precipitation values ≥ 6.0 mm. The 

streamflow response trend for the 3-year bins associated with 

precipitation values ≥ 35.0 mm, while not statistically significant, showed consistent increases over the study period. The 

lack of statistical significance for the trend in the 3-year bin 

data is due to fewer observations than the 10-year bin data.

-50

0

50

100

150

200

250

300

350

1st Decade 2nd Decade 3rd Decade 4th Decade 5th Decade

Transition by Decade

% Cumulative Change from First Decad

e

 >

 >

 >

 >

 >

 >

14.00 m 3 s– 1

 9.00 m 3 s

1.50 m 3 s

 0.80 m 3 s

 0.50 m 3 s

0.25 m 3 s

– 1

– 1

– 1

– 1

– 1

Figure 9. Historical streamflow frequency patterns for the upper Accotink Creek subwatershed as derived 

from data in table 3. Categories defined as the daily streamflow (m3 s-1) above a given level. Change is 

reported per flow category as cumulative percent change in the frequency of streamflow occurrences from 

the first decade. The historical daily mean streamflow for the upper Accotink Creek is 0.8 m3 s-1.
181

Our results imply that the amount of precipitation required to produce a given level of streamflow has 

decreased over time. The amount of precipitation necessary 

to produce a streamflow of 12 m3 s-1 (bankfull discharge 

for the first decade, fig. 2) dropped from approximately 80 

mm in the first decade to approximately 55 mm in the final 

decade (fig. 6). An increase in the frequency of large flows 

is a consequence (table 3). The increase in frequency of 

larger discharge streamflows over time implies a change in 

the stream’s disturbance regime related to the volumes. We 

found that it was four times more likely to record a mean 

daily streamflow ≥ 14.0 m3 s-1 in the last decade of our study 

than in the first. The altered flow regime is further illustrated 

by figure 9 (derived from table 3) where percent cumulative changes in specific streamflow cutoff levels reflect the 

historical change in streamflow pattern. Henshaw and Booth 

(2000) observed that increasing urbanization was related to 

decreases in the frequency of flows above the mean. A similar pattern is noted here, where a decreasing frequency of 

daily flows above the historical mean (0.8 m3 s-1) is observed. 

Further, observations show an increase in hydrologic variability at all given streamflow frequency ranges during 

the study period. The impact is most pronounced at higher 

volumes but is also evident at low flow levels. The hydrologic alteration of lower flow volumes agrees with Rose and 

Peters (2001) where a transition to shorter recession periods 

was related to urbanizing basins. This altered flow regime 

is indicative of an increasingly “flashy” streamflow prone 

to short-term high flows and a quick recession back to base 

flow. The changing stream ecology impacts the benthic and 

aquatic habitat as: (1) larger and more frequent pulse events 

in the form of peak discharges well above the historical mean 

and (2) less frequent sustained flows at or below the historical mean. 

As illustrated in figure 6, the data appear to form two 

groupings of slopes over time - suggesting a shift in the 

streamflow discharge pattern during the study period. Under 

“normal” precipitation conditions, the level at which significant change in historical mean daily streamflow response 

first occurred was between 13 percent and 21 percent 

impervious surface area (Mann-Whitney U test statistic = 

10028.50, df = 1, p = 0.002) for the 3-year bins, and between 

the 1st and 2nd decades (Mann-Whitney U test statistic = 

107649.00, df = 1, p = 0.002) for the 10-year bins. The 13 

percent impervious surface category and the 1st decade 

category appear to be transitional zones, above which permanent alteration of streamflow discharge is observed. A 

more precise relationship of impervious surface area and 

streamflow response might be obtained if the study were 

expanded to include photographic coverage between 1963 

and 1971. However, we can generally state that a change in 

mean daily streamflow response in this basin occurred in the 

80

120

160

200

240

280

1940 1950 1960 1970 1980 1990 2000

Year

Streamflow response (m3 s-1 

m-1)

3-Year bin (35mm) 10-Year bin (35mm)

3-Year bin (6mm) 10-Year bin (6mm)

Figure 10. Trends of streamflow response value (m3 s-1 m-1) for the upper Accotink Creek subwatershed at 3-year and 10-year 

bins associated with precipitation cutoff values ≥ 6 mm and ≥ 35 mm. Data points for 10-year category are plotted at approximate mid-decade (1953, 1963, 1973, 1983, 1993). Data points for the 3-year category are plotted per photo year.

Discussion
182 Changes in Anthropogenic Impervious Surfaces, Precipitation, and Daily Streamflow Discharge

mid-1960s time-period. This trend is observed at all precipitation cutoff values used in our investigation: > 0 mm (fig. 

6), ≥ 6.0 mm (fig. 7, fig. 8) and ≥ 35.0 mm (tables 4 and 5). 

In addition, during the period of rapid impervious surface 

growth (1949-1979), there is a corresponding rise in daily 

streamflow response. In the subsequent period (1980-1994), 

slower impervious surface growth corresponds to a slower 

rise in streamflow response. It is also possible that this later 

period was influenced by detention basins that were widely 

introduced into Fairfax County in the mid-to-late-1970s (A. 

Rayyan, personal commun., 2000).

Ecological studies such as Klein (1979), Limburg and 

Schmidt (1990), Weaver and Garmen (1994) and Wang and 

others (2001) utilized land-use/land-cover information to 

derive an impervious component and found that significant 

ecosystem change occurred at approximately 10 percent 

to15 percent impervious surface area. The results presented 

here (significant hydrological change between 13 percent 

and 21 percent impervious surface area) agree with these 

investigations. However, the reasons for agreement are 

unknown. As speculated in Wang and others (2000), it is 

possible that instream changes in species composition noted 

in urban studies are primarily due to changes in the variability of frequency and magnitude of streamflow disturbance 

brought about by impervious surfaces. However, the connection between the change in stream disturbance regime 

and changes in stream species composition is not supported 

by this investigation or by the current science. While the 

literature is abundant with investigations that have related 

impervious surface and stream biota there have been few 

investigations that specifically relate the historical relationship of impervious surface area, streamflow and stream biota 

(Moscript and Montgomery, 1997) and none that specifically address this issue at the daily temporal scale. We did 

not have a long-term biota data set from which to assess this 

issue and suspect that, with few exceptions, consistent longterm stream sampling data in urban environments (coincident 

with stream gages) are rare. An issue for future research 

would be capturing and analyzing fine temporal-scale, 

long-term stream data (streamflow and biota) along with 

explicit impervious surface data to determine specific linkages between changes in historical hydrologic pattern and 

in-stream biological communities in urbanizing watersheds. 

The production of stream-flow pattern metrics based on the 

de-construction of historical daily streamflow variability in 

urbanizing basins, as simply illustrated in figure 9, might be 

a step towards determining the exact mechanisms by which 

impervious surfaces and hydrologic change impact stream 

biota.

Ecological change due to hydrological disturbance has 

specifically been addressed in Biggs (1995) and Poff and 

Allen (1995). These studies, while not specifically addressing 

an impervious surface issue, reveal the biological impacts 

related to altered flow regimes and changing hydrologic 

pattern. Cairns (1995) discussed the concept of ecological 

elasticity and urban runoff and concluded that “The effects 

of closely spaced pulses of urban runoff, which do not permit 

full recovery to occur, undoubtedly have a cumulative impact 

... but the cumulative impact of multiple, closely spaced 

pulses is not well understood.”

Figure 10 illustrates the general agreement on streamflow response value between the 3-year and 10-year bins. We 

assumed that high variance associated with the 3-year windows (wet-year/dry-year impacts and smaller sample sizes) 

would necessitate a decade bin analysis to accurately characterize an overall streamflow trend. However, the agreement 

between the differing temporal resolutions suggests the 3-

year window has been minimally influenced by wet-year/dryyear precipitation phenomena and accurately characterizes 

the landscape, precipitation, and runoff relationship. This 

agreement between the two temporal scales allowed us a 

higher level of confidence in the relationship of impervious 

surface percent and streamflow response.

Manual analysis and compilation proved to be a highly 

accurate method for the mapping of impervious surfaces. 

However, there were three areas subject to interpretation 

confusion. Sidewalks were not always compiled due to a lack 

of clearly definable edges. This was due primarily to the spatial resolution of the scanned imagery. Pixel size (1 m2

) was 

based on USGS DOQQ standards, which provided a standard 

mapping methodology across all years of coverage. In many 

areas however, the resolution precluded discrimination of 

smaller features such as sidewalks that often are imaged 

at sub-pixel resolutions. Open compacted soils related to 

construction zones were possibly mis-classified due to the 

inability of aerial photography to determine a measure of 

imperviousness for these soil areas. Single-family rooftops

were subject to confusion due to the density of canopy cover 

over portions of the residential areas. In numerous cases, the 

density of woody overgrowth precluded clearly delineating 

impervious polygon boundaries at the parcel level. Due to 

the number of rooftops increasing over time, this source of 

confusion could possibly lead to an increasing amount of 

error in the TIA per photographic coverage over time.

Conclusion

Using an empirical approach, we have attempted to 

characterize the long-term impact of impervious surface 

growth on mean daily streamflow discharge. Results show 

that, over the study time period, daily streamflow response 

values coinciding with “normal” and “extreme” precipitation occurrences have increased approximately 50 percent 

and 100 percent respectively, while the percent of anthropogenic impervious surface area has increased 10-fold. Results 

suggest that a statistically significant change (p < 0.05) 

in streamflow response occurred between the 13 percent 

(1963) and 21 percent (1971) impervious surface levels for 

the 3-year bins and between the 1st (1948-1957) and 2nd 

(1958-1967) decades for the 10-year bins. Results also indicate that a statistically significant (p < 0.05) disturbance in 
183

the frequency of mean daily streamflow levels has occurred 

between the flow range of ≥ 0.25 m3 s-1 and ≥ 14 m3 s-1. This 

particular alteration of the physical ecology is suggestive of 

an urbanizing basin prone to “flashy” flows that bring about 

an increased variability in streamflow at the daily temporal 

scale.

Anthropogenic impervious surface, as a landscape variable, is not the only driver of changes in streamflow response 

in urbanizing basins. More research is necessary to determine the historical relationship that streamflow has with 

other individual landscape variables such as forest patches, 

grasslands and agricultural areas, riparian area as well as the 

effects of integrated landscape pattern. In addition, in historical investigations, there is a need to further define historical 

precipitation patterns and their possible impacts on streamflow rate. However, there is historical evidence, in the upper 

Accotink Creek subwatershed, to suggest that anthropogenic 

impervious surface growth is an indicator of statistically significant response in streamflow discharge.

The methodologies utilized in this investigation offer a 

means to discern the historical relationship between landscape, precipitation, and streamflow. Utilizing these to formulate historical integrated assessments can be a research 

mechanism for determining the relative health of evolving 

stream eco-systems. However, in order to implement this 

method over larger landscape areas, a more efficient method 

of accurately measuring impervious surfaces is required. To 

that end, the mapping portions of this investigation will be 

used to study the classification accuracy of coarser resolution remote sensing data such as LANDSAT MSS and TM 

data, and such evolving image classification methods as data 

fusion and sub-pixel analysis. As noted by Booth and Jackson (1997), the lack of consistent methodology for determining impervious surface area as well as the lack of reporting 

of how the parameter was generated inhibits a thorough scientific comparison of results. A common mapping framework 

for impervious surfaces needs to be established before the 

myriad integrated impacts of this ecological indicator can be 

fully understood.

Acknowledgments

We specially thank Maliha Nash for her advice regarding the statistical relationship of precipitation and streamflow. We also thank Chad Cross, Dan Henry, K. Bruce 

Jones, Mary Lacerte-Benger, E. Terrence Slonecker, and 

Peter Stokely, for their assistance in this effort. The efforts 

and recommendations of two anonymous reviewers are 

greatly appreciated.

The views expressed here are those of the individual 

authors and do not necessarily reflect the views and policies of the U.S. Environmental Protection Agency (EPA). 

The manuscript was prepared by scientists in the USEPA’s 

Office of Research and Development (ORD) and has been 

administratively reviewed and approved for publication.

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185

Introduction

The watershed of Tampa Bay covers approximately 

5,700 km2 and encompasses most of Pinellas, Hillsborough, 

and Manatee Counties and portions of Pasco, Polk, and 

Sarasota Counties. Four major sources of surface water—

the Hillsborough, Alafia, Little Manatee, and Manatee 

Rivers—flow into the bay. The largest municipalities within 

the watershed are Tampa, St. Petersburg, Clearwater and 

Bradenton. Tampa Bay is situated on the Gulf coast of westcentral Florida with an areal extent of approximately 1,030 

km2, making it one of the largest open-water estuaries in the 

southeastern United States.

Seagrasses are one of the most important components 

of the Tampa Bay’s ecosystem and are found in the shallow 

waters along its shoreline. They play an important role in 

reducing coastal erosion by binding sediments and moderating the impacts of waves and strong currents (Johansson 

and Greening, 2000; Lewis and others, 1982). In addition, 

seagrasses provide valuable habitat for a variety of economically and ecologically important marine species including 

shrimp, bay scallop, spotted sea trout, red drum, a variety 

of turtles, and manatee for foraging, shelter, and protection from predators within these dense underwater pastures 

(Avery, 1991; Johansson and Greening, 2000; Kurz and 

others, 2000; Phillips, 1960; Stowers and others, 2002). 

Compared to bare sand bottom areas of the bay, seagrass 

beds harbor 50 percent more fish and invertebrates (Stoner, 

1980; Lewis and others, 1982; Livingston, 1982). 

Seagrass Declines

To thrive, seagrasses require sunlight and clear water 

(Boler and others, 1991; Dixon, 2000). Because of these 

requirements, seagrasses are usually found in shallow water, 

however, they can grow at depths of 32 m and have been 

found in clear water at 68 m. Seagrasses also prefer to live 

in shallow coastal waters where there is shelter (such as 

sand bars) from winds, wave action, and strong currents that 

could create turbulent muddy water (Phillips, 1962). Because 

of their sensitivity to water clarity and nutrient loading, 

seagrasses function as a key indicator of estuarine health in 

Tampa Bay (Johansson and Greening, 2000).

Seagrass meadows in Tampa Bay have declined noticeably since settlement of the region began at the end of the 

19th century (Avery, 2000; Johansson, 2002; Lewis, 2002). 

When human settlement commenced in the late 1870’s, seagrasses extended as vast aquatic meadows along the shore of 

the bay, as shown in figure 1, and are believed to have covered on the order of 31,000 ha (Lewis and others, 1982). Following World War II, urban growth increased significantly 

while seagrasses declined in areal extent from approximately 

16,500 ha in 1950 to their smallest coverage of about 8,700 

ha in 1982 (Lewis and others, 1991). These declines are 

attributed to a variety of human-induced disturbances that 

include destruction of habitat (Stowers and others, 2002; 

Zarbock, 1991), decreased water clarity (Boler and others, 

1991), and declining water quality (Carlson and others, 

2002).

Urban Growth

Tampa Bay is one of the fastest growing metropolitan 

areas in the country. From 1940 to 1999 urban land area 

increased approximately six fold. Until the late 1970s, the 

estuary was characterized by decades of neglect, alteration of 

the shoreline by industrial and residential development, and 

discharge of barely treated wastewater directly into the bay 

(Johansson and Lewis, 1992; Johansson, 1991; Greening, 

2002). Pollution and deteriorated water quality and clarity, 

together with damage from dredging operations and scarring 

by boat propellers, seriously affected the extent and vigor 

of seagrasses in the bay. Hillsborough Bay, for example, 

had the poorest water quality in Tampa Bay and eutrophic 

conditions had become evident by the 1960s (Johansson and 

Greening, 2000; Lewis and others, 1998; Lewis and others, 

1982). Discharges into Hillsborough Bay from fertilizer 

industries near the perimeter and from the City of Tampa’s 

primary wastewater treatment plant were the main pollutant sources (Johansson, 1991). Little wonder that seagrass 

extent decreased about 50 percent from 1940 to 1982 (fig. 2). 

It wasn’t until 1979 that the state legislated that all sewage 

treatment plants discharging into the bay were required to 

meet strict treatment standards (Johansson and Greening, 

2000).

Urban Growth and Seagrass Distribution Trends in 

Tampa Bay, Florida

By Mike Crane1 and George Xian2

1

U.S. Geological Survey, EROS, Sioux Falls, SD 57198.

2

SAIC Contractor to U.S. Geological Survey, EROS, Sioux Falls, SD 57198.
186 Urban Growth and Seagrass Distribution Trends in Tampa Bay, Florida

Nutrient Loading

Nutrients such as nitrogen and phosphorus are introduced into the estuary by urban stormwater, agricultural 

runoff, leachate from septic tanks, sewage treatment plants, 

and atmospheric deposition from powerplants and cars. 

Specific sources included the Alafia River and the fertilizer 

industry within its basin, the City of Tampa’s wastewater 

plant at Hookers Point, the U.S. Phosphoric Products Company, and the Nitram Chemical Company (Johansson, 1991). 

Before a mandated reduction in discharges began, it was 

estimated that the Alafia River and the chemical companies 

supplied 63 percent of the total nitrogen and 94 percent of 

the total phosphorus entering the bay (Johansson and Lewis, 

1992). The Hookers Point facility discharged 40 percent of 

the total nitrogen and less than 5 percent of total phosphorus 

(Johansson and Lewis, 1992). Excessive levels of these nutrients stimulate the growth of algae in the bay. As algae grow, 

they block sunlight needed by the submerged aquatic vegetation of the bay. Thus, algal blooms contribute to declines 

1879 1940

Old Tampa

Bay

Hillsborough

Bay

Figure 1. This sequence of maps depicts increases in urban development and changes in seagrass distribution in Tampa Bay from 1879 

to 1999.

1963 1972

Old Tampa

Bay

Hillsborough

Bay

1992 1992

Old Tampa

Bay

Hillsborough

Bay

Old Tampa

Bay

Hillsborough

Bay

Urban Water Seagrass
187

in seagrass beds. Figure 3 depicts the correlation between 

urban sprawl and decline in seagrasses from 1940 to 1982. 

The increase in seagrasses between 1982 to 1998 (fig. 2) is 

associated with a significant reduction in nitrogen loading 

to Tampa Bay through better treatment of discharge from 

domestic and industrial point-sources. A concurrent reduction of agriculture and commercial/industrial water use in the 

Tampa Bay watershed led to a further decrease in nutrient 

loading and helped seagrasses recover after the early 1980’s 

(Xian and Crane, 2003). 

Conclusions

Eutrophication, dredging, and urban sprawl are primary 

factors in declines of the extent of seagrasses in Tampa Bay. 

The process of urbanization continues to characterize the 

Tampa Bay region and threatens to negatively impact seagrasses distributed around its margins. Efforts to improve 

water quality and benthic habitats started in the 1970s and, 

as a result of advanced wastewater treatment, succeeded in 

reversing the degradation trend. By 1996 seagrasses had been 

restored to about 11,000 ha (Johansson and Greening, 2000). 

Recently the trend appears to have reversed, and once again 

seagrass extent is declining (Tomasko, 2002). Further studies 

are needed to better understand urban development and its 

impact on the seagrasses of Tampa Bay, as well as development of a long-term mitigation strategy that provides for the 

sustainability of this valuable aquatic resource. 

References

Avery, W., 2000, Monitoring submerged aquatic vegetation 

in Hillsborough Bay, Florida, in Bortone, S.A., Seagrasses: 

CRC Press, p. 137-145.

Avery, W.M., 1991, Status of naturally occurring and 

introduced Halodule wrightii in Hillsborough Bay: 

Proceedings, Tampa Bay Area Scientific Information 

Symposium 2, Tampa Bay, Fla., p. 177-187.

Boler, R.N., Malloy, R.C., and Lesnett, E.M., 1991, Surface 

water quality monitoring by the Environmental Protection 

Commission of Hillsborough County: Proceedings, Tampa 

Bay Area Scientific Information Symposium 2, Tampa, Fla., 

p. 111-135.

Carlson, P.R., Yarbro, L.A., Peterson, B.J., Ketron, A., Arnold, 

H., Madley, K.A., 2002, The influence of sediment sulfide 

on the structure of south Florida seagrass communities: 

Proceedings, Seagrass Management—It’s Not Just 

Nutrients!, St. Petersburg, Fla., p. 215-227.

Dixon, L.K., 2000, Establishing light requirements for the 

seagrass Thalassia testudinum—An example from Tampa 

Bay, Florida, in Bortone, S.A., Seagrasses: CRC Press, p. 

9-31.

Fletcher, D., 1991, Changes in climate, sea level, and Tampa 

Bay: Proceedings, Tampa Bay Area Scientific Information 

Symposium 2, Tampa Fla., p. 101-104.

Greening, H., 2002, Implementing the Tampa Bay seagrass 

restoration management strategy: Proceedings, Seagrass 

Management—It’s Not Just Nutrients!, St. Petersburg, Fla., 

p. 29-37.

Johansson, J.O.R., 2002, Historical overview of Tampa Bay 

water quality and seagrass—Issues and trends: Proceedings, 

References

1930 1940 1950 1960 1970 1980 1990 2000

Year

8000

10000

12000

14000

16000

0

40000

80000

120000

Sea grass (ha)

Urban (ha)

Figure 2. This graph compares urban growth and seagrass variation in 

Tampa Bay. The red line represents urban growth and shows a steady 

increase from about 20,000 ha in 1940 to more than 140,000 ha in 1990. 

During the same period, the green line shows a decline in seagrasses 

from more than 16,000 ha to a low of 8,700 ha in 1982, then recovering 

dramatically to more than 13,000 ha. Since 1998, seagrasses have 

exhibited further decline.

10000 30000 50000 70000 90000

Urban (ha)

6000

8000

10000

12000

14000

16000

Sea grass (ha)

Figure 3. This poly-fit plot shows the strong relationship between 

the extent of urban area and seagrasses in Tampa Bay from 1940 

to 1979.
188 Urban Growth and Seagrass Distribution Trends in Tampa Bay, Florida

Seagrass Management—It’s Not Just Nutrients!, St. 

Petersburg, Fla., p. 1-10.

Johansson, J.O.R., and Greening, H., 2000, Seagrass 

restoration in Tampa Bay: A resource-based approach to 

estuarine management, in Bortone. S.A., Seagrasses: CRC 

Press, p. 279-293.

Johansson, J.O.R., and Lewis, R., 1992, Recent improvements 

of water quality and biological indicators in Hillsborough 

Bay, a highly impacted subdivision of Tampa Bay, Florida, 

USA: Amsterdam, Elsevier Science Publishers B.V., 

Science of the Total Environment, Supplement 2, p. 1199-

1215.

Johansson, J.O.R., 1991, Long-term trends of nitrogen loading, 

water quality and biological indications in Hillsborough 

Bay, Florida: Proceedings, Tampa Bay Area Scientific 

Information Symposium 2, Tampa, Fla., p. 157-176.

Kurz, R.C., Tomasko, D.A., Burdick, D., Ries, T.F., Patterson, 

K., and Finck, R., 2000, Recent trends in seagrass 

distributions in southwest Florida coastal waters, in

Bortone, S.A., Seagrasses: CRC Press, p. 157-166.

Lewis, R., 2002, The potential importance of the longshore 

bar system to the persistence and restoration of Tampa Bay 

seagrass meadows: Proceedings, Seagrass Management—

It’s Not Just Nutrients!, St. Petersburg, Fla., p. 177-183.

Lewis, R., Clark, P., Fehrings, W.F., Greening, H.S., 

Johansson, R.O., and Paul, R.T., 1998, The rehabilitation 

of the Tampa Bay estuary, Florida, USA, as an example of 

successful integrated coastal management: Marine Pollution 

Bulletin, v. 37, n. 8-12, p. 468-473. 

Lewis, R., Haddad, K., and Johansson, J.O.R., 1991, Recent 

areal expansion of seagrass meadows in Tampa Bay, 

Florida—Real bay improvement or drought-induced?: 

Proceedings, Tampa Bay Area Scientific Information 

Symposium 2, Tampa, Fla., p. 189-192.

Lewis, R.R., Durako, M.J., Moffler, M.D., Phillips, R.C., 

1985, Seagrass meadows of Tampa Bay—A review: 

Minneapolis, Minn., Burgess Publishing Co., Proceedings, 

Tampa Bay Area Scientific Information Symposium, p. 210-

246.

Livingston, R., 1982, Trophic organization of fishes in a 

coastal seagrass system: Marine Ecology—Progress Series, 

v. 7, p. 1-12.

Phillips, R., 1962, Distribution of seagrasses in Tampa Bay, 

Florida: Florida State Board of Conservation Special 

Scientific Report No. 6, p. 1-12.

Phillips, R., 1960, Observations on the ecology and 

distribution of the Florida seagrasses: Professional Papers 

Series, Number Two, Florida Marine Laboratory, St. 

Petersburg, Fla., p. 1-72.

Stoner, A., 1980, The role of seagrass biomass in the 

organization of benthic macrofaunal assemblages: Bulletin 

of Marine Science, v. 30, n. 3, p. 537-551.

Stowers, J.F., Fehrmann, E., and Squires, A., 2002, Seagrass 

scarring in Tampa Bay—Impact analysis and management 

options: Proceedings, Seagrass Management—It’s Not Just 

Nutrients!, St. Petersburg, Fla., p. 47-54.

Tomasko, D., 2002, Status and trends of seagrass coverage 

in Tampa Bay, with reference to other southwest Florida 

estuaries: Proceedings, Seagrass Management—It’s Not Just 

Nutrients!, St. Petersburg, Fla., p. 11-20.

Weisberg, R.H., and Williams, R.G., 1991, Initial findings 

on the circulation of Tampa Bay: Proceedings, Tampa Bay 

Area Scientific Information Symposium 2, Tampa, Fla., p. 

49-66.

Xian, G., and Crane, M., 2003, Impact of urban development 

on seagrass in Tampa Bay: Proceedings, 19th National 

Environmental Monitoring Conference, Arlington, Virginia, 

No. 23.

Zarbock, H.W., 1991, Past, present and future freshwater 

inflow to Tampa Bay—Effects of a changing watershed: 

Proceedings, Tampa Bay Area Scientific Information 

Symposium 2, Tampa, Fla., p. 23-33.
189

Introduction

Meyer (1995) describes land use as “the way in which, 

and the purposes for which, human beings employ the land 

and its resources: for example, farming, mining, or lumbering” and land cover as “the physical state of the land 

surface: as in cropland, mountains, or forests.” The definition of the term land cover has broadened in recent usage 

to include human structures such as buildings or pavement 

(impervious surfaces) and other aspects of the natural 

environment, such as soil type, biodiversity, and surface 

and groundwater (Meyer, 1995). As human population and 

activities have increased over time, changes in land-use and 

land-cover change due to human activities have become 

major factors in changes in ecological processes at local 

to global scales (Groom and Schumaker, 1993; Houghton, 

1994; Imhoff, 1994; Ojima and others, 1994).

R.T. Paine (1966, 1969) described a “keystone predator” as one controlling species diversity by selective predation upon a competitive dominant. This definition was 

expanded to that of a “keystone species” (Naiman, 1986), 

not necessarily a predator, where a species alters the ecosystem by modifying large areas of habitat to make it more 

suitable for their occupation thereby altering the community structure without having direct trophic effects on other 

species (that is, a beaver altering a stream community by 

dam building). “Keystone species” have been even more 

broadly defined (Power and others, 1996) as those having 

large effects on community structure or ecosystem function (that is, high overall importance) relative to abundance 

(that is, high community importance) and who perform 

roles not performed by other species or processes (Kotliar, 

2000). Using the broader definitions, human beings act as a 

keystone species—a species that regulates ecosystem structure and function. Human activates alter not only their own 

environment but also act directly and indirectly to structure 

the ecosystem for all other organisms. Some authors argue 

that the impact of humans has become so great on a global 

scale that the current geological epoch should be renamed 

the “anthropocene” (Crutzen and Stoermer, 2000; Crutzen, 

2002). 

Recently, human activities have increased to a level 

where they dominate the Earth’s global ecosystem. Between 

one-third and one-half of the land surface of the planet 

Earth has been transformed by human activity (Vitousek 

and others, 1997) with approximately 40 percent of the 

Earth’s terrestrial vegetated surface impaired in its ability to furnish benefits due to human activity (Daily, 1995). 

Humans appropriate more than 30 percent of terrestrial net 

primary production (Vitousek and others, 1986; Rojstaczer 

and others, 2001) and human use accounts for 25 percent of 

the world’s total evapotranspiration and 50 percent of the 

accessible runoff supply of fresh water (Postel and others, 

1996). 

The consequences of land-use and land-cover change 

at a global scale are not easily measured and quantified but 

have impacts on global processes such as climate change 

(Houghton and others, 1999; Hurtt, and others, 2002), 

resource depletion (Peskin, 1990; Daily, 1997), nutrient 

and hydrologic cycling (Nobre and others, 1991; Rasmussen and others, 1998), biodiversity (Wilson, 2002), and, 

ultimately, the carrying capacity of the Earth for human 

populations (Cohen, 1995a). At a regional scale, the 

impacts of agriculture, deforestation, and development on 

regional climate have been greater than global changes in 

CO2 up to the current time (Couzin, 1999; Caspersen, and 

others, 2000; Schimel, and others, 2000). In the United 

States, urbanization alone may be responsible for half of 

the observed decrease in temperature range over the last 

half-century in the 48 conterminous States (Kalnay and 

Cai, 2003). Except for the potentially catastrophic effects 

of global climate change, for terrestrial ecosystems, future 

global biodiversity is most dependent upon changes in 

human population and land-use (Sala and others, 2000; 

Wilson, 2002).

This report will not attempt to examine in detail the 

specific consequence of each potential change in land-use 

and land-cover at a global scale. Rather, it will concentrate on the ecological impacts of land-use and land-cover 

change (LULCC), linking LULCC to specific environmental changes, drivers of LULCC and their resulting stressors 

and ecological impacts, and regional and global patterns of 

change.

An Overview of Stressors and Ecological Impacts 

Associated With Regional and Global Patterns of 

Population, Land-Use, and Land-Cover Change

By S. Taylor Jarnagin1

1

Research Ecologist, U.S. EPA Landscape Ecology Branch, Environmental 

Photographic Interpretation Center (EPIC), 2D115 USGS National Center, 

MS 555, 12201 Sunrise Valley Drive, Reston, VA 20192-0002, 

jarnagin.taylor@epa.gov.
190 Overview of Stressors and Ecological Impacts Associated With Regional and Global Patterns of Change

Ecological Impacts of Land-Use and 

Land-Cover Change

Human-induced land-use and land-cover change produces 

both direct and indirect effects across spatial and temporal 

scales. Some effects are transitory in nature and others, such 

as regional desertification, may produce changes in hydrology that result in the loss of biotic potential (Schlesinger and 

others, 1990; UNEP, 1992) and are essentially irreversible 

on any time scale relevant to human societies (Glantz and 

Orlovsky, 1983; von Hardenberg and others, 2001). Humaninduced species extinctions are also irreversible and are 

occurring at unprecedented rates as are the human-induced 

landscape changes to which those extinctions have been linked 

(Melillo and others 1985; Skole and Tucker 1993; Sisk and 

others, 1994; Pimm and others, 1995). 

At the spatial scale of the watershed, landscape and landuse patterns, along with hydrology and climatology, play a 

critical role in determining sediment and pollutant loading to 

aquatic ecosystems. Understanding the impact of land-use and 

land-cover change is critically important as the environmental 

protection of water quality evolves from the detection of endof-pipe sources of water pollution, such as sewage and industrial waste, towards a watershed management approach based 

primarily upon detection of non-point-source (NPS) pollution 

and Total Maximum Daily Load (TMDL) science (USEPA, 

1994; USEPA, 2002). 

Direct and immediate effects of human-induced landuse and land-cover change result from the conversion of 

land cover from a “natural,” undisturbed state to one meeting human needs, such as housing, agriculture, and forestry. 

Direct effects on habitat (conversion, loss, simplification, and 

fragmentation) lead to increased erosion from altered surfaces 

and changes in surface and ground-water hydrology, peak flow 

and flood frequency, nutrient cycling, and solar radiation and 

thermal balance (Peterjohn and Correll, 1984; Saunders and 

others, 1991; Franklin, 1992; Inman, 1995; Inman, 2000). 

Indirect effects of land-use and land-cover change are 

seen in ecosystem consequences, such as changes in ecosystem function; shift in species composition; increased spread of 

invasive species and infectious diseases; loss of biodiversity; 

increased levels of sediments, metals, and pesticides in surface 

and ground water; increased air pollution; and increased temperature of surface water and air (Woodley and others, 1993; 

Daily, 1997; Daily and others, 1997; Wilcove and others, 

1998; Binder and others, 1999; Harvell and others, 1999; 

Barnes and others, 2000; Doyle and others, 2001; Jones and 

others, 2001a; Harvell and others, 2002). 

Local-scale habitat loss and fragmentation breaks up species metapopulations at a regional scale (Verboom and others, 

1991) and results in species extinctions and loss of biodiversity (Lubchenco and others, 1991; Kattan and others, 1994; 

Koopowitz and others, 1994; Noss and Cooperrider, 1994; 

Short and Turner, 1994; Knick and Rotenberry, 1995). At the 

regional to continental spatial scale, habitat loss, fragmentation, and simplification are the most important causes for the 

loss of biodiversity (Turner 1989; Turner and others 1991; 

Saunders and others 1991; Noss and others 1995). 

Some research has indicated that reduced connectivity 

(increased habitat fragmentation) can have beneficial effects. 

Habitat fragmentation can be beneficial to a species if it 

reduces the spread of disease either by reducing intraspecific 

contact or by reducing the degree of interspecific disease 

transmission (Hale and others, 2001). Increasing habitat 

fragmentation could theoretically limit human disease transmission by reducing the contact between disease vectors and 

human populations or even by fragmentation reaching the 

“extinction threshold” (Lande, 1987) of a vector. An extinction 

of a disease vector metapopulation due to habitat fragmentation could produce a result indistinguishable from reaching an 

epidemiological eradication threshold through immunization 

(Bascompte and Rodríguez-Trelles, 1998). However, these 

theoretical positive human health aspects of reduced biodiversity and increased fragmentation are countered by empirical 

studies that show that increasing species diversity and reduced 

habitat fragmentation reduces the spread of Lyme disease 

(Allan and others, 2003; Ostfeld and Keesing, 2000).

Quantifying these changes at a global scale is a difficult 

task. One approach uses ecological economics. Recently, 

attempts have been made to assess the financial value of goods 

and services delivered by intact, natural ecosystems at a global 

scale (Costanza and others, 1997; Costanza, 1998; Daily, 

and others, 2000; Balmford and others, 2002). Although the 

amounts estimated are open to debate, the values estimated 

for ecosystem services ($33 trillion) are on the same order 

of magnitude as estimated for global gross domestic product 

(GDP, $18 trillion). These efforts have prompted criticism 

that has focused on the difficulties of valuing ecosystem 

goods and services (Toman, 1998; Turner, 1998; Nunes and 

van den Bergh, 2001). For example, some authors argue that 

urban sprawl reduces biodiversity (Wilson, 1986; Pope, 1999; 

Wilson, 2002), and others argue that economically important 

species, specifically white tail deer, have improved habitats 

due to sprawl (Dunn, 1999). 

Human-induced land-use and land-cover change can act 

to increase the economic value of the land changed and do not 

necessarily imply a degradation of the land (Meyer, 1995). 

Although the use of ecological economics provides a means 

to try to quantify land-use and land-cover change at a global 

scale, it is likely the concept is more useful as a means to organize discussion than a method of direct quantification. 

Another approach to quantifying land-cover changes at 

a global scale is to try to map generalized land classes and 

quantify changes over time. This is a daunting task for largescale areal assessments. Studies that clearly relate generalized land-use and land-cover change at a synoptic or national 

scale to specific degradation of ecosystem values are few and 

sometimes contradictory (Turner and others, 1993). This may 

be in part due to a shifting of ecological consequences at the 

global scale. For example, the percentage of forested land has 

increased in the conterminous United States during the past 
191

100 years, while the population, standard of living, and overall 

consumption of wood products has increased over that same 

period (Sisk, 1998). This may be due in part to increased globalization in resource extraction (for example, the increased 

utilization of wood products harvested outside the conterminous United States in the example of increased forest landcover). The “exportation” of human-induced landscape change 

by more developed to less-developed countries has long been 

a problem for modelers of sustainable development (Arrow 

and others, 1995). Global consequences of local preservation 

of resources need to be considered in sustainable development 

models.

Another difficulty in correlation studies is caused by the 

use of a small number of nonspecific land-use/land-cover 

classes during classification. A closed canopy, old growth 

forest is quite different biotically and functionally than a 

recently harvested tract of land managed for timber harvesting. However, both areas might wind up classified as “forest” 

in a continental scale analysis utilizing a limited number of 

classes. Another confounding variable is the cyclical spatial 

and temporal nature of land-use change. Whereas the overall 

percentage of a given land-cover type in a watershed may 

remain relatively constant over time, there may still be an 

environmentally significant portion of land-cover conversions 

among classes during the period of consideration for example, 

between “barren” and “forested” (Mehaffey and others, 1999; 

Mehaffey and others, 2001). Although global-scale associations between generalized land-cover and land-use change and 

global-scale changes in ecosystem functioning may be weak, 

at local and regional scales there are many studies that link the 

conversion of land-cover to direct human use and occupation 

(urbanization) to degradation of environmental quality and 

ecosystem function (Klein, 1979; Schueler, 1994; Barnes and 

others, 2000; Doyle and others, 2001, Capiella and Brown, 

2001).

Urbanization, Impervious Surfaces, 

and Aquatic Systems—Linking 

Land-Use and Land-Cover Change 

to Specific Environmental Change

Urbanization produces changes in land-use and landcover that are measurable and quantifiable from local to continental and global scales using techniques ranging from field 

survey to satellite imagery (Elvidge and others 1997a; Elvidge 

and others 1997b; Sutton and others, 1997; Imhoff and others, 

1998). Anthropogenic impervious surfaces include roads, rooftops, parking lots, driveways, and sidewalks and are direct, 

quantifiable results of the urbanization transformation of the 

natural land-cover. The impacts of urbanization that have been 

linked to impervious surfaces include changes in air quality; 

habitat loss and fragmentation; and changes in water quality (Schueler, 1994; Arnold and Gibbons, 1996; Benfield and 

others, 1999; Barnes and others, 2000). Because of the way 

that aquatic ecosystems act to focus and reflect land-use and 

land-cover changes within their watersheds, studies of aquatic 

systems have provided some of the best evidence quantitatively linking specific and measurable land-cover and landuse change (particularly impervious surfaces) to specific and 

measurable changes in ecosystem function and water quality 

(Jones and others, 1997; Jones and others, 2000; Jennings and 

Jarnagin, 2002). 

At the spatial scale of the watershed, quantifiable changes 

in land-cover can be linked to specific responses in environmental variables. For example, the amount of impervious 

surface in a watershed is a landscape indicator integrating a 

number of concurrent interactions that influence a watershed’s 

hydrology (Schueler, 1994). Increases in the amount of impervious surfaces in a watershed have been related to changes in 

the way in which streamflow responds to precipitation (Jennings and Jarnagin, 2002) and the frequency of disturbance 

events. Changes in impervious surfaces result in measurable changes in stream hydraulics and stream bank stability 

(Hammer, 1972; Leopold, 1973; Booth, 1990; Henshaw and 

Booth, 2000). Klein (1979) and Galli (1991) related increases 

in impervious surfaces to increases in stream temperature. 

Jones and Clark (1987) and Kennen (1999) discussed the 

lack of benthic diversity associated with urban environments. 

Studies by Limburg and Schmidt (1990), Weaver and Garman 

(1994), May and others (1997), and Wang and others (2001) 

related changes in fish communities to increases in urbanization. Anderson (1968), Hollis (1975), and Rose and Peters 

(2001) discussed the impacts of urbanization and impervious 

surfaces on streamflow, whereas Biggs (1995) and Poff and 

Allen (1995) specifically related disturbance from streamflow 

variability to significant changes in aquatic habitat structure. 

Schueler (1994) observed that imperviousness of a watershed 

is the primary gauge of urban stream ecosystem health and 

hypothesized that a threshold for urban stream stability and 

habitat quality exists at approximately 10 percent to 20 percent 

watershed impervious surface area. Elvidge and others (2004) 

estimated that the total surface area of the lower 48 States 

covered by impervious surfaces was approximately 112,610 

km2, an area nearly the size of the state of Ohio or nearly 1.4 

percent of the surface of the area of the lower 28 States. 

Drivers of Land-Use and Land-Cover 

Change

Ecological footprint—A useful organizing construct in 

understanding land-use and land-cover change is the “ecological footprint.” Simply stated, the ecological footprint is a 

quantified measure of the affect a given population or activity 

has on the environment (Wackernagel and Rees, 1996). One 

of the earliest descriptions of an ecological footprint is the 

I=P*[F(P)] relation, rewritten to I=PAT [Impact = Population 

* Affluence * Technology] (Commoner, 1971; Ehrlich and 

Drivers of Land-Use and Land-Cover Change
192 Overview of Stressors and Ecological Impacts Associated With Regional and Global Patterns of Change

Holdren, 1971; Commoner, 1972; Holdren and Ehrlich, 1974; 

Ehrlich and Ehrlich, 1990). There have been several computations of ecological footprints that try to estimate values at 

national and global scales (for example, see Wackernagel and 

others, 1999). These efforts have been criticized for many of 

the same reasons as the attempts to value ecosystem services, 

including difficulties in valuing services, accounting for trade, 

and dealing with multiple spatial scales (van den Bergh and 

Verbruggen, 1999; van Kooten and Bulte, 2000). Even if 

the specific values computed from ecological footprint computations are controversial, a main benefit derived from the 

concept of the ecological footprints is their use to direct discussion of environmental stressors and their impacts (Ehrlich 

and Ehrlich, 1990). Rewriting the ecological footprint equation as EI = P * (C/E), [Environmental Impact = Population * 

(Consumption/Efficiency)] helps to illustrate simply the functional drivers to land-use and land-cover change and the fact 

that there are magnifiers and modifiers to human population 

that affect environmental impact.

Population, consumption, and efficiency—The most obvious and largest driver to changes in land-use and land-cover is 

the growth in human population. There is a minimum requirement for each person for space, food, water, air, and the landuses associated with those requirements. More people require 

more resources and either a greater conversion of natural landcover to human-altered land-cover or a more intensive use of 

existing human-altered land-cover. This per capita driver to 

changes in land-use is magnified by lifestyle factors (that is, 

increased consumption offset by increases in efficiencies). 

When Malthus first published “An Essay on the Principle 

of Population” in 1798, there were approximately one billion 

humans on the planet Earth. It took another 125 years to add 

another billion (1925), 35 years to add the next billion (1960), 

15 years to add the next billion (1975), 11 years to add the 

next billion (1986), and 13 years to add the next billion (a total 

of six billion in 1999) (Cohen, 1995b; UNFPA, 1999). Given 

the difficulties of estimating current populations (Cohen, 

1995b), much less how many people the Earth can support 

(Cohen, 1995a), future estimates of global population change 

are uncertain. The best current probabilistic estimates of future 

global human population predict a peak in the range of eight to 

ten billion within the next century, with nine billion as the best 

estimate (Smil, 1999; UN, 1999a; Keilman, 2001; Lutz and 

others, 2001). 

Future changes in the consumption and efficiency factors of the global population will have major impacts on the 

ultimate resource requirements, pressures placed on global 

resources, and the resultant changes in land-use and landcover to be expected. These changes can act to either increase 

or reduce the ecological pressure of the population. For example, in 1800, each of the one billion humans required approximately 20,000 square meters of agricultural land to obtain 

a year’s supply of food, whereas in 1999 it only took 2,000 

square meters of agricultural land to supply each human with 

food (Evans, 1998). Although this increase in efficiency has 

acted to reduce the potential total agricultural land-use requirement (the pattern of land-use), this has happened only because 

of advances in agricultural techniques such as pesticide use 

and biological pest control, plant breeding, and genetics 

(Trewavas, 2002) and massive inputs of human-fixed nitrogen fertilizer that now roughly equal the entire natural global 

nitrogen-fixation amount (Vitousek and others, 1997). This 

change in the process of land-use has resulted in increased 

nutrient cycling, contamination of surface and ground water, 

and excessive fertilization of aquatic ecosystems resulting 

in eutrophication of coastal and estuarine ecosystems (Smil, 

1997). Similarly, Cohen (1995a) notes that although human 

population roughly quadrupled from 1860 to 1995, each 

human’s use of energy increased roughly 20 times, resulting in 

a 100 times increase in total global energy consumption (and 

concurrent land-use impacts due to energy extraction, mining 

activity, pollution, transportation, and energy production.). The 

intensity of human activity per unit of land-use may have ecological impacts that are just as important, or more important, 

as the land-use itself.

Urbanization—Along with the rapidly expanding human 

population, changes in the pattern of human habitation have 

had major consequences for land-use and land-cover changes 

in the past several centuries. While the global population 

roughly tripled from 1900 to 2000, the number of people 

living in urban areas increased by a more than tenfold (WRI, 

1996). Nearly half the world’s population now lives in urban 

areas and virtually all of the population growth expected 

during 2000 to 2030 will be concentrated in the urban areas 

of the world (UN, 1999b; UNEP, 2002). By 2050, 65 percent 

of the total population is expected to be urban (UN, 2001). 

Urbanization increases with development—about 84 percent 

of the population in developed areas of the world is expected 

to be living in urban areas by 2030 (UN, 1999b; UNFPA, 

1999). About 60 percent of the current global population lives 

within 100 km of the coastline and this proportion is expected 

to increase (WRI, 1996). More than half of the U.S. population 

lives in the coastal zone and more than half of the projected 

increase in population expected over the next 15 years will 

occur in that zone, which only covers 17 percent of the land 

area of the 48 conterminous U.S. States (Beach, 2002). 

For much of human history, urban centers have tended 

to be regional aggregations consisting of a densely populated 

core surrounded by a less populated region from which the 

urban core drew resources. Megacity complexes now cover 

large areas of territory—they have grown large enough to 

alter regional weather (Shepherd and others, 2002). The urban 

megacity acts to concentrate the products of land-cover change 

from a wide, even global, area. Long-distance transport of 

agricultural and resource extraction products from the conversion of grassland to agriculture and forest to timber harvest are 

processed by the concentrations of population and the waste 

products discharged in the air and watersheds of those urban 

concentrations. This has particular significance for coastal 

ecosystems. 

Land-use conversion has also tended to concentrate on 

the most productive land. For most of the last 9,000 years of 
193

human history, population growth has been dependent upon 

the conversion of natural systems to agricultural production 

and has been concentrated in areas with the most productive 

soils where sources of freshwater were available (Kates and 

others, 1990). Using satellite-derived imagery, Imhoff and 

others (1997) estimated that urban areas accounted for 2.7 

percent of the surface area of the 48 conterminous States, 

an area approximately equal to the state of Minnesota or 

one-half the size of California. They also determined that 

the growth of urban areas was concentrated on the better 

agricultural soils. In addition to the recent concentration of 

population in coastal urban areas, recent changes in the pattern of urban development have acted to magnify the impact 

of urbanization and the amount of land-cover converted to 

urban use. Collectively, these changes have come to be called 

“urban sprawl” (Benfield and others, 1999).

Regional patterns of change (suburbanization/urban 

sprawl)—There has been a major shift in the pattern of urban 

land-use in the United States over the past two centuries. 

The population of the United States has grown increasingly 

urban in distribution since colonial times. The increase in 

urban population has accelerated in the past 100 years, while 

the number of Americans living in rural areas has remained 

fairly constant (fig. 1). Using the 1990 U.S. Census Bureau 

definition of urban, suburban, and rural, the U.S. Council on 

Environmental Quality (USCEQ, 1997) tabulated the U.S. 

population over the past 50 years of growth and migration. 

Their result shows that from 1950 to 1996, the urban population increased by 63 percent, the rural population decreased 

by 19 percent, and the greatest relative change occurred in 

the suburban population, an increase of 274 percent. Their 

analysis calculated a 1950 to 1996 urban:suburban:rural 

population proportion shift as 33:23:44 to 30:50:20 while the 

total population increased from 150.7 to 265.3 million (fig. 

2). 

Since 1950, the U.S. Census Bureau has issued several 

revisions in their definition of metropolitan areas that have 

complicated historical trend analyses (Meyers and Simon, 

1994; Imhoff and others, 1998; U.S. Bureau of the Census, 

1998). Considering how difficult it is to precisely define an 

“urban area,” it is perhaps not surprising that many definitions of “urban sprawl” exist and that different conclusions 

can be reached depending on the definition used (Hess, 

2001; personal commun.). In general, when an urban area is 

adding urbanized land at a rate faster than it is adding population, it is experiencing sprawl. A good metric to quantify 

urban/suburban sprawl is the “per household land consumption” (PHLC), which is calculated by dividing the total area 

of developed land by the number of households in that area 

takes into account both residential and nonresidential uses 

(EPA, 2000). Using this metric over the period 1965-1990, 

the per household land consumption rate for the study area 

(Montgomery County, Pennsylvania) increased from 0.80 

acres of developed land per household in 1965 to 1.14 acres 

per household in 1990. 

Another metric of increased sprawl is the increase in 

miles driven per capita. Wallis and others (2001) found that 

between 1983 and 1997, Broward County’s population grew 

by 38 percent and the number of licensed drivers increased 

by 31 percent. However, the number of miles driven on 

county freeways increased by 177 percent, more than four 

times the rate of population increase and five times the 

increase in the number of drivers. Nationwide, from 1960 

to 1995, the population of the United States increased by 48 

Figure 1. U.S. population in urban and rural areas, 1790-1990. Data from U.S. Department of Commerce, Bureau of 

the Census, table 4, available on the Web at http://www.census.gov/population/censusdata/table-4.pdf.

Drivers of Land-Use and Land-Cover Change
194 Overview of Stressors and Ecological Impacts Associated With Regional and Global Patterns of Change

Figure 3. Per capita energy consumption versus per capita gross domestic product (GDP) by wealth grouping. Data from U.S. Department of Energy, Energy Information Administration, Washington, D.C. GDP dollar 

estimates for all countries are derived from purchasing power parity (PPP) calculations rather than from 

conversions at official currency exchange rates, normalized to 1995 U.S. dollars. Most of the GDP estimates are 

based on extrapolation of PPP numbers published by the U.N. International Comparison Program (UNICP) and 

by Professors Robert Summers and Alan Heston of the University of Pennsylvania and their colleagues.

0

20

40

60

80

100

120

140

1950 1960 1970 1980 1990 1996

Year

Population (millions)

Urban Suburban Rural

Figure 2. U.S. population in urban, suburban, and rural areas, 1950-1996. Data from U.S. Council on 

Environmental Quality, The 1997 Annual Report of the Council on Environmental Quality, Part III Environmental Data and Trends, table 1.4, available on the Web at http://ceq.eh.doe.gov/nepa/reports/1997/

tables.pdf. Urban refers to population inside central cities of metropolitan areas (MAs). Suburban 

refers to MA population in suburbs outside central cities. Rural refers to nonmetropolitan population. 

MAs are defined for each population census.
195

percent while the number of miles driven per capita by that 

population (“vehicle miles traveled” or “VMT” per capita) 

increased by 132 percent (USBTS, 1998). The increasing per 

capita space utilized and mobility of the population acts as a 

multiplier on the population increase to magnify the human 

impact and the land-use conversion required to meet human 

needs.

Urban sprawl and associated land-uses have been cited 

as primary drivers of declines in environmental conditions 

in the United States (Flather and others, 1998) and other 

areas of the world (Houghton, 1994). Fulton and others 

(2001) found that the trend towards sprawl was widespread 

in the United States and that from 1982 to 1997 about 25 

million acres of land was converted to “urban” from some 

other land-cover class. Nationally, forested land-cover is 

being maintained or increased while agricultural land is 

being lost (Sorenson and others, 1997; Sisk, 1998). Using 

satellite imagery and GIS in a regional scale analysis, Jones 

and others, (2001b) grouped land-cover in the mid-Atlantic region into six classes. They found that from the early 

1970’s to the early 1990’s, out of the six land-cover classes, 

the urban land-cover class increased the most and agriculture land-cover decreased the most while forests, water, and 

barren land experienced small increases and emergent wetlands had very small decreases. 

Global Economics and Prospects for 

Land-Use and Land-Cover Change

Except for the potentially catastrophic effects of global 

climate change at a global scale over the next century, the 

future human impact on land-use and land-cover change will 

be determined by three factors: (1) the additional three billion people who will be added to the human population—primarily in the less developed nations (UNPD, 2001), (2) the 

standard of living achieved by the nine billion inhabitants 

of the planet (consumption), and (3) the efficiency of the 

global population’s conversion of the Earth’s landscape to 

meet human needs. The traditional development path for the 

most highly developed countries today has been to achieve 

a higher standard of living through a higher expenditure of 

energy and a more intensive use and extensive conversion of 

land-cover to human use. If we group the current world population in roughly equal size groups sorted by natural breaks 

in affluence (table 1, with “affluence” defined as the per 

capita Gross Domestic Product (GDP)—roughly proportional 

to “standard of living”) and plot those groups versus the per 

capita energy consumption (roughly proportional to environmental impact) for these groups, we can visually see the gap 

that separates the most developed nations from the rest of the 

world (fig. 3). How the human population of approximately 

nine billion people impacts global land-use and land-cover 

change patterns in the next century will be determined by 

the degree to which the seven billion people represented in 

the lower left corner of the graph improve their standard of 

living without mirroring today’s consumption pattern of the 

two billion people who will be living in the most developed 

nations.

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