Quantifying Urbanization and Fragmentation of Agricultural and
Forested Lands in Kenosha County 1988-2008
By
Sarah Geise
An Undergraduate Thesis Proposal
Submitted in Partial Fulfillment for the Requirements of
Bachelor of Arts
In
Geography and Earth Science
Carthage College
Kenosha, WI
April 2011
Quantifying Urbanization and Fragmentation of Agricultural and
Forested Lands in Kenosha County 1988-2008
Sarah Geise
Abstract
Kenosha County’s proximity to Milwaukee and Chicago limited industry to
manufacturing in the 20th century. In the past two decades Kenosha has increased its role in
commercial economy. This resulted in changes in land use which impacted agricultural and
forested lands. Kenosha’s agricultural lands are of both state and national importance for food
production. Forests are useful for their aesthetic appeal, recreational purposes, as an aid to
prevent runoff, and as storage for environmental carbon. Due to changes in the economy and
their resulting changes on the physical environment, it was hypothesized that there has been a
higher percent of agricultural and forested land converted to residential and commercial uses in
Kenosha County between 1988 and 2008 along Interstate 94 and in townships which share a
boundary with Illinois compared to Kenosha County as a whole. It was also hypothesized that
county subdivisions (towns, villages, or cities) with greater population growth exhibited higher
levels of fragmentation of agricultural and forested land. To quantify and spatially analyze
changes in Kenosha, two classified land cover/use maps were derived from satellite images of
1988 and 2008 respectively using Idrisi image processing software. Both 1988 and 2008
classifications yielded 75% accuracy. Idrisi’s Land Change Modeler was used to quantify the
change in area from one land use/cover to another. Percent changes along I-94 were not higher
than those of the whole county for both agriculture and farmland and only a portion of the
subdivisions on the Illinois border exhibited greater changes in the two specified classes than the
county as a whole. Fragmentation of agricultural and forested lands was calculated using spatial
metrics from Fragstats software. As hypothesized, the subdivision with the greatest population
growth also exhibited more fragmentation between 1988 and 2008. This study serves as an
evaluation of the changing landscape of Kenosha since the County’s 1991 Corridor Plan,
provides useful insights for the 2035 Zoning Plan and will potentially advocate for increased
conservation of important forested and agricultural lands.
1
Table of Contents
Abstract………………………………………………………………………………….…1
List of Figures……………………………………………………………………………...3
Lists of Tables……………………………………………………………………………...4
Literature Review………………………………………………………………….............5
Introduction.....................................................................................................….......5
Suburban Expansion and Sprawl.……………………………………………….….5
Managing Sprawl….………………………………………………………………10
Use of Remote Sensing and GIS to Study Land Use/Cover Change…..…...11
Quantifying Fragmentation………………...……………………………………...13
Forests in Wisconsin………………………………………………………………16
Agriculture in Wisconsin…… ……………………………………………...…… 18
Study Area Background...………………………………………………………... 19
Kenosha Corridor Land Use Plan…………………………….……………….…...22
Hypotheses………………………………………………………………………………...24
Methodology.………………………………………………………………………….…..26
Data Acquisition...................……………………………………………………...26
Image Correction and Classification ……….………………………………….....27
Change Detection Analysis………………………………………………………..28
Quantifying Landscape Fragmentation……………………………………………29
Results………………………………………………………………………………..........30
Classification….…………………………………………………………...........…30
Accuracy Assessment……………………………………………………………...33
Spatial Trends……………………………………………………………………...34
Fragmentation Results………………………………………………………..........39
Discussion…………………………………………………………………………….........42
Recommendations…………………………………………………………………45
Future Research…………………………………………………………………....46
Acknowledgements..............................................................................................................47
Works Cited..........................................................................................................................48
2
List of Figures
Figure 1: Land use/Cover Kenosha County 1988
30
Figure 2: Land use/Cover Kenosha County 2008
31
Figure 3: Mean Signature Comparison Chart for 1988 Land Use/Cover Map
32
Figure 4: Mean Signature Comparison Chart for 2008 Land Use/Cover Map
32
Figure 5: Trend of Change from Agricultural to Residential Land Use/Cover 1988-2008
34
Figure 6: Trend of Change from Agricultural to Commercial Land Use/Cover 1988-2008
35
Figure 7: Trend of Change from Forest to Residential Land Use/Cover 1988-2008
36
Figure 8: Trend of Change from Forest to Commercial Land Use/Cover 1988-2008
37
3
List of Tables
Table 1: Sprawl Archetypes
7
Table 2: Costs of Sprawl
9
Table 3: Landscape Metrics selected and calculated for
land-use/cover change study in Indiana
16
Table 4: Error Matrix/Accuracy Assessment of 1988 Classification
33
Table 5: Error Matrix/Accuracy Assessment of 2008 Classification
33
Table 6: Changes in Area of Agricultural Land
to Residential or Commercial Land Use/Cover
38
Table 7: Changes in Area of Forested Land
to Residential or Commercial Land Use/Cover
39
Table 8: Population Change of Kenosha County Subdivisions 1988-2008
39
Table 9: City of Kenosha Agricultural Fragmentation Indices
41
Table 10: City of Kenosha Forest Fragmentation Indices
41
Table 11: Town of Paris Agricultural Fragmentation Indices
41
Table 12: Town of Paris Forest Fragmentation Indices
41
4
Literature Review
Introduction
Expansion of urban areas has been a prevalent occurrence within the United States over
the past century and has been occurring more rapidly in recent decades. Southeastern Wisconsin
has seen many changes in the built environment. Kenosha County is a prime example of urban
expansion in Southeast Wisconsin and is especially important to study as it is located between
two major urban centers of Milwaukee and Chicago. As a result of Kenosha’s location it is a
target for those coming from both the north and the south and has developed a need to establish
its own identity. The county has undergone many changes in the past 20 years which include
many new subdivisions and increased commercial developments and this has changed the
amount of other land cover types in the county. This study is intended to quantify that between
1988 and 2008 a higher percentage of agricultural and forested land was converted to residential
and commercial uses and the remaining agricultural and forested land was more fragmented
along Interstate 94 and townships that share a boundary with Illinois compared to the county of
Kenosha as whole. Selected methods for studying land use/cover change and fragmentation will
be examined which provide a basis for this study. The historical and recent changes occurring in
the Kenosha area will be discussed as well to provide an idea of why the land use/cover has been
changing and needs to be quantified.
Suburban Expansion and Sprawl
Urban and suburban expansions have become very hot topics as open land continues to
disappear and cities grow outward. This is a trend seen not only in developed countries like the
5
United States, but in the developing world as well. Cities and suburbs have grown and changed
from more single-centered urban areas with concentric rings of surrounding farmland (Von
Thunen’s Model) to more poly-centric urban areas where more concentrated densities of houses
and businesses exist in multiple places within a given area (Galster et al. 2001). In doing so,
urban areas have converted large quantities of farmland and consumed forest lands, both of
which were some of the most prevalent features of the Midwest. Expanding urban centers can
also lead to increased driving times from the home to place of employment and increased
emissions from vehicles which can result in negative impacts on air quality. Poly-centric urban
areas can be seen as a negative impact of sprawl but can also be beneficial once an urban area
reaches a certain size (Ewing 1994). Not only are the forested and agricultural lands that are
being paved over affected by urban expansion, but the areas surrounding the newly built
environment are subject to the redirected environmental processes that once operated on a
different category of land cover. Urban expansions have also been linked to fragmented or
disconnected cultures. Expansion of urban areas has impacted the land and the air around built
environments and it has cycled back to have repercussions on those who utilize urban areas.
One phenomenon associated with urban and suburban expansion is described by the term
suburban sprawl. Sprawl is an ambiguous word which may encompass the patterns of land use,
the process of expanding the built environment, or the causes and consequences of how the
environment is altered as a result of particular land uses (Galster et al. 2001). According to
Ewing, any “undesirable land use pattern” can be considered as sprawl. While sprawl has
multiple definitions and can be measured by varying degrees, past scientific literature on the
subject has shown many commonalities, some of which are presented in Table 1.
6
Table 1: Sprawl Archetypes. Adopted from Ewing, Reid H. “Characteristics, Causes, and Effects
of Sprawl: A Literature Review.” Environmental and Urban Studies Volume 21, Issue 2 (1994).
Low density development can vary depending on the study describing it but is defined by
the United States Census Bureau as less than 1,000 people per square mile. Strip development
presents a concern of sprawl measurement as it limits accessibility by providing a row of
continuous commercial development, blocking access to other land uses and increasing drive
times to reach other types of development. Scattered development considers the fragmentation
patterns of growth and is related to leapfrog development. Leapfrog development occurs when
some sites are not developed as the surrounding area is developed. This type of development is
typically only a concern at certain points in time as the unoccupied lands tend to be filled at later
dates, typically due to speculation of current owners attempting to make more money in the long
run. Besides the factor of time, the distance between developments in leapfrog patterns can also
be an important factor when considering how sprawling an environment is (Ewing 1994). This is
by no means an exhaustive list of definitions/descriptions of patterns of sprawl. Many more
papers have been written attempting to define and quantify sprawl since this list was generated.
7
The archetypes of sprawl shown in the table are just some of the most basic descriptors of sprawl
which create a foundation for studying the patterns of urban/suburban development. It is
beneficial as this study will mainly focus on the patterns associated with urban/suburban
expansion in Kenosha County between 1998 and 2008.
Table 2 discusses the basic costs associated with suburban expansion (Ewing 1994).
Psychic costs include environmental deprivation and deprivation of access. In these two
situations people feel disengaged with the community or cut off from community events due to
lack of transportation (Popenoe 1979). Excess travel and congestion are results of either more
spread out development or lack of mix-use development. Energy and environmental costs come
from longer commutes in miles or time as a result of congestion (Ewing 1994). Infrastructure and
public service costs are higher in lower density areas (Priest et al. 1997). Downtown decay
results from businesses moving out of the central city and is costly due to decreases in tax base
as a result of the shift. According to Ewing (1994), “Because lands most suitable for growing
crops also tend to be most suitable for ‘growing houses’ (being flat and historically near
settlements), a disproportionate amount of prime farmland is lost to urbanization.” As this work
intends to quantify the changes in agricultural lands, it is important to note that the loss of
agricultural lands is considered a cost, meaning that some value is being lost. This study will,
therefore, assist Kenosha County in determining the quantity and the “cost” of local urban
expansion.
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Table 2: Costs of Sprawl. Adopted from Ewing, Reid H. “Characteristics, Causes, and Effects of
Sprawl: A Literature Review.” Environmental and Urban Studies Volume 21, Issue 2 (1994).
Two additional, historic landscape patterns associated with suburbanization include edge
nodes and rural fringes. Edge nodes can be described as road side commercial strips which are
scaled for automobiles. Not only is a car a necessity to get from one’s home to a commercial area
but vehicles are also needed to traverse between commercial areas within an edge node. Big box
retailers such as Wal-Mart, chain stores, franchises and malls all prevail in edge nodes (Hayden
2003, Chapter 8). The concept of an edge node as a pattern of development is significant to this
study as Kenosha has many major roadways, including Interstate 94, which cut through the
county with high quantities of commercial development along it. The area surrounding I-94 may
be an edge node of Kenosha and the development of land within certain distance of this major
transportation artery should be examined.
Correlated to edge nodes is the rural fringe. Typically residents will live in the rural
fringe and work in edge nodes. Residents in rural fringes have a tendency to either highly
support development near the property or severely oppose development. The opposition of
9
development by current residents or high land prices near current developments can lead to leap
frog developments which fragment the land, especially agricultural and forested land. Also
related to the pattern of rural fringe development are longer commutes, sometimes as lengthy as
two hours (Hayden 2003, Chapter 9). Kenosha’s proximity between two large urban centers may
make it susceptible to being a rural fringe development to accommodate commuters from
Milwaukee and Chicago as well as fragmentation as a result of leapfrog developments.
Managing Sprawl
In order to combat the negative impacts of urban sprawl Urban Growth Boundaries and
greenbelts have been implemented globally with examples in the United States. A prime example
of the Urban Growth Boundary is found in Portland, OR. This is a legal boundary which
discriminates between urban and rural land and prevents growth in rural areas. While this
boundary was enlarged in 2002 it has continued to minimize loss of farmland in Oregon at a
more effective rate than surrounding states (Carter-Whitney 2008, 22). The concept of greenbelts
dates back to pre-industrial revolution times (Ali 2008, 534). Definition and uses of greenbelts
vary by the location and its intended purpose. Greenbelts need little definition but are generally
more vegetated areas surrounding, sometimes completely encompassing cities. There are many
purposes for greenbelts which may include: minimizing urban sprawl, improving ecological
conditions in and around urban areas, providing areas for recreation, preserving farmland and
encouraging infill development (Wei-feng; Ali 2008, 534). These methods for managing urban
expansion and its impacts may have implications for Kenosha if results of this study show large
amounts of fragmentation and large losses of agricultural and forested lands in the county.
10
Use of Remote Sensing and GIS to Study Land Use/Cover Change
This project will employ Landsat 5 thematic mapper (TM) images. The Landsat 5
satellite was released into orbit on March 1, 1984 and is still in use today. The thematic mapper
is a sensor that provides finer details of Earth’s spectral qualities and is an updated version of the
multispectral scanner subsystem employed on earlier Landsat satellites (Campbell 2008, 161).
TM images also have finer spatial resolution, greater geometric accuracy, and a larger range of
brightness values. These images have 30 meter spatial resolution (about .09 ha or .22 acres) for
bands 1-5 and 7, and a resolution of 120m for band 6. Band one has a spectral definition of bluegreen from .45-.52 µm, band two is green from .52-.6 µm, band three is red from .63-.69 µm,
band four is near infrared from .76-.9 µm, band five is mid-infrared from 1.55-1.75 µm, band six
is far infrared from 10.4-12.5 µm, and band seven is also mid-infrared but is from 2.08-2.35 µm.
(Campbell 2008, 175). The Landsat 5 satellite collects data as it travels northeast to southwest on
the illuminated hemisphere of Earth and captures data at 185 km spans with cycles of 16 days
(Campbell 2008, 178).
Numerous studies have employed TM images for land use/cover classifications to study
agriculture, forestry and urbanization. A study was done by Sun (2006) of South-Central Indiana
in Monroe County. This study examined changes in forest-cover and their particular effect on the
urban fringe and in relation to socio-demographic variables using satellite imagery and a variety
of other datasets such as aerial photographs and township boundaries. Three different years of
satellite imagery were used to examine changes over a twenty year time span in Sun’s study of
South-Central Indiana (Sun 2006, 42 and 62). This study seeks the same goal of quantifying land
use/cover change but will not have a mid-point classification. While Sun’s change detection
analysis focused on reforestation, deforestation and areas of no change (Sun 2006, 64), this study
11
seeks to quantify changes specifically to urban and commercial uses. Sun’s study showed
increased reforestation but medium density residential areas showed the second highest
percentage of deforestation, which suggests that this study may show decreases in forested lands
near expanding residential areas (Sun 2006, 84). A main difference between land changes found
in the Indiana study and those expected in this study is smaller amount of change in forested
lands along major roads (Sun 2006, 101).
Changes in land use/cover are also being done using TM imagery for different countries.
A study by Seto et al. (2002) was done on the quickly developing Pearl River Delta in China.
This study utilized brightness, greenness and wetness values from TM images to map land
use/cover change. A physical field assessment provided 93.5 % accuracy of the TM image
classifications (Seto et al. 2002, 1). This study also utilized a supervised maximum likelihood
classification of the TM images once land use/cover classes were determined. As the maximum
likelihood classification provided inaccuracies in a number of classes, image clustering and
segmentation reclassification were utilized as well as manual editing of classes (Seto et al. 2002,
11-12). If initial inaccuracies in this study show large errors these reclassification methods may
need to be utilized in addition to the initial supervised classification.
Closer to the intended study area of Kenosha was a Chicagoland study that was
completed using TM images as well. Supervised classification of the time-series images were
performed again using the maximum-likelihood classification algorithm. Historical aerial
photographs and county land-management data were used to assist with the classification of the
satellite imagery (Wang and Moskovits 2001, 837). The Chicago wilderness study needed
additional accuracy adjustments as 30 m was too large to identity some of their subcategories
(Wang and Moskovits 2001, 388). This should not be a problem for this study as most of the
12
categories are based on Anderson Level I land use/cover classification. Images taken from late
April assisted in the classification of wetlands and floodplains as that month coincides with peak
discharge of rivers. This information along with ancillary data from the national wetlands
inventory will be helpful for deciphering the difficult class of wetlands.
All of the above mentioned studies have used thematic mapper images to determine land
cover classifications of various regions, including the county region at which this study will take
place. These studies have also demonstrated that high levels of accuracy in land use/cover
classification are achievable either through utilization of supervised maximum likelihood
algorithm or a combination of supervised classifications and additional editing tools and
ancillary data.
Quantifying Fragmentation
Fragmentation is important to study for both forests and farmlands. In forests,
fragmentation can lead to reduced movement of plants and animals, which can result in species
elimination and decreased species diversity in an area. Species that require more interior space in
forest patches may be adversely affected through loss of their habitat due to fragmentation as
well. When farmland is fragmented farmers are sometimes forced to convert to specialized crops
to accommodate smaller field sizes or pressured further to sell their land for urban uses (Delbecq
and Florax, 2010). Changes to more fragmented forests and agricultural land from urban
development can affect the biodiversity in both land use/cover types.
The type of software that will be used to analyze fragmentation of agricultural and
forested land is called Fragstats (http://www.umass.edu/landeco/research/fragstats/
13
fragstats.html). Fragstats is free software that can be downloaded through the University of
Massachusetts-Amherst website. It contains a diverse set of landscape metrics that can be used to
map patterns of fragmentation.
There are four common types of data that are associated with landscape patterns which
include: spatial point patterns, linear network patterns, surface, and categorical map patterns. The
two most important types of data for this study will be linear network patterns and categorical
map patterns. Linear network patterns are made up of corridors which connect to nodes and can
be distinguished through their composition or width. This will be useful for studying the
connectivity of the built environment through road networks and to see if they provide a cause
for fragmentation. The most applicable pattern for this study is the categorical map pattern.
Categorical maps provide distinct patch outlines through either vector outlines or the grid cells of
raster data as this study will.
There are three common levels at which spatial metrics are calculated; these include
patch, class and landscape level metrics. Patches are the main element for categorical maps. The
patches can be evaluated on an internal or external basis but individually have a limited amount
of metadata associated with them and are, therefore, generally not interpreted on their own. Class
level metrics consist of a group of patches that have a similar characteristic. This level of metric
can be aggregated using a variety of statistical measures to create an over-arching perspective of
patch types, usually indicating the quantity and dispersal of a patch class. It is well utilized for
studies of habitat fragmentation. For the purposes of this paper the fragmentation of forest
habitats as they are affected by anthropogenic forces will be studied using this level. Lastly,
landscape-level metrics include the cumulation of all patches and classes within the entire study
area/landscape. They can be aggregated statistically similar to class level metrics and provide the
14
broad picture for the area under consideration. The metric level is important to define based on
the object of study so that correct and meaningful results may be achieved (McGarigal et al.
2002).
There are also an extensive variety of class-level and landscape-level components to
choose from to use for analysis so it is important to identify those that are the strongest indicators
of fragmentation. From the study conducted by Cushman et al. (2008), seven landscape structure
components that explain the most variation among the 49 metrics tested include: aggregation,
large patch dominance, shape and correlation length of large patches, patch size variation,
edge/patch density, mean patch size and edge + aggregation. The three most important
landscape-level measurements were contagion/diversity, large patch dominance, and
interspersion/juxtaposition. In a similar study conducted on forest fragmentation in Indiana the
metrics used included: class area, percentage of landscape, number of patches, patch density,
largest patch index, total edge, edge density, and perimeter-area fractal dimension (Sun 2006, 65)
(Table 3). This list provides a basis for the metrics that will be used within this study.
15
Metric
Class Area
Percentage of
Landscape
Description
CA equals the sum of the areas of all patches of
the corresponding patch type
PLAND equals the percentage of the landscape
comprised of the correpsonding patch type
Number of Patches
NP equals the number of patches in the landscape
Patch Density
PD equals the number of patches in the landscape,
divided by the total landscape area
Units
Range
CA > 0,
Hectares without limit
Percent 0≤ PLAND ≤ 100
NP ≥ 1,
None
without limit
Number PD >0,
per 100 constrained by cell
hectares size
LPI equals the area (m2) of the largest patch in the
landscape divided by total landscape area (m2);
LPI equals the percent of the landscape that the
Largest Patch Index largest patch comprises
TE equals the sum of the lengths (m) of all edge
Total Edge
segments involving the corresponding patch type
ED equals the sum of the lengths (m) of all edge
segments in the landscape, divided by the total
Edge Density
landscape area (m2)
Perimeter-Area
Fractal Dimension
Percent 0≤ LPI ≤ 100
TE ≥ 0,
Meters without limit
Meters
per
ED ≥ 0,
hectare without limit
PAFRAC equals 2 divided by the slope of regression
line obtained by regressing the logarithm of patch area
(m2) against the logarithm of patch perimeter (m).
PAFRAC approaches 1 for shapes with very simple
perimeters such as squares, and approaches 2 for
shapes with highly convoluted, plane-filling perimeters. None
1 ≤ PAFRAC ≤ 2
Table 3: Landscape metrics selected and calculated for land-use/cover change study in Indiana.
Sun, Wenjie. “A GIS-Based Integrated Approach to Explore Land-use/cover Change Dynamics
in South-central Indiana.” PhD diss., Indiana University, 2006.
Forests in Wisconsin
According to the Millennium Ecosystem Assessment, the last 50 years have seen greater
changes in the ecosystems than any other similar quantity of time in history (Waller and Rooney
2008, 6). Great changes have been seen in forests which are economically valuable natural
resources exploited and removed over the centuries since the settlement of Wisconsin. The loss
of forested lands results in a decrease of aesthetic appeal to those who value natural beauty as
well as the destruction of habitats for wildlife and loss of hiking and other recreational activities.
16
With decreases in forested land there have been decreases in carbon sequestration which can
result in decreased air quality and increased global warming. Increased run off can also become a
significant problem when large amounts of vegetation is removed.
Wisconsin’s vegetation has undergone massive changes over the last 200 years. In the
19th century, prairies, oak woodlands and oak savannas dominated Southeastern Wisconsin. In
places where there are now houses along Lake Michigan there used to be major concentrations of
forests. Between 1850 and 1950 there was an increase in settlements which suppressed wildfires
and allowed the oak savannas to succeed into the oak woodlands and eventually into such trees
as maples. This succession was concurrent with fragmentation due to cropland, pastures, towns,
and urban areas. Fragmentation of forests leads to reduced movement of plants and animals,
which results in species elimination and decreased species diversity in an area. In a study that
compared a set of forested lands between 1940 and 2000, 12% of the original sampled species
were lost and in place of these species’ habitats were mostly residential and commercial
development. Forested areas nested within suburban or farm lands have had increased rates of
species loss as well (Rogers et al. 2008). A hopeful thread in the decrease of forested areas is the
574 acres of managed forest land within Kenosha County (The City of Kenosha Comprehensive
Plan). Southeastern Wisconsin has been deeply impacted by humans shaping the landscape, first
through the development of agricultural land and then by decreasing quantities of farm and
forested lands due to residential and commercial development. This trend is expected to be seen
through a more recent study of Kenosha’s development.
17
Agriculture in Wisconsin
Kenosha County’s soils are important for agricultural land as they were produced by
glaciation. Moraines created by glaciation are dominant features of the Kenosha area which
contain glacial till made of silt and clay that were picked up, carried by the glacier and deposited
in Southeastern Wisconsin. Outwash from melting glaciers and till produced rich soils for the
growth of prairies and then the prairies provided rich layers of dark, organic soils that are
effective for producing crops.
Post-glaciation, Kenosha’s agricultural history began around 1833 when the first land
surveying began in the southern and eastern portions of the state. “Surveying,” according to
William Cronon (1991, 102), was purposed “to turn the land into real estate”. Once surveying
was complete the prairies, savannas and wetlands of Southeast Wisconsin were rapidly converted
to agricultural land use. A century after lines had been drawn segmenting the county, land
management efforts began with conservation practices on farms which sought to farm along the
shape of the land rather than the shapes drawn by the government (Meine 2008).
In order to ensure the protection and responsible use of limited natural resources, The
City of Kenosha Comprehensive Plan has detailed information on Kenosha’s agricultural land.
According to the Plan, 73% of the city of Kenosha is covered in Class I and Class II farmland
which is designated “National Prime Farmlands”. Additionally, 16% of the city is covered in
Class III farmland that is considered “Farmlands of Statewide Importance”. Starting with Class II
there are restrictions to the crops that can be grown in each soil type and/or the amount of
management needed to conserve the soils. Settlements tend to occur next to/in areas where
agriculture is productive and expanding settlements tend to cover the same agricultural land that
first helped sustain the community. To maintain Kenosha’s valuable farmland, a County
18
Farmland Preservation Plan was adopted in 1981 and is still in effect with plans to enhance
preservation to adapt to the changing environment of Kenosha (The City of Kenosha
Comprehensive Plan 2010 and Department of Planning and Development 2009).
Study Area Background
Kenosha has undergone many changes, some already mentioned, since it became a
county. The county started with an agricultural economy but began rapid industrialization in the
early decades of the 20th Century bringing along with it an abundant increase in population. The
population for the county went from approximately 22,000 in 1900 to 120,000 in 1975 (Jensen
1976, 127) and almost 165,000 estimated for 2008 (U.S. Census, 2008). With an increase in
population came a trend that has been seen throughout the United States, the number of farms in
Kenosha has shown an overall decrease from 1,300 farms in the county in 1900 (Jensen 1976,
127) to 460 farms in 2007 (United States Department of Agriculture, 2007). The average farm
size has also increased from 97 acres in the 1930s (Jensen 1976, 128) to 183 acres in 2007
(United States Department of Agriculture, 2007) as a result of the industrial age bringing
machinery into the realm of agriculture and the advent of corporate farms. Between 1900 and
1975 the percent of land in Kenosha County that is used as farmland has decreased from 96.7%
to about 60% as well. These changes have prompted the consideration of maintaining a
“greenbelt” surrounding the city of Kenosha since at least 1975 (Jensen 1976, 128).
Prominent crop and farm exports at the end of the 19th century and the beginning of the
20th century included wheat and dairy. Wheat was grown in abundance until the Civil War and
then dairy farming and milk production took over around 1900. Exports of dairy products to
Chicago boosted Kenosha’s interaction with the larger city and helped to develop the flow of
goods from Kenosha to Chicago that was maintained throughout the last century (Jensen 1976,
19
129-130). As the century progressed, the trend from livestock farming reverted back to crop
production. Crops of importance in the early 1900s were Barley (used by breweries in
Milwaukee mainly between 1910 and 1920), oats, hay, and corn. Corn has been the highest
harvested crop in Kenosha County since the late 1920s. In the 1930s soybeans became a
significant crop in the county and were a booming crop export in the 1970s due to foreign
demand. Wheat has made appearances in the county throughout the last century when the market
was high (Jensen 1976, 138-9). Truck crops such as cabbage played a role in Kenosha’s
agriculture early on and, despite disease (Jensen 1976, 140), have remained in production even
now. Today, corn ranks highest in acres planted, followed by soybeans, forage and wheat
(United States Department of Agriculture 2007).
Transportation availability has been the catalyst for productive agriculture and has also
been responsible for the economic changes seen throughout the county over the last 100 years.
Goods first traveled by water on the Great Lakes until the railroad came. The railroad was the
most important link in Kenosha, bringing passengers and produce to Chicago and Milwaukee
(Jensen 1976, 148). “Farm-to-market roads” were of great significance as well. Green Bay
Military Road (Hwy 31 now) was the first road in Kenosha and remains an important
thoroughfare today with a variety of land uses lining it (Jensen 1976, 149). Many other major
highways developed between 1933 and 1938 which included Hwy 41 (Interstate-94 currently),
which runs twelve miles through the county, and 100 miles of state highways that connect I-94 to
city of Kenosha (Jensen 1976, 150).
Increased transportation has also been a problem for farm owners as it has allowed
increased urban development into agricultural areas. As a result of increased residential or
commercial uses, farm property taxes have been raised disproportionately to the value of the
20
farmland, making farm life and crop production more difficult (Jensen 1976, 165). A study in
1957 of Kenosha County showed farmers were wary of non-farm residential developments being
located near them (Jensen 1976, 169). These concerns have most likely increased as urbanization
increased in the last fifty years.
Along with farming, Kenosha has been a major manufacturing hub. It has been unable to
compete with Milwaukee and Chicago in other industries until more recently. Durable goods
were the staple of industry in Kenosha with a number of brass companies in the early 20th
century as well as textile and garment manufacturers (Keehn 1976, 179). By the 1920s the auto
industry had hit Kenosha and employed the most people in the county (Keehn 1976, 180). The
dominance of auto manufacturing in Kenosha coincided with accelerated use of vehicles and
added to the spread of people from the central city (Ross 1976, 463). Due to large emphasis on
manufacturing, Kenosha was more susceptible to trends in the national market and fears of large
manufacturers closing threatened the county (Keehn 1976, 211).
Fears of a major manufacturer closing was justified in 1988 when the Chrysler plant, the
owner of Kenosha’s long held American Motors Corporation, closed and left 5,300 people
unemployed (Bachman 1988). This closing was a hard blow to Kenosha but the company did
provide funds to the Kenosha Area Development Corporation, United Way, and the Kenosha
Achievement Center and parcels of land were released back to the city (Moran 1991, 15).
After this devastating loss to the community there was a major push for the diversification of
Kenosha’s economy (Moran 1991, 9-10). In addition to diversifying the economy citizens of the
City of Kenosha surveyed also hoped for the city to be a technology, service industry, retail and
education center Those surveyed also preferred not to be a suburb of Chicago (Moran 1991,
Appendix B).
21
In the year following the closing of Chrysler, the City of Kenosha 1990 Economic
Report and Financial Highlights listed “record construction activity, resurgent population growth,
creation of 4000 jobs outside the auto industry, and a boom in residential activity…fueled by
Illinois residents looking for lower housing costs and easy access to their jobs in the Chicago
market” (Moran 1991, 11). Another significant jump towards diversification came with the
annexation of the Town of Pleasant Prairie into the City of Kenosha in 1989 for the Lakeview
Corporate Park discussed in the next section. The economic transformations that Kenosha has
experienced over the last century have caused great changes in the dispersion of residential and
commercial land use. Major economic changes and the shifting opinions of Kenoshans have
gradually shifted the boundaries of the city into the rest of the county and the impacts to long
standing agricultural production and old growth forested lands must be examined.
Kenosha Corridor Land Use Plan
The Department of City Development for Kenosha created a corridor land use plan which
included the land between Highways 50 and 142 and Interstate 94 in 1991. This plan highlighted
reasons for expected growth for this area which included construction of Lakeview Corporate
Park and relocation of people from Chicago to Kenosha (Department of City Development).
Lakeview Corporate Park is in use currently and offers diverse development sponsored by
Wisconsin Energy Corporation and Center Point Properties, a Chicago region developer, which
suggests the growing interests of those from Chicago in the Kenosha area. It boasts of its 2400
acres’ proximity to the Interstate and major airports in two nearby cities as well as an
aesthetically pleasing park-like environment, a nature conservancy, indoor and outdoor activities
and the Radisson Hotel and Convention Center among many other businesses (Wispark LLC).
22
According to the corridor land use plan, Kenosha sought to improve their image through quality
development of a variety of land uses such as housing, recreation, commercial and industry.
While this plan did intend for large amounts of impervious land cover development it
also aspired to work around unique natural areas. This is promising for the forested regions
within the corridor but cannot speak to the fragmentation of those remaining areas. The
Department of City Development also sought to “encourage land use densities that allow for a
transition from the high densities of the city to the lower densities of the suburban/rural area”.
Based on this statement is appears that Kenosha intended to sprawl out towards the rural areas
rather than to provide a compact city. Furthermore, this plan states that a “linkage” between the
interstate and the city as well as a need for continuous development along the interstate
(Department of City Development). Continuous development along the interstate of residential
or commercial could be deemed positive such that it is not sprawling but could also lead to
fragmentation of agricultural and forested lands in/surrounding that corridor.
23
Hypotheses
Based on the literature reviewed and long-term observation of Southeastern Wisconsin, a
study of Kenosha County will be conducted with respect to the following research questions:
Null Hypothesis 1: An identical percent of agricultural land has been converted to residential
and commercial uses in Kenosha County between 1988 and 2008 within two miles of Interstate
94 compared to Kenosha as a whole.
Alternate Hypothesis 1: There has been a higher percent of agricultural land converted to
residential and commercial uses in Kenosha County between 1988 and 2008 within two miles of
Interstate 94 compared to Kenosha as a whole.
Null Hypothesis 2: An identical percent of forested land has been converted to residential and
commercial uses in Kenosha County between 1988 and 2008 within two miles of Interstate 94
compared to Kenosha as a whole.
Alternate Hypothesis 2: There has been a higher percent of forested land converted to residential
and commercial uses in Kenosha County between 1988 and 2008 within two miles of Interstate
94 compared to Kenosha as a whole.
Null Hypothesis 3: An identical percent of agricultural land has been converted to residential and
commercial uses in Kenosha Country between 1988 and 2008 in townships which share a
boundary with Illinois compared to the rest of Kenosha.
Alternate Hypothesis 3: There has been a higher percent of agricultural land converted to
residential and commercial uses in Kenosha Country between 1988 and 2008 in townships which
share a boundary with Illinois compared to the rest of Kenosha.
24
Null Hypothesis 4: An identical percent of forested land has converted to residential and
commercial uses in Kenosha Country between 1988 and 2008 in townships which share a
boundary with Illinois compared to Kenosha as a whole.
Alternate Hypothesis 4: There has been a higher percent of forested land converted to residential
and commercial uses in Kenosha Country between 1988 and 2008 in townships which share a
boundary with Illinois compared to Kenosha as a whole.
Null Hypothesis 5: County subdivisions (towns, villages, or cities) with greater population
growth exhibit stagnant levels of fragmentation of agricultural land.
Alternate Hypothesis 5: County subdivisions (towns, villages, or cities) with greater population
growth exhibit higher levels of fragmentation of agricultural land.
Null Hypothesis 6: County subdivisions (towns, villages, or cities) with greater population
growth exhibit stagnant levels of fragmentation of forested land.
Alternate Hypothesis 6: County subdivisions (towns, villages, or cities) with greater population
growth exhibit higher levels of fragmentation of forested land.
25
Methodology
Data Acquisition
This study examines Kenosha County during the years 1998 and 2008 to assess changes
in land cover and land use. The two Thematic Mapper (TM) satellite images used for land cover
analysis were acquired from the USGS Archive and Available Scenes webpage
(http://landsat.usgs.gov/USGS_Archive_and_Available_Scenes.php). Each of these images had a
resolution of 30 x 30 m. Once at the archives page a link was followed to a site called Earth
Explorer. From this page, the Landsat Archive, L4-L5 dataset was selected and Kenosha County,
WI was entered as the search criteria. Acquired images are both from Landsat Satellite 5 from
the Thematic Mapper (TM) sensor. The date of the first image used is April 8, 1988 and the
second image is from April 15, 2008. Both images are from path 23 and row 30 and are cloudfree. Imagery from 2008 could be downloaded directly from the website but 1988 imagery had to
be ordered through the website and then downloaded. Both images were free and came in
compressed zip files that needed to be extracted.
Historical aerial photographs were also obtained from the USGS Archives for 1986 to
provide a finer spatial resolution for training sites and accuracy assessments for the 1988
classification. The aerial photographs used were at a scale of 1:80,000. Google Earth historical
images from September 2008 were to provide training sites and accuracy assessments for the
respective year.
Classification of wetlands for 2008 was based on data from the National Wetlands
Inventory Wetlands Mapper found online as of 2010 (http://www.fws.gov/wetlands/Data/
Mapper.html). The National Land Cover Dataset (NLCD) was downloaded from the USGS
Archives as well for 1992 (http://landsat.usgs.gov/USGS_Archive_and_Available_Scenes.php).
26
Locations of wetlands listed in the 1992 NLCD were used as training sites for wetlands in the
1988 TM images.
County subdivision population data for cities, villages and towns of Kenosha County
were also downloaded from the U.S. Census Bureau’s website to represent both time periods
being examined. Population data used to represent the 1988 time period are from the “1990
Census, Summary File 1”. Data used to represent 2008 population are the 2008 annual
population estimates. Both years of Census data were downloaded directly as Excel files which
were imported into ArcMap and joined to existing spatial data.
Image Correction and Classification
Both images have been processed by the USGS and were geometrically corrected already.
A land cover/land use, categorical map was produced for both 1988 and 2008 using the
respective year’s Landsat TM imagery and IDRISI software. The TM imagery encompassed by
Kenosha County was imported from a .tiff file into the Idrisi format. A supervised classification
was performed using a maximum likelihood classifier, based on training sites obtained from
1986 historical air photos and 2008 Google Earth historical images respectively. Training sites
are select areas that are representative of the categories being classified. Classes include:
agricultural land, forested land, water, wetlands, residential land uses, and commercial land use.
Residential land uses will incorporate both houses and their accompanying landscapes (lawns).
Commercial classification will be used in a broader sense for this study. Developed areas which
include industrial land uses and transportation corridors will be classified as commercial in
addition to the typically zoned commercial areas. Ten to twenty training samples were taken per
class until a satisfactory spectral signature was developed.
27
Once the supervised classification was complete, an accuracy assessment was performed.
This was done by generating sixty random points (ten for each class) using ArcMap’s random
point generator function and the spatial extent of Kenosha County. A direct comparison of the
classified images and the corresponding higher-resolution aerial photographs at the random
points was then performed. An error matrix was produced in Excel to document the comparison
and provide the percent accuracy of the classification. This matrix also tells omission or
commission errors of the classification.
Change Detection Analysis
Change detection between the two time periods was accomplished by using Land Change
Modeler (LCM) in IDRISI. The classified land use/cover images were used as the input files.
Idrisi only allowed a rectangular extent, so initially the bounding envelop instead of the actual
county boundary was used to derive the change map. After the change map was created with
LCM, it was exported to ArcMap. From this exported map, a series of land cover change maps,
including one for the county, one for the two miles buffer zone along Interstate 94, and several
for the county subdivisions (towns, cities, villages) adjacent to the Illinois border were created
using the Extraction by Mask tool in ArcMap. Land Change Modeler was then run for the
buffers and areas adjacent to Lake County to show the percent change of agricultural and
forested land converted to residential and commercial uses. Changes were compared for the area
within the two mile buffer of I-94 to the county percent change and the county subdivisions
adjacent to Lake County were compared to the percent change for the county to determine if the
hypothesized increase in residential and commercial lands in the queried out/buffered areas are
greater than the percent of change for the county. Land Change Modeler presents the gains and
28
losses by category of land use/cover in both graph and map formats and the specific transitions
that took place between categories. Gains and losses are documented in hectares. The Spatial
Trend of Change function in IDRISI was also utilized to map the trend of the changes. Four
maps were created showing changes from agricultural to residential use, agricultural to
commercial uses, forest to residential uses, and forest to commercial uses.
Quantifying Landscape Fragmentation
Fragstats software (http://www.umass.edu/landeco/research/fragstats/downloads/
fragstats_downloads.html) was used to determine the spatial patterns of agricultural and forested
lands. The spatial metrics calculated using Fragstats include class area, percentage of landscape,
number of patches, patch density, large patch index, total edge, edge density, and perimeter-area
fractal dimension. For a description of each of these spatial metric measurements see Table 3.
These subdivision cover maps were queried out for the areas with the highest and lowest
population growth. Fragmentation indices of the two subdivisions were then created using
Fragstats and compared between 1988 and 2008.
29
Results
Classification
Two maps of land cover classification were created using Idrisi, one for 1988 and one for
2008. Both maps are shown below.
Figure 1: Land use/Cover Kenosha County 1988. Derived from 30m resolution TM satellite
images in Idrisi.
30
Figure 2: Land use/Cover Kenosha County 2008. Derived from 30m resolution TM satellite
images in Idrisi.
Shown below is a signature comparison from the training samples used to make Figure 1
(Figure 3). Agricultural and residential signatures have almost exactly the same signature means.
While water, wetlands and forest all had similar signatures for bands 1-3, band four had separate
signature means. Commercial is identifiable as having a spectrally dissimilar mean to all other
categories. Also shown below is a signature comparison from the training samples used to make
Figure 2 (Figure 4). Agricultural and residential categories have spectrally similar signatures in
31
all four bands much like those for 1988. The signature for the commercial class is less
Reflectance
distinguished from the other classes than that for the 1988 map.
1
2
4
3
Bands
Reflectance
Figure 3: Mean Signature Comparison Chart for 1988 Land Use/Cover Map
1
2
Bands
3
4
Figure 4: Mean Signature Comparison Chart for 2008 Land Use/Cover Map
32
Accuracy Assessment
Classifications for both years were assessed at 75% accuracy. Error matrices were
created through the accuracy assessment performed (Table 4 and 5). The four main categories
being examined for this thesis are shown as having 90% (agricultural), 60% (forested), 90%
(residential), and 80% (commercial) accuracy (Table 4). The same categories are shown with 90%
(agricultural), 80% (forested), 90% (residential), and 50% (commercial) accuracy for the 2008
map (Table 5). Wetlands had the lowest class accuracy for both time periods and water was
consistently the most accurate at 100% both years (Tables 4 and 5).
Google Image →
Classfication
↓
Water Agricultural Wetlands Forest Residential Commercial Sum of Category Accuracy of Class
Water
10
0
0
0
0
0
10
100%
Agricultural
0
9
0
0
0
1
10
90%
Wetlands
0
5
5
0
0
0
10
50%
Forest
0
4
0
6
0
0
10
60%
Residential
0
0
0
0
9
1
10
90%
Commercial
0
2
0
0
0
8
10
80%
Table 4: Error Matrix/Accuracy Assessment of 1988 Classification
Google Image →
Classfication
↓
Water Agricultural Wetlands Forest Residential Commercial Sum of Category Accuracy of Class
Water
10
0
0
0
0
0
10
100%
Agricultural
0
9
0
1
0
0
10
90%
Wetlands
0
4
4
2
0
0
10
40%
Forest
0
0
2
8
0
0
10
80%
Residential
0
1
0
0
9
0
10
90%
Commercial
0
3
0
0
2
5
10
50%
Table 5: Error Matrix/Accuracy Assessment of 2008 Classification
33
Spatial Trends
From the classified images of 1988 and 2008 four maps were created with Idrisi’s Land
Change Modeler Spatial Trend of Change function to show the conversion of both agricultural
and forested land to residential and commercial cover/use. Figure 5 shows the change from
agricultural to residential. The areas with the most change were located in the Village of Pleasant
Prairie, the central part of the City of Kenosha, and the eastern side of the Town of Somers.
Along the lake downtown Kenosha is where the least amount of change was found. Figure 6
shows the conversion of agricultural land to commercial cover/use. Again, the areas with the
most change are in Pleasant Prairie, Kenosha and part of Somers.
Figure 5: Trend of Change from Agricultural to Residential Land Use/Cover 1988-2008.
34
Produced using Idrisi’s Land Change Modeler
Figure 6: Trend of Change from Agricultural to Commercial Land Use/Cover 1988-2008.
Produced using Idrisi’s Land Change Modeler.
Figure 7 shows the conversion trend of forested lands to residential uses. The area with
the most change between these two classes is in the middle of the county and is centered on
Paddock Lake. Other areas with a loss of forested land to residential uses include the southern
edge of the county mostly through Salem, the western edge near Genoa City, Randal and Twin
Lakes and the northeastern section of the Town of Somers. Figure 8 shows the conversion of
forested lands to commercial uses. Changes between these two categories are highest centered on
the western region of the City of Kenosha.
35
Figure 7: Trend of Change from Forest to Residential Land Use/Cover 1988-2008. Produced
using Idrisi’s Land Change Modeler.
36
Figure 8: Trend of Change from Forest to Commercial Land Use/Cover 1988-2008. Produced
using Idrisi’s Land Change Modeler.
After making the trend maps of changes shown above, the percent area of forested and
agricultural lands converted to residential and commercial were computed from the classified
maps. Table 6 shows that a lower percent of agricultural land was converted to residential or
commercial uses within a two mile buffer zone of I-94 at 8.96% than the county as a whole at
9.66%. This supports the rejection of my null hypotheses for the agricultural land in the buffer
zone along the interstate. Furthermore, there were only two out of six county subdivisions
sharing a border with Illinois that had larger percentages of agricultural land converted to
residential or commercial uses than the county as a whole. These two subdivisions are Genoa
37
City with a total change of 12.25% and Pleasant Prairie with one and a half times the total of the
county at 15.40%. This does not entirely allow the rejection of the null hypotheses for
subdivisions along the border with Illinois having a larger percentage of conversion away from
agriculture to residential and commercial uses. Additionally, a higher percentage of agricultural
lands were converted to residential uses than commercial in subdivisions along the border.
Location
Kenosha County
2 mi Buffer of I-94
Bristol
Genoa
Pleasant Prairie
Randall
Salem
Twin Lakes
Agricultural-Residential Agricultural-Commercial Total Change
7.06%
2.60%
9.66%
6.08%
2.88%
8.96%
5.78%
1.84%
7.62%
10.26%
1.99%
12.25%
11.08%
4.32%
15.40%
6.26%
1.83%
8.09%
6.82%
1.86%
8.68%
5.74%
1.50%
7.24%
Table 6: Changes in Area of Agricultural Land to Residential or Commercial Land Use/Cover
Changes in forest show some similar changes to those of agriculture. A smaller percent of
forested land was converted to residential or commercial uses along a two mile buffer of I-94
at .53% than the county as a whole at .78%. This lower percentage allows the rejection of the
null hypothesis about forested land conversion within the two mile buffer of the interstate.
Unlike the changes in agricultural conversions, there were three county subdivisions that
experienced higher proportions of change from forested land to residential and commercial uses.
These divisions were Bristol with .93% change, Salem with a .91% change, and Randall with the
highest change of forested land at 1.05%. These three counties allow the rejection of the null
hypothesis concerning forested land in counties bordering Illinois. Again, a higher percent of
forested lands were converted to residential uses than commercial uses in counties along the
Illinois border (Table 7).
38
Location
Kenosha County
2 mi Buffer of I-94
Bristol
Genoa
Pleasant Prairie
Randall
Salem
Twin Lakes
Forested-Residential
0.65%
0.40%
0.78%
0.31%
0.36%
0.96%
0.81%
0.63%
Forested-Commercial Total Change
0.13%
0.78%
0.14%
0.53%
0.15%
0.93%
0%
0.31%
0.12%
0.49%
0.09%
1.05%
0.10%
0.91%
0.04%
0.67%
Table 7: Changes in Area of Forested Land to Residential or Commercial Land Use/Cover
Fragmentation Results
To test the hypothesis that regions with greater population growth exhibited higher levels
of fragmentation the population change between 1988 and 2008 was determined. The City of
Kenosha was the subdivision with the highest population change. Genoa City had the lowest
population change but was not used due to its negligible size compared to the landscape being
studied. The Town of Paris, the subdivision with the second smallest population change, was
used instead (Table 8).
Location
1990 Population 2008 Population Change in Population
0
36
36
Paris
1482
1539
57
Brighton
1264
1554
290
Wheatland
3263
3569
306
Paddock Lake
2662
3138
476
Silver Lake
1801
2522
721
Randall
2395
3151
756
Bristol
3968
4928
960
Twin Lakes
3989
5752
1763
Somers
7861
9788
1927
Salem
7146
11691
4545
Pleasant Prairie
11998
19847
7849
Kenosha
80352
96950
16598
Genoa City
Table 8: Population Change of Kenosha County Subdivisions 1988-2008
39
The City of Kenosha’s agricultural lands experienced more fragmentation than the
agricultural lands of the Town of Paris which had less population growth. Paris had an increase
in its percent of agricultural lands of 8.3611% whereas the City of Kenosha lost 30.27% of its
agricultural lands. The patch density also decreased about .2 patches more in Kenosha than it did
in Paris making the overall landscape more fragmented in Kenosha. The largest patch also
increased for Paris whereas the City of Kenosha experienced a decrease in the largest patch
index showing that Kenosha, the subdivision with the highest population growth, exhibited a
trend towards a more fragmented agricultural landscape (Tables 9 and 11).
Forested land in the City of Kenosha experienced a trend towards a more fragmented
landscape than that of Paris as well. Again, Paris’s forested areas showed an increase in the
percent of land it occupied in the subdivision of 1.1266% compared to the City of Kenosha’s
decrease of about 8%. The patch density also decreased far more in Kenosha by 31.633 while
Paris had a smaller patch density decrease of -3.009. Paris’s largest patch index for forested land
increased by.029 while the City of Kenosha’s LPI decreased by .057 (Tables 10 and 12). Overall,
the fragmentation indices support the rejection of the corresponding null hypotheses by
suggesting that the subdivision with the highest population growth exhibited more fragmentation
of agricultural and forested land between 1988 and 2008 than those with the least population
growth.
40
CA
PLAND NP
PD
TE
ED
PAFRAC LPI
1988 4146.57 42.8251
1977 20.4182
2379240 245.7243 1.6348
1.2786
2008 849.24 12.5556
1184 17.5048
480780 71.0807
1.515
1.147
Change -3297.3
-30.27
-793 -2.9134 -1898460 -174.644 -0.1198 -0.1316
Table 9: City of Kenosha Agricultural Fragmentation Indices
CA
PLAND NP
PD
TE
ED
PAFRAC LPI
1988 882.09 9.1101
3312 34.2058
786210 81.1986
1.466
0.238
2008
73.62 1.0884
174 2.5725
48480
7.1675 1.3864
0.181
Change -808.47 -8.0217 -3138 -31.633
-737730 -74.0311 -0.0796
-0.057
Table 10: City of Kenosha Forest Fragmentation Indices
1988
2008
Change
CA
PLAND NP
PD
TE
ED
PAFRAC LPI
4136.67 44.4564 1061 11.4025 1505460 161.7903 1.5538 19.0465
4929.03 52.8175 852 9.1297 1299690 139.2696 1.5055 27.6832
792.36 8.3611 -209 -2.2728
-205770 -22.5207 -0.0483
8.6367
Table 11: Town of Paris Agricultural Fragmentation Indices
CA
1988
2008
Change
PLAND NP
PD
TE
ED
PAFRAC LPI
395.01 4.2451 766 8.2321
249840 26.8501 1.4817
0.6268
501.3 5.3717 488 5.2292
201060 21.5448 1.3709
0.6558
106.29 1.1266 -278 -3.0029
-48780 -5.3053 -0.1108
0.029
Table 12: Town of Paris Forest Fragmentation Indices
41
Discussion
While other studies have achieved accuracy in the 90th percentile, the maps produced in
this study had a fairly low accuracy of 75% as a result of many factors (Sun 2006). While the
satellite images were cloudless and came radiometrically and geometrically corrected from
USGS, there was a thin, white haze that appeared on the 1988 imagery. This may have caused
spectral confusion and led to misclassification in areas covered by the haze. Another challenge to
the classification of these images was a lack of definition in classes. More categories may have
aided in a more accurate classification. Example locations that may have caused confusion were
a golf course or other urban grassland areas. Due to a lack of ancillary data, the land uses/covers
of these areas may not have been properly classified. Golf courses were classified as commercial
areas during the accuracy assessment because of their use by the public. This exemplifies an
extreme case in which land cover and land use cannot be used interchangeably. Another limiting
factor in making a more accurate classification was a lack of aerial photographs from 1988.
Photographs available for the project only provided detailed coverage data for approximately a
fifth of the county on the eastern side. This limited the analysis of classes such as agriculture to a
smaller region where they may have been less prevalent or their classification may have not been
as accurate as other areas in the county.
The error matrices show rather large proportions of agricultural lands misclassified as
wetlands. This was a surprising result as the signature comparison charts show the two classes as
having distinct means from one another for both years. A reasonable explanation for this error is
based on the time of year the images were produced. April is a very wet time of the year and
may have caused agricultural fields to flood, causing the confusion. Another error that occurred
was a large portion of agricultural lands were classified as forest. This may have been due to
42
dark soil on farmland that was spectrally similar to the darker shades associated with the forest
areas. This may also be related to the time of year the images were taken. In April the leaves are
off deciduous trees which could have produced a spectral signature similar to the brown tones of
agricultural lands instead of a signature indicating a lot of living vegetation.
Unlike the unexpected classification errors, the spatial trends of change between classes
appear logical and follow the alternative hypotheses that residential and commercial uses are
spreading out towards the interstate and are edging nearer to the Illinois border. Downtown
Kenosha is already highly developed with businesses and residential areas and contained little
agricultural lands at the start of the study period as a result there was very little change shown for
the shift away from agriculture. The trend map of agricultural to residential land change
demonstrates a somewhat circular expansion of the city radiating from downtown. This expands
through the outer edges of the city of Kenosha, through Somers and into Pleasant Prairie. With
the development of the Kenosha Corridor Plan in 1992, Pleasant Prairie and the eastern ridge of
the City of Kenosha were slated to grow commercially and residentially. Commercial and
residential growth is centered on the areas affected by the Plan.
Figures 7 and 8 show the change of forested land to residential and commercial uses.
Similar to the changes in agricultural land, the western edge of the City of Kenosha and parts of
Pleasant Prairie were converted from forested land to commercial uses. Once more, that area
included parts of the Kenosha plan which emphasized commercial expansion. The largest change
from forest to residential use was centered on the Paddock Lake region. This region has
noticeable forest patches surrounding it according to the 1988 classification and has expanded as
a lake suburb community since then according to the 2008 image. Overall, Paris, the subdivision
with the lowest population growth, has seen little conversion of agricultural or forested land to
43
either residential or commercial uses. This follows the hypothesis that areas with lower
population growth would experience less change in farmland and forest land.
While the trend maps quite closely follow the hypothesis presented about location of
changes, the percentile comparisons of predicted areas of change produced a different result.
Neither year showed a greater amount of agricultural or forested land converted to commercial or
residential along the interstate buffer compared to the county as whole. This may have been a
result of only the southern portion of I-94 in the county becoming urbanized and the northern
portion remaining less farmland along the interstate. Even though the percent was not greater for
the buffer it was very similar for agricultural changes with the county being at 9.66% and the
buffer at 8.96%. Pleasant Prairie’s percent change of agricultural land was higher than the total
as hypothesized. This percent is also supported by the spatial trend of change maps presented.
Genoa City also experienced a greater change of agricultural lands to commercial and residential
compared to the county as a whole. A factor for this may have been the diminutive size of the
subdivision compared to the county as a whole which created a disproportionate amount of
change compared to the area of the subdivision.
Forested lands experienced very little change in the county and its subdivisions. This
overall trend of persistence for forested lands may be attributed to the 574 acres of managed
forest land within Kenosha County. The low percent change of forested land within two miles of
the interstate may be attributed to the riparian nature of the forest in this area. Randall is the
subdivision with the highest percent change of forested lands to residential and commercial uses.
This was expected due to its location along the Illinois border. The reason for this may be
attributed to possible residential growth of commuters from the Chicago area who wanted a more
rural area residence. Bristol and Salem also had higher percentages of forested land converted to
44
residential and commercial uses. This is shown by trend maps as well. However, Salem’s change
is focused near the Illinois border and Bristol’s change is focused towards the center of the
county.
In addition to trends of conversion of forested and agricultural land to residential and
commercial uses, the area with the most growth in the county has also experienced more
fragmentation of agricultural and forested uses. While this change is logical, the fragmentation
indices may have been skewed as a result of the confusion between forested and agricultural
lands in the classification as indicated by the error matrices.
Recommendations
Based on the classifications and analysis currently produced by this study it is
recommends that Kenosha County impose an urban boundary. Due to the curved transition of
urbanized lands in this study seen throughout Pleasant Prairie, the City of Kenosha and the Town
of Somers a more circular urban boundary imposed at the western extent of the City of Kenosha
would be the most fitting. In order to be effective throughout the county not only would an urban
boundary around the City of Kenosha and the currently urbanized areas of Pleasant Prairie and
Somers be necessary but urban boundaries around the lakes in the county should also be created.
Many forested and wetland areas that provide shelter to wildlife and enhance the natural
aesthetics of the land are located along lakes in the county. Limiting growth in and/or protecting
riparian forest areas would allow for continued recreation and beauty along the lakes as well as
provide a buffer zone against the existing pollutants from the currently urbanized areas. Urban
boundaries that encompass the City of Kenosha and surround the lakes would prevent any further
loss of forested land that could be used for recreation, animal habitats and providing stability of
45
the soil. These suggested boundaries would also decrease the loss of prime agricultural lands that
are of statewide and county importance.
Future Research
Due to the relatively low accuracy of the land use/cover classifications of this study,
further research is recommended. For higher accuracy, a study using a larger collection of aerial
photographs could be beneficial. Additional ancillary data for the county such as zoning maps
from the years being studied would provide a better idea of the likely land cover/use in the area.
In order to provide a more accurate classification, manual editing of the classifications in
addition to other ancillary data would provide better knowledge of the actual changes in
experienced in the county and their magnitude. A study conducted with the land use/cover in
Lake County IL, which is adjacent to Kenosha, would provide insight to why changes are
occurring within Kenosha as well.
46
Acknowledgements
I would first like to thank my academic advisor and professor Dr. Joy Mast and my
primary thesis advisor and professor Dr. Wenjie Sun for all of their guidance in completion of
this thesis. They have answered my questions and talked me through both my thesis and my
journey to graduate school. I would also like to thank all of the other professors in the Geography
and Earth Science department for their academic knowledge and support. I also owe many
thanks to Dr. Julio Rivera for teaching me GIS. Last but not least, thank you to all of my friends
who have supported me through the completion of this thesis.
47
Works Cited
Ali, Amal K. “Greenbelts to Contain Urban Growth in Ontario, Canada: Promises and Prospects.”
Planning, Practice, and Research 23, no. 4 (November 2008): 533-548.
Bachman, Dave. “Plant Closing Inevitable, But Shocking.” Kenosha News, December 31, 1988.
Pg 2-3.
Campbell, James B. Introduction to Remote Sensing. New York: The Guilford Press, 2008.
Carter-Whitney, Maureen. “Cinching Sprawl: Worldwide Experience With Greenbelts Can Help
Calgary Protect Its Near-Urban Lands.” Alternatives Journal 34, no. 3 (2008).
Cushman, Samuel A., Kevin McGarigal, Maile C. McNeel. “Parsimony in Landscape Metrics:
Strength, Universality, and Consistency.” Ecological Indicators (2008): 691-703.
Cronon, William. Nature’s Metropolis: Chicago and the Great West. New York: W. W. Norton,
1991.
Delbecq, Benoit A., and Raymond J.G.M. Florax. “Farmland Allocation Along the Rural-Urban
Gradient: The Impacts of Urbanization and Urban Sprawl.” Paper presented at the Agricultural
and Applied Economics Association July 25-27, 2010.
Department of City Development. “Corridor Land Use Plan.” (1991).
http://www.kenosha.org/departments/development/pdf/NP_plans/CorridorNP.pdf. (Accessed
October 28, 2010).
Department of Planning and Development. “Farmland Preservation Program.”
http://www.co.kenosha.wi.us/plandev/conservation/farmland.html (Accessed October 28, 2010).
Ewing, Reid H. “Characteristics, Causes, and Effects of Sprawl: A Literature Review.”
Environmental and Urban Studies Volume 21, Issue 2 (1994).
Ewing, Reid H, and Rolf Pendall, and Don Chen. “Measuring Sprawl and Its Impact.”
Galster, George, Royce Hanson, Michael R. Ratcliffe, Harold Wolman, Stephen Coleman, and
Jason Freihage. “Wrestling Sprawl to the Ground: Defining and Measuring and Elusive Concept.”
Housing Policy Debate Volume 12, Issue 4 (2001).
http://www2.gulfofmaine.org/ESIPPlanning/subcommittees/coastaldevelopment/CoasDevRefDo
cs/Wrestling%20Sprawl%20to%20the%20Ground%20Defining%20and%20Measuring%20an%
20Elusive%20Concept.pdf.
Hayden, Dolores. “Edge Nodes.” In Building Suburbia: Green Fields and Urban Growth, 18202000, Chapter 8. New York: Pantheon Books, 2003.
48
Hayden, Dolores. “Rural Fringes.” In Building Suburbia: Green Fields and Urban Growth,
1820-2000, Chapter 9. New York: Pantheon Books, 2003.
Jensen, Don. “Agriculture and Farming.” In Kenosha in the 20th Century, edited by John A.
Neuenschwander, 127-174. Kenosha, WI: Kenosha County Bicentennial Commission, 1976.
Keehn, Richard H. “Industry and Business.” In Kenosha in the 20th Century, edited by John A.
Neuenschwander, 175-222. Kenosha, WI: Kenosha County Bicentennial Commission, 1976.
McGarigal, K. SA Cushman, and McNeel, E Ene. “FRAGSTATS: Spatial Pattern Analysis
Program for Categorical Maps.” Computer software program produced by the authors at the
University of Massachusetts, Amherst. Available at:
http://www.umass.edu/landeco/research/fragstats/fragstats.html
Meine, Curt. “The View From Man Mound.” In The Vanishing Present: Wisconsin’s Changing
Lands, Water, and Wildlife, Chapter 7. Chicago: University of Chicago Press, 2008.
Moran, Patrick E. “Kenosha: City on the Grow.” PhD diss, Carthage College, 1991.
NOAA National Severe Storms Laboratory. “Historical Weather Data Archives.”
http://data.nssl.noaa.gov/dataselect/nssl_options.php?outputtype=list (Accessed December 15,
2010).
Popenoe, D. “Urban Sprawl-Some Neglected Sociological Considerations.” Sociology and Social
Research 63 (1979), no. 2, 255-268.
Priest, D. et al. “Large-Scale Development: Benefits, Constraints, and State and Local Policy
Incentives.” Urban Land Institute (1997), 37-45.
Rogers, David, Thomas P. Rooney, and Rich Henderson. “From the Prairie-Forest Mosaic to the
Forest: Dynamics of Southern Wisconsin Woodlands.” In The Vanishing Present: Wisconsin’s
Changing Lands, Water, and Wildlife, Chapter 7. Chicago: University of Chicago Press, 2008.
Ross, Nelson Peter. “Architecture, Planning, and Transportation.” In Kenosha in the 20th Century,
edited by John A. Neuenschwander, 387-500. Kenosha, WI: Kenosha County Bicentennial
Commission, 1976.
Seto, K. C., C. E. Woodcock, C. Song, X. Huang, J. Lus, and R.K. Kaufmann. “Monitoring
land-use change in the Pearl River Delta using Landsat TM.” International Journal Remote
Sensing Vol 23, No 10 (2002): 1985-2004.
Sun, Wenjie. “A GIS-based integrated approach to explore land-use/cover change dynamics in
south-central Indiana.” PhD diss., Indiana University, 2006.
49
The City of Kenosha Comprehensive Plan. (2010).
http://www.kenosha.org/departments/development/pdf/COMP_plan/CH-3-AGRICULTURE.pdf
(Accessed October 28, 2010).
United States Census Bureau. “Detailed Tables-American Factfinder.”
http://factfinder.census.gov/servlet/DTTable?_bm=y&-context=dt&ds_name=PEP_2008_EST&-mt_name=PEP_2008_EST_G2008_T001&-CONTEXT=dt&tree_id=808&-all_geo_types=N&-geo_id=05000US55059&-search_results=01000US&format=&-_lang=en (Accessed December 10, 2010).
United States Department of Agriculture. “2007 Census of Agriculture County Profile: Kenosha
County Wisconsin.”
http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/County_Profiles/Wisconsin
/cp55059.pdf (Accessed December 10, 2010).
United States Geological Survey. “USGS Archives and Available Scenes.”
http://landsat.usgs.gov/USGS_Archive_and_Available_Scenes.php (Accessed December 15,
2010).
U.S Fish and Wildlife Service. “Wetlands Mapper.” National Wetlands Inventory.
http://www.fws.gov/wetlands/Data/ Mapper.html (Accessed December 10, 2010).
Waller, Donald M., and Thomas P. Rooney. “Assembling the Puzzle.” In The Vanishing Present:
Wisconsin’s Changing Lands, Water, and Wildlife, 6. Chicago: University of Chicago Press,
2008.
Wang, Yeqiao, and Debra K Moskovits. “Tracking Fragmentation of Natural Communities and
Changes in Land Cover: Applications of Landsat Data for Conservation in an Urban Landscape
(Chicago Wilderness).” Conservation Biology Vol 15, No 4 (2001): 835-843.
Wei-feng, Li, Ouyang Zhi-yun, and Wang Ru-song. “Land Potential Evaluation for Large-Scale
Greenbelt Development at Urban-Rural Transition Zone: A Case Study of Beijing China.”
http://www.isprs.org/proceedings/XXXVI/8-W27/li.pdf. (Accessed October 28, 2010).
Wisconsin’s Working Land Initiative.
http://www.co.kenosha.wi.us/plandev/conservation/farmland.html. (Accessed October 28, 2010).
Wiscpark LLC. “Lakeview Corporate Park.” http://www.wispark.com/parks/lakeview_park.htm.
(Accessed October 28, 2010).
50