forecasting and assessing the impact of urban sprawl in...

Lakes & Reservoirs: Research and Management

2002

7

: 271–285

Forecasting and assessing the impact of urban sprawl in coastal watersheds along eastern Lake Michigan

Bryan C. Pijanowski,

1*

Bradley Shellito,

2

Snehal Pithadia

3

and Konstantinos Alexandridis

4

1

Basic Science and Remote Sensing Initiative, and Departments of

2

Geography,

3

Electrical and Computer Engineering and

4

Agricultural Economics, Michigan State University, East Lansing, Michigan, USA

Abstract

The Land Transformation Model (LTM), which has been developed to forecast urban-use changes in a grid-basedgeographical information system, was used to explore the consequences of future urban changes to the years 2020 and 2040using non-urban sprawl and urban-sprawl trends. The model was executed over a large area containing nine of the majorcoastal watersheds of eastern Lake Michigan. We found that the Black-Macatawa and Lower Grand watersheds willexperience the most urban change in the next 20–40 years. These changes will likely impact the hydrological budget, mightreduce the amount of nitrogen exported to these watersheds, result in a significant loss of prime agricultural land and reducethe amount of forest cover along the streams in many of these watersheds. The results of this work have significantimplications to the Lake Michigan Lake Area Management Plan (LaMP) that was recently developed by the United StatesEnvironmental Protection Agency.

Key words

coastal watersheds, ecological assessment, Lake Michigan, land-use change, prime farmland, riparian zones,

urban sprawl.

INTRODUCTION

The Land Transformation Model (LTM; Pijanowski

et al

.1997; Pijanowski

et al

. 2000; Pijanowski

et al

. 2002) hasbeen developed to simulate land-use change in a variety oflocations around the world. The LTM uses populationgrowth, transportation factors, proximity or density ofimportant landscape features (such as rivers, lakes, recre-ational sites and high-quality vantage points) as inputs tomodel land-use change. The model relies on GeographicInformation Systems (GIS), Artificial Neural Network(ANN) routines, remote sensing and customized geospatialtools, and can be used to help understand what factors aremost important to land-use change. Information derivedfrom an historical analysis of land-use change can be usedto conduct forecasting studies (Pijanowski

et al

. 2002).

Land-use data from remote sensing is used for model inputsand calibration routines.

Artificial Neural Networks are powerful tools that use amachine learning approach to numerically solve relation-ships between inputs and outputs. They are used in avariety of disciplines, such as economics (Fishman

et al

.1991), medicine (Babaian

et al

. 1997), landscape classifi-cation (Brown

et al

. 1998), mechanical engineering (Kuo &Cohen 1999) and remote sensing (Atkinson & Tatnall1997). The use of neural networks has increased sub-stantially over the last several years because of theadvances in computing performance (Skapura 1996) and ofthe increased availability of powerful and flexible ANNsoftware. The GIS is a powerful spatial data managementand analysis tool that can be used to perform spatial-temporal analysis and modelling.

Recently, the US Census Bureau released its year 2000population census for the entire US. Of the 14 counties inMichigan that experienced a 10 000-person or greaterincrease in population, seven (50%) are located in countiesalong the Great Lakes’ coast. Four (Kent, Grand Traverse,Ottawa and Allegan) of these seven are located

�

50 kmfrom Lake Michigan.

*Corresponding author. Email: [email protected]

Present address: Department of Zoology, 203 Natural

Science Building, Michigan State University, East Lansing,

Michigan 48824, USA.

Accepted for publication 24 June 2002.

272 B. C. Pijanowski

et al

.

Also in 2000, the US Environmental Protection Agency(United States Environmental Protection Agency 2000)issued a report updating its Lake Michigan LakewideManagement Plan (LaMP). This report outlined six majorgoals aimed towards improving the quality of the LakeMichigan ecosystem. One of these goals was to ‘make landuse, recreation, and economic activities sustainable andsupportive of a healthy ecosystem’. The Lake MichiganLaMP seeks to develop a systematic and comprehensiveecosystem approach that attempts to protect the sustain-ability of ‘open lake water and the watersheds that comprisethe lake basin’. One such approach is to develop indicatorsof ecosystem health that can be applied lake basin wide.

The purposes of this paper is to: (i) present our approachof the modelling of land-use change using the LTM alongLake Michigan’s coastal watersheds; (ii) summarize theresults of the model executions; (iii) apply several eco-logical

assessment

metrics

to

past,

current

and

futureland-use changes along Lake Michigan’s coastal water-sheds; and (iv) discuss the implications of the model resultsin the context of the Lake Michigan LaMP.

BACKGROUNDArtificial neural networks

Artificial Neural Networks are computer simulation toolsdesigned to emulate the functionality of mammalianneurones

(Rosenblatt

1958).

They

typically

consist

ofmany hundreds of simple processing units that are wiredtogether like synapses in a complex communication net-work. Each unit or node is a simplified model of a realneurone that fires (sends off a new signal) if it receives asufficiently strong input signal from the other nodes towhich it is connected. The strength of these connectionsmight be varied in order for the network to performdifferent tasks corresponding to different patterns of nodefiring activity. The ANNs are specified by a variety offactors, such as network topology, node functions, trainingalgorithms and learning rules (Principe

et al

. 2000).There are several benefits to using neural networks over

using alternative approaches such as mathematical modelsthat have explicit equations. First, neural nets possess theability to perform well on data that they have not seenbefore. This feature of neural nets is referred to as‘generalization’. A second possible benefit of using neuralnets over a mathematical model is that neural nets are notadversely affected by errors in the original data and, there-fore, might not require data to be free of errors. Therefore,data sets created from imperfect methods, such as theclassification of land use from satellite remote sensingwhere all locations might not have the correct land-use

assignment for use, might not overly affect the outcomes ofa neural network model.

Neural nets are also very useful if state equations havenot been developed that describe the relationship that isbeing modelled. This is usually the case in systems thathave strong anthropogenic factors acting in complex ways,such as in the process of land-use change. Because neuralnets are non-linear systems, it is likely that a neural net willreach a solution numerically that adequately describes therelationship between input and output.

Land-transformation model

Detailed descriptions of the LTM can be found elsewhere(Pijanowski

et al

. 2000; Pijanowski

et al

. 2002). Briefly, theLTM modelling process includes the following procedures.

We first process the inputs to the model using a series ofbase layers stored and managed within a GIS. These baselayers store information about land uses (such as agri-culture parcels and urban areas) or features in thelandscape

(e.g.

roads,

rivers

and

lakeshores).

Grid

cellsare coded to represent drivers as either binary(presence = ‘1’ or absence = ‘0’) or continuous variables(e.g. elevation) depending on the type of attribute.Figure 1(a) shows a map of example driver cells forhighways where the presence of a highway is coded in theGIS as a ‘1’ (black) and the absence as a ‘0’ (white).

Next, we use the GIS to apply spatial transition rules thatquantify the spatial effects that driver cells have on land-usetransitions (Pijanowski

et al

. 2002). We use four classes oftransition rules: (i) neighbourhoods or densities; (ii) patchsize; (iii) site specific characteristics; and (iv) distance fromthe location of the nearest predictor cell. Neighbourhoodeffects are based on the premise that the composition ofsurrounding cells has an effect on the tendency of a centralcell to transition to another use. Patch sizes relate the

Fig. 1.

An example of how the Land Transformation Model

spatially codes drivers (a) and spatial-interaction values (b).

Urban sprawl along Lake Michigan 273

variable values of all cells within a defined patch (e.g.parcel) to likelihood of land-use transition. Site-specificcharacteristics are values assigned to a cell based on abiophysical or social characteristic that is specific to eachgrid cell. An example site-specific characteristic is the loca-tion of high-quality views. The distance spatial transitionrule relates the effect of the Euclidean distance betweeneach cell and the closest driving variable. Figure 1(b)shows a map of distance to highway driving variablecreated using the GIS on driving cells from Fig. 1(a).

Certain locations are coded so that they do not undergotransitions. This is necessary for areas within whichdevelopment is prohibited, such as public lands. We codecells with a ‘1’ if a transition cannot occur; all other locationsare assigned a ‘0’. All such layers are then multipliedtogether to generate one single layer that we call the‘exclusionary zone’.

In our next step, we integrate all driving variables(Pijanowski

et al

. 2002) using one of three differentintegration

methods:

(i)

multicriteria

evaluation

(MCE);(ii) ANN; and (iii) logistic regression (LR). Each integ-ration procedure

requires

a

different

type

of

datanormalization.

With

all

of

the

integration

methods,

thecell size (e.g. 100

�

100 m in the present analysis) andanalysis window are set to a fixed base layer. The outputfrom this step is a map of ‘change likelihood values’ thatspecifies the relative likelihood of change for each cell

based on the result given for the cell’s vector of predictorvariable values.

In the final step, which is called ‘temporal indexing’, theamount of land that is expected to transition to urban overa given time period is determined using a ‘principle indexdriver’ (PID) (Pijanowski

et al

. 2002). This can be thoughtof as the ‘demand function’. Most often, a PID is based onpopulation forecasts developed from a demographic oreconomical analysis scaled to the amount of urban land inuse. Such per capita requirements for land (i.e. the numberof hectares of developed land per person) are calculatedsuch that the total amount of new urban land at a later timeis as shown in equation 1:

dP

i

U

i

(t) =

(

–––––

)

* A

i

(t) (1)

dt

i

where U is the amount of new urban land required in thetime interval t, i is the unit (e.g. county) of for which thepopulation statistics are available, P is the population size inany given area in a given time interval and A is the percapita requirements for urban land.

The LTM creates an output that assigns locations with a‘1’ if it is projected to transition to urban and a ‘0’ if no suchtransition is expected. The PID is used to determine thenumber of cells that need to transition to urban. Projectionsare made by selecting the appropriate number of cells inpriority order, which is based on the change likelihood.

Fig. 2.

Hydrology of the study area shown in relationship to the entire Great Lakes’ Basin (a). The nine major watersheds (b) are also shown.

The study area’s major streams and rivers are shown in part c.


et al

.

METHODSStudy-area description

Nine coastal watersheds (Fig. 2) located along the easternshores of Lake Michigan were selected for this study.These nine watersheds correspond to the US GeologicalSurvey’s 1:250 000 hydrological unit codes (HUC). Thesenine watersheds are located in 39 of Michigan’s 83counties. Located in the coastal watersheds are the majorcities of Grand Rapids, Kalamazoo, Battle Creek, Holland,Muskegon, Traverse City, Charlevoix and Petoskey. Thesewatersheds encompass approximately 29% of the entireland area for the state of Michigan (including the UpperPeninsula).

We used the Michigan Resource Information System(MiRIS) land-use database, which was developed by theMichigan Department of Natural Resources. This databasecontains land use/cover to Anderson Level III (Anderson

et al

. 1976) for areas larger than 2.5 acres. Land use/coverwas interpreted from 1:24 000 aerial photography from1978. Many Michigan counties have updated their MiRISland-use databases. Landscape features were also derived

from the MiRIS line database. Each of these layers, such asrivers, roads, railroads and political boundaries wereextracted by county and joined using the Arc/Info 8.0 GIS(Environmental Systems Research Institute 1999). Loca-tions of public lands, which are assumed not to undergodevelopment, were acquired from the Michigan Depart-ment of Natural Resources Land and Water ManagementDivision. Population data by minor civil division (MCD) foreach of the 39 counties were obtained from the MichiganInformation Center (MIC) web site and joined to theMichigan

MCD

GIS

database.

All

data

were

convertedinto an Albers Equal Area projection for modelling.

Figure 3 shows a map of the study area and its land usein 1978. Areas in red denote urban (residential, com-mercial, retail, industrial), yellow denotes agriculture,green denotes forest, blue is open water and brown iswetlands. In 1978, over a third (34%) of the area wasagriculture, nearly 44% was forest and slightly less than 5.6%was in urban. Other land uses in the study area in 1978include open water (3%), wetlands (4%) and barren/dunes(<1%).

Fig. 3.

The distribution of land uses in 1980 across the study area (a) and in the largest populated area around Grand Rapids (b).


Urbanization trend analysis

We conducted an analysis of the relationship betweenurban growth and population change to parameterize ourmodel. Changes in the amount of urban area in 1978 (ascontained in the MiRIS land-use database) were comparedto land-use databases from 17 counties that updated theirland-use GIS data. Seventeen counties, including eightlocated in the current study, were included in this analysis.Between this 17-year (1978–1995) period, the amount ofurban in these counties increased from 474 574 ha to595 321 ha, representing a 25.22% increase in urban. Duringthis same time period, the total population of these 17counties increased from 6 128 158 to 63 047 000 people, a2.88% increase in population. This ratio, which we call the

urban expansion index, represents an 8.76-fold increase ofurban usage in relation to the population increase. Thislarge ratio of urban increase to population increase is likelydue to: (i) a large exodus of residents from inner cities tothe suburbs and more rural locales; and (ii) larger parcelsfor residential use, especially in the more rural commu-nities (Wyckoff 2002).

The Planning and Zoning Center (Rusk 1999), Lansing,Michigan, conducted an independent analysis of the pro-portion of urban increase to population increase and foundthat many areas in Michigan undergo different rates ofurban expansion to population expansion. They found thatthe Ann Arbor metropolitan area, which is located outsideour study area, had the lowest urban expansion index in thestate at 2.3.

We examined the consequences of future urban expan-sions using the LTM applying an urban expansion rate of2.3 (which we call the ‘non-sprawl’ scenario) and a secondcondition that assumes the statewide average trend of 8.76.We consider the latter trend as an ‘urban sprawl’ scenario.The model was executed using a 1978 (hereafter asreferred to as 1980) as a baseline and forecasting to 1995,2020 and 2040. We also compare here ‘sprawl’ versus ‘non-sprawl’ growth scenarios for 2020 and 2040.

Parameterization of the land-transformation model

We created a ‘base’ GIS layer that was used to register allspatial calculations. This base layer contained cells thatwere 100

�

100 m (1 ha) in size in a grid with 4503 rows

Fig. 4.

The percentage of urban use in the study area over the

temporal periods and between the baseline and sprawl conditions.

(–

�

–) Non-sprawl, (–

�

–) sprawl.

Fig. 5.

The amount of urban (%) in the nine major Michigan watersheds of Lake Michigan. Both urbanization trends, non-sprawl and sprawl

are shown.


et al

.

and 1713 columns. There were 4 254 077 active cells in thisgrid. Note that some of the cells were located in the grid butwere inactive because they fell outside the coastal water-shed study area. The following driving variables weredeveloped statewide (the area was reduced for this currentanalysis).

Distance to transportation

Previous studies (Pijanowski

et al

. 2002) have found astrong correlation between the proximity to county roadand future urban development. We used the GIS tocalculate the distance each cell is from the nearest countyroad. In addition, we also calculated the distance each wasfrom the nearest highway and highway interchange.

Proximity to amenities

We also calculated the distance each cell was from thenearest urban cell (as a surrogate to proximity to urbanservices), the distance from lakes and rivers (high demanddevelopment locations) and to the nearest Great Lakes’shoreline.

Exclusionary zone

Areas that cannot be developed were aggregated into oneGIS layer and used to prohibit the model from assigningland-use change probabilities to these locations. All publiclands, previous urban (including transportation) and loca-tions of surface water were assigned to the exclusionaryzone.

Population forecasts

Decadal population total census data by MCDs wereobtained from MSU’s Center for Remote Sensing. Decadaltotal population for 1950–1990 were used in Microsoft Excel2000 trend analyser to extrapolate total population for eachMCD to the year 2000, 2020 and 2040. Statewide populationtotals for the forecasts were then compared to the USCensus Bureau’s Michigan statewide population forecasts,and then adjusted evenly across all MCDs to matchstatewide forecast numbers (decreased our totals for 2020by 8% and our 2040 totals by 12%). In addition, the amountof urban per MCD was calculated using equation 1 so thata per capita urban use per unit (i.e. MCD) was derived. This

Fig. 6.

A map of small watershed basins and the percent composed of urban. (

�

) 0–15%, ( ) 15–50%, ( ) 50–100%).


per capita urban use requirement value was then used toadjust the urban-use demand for future years based onpopulation forecasts. We calculated the amount of futureurban by using the 2.3 and 8.76 ration for urban useincrease to population increase, for non-sprawl and sprawlscenarios, respectively.

Neural network

Driving

variables

stored

in

the

GIS

were written toASCII grid representations and then converted to atabular format such that each location contained itsspatial configuration value (i.e. each location was aninput vector into the neural net) from each drivingvariable grid. This reformatting to a tabular arrangementwas necessary for input to the neural net software. Theneural net model was based on that of Pijanowski et al.(2002). Details of how neural net operate is found inAppendix 1 of this paper for readers requiring additionaltechnical information.

Ecological assessment of urbanizationThe US Environmental Protection Agency has embarkedon a series of pilot projects in the Mid-Atlantic region of theUS that attempt to develop landscape-pattern metrics thatcan be used to assess the ecological quality of watershedsand communities. A recent study conducted by Jones(1997) examined how over 20 different landscape metricscould be used to assess the ecological condition of water-sheds within the region. We selected several of thesemetrics for use in this current study and examined howthey changed over time and across the two urbanizationtrends.

Percent urban in watershedThe total amount of all urban, including residential, retailand industrial, was calculated for each watershed and thendivided by the watershed size. As watersheds become moreurban, hydrological properties are expected to change.(Boutt et al. 2001) More urbanization will result in more

Fig. 7. Nitrogen loading (kg/ha/year) for each of the small watershed basins in the study area. (�) 0–25, ( ) 26–50, ( ) 51–75, ( ) 76–100,

(�) >100.

278 B. C. Pijanowski et al.

surface run-off, less recharge into groundwater and morewater evaporated back into the atmosphere (Leopold 1973).

Human-use indexHumans generally use the land for two primary purposes:for housing and to grow food. These uses generally nega-tively impact wildlife populations. The US EnvironmentalProtection Agency has developed a land-use metric thatquantifies the amount of urban or agriculture within a fixedneighbourhood window. We used this approach as part ofour ecological assessment using the same window sizeproposed by Jones (1997), storing the number of cells thatwere urban or agriculture within a 250 � 250 m neighbour-hood window.

Amount of forest along streamsForested riparian zones are well known to reduce sedimen-tation and nutrient loadings to streams as well as providinghabitat for important plant and animals. We calculated theamount of forest cover buffering streams in all watershedsand divided this by the total length of all streams in thatwatershed. We examined the proportion of streamsbuffered by forests across the nine major watersheds inthis study area.

Nitrogen loading from usesAgriculture and urban uses might have negative impacts onstream-water quality by increasing the amount of nutrientsinto surface- and groundwaters (Daniel et al. 1998).Sources of these nutrients include fertilizers and human/animal waste. Following guidelines by Jones (1997), weused a nitrogen export coefficient adopted by the USEnvironmental Protection Agency (Young et al. 1996) toestimate the amount of nitrogen that might result from allland uses in each minor watershed. This total was thenadjusted to account for each watershed’s size and reportedin kg/ha/year.

Loss of prime farmlandA large proportion of Michigan’s southern LowerPeninsula contains soils suitable for many importantcrops, such as corn, soybeans and wheat. We used theMichigan Comprehensive Resource Inventory andEvaluation System’s GIS database containing the statusof prime agricultural soils. This information, stored in a1-km grid database, codes the proportion of soils in alarge survey area that is prime farmland. We extractedfrom the database all areas that contained more than 80%of the area with prime farmland. These areas were then

Fig. 8. The human-use index (a) for 1980 and 2020 sprawl scenarios. A comparison of 1980–2040 non-sprawl and 1980 and 2040 sprawl (b).


examined in relation to locations of current and futureurban areas.

Distance of urban from the Great Lakes’ shoreline

We calculated the distance each urban cell is from thenearest Lake Michigan shoreline. In many areas in theGreat Lakes’ Basin, the most rapid urban development isoccurring along coastal environments. Increasing thepopulation density of people along the shoreline is likelyto increase negative anthropogenic impacts on the GreatLakes’ ecosystem. We plotted the amount of urban for eachtime period and urbanization trend at 1-km intervals fromthe shoreline.

RESULTSIn 1980, over 5% of the study area was composed of urban(Fig. 4). By 1995, we estimate that over 7% of the study areawill be urban. Population of the area is expected to increaseby 10% between 1995 and the year 2020. Scaling the amountof urban use as a ratio that is the same as 1980 will result inurban taking up as much as 8% of the nine watersheds. Bythe year 2040, using the same ratio as the 1980 populationper capita urban use, nearly 10% of the study area will becomposed of urban. However, if we assume that the amountof urban being used per person is the same as that in 1995,then over 12% of the study area will be urban by 2020 andover 21% of the study area will be urban by the year 2040.This latter value represents a quadrupling of the amount ofurban with only a 25% increase in population.

The percentage of urban usage per watershed over themodelling time frame and with and without sprawl isshown in Fig. 5. Nearly all watersheds begin in 1980 withless than 10% urban; one, the Manistee Watershed, whichis composed mostly of state forest land, is less than 3%urban. By 1995, we estimate that three watersheds(Kalamazoo, Lower Grand and the Black-Macatawa) willexceed 10% urban use in total area. By the year 2040,assuming constant urbanization rates, these same threewatersheds will be over 15% urban. In contrast, if weassume a sprawl scenario, by the year 2020, six of the ninewill be composed of 10% or more urban use; one (the Black-Macatawa), will be over 33% urban. By the year 2040, againassuming sprawl ratios, all but one will be more than 10%urban; the Black-Macatawa watershed will be nearly 44%urban.

We used the GIS to calculate the percentage of urban usewithin each of the smaller watersheds in order to examineimpacts at smaller spatial scales (Fig. 6). Note that in 1980,only a few watersheds contained 50% or more urban. Thesewatersheds represented the larger cities (e.g. GrandRapids, Kalamazoo) that are located in the study area.Assuming a constant rate of urbanization, by the year 2040,more than twice the number of small watersheds willcontain �50% urban. Several of these will be located alongthe coast of Lake Michigan. However, assuming a sprawltrend, by the year 2040, a majority of the coastal watershedslocated in the southern half of the Lake Michigan studyarea will contain �50% urban. In addition, the cities ofGrand Rapids and Kalamazoo will also have their

Fig. 9. The amount of prime

farmland (a) replaced by urban use

for 1980, 2040 non-sprawl and

2040 sprawl scenarios. (b) Distri-

bution of the prime farmland is also

shown.


neighbouring watersheds have increased amounts ofurban.

Estimates of nitrogen export, derived from the USEnvironmental Protection Agency’s nitrogen export land-use based coefficient model, shows that increasing theamount of urban will likely result in reduced nitrogenloadings to the study area’s watersheds (Fig. 7). This isprobably a result of the loss of agricultural lands in the areathat contribute more nitrogen to the land than does anyother land use.

The human use index, which is designed to assess theamount of human use of the land for agriculture and urbanacross scales, is shown in Fig. 8(a). In 1980, a majority of

the study area is light coloured, reflecting the low level ofhuman use of the land. In contrast, a large portion of theBlack-Macatawa watershed is coloured dark, indicatingthat its use is mostly urban or agriculture. Still, largeportions of the northern area remain light coloured. Theseareas, which are state forested lands, are assumed toremain as forests.

Figure 8(b) shows two different human-use indexchange calculations that were made between 1980 and thenon-sprawl trend to 2040 and 1980 and the sprawl trends to2040. The 2040 sprawl trends clearly show a much greaterincrease in human use along the coastline of LakeMichigan.

Fig. 10. Percentage of major river

banks composed of forest by major

watershed.

Fig. 11. The amount of urban

(ha) as a function of distance from

the Lake Michigan shoreline.


Figure 9(a) shows the locations of prime farmland in thestudy area. In 1980, slightly more than 2% of the entirestudy area’s prime agricultural land was urbanized(Fig. 9b). By 2040, we estimate that nearly 4% of this area(almost a doubling) of the prime farmland will be convertedto urban. Assuming a sprawl trend, the LTM estimates that8% of the study area’s prime farmland will be lost to urbandevelopment. In addition to this loss, many areas thatsupport specialized crops, such as fruits and vegetables,which are currently not considered prime farmland, mightalso be lost. These specialized crops (e.g. apples, blue-berries) are high income crops.

The percentage of the length of all streams in eachwatershed that are buffered by forests (e.g. that contain ariparian zone) is shown in Fig. 10. Many of the watershedsshould experience relatively small changes in the streamcover composition. Only one (Black-Macatawa), will haveless than 30% of its stream length composed of non-forestcover given a sprawl trend to the year 2020 and 2040.Manistee, because it is located in a state forest, will havenearly 80% of all stream banks buffered by forest.

The amount of urban use, expressed in total hectares, asa function of distance from Lake Michigan, is illustrated inFig. 12. Note that even in 1980, there was more urbanlocated within 10 km of the Lake Michigan shore than anyother distance from lakeshore category. In addition, thegreatest differential impact of urban sprawl occurs between20 and 30 km from the shoreline.

DISCUSSIONWe used the GIS and ANN LTM to forecast urban use tothe years 2020 and 2040 using two trends: the most his-torically conservative urban to population expansion ratio,2.3, we called the ‘non-sprawl’ scenario, and a statewideaverage urban to population expansion ration of 8.7. In thefirst trend, we estimate that the entire study area, com-

posed of the none of the major coastal watersheds ofeastern Lake Michigan, will be composed of 8.2 and 9.6%urban in the years 2020 and 2040, respectively. For thesprawl scenario, we estimate that the study area will becomposed of 13.2 and 20.4% urban in 2020 and 2040,respectively.

Of the nine major watersheds in the study area, under anon-sprawl scenario, the Lower Grand watershed wouldcontain the greatest proportion of urban use. However,under a sprawl scenario, the Black-Macatawa and theKalamazoo watersheds would contain the greatest pro-portions of urban. The Black-Macatawa watershed could beas much as 40% urban by the year 2040. A majority of thisgrowth will occur along the small coastal watershedcommunities in south-western Michigan.

The results of the LTM simulations also suggest that theBlack-Macatawa watershed could experience the greatestloss of forested riparian cover. This is important becausethis watershed currently contains the least amount of forestcover along its streams.

There is also the potential for a significant loss of primefarmland in this study area. We estimate that there could bea loss that is proportional to the amount of urban growth.Although it is possible for there to be greater loss of primefarmland because significant urban development will notoccur over large areas of prime farmland, more than 8% ofthe study area’s prime farmland could be lost by the year2040 under a sprawl scenario. This loss is irreversible.

Another significant finding of our work is that many ofthe small coastal watersheds in this study area will be morethan 50% urban by 2040 assuming a sprawl scenario. Thus,inputs from the urban environment are likely to have thelargest impact along the Lake Michigan shoreline.

We also found that the Human Use Index proposed byJones (1997) is useful to visualize large-scale and long-termimpacts of urban changes. The original work by Joneswas conducted on static digital land-use maps. We showthat it is also a useful metric for temporal sequences ofchanges.

Finally, we were able to show that one possible benefit offuture urbanization trends might be reduced nitrogenloadings into the surface- and groundwater systems. Thiswould result from a large loss of agricultural land, especi-ally in the southern portion of our study area. However, ifour simple nitrogen-loading model underestimates theamount of fertilizer being applied to urban areas, then thisbenefit might not result from the protected urbanization.

Policy implications of the LTM forecastsThe purpose of the US Environmental Protection Agency’sLaMP is to develop a strategy to assess, restore, protect and

Fig. 12. Artificial neural network and the two main types of

calculations that are made: feed forward of weights and back

propagation of errors.


monitor the ecosystem health of a Great Lake. A LaMPhelps to coordinate the work being conducted by allgovernment and non-government agencies responsible fordeveloping environmental and economic policies impactingthe health of a Great Lake. All plans are open to publicreview and input so that all potential stakeholders can havea ‘voice’ in the content of the LaMP and, subsequently, onthe health of the Great Lakes.

One of goals of the Lake Michigan LaMP was to ‘makeland use, recreation, and economic activities sustainableand supportive of a healthy ecosystem’. The LaMP seeksto develop a systematic and comprehensive ecosystemapproach that attempts to protect the sustainability of ‘openlake water and the watersheds that comprise the lakebasin’.

The LTM forecasts presented here can be used to antici-pate the potential consequences of future changes so thatpolicy can be developed that might mitigate any negativeimpacts to the quality of the watersheds that comprise thelake basin. Developing sound policies that increase theefficiency of decision-making and the ability to respond intimely manner so that anticipated changes are mitigated isa common goal in any policy formulation (Heathcote 1998).

In its assessment of the potential environmental impactsfrom land-use changes, the LaMP identified severalspecific types of land-use trends were of major concern.These land use trends, and the implications from ourstudy follow.

Loss of natural habitatNatural habitat provides required resources for wildlife.The Human Use Index calculated on land-use changesprojected by the LTM show clearly that coastal naturalhabitats are likely to be greatly impacted in 20–40 years andthat wildlife habitat in the southern extent will becomegreatly reduced in the future.

Threats to coastal-inland wetland systemsLand-use change alter land-surface characteristics which,in turn, modify surface- and groundwater interactions(Schramm 1980). Reducing groundwater recharge mightnegatively impact wetland hydrological dynamics (Salamaet al. 1999). Our results show that a majority of the smallwatersheds in the southern half of our study area willundergo so much future urbanization that they will becomehydrologically impaired.

Threats to coastal lake shore system, including the Lake Michigan sand dunes

The coastal sand dunes are clearly sensitive environmentsthat are potentially threatened by expanding residential

development along the shoreline of Lake Michigan. TheLTM projections indicate potentially severe impacts tothese sensitive habitats due to extensive residential andsprawled development along the Lake Michiganshoreline.

Impairment of surface water quality such as rivers and inland lakes

The intricate system of tributaries contributing to LakeMichigan is clearly subject to impacts from urban andsuburban land-use changes and its population-relatedimplications. Impacts resulting from practices such aschannelization, dredging and filling, damping, sedimen-tation, loss of riparian vegetation, eutrophication andflooding are all positively correlated with urbanization. TheLTM methodology and estimations are capable of identi-fying specific areas of concern where policy measures andpossibly specific best management practices (BMPs) canbe implemented in order to ease or mitigate anticipatedimpacts.

Human health and non-point source pollution impactsfrom water quality are often caused by pathogens anddrinking water contamination, a direct result of urbaniz-ation. The LTM estimations can reliably correlate andindicate potential areas and sources of concern.

CONCLUSIONA land-use forecast model such as the LTM can generateuseful information about possible environmental impactsof future urbanization. We were able to show that: (i) thegreatest amount of new urban will be along the coastalwatersheds of Lake Michigan; (ii) some watersheds willbecome hydrologically impaired from the increasedamount of imperviousness from urbanization; (iii) someriparian habitats might be lost along major rivers drainingto Lake Michigan; (iv) the amount of natural habitat will bereduced, thus threatening the sustainability of wildlifepopulations; and (v) the amount of prime farmland lost willbe proportionally greater than the increase in urban areas.In addition, the model predicted that the Black-Makatawawatershed is likely to have the greatest impact fromfuture urbanization. We suggest that this watershed beexamined more carefully by policy-makers and resourcemanagers.

ACKNOWLEDGEMENTSThis work was supported in part from National Aero-nautical and Space Administration grant NAG13-99002 tothe University of Minnesota, University of Wisconsin andMichigan State University entitled ‘An Upper Great LakesRegional Environmental Science Applications Center


(RESAC)’. Work was also supported in part by a grant fromthe Great Lakes Fisheries Trust, Lansing, Michigan.

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APPENDIX 1This appendix is designed to provide some of the technicaldetails of how the artificial neural network is configured foruse in the LTM. In addition, where appropriate, somehistorical context is also presented.

The LTM uses what is referred to as a multilayer percep-tron (MLP) neural net. The MLP was first described byRumelhart et al. (1986) and it is one of the most widely usedANNs. The MLP consists three different types of layers:input, hidden, and output (Fig. 11) and, thus, can be usedto identify relationships that are non-linear in nature. TheANN algorithms calculate weights for input values, inputlayer nodes, hidden layer nodes and output layer nodes byintroducing the input in a feed-forward manner thatpropagates through the hidden layer to the output layer.The signals propagate from node to node and are modifiedby weights associated with each connection. The receivingnode sums the weighted inputs from all of the nodesconnected to it from the previous layer. The output of thisnode is then computed as the function of its input called theactivation function. The data moves forward from node tonode with multiple weighted summations occurring beforereaching the output layer. The mathematical expressionsfor each component of the neural network followguidelines from Reed and Marks (1999), and are shownin equation 1:

Ui = � wijxj + bi (1)j0000

where wij is the jth synaptic weight assigned to a node i, andxj contains the output value from node j of the previouslayer; Ui is the summed scalar of all weights and outputvalues from the previous layer. A scalar bias or threshhold,bi, is added to optimize the number output values can beplaced into the correct output category. The scalar, ui, isthen passed through a non-linear function, which is some-times referred to as the squashing function or activationfunction, as shown in equation 2:

yi = f(ui) (2)

where the function in is either a logistic, tanh or stepfunction. Logistic functions are used most often.Logistic functions create output values that range from0.0 to 1.0.

Weights in an ANN are determined by using a trainingalgorithm, the most popular of which is the back propa-gation (BP) algorithm. The BP algorithm randomly selectsthe initial weights, then compares the calculated output fora given observation with the expected output for thatobservation. The difference between the expected andcalculated output values across all observations is summar-

ized using one of several statistics, such as the sums ofsquares, or more preferably, the mean squared error(MSE). After all observations are presented to the network,the weights are modified according to a one of severaldifferent types of learning rules. The Widrow-Hoff learningrule or delta rule (Skapura 1996) is used most often and ithas form as shown in equation 3:

x�w = �(t–u) –––– (3) � x �2

where ç is the constant learning rate, t is the value of thetarget or true output, u is the value of the estimated outputand x is the value of the input at location j. Learning rulesare applied so that the total error is distributed among thevarious nodes in the network. This process of feedingforward signals and back-propagating the errors isrepeated iteratively (in some cases, thousands of times)until the error stabilizes at a low level.

ANN parameterization of the LTMThe Stuttgart Neural Network Simulator (SNNS) wasutilized for training and testing. To reduce the possibilitythat the neural network would ìovertrainî duringtraining, every other cell was presented to the neuralnetwork. The SNNS ìbatchmanî utility was used tocreate, train and test the neural network. A backpropagation, feedforward neural network, with one inputlayer, one hidden layer and one output layer was utilized.The neural network input layer contained seven nodes(one node for each driving variable) and six nodes inthe hidden layer. The output layer contained binary datathat represented whether a cell location changed to urban(1 = change; 0 = no change) during the study period for thetraining exercise (1978–1995). Cells located within theexclusionary zone were removed from both the trainingand testing exercises.

Each value in an entire predictor variable grid was stand-ardized a linear normalization that transforms the input sothat there is a mean of zero with a unit standard deviation,as shown in equation 4:

xj – xxs = –––––– (4)

�j

where xs is the scaled value, xj is the raw value, is the meanof all xj, and Ûj is the standard deviation of all xj.

The neural network contained a logistic activationfunction with a bias (Reed & Marks 1999). The Widrow-Hoff learning rule or delta rule (Skapura 1996) was used fortraining. The learning rule was applied so that the total


error is distributed among the various nodes in thenetwork.

Following Pijanowski et al. (2002), the neural net trainedon the input and output data for 500 cycles. Previousexperience of training urban change data showed that 500cycles gave a minimum mean square error between themodelled output and presented data. After training wascompleted, the network information containing all of theweights, biases and activation values was saved to a file.This file was examined for unusual values in activationfunctions or bias.

The testing exercise that followed used drivingvariable input from all cells (except those located in theexclusionary zone) in the study locations but with theoutput values removed. The network file generated fromthe training exercise was used to estimate output values foreach location. The output was estimated as values from

0.0 (not likely to change) to 1.0 (likely to change); theoutput file created from this testing exercise is called aìresultî file. The actual number of cells undergoingtransition during the study period for each county was thenused to determine how many cells from each county ìresultîfile should be selected to transition. Cells with valuesclosest to 1.0 were selected as locations most likely totransition. A C program was written that performed thiscalculation such that if cells possessed the same valuebut only a subset of them needed to be selected totransition, then the necessary number of cells of equalvalue was randomly selected from the pool of availablecells. We converted the file back into a GIS format byreintroducing those locations taken out as part of theexclusionary zone. Once in the GIS, the output wasanalysed per the procedures outlined in the Resultssection of this paper.

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