Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH...

ORIGINAL ARTICLE

Modelling urban growth evolution and land-use changes usingGIS based cellular automata and SLEUTH models: the caseof Sana’a metropolitan city, Yemen

Mohamed Al-shalabi • Lawal Billa •

Biswajeet Pradhan • Shattri Mansor •

Abubakr A. A. Al-Sharif

Received: 16 December 2011 / Accepted: 18 November 2012 / Published online: 1 December 2012

� Springer-Verlag Berlin Heidelberg 2012

Abstract An effective and efficient planning of an urban

growth and land use changes and its impact on the envi-

ronment requires information about growth trends and

patterns amongst other important information. Over the

years, many urban growth models have been developed and

used in the developed countries for forecasting growth

patterns. In the developing countries however, there exist a

very few studies showing the application of these models

and their performances. In this study two models such as

cellular automata (CA) and the SLEUTH models are

applied in a geographical information system (GIS) to

simulate and predict the urban growth and land use change

for the City of Sana’a (Yemen) for the period 2004–2020.

GIS based maps were generated for the urban growth

pattern of the city which was further analyzed using geo-

statistical techniques. During the models calibration pro-

cess, a total of 35 years of time series dataset such as

historical topographical maps, aerial photographs and

satellite imageries was used to identify the parameters that

influenced the urban growth. The validation result showed

an overall accuracy of 99.6 %; with the producer’s accu-

racy of 83.3 % and the user’s accuracy 83.6 %. The

SLEUTH model used the best fit growth rule parameters

during the calibration to forecasting future urban growth

pattern and generated various probability maps in which

the individual grid cells are urbanized assuming unique

‘‘urban growth signatures’’. The models generated future

urban growth pattern and land use changes from the period

2004–2020. Both models proved effective in forecasting

growth pattern that will be useful in planning and decision

making. In comparison, the CA model growth pattern

showed high density development, in which growth edges

were filled and clusters were merged together to form a

compact built-up area wherein less agricultural lands were

included. On the contrary, the SLEUTH model growth

pattern showed more urban sprawl and low-density

development that included substantial areas of agricultural

lands.

Keywords Urban growth � Land-use change � Remote

sensing � GIS � Cellular automata � SLEUTH � Sana’a

Introduction

Most of damages and harmful effects in environment are

caused by anthropogenic activities. The changes of land

use and land cover occur due to urbanization caused by

unplanned and uncontrolled urban sprawl which leads to

change nature, destroy green cover and pollute the water

resources. The present needs are analysis, understanding,

modelling urban growth evolution and land use changes to

save and provide suitable and safe environment for the

mankind. Urban growth modelling is essential for analyt-

ical and the prediction of the dynamics of urban growth.

GIS technique has been used to study, analyze and corre-

late urban activities and land use changes and its effects on

ground water and environment. Most of these studies

M. Al-shalabi

Department of Geography, University of Sana’a, Sana’a, Yemen

L. Billa

School of Geography, University of Nottingham,

Malaysia Campus, Semenyih, Malaysia

B. Pradhan (&) � S. Mansor � A. A. A. Al-Sharif

Department of Civil Engineering, Faculty of Engineering,

Geospatial Information Science Research Center (GISRC),

University Putra Malaysia, Serdang Selangor, Malaysia

e-mail: [email protected]; [email protected]

123

Environ Earth Sci (2013) 70:425–437

DOI 10.1007/s12665-012-2137-6

revealed that the urbanization and change in land use are

important factors affecting the water resources and envi-

ronment (Bathrellos et al. 2008). Urban models have been

used to forecast future changes or trends of development, to

describe and assess impacts of future development, and to

explore the potential impacts of different policies. The

geographical information system (GIS) offers a powerful

tool for the spatial analysis of a multi-dimensional phe-

nomenon (Youssef et al. 2011). The susceptibility map is a

practical tool in natural and urban planning. It reflects the

availability of lands for urbanization and other require-

ments. Many factors such as slope, aspect, road network

and land use are generally used during the model building

(Bathrellos et al. 2009; Rozos et al. 2011; Al-shalabi et al.

2012). In the literature, it is common to encounter meth-

odologies using spatial multiple criteria analysis in GIS

environment for urban growth management. The Analytic

Hierarchy Process (AHP) and GIS has been popularly used

to predict urban growth using many factors that affects

future growth and water demand (Panagopoulos et al.

2012). In the last two decades, a lot of research has been

done on urban spatial modelling due to increased com-

puting power, improved availability of spatial data, and the

need for innovative planning tools for decision support

(Geertman and Stillwell 2004; Brail and Klosterman 2001;

Bathrellos et al. 2012; Biro et al. 2011; De Abreu and Filho

2011; Jabbar and Zhou 2011; Lv et al. 2011; Peng et al.

2011; Pradhan 2011). Urban system models have shown

potential in simulating the complexity of dynamic urban

processes and can provide an additional level of knowledge

and understanding of spatial and temporal change (Wu and

Silva 2009; Kaya and Curran 2006; Pettit et al. 2002).

These aforementioned models include both macro and

micro integrated cellular automata (CA) models (RIKS

model) (Engelen et al. 1997), fuzzy CA model (Wu 1996),

ANN CA model, (Li and Yeh 2002), multi-CA model

(Cecchini and Rinaldi 1999) and the SLEUTH urban

growth model that has been widely applied in the United

States and many other parts of the world (Jantz et al. 2004;

Leao et al. 2004; Yang and Lo 2003; Esnard and Yang

2002; Silva and Clarke 2002; Clarke et al. 1997; Zeug et al.

2006).

These models have been useful in planning and decision

support for urban environmental management. They are

applied together with other models, such as; the logistic

regression model to identify the significant variables and

rules that differentiate urban or city from rural and forest

environments; the relative probability model which uses

spatial interactions of neighbourhood, distance, patch size

(parcels), and site-specific characteristics; and the focus

group involvement to create a human input layer, set the

growth scenarios, evaluate predictions and disseminate the

information (Allen and Lu 2003).

In a data deficient country like Yemen, urban environ-

ment planning and management have many challenges and

most particularly the lack of a mechanism to forecast and

predict urban growth trends and patterns. In Yemen,

development in uncontrolled and planning is haphazard,

lacking any clear vision about the future. For that reason,

cities have many socio-economic, infrastructural and

management problems. In this study, two urban growth

models, i.e. the CA and the SLEUTH are applied in

modelling the urban growth trend of Sana’a the biggest city

in Yemen and to predict and forecast the growth pattern

from the year 2004–2020. These advanced urban growth

models have been developed using the factors by consid-

ering certain assumptions which have been popularly

applied in many cities around the world (ESCWA 2007;

Pedro and Zamyatin 2006). This study tests the applica-

bility of these models in a city where growth condition may

be different; it also compares the output growth pattern of

the two models.

Urban growth modelling using cellular automata (CA)

and the SLEUTH models

Cellular automata (CA) models were developed for many

modelling purposes but have been popularly applied in the

area of modelling urban studies and growth processes

depending upon their transition rules, and calibration

methods (Couclelis 1985, 1997). Many forms of urban CA

have been used to simulate the spatial pattern of an actual

city or a synthetic city (Batty 1997; Yeh and Li 2001).

Wagner (1997) explored the potential use of CA in urban

planning and discussed theoretical obstacles in incorpo-

rating CA models in geographical context. These models

have been used to study the evolution of urban land use,

modelling urban forms; urban models and many other

urban studies (White and Engelen 1993, 1994; Clarke et al.

1997, Clarke and Gaydos 1998a, b; Batty and Xie 1997;

Batty 2000). In recent years, new CA models, such as

URBANSIM, UPLAN, SLEUTH have been used to fore-

cast future changes, trends of development, describe and

assess impacts of future development, and to explore the

potential impacts of different policies (Herold et al. 2001).

CA models have many advantages in the modelling of

urban processes that include the abil-ity to perform spatial

dynamics, and time explicitly. After successfully analysing

the similarities and capabilities of CA, Wagner (1997)

proposed that CA can be considered as analytical engine of

GIS. Raster GIS with map algebra can be integrated with

enhanced capabilities as discussed by Takeyama and

Couclelis (1997). CA are considered to have a ‘‘natural

affinity’’ with raster data. It has similarities with GIS, such

as the representation of attribute information in a layered

426 Environ Earth Sci (2013) 70:425–437

123

fashion, and the manipulation of information with opera-

tors (Overlay in GIS, Transitional rules in CA). The focal

sum or focal mean functions of GIS has direct analogous

with neighbourhood functions. Because of its natural

affinity with GIS, it was obviously adopted by geographers

as a tool for modelling spatial dynamics (Singh 2003).

Clarke et al. (1997) introduced the Clarke urban growth

model, a precursor to the SLEUTH model for simulating

historic change. The model aids the illustration and expla-

nation of growth processes at a regional scale and predicts

future urban growth trends. The model was successful in

simulating urban change between 1900 and 1990 for the San

Francisco area, and was later applied to the Baltimore

Washington corridor (Clarke and Gaydos 1998a, b), where

calibrations and long term predictions for both San Francisco

and Baltimore Washington were presented, allowing for an

effective comparison to be made between the growth pat-

terns and processes of the two urban systems. The SLEUTH

model is now in a public domain C-language source code,

available for download with documentation online from

USGS website at http://www.ncgia.ucsb.edu/projects/gig.

The model simulates land use change as a consequence of

urban growth by working on a grid space of pixels, with a

neighbourhood of eight cells of two cell states (urban/non-

urban), and five transition rules that act in a sequential time

steps. The states are acted upon by behaviour rules, and these

rules can self-modify to adapt a place and simulate change

according to what have been historically the most important

characteristics.

SLEUTH requires five GIS-based inputs: urbanization,

land use, transportation, areas excluded from urbanization,

slopes, and hill-shading as a background. The input layers

must have the same number of rows and columns, and

should be correctly geo-referenced. For statistical calibra-

tion of the model, urban extent must be available for at

least four time periods. Urbanization results from a ‘‘seed’’

urban file with the first urban year used, with at least two

road maps that interact with a slope layer is used in order to

allow the generation of new nuclei for outward growth.

Besides the topographic slope, a constraint map represents

water bodies, natural and agricultural reserves. After

reading the input layers, initializing random numbers and

controlling parameters, a predefined number of interactions

take place that correspond to the passage of time. A model

outer loop executes each growth history and retains sta-

tistical data, while an inner loop executes the growth rules

for a single year.

Study area

The city of Sana’a, Yemen is located within the latitudes

15� 100 0000 and 15� 300 0000 North and longitudes 44� 050

0000 and 44� 200 0000 East (Fig. 1). The total area was

15,284.84 km2 and the built up area was around

138.66 km2 in 2003 (Al Shaibi et al. 2006). The city has a

moderate climate all year round, due to its location at about

2,200 m above sea level. Sana’a city is located to the

Northern-central part of Yemen in a high valley extending

from south to north. The old city has a unique urban and

architectural design that dates back to over 2,500 years. In

2004 the total population of Sana’a was estimated at

1,747,834 (Al Shaibi et al. 2006; Alderwish and Almatary

2011).

Data and methodology

Various data used in this study includes Quick bird satellite

imagery (0.60 m) acquired in 2003, aerial photography

(4 m) acquired in 1994, digital contour line map (10 m

interval), hydrological map 1:50,000, road network and the

city master plans maps from the Ministry of Public Works

and urban planning and the statistical information for dif-

ferent years from Central Statistical Organization in

Yemen. In developing countries the availability of data is a

major concern for this kind of studies. In this study, the

available data were collected between the years

1978–2003, this made it possible for the calibration process

to register the progression of urban growth signature over

the time period. Data for growth after 2003 were not

available so could not be included in the calibration pro-

cess. Data generally comprised maps of different types,

dates, scales and time, those were pre-processed into a

uniform geo-reference to create a profile of urban extent of

Sana’a city over space and time and organized into spatio-

temporal GIS database. Other types of data such as statis-

tical information were collected from various reports

published by different ministries and departments in

Yemen in 1999. These data was further classified and

clipped to the map extent (Hurskainen and Pellikka 2004)

and transformed to the same cell size raster grids at 45

meter resolution. This resolution was used for all data

layers. The grid dimensions were 527 columns by 811

rows. These datasets were also used as an input for the CA

and SLEUTH model calibration process.

Prediction of urban growth and land use change using

CA transition rules

A suitability map was used to estimate the annual demand

of land for urban development depending on the historical

growth of Sana’a city. The evolution of a cell was deter-

mined by the suitability value and the number of cell that

were developed. The cells were then calculated to establish

how many cells had attained a particular state of a time

Environ Earth Sci (2013) 70:425–437 427

123

http://www.ncgia.ucsb.edu/projects/gig

transition rule (Xi et al. 2009; Watkiss 2008). Cells are

allocated to a particular state by selecting them from the set

of available (the cells which can undergo transition) cells

that are spread over the city. Subsequently, the cell can-

didatures were evaluated using multi criteria analysis

(MCA) technique to determine their possible inclusion in

the model. Equations 1 and 2 ere used for formulation of

the model (Samat 2005).

utþ1i;j ¼ f ut

i;j;Xti;j; S

ti;j

� �ð1Þ

where, utþ1i;j = the state of the cell at row i and column j at

time t ? 1; uti;j = the state of the cell at row i and column j

at time t; Xti;j = the development of cells within the

neighborhood of the cell at row i and column j; and

Sti;j = the suitability score for the cell at row i and column j

for urban development.

In the above equation, the function f is formulated using

IF, THEN and ELSE statements as shown in Eq. 1. The

repeated application of this rule produced a complex spatial

pattern.

IF ðSti;j � Xt

i;j � threshold value); ð2Þ

Then utþ1i;j = urban; Else utþ1

i;j = non-urban.

The urban growth rules involve the selection of a

location by investigating the spatial properties of the

neighboring cells, and urbanizing the cell under consider-

ation based on a set of weighted probabilities using land-

use and suitability map. High priority and suitable areas

such as the master plan areas and proximity to existing

developed areas are given a score factor of 5. The unsuit-

able and development restricted lands are given a score of

0. The details of these score factors are developed for

transition rule processes (Table 1). After cells have been

assigned score, the top scoring cells (cell available for

transition) are allocated to particular states (land use type)

using Eqs. 1 and 2 and afterwards each cell is assigned to

an integer value based on land use classes.

Prediction of urban growth using the SLEUTH model

SLEUTH is a moniker for the input data required to use the

model: slope, land use, exclusion, urban, transportation and

hill shade. It employs a raster data (8-bit GIF) as input

format but it does not use GRIDs directly. For the mod-

elling and prediction of urban growth, the model supports

three different modes: test, calibration, and prediction.

The model is computation-intensive especially in the

Fig. 1 Location map of the Sana’a city and its urban extent


123

calibration mode (Xi et al. 2009; Candau 2002). The

computation time varies from hours to days depending on

the spatial resolution and the image size. According to

Jantz et al. (2004), it requires a several days to calibrate the

urban growth for a medium-size city. The outputs include

a series of GIF images, representing the yearly urban

scenarios, which can be compiled into time series for

animation that illustrates the urban growth pattern and

change over time. Figure 2 shows the structure of the

SLEUTH model as illustrated by Yang and Lo (2003).

Model calibration

Urban areas throughout the world grow at different rates

and in different ways due to varying economic conditions

and environmental constraints (Kaya and Curran 2006;

Daniels 1999). Urban growth modelling (UGM) of Sana’a

city will thus require predictions that are consistent with

the factors and constraint with the city. Sana’a city model’s

coefficient values were derived from historical dataset that

was ‘‘fit’’ to area through the UGM’s calibration phase

using the brute force method (Goldstein et al. 2004). The

model was first run in a calibration phase to obtain a

suitable set of parameters which was further refined in

the sequential calibration steps. The calibration allows the

Table 1 The factors and scores effect of the transition rule

Factors Score

Proximity to existing developed areas 5

Proximity to prioritized land (master plan proposed

areas)

5

Suitability factors

High suitable 5

Suitable 4

Moderate suitable 3

Less suitable 1

Unsuitable Restricted

Land use factors

Agricultural land 5

Industry land Restricted

Mountains 1

Green areas Restricted

Other land use Restricted

Fig. 2 General structure of the SLEUTH model (after Yang and Lo

2003)

Table 2 The parameters of the coarse, fine, final calibration (45

meter pixel size)

Dispersion Breed Spread Slope Road

gravity

Parameters of coarse calibration

Start 1 1 1 1 1

Step 25 25 25 25 25

Stop 100 100 100 100 100

Possible units 5 5 5 5 5

Monte Carlo

iterations

4

Possible

combinations

3,125

Elapsed time 4 Days, 8 h, 7 min and 3 s.

Parameters of fine calibration

Start 80 70 20 45 45

Step 5 5 5 5 7

Stop 100 85 35 65 100


Monte Carlo

iterations

6

Possible

combinations

4,800

Elapsed time 7 days, 3ours, 49 min and 13 s

Parameters of final calibration

Start 88 78 17 40 94

Step 1 1 1 2 1

Stop 93 84 23 56 98


Monte carlo

iterations

8

Possible

combinations

1,3,230

Elapsed time 9 days, 4 h, 7 min and 33 s


123

model to simulate urban growth from the past into the

present with a very high degree of fit between the simulated

years and the control years. The coarse calibration, took 25

unit steps for each entire coefficient space, for all coeffi-

cients. The fine calibration, took 5 unit steps and the final

calibration took either 1–2 unit steps through the coeffi-

cient space. Table 2 shows a summary of the parameters

and processing outputs obtained in the calibration.

Results and discussion

The prediction of urban growth change was performed by

using GIS-based CA transition rule. The annual demand of

land was estimated based on the models calculation of rate

of change and urban growth for the calibration period/

historical data (1978–2003). The results presented in

Table 3 shows that urban growth/extent of 138.6 km2 in

2003 will increase to 149.63 km2 in 2005, then to

183.62 km2 in 2010 and 272.23 km2 in 2020 respectively,

with an average annual growth rate of 4.05 %. The pre-

diction of the urban growth extent was paced with a dif-

ference of five years (2005, 2010, 2015 and 2020) so as to

allow enough significant growth during the period to reg-

ister a change in the prediction. The graphical presentation

of predicted urban growth is shown in the sampled results

for the year 2010 (Fig. 3). The figure shows the growth

pattern using a GIS-based CA model are compact in the

suitability lands, i.e. the growth has filled spaces and has

merged many clusters together around existing built-up

areas and occupies the prioritized land in the master plan.

In this study, land-use was defined as state of cells at

time ‘t’, represented by five major categories: built-up area,

agricultural land, industry land, green areas, and moun-

tains. The suitability land was represented by five catego-

ries: high suitable, suitable, moderate suitable, less

suitable, and unsuitable. The sampled results obtained from

the prediction modelling for the year 2010 are presented

here for better discussion and explanation of the growth

and change of the land-use. Table 4 and Fig. 4 show the

spatial growth and land-use changes respectively. Agri-

cultural land is reduced from 444,742 km2 in 2003 to

339 km2 in the year 2010, while the green areas was

increased from 6.62 km2 in 2003 to 24.9 km2 in 2010. The

industrial land does not show any changes because; it has

Table 3 Prediction of spatial urban growth in Sana’a City

2004–2020

Years Area (pixel) Area (km2) Annually developed cells

2004 71,052 143.88 2,578

2005 73,891 149.63 2,839

2006 76,912 155.75 3,021

2007 80,130 162.26 3,218

2008 83,529 169.15 3,399

2009 87,061 176.30 3,532

2010 90,676 183.62 3,615

2011 94,407 191.17 3,731

2012 98,342 199.14 3,935

2013 102,372 207.30 4,030

2014 106,550 215.76 4,178

2015 110,917 224.60 4,367

2016 115,408 233.70 4,491

2017 119,986 242.97 4,578

2018 124,647 252.41 4,661

2019 129,461 262.16 4,814

2020 134,436 272.23 4,975

Fig. 3 Predicted growth areas for 2010 using the CA model

Table 4 Land-use change for the year 2010

Land use 2003 2010 Value

Built up area 138.66 184.75 45.74

Agricultural land 444,742.36 399.04 -43.43

Mountains 229.48 227.17 -2.31

Green area 6.62 24.90 18.28

Industry 10.12 10.12 None

Other land use 11.30 11.30 None


123

already been included in the western part of the city as

future industrial area as documented in the master plan

1999.

Validation of the CA model

The validation of the modelling results of CA was carried

out by predicting the spatial urban growth from 1994 to

2003 and comparing it with the actual growth pattern of the

city in 2003. The validation result (Table 5) shows an

overall accuracy of 99.6 %. This high level of accuracy

was achieved because very high spatial data was used in

the processing. The producer accuracy however was

83.3 % while the user’s accuracy was 83.6 %. Considering

the high level of accuracy achieved by the prediction, the

dataset (1994–2003) was further used in the application of

the model. The graphical maps of the actual urban area of

2003 and predicted were overlaid for visual comparison

(Fig. 5).

Fig. 4 The change in land-use from 2003 to 2010 using GIS-based CA model

Table 5 Validation result of the GIS-based CA prediction for the

year 2003

Actual

pixels

Predicted

pixels

Correct

pixels

Overall

accuracy

%

Procedure’s

accuracy

%

User’s

accuracy

%

68,451 68,688 57,204 68,451/

68,688

57,204/

68,688

57,204/

68,451

99.6 83.3 83.6

Fig. 5 Actual and predicted urban extent for the year 2003


123

Results of the SLEUTH urban growth model

The five coefficient values that were obtained in the cali-

bration process were used to predict the urban growth from

2003 to 2020 for the city of Sana’a. Table 6 shows the

results of the predicted urban growth. The result indicated

that urban area will increase dramatically from 138.6 km2

in 2003 to 143.75 km2 in 2005,159.18 km2 in 2010 and

199.71 km2 in 2020. The growth patterns indicate a very

high value of diffusion and breed and also showed very

high road gravity coefficient and low slope coefficients.

Growth clearly occurred at the urban fringe resulting

organic growth and increase in suburbs. Very high value of

diffusion can be attributed to the fact that the city has no

zoning plan which has allowed unrestricted outward

growth in all directions. Table 7 shows the effect of the

urban growth on land-use change. The annual probabilities

transition from agricultural land to urban land is about

11.92 % and the annual transition for the mountain area is

0.03 % with 0.065 km2 per year. The graphical results of

the SLEUTH model prediction of urban growth for the year

2010 is shown in Fig. 6. In this map it can be seen that the

outward growth is scattered all over the map. The predic-

tion of land-use change for the same year is shown in

Fig. 7.

Comparison between the CA and SLEUTH model

A comparison was made between the two sampled mod-

elling results for the year 2010 for both CA model and

SLEUTH model to understand the dynamics of transitional

probability change and also to estimate the variance

between the two modelling approaches. Figure 8 shows an

Table 6 Model prediction of growth extent of Sana’a urban area

(2004–2020)

Years Area (pixel) Area (km2) Annually developed

cells

2004 69,730 141.20 1,281

2,005 70,990 143.75 1,260

2006 72,413 146.65 1,423

2007 73,858 149.56 1,445

2008 75,415 152.72 1,557

2009 76,993 155.91 1,578

2010 78,609 159.18 1,616

2011 80,312 162.63 1,703

2012 82,028 166.11 1,716

2013 83,744 169.58 1,716

2014 85,722 173.59 1,978

2015 87,667 177.53 1,945

2016 89,789 181.82 2,122

2017 91,885 186.17 2,096

2018 93,988 190.33 2,103

2019 96,257 194.92 2,269

2020 98,622 199.71 2,365

Table 7 Annual land-use

transition probabilitiesUnclass Urban Agricultural Mountains Park Industry Airport Others

Unclass 99.57 0.02 0.31 0.11 0 0 0 0

Urban 0 98.89 1.08 0 0 0 0.02 0

Agricultural 0 11.92 86.87 0.69 0.01 0 0.5 0

Mountains 0.03 0.03 0.08 99.73 0 0 0.13 0

Park 0 0 0 0 100 0 0 0

Industry 0 0 0 0 0 100 0 0

Airport 0 0.05 0 0 0 0 99.95 0

Others 0 0 0 0 0 0 0 0

Fig. 6 Predicted growth areas for 2010 using the SLEUTH model


123

overlay comparison of the two modelling results. The

SLEUTH model results showed a wider spread of the

growth patterns and smaller urban clusters, while the GIS-

based CA model showed a compact growth pattern where

small clusters are merged together. The CA grow pattern

reflected the proposal made in the Sana’a master plans

where it is suggested to incorporate adjacent lands into the

city’s residential development. In Fig. 9 the comparison

results are presented based on the five land-use classes. It

showed that the predicted growth for 2010 using the CA

model is about 184.75 km2 when compared to 159.18 km2

Fig. 7 Predicted land-use change for 2010 using the SLEUTH model

Fig. 8 Comparison of prediction results of 2010 for the CA and

SLEUTH models

24.911.3

24.911.310.12

159.18

424.45

228.33

10.12

184.75

399.04

228.17

0

50

100

150

200

250

300

350

400

450

landBuilt up Agricultural Mountains Industry Green land others

Km

2

area

SLEUTH Model GIS-based CA Model

Fig. 9 Statistical comparison of the CA and SLEUTH models in the

land-use change prediction


123

using the SLEUTH model. In general the results from the

SLUETH model showed extensive urban sprawl and spatial

distribution when compared with the CA model.

The verification of the predicted urban growth for

Sana’s city by field measurement was not possible due to

logistical constraints. Moreover, due to scarcity of high

spatial resolution satellite images for prediction time zone,

the verification of the models application was conducted

using the Google images (Digital Globe image 0.6 m) in

Google Earth free software, that provide the capability of

time tuning through 2003–2010. The predicted results for

the year 2010 was possible by overlaying the images on the

google images. For the validation purpose, we chose the

Google image of 2010. A comparison was thus made by

visual validation process that showed significant similarity

between the existence and predicted urban growth for the

Sana’a. By logical inference and visual inspection, the

study concluded that where the predicted urban growth

shows a good correlation over the Google images. Fig-

ures 10 and 11 show the validation results of 2003 and

2010 that inevitably support the predicted spatial urban

growth in Sana’a.

The actual urban growth and land use changes in Sana’a

city that occurred from 2003 to 2010 are shown in Figs. 10

and 11 respectively. These figures give a clear assessment

and comparison of the results of urban sprawl and land use

changes that we have predicted using CA and SLUETH

models used in this study. By comparing the results of both

models with the real urban growth from 2010 (Figs. 10 and

11), it is evident that that models employed in this study is

sufficient enough for prediction of urban growth.

The advantage of GIS-based CA is that it generated

results with geographical reference and also handles large

amount spatial data which allows for the creation of

Fig. 10 Validation of Urban growth predicted using CA-model for the year 2010


123

constraints and other criteria by assigning the weight that

helped in determining whether sites were suitable or

unsuitable to the growth modelling. The prediction by GIS-

based CA model reflected the projections of the master

plan; thus can be used to develop master plan projection to

support planning and decision making in the future. The

prediction also shows a growth trend towards increased

urban expansion due to increasing population growth. The

pattern of urban growth would be greatly influenced by the

controlled and protected lands and the physical character-

istic of the terrain such as mountains ranges in northeast

part of the city. The limitations of the model are highly

dependent on the data quality, i.e. data of higher quality

results in more accurate outputs. Other weaknesses

affecting the models are growth in multiple directions and

also their inability to recognize new urban development

that occurred away from existing urban areas.

The SLEUTH model was found to be useful in visual-

izing and quantifying spatial growth extent. It also showed

saving of about 25.41 km2 of growth encroachment on

agricultural land-use when compared with the CA model

because its growth trend has more sprawl and low-density

development patterns that leads to substantially less con-

sumption of agricultural lands. The limitation of the

SLEUTH model is that, it is very sensitive to the spatial

resolution of data. Consequently, a higher resolution data

improves the quality of data and the model output. The

SLEUTH model also require high end computational

capabilities such as the use of parallel computing envi-

ronment, making this model extremely difficult to apply in

Fig. 11 Validation of Urban growth predicted using SLEUTH model for the year 2010


123

a developing country with limited data inputs. It also

observed that the composite results of the optimum values

for the diffusion, spread, slope and road gravity parameters

show successive improvement in the parameters that con-

trol the behavior of the system.

Conclusions

Urban modelling is an important technique for forecasting

and studying urban dynamics to understand the potential

impact of growth and future development. This study

found that the major urban growth occurred in agriculture

areas and around unplanned (low price) areas outside

township boundaries where no infrastructure facilities

(such as drainage systems and disposal management sys-

tem) exist. This reflects the lack of clear policy that could

control and guides urban sprawl in the city. This situation

will results in serious socioeconomic and environmental

problems in the near future. In this study the growth is

mainly affected by topography and road networks. It is

necessary to accommodate the fast population growth by

following clear plan that take into consideration of the

socioeconomic conditions, and exploit the growth factors

to put clear policy and regulation by providing facilities

and infrastructures which encourage the urban growth in

controlled planned areas. The CA and SLEUTH are some

of the models that have been extensively used in growth

modelling. Although these techniques are useful for fore-

casting and prediction for future growth which support

planning and decision making, they have seen little used

in developing countries such as Sana’a, Yemen where

their impact could be greatly appreciated. In this study the

results of their application for prediction and simulation of

the urban growth and land use change in Sana’a city

showed a high overall accuracy of 99.6 %, producer

accuracy of 83.3 % and user accuracy of 83.6 %. The

growth pattern of CA model presented a compact and high

density development, while the SLEUTH model presented

an urban spread pattern with very low-density develop-

ment. The study concluded that both models are very

useful urban modelling tools and enable the prediction and

generation of different urban growth scenarios in support

of planning and decision making. By coupling the models

GIS, output results showed geo-referenced map that

helped to identify and demarcate specific locations for the

implementation of planning policies. Implementation of

the SLEUTH model however required many datasets and

high end computing power for processing; this will be a

major limitation in developing countries that have defi-

cient resources. The study demonstrated an efficient

implementation of the CA and SLEUTH models coupled

with GIS in urban growth and land-use modelling and

allowed the testing of different policy alternatives on

growth scenarios.

Acknowledgments The study benefited from the academic schol-

arship provided by the Ministry of Higher Education Yemen. Authors

are also very thankful for the support given by various ministries and

departments in Yemen with the provision of data and other infor-

mation used in the study. Thanks to two anonymous reviewers for

their useful comments on the earlier version of the manuscript.

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