GEOSPATIAL AND GEOENGINEERING APPLICATION FOR URBAN DYNAMICS

MAPPING: A CASE STUDY OF RIYADH, KINGDOM OF SAUDI ARABIA

Iqra Atif and Muhammad Ahsan Mahboob Institute of Geographical Information Systems, School of Civil and Environmental

Engineering, National University of Sciences and Technology, Islamabad, Pakistan. *Corresponding Author: Iqra Atif Phone: 92-51-9085-4491 (Office)

Email: iqraphd13@igis.nust.edu.pk ABSTRACT

Timely and accurate change detection of Earth’s surface features is extremely important

for understanding relationships and interactions between human and natural phenomena

in order to promote better decision making. In this paper spatial and temporal dynamics

of landcover changes and urban expansion of Riyadh, Kingdom of Saudi Arabia (KSA)

were quantified using Landsat images, a supervised classification algorithm and the post-

classification change detection technique in GIS. The analysis revealed that significant

growth of built-up areas over the study period resulted major decrease in the area of

barren land. Results shows an increase of 37% in urbanization with 6% decrease in

barren land from 2000 to 2013. The major increase in urbanization was found at 15 km

from the center of city and at the same distance the decrease in barren land was started.

As reliable and current data are lacking for KSA, the landcover maps produced in this

study will contribute to both the development of sustainable urban land use planning

decisions and also for forecasting possible future changes in growth patterns.

INTRODUCTION

Human settlement patterns are changing quickly around the world, increasing global

population resulting in urban expansion. Fast urban development is of great concern for

urban planners as it has a significant urban environmental impacts (Adams et al., 2015;

Gulgun et al., 2009; Ha et al., 2003; Han et al., 2003 and Leon et al., 1999). In 2009, over

3.4 billion people in the world lived in urban areas, and this number is projected to

increase to 6.5 billion by 2050 (Hay et al., 2005 and Almeida et al., 2014). Typically, the

urban sprawl appears in cities which have many natural resources and may be more

industrialized to live. In the desert like cities the urban sprawl is a big problem because of

its limited natural resources and the cost of development. There are many desert cities in

the world which experienced sprawl and the large urban growth (Avino et al., 2004;

Miranda et al., 1998 and 1996 and Weng, 2001). Desert cities usually do not have a lot

of resources that become qualified for expansion processes. However, some developers

believes in the idea “smart growth” where by using the modern technologies the desert

cities can be converted into spatially planned changes. These changes in spatial structure

in turn transform ecological functions, such as hydrological systems, biodiversity and

vegetation (Baker et al., 2011 and Bektas, 2014). Also these changes has negative

impacts on environmental factors such as biota, soil, topography, surface and

groundwater, and human structures. Land cover changes may be classed as natural or

anthropogenic (Benza et al., 2016). With these rapid development it is very important to

monitor, map and make proper databases of these changes using change detection

techniques. Change detection is the technique of categorizing differences in the state of

an object or phenomenon by observing it at different times (Cohen et al., 2016 and Correia

et al., 2016). The land cover change is widely recognized as a risk to several biomes

including forests, grasslands, wetlands, tundra and deserts (Dale et al., 1996; Dong et

al., 2008 and Dadras et al., 2014).

In Saudi Arabia, where more than 70% of total area is desert (Duncan et al., 2012 and

Efe et al., 2012), have been changed significantly during the last 30 years since the

government initiated an forceful program of expansion, funded by huge oil revenues

(Engel-Cox et al., 2004 and Etzion et al., 2014). Saudi Arabia is known as a nation

boasting one of the most fast settlement developments in the Middle East (Li et al., 2008

and Liese et al., 2010). Multi-temporal data is helpful for providing a better understanding

of the complex land cover change patterns (Lin et al., 2008; Zhou et al., 2009). The

repetitive coverage of satellite images can be analyzed using digital image processing

techniques which provide the capability to discover changes to the earth’s surface through

space-borne sensors (Mallupattu et al., 2013).

The purpose of this paper is to examine the spatial pattern of the enlargement of Riyadh,

the capital of the Kingdom of Saudi Arabia, which is the fastest growing cities in the Middle

East.

MATERIALS AND METHODS

Study Area

The study area of this research is the capital of Saudi Arabia, Riyadh City as shown in

figure 1. The city is situated in a desert at about 400km from the Gulf and 1000km from

Red sea of Saudi Arabia. Riyadh has experienced rapid urbanization particularly during

the oil boom period in the 1970s and 1980s. Rapid urban expansion of the city has been

driven by oil economy and automobile industry. Currently the population of the city is

5.451 million which is expected to be 11 million (from 2.8 million in 1992 and 4 million in

2008) by 2020 (Markogianni et al., 2014).

Fig. 1- Study area map of Riyadh city.

Datasets:

Landsat satellite data was acquired form United States Geological Survey server. Landsat

data of multispectral TM, ETM+ and OLI sensors was acquired to analyze the urban

expansion in Riyadh city. The data collected from secondary sources include the

administrative boundary map. The details are given in table 1.

Table 1: Different type of data used

Sr. no Type of data used Scale/resolution Years

1 Landsat TM image 30 m 2003

2 Landsat ETM+ image 30 m 2013

3 Administrative boundary Online Source (http://gadm.org/)

Methodology

Understanding the dynamic phenomenon, such as urban sprawl/growth, requires land

use change analyses, urban sprawl pattern identification. ERDAS (Leica) and ArcGIS

software (ESRI) have been used to produce numerous thematic layers, like,

administrative boundary map, roads, railway network and administrative boundary map

using the visual analysis and open street map data.

The typical image processing techniques, such as image extraction, rectification,

restoration, and classification have been used for the analysis of three satellite images

(2003 and 2013). ERDAS imagine software has been used for image analysis. First of all,

atmospheric correction has been applied using enhanced dark object subtraction

technique to take all the images at common reference spectral characteristics (Mason et

al., 2009). Further, these subtracted images have been stretched to 8 bit digital number

range. Satellite images have been studied thoroughly to determine the probable land use

classes. Spectral profiles have been drawn to determine the severability and relative

difference in pixel values of different land use classes in different spectral bands (Merem

et al., 2008). Four separable land cover classes have been identified, such as urban area,

vegetation, water and barren land. Initially, supervised classification using MLC algorithm

has been performed for the classification of various images (Mohan et al., 2015). To

enhance the classification accuracy, knowledge-based expert system was used for post-

classification refinement of initially classified outputs (Ramadan et al., 2004).

Then the accuracy assessment of classified images is performed using ground truth data.

More than 650 points for each year are collected and finally an error matrix is generated.

Development of Spatial Zones

The mean of the Riyadh was calculated using spatial mean model. The spatial mean

model take the extent of the area and estimate a mean based on spatial distribution of

features in the study area. After calculation of the spatial mean, the spatial urban zones

was developed. A multi-ring buffer was applied on the city mean and total 12 zones with

5 km distance was developed. The distance of the first zone from the mean was 5 km

and progressively the distance of last zone was 60 km from the mean. Zonation map for

classified landcover extraction is shown in figure 2.

Fig. 2- Zonation map of Riyadh city.

RESULTS AND DISCUSSION

By using remote sensing and GIS techniques, urban sprawl of Riyadh city were identified.

Supervised image classification techniques were applied to extract areal change of

landcover in different zones. The results show that there is a significant increase in

urbanization.

Detecting Urban Growth Using Supervised Classification:

The supervised classification of the satellite images into built-up and non-built-up areas

for two temporal instants has resulted in the creation land cover of Riyadh city, which

define the urban extents of specified times in specific zones (increasing in Km from the

origin of city). Figures 3 and 4 showing the Landcover classification of Riyadh city in

different zones in year 2000 and 2013.

Fig. 3- Landcover classification of Riyadh city in different zones in year 2000.

Fig. 4- Landcover classification of Riyadh city in different zones in year 2013.

From the classification results increase in urban area was observed from 2000 to 2013,

with the decrease in barren land. Barren land converted into vegetation, water bodies and

built up area due to increase in population. Improved water supply and irrigation system

resulted in increase in vegetation landcover.

Fig. 5- Total Change in Landcover from 2000 to 2013.

The graph of landcover area classification depicting an increase of 37% in urbanization

with 6% decrease in barren land shown in figure 5. This clearly indicated that the capital

of Kingdom of Saudi Arabia is developed in 13 years and still developing. The water and

vegetation class was also found to be increase with 64 and 25 % respectively.

The increase in vegetation is a good and positive sign in urban development as many

researchers insist that vegetation should also be increase with increase in urbanization.

Fig. 6- Temporal Change in Built-Up from 2000-2013.

0

1000

2000

3000

4000

5000

6000

7000

Water Built up Barren Land Vegetation

Are

a Sq

Km

Total Change in Landcover from 2000 to 2013

Year 2000

Year 2013

0

50

100

150

200

250

300

5 10 15 20 25 30 35 40 45 50 55 60

Are

a Sq

Km

Zones

Temporal Change in Built-Up from 2000-2013

2000

2013

In 2003 the average built up area was about 745.55 Km2 which increased by 276.795

km2 in 2003 Shown in figure 6. Abrupt change was observed in zone 20 and 25. Increase

in built up area was 107.96 Sq km and 56.60 Sq Km in zones 20 and 25 respectively. Al-

Riyadh carries the largest share of Saudi population (23.3%). With an increase in the

number of Saudi natives in a region, the number of non-Saudi also increases. In other

words, rural to urban migration of Saudi natives leads to an increase in non-Saudi

population in the urban area.

Fig. 7- Temporal Change in Water from 2000-2013.

Water spatio temporal distribution shows increase in water body shown in figure 7. Water

supply in Saudi Arabia is considered as challenging. One of the main challenges in Saudi

is water scarcity. This problem has improved during the studied period. Saudi

Government has made substantial efforts to overcome water shortage. Significant funds

have been take on in seawater desalination, water distribution, and sewerage and

wastewater treatment. Today about 50% of drinking water comes from desalination, 40%

from the mining of non-renewable groundwater and only 10% from surface water in the

mountainous South-West of the country (Thomas et al., 2011 and Tong et al., 2009). In

0

5

10

15

20

25

5 10 15 20 25 30 35 40 45 50 55 60

Are

a Sq

Km

Zones

Temporal Change in Water from 2000-2013

2000

2013

2000 the average area of water bodies was about 33.6 km2 that increase about 21.42

km2 in 2013 leads to total average coverage is approx. 55.0575 km2.

Fig. 8- Temporal Change in Barren Land from 2000-2013.

Barren land has reduced which converted into built up, vegetation and water bodies due

to increase in population. In 2000 the areal coverage of barren land was 5743.22 Km2

which reduced by area of 346.81 Km2 in 2013 shown in figure 8.

Fig. 9- Temporal Change in Vegetation from 2000-2013

Increase in vegetation was observed from 2000 to 2013 shown in figure 9. In 2000 the

area was 334.01 Km2 which increase up to 417.17 Km2 in 2013. The total increase in

area was about 83.15 Km2. Center pivot irrigation in Saudi Arabia is typical of many

isolated irrigation projects scattered throughout the arid and hyper-arid regions of the

0

200

400

600

800

1000

5 10 15 20 25 30 35 40 45 50 55 60

Are

a Sq

Km

Zones

Temporal Change in Barren Land from 2000-2013

2000

2013

0

10

20

30

40

50

60

70

5 10 15 20 25 30 35 40 45 50 55 60

Are

a Sq

Km

Zones

Temporal Change in Vegetation from 2000-2013

2000

2013

Earth. Nonrenewable Fossil water is mined from depths as great as 1 km (3,000 ft),

pumped to the surface, and distributed via large centre pivot irrigation feeds. The circles

of green irrigated vegetation may comprise a variety of agricultural commodities from

alfalfa to wheat. Diameters of the normally circular fields range from a few hundred meters

to as much as 3 km (1.9 mi). Due to central pivot system technologies transformed the

vast tracts of the desert into fertile farmland.

Fig. 10- Areal Change in Landcover from 2000 -2003

The pyramid graph of the zonal change in Riyadh from 2000 to 2013 (figure 10) shows

that the increase in urbanization was started at 15 km from the city mean and at the same

distance the decrease in barren land was started. After this distance the increase in

urbanization and decrease in barren land was continued at 30 km. The maximum

increase in urbanization was found at 20 km from the city mean with maximum decrease

in barren land. In the same zone vegetation class was also found to be increased. At the

-150 -100 -50 0 50 100 150

5

10

15

20

25

30

35

40

45

50

55

60

AREAL CHANGE SQ KM

ZON

ES

Areal Change in Landcover from 2000 -2003

Vegetation

Barren Land

Built up

Water

fringes of the city the vegetation was found to be increased with decrease in barren land

i.e. this barren land was converted into vegetation.

CONCLUSION

This study has assessed landcover changes and the dynamics of urban expansion in

Riyadh, Kingdom of Saudi Arabia (KSA) using GIS and Remote Sensing data. Urban

expansion was quantified for the last 13 years using the post-classification comparison

technique. Riyadh was found to have experienced rapid changes in landcover, particularly

in built-up/urban areas. Analysis revealed that urban areas increase by 37% with 6%

decrease in barren land from 2000 to 2013. The water and vegetation class was also

found to be increase with 64 and 25 % respectively. The increase in vegetation is a good

and positive sign in urban development as many researchers insist that vegetation should

also be increase with increase in urbanization.

In the future, the spatio-temporal modeling of urban sprawl may be done and will help us

to better understand the evolved urban patterns of Riyadh.

Geospatial techniques including Geographical Information System (GIS) and Remote

Sensing (RS) is essential for dealing dynamic phenomenon, like urban sprawl. Without

remote sensing data and GIS analysis, one may not be able to monitor and estimate the

urban sprawl effectively over a time period, especially for elapsed time period.

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