Land use and cover change assessment using Remote Sensing ... · International Journal of Geomatics and Geosciences Volume 3 Issue 3, 2013 554 2. Study area The study focuses on Dohuk,

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 3, No 3, 2013

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4380

Submitted on January 2013 published on March 2013 552

Land use and cover change assessment using Remote Sensing and GIS:

Dohuk City, Kurdistan, Iraq (1998-2011) Jambally Mohammed

School of Planning, University of Duhok, Dohuk, Kurdistan region of Iraq

[email protected]

ABSTRACT

Land cover undergoes continuous changes around the world, especially in highly populated

areas. This phenomenon can be attributed to human activities including population growth

and the need of more housing. Dohuk, like many other cities in Iraq, has undergone a rapid

urban growth mainly due to the population growth after 2003. With the absence of efficient

urban planning systems and regulations in the past, this city has witnessed uncontrollable

urban growth with adverse environmental impact. This growth has encroached upon

peripheral agricultural lands, caused considerable lack of open spaces, and resulted in a

degraded urban environment, e.g. higher proportion of concrete and asphalt surfaces and

increased temperature in summer. This paper follows an integrated approach of using remote

sensing and geographic information system (GIS) to measure and analyze the urban growth

in Dohuk city over three periods of time (1998, 2007, 2011). Its main goals are to

demonstrate the effective use of such modern techniques in mapping, identifying, and

assessing land use/land cover (LULC) changes and urban growth trend and in addressing the

current lack of urban growth information by providing relatively accurate data to help the

planners in identifying the driving forces behind current LULC changes and in managing the

urban growth in a more systematic manner. Spatial patterns of LULC change were identified

through LULC classification and change detection analyses conducted on multi-temporal

Landsat Thematic Mapper (TM) data. Three Landsat satellite images for the study area for

1998, 2007 and 2011 were processed and classified into three LULC categories: Vegetation,

Barren Land, and Urban/Built-up Land. The results show a remarkable increase in the

urban/built-up area with corresponding decreases in the barren land and vegetation areas

during 1998 and 2011. The paper also demonstrates that the city is growing rapidly which

could assimilate more agricultural and rural areas.

Keywords: Land Use/Cover, GIS, Remote Sensing, Urban Growth, Landsat imagery, Dohuk.

1. Introduction

Urbanization is a significant global phenomenon and it becomes a great concern for many

parts of the world. Thompson Warren defines this concept as the "movement of people from

agricultural-based communities to other larger communities that primarily depend on

government, trade, manufacture" or personal interests as main activities (Sociology Guide,

2012). Urbanization is an inevitable process in the areas that experience rapid economic

developments and population growths. Urban growth accelerates movement of residential

and commercial land uses to rural areas, thus creating noticeable effects on land use through

sprawl (Rimal, 2011). It has an adverse impact on the urban map, especially when it involves

movement of large populations from rural to urban areas which creates a subsequent increase

in the urban population and conversion of other types of land to uses associated with the

growth of populations and economy (Sociology Guide, 2012). Urban growth and urban

Land use and cover change assessment using Remote Sensing and GIS: Dohuk City, Kurdistan, Iraq

(1998-2011)

Jambally Mohammed

International Journal of Geomatics and Geosciences

Volume 3 Issue 3, 2013 553

development or urbanization are two major factors that influence land uses and plague most

urban areas throughout the world. They bring unrestrained impact on land use/land cover

(LULC) change and patterns, and create critical imbalances among patterns. LULC changes

have become the main challenge of the present world, and they create severe threats for major

cities of the world (Rimal, 2011). Masser (2011) noted that monitoring and evaluating urban

change is a major issue in urban planning and management throughout the third world.

An unplanned urbanization process is now a major problem, especially in the developing

countries where there is lack of consistent and reliable data including spatial data. Such

situation is highly evident in Iraq where conflicts, political and factional instability, and

subsequent internal displacements and migration; population growth; and improved economic

opportunities are the driving forces behind the high level of urban growth, particularly in the

big cities of the northern Kurdistan Region that enjoys a complete state of security and

economic stability. Reference (Guttenberg, 1959) indicated that land use is a key term in the

language of city planning. A success of urban planning requires a proper regulation of land

uses in order to avoid uncontrollable urban growth or sprawl. It is also known that land use

plans are implemented through land use ordinances and regulations, such as zonings, and that

the urban growth boundary is one form of land-use regulation for controlling sprawl and

random movements from urban centers towards agricultural countryside. However, such

means were not effective in Dohuk or are still not strongly enforced there. This could also be

attributed to the absence of an efficient system for acquiring data about the local urban

movements and patterns.

In view of these facts, there should be a wise and rational use of lands in order to make a

balance between the life necessities that use lands as a source. While urban areas are usually

dominated by human influence and the associated land use patterns, an efficient method

should be used to deal with land use change processes. Such method certainly formulates the

basis of any sound urban planning and should be built upon reliable data. Policy makers and

planners need to make use of all available tools and data to detect LULC changes and urban

growth trends to make sound and precautious urban plans and sprawl control/mitigation

systems. Reliable and accurate data are required for development of urban plans; however,

such data cannot be easily obtained without using an efficient technology such as satellite

imagery. Remote sensing, which is the extraction of information about an object on the earth

without making physical contact with it (Robert, 2007; John, 2007), is considered as a

powerful tool in the change detection of the land surface; therefore, urban growth is highly

involved in remote sensing and GIS applications (Al Fugara, B. Pradhan and T. Mohamed,

2009; Geymen and Baz, 2008; Treitz and Rogan, 2003). Masser (2011) also demonstrated

that GIS is an appropriate tool for applications in the field of urban planning and management

because it integrates information from different sources. Remote sensing and GIS have been

proved to be effective and accessible means for extracting and processing spatial information

obtained from satellite and aerial images for monitoring urban growth. The correlation

between population growth and urban growth can be determined from the Landsat-derived

change maps (Masser, 2011).

This study intends to determine the extent of LULC change in Dohuk city throughout the

period under study (1998-2011), to highlight the driving forces behind these changes, and to

suggest appropriate recommendations to address the current rapid urban growth. While

accurate and LULC information/databases are not available for the study area, this paper uses

the powerful technology of remote sensing and GIS to process three Landsat satellite

imageries for the detection, measurement, and analysis of LULC changes in this part of Iraq.


(1998-2011)

Jambally Mohammed



2. Study area

The study focuses on Dohuk, a city located between latitudes (N 36º48’32’ and N 36º53’15’)

and longitudes (E 42º55’29’ and 43º0’34’), as shown in figure 1. Dohuk is situated in the far

northwest of Iraq, about 470 km north of Baghdad, the capital of Iraq. This city is the center

of Dohuk province, one of the three provinces of Iraqi Kurdistan Regional Government

(KRG), and the center of Dohuk district, one of the six districts of Dohuk province. Dohuk is

a city of interest, notably because of its historical and geographical characteristics. It has a

strategic location since the international strategic transport road that connects Iraq to Turkey

and Syria passes through its territory. Dohuk is situated in a wide valley extended between

two opposing mountain ranges, namely Bekher Mountain in the north and Zawa Mountain in

the south, as shown in figure 2. From the east, it is bordered by Etit village at the foothill of

Mamseen Mountain. From the west, it is opened to Semel agricultural plain. Therefore, it

takes the shape of an irregular long triangular strip and it is elongated from east to north-west.

Two small streams flow through Dohuk with decreased water level in summer. Both rivers

meet in the south-west of the city to form Dohuk River that flows into Mosul Lake, about 22

km south west of Dohuk. The water of these streams is used for irrigating the fruit farms that

historically spread on their banks. Dohuk Dam was built over Dohuk valley catchment to the

Figure 1: Location of the study area


(1998-2011)

Jambally Mohammed



north of the city during 1980-1988 for irrigating vast rain-fed agricultural lands, which are

located to the west of the city center and extend up to neighboring Semel district (Chatty,

2010; Brendan, M. John and S. Khaled, 2005; Wikipedia). However, this dam has not been

used for the intended irrigation purposes and the targeted agricultural lands have been

gradually converted into urban uses. The dam does not fall within the study area

Dohuk has undergone a fundamental change in LULC patterns due to accelerated economic

development since 2003. Urban growth has been speeded up and considerable agricultural

lands have disappeared. This urban march has greatly accelerated especially because of lack

of appropriate land use planning including a balance between urban/built-up and other land

uses especially green cover. As a result, Dohuk has been growing at unprecedented rates,

creating extensive urban landscapes and facing growing problems of urban growth and loss

of open spaces and vegetation. This city has expanded remarkably over the last 35 years.

Landlocked by mountains on three sides, Dohuk has grown as a 2 x 2 km small compact town

with an agricultural rural-characteristic community of an estimated population of 70,000 in

1977 to an approximately 15 x 3 km city of over 400,000 persons now. The greatest urban

development took place during 1973-1984, 1986-1994, and 2006-2011. The initial settlement

was historically established in a central location in the valley between the two mountains,

shown in figure 3. It was initially inhabited by a limited number of families who occupied

certain close-knit neighborhoods. This location was surrounded by eleven villages with a

radius distance of about four km. Since 1970s, the compact city center started to expand

taking a radial shape and encroaching upon the agricultural lands primarily in the western and

eastern parts inside the valley. Over time, more residential neighborhoods were established

around the city center, and the expansion moved further during 1980s to annex the southern

parts to reach the foothills of Zawa Mountain. The expansion had doubled towards the

Figure 2: Aerial image of the study area, 2010


(1998-2011)

Jambally Mohammed



western open spaces in late 1990s, registering slower population densities. After 2000, the

city began to expand further toward the western plains, eastern countryside, and marginal

lands at the foothills of the two mountains, as shown in figure 4. (Dohuk Urban Planning

Directorate, 2007). Due to insufficient land allocation and policies to cope with the

challenges of shelter delivery and poor living conditions of the people, informal settlements

and slums, characterized by weak housing structures and poor services, have plagued the city

at the periphery. Still some new areas had been developed as informal settlements that were

originally villages located within Dohuk periphery and later annexed to the city urban extent.

The population density in Dohuk ranged from less than 50 persons per hectare to more than

250 persons per one hectare, based on the residential areas in 2007. For example, in older

neighborhoods, which were established around the city center prior to 1990s, the population

density reached over 250 persons per hectare (Dohuk Urban Planning Directorate, 2007).

With the improved living conditions, increased marriages, and subsequent need of new

families to separate from nuclear families, the need arouse for more housing. With the

economic boom witnessed by the region after 2003, there has been an increased reduction in

the amount of lands allocated for open spaces or conservation, and there has been a

remarkable encroachment of urban development upon these zones. According to Master Plan

Dohuk City 2007, about 68% of Dohuk area was residential in 2006 and that this city has

preempted eight out of eleven villages on its periphery to annex them as urban quarters (see

Figure 3: Views of Dohuk city in 1950s and in 2007

Figure 4: Development Phases of the study area (Source: Master Plan Dohuk City, 2007)


(1998-2011)

Jambally Mohammed



figure 5). Because of the high value of land and inability of most inhabitants to acquire larger

parcels of lands, the prevalent housing types in Dohuk is attached, single-family, 1-2 story

houses (usually 200 sq. meter). The houses are often are placed wall-to-wall and back-to-

back with relatively no open spaces in between. As a result, the compact horizontal expansion

has been the common housing mode.

3. Methods and materials

3.1. Data preparation

The study area is limited to the current administrative border of Dohuk city which was

digitized and processed by the author using ArcMap 9.3.1, the latest version of Google Earth

image as well as the handy functions of Erdas Imagine 9.2. Meanwhile, a combination of

softwares (Elshayal Smart Web on Line Software, Global Mapper, and Ozi Explorer) was

used to download (from Google Earth), rectify, and project two satellite images for the city

for October 2004 and May 2010 for use in processing the Landsat images. The study is based

on three 7-band TM images (with a resolution of 30 x 30 m pixel) acquired for the study area

(on 13 June 1998, 14 June 2007, and 3 July 2011) from Landsat 4 and Landsat 5. These

images were obtained with path/row 17/34 from the U.S. Geological Survey website. Using

Erdas Imagine, five bands (1, 2, 3, 4 and 5) of each image were stacked, rectified to Universal

Transverse Mercator coordination system (WGS 1984, zone 38N), and masked to the

boundary file to subset the study area. The processed images are presented in figure 6.

3.2. Image classification

Multispectral image classification is used in remote sensing to categorize all pixels in an

image to produce thematic maps of the existing land cover (Levin, 1999). Spectral

information represented by the digital numbers in one or more spectral bands is used to

classify each individual pixel. Two types of classification (supervised and unsupervised) are

used for processing satellite images. Each of these methods groups the pixels of an image into

homogeneous classes in order to portray the spatial distribution of different features detected

Figure 5: Peripheral villages around historical Dohuk city center


(1998-2011)

Jambally Mohammed



Figure 6: Masked Landsat images of the study area


(1998-2011)

Jambally Mohammed



by the satellite sensor. The choice of the methodology depends on the strategy of sampling

pixels and the analyst’s knowledge of the study area (Favretto, 2006). The supervised

classification depends on the experience and accuracy of the user and his optical capability in

detecting the signature differences between various patterns in the satellite image while the

unsupervised classification doesn’t require a direction from the user and it depends on the

computer to separates the pixels into classes (Sabins, 1997). Since the supervised

classification does not efficiently differentiate between various LULC patterns, the

unsupervised classification was used in this study to provide more accurate results. The three

masked TM images were classified into 50 classes, which were later recoded into three major

categories: Vegetation, Barren Land, and Urban/built-up Land, following the land cover

classification scheme defined by Anderson et al (Anderson, Hardy, Roach and Witmer, 1976).

Shapefiles of major roads and streets within the study area in 1998, 2007, and 2011 were

digitized, processed, and superimposed (forced) on the respective classified imagery to

produce the final LULC map. The author's familiarization with the study area, availability of

Google Earth's imagery for 2004 and 2010, and ground control points have helped in proper

identification of the LULC classes. A set of rules was set to ensure a proper classification

process. For example, the land used as a parking lot or a dirt road was classified under

Urban/Built-up, an agricultural field cultivated to cereal crops or a natural pasture land was

classified under Barren Land, and a park with green turf or a regularly vegetated grapevine or

almond farm was classified under Vegetation. The classified images are presented in figure 7.

3.3. Accuracy assessment

Accuracy assessment is a valuable tool and a critical step for determining the quality of the

information derived from remotely sensed data. This process defines the degree of coherence

of the classified image with the ground truth (Congalton, & Green, 1999; & Martellozzo &

Clarke, 2011). The accuracy of an image classification depends on the number of samples

taken for each class and the quality of reference images used for analysis. The accuracy

assessment usually evaluates the effectiveness of classifiers (Ngigi et al., 2008) with the help

of field data by testing the statistical significance of a difference through computation of

kappa coefficients (Congalton, Oderwald & Mead, 1983) and the overall accuracies. A

considerable number of (reference) pixels are taken from the classified image and compared

with a reference map of higher authority to evaluate correctness of the classification process

(Jensen, 1996). This comparison is built on an error matrix (overall accuracy) and use of a

sampling strategy for selection of pixels. The Kappa coefficient ranges from 0 to 1; values

higher than 0.7 are considered acceptable, while those equal to or lower than 0.4 identify a

very low correlation between the classified image and the ground truth (Jensen, 1996).

Table 1: LULC classification scheme for the study area

LULC Type Description

Vegetation

Irrigated fruit orchards, rain-fed forest trees (deciduous and evergreen), irrigated

and rain-fed grapevines, rain-fed almond farms, plant nurseries, parks, gardens,

and playgrounds with green turf.

Barren Land

Seasonal croplands (wheat, barley, etc.), sparsely vegetated rain-fed pastures and

grasslands, mountainous and rocky areas, and other lands that are not disturbed by

urban development or encroachment.

Urban/

Built- up

Land

Residential, commercial, services, utilities, public and private infrastructure;

buildings, roads, concrete and asphalt surfaces, built-up and other lands disturbed

by development or human intervention.


(1998-2011)

Jambally Mohammed



Figure 7: Classified Landsat images of the study area


(1998-2011)

Jambally Mohammed



The accuracy for each of the three classified images was assessed taking as a reference

available images and maps of respective time period. This process was supplemented with

previous knowledge and ground checks. Accuracy assessment was performed for a total of 50

pixels on each classified image, using random sampling strategy for selection of the pixels. In

addition, a set of ground control points (150) was collected from well-known locations and

landmarks such as old public buildings and hills in the study area with a hand-held GPS

receiver in the summer of 2012. Of these field data, 60% were used for classification, while

the balance was used for the accuracy assessment. This information was supplemented with

Google Earth and the author’s previous and current knowledge of the object scene of the

study area.

Table 2: Summary of error matrixes for the classified images for the study area

Reference Map Image Overall

Accuracy

Kappa

Index

Topographic map 1998 + Google Earth image

2004 + ground control points Landsat 1998 85.33% 0.7473

TerraServer image 2007 + ground control points Landsat 2007 86.00% 0.7834

Google Earth image 2010 + ground control points

(ground checks) Landsat 2011 90.00% 0.8438

The results indicate that the overall accuracy for the three classified Landsat images is above

the minimum acceptable level of accuracy (85%) to be used for efficient LUCC analysis and

modeling (Pontius, 2003). This is the accuracy value for reliable land-cover classification set

by Anderson et al. (Anderson, Hardy, Roach and Witmer, 1976). The Kappa coefficient, a

measure of agreement, can also be used to assess the classification accuracy (Congalton,

1991). This coefficient often appears to be low (Muzein, 2006) as it considers both the actual

agreement in the error matrix and the chance agreement (Congalton, 1991). Kappa coefficient

calculated for the three images for 1998, 2007 and 2011 are 0.74, 0.78 and 0.84, respectively;

these are acceptable values since they are above the threshold of 0.7 (Jensen, 1996).

3.4. Image analysis

Remote sensing techniques have been proved as a valuable tool in mapping urban areas and

becoming data sources for the analysis and modeling of urban growth and land use change

(Batty and Howes, 2001). Remote sensing provides spatially consistent data sets with large

geographical coverage, high spatial detail, and high temporal frequency (Martin, 2003). It can

also provide consistent historical time series data (Batty and Howes, 2001). There are many

techniques that can be used for urban LULC mapping for change detection from satellite data

(Masek, et al., 2000). However, selection of a good change detection method could be

sometimes difficult (Lu and Weng, 2004). Therefore, effective image classification for urban

LULC mapping and change detection depends on many factors, with main consideration

given to the selection of available multi temporal data, processing and method of image

classification.

In this study, LULC change detection and analysis are based on a series of afore-mentioned

data and image processing operations. Spatio-temporal LULC detection and analysis were

carried out using unsupervised pattern classifier. Unsupervised classification with 50 sample

points was run on a composite of five bands (1, 2, 3, 4 and 5) for each of the three Landsat


(1998-2011)

Jambally Mohammed



images. The produced LULC thematic layers were used as a basis for the detection and

analysis of urban change in the study area by grouping the spatial features associated with the

LULC into three major categories: Vegetation, Barren Land, and Urban/Built-up Land, as

illustrated in figure 7 and table 3. LULC change results are also obtained by comparing the

reclassified raster images of two time periods (Anurag, 2012). For this end, ESRI ArcGIS

with its Spatial Analyst Extension was used for proofing the LULC detection and analysis

done in Erdas Imagine. Overlay analysis was done using the raster calculator tool in ArcGIS.

The classified raster maps of LULC data for the three respective years were subtracted from

each other to find areas of LULC change over two time spans (1998-2007 and 2007-2011), in

addition to an overall span (1998-2011), as presented in Figure 8. For example, the

reclassified raster image of 2007 was subtracted from the respective image of 1998 to find

out the changes in LULC (the three categories) during 1998-2007. To isolate areas of change,

the produced image was reclassified, maintaining the cells in the areas with changes. The

same procedure was applied to the two reclassified images of 2007 and 2011, and also of

1998 and 2011 to detect the LULC changes during 2007-2011 and 1998-2011, respectively.

The three final images present an overall picture of the degree and directions of changes in

LULC in the study area. On the basis of these images, the change in the urban/built-up land

cover was detected and mapped for the three periods of 1998, 1998-2007, and 2007-2011.

Figure 9 shows three new reclassified rasters over the respective periods as superimposed on

each other to produce a final map that outlines changes in the urban/built-up land cover

during the afore-mentioned three periods.

4. Results and discussion

4.1. Change detection analysis

An estimation of the area of each classified LULC type or surface was made on the basis of

the number of existing pixels. The three LULC types were computed by multiplying the pixel

dimensions (30m x 30m) by the total number of pixels on respective surfaces. As shown from

Table 3 and Figure 10, a remarkable increase was registered for the urban/built-up land in

parallel to decreases in the barren land and vegetation during 1998-2007 and 2007-2011. The

area of urban/built-up land registered a continuous, but irregular, increase of 191% during

1998-2007 and 40% during 2007-2011. Oppositely, the area of barren land decreased on a

steady basis by -11% during 1998-2007 to -22% during 2007-2011. As for the area of

vegetation, there was a decrease of -50% during 1998-2007 but it was reversed to an increase

of 26% during 2007-2011.

Table 3: Area classified for each land cover type for the study area

Class

Area

1998 2007 2011

Acre Sq.

Km % Acre

Sq.

Km % Acre

Sq.

Km %

Vegetation 4,795 19 20.65 2,393 10 10.30 3,025 12 13.03

Barren Land 16,260 66 70.01 14,523 59 62.53 11,374 46 48.98

Urban/built-

up Land 2,169 9 9.34 6,308 26 27.16 8,825 36 38.00


(1998-2011)

Jambally Mohammed



Figure 8: LULC change in the study area


(1998-2011)

Jambally Mohammed



The total percentages of changes, presented in table 4, in the urban/built-up land, barren land,

and vegetation during 1998-2011 are 231%, -32%, and -24%, respectively. The increase in

the urban/built-up area is attributed to the population growth and immigration of large

populations from the rural areas and other parts of Iraq with deteriorated security sit uation to

the study area, improved living conditions of the local people and the subsequent increased

demand on housing, and extensive horizontal expansion of the study area. Such urban

expansion was made at the expense of barren land and vegetation. It is to be noted that the

increase in the vegetation cover gained during 2007-2011 was attributed to more attention

paid by the government to improving the green cover by rehabilitating/establishing parks and

gardens, planting more trees, and encouraging vertical expansion through construction of

apartment complexes with acceptable proportions of green cover (trees, parks, green turf).

In general, the biggest increase in the urban/built-up land is observed in the western area, a

solid indicator that the rapid urban growth of this city will continue in that direction to

assimilate more agricultural lands and annex neighboring Semel town. This is evident, as

shown in figure 9, from the existing topography – enclosure by mountains form the north and

the south – that pushes the urban growth more in the western direction.

Table 4: Observed growth in LULC types (in km2)

LULC Class 1998-2007 2007-2011 Total

Vegetation -50% 26% -24%

Barren Land -11% -22% -32%

Urban/Built-up Land 191% 40% 231%

4.2. Driving forces

There are several factors that have directly and indirectly caused considerable changes to

Dohuk LULC. Population growth is a major one. The total population of this city has steadily

increased by 54% during 1998-2007 and by 14% during 2007-2011, with an annual growth

Figure 9: Urban/Built-up extent of the study area


(1998-2011)

Jambally Mohammed



rate of 6.8% in early 2000s. This increase can be attributed to the promoted birth growth as

the inhabitants are mostly governed by inherited social traditions that encourage early age

marriages and having more children. This accounts for the average family size to reach 6.7

persons in 2000s. This tradition, combined by improved living conditions of the residents and

a stabilized region, has led many individuals to get married at early ages (Dohuk Urban

Planning Directorate, 2007; UNDP, 2004). During fourteen years (from 1998 to 2011),

Dohuk urban population grew by 76%, about three times less than rate (231%) of urban/built-

up growth. This means that land was being inefficiently used.

Table 5: Urban growth vs. population growth in the study area

1998-2007 2007-2011 Total

Urban/built-up growth 191% 40% 231%

Population growth 54% 14% 68%

Immigration for employment and better living opportunities is another fact that has

contributed to the increased urban growth in Dohuk. Since mid 2000s, large numbers of the

rural population immigrated to Dohuk, seeking better employment opportunities and access

to services. This can justify the fact that the population of this city represented about 30% of

the overall population of Dohuk province in 2006 (Dohuk Urban Planning Directorate, 2007;

Swiss Refugee Council, 2007). Compulsory displacement of people functions similarly.

Dohuk has received an influx of people displaced from the central and southern troubled Iraqi

areas, especially Baghdad where there have been widespread violence and collapse of law

and order after 2005. Dohuk, like other KRG areas, has enjoyed since 2003, a safe and stable

situation and prosperous business opportunities that have attracted such great numbers of

internally displaced people (IDPs). For example, there were 11,482 IPDs in Dohuk city in

2007 (UNHCR, 2007).

Improved economy, living conditions, and purchasing power of the inhabitants have also

contributed to the rapid growth of the study area. Since 2003, Kurdistan region including

Dohuk has experienced an economic boom because of a combination of factors including the

Figure 10: Trend of LULC change in the study area, 1998-2011


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Jambally Mohammed



established government and public administration, staple political and security situation, and

favorable business environment. This boom is largely attributed to the increased financial

resources provided for KRG provinces. Unlike the previous period (before 2003) when

Dohuk province mainly depended on tax revenues generated from the imports and

international trade with Turkey, now Dohuk receives a stable share of funds from KRG

budget, which is about 17% of the total annual Iraqi oil-based budget (USAID, 2008). As a

result, the number of public employees with good earnings has increased remarkably in

Dohuk. This factor together with the free distribution of housing land plots to the employees

has enabled many of them to have access to new houses.

5. Conclusions and recommendations

The present study has detected and assessed the trend of urban LULC in Dohuk city, using an

integrated approach that included GIS and remote sensing tools as well as statistical

calculations. It has demonstrated an effective utilization of this approach in detecting LULC

change and assessing extent of urban growth without depending on costive and inefficient

land surveys. It has shown a considerable increase in the urban/built-up area during 1998-

2011. It has presented a clear picture of a potential occurrence of sprawl toward the western

plain in view of the current urban growth trend. This growth is expected to increase with

more encroachment upon agricultural lands and open spaces, if a wise urban planning

approach is not developed and enforced. Meanwhile, the study has addressed the gap in the

lack of information about the existing LULC types which can assist the planning authorities

in formulating a solid planning system for the growth of the study area. The output of this

research can be used as a model to trigger similar initiatives at official levels, namely by the

planning authorities, in an effort to detect and measure LULC change, and address current

urban problems.

In view of the study results, a set of recommendations are presented here for creating a

balance between the urban/built-up and green cover in Dohuk and ameliorating its urban

environment and climate. A holistic approach needs to be adopted for increasing the green

cover in the city at the expense of cement and asphalt surfaces. This can be achieved through

various means such as plantation of more trees, especially on sidewalks in the residential

areas and on public/private properties. This also requires community awareness in the

adverse impact of current inefficient urban form and the importance of establishing an

environment friendly city. Meanwhile, a study needs to be conducted on the possibility of

reforesting the barren slopes and foothills of Bekher and Zawa mountains. Accordingly, an

action plan can be developed to bring back the green cover to at least some portions of the

two mountains on an efficient and sustainable manner. A more rational and favorable vertical

housing approach with international standards should be enforced and encouraged to

minimize the horizontal compact development. In addition, affordable green, environment-

friendly housing projects should be encouraged. Most importantly, the planning and decision-

making authorities must integrate new technologies, such as remote sensing and GIS into

their decision making process. Using remote sensing data and information to understand the

dynamics of the urban environment may contribute to better urban policy and management.

Acknowledgements

The author would like to thank the Department of Geography at Texas Tech University,

Texas, USA for their generous assistance in enabling him to acquire a high-resolution

TerraServer imagery for the study area which was used as part of this study. Special thanks


(1998-2011)

Jambally Mohammed



go to Dr. Tina Delahaunty, Associate Professor at the same department for taking great

efforts in providing guidance and in supervising major steps of the implementation of this

study.

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Land use and cover change assessment using Remote Sensing ... · International Journal of Geomatics and Geosciences Volume 3 Issue 3, 2013 554 2. Study area The study focuses on Dohuk,