The goal of this study is to spatially portray Taif’s urban expansion and determine for last 30 years, from 1990 to 2020. It is only including the residential neighborhoods approved by the Taif Municipality, which is responsible and organized for urban planning in the city. The geographical location of the city of Taif is a vital crossroad between eastern and western parts of the Kingdom of Saudi Arabia , which made it a tourist destination, as well as commercial and agricultural preference for many years, as it was considered the summer capital of the KSA. Moreover, it serves as the entrance to Makkah city from the eastern side. The proposed study has necessitated because the lack of recent scientific studies that dealt with the spatial analysis of urban expansion and its trends in the city of Taif and follow the stages of expansion during periods of time by relying on remote sensing and geographic information systems (GIS) techniques. The many development projects in the city of Taif, such as Taif International Airport, the new Taif project, and other projects, which will cause an increase in demand for residential, commercial, industrial and service units have also prompted the proposed study. This was investigated using a multitemporal Landsat data for the years of 1990, 2002 and 2020, as well as census data from 1990 to 2020, along with Remote Sensing (RS) and Geographic Information System (GIS) techniques. The results revealed that over the last 30 years, urban land cover has increased by 20 , 448 (ha) whereas other land covers, such as green area, have decreased significantly by 14 , 554 (ha). The results also indicate that the increase in urban areas amounted to 114.8% during the period from 1990 to 2020. The locations of new developments such as Taif airport, Taif university, Ministry of Housing projects, etc. were located to the North and Northeast. This is due to the area’s topography, which played a major role in determining the direction of urban expansion. According to the study, multiple urban centers, rising low-density dispersed communities, and leapfrogging growth were all hallmarks of urban expansion in Taif. The study demonstrated that Taif is at risk of ecosystem loss as a result of continued urban expansion. To ensure environmental sustainability, the current effort asks for actions that will restrict urban sprawl and prepare the city for future growth.
The Kingdom of Saudi Arabia (KSA) has witnessed rapid urbanization since 1950, with increased the population in urban regions has from 21% in 1950, through 58% in 1975, to 83% in 2015, and is estimated to reach 86% by 2030 [
The name Taif means “encompassing” in Arabic [
visitors from across Saudi Arabia by its a pleasant climate [
The geology of Taif is characterized by the fact that they are mountainous rocks. However, Taif city is subdivided into two distinct regions, a mountainous region in the west and in the southwest, and a desert region in the northeastern and eastern parts of the city. There are also more than 400 mountain peaks in the province, varying in height and size, and most of them are concentrated in the western and southern parts of Taif. The highest peak is Mount Dhaka, 2585 meters above sea level. Generally, Taif is located at an average elevation of 1800 meters above sea level. The present study utilized Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) with 1 arc-second resolution (30 meters) to show the topography of Taif, which can be seen in
Population growth and population density are important factors for urban studies, which help to explain many changes that occur in the region. The population of any area affects the city’s environment and the land use. Migration from the countryside is also considered one of the most important reasons for continuous urban growth. The population in Taif city was 381,000 in 1990. In 2010, the population of the city of Taif was 582,000 people, according to the population census approved by the General Authority for Statistics. At the present time, the population of Taif city is expected to reach 689,000 people, and expected to reach 775,000 in the next ten years.
| Year | Population | Growth Rate |
|---|---|---|
| 1990 | 381,000 | - |
| 1995 | 438,000 | 2.82 |
| 2000 | 482,000 | 1.92 |
| 2005 | 529,000 | 1.88 |
| 2010 | 582,000 | 1.92 |
| 2015 | 640,000 | 1.92 |
| 2020 | 689,000 | 1.49 |
This section describes the methodology to be used to analyze urban expansion in Taif to achieve the research objectives. Multi-temporal Landsat images for the year of 1990, 2002 and 2020 were checked to ensure the comprehensiveness of these images to cover the study period. These Multi-temporal Landsat images will used to create urban expansion map using ERDAS Imagine 2014 and ArcGIS 10.2. Details of the process are presented in subsequent sections.
Satellite images of Taif city were downloaded from the United States Geological Surveying (USGS) website (www.earthexplorer.usgs.gov). These images are for different periods of time by Landsat 4, 5, and 8 satellites. Dates of acquisition of each year as well as the sensor name are as follows:
- Landsat 4 image, captured on 27/8/1990.
- Landsat 5 image, captured on 19/7/2002.
- Landsat 8 image, captured on 16/10/2020.
After obtaining satellite images for the study period, these images undergone preprocessing stages. In order to prepare the satellite images for analysis, interpretation, and classification various steps and processes are available. Numerous researchers and scientists have used different techniques to improve the quality of satellite images such as geometric correction, radiometric correction, enhancement, classification etc. [
There are two types of classification techniques, that are widely used in the scientific community to analysis and assign land cover classes to pixels. They are supervised and unsupervised classifications which are used according to several criteria, conditions and use cases. In this study, we have used a supervised maximum likelihood classification to classify images of 1990, 2002 and 2020 by virtue of our knowledge of the studied area, which makes us avoid as much as possible the spectral problems in the image such as the spectral similarity between terrestrial objects and the weakness of the rays reflected or emitted from those objects as shown in
Accuracy assessment is one of the most important final steps in classification process. In the general term, it is used for comparing the land cover classification results to geospatial data that are assumed to be true as Google earth or Google map. The purpose of accuracy assessment is to quantitatively assess how effectively the pixels were sampled into the correct land cover classes [
| NO | Classes | Definitions |
|---|---|---|
| 1 | Urban Area | Areas consisting buildings |
| 2 | Green Area | Area consisting farms and bushes |
| 3 | Space Area | Areas consisting mountains, flat land, and others |
As a result of selecting pure training sites within each class, considering intra-class variability, and distributing training areas evenly over the study area, we were able to accurately classify the images. The overall accuracy of image of the 2020 Landsat 8 was 93.08%, and Kappa coefficient was 0.891. Classification of urban area had moderately high user accuracy and high producer accuracy. Green area had moderately high user and producer accuracy. Furthermore, space area had high user accuracy and moderately high producer accuracies of 100% and 87.1%, respectively.
| Classes | ||||||
|---|---|---|---|---|---|---|
| Class | Urban Area | Green Area | Apace Area | Row Total | User’s Accuracy | |
| Classified data | Urban Area | 42 | 1 | 7 | 50 | 84 |
| Green Area | 0 | 25 | 1 | 26 | 96.15 | |
| Space Area | 0 | 0 | 54 | 54 | 100 | |
| Column Total | 42 | 26 | 62 | 130 | 278.15 | |
| 100 | 96.15 | 87.1 | Overall Accuracy | 93.08% | ||
| Kappa coefficient | 0.891 | |||||
| Land cove | 1990 | 2002 | 2020 | |||
|---|---|---|---|---|---|---|
| Area (ha) | % | Area (ha) | % | Area (ha) | % | |
| Urban Area | 17,809 | 2.02 | 25,502 | 2.89 | 38,257 | 4.34 |
| Green Area | 33,093 | 3.75 | 27,146 | 3.08 | 18,539 | 2.10 |
| Space Area | 831,420 | 94.23 | 829,710 | 94.03 | 825,520 | 93.56 |
| Total | 100% | 100% | 100% | |||
Furthermore, the classification result for the 2020 map showed an increase of urban areas of 4.34% and a reduction in the other land covers.
According to results, urban land greatly increased within 30 years. Urban growth cannot be greatly emphasized or explained without taking a closer look at land use.
While the green area decreased from 33,093 (ha) in 1900 to 27,146 (ha) in 2002, then 18,539 (ha) in 2020, that shows the decrease by 44% over the past 30 years and at an annual rate of 1.95%. There is a strong relationship between urban growth and green lands. This is an inverse relationship whereby the increase in urban growth leads to a decrease in green lands as shown in
Through the cartographic treatment of the three images for the years 1990, 2002 and 2020, the study noted that the urban expansion of the city of Taif did not include all directions in the same way. As the expansion included the north and northeastern sides. However, there are several factors that influenced the determination of the direction of urban expansion, the most important factors are as follows:
The topography of the area greatly affected the direction of urban expansion. The north and north-east sides have fairly flat slope, i.e., 0 to 25 degrees, which allows for construction and reconstruction. Unlike the southern and west sides, where there is rugged mountainous terrain that represents a natural obstacle to urban expansion. This indicated that topography has an effect on urban expansion which supports the findings of Alqurashi, Kumar, Al-Ghamdi [
| Year | urban growth | vegetative growth |
|---|---|---|
| 1990 | 17,809 | 33,093 |
| 2002 | 22,502 | 27,146 |
| 2020 | 38,257 | 18,539 |
| Correlation coefficient R | −0.980539842269745 | |
| Coefficient of determination R2 | 0.961458382278375 | |
The northern side contains the old and new Taif Airport, Taif University (TU), and an important group of other government facilities and luxury shops that attract residents, and this is evident in
Due to the remoteness of the northern side from the original nucleus of the city of Taif, or the so-called old city center, the price of residential lands was appropriate and accessible to a wide category of citizens, especially during the period of the high price of oil in the world, from which the KSA benefited greatly, which was reflected on the purchasing power of the Saudi citizen. Furthermore, the role of the state in facilitating real estate loans for citizens who are eligible to get some financial support from a local bank for purchasing and reconstruction of land.
Taif has experienced a positive annual urban growth rate of 2.5% over the last 30 years. The urban growth can be attributed to the natural increase in population. The population of Taif city has increased from 381,000 in 1990 to 689,000 in 2020, which the expected census stats. Furthermore, young people who live on the outskirts of Taif area strive for a better lifestyle, education and employment; therefore, they tend to migrate from rural areas to Taif. Given the time period considered in this study, a correlation between population increase and urban growth can be revealed. Population growth along with economic diversification is the main driver for urban growth in the any city. In addition, more people are being drawn to urban areas by push and pull factors. Therefore, the increase of Taif’s population over the years has contributed to the city’s urban expansion, i.e., increasing the number of residents resulted in an expansion of urban land cover and urban extension. The growth in the number of people living in towns and cities is known as urbanization. Urbanization happens primarily as a result of people migrating from rural to urban areas, resulting in an increase in the number of the urban population and the size of urban areas. Land use, economic activity, and culture all alter as a result of population fluctuations. Historically,
urbanization has been linked to considerable economic and social changes. Therefore, there is a direct relationship between the number of population and urban expansion; in other words, an increase in the population leads to an increase in urban expansion (
Although urbanization creates many economic opportunities, it exerts pressure on different ecosystems. However, establishing a spatial pattern and planning the sites of new constructions is critical in assisting city planners in managing the hazards associated with sprawl in order to maintain human and environmental sustainability. This study came to identify the amount of urban expansion and its trends in the city of Taif through the achievement of the objectives of the study previously mentioned, which were successfully achieved.
The results showed that over the last 30 years, urban land cover has increased by 20,448 (ha) or 114.8% at a rate of 2.5% per year, indicating a continuing increase in urban growth. The analysis revealed that; 1) the southern and western sides are mountainous and rugged areas that represent a natural obstacle to urban expansion, and construction in them is at a high cost. On the other hand, the Northern and Northeastern sides have somewhat flat terrain, which helps reduce the cost of construction, especially after the rise in the prices of building materials in general in recent years. The slope is one of the factors that did not help the urban expansion towards the south and west of Taif, as it is not allowed to build in areas where the slope is more than 30%. And 2) government support by facilitating real estate loans for citizens through the former real estate bank, which had a major role in urban expansion. Also, the Ministry of Housing’s initiatives in Taif city included four housing projects on a total area of around 19 km2. There are 16,319 housing units in these developments. This is further evidence of the government’s support for urban expansion. In this period, most development and service projects in Taif, such as education, health, and services (Taif airport, Taif University, housing projects, etc.) are located in the North and North-east of the city of Taif. As a result, urban expansion was influenced by these services as a pull factor.
Future Prospects and RecommendationsThe observed trajectory of urban expansion in Taif over the last 30 years has necessitated additional research. The future study may include a prediction model that can estimate the extent of urban expansion over the next 30 years. It may be used to educate planning authorities about the level of urban expansion for which they should prepare. The following future prospects and recommendations can be made based on this research:
- Due to urban expansion in a specific direction, Taif will faces many problems such as traffic congestion, drinking water shortages, sewerage disposal issues, and a slew of other environmental and socioeconomic issues that cannot be handled without addressing issues related to urban development.
- To better understand the causes and implications of urban expansion, future research might include aspects such as land value, education, quality of life, and economic activities.
- Planning authorities and those related to urban development should take the topographic factor into consideration, and do not allow construction in valleys to maintain safety.
- Supporting the trend towards vertical construction, such as residential apartments, and moving away from horizontal construction, such as residential villas that consume larger areas.
- For the formulation of adequate plans and bi-laws for city planning and development, environmental factors, socio-economic prospects, and land values should be combined.
- Further environmental impact such as, water pollution, air pollution, ecological imbalance, and temperature rise can be studied in response to growth of urban in Taif.
The authors declare no conflicts of interest regarding the publication of this paper.
Alharthi, B. and El-Damaty, T.A. (2022) Study the Urban Expansion of Taif City Using Remote Sensing and GIS Techniques for Decision Support System. Advances in Remote Sensing, 11, 1-15. https://doi.org/10.4236/ars.2022.111001