Ahmed Bindajam

Department of Architecture, College of Engineering, King Khalid University

Kingdom of Saudi Arabia

aaadajam@gmail.com

Abstract

It is recognized that ambient air temperatures (Ta) inside urban cores are higher than that in their rural surroundings, forming what is known as the urban heat island (UHI) phenomenon. During the summer in hot and dry regions, the UHI intensity is significantly influenced by the extreme direct solar radiation and leads to outdoor thermal discomfort during the entire day. The present study aims to investigate the thermal performance of three different urban configurations and street geometries in the metropolitan city of Buraydah, Saudi Arabia: traditional compact, modern attached, and modern detached. To ensure that the urban configuration is the only factor for comparison, vegetation was excluded and materials for buildings and streets were unified in all examined study cases. The evaluation of the ambient air temperature (Ta), street surface temperature (Ts) inside the urban canyon, and mean radiant temperature (Tmrt) was conducted by utilizing the three-dimensional numerical software Envi-met 4.0. The three variables Ta, Ts, and Tmrt were measured at the center of the street in each urban configuration. The investigation of this study has provided a better understanding of the role of urban form configurations in forming the UHI that affect the microclimate in hot and dry regions, which has therefore helped to generate guidelines of urban design and planning practices for a better thermal performance in cities. In particular, the study has contributed to the validation of the impacts of urban canyons on the temperature variations in built environment.

Key Words: GIS, Urban Heat Island UHI, land surface temperature, LANDSAT

Background

GIS technology provides a wonderful and powerful environment for entering, manipulating, analyzing, and displaying digital data from different sources necessary for various applications. Satellite remote sensing collects multispectral, multiresolution, and multi-temporal data, and converts them into valuable information for understanding and monitoring urban land processes and for building urban land cover datasets. The integration of these two technologies, Remote Sensing and Geographic Information System (GIS), has been widely applied and been accepted as a powerful and effective tool in detecting urban land use and land cover change, and environmental modelling applications.

An Urban Heat Island (UHI) is an urban area, which is significantly warmer than its surroundings. As many city residents know, it is often warmer in the city than in surrounding rural areas during hot fine weather, especially at night. This phenomenon is referred to as the UHI (Streutker, 2003).

Figure 1: Urban heat island profile, Source: (drawn by author)

In urban areas, natural elements (e.g., vegetation, water bodies) are replaced by manmade elements (e.g., buildings, asphalt, and paved) surfaces with thermal properties (e.g., albedo, thermal conductivity, and emissivity) different from non-urban areas (see Figure 1). Howard (1833) was the first to observe that urban temperatures were higher than those in nearby rural areas, an effect termed as an UHI (Manley, 1958). Urban climate, summarized by multiple teams (Bomstein, 1987; Yoshino, 1975; Landsberg, 1981; Ohashi & Kida, 2001; Oke, 1987; Oke & Bomstein, 1981; Shepherd et al., 2004), have shown that under clear skies and light wind conditions, cities are warmer than surrounding rural environments by up to 10°C. According to Grater (2006), the principle cause of UHI is the change to the form and composition of the land surface and atmosphere.

Wan and Noor (2005) mentioned that analyzing the UHI using the conventional methods (i.e., filed survey) for collection data on automobile transects or weather station networks is no longer practical or effective. It is almost impossible to obtain the temperature at different location at the same time in a big area. The urban development and urban sprawl rapidly growths in certain cities in Saudi Arabia without proper considerations of the vegetation, landscape, and building characteristics have caused cities to experience higher temperature.

The specific problem is simply stated as this question:

How UHI could be affected by different land features and urban sizes within the cities using satellite remote sensing and GIS technologies in the Saudi Arabian cities?

Objectives

The following are the objectives of the study:

1. To generate the surface temperature map for the study area using remote sensing method.

2. To analyze the effect of urban landscape features, such as buildings and vegetation, on the surface temperature within the study area.

Methodology

In any research project, a proper and clear planning and execution of the proposed methodology is critical. The methodology for this paper is organized into three main stages: (1) selection of study area, (2) data processing, and (3) analysis of the results.

The Study Areas

Buraydah is the capital and the largest city of Qassim region (see Figure 2) located in 26.3592° N, 43.9818° E with an area of 1,291 km² and population of 614,093 (as officially counted in 2010) 99. According to Al-Qassim municipality (2017) it is located in the Middle Eastern part of the Qassim region on the edge of the great Ruma valley. It surrounds a range of hills and sandy highlands, which are very fertile agricultural lands because of the easy extraction of water from its surface covered with a layer of limestone and gypsum. The weather is generally arid climate desert due to the surrounding sandy areas and the lack of rain. In Buraydah, it is hot in summer, cold winter, and relatively relative humidity, and dominated by the north wind, which works to reduce heat in the summer, and the Northeast, which works to reduce heat in winter. Buraidah is also famous for its agriculture and its date farms and has a global market for date trade.

Figure 2: Buraydah within Qassim region, Source: http://search.landinfo.com/

Data Processing

An essential stage before the real processing and analysis of the image should be carried out, which is called data preprocessing. Data preprocessing consists of two main steps, i.e. image rectification for LANDSAT 8 satellite image, LANDSAT 8 satellite image is subset into the study areas and image overlay that will make easy to analyze the effect of different landscape features on the surface temperature.

Table 1

LANDSAT8_OLI & TIRS

Thermal band of the LANDSAT 8 image is used to derive the land surface temperature map. Thermal band (band 10) from ERDAS IMAGINE software can directly be read in the ArcGIS 10.2 or any GIS software.

Following Meta data values are used for calculation:

• Radiance Add Band 10 = 0.10000

• Radiance Mult Band_10 = 0.0003342

• K1 Constant band 10 = 774.8853

• K2 Constant Band 10 = 1321.0789

Figure 3: Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)

As shown in Figure 4, to generate the land surface temperature, few steps are applied in ArcGIS 10.2, and they are as follows:

1. Top of Atmosphere (TOA) Radiance

Using the radiance rescaling factor, Thermal Infra-Red Digital Numbers can be converted to Top of Atmosphere (TOA) spectral radiance.

Lλ = ML * Qcal + AL

Where:

Lλ = TOA spectral radiance (Watts/ (m2 * sr * μm))

ML= Radiance multiplicative Band (No.)

Qcal= Quantized and calibrated standard product pixel values (DN)

AL= Radiance Add Band (No.)

2. Top of Atmosphere (TOA) Brightness Temperature

Spectral radiance data can be converted to top of atmosphere brightness temperature using the thermal constant Values in Meta data file.

BT = K2 / ln (k1 / Lλ + 1) - 272.15

Where:

BT= Top of atmosphere brightness temperature (°C)

Lλ= TOA spectral radiance (Watts/ (m² * sr * μm))

K1= K1 Constant Band (No.)

K2= K2 Constant Band (No.)

Figure 4: Overall chart of the data process

3. Normalized Differential Vegetation Index (NDVI)

The Normalized Differential Vegetation Index (NDVI) is a standardized vegetation index which Calculated using Near Infra-red (Bnad 5) and Red (Band 4) bands.

NDVI = (NIR – RED) / (NIR + RED)

Where:

RED= DN values from the RED band

NIR= DN values from Near-Infrared band

4. Land Surface Emissivity (LSE)

Land surface emissivity (LSE) is the average emissivity of an element of the Earth surface calculated from NDVI values.

PV = [(NDVI – NDVI min) / (NDVI max + NDVI min)] ^2

Where:

PV= proportion of vegetation

NDVI= DN values from NDVI image

NDVI min= minimum DN values from NDVI image

NDVI max= maximum DN values from NDVI image

E = 0.004 * PV + 0.986

Where:

E= land surface emissivity

PV= proportion of vegetation

5. Land Surface Temperature (LST)

The Land Surface Temperature (LST) is the radiative temperature, which calculated using Top of atmosphere brightness temperature, Wavelength of emitted radiance, and Land Surface Emissivity.

LST = (BT / 1) + W * (BT / 14380) * ln (E)

Where:

BT= Top of atmosphere brightness temperature (°C)

W= Wavelength of emitted radiance

E= Land Surface Emissivity

Result and Analyses

As shown in Figure 5, the LST ranged between 21.2°C to 45.9°C. The UHI map clearly shows that the vegetation and water body record the lowest value of LST, while open area with stone land features records the highest value of LST.

Figure 5: SLT map for Buraydah city

It is clear in the map that the old areas of Buraydah (e.g., Alkubaib area) showed a random urban heat island map, while the new and newly built areas, such as north Buraydah (e.g., Sultanah) showed a regular diversity of surface land temperature (see Figure 6).

Figure 6: SLT map overlaid with google satellite image

Moreover, results obtained (SLT maps in Figure 5) have shown that the generated surface temperature of residential and commercial areas in the city center of Buraydah (e.g., Alkubaib area) are lower than the undeveloped area, which is located beyond the city due to the exposure to sun radiation all the daytime. As shown in Figure 7, Alkubaib area has very narrow streets with buildings that are close to each other. The urban fabric in this area lake of open spaces and wide streets, which cos the shade most of the time.

Figure 7: Ratio of building height to street (1:1) in Alkubaib area, (drawn by author)

On the other hand, the UHI map shows high SLT in the street that has wide ratio compared to the height of the attached buildings. For instance, in Mulayda area, as shown in Figure 8, the building to street height ratio is 1:6 which records 41.93°C in the LST.

Figure 8: Ratio of building height to street (1:6) in Mulayda area, (drawn by author)

The building to street height ratio shows a significant effect on the UHI. For instance, Omer ibn Alkatab street has different ratio of building to street height, as seen in Figure 9.

Figure 9: SLT map shows Omer ibn Alkatab Street

Figure 10: Ratio of building height to street (1:2) in Omer ibn Alkatab St. (drawn by author)

Figure 11: Ratio of building height to street (1:3) in Omer ibn Alkatab St. (drawn by author)

Figure 12: Ratio of building height to street (1:4) in Omer ibn Alkatab St. (drawn by author)

As shown in Figures 10, 11, and 12, it is noticed that the building to street height ratio change along the way to the east direction which cause different LST as shown in the UHI map. The building to street height with 1:2, 1:3, and 1:4 ratio records 39.12 °C, 40.51°C and 41.53°C, respectively.

Findings and Conclusion

Findings have shown that there is a strong correlation between spectral reflectance of Thermal Infrared (TIRS) 1of LANDSAT 8 image and surface temperature. Temperature maps derived from satellite images have shown the differences in surface temperature for different land cover, building density, and ratio of building height to street wide. The highest temperature found was in (Mulayda area) with temperature of 41.93 C, followed by (Omer ibn Alkatab area) vary at three segments, with higher temperature degree as we go to the east direction 39.12 °C, 40.51°C and 41.53°C, respectively, and finally by the lowest surface temperature found in (Alkubaib area) with temperature of less than 37.57 C.

This paper explained the differences between the urban fabrics that get less impact by UHI phenomena. In Buraydah city, it was found that the ratio of the street wide to building height makes a significant effect on the UHI, so it is important to distinguish between the relationship of street canopy ratios and the heat temperature on the hot arid climate. The natural features, such as water and vegetation, played a remarkable help to mitigate the UHI impact. The city growth and development should be within the city rather than beyond as it will create better diversity of surface land temperature.

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