Impact of Industrial Areas on Surface Temperature
Using Thermal Infrared Remote Sensing and GIS techniques.
A Case study Of Jubail City, KSA.
El-Nahry, A. H. and Rashash,A *
* Department of Continuous Studies, Division of Scientific Training and Continuous Studies
National Authority for Remote Sensing and Space Sciences,Cairo,Egypt
Abstract
During the last decade, Jubail City became the biggest industrial area, in Saudi
Arabia with population more than 250,000 people. Urban expansion has reached to
suburban areas along Arabian gulf. Thermal infrared remote sensing proved its
capability in monitoring temperature and effecting microclimate in urban areas. The
purpose of this study is to evaluate the use of Thermal Infrared Remote Sensing Landsat ETM+ data band 6.1-, for assessing temperature differences in Jubail City
and comparing the relationships between urban surface temperature and land cover
types (specially industrial areas and green area). The study showed the increament
of urban surface temperature near industrial area in comparison with suburban
areas, the centre of heat island was concentrated above the industrial area and its
adjacent near urban areas, also building is one of the factors that reflect more heat
and it is responsible of raising the surface temperature at urban area rather than the
development area and gardens. Iron and steel factories raise the temperature to 80
o
C, affecting the temperature of nearby areas, this effect may extend to the distance
between 500 - 2000 meter that could be considered as a buffer zone. It could pose
serious environmental problems for the inhabitants in, Jubail area (e.g., thermal
pollution).
Keywords: surface temperature, thermal Infrared, heat island, industrial area,
green areas.
* Corresponding author. Tel.: +201225640454 E-mail address: [email protected] (Prof.Dr.Alaa. El-Nahry) ;
E-mail address: [email protected] (Dr.Abdel-Nasser Rashash)
1. Introduction
The climatic elements are almost observed by climate stations in cities, almost each city has
one station. In many cases, it doesn’t express actual climate variability and microclimate
conditions.
Also, the climate station May not be found in the city under investigation therefore data could
be got from the neighbouring stations. So, the thermal remote sensing is urgent because it
has the capability to cover great areas in the city within the scene providing more thermal
condition details. Remotely sensed TIR data are unique sources of information to define
surface heat islands,(Weng, Q., 2009).
The thermal environment in urban areas is characterized by the heat island phenomenon
affecting energy, human health and environmental conditions. Ground-based observations
reflect only thermal local condition around the station. Meanwhile using remote sensing
thermal bands enabled the researcher to get the thermal condition for each pixel in the
image.
Nowadays thermal remote sensing has been used over urban areas to assess the
heat island and climatic conditions. Until present, there are many studies concerning
heat island (UHI) on regional and global climate (Rajasekar, U. and Weng, Q., 2009;
Zhanga, H., Kainz, W.,2012; Weng, Q. 2009; Weng, Q., Lu, D. Schubring, J.,
Quattrochi, D.A., Luvall, J.C., 1999; Voogt J.A., Oke T.R., 2003)
urban
urban
Li, Y.,
2004;
Remotely sensed thermal infrared (TIR) data have been widely used to retrieve land surface
temperature (LST) (Quattrochi and Luvall, 1999; Weng et al., 2004). The recent
development of high resolution satellite images means that detailed analyses could be
expected.
To estimate the thermal condition of land surface by satellite image, it is necessary to find
the relationship between the surface temperature, surrounding topography and land cover
/use (Weng, Q., 2009).
To estimate land surface temperature (LST) from satellite thermal data, the digital number
(DN) of image pixels needs to be converted into spectral radiance using the sensor
calibration data (Markham,B.L and Barker,J.L.,1987).
However, the radiance converted from digital number does not represent a true surface
temperature but a mixed signal or the sum of different fractions of energy. These fractions
include the energy emitted from the ground, upwelling radiance from the atmosphere, as well
as the downwelling radiance from the sky integrated over the hemisphere above the surface.
Surface temperature can be estimated daily using thermal bands of NOAA/AVHRR.
However, the data with 1.1 km spatial resolution was not suitable for urban temperature at
the micro-level, which does not allow the recognition of different land cover types within the
pixels.
Landsat ETM+ with 60 m. spatial resolution of thermal infrared band enables experts to
define the more detailed surface temperature. (Weng, Q., 2009).
This research aims to evaluate the use of Landsat ETM+ data for identifying temperature
differences in urban areas, to analyze and compare the relationship between urban surface
temperature and land cover types, and to estimate the impact of industrial areas upon
adjacent area.
2. Study area :
The study area is located at the East of Saudi Arabian peninsula on the coast of Arabian
golf, it represents the desert area with extremely high temperature in summer (Fig.1).
In 1977, Jubail, on Saudi Arabia's Gulf Coast, was a small fishing community of sum of
8,000 inhabitants. Today, it contains the largest civil engineering projects in the world.
Nowadays, It is represented by the major part of Jubail City and some surrounding areas,
which is reported to have rapid built-up expansion since the last decade resulted in air
pollution and greenhouse gas emission problems, that seriously impact the human health.
Fig. 1: Map of the study area (Jubail City, KSA)
3. Methodology
The following procedures were carried out to derive the digital surface temperature, generate
the temperature colour map, analyze the data, create buffer around urban heat island and
deriving spectral profile land cover.
The Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors
acquire thermal temperature data and store this information as a digital number (DN) with a
range between 0 and 255 in thermal band (band 6.1 in ETM+). It is possible to convert these
DNs to degrees Kelvin using two processes.
The first process is to convert the DNs to radiance values using the bias and gain values
specific to the individual pixel. The second process is to convert the radiance data to
degrees in Kelvin. The third one is to convert the temperature in Kelvin to the temperature
Celsius
Fig 2. Flowchart showing the major steps of research procedures
3.1 Conversion the Digital Number (DN) to Spectral Radiance (Lλ):
Radiance (W/m2* sr * µm) in TM band 6.1 (high gain on ETM+) were calculated from digital
numbers (DN) using standard NASA equations to correct gain and offset at the detector . In
TM and ETM+, band 6 captures the radiant thermal energy between 10.4 and 12.5 Am, at
the atmospheric window between O3 and CO2 atmospheric absorptions.
The spectral radiance (Lλ) is calculated using the following equation (USGS, 2001):
Where,
Lλ =Spectral radiance at the sensor's aperture (W/m2* sr * µm)
DN = Quantized calibrated pixel value (Qcal)
QCALMIN = Minimum quantized calibrated pixel value corresponding to LMINλ [DN] = 1
QCALMAX = Maximum quantized calibrated pixel value corresponding to LMAXλ [DN] = 255
LMIN= Spectral at-sensor radiance that is scaled to Qcalmin (W/m2* sr * µm)
LMAX = Spectral at-sensor radiance that is scaled to Qcalmax (W/m2* sr * µm)
(G.Chander, B. et al., P897)
3.2 .Conversion the Spectral Radiance to Temperature in Kelvin
The ETM+ thermal band data could be converted from spectral radiance (as described
above) to a more physically useful variable. This is the effectiveness at-satellite
temperatures of the viewed Earth-atmosphere system under an assumption of unity
emissivity and using pre-launch calibration constants.
Assuming surface emissivity = 1 (USGS, 2001), the following equation to convert radiance to
temperature was used as follows:
(
)
Where,
- T = temperature in Kelvin
- K1 = 666.09
- K2 = 1282.71
- Ly = Spectral radiance
Table .1 shows ETM+ thermal band calibration constants
Table. 1 : ETM+ thermal band calibration constants.
Constant
(Units)
L7 ETM+
K1
(W/m2* sr * µm)
666.09
K2
(Kelvin)
1282.71
Source: USGS, 2001.
3.3 Conversion the Temperature in Kelvin to the Temperature “Celsius”
The temperature in Celsius was calculated as the following equation:
T(oC) = T – 273.13 (Aniello et. al.,1995)
where :
T (oC) = Temperature “Celsius”
T
= Temperature “Kelvin”
273.13 = Zero Temperature “Kelvin”
4. Results and Discussions
The Landsat 7 ETM+ was acquired in May, 2001. Band combination of 7 4 2 was used to
give maximum information of land cover /use relevant to the investigated area, i.e. industrial
zone, residential area of inner city with high density and its expansion, gardens, sand and
water.
The thermal energy responses of different landforms indicated the great variation in surface
temperature of different surface patterns. Land surface temperature was extracted from
thermal band 6.1 of Landsat 7 ETM+ (Fig. 3). Analyses indicated that, the industrial,
residential areas represent the highest surface temperature meanwhile vegetation and water
bodies exhibit the lowest one.
Fig. 3. Band combination of channels 7, 4 and 2 of ETM+ in 2001
Figure No.5 shows the colour palette of surface temperature, where Industrial zones with red
colour exhibited the highest temperature (from 50oC to 60oC) due to the aluminium roof
material plus the thermal energy resulted from production activities.
Fig. 4. Thermal band(6.1) of Landsat 7 ETM+ of Jubail city .
It is noticed that, factories could be considered the main source of heat in the Jubail
industrial area as well as buildings. Those two elements responsible of raising the surface
temperature at urban area.
rather than the development area and gardens. (Asmat et al., 2003).
The cooler areas that have temperature in the range of 37oC to 42oC (green and cyan
colour) are those supported by vegetation. This is the result of dissipating solar energy by
absorbing surrounding heat and evaporation process from the leaves as well. The
relationship and correlation between surface temperature and land cover types is
elaborated, as shown in Figure 6.
Fig. 5. Surface temperature distribution of the study area
Land
cover
body
Fig.6. Thermal signature of land covers types in Jubail
Fig.7. Thermal cross section
Fig.8. Thermal profile
Thermal cross section shows difference between iron& steel factory and adjacent areas. The
temperature in the perimeter of iron factories raises to 80 oC, affecting the temperature of
nearby areas, this effect may extend to the distance between 500 - 2000 meter that could be
considered as a buffer zone, (Figure. 9)
Fig.9.Hot spot buffer zone of iron factories
5. CONCLUSIONS
Surface temperature could be directly derived from remotely sensed data, which provides a
powerful way to monitoring urban environment and human activities. This information
enhances understanding of urban environment.
The ETM+ data thermal band with 60 m spatial resolution help in estimation of surface
temperature variations and getting more accurate estimation of the urban temperature.
Relationship between urban surface temperature and land cover types enabled us to find out
the best solution for urban planning strategies that meet heat island reduction.
It is advised to surround the industrial areas by green belt buffers to more than 500 m for
improving temperature condition and to decrease pollution effects to the acceptable limits.
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