=Paper=
{{Paper
|id=Vol-1570/paper14
|storemode=property
|title=Diurnal and Seasonal Surface Temperature Variations: A Case Study in Baghdad
|pdfUrl=https://ceur-ws.org/Vol-1570/paper14.pdf
|volume=Vol-1570
|authors=Mustafa Naem,Robert Corner,Ashraf Dewan
}}
==Diurnal and Seasonal Surface Temperature Variations: A Case Study in Baghdad==
https://ceur-ws.org/Vol-1570/paper14.pdf
Diurnal and Seasonal Surface Temperature
Variations: A Case Study in Baghdad
Mustafa Naem, Robert Corner, and Ashraf Dewan
Department of Spatial Sciences, Curtin University, WA, Australia;
Emails: m.hamoodi@postgrad.curtin.edu.au (M.N.); r.corner@curtin.edu.au (R.C.);
a.dewan@curtin.edu.au (A.D.)
SUMMARY
Urban land use and land cover (LULC) classification is an important technique to study a variety of
applications in remote sensing, especially in urban climate and environment. In this study, a new
approach was applied to classify urban LULC area into four main categories using Landsat TM. This
approach used the impervious surface area (ISA) technique by fusing the night thermal band with day
multispectral bands of Landsat data. In addition, masks of water and vegetation cover were applied to
extract their categories. In the second part, diurnal and seasonal variations in surface temperature
were analysed using LST maps. Landsat TM images during daytime and night time for Summer and
Winter in 1990 have been utilised to estimate surface temperature and spectral indices (MNDWI and
NDVI).
Keywords: Impervious surface area, Classification, Land surface temperature, Urban Heat Island,
Baghdad
INTRODUCTION
The earth’s climate has been significantly affected through long-term land use and land cover (LULC)
changes, as a result of the interrelationship between land surface and the atmosphere at local and
regional scales [1]. LULC changes are associated with human use of land resources [2]. Some of the
most important issues that have affected the ecosystem are urbanisation and other practices of land use
attributable to human activities [3, 4]. According to previous studies, the main effect of urbanisation is
to cause variation in urban surface temperature [5]. Many studies have examined urban heat islands,
and have documented increases in temperature for many cities around the world in recent decades.
However diurnal and seasonal variability in temperature are associated with environmental effects
such as water vapour, cloudiness and precipitation [6], as well as geographical location. In addition,
population changes and urban growth have led to significant changes in LULC. As a result, the
phenomenon of urban heat islands over cities occurs [7]. The urban thermal environment is locally
affected by the diurnal and seasonal variability in surface temperature for LULC types, especially in
soil moisture and tree canopy through evaporative cooling [8].
Thermal remote sensing is a useful tool for examining diurnal and seasonal variations in land
surface temperature (LST) in a variety of environmental domains. It has been widely used in studies
of climate change and urban heat islands [1, 9].The study carried out by [8], examined the relationship
between landscape pattern and land surface temperature over four seasons in Indianapolis in the
United States. Work by [10] investigated the response of urban heat islands to seasonal variation of
LULC in Piracicaba, Brazil. The surface temperature in the city of Delhi across four seasons was
studied by [11] using Landsat 5 Thematic Mapper. In the studies above, different approaches were
used in analysing diurnal and seasonal variations of temperature with LULC patterns. However, the
use of day and night Landsat images to investigate diurnal and seasonal surface temperature is rarely
found. Numerous LULC classification methods have been using a range of satellite data, but there are
still some challenges. In urban areas these relate to mixed pixels among LULC patterns, especially
between built-up and bare land.
It is therefore difficult to obtain an optimal result using standard classification methods in an urban
area. In this study a new method was applied to Landsat TM images by fusing a night thermal band
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with day multispectral bands in order to improve ISA mapping performance. This study is divided into
two parts. First, this method was applied to classify the Landsat TM image of the study area into four
main categories (built-up areas, green areas, bare land and water bodies). Secondly, the spatial
distribution of temperature was visualised to locate warmer and cooler densities of LULC patterns.
The main objective of this research is to investigate trends of diurnal and seasonal surface
temperature variability across different types of LULC over the central Baghdad area using Landsat
TM images.
STUDY AREA
The study area of central Baghdad province is located in central Iraq on of the Tigris River with
geographic coordinates latitudes 33° 10.77' N to 33° 29.26' N, longitudes 44° 11.55' E to 44° 34.23' E
as shown in Figure 1. The total area of the administrative boundary of Baghdad mayoralty is 870 km2.
The climate of Baghdad is described as a hot arid and subtropical desert climate type (BWh)
according to Köppen’s climate classification system, dry and extremely hot in summer with short,
cool winters [12].
MATERIALS AND METHODOLOGY
Datasets:
Landsat TM images were downloaded from the U.S. Geological Survey. The images were acquired
for two seasons during daytime and night-time in 1990 (Table 1). Those images were chosen based on
availability, especially during the night. Images within a window of five days between day and night
for each season were chosen. This early date was used as they will form a baseline for further work.
Table 1. Landsat TM Images of Baghdad central area
Season D/N Acquisition date Local time Path Row
Landsat 5_TM Day 12-08-1990 09:53:36 am 168 037
Summer
Landsat 4_TM Night 09-08-1990 09:22:06 pm 034 207
Landsat 4_TM Day 08-01-1990 10:06:14 am 168 037
Winter
Landsat 4_TM Night 13-01-1990 09:25:53 pm 034 207
Data pre-processing:
The dark object subtraction method (DOS) [13] was applied to remove atmospheric effects. It was
conducted in a two-step process using ENVI 5.1 Software. Firstly, digital numbers (DN) were
converted to top of atmosphere (TOA) reflectance. Then, the values of the darkest pixels were
subtracted from the entire image. The images were co-registered in order to enable direct comparison
of values between images.
Image classification:
A proposed new method requiring fusion of the Landsat night thermal band with daytime
multispectral bands was used in order to increase delimitation of LULC patterns, especially for built-
up areas.
This method extracts the LULC categories sequentially using different techniques. The water
category is extracted using MNDWI, the ISA method was used to extract built-up areas and next,
vegetation cover was estimated using NDVI.
Water masking:
Water has similar spectral characteristics with some LULC surfaces such as asphalt, shadows and
green area. Thus, it is necessary to mask out water from the images before further processing. Water
masking was carried out using the modified normalised difference water index (MNDWI), as
developed by [14]. The MNDWI was calculated for Landsat TM images using equation (1). The water
features were determined by threshold values, then, converted to polygons.
((()*+,-)/(()*+,0))
!"#$% = ((()*+,-)1(()*+,0)) (1)
Where:
ρ(band2) is the spectral reflectance of green band
ρ(band5) is the spectral reflectance of short wave-infrared band
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ISA:
Impervious surfaces prevent water infiltrating into the soil, such as building rooftops, streets, parking
lots and sidewalks [15]. ISA techniques have been used to extract urban areas from remotely sensed
data. Principle component analysis (PCA) was used to convert the fused images, with a water mask
applied, to a new dataset. Endmembers for high albedo surfaces, low albedo surfaces, green areas and
bare land were chosen from the scatterplots of the PCA bands. Then, linear spectral unmixing was
used to convert the image into four fractional images. The high and low albedo fraction images were
combined to one image showing ISA as described by [16]. The ISA image was converted to polygons
to facilitated post classification editing.
NDVI:
Normalised difference vegetation index (NDVI) is a vegetation index which is widely used to describe
vegetation characteristics such as green biomass and chlorophyll content. It is calculated from the red
band and near infrared band for Landsat TM image using the equation (2) and its values range from (-
1 to 1) [17].
('(()*+,).'(()*+/))
!"#$ = (2)
('(()*+,)0'(()*+/))
Where:
ρ(band3) is the spectral reflectance of red band
ρ(band4) is the spectral reflectance of near infrared band
The NDVI values were calculated with threshold values for determining vegetation cover being
manually selected. The resulting image was reclassified and converted to polygons and masked from
the image as a separate category.
Bare land:
Based on the study area dataset (polygon file) the three categories, water, ISA and vegetation cover
were clipped out from this file and the remaining areas represent the fourth category that is bare land.
LST mapping:
LST maps were prepared by using Arc GIS 10.2. Firstly, surface temperatures were retrieved using
the equation (3) from the spectral radiance values of the Landsat 4 and 5 TM thermal band [18].
23
1 = 4* (26 / 89 0 6) (3)
Where: T is the effective at-satellite temperature in Kelvin, K1 and K2 are specific thermal band
calibration constants 1 and 2 respectively for the TM 4 and 5, Lλ is spectral radiance W/(m2· sr· μm).
RESULTS AND DISCUSSION
Statistical details for the LULC classes, including the minimum, maximum and average surface
temperature of diurnal and seasonal maps in summer and winter were calculated. Comparisons of
thermal behaviour of LULC categories between day and night, and between summer and winter were
conducted using land surface temperatures. The spatial distribution of land surface temperatures was
analysed using the LST maps.
Urban LULC classification
The urban LULC classification is shown in Figure 2 and shows a reasonable representation of the
main LULC categories, including ISA (built-up and man-made area), water (river and artificial lakes),
green area (orchard, agricultural fields and vegetation) and bare land (open space without artificial and
vegetative cover, and cultivated land).
The classified map was checked for accuracy using a historical high resolution aerial photograph
using the error matrix method. 125 random points were generated on the reference image and
attributed to the four categories, then converted to a raster layer with 30 m resolution and combined
with the classified map. The overall accuracy and Kappa coefficient of classified map were 86.6% and
0.81, respectively.
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Diurnal and Seasonal variations
Diurnal variations:
Diurnal variation is defined as the change in temperature from day to night, as a result of the daily
rotation of the Earth and is essentially the difference between maximum and minimum temperatures
[19]. Here the twice-daily surface temperatures estimated from Landsat TM images at around 10:00
AM and 9:24 PM, the local time of the satellite overpasses, were used to compare between LULC
categories. The diurnal variations in surface temperature for LULC categories are shown graphically
in Figure 3 and 4 for summer and winter. Analysis of the summer LST maps, graph and table in
Figure 3 shows that the LST parameters (min, max and mean) for all LULC categories decreased
gradually from day to night. The LST of bare land was highest during the day, followed by built-up
area, green cover and water, while at night built-up areas had the highest LST, followed by bare land,
water and green cover. Bare land and green cover categories showed the highest diurnal range in LST,
whereas the range for built-up areas and water categories was lower.
Figure 1. The study area of Baghdad city Figure 2. Classified map of major LULC
(TM image, bands 4, 3, 2) categories for Baghdad city in 1990
Figure 4 shows that in winter, the LST values for all LULC categories fell sharply at night. Bare
land had the highest LST and water the lowest LST during the day. But at night water had the highest
LST and green area the lowest. The nightly decrease in the LST values for green areas and bare land is
more severe than for built-up areas and water which release heat more due to their higher heat
capacity. This situation occurs in both summer and winter, but the difference of the diurnal LST
values is more noticeable in winter than summer.
Figure 3. LST maps in summer during daytime and nighttime
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Figure 4. LST maps in winter during daytime and nighttime
Seasonal variation:
The seasonal surface temperature variations of LULC categories during day and night are shown in
Figure 5. The summer values of LST were higher than those in winter, for both day and nighttime.
This is as expected given the seasonal differences cloud cover and insolation. It can be seen that the
contrast between summer and winter, based on of the spatial distribution of mean LST is around 20 °C
during daytime but at nighttime increases to almost 30 °C.
The highest summer daytime LST was found on bare land (47.2 °C), followed by built-up area
(43.9 °C), green area (39.4), and water (32.1). During the summer nighttime, there is a little difference
in temperature between all categories. The highest summer nighttime value of LST was found on
built-up area (34.2 °C), followed by water (31.1 °C), bare land (30.5 °C), and green area (29.3 °C).
Winter daytime, LST values were highest on bare land and green area (20.7 °C) and (19.8 °C) and,
lower for built-up area and water (18.9 °C) and (15.7 °C), respectively. On winter nights there is a
change in the order of these categories during winter nighttime. Higher values were observed in water
and built-up area (3.95 °C) and (3.52 °C), respectively, and lower for green area and bare land (0.87
°C) and (1.62 °C), respectively.
In general high temperatures occur in the summer season for all LULC categories of the study area
compared with the winter season, for both times day and night.
Daytime Nighttime
Category Summer Winter Category Summer Winter
Min. Temp Max.Temp Mean. Temp Min. Temp Max.Temp Mean. Temp Min. Temp Max.Temp Mean. Temp Min. Temp Max.Temp Mean. Temp
(°C) (°C) (°C) (°C) (°C) (°C) (°C) (°C) (°C) (°C) (°C) (°C)
Bare land 45.18 52.00 47.22 14.87 24.80 20.71 Bare land 27.37 34.79 30.56 -3.62 5.01 1.62
Built up 39.65 45.95 43.90 17.20 20.39 18.94 Built up 36.39 29.48 34.29 -1.39 6.55 3.52
Green area 36.40 46.72 39.49 16.74 23.06 19.87 Green area 34.79 24.37 29.36 -4.18 5.01 0.87
Water 30.08 40.85 32.10 13.92 19.04 15.78 Water 33.99 28.64 31.10 -1.39 9.06 3.95
Figure 5. LST in summer and winter during daytime and nighttime
CONCLUSION
This study has assessed an urban LULC classification method that has been developed using historical
daytime and nighttime Landsat TM images for Baghdad city in 1990. The study shows that extraction
of ISA by fusing the night thermal band with multispectral Landsat image has improved classification
Proc. of the 3rd Annual Conference of Research@Locate 69
in term of built-up area. This method solves the problem of mixing between built-up and bare land.
However, its use is limited because night thermal images from Landsat satellite are rare. The study,
furthermore, has examined diurnal and seasonal variations in surface temperature using LST maps
which were produced from Landsat images.
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