the surface heat island effect of urbanization: spatial- temporal...
TRANSCRIPT
THE SURFACE HEAT ISLAND EFFECT OF URBANIZATION: SPATIAL-
TEMPORAL ANALYSIS
Aliihsan Şekertekin1,2, Senol Hakan KUTOĞLU1, Aycan Murat MARANGOZ1, Sinasi
KAYA3
Abstract
The temperature of Earth’s surface has been rising since the last century. Thus, it is of great importance
to study on climate change for the scientists. By the help of remote sensing technologies, scientists widely
use satellite images in order to investigate and visualize the effects of global warming. Urban Heat Island
(UHI) is one of the most important expressions for studying climate change. As the urban areas grow in a
city, UHI effect becomes bigger. In other words, the temperature of the urban areas would become higher
than its surroundings. The aim of this study is to retrieve Surface Heat Islands (SHIs) on the basis of Land
Surface Temperature (LST) and to evaluate the changes in SHIs in two different years. The city center of
Zonguldak was chosen as study area and Landsat 5 satellite data were used as materials. LST maps
generated using Landsat data were utilized in order to obtain SHIs. Landsat 5 satellite data were
acquired on 11.09.1987 and 29.09.2011, respectively. Besides, Land Use Land Cover (LULC) maps of the
study area were generated using high resolution satellite images and these classified images were
analyzed on the basis of urban heat island. The obtained results revealed that from 1987 to 2011 the size
of the SHIs expanded considerably. As in our case study, urbanization causes the global warming and to
overcome this challenge, city planners and decision makers should consider using appropriate materials
(not absorbing sun radiation) while constructing buildings, roads etc.
Key Words: Land Surface Temperature, Urban Heat Island, Urbanization, Spatial-Temporal Analysis,
Landsat
1. INTRODUCTION
Climate change has been one of the most important issues in recent years and it has been
accelerating for the last twenty years because of the increase in greenhouse gases, urbanization,
deforestation etc. Increasing world population and the growing industrial demands have created
the need for protecting the environment and maintaining the industrial developments in order to
meet the requirements of sustainable development (Jüttner et al. 2000, Sekertekin et al. 2016).
1 Bulent Ecevit University, Engineering Faculty, Department of Geomatics Engineering/Resh. Assist., Prof., Assist.
Prof., (aliihsan_sekertekin, kutogluh, aycanmarangoz)@hotmail.com 2 Cukurova University, Ceyhan Engineering Faculty, Department of Geomatics Engineering/Resh. Assist.,
[email protected] 3 Istanbul Technical University, Faculty of Civil Engineering, Department of Geomatics Engineering / Assoc. Prof.,
Urbanization refers to as the change of rural lifestyles into urban ones (Antrop 2004; Kaya 2007).
In addition to rapid population growth in cities, people living in rural areas emigrate to urban
ones in an effort to benefit from the potential job opportunities, different social activities etc.
(Sekertekin et al. 2016). As urban areas grow, their landscapes change. In other words, buildings,
roads, and other infrastructures replace open land and vegetation (EPA 2008; Sertel et al. 2008;
Demirel et al. 2008; Kaya et al. 2014).
Surface temperature is one of the most important climate parameters. Thus, it is of great
importance to study on Land Surface Temperature (LST). Temporal variabilities on LST and its
relation to land cover change in urban areas are significant to study regional climate change
(Nguyen et al. 2015). Remote sensing technology is an effective way to obtain LST maps using
satellite data, and the most common LST retrieval methods used in remote sensing applications
include split-window (Sobrino et al. 1996), temperature/emissivity separation (Gillespie et al.
1998), mono-window (Qin et al. 2001), and single-channel (Jimenez-Munoz and Sobrino 2003)
algorithms.
The Urban Heat Island (UHI) is the phenomenon that the cities or urban areas have more
temperature values than its surroundings (rural areas). SHI is one of the types of UHI and it can
be observed by airborne aircrafts, satellites etc. The aim of this study is to present the effects of
urbanization on LST and to retrieve Surface Heat Island (SHI) maps of the urban areas (city
centers).
2. STUDY AREA AND MATERIAL
In order to investigate the effects of urbanization on LST Zonguldak city center and its
surrounding was chosen as urban area (Figure 1). Zonguldak is located on the coast of Western
Black Sea region of Turkey. The city is also one of the most forested cities in Turkey. However,
the exploitation of the coal reserves, progressive deforestation, massive loss of wetlands,
expansion in facilities of industrial enterprises and urbanization have caused loss of woodland
and vegetative areas. Urban expansion in Zonguldak has accelerated in recent years; hence, it is
important to analyze the study area with regard to LST.
Figure 1 the city of Zonguldak, Turkey.
Landsat 5 satellite data, acquired on 11.09.1987 and 29.09.2011, were used to retrieve LST
images. In addition to Landsat data, high resolution satellite images were utilized to obtain Land
Use Land Cover (LULC) maps of the study area on the basis of SHI. Furthermore,
Meteorological data namely near surface temperature and humidity were obtained from the local
meteorological station, and they were used in mono-window algorithm.
3. METHODOLOGY
LST retrieval algorithms using remote sensing data are dependent on data specifications such as
spectral features and number of thermal bands. Considering LST retrieval from Landsat 5 TM
sensor data, it is usually preferred one of the algorithms, namely radiative transfer equation
method, mono-window algorithm and single-channel algorithm. In this research, mono-window
algorithm was utilized due to its practicability. The method includes three main parameters,
namely emissivity, atmospheric transmittance and effective mean atmospheric temperature (Qin
et al., 2001). The flow chart of the mono-window algorithm for Landsat 5 data is presented in
Figure 2.
Figure 2 the flow chart of the mono-window algorithm for Landsat 5 data.
As seen in Figure 2, red band (3. band), near infrared band (4. band) and thermal band (6. band)
of Landsat 5 data are utilized in the algorithm. Data pre-processing includes registering the
images each other, clipping as including the study area and layerstacking the bands to obtain
multi-band image. After pre-processing, the digital numbers of the bands are converted to the
radiance values using equation [1].
Lλ= [LMAXλ - LMINλ
QCALMAX-QCALMIN] x [QCAL-QCALMIN]+ LMINλ [1]
where Lλ is spectral radiance at the sensor's aperture (MW.cm-2.sr-1.μm-1), QCAL is the quantized
calibrated pixel value in DN, LMINλ is the spectral radiance scaled to QCALMIN, LMAXλ is the
spectral radiance scaled to QCALMAX, QCALMIN is the minimum quantized calibrated pixel value
in DN and QCALMAX is the maximum quantized calibrated pixel value in DN (Landsat Project
Science Office 2002). Then red band’s and near infrared band’s radiance values are converted to
reflectance and thermal band’s radiance values are converted to brightness temperature using
equation [2] and [3], respectively.
ρp=
π ⋅ Lλ ⋅ d2
ESUNλ ⋅ cos θs
[2]
where ρp is unitless planetary reflectance, Lλ is the spectral radiance at the sensor's aperture, d is
the Earth-Sun distance in astronomical units, ESUNλ is the mean solar exo-atmospheric
irradiances and θs is the solar zenith angle in degrees (Landsat Project Science Office 2002).
T=K2
ln (K1
Lλ+1)
[3]
where T is effective at-satellite temperature in Kelvin, K1 is calibration constant 1, K2 is
calibration constant 2, Lλ is the spectral radiance at the sensor's aperture.
The next step is the estimation of Land Surface Emissivity (LSE) by using Normalized
Difference Vegetation Index (NDVI). Van de Griend & Owe (1993) proposed an operative
equation [4] for LSE (εi) retrieval from NDVI value when NDVI values range from 0.157 to
0.727. In this study, equation [4] was used to estimate LSE as well.
εi= 1.0094+0.047ln(NDVI) [4]
After that, the estimation of mean atmospheric temperature (Ta) via near surface temperature (To)
was proposed by Qin et al. (2001). Estimation of the last parameter, atmospheric transmittance
(τi), could be estimated from water vapor content (wi) as demonstrated in Table 1 (Qin et al.,
2001). Water vapor content can be obtained from the meteorological stations or can be calculated
using near surface temperature and relative humidity.
Profiles Water Vapor
(wi)(g/cm2)
Transmittance
estimation equation (τi)
Squared
correlation Standard Error
High Air
Temperature
0.4-1.6 0.974290-0.08007×wi 0.99611 0.002368
1.6-3.0 1.031412-0.11536×wi 0.99827 0.002539
Low Air
Temperature
0.4-1.6 0.982007-0.09611×wi 0.99563 0.003340
1.6-3.0 1.053710-0.14142×wi 0.99899 0.002375
Table 1 the estimation of the atmospheric transmittance using water vapor.
The parameters stated above are placed into mono-window algorithm, which equation is given
below.
Ts={a⋅(1-C-D)+[b⋅(1-C-D)+C+D]⋅Ti-D⋅Ta}÷C [5]
a = −67.355351, b = 0.458606, C = εi × τi, D = (1 − τi)[1 + (1 − εi) × τi]
where Ts is the LST in Kelvin, Ti is the brightness temperature in Kelvin calculated using the
equation [3], Ta is the effective mean atmospheric temperature, τi is the atmospheric
transmittance value, εi represents LSE estimated by NDVI method using equation [4], a and b are
the algorithm constants, C and D are the algorithm parameters calculated by means of emissivity
and transmittance as seen above.
Urban Thermal Field Variance Index (UTFVI) was used to evaluate the SHI effect (Zhang et al.,
2006). UTFVI can be calculated using the equation [6] as below:
UTFVI=Ts
Ts-TMEAN
[6]
where Ts is the LST in Kelvin and TMEAN is the mean LST value of the whole study area in
Kelvin. The threshold values for the ecological evaluation index related to UTFVI are presented
in Table 2.
Urban Thermal
Field Variance Index
Urban Heat Island
phenomenon
Ecological Evaluation
Index
<0 None Excellent
0.000-0.005 Weak Good
0.005-0.010 Middle Normal
0.010-0.015 Strong Bad
0.015-0.020 Stronger Worse
>0.020 Strongest Worst Table 2 the threshold values for the ecological evaluation index related to UTFVI.
4. RESULTS
LST images were created using mono-window algorithm abovementioned and classified using
threshold method (Figure 3). As it is understood in the figure 3, LST values have increased
significantly for the study area. The mean LST values were observed to increase by about 2 ᵒC,
from 1987 to 2011. Although the second image acquired at the end of the September compared to
the other one (acquired on 11.09.1987), LST values are really higher than the image acquire on
1987.
Figure 3 LST variations in the study are from 1987 to 2011.
As SHI evaluation of the study area, UTFVI was generated using LST images, and UTFVI
images were also classified using threshold method (Figure 4).
Figure 4 UTFVI variations in the study are from 1987 to 2011.
In Figure 4, it is clearly seen that two extreme levels of UTFVI were observed in the study site.
The classified UTFVI images can be also associated with SHIs and each class in UTFVI images
presents SHIs. The urban development in Zonguldak city center and its central district from 1987
to 2011 can be analyzed from the Figure 4. Because of the urban development and expansion, the
spatial sizes of SHIs have increased year by year. Destruction of woodland and vegetative lands
and expansion in concrete structures are the main reasons that lead to increase in LST and SHI
effect.
In addition to LST and UTFVI images, two high resolution satellite images acquired on
27.08.2009 and 29.08.2011 were processed to extract LULC maps (Figure 5). Supervised
classification method was implemented and the kappa values as an indicator of the classification
results were 0.81 and 0.85, respectively. As classes of LULC maps, sparse vegetation area, Black
Sea, dense forest area and possible SHI areas are considered. Possible SHI areas consist of city
centers, open lands, sandbanks, open pit mining area etc. In other words, areas without vegetation
and water body are the possible SHI areas. Although the time difference between the images is
just two years, the expansion of possible SHI areas is clear from the Figure 5. As spatio-temporal
analyzes of the images, Table 3 is presented below. As it is understood from Table 3, Possible
SHI areas have expanded. Urban development in the study area between these years has
accelerated and steel goes on, and that is the main reason of the expansion.
Figure 5 LULC maps of the study are on the basis of SHI evaluation.
SPATIO-TEMPORAL VARIATIONS IN
LULC (%)
27.08.2009 29.08.2011
Sparse Vegetation 13.22 13.49
Black Sea 30.13 28.93
Dense Forest 33.95 29.34
Possible SHI Areas 22.70 28.25
Table 3 Spatio-Temporal Variations In LULC (%).
5. CONCLUSION
LST is an indicator of the climate change and it is of great importance to monitor the variations in
LST so as to evaluate UHI effect. SHI is one of the types of UHI and it can be observed by
airborne aircrafts, satellites etc. In this study, LST images were generated using mono-window
algorithm. Then, UTFVI images were extracted from the LST values since UTFVI could be
considered as an indicator of SHI. In order to support the results of UTFVI images, two high
resolution satellite images were classified using supervised classification method. City centers,
open lands, sandbanks, open pit mining area etc. were considered as possible SHI areas in the
LULC image. It is observed that the areas of the possible SHI areas from 2009 to 2011 have
increased. The obtained results showed that the surface temperature of the study area have rapidly
increased in recent years.
Remote sensing technology is an effective way to understand the behavior of the world and to
evaluate the changes on Earth. Regional climate studies, especially in rapid urbanized areas, can
be observed using satellite imagery and urgent action plans can be managed by the help of
analyzes. Furthermore, decision makers and policy makers should also take climate parameters
into account for the sustainable development.
ACKNOWLEDGEMENTS
Geoeye and Quickbird2 satellite images have been provided by BEUN Scientific Research
Project: 2012-17-12-03.
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