the effect of land cover and land use on urban heat island ... · wereover 31.3 ℃, and the ratio...
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The effect of land cover and land use on urban heat island in
Taiwan
Feng-Chi Liao1;Ming-Jen Cheng
2;Reuy-Lung Hwang
3;Wen-Shan Yang
4
1Program in Civil and Hydraulic Engineering, Feng Chia University, Taichung, Taiwan
2Department of Architecture, Feng Chia University, Taichung, Taiwan
3Department of Architecture, National United University, Miaoli, Taiwan
4Program in Civil and Hydraulic Engineering, Feng Chia University, Taichung, Taiwan
Abstract
The global warming get worse as problems on highly land developed, other artificial heat
influences, and especially it causes that urban heat island effect with highly intensive
population and makes the living environment worse. Finally, we mainly investigate the
influence of land use on urban heat island (UHI) and study impact factors of urban micro-
climate in Taiwan. In the study, we established 20 different measuring points in Taichung,
Taiwan (120°40’E, 24°08’N) with fixed-point monitoring. We analyzed the correlation of land
use pattern factors and variations in UHI strength by buffering analysis, ANOVA, multiple
regression analysis, and then we established air temperature regression model. Finally, we
found that it had the highest correlation between the building area and UHI strength, and the
results shows that the average UHI strength during the period of experiment is about 0.96℃
and the maximum vaule is 1.49℃.
Keywords: land use, urban heat island (UHI), fixed-point monitoring, multiple regression
analysis
1. Introduction
After the Industrial Revolution, urbanization is an apparent appearance in every country, and
the environment has become different from the past decades.All the research indicate that the
microclimate in urban has become more and moreheater. The urban heat island effect has
happened in some city in Europe, such as London. Other Cities in different country also have
the same problem. For example, the temperature in Tokyo city has risen up since 1920, and
the upward trendexceeds the average of the Tokyo County. Therefore, Sciences had attached
importance to the urban microclimate since the 20th century. Surveyed all the reverences in
the world, the factor which affect the urban microclimate includedthe sky view factor (SVF),
surface albedo, ratio of green cover, building height/street width (H/W), and land use/land
cover (LCLU). The LCLU composition includes the buildings, streets, plants, and water,
which can rise or decrease the temperature. It might be the improvement factors of the urban
microclimate.
Because of the references on urban climate didn’t take the types of LCLU as the main factor,
therefore the purpose of this study will includeconstructing the database of urban
microclimate in midland of Taiwan, surveying the correlation between the Land Use and the
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urban microclimate, analyzing how the Land Use effects the urban heat island effect in
Taiwan.
2. Materials and Methods
2.1. Definition of land cover and land use
As Fig.1 shows, the LCLU patterns around the survey point could be classed into two
categories, the hard or the soft pavement.And it also could be classed into five factors: (1)the
building area(Ba), which included all the structures; (2)the paved area(Pa), which included the
asphalt road, the sidewalk, and the impermeable pavement; (3)the free area(Fa), which
included the back yard, the grassun-shade by trees, and theun-paved soil surface; (4)the green
area(Ga), which shaded by trees; (5)the water area(Wa), which included the rivers, the lakes,
and the ponds.
2.2. Field measurement
Fig.1 Description of LCLU patterns. Fig.2 Location of Taichung City and 20 measuring spots.
Table 1 Locations description on LCLU patterns.(radius:150 meters)
A B C D E
F G H I J
K L M N O
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P Q R S T
In this study, the fieldwork is surveying in fixed-point monitoring. The measurement spots
had to be consistent in the impact of traffic, street orientation, street and building aspect ratio,
and green trees. In order to obtain the variationof the temperature and enhance its credibility,
we chose 20measurement spots, which were distributed in the axis of downtown to the
outskirts of Taichung City and around the dense population and activities areas, as Fig. 2
shows. We carry out the fieldwork and analyze the aerial photographs of themeasurement
spots to diagram the LCLU patterns. And then we differentiate and quantify the composition
of LCLU patterns around the measurement spots, as Table 1 shows.
Then, we monitored the temperature and humidity in July to September 2008 and 2009 with
the iLog sensor. We set a well-ventilated white cover on top of the sensor, in order to avoid
direct exposure to the sun and rain. And we also used wooden structure to set the sensor on a
light pole at 3 meters high and at least 1.5 meters far away the buildings to avoid the artificial
damage, and reduce the effect of radiant heat from the buildings and grounds. In addition,
sensors were all located on the north side of streets as the different of sunshine duration.
Fig.3 Description of setting i-Log sensor.
3. Results and Discussion
3.1. The contrast between local temperature
After surveying, we had 68 valid data by excluding cloudy and rainy days, and we took the
climatic data from Taichung meteorological station to be the reference. Based on the related
research and the buffering analyzing with a 50m/100m/150m/200m/300m radius of the
measurement locations, it indicates that the parameters of the microclimate is most related to
the Land Use patterns within a 150m radius, therefore, we take i as the criterion for this study.
The Fig. 4 shows that all the data of each measuring spots and the Land Use patterns.
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(1) The daily average temperatures
The different LCLU patterns and the artificial heats primarily affected the daily average
temperature of each measurement spot. We found that the daily average temperature of spot K
is 31.47℃, which is the highest among all. The daily average temperature of spot F, N, and Q
wereover 31.3℃, and the ratio of hard pavement in these three spots was more than 80%.
Furthermore, the daily average temperature of spot J is 29.94℃, which is the lowest, and the
ratio of soft pavement in J spot is 53%.
Besides, the data indicated that the maximum temperature of each day is affected not only by
the different LCLU patterns, but also by the size of surface heating area around the measuring
location and the artificial heats.
Fig.4 Collection of the climatic data and LCLU patterns for each spot.
(2) The average daytime temperatures
In daytime, we found that the building is an endothermic factor in daytime, but its shadow
will reduce the temperature around. The more Pa will accelerate the absorption of heat in the
surface, and then increase the temperature. The Fa could heat up the air faster, even if it
wasn’t an endothermic factor. On the contrary, the Ga can reduce the temperature of
measuring spots, and the Wa could cooldown around.
(3) The average nighttime temperatures
The temperature of each measuring location had the same variation phenomenon at nighttime.
The difference in the average of nighttime temperature of each measuring spot is 1.9℃. On
the contrary, the building and the artificial pavement would radiate the heat slowly, which
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they absorb in daytime. On the contrary, the free area,green area, and water could cool down
the temperature of measuring location.
(4) Temperature differences
To explore the difference of the temperature between downtown and outskirts , we analyzed
the data of spot B and J first, and we discovered that the difference of the daily average
temperature was 1.54℃, and the maximum temperature difference was 2.23℃occurred at
20:00. As Fig. 5 shows, the difference of the temperature had the same variation phenomenon
at night. The average difference between spot B and J was 1.96℃.
Secondly, exploring the overall temperature difference between the whole cityand the
outskirts, we analyzed the data of location J and the highest temperature spot of all, then we
measured the UHI strength by the average of temperature difference between spot J and
others. The results indicated that the temperaturedifferences of spot Balmost consistent with
spot J. The temperature difference was lower as 1.18℃ in the morning, but it significantly
increased in the afternoon, especially at the measurement spot with larger ratio of the Pa and
Ba. And the maximum temperature difference was 3.85℃ occurred at 19:00. In brief, all the
maximum temperature of every measurement spots were higher than spot J, and the average
temperature differences was 2.1℃. The maximum UHI was 1.49℃ occurred at 19:00.
Fig.5 The daily mean temperature difference
3.2. Quantitative analysis
(1) One way ANOVA
In this study, we analyzed the relationship between the climate data and the LCLU patterns by
ANOVA for regression. Table 2 shows that the daily average temperature was obvious related
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to the Pa, Fa, and Ga. The area of Pa was obvious related to the average daytime temperature,
and the average nighttime temperature was obvious related to the Ba, Fa, Ga, and Wa. by
differential thermal analysis,we found that the daily average temperature difference was
obvious related to the Pa and Ga. The Pa was obvious related to the average daytime
temperature difference, and the average nighttime temperature difference was obvious related
to the Ba, Fa, and Ga.
(2) Multiple Regression
With stepwise regression, we confirmed the significant factor by buffering analysis and one-
way analysis of variance. And then we analyzed the LCLU patterns in accordance withthe
average temperature of the whole day, the daytime, and the nighttimeby multiple regression
analysis. And Table 3 shows the results.
Table 2 The P value of the significance for LCLU factors to air temperature(α=.05)
Factors Taavg △Taavg UHI strength (△Ta at 19:00) daily day night daily day night
Ba .070 .990 .000 .092 .174 .000 .004
Pa .033 .048 .194 .023 .034 .068 .246
Fa .007 .098 .000 .069 .964 .004 .083
Ga .043 .123 .004 .011 .133 .000 .000
Wa .325 .179 .007 .511 .053 .172 .776
Table 3 The multiple regression model for air temperature and LCLU
Parameter Formula R2
Daily_Taavg 32.21-1.64×Ba-0.32×Pa-4.39×Fa-2.56×Ga 0.54
Day_Taavg 31.96+2.2×Pa-2.37×Fa-0.1×Ga+6.14×Wa 0.27
Night_Taavg 29.35-0.34×Ba-3.47×Fa-2.18×Ga-3.78×Wa 0.86
Daily_△Taavg 0.96-3.55×Ba-2.35×Pa-4.52×Fa-4.87×Ga 0.48
Day_△Taavg 1.49-0.34×Ba+1.77×Pa-0.99×Ga+9.74×Wa 0.30
Night_△Taavg 0.44+2.97×Ba+3.26×Pa+0.69×Fa+0.98×Ga 0.81
UHI strengthavg(19:00) (Σ0.36+0.86×Ba+0.83×Pa-2.84×Fa-1.31×Ga)/n 0.69
4. Conclusions
In this study, we analyzed the temperature by fixed-point field measurement and regression
analysis. We explored that the hard pavement, such as the Ba, and Pa,would absorb heat at
daytime and radiate the absorbed heat at nighttime. Resulting in the spot with more ratio of Pa
cooled down slower than with more ratio of soft pavement, such as Fa, Ga, and Wa. More Ga
and Fa area would cool down the urban microclimate more, especially at night. On the
contrary, more Ba and Pa area would heat up the urban microclimate, especially at daytime.
By analyzing the temperature difference of each spot, the LCLU pattern wasn’t related to the
temperature difference of daytime. We confirmed the Pa and Ga were the main impact factor,
but the shadow of buildings could due to the downtown temperature higher than the outskirts
in the morning. The Ba, the Fa, and Ga obvious related to the average temperature difference
of the nighttime. During the measurement period, the average UHI is 0.96℃. The maximum
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UHI was 0.96℃occurred at 19:00 because of the heat absorption and radiation of the Pa, and
the outskirts cooled down faster than downtown.
Finally, we established the correlation model of the LCLU patterns and the temperature by
multiple regressions. And we explored that the LCLU pattern obvious related to the average
temperature of the nighttime and the average nighttime temperature difference of each
measuring spot. The maximum temperature difference of whole day was 4℃because of the
different LCLU patterns. The difference of maximum average temperature was 2.52℃, and
the difference of maximum UHI was 3.7℃. In brief, the LCLU pattern was an important
impact factor to the urban microclimate.
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