Academic Article
Journal of Heat Island Institute International Vol.7-2 (2012)A New Urban Planning Approach for Studying Heat Islands at the Community Level
Kun LI*
1Borong LIN*
1Dong JIANG*
2*1 School of Architecture, Tsinghua University, Beijing, P. R. China
*2 Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing, P. R. China Corresponding author email: [email protected]
ABSTRACT
The urban heat island (UHI) effect is a necessary issue to consider in urban planning. However, an effective way to study UHIs is presently lacking. This paper tries to incorporate the use of remote sensing technology to establish an urban planning approach that takes into account UHIs at the community level and to provide a scientific means of studying the heat environment for urban planners. Tsinghua University is used as a case study for analyzing the heat environment, using the aforementioned approach. Various underlying surfaces make different contributions to the UHI on the campus and should be considered accordingly. Therefore, research on UHIs is necessary for urban planning, and good planning projects can reduce UHIs.
1. Introduction
Urban planning is one of the main causes of urban heat island (UHI) generation. Unreasonable planning can result in exacerbation of the UHI effect, worsening the microclimate environment, and lowering the level of human settlement in the community, and resulting in greater energy consumption and pollution. Thus, urban planners should seek an approach that suits the local climate and decreases the UHI effect.
Remote sensing, a tool for space study, was used in this research to develop an urban planning approach based on the study of UHIs. Remote sensing can deduce the condition of underlying surfaces, based on different resolutions (Yang 2006).
Satellite images present the overall condition of an area and record the status of all its characteristics, and researchers can use these digital data to deduce urban planning indexes and make sound, scientifically based decisions. The UHI effect is important for the human environment at the community level, which is the focus of this research. Therefore, the analysis of UHI at the community level, using remote-sensing technology, is an important way to assist in urban planning and make it more reasonable.
2. Methodology
2.1 Research Approach
The approach to studying the relationship between UHI and urban planning should minimize the limitations of each profession and should address the heat environment at the
community level. The practical flow can include the following steps.
The community’s condition can be analyzed at a medium level. This step uses medium-resolution satellite images to deduce indices for the underlying surface (e.g., surface temperature index and vegetation index) to analyze the medium-level characteristics of the studied community and its heat environment.
Then high-resolution satellite images can be used to analyze the land use of the studied community, including its underlying surface classifications and landform characteristics.
In this step, the planners analyze the characteristics of planning and its influence on the UHI, combined with the result of medium-resolution satellite images. Thus, the flaws in community planning can be identified.
Based on these studies and on the existing condition of buildings, roads, and the landscape, final revision plans can be summarized and provided as a reference for other relevant projects.
2.2 Points for Attention
This approach is divided into several small parts, based on the requirements of UHI research. In the real-world application, some matters should receive attention. Digital study should be closely related with real design and planning in the target community. Therefore, the approach can have applicability for real UHI research.
Research on UHIs should not be limited to the detection of the temperature index and heat field, but should include the
indices of vegetation overlay, water surface, and styles of buildings. Then analyses of the inside mechanisms of the heat field can be performed. The technology of remote sensing needs many environmental parameters because interchange parameters can produce many underlying surface indices as factors relevant to UHI research and planning projects.
Representation of the heat environment as one spot and one time only yields a static value; however, this value changes continuously. The characteristics and spatial structures of heat environments based on changes of time and environment in the city should be the focus of this research.
2.3 Relevant literature
Many research papers address the UHI effect, but these all have some flaws. Kato and Yamaguchi (2005) used ASTER and ETM+ data to study the UHI effect and separate anthropogenic-heat discharge and natural-heat radiation from sensible heat flux. But their research focused on the analysis of medium-resolution satellite images and could not study the characteristics of underlying surfaces in small detail. Ji et al.
(2008) studied the technique of regional wind-environment simulation, based on geographic information system (GIS) data.
But GIS data cannot be obtained easily. Ben-Dor and Saaron (1997) used airborne-video thermal radiometry as a tool for monitoring microscale structures of the UHI. But this study was very expensive and could not be widely used. Wang et al. (2008) studied factors of the thermal environment on a campus using experimental data (e.g., outdoor air temperature, relative humidity). Their research required extensive manual work and was influenced by subjective decisions.
The above studies paid attention to one point and had limitations at the other points. The approach of the present work establishes a system of UHI study involving various levels of remote satellite images, which can make the urban planning
process more scientific and rational.
3. A Case Study of this Approach
The UHI study approach is shown through the case study of a large community zone in Beijing that was developed with the steps mentioned above. The study used a high-resolution satellite image and a medium- resolution satellite image as resources. The medium-resolution image was a ASTER satellite image1; it had five thermal infrared bands with resolutions of 90 m. The thermal infrared bands were used to deduce the brightness temperatures of underlying surfaces (Zhao et al.
2009). The brightness temperature reflects the structure of the UHI and can build a relationship between the UHI and the underlying surfaces. The high-resolution satellite image used CBERS-02B HR images2 with a spatial resolution of 2.35 m. It can represent the information of underlying surface and can investigate the study area carefully.
The case study concentrated on Tsinghua University, which is a university in Beijing, China P. R. It is a special community that is divided into several parts (e.g., teaching area, dormitory area, greening area). Vegetation on the underlying surface of the campus is plentiful and concentrated. Building on the underlying surface is orderly, and the water surface is obvious. Thus, this study area has usual underlying surfaces and can reflect the characteristics of a UHI.
1 The data from the ASTER satellite used in this paper was provided by China Remote Sensing Satellite Ground Station: Http://www.rsgs.ac.cn.
2 The data from the CBERS satellite used in this paper was provided by the China Centre for Resource Satellite Data and Applications (CRESDA): http://www.cresda.com.
Figure 1. The image of brightness temperature calculated from an ASTER
satellite image
Figure 2. The image of NDVI calculated from an ASTER
satellite image
Figure 3. Scatter diagram of brightness temperature (°C) and NDVI
The date of the ASTER satellite image is April 22, 2006.
The brightness temperature was used to deduce the heat structure of this region. Though it is not the real surface temperature and has deviations made by reflection and refraction of sunlight, it can represent the UHI phenomenon well in a small-scale city or city region with approximately the same moisture and atmosphere (Chen et al. 2004). Figure 1 shows the status of the brightness temperature of Tsinghua University. The structure of the UHI is a sequential and illegibility field and has no clear boundary, which can be seen in the Figure 1. So the analysis of the temperature field focuses on its distribution of temperature segments. For example, it can be seen that the parts that have a high-brightness temperature lie in the areas of high-density buildings on the campus.
Next, we obtained the index of vegetation, which is called NDVI (Figure 2). NDVI represents the condition of vegetation coverage (Bi et al. 2005). Using the ASTER satellite image as an example, the derivation of NDVI uses the second and third bands, and the value is the ratio of difference-value and sum of two bands (Qin and Chen 2008).
Then, these two values of indices were connected to see the relationship between brightness temperature and the greening condition of the underlying surface. The ASTER satellite image used was generated in the spring, so it shows that good vegetation can effectively relieve the UHI. The eastern part of the campus has many big buildings and a comparatively high temperature. The landscape area, in contrast, has lower temperatures. Following these tendencies, a series of point values of brightness temperature and NDVI was selected to form a scatter diagram (Figure 3). In this figure, the Y axis is the value of the brightness temperature and the X axis is the value of NDVI, which can be used to do some studies. These two indices have an inverse relationship.
Then regions of interest were chosen to see the distribution of some special regions. For example, when places where the values of NDVI exceed zero were chosen, and the results show that the campus has very good greening coverage.
Most regions have a moderate temperature. Then the regions of interest were changed to those with higher NDVI value and lower brightness temperature value. This time, the regions chosen were greatly reduced and included parts of the landscape area and administrative area, which have buildings of low
density and many trees. Then the regions of low NDVI and high brightness temperature were chosen. Those regions included parts of the teaching areas and research institutes, which have some high buildings and fewer trees. Similar studies were done and it was shown that dormitory areas have a moderate balance of NDVI and brightness temperature; this is because the dormitory areas have many buildings that are not very high and have considerable green areas.
The above research was based on the medium-resolution satellite image. However, this image has some limitations because it cannot explore underlying surfaces well.
High-resolution satellite images have no thermal infrared band, but can clearly analyze the underlying surfaces, especially in small-scale city regions, and thus can remedy the shortage of medium-resolution satellite images and be used for deeper research. In our case, three different areas were selected to do the classification of underlying surfaces and to make the condition of these areas clear, using the high-resolution band of a CBERS satellite image with resolution of 2.35 m and taken on December 6, 2007. One pixel in the CBERS image represents an area of 2.35 m x 2.35 m at the real underlying surface, and ground objects (e.g., buildings, roads, and water surfaces) can be seen clearly. Finer classifications and follow-up studies can be done by calculating the amount and property of the pixels. The dates of the ASTER image and CBERS image used in this research were close, so the underlying surface had little change, and thus these images can be used together.
The first area selected was the teaching area, which is in the eastern part of campus (Figure 4). The main types of underlying surfaces in this area are buildings, roads and piazzas, lawns, and forests. Their percentages are presented in Table 1. It can be seen that buildings occupy a large percentage and will raise the brightness temperature. Roads and piazzas also have a large percentage and do the same function. However, buildings are arranged in orderly ranks along the roads. This benefits the ventilation and can take the heat out. Moreover, vegetated surfaces have a sizeable area and can decrease the intensity of the UHI. Although this area is a high-temperature part of the campus, it remains in an appropriate condition.
The second area selected was the administrative area on campus. This area is also made up of buildings and roads, water surfaces, lawns, and vegetated surfaces. The area’s greening
Figure 4. Teaching area of Tsinghua University
Figure 5. Administrative area of Tsinghua University
Figure 6. Dormitory area of Tsinghua University
coverage occupies a large percentage and has a beautiful landscape (Figure 5). The greening surface here can prevent the radiation of sunlight and absorb much heat. The water surface can also stabilize the temperature. Buildings and roads in the area are scarce, and many of them are sheltered by trees. So the temperature of this area is relative low.
The third area selected was the dormitory area on campus, which has a proper proportion of building and plant areas (Figure 6). The classifications of underlying surface include buildings, roads, and vegetated surfaces. Buildings are arranged in rows, and the spaces between buildings are enough for ventilation. Vegetated surfaces are in abundance among the buildings, and the greening condition of the adjacent area is good. Most buildings in this area are multilayered buildings and are not very high. All these factors can result in a reduced UHI effect.
4. Conclusion
From these studies, it can be seen that this new research approach can study the UHI effectively, based on the frame of urban planning. Conclusions can be drawn as follows:
1. Traditional urban planning cannot provide effective measures to detect the underlying surface and the structure of UHIs. Our approach uses remote sensing technology to do relevant research, which can guide real-world planning projects more scientifically.
2. A UHI of small scale is hard to study because high precision is required for the observation of ground objects. The
more serious, but too few buildings limit the requirements of human settlements. Therefore, urban planning measures (e.g., multilayered building, enough space between buildings and planted vegetation) can be efficient in decreasing the UHI effect.
5. Acknowledgments
This research was supported by NSFC No.50878111 and the National 11th-five Research Plan Project No.2006BAJ02A02.
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Percentage 100% 35.0% 7.6% 11.6% 3.2% 42.6%
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Percentage 100% 37.8% 32.0% 30.2%
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(Received Feb 9, 2012, Accepted Oct 10, 2012)
