Evaluation of surface urban heat island intensity in arid environments (case study: Isfahan metropolitan area)

doi:10.21203/rs.3.rs-1591895/v1

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Evaluation of surface urban heat island intensity in arid environments (case study: Isfahan metropolitan area)

https://doi.org/10.21203/rs.3.rs-1591895/v1

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Heat island is formed due to the expansion of a city in a vast geographic area. The traditional index is used to describe the urban heat island based on the temperature difference between the urban and rural areas. Other indices have been suggested in the literature for this purpose. However, the heat island characteristics depend on the background climate of the place where the city is located. Therefore, an index was defined based on the background climate of the Isfahan metropolitan area in order to quantify the heat island intensity. This new index is based on the representative pixels of the urban and the non-urban areas. For this purpose, the MODIS land cover type product (MCD12Q1) data was used to distinguish between the urban and non-urban areas. Also, the data from the MODIS/Terra/Aqua land surface temperature product (MOD11A1/MYD11A1) from 2000 to 2016 was utilized for the day time and night time to study the heat island intensity. Then, the representative pixels of urban and the non-urban areas were identified using the spatial correlation method and the heat island index was calculated for the metropolitan area of Isfahan. The study showed that the frequency distribution for the nighttime heat island index in the metropolitan area of Isfahan follows a normal distribution and is often 3.5-4 K above the temperature of the surrounding areas at night. Investigating the temporal and spatial behavior of surface urban heat island in the metropolitan area of Isfahan showed that the intensity of the surface urban heat island is maximum in January and becomes weaker in summer. The 365-day floating mean of the surface urban heat island showed that this index has increased in recent years. The core of heat island in the metropolitan area of Isfahan is extended to downtown areas where the oldest part of the city is located.

MODIS/Terra/Aqua/land surface temperature

urban heat island intensity

representative pixel method

Isfahan metropolis

Urban heat island (UHI) is one of the environmental hazards that has emerged due to the urbanization and is often defined by the difference in the air temperature of a station in the city center with that of the station outside the city (Stone 2007), which is called air urban heat island(Arnfield, 2003, Peng et al. 2011).

In practice, the urban and suburban temperatures can be compared by different methods. Some of these methods are: the difference in temperature of one or more pairs of urban and suburban stations (e.g. Nasrallah et al. 1990), difference in city temperature between weekdays and weekend (Figuerola & Mazzeo, 1998), comparing the time series of temperature in one or more urban stations with one or more rural ones (e.g. Magee et al. 1999), time series trend analysis of temperature in an urban weather station (e.g. Tereshchenko & Filonov, 2001), a network of fixed weather stations inside and outside the urban areas (e.g. Morris et al. 2001, Golroudbary et al. 2018), and measurement of temperature cross-section from one city corner to another (Unger et al., 2001). Such heat island identification methods represent the traditional approach to the study of the urban heat island.

In the traditional approach, the definition of urban heat island is based on the comparison between the temperatures of urban and suburban areas (Cai & Du, 2009, Shigeta et al. 2009), but it is not easy to distinguish between the urban and suburban areas (Schwarz et al. 2011). Besides, it is controversial in the traditional approach that the temperature difference between a pair of weather stations one in the city and the other outside the city is the basis for the definition of the urban heat island. This is because the temperature difference between two stations (urban and suburban) depends on their location, distance, and geographical conditions. By choosing pairs of stations different from the urban heat island index in a single city, different values will be obtained. Because the urban land cover is very heterogeneous, one or even several stations cannot be the representative of city temperature. On the other hand, the land cover type outside the city may greatly vary, and depending on the land cover type the station outside the city is located on, different temperatures will be obtained. Hence, not only we cannot achieve a reliable and consistent UHI index for a city, but also the heat island obtained in this way will not be comparable for different cities (Jin, 2012).

There is no consensus among the researchers that the difference in temperature between which urban district can be the basis for the definition of heat island. Some researchers considered the difference between the temperature of the fully developed and the less developed parts of the city, while others considered the difference between the two different built-up areas. Furthermore, in the traditional heat island survey method that is based on the station data, the spatial structure of the heat island cannot be identified.

Due to the city size, scarcity of stations in the city, and impact of surrounding conditions on the station data, the comprehensive information on the urban heat island cannot be achieved only by relying on such method. For this reason, the remote sensing data has been increasingly used for the analysis of heat island (Majkowska et al. 2016).

Using the satellite data, the temporal and spatial structure of the land surface temperature (LST) can be revealed by such a resolution that can well distinguish between the urban and suburban areas (Peng et al. 2011).

Satellite data covers large areas with high spatial resolution, and thus, is a better tool to define the urban heat island intensity (Jin, 2012). The heat island calculated from the LST data derived from remote sensing is called surface urban heat island (Arnfield 2003, Voogt & Oke, 2003). Accordingly, the urban heat island intensity is the difference between the mean spatial temperature of urban and suburban/rural areas (Rizwan et al. 2008). Since the air temperature behavior is different from the LST, the time of measuring the temperature and the approach chosen to calculate the heat island index are both effective in the characteristics of the resulting heat island. In this case, the surface urban heat island reflects the difference in the LST patterns in different urban areas (Schwarz et al. 2011).

The advances made over the past two decades show that the urban heat island is much more diverse than what was previously thought. Depending on where the (air or surface) temperature is measured by what tool (station or satellite sensor), the resulting heat island will show different characteristics (Arnfield 2003). In the clear sky conditions, the higher the surface temperature, the higher the air temperature and the thicker the urban boundary layer. Since the land surface temperature fluctuations in the daytime is higher than the air temperature and there is a lag time between them, the surface heat island is stronger than the air heat island and its fluctuations are greater (Peng et al. 2011, Li et al. 2017). Although the LST measured by satellites is correlated to the air temperature measured at the weather stations, the seasonal and diurnal variations are not necessarily identical, as the LST is linked to the energy exchange processes in the land surface and has little dependence on its upper air column. As a result, the heat islands relying on the land surface temperature are stronger during the night and day, but the heat islands relying on the air temperature are only strong at night (Jin, 2012). This is because the shading of buildings is an important reason for the weak UHI in the daytime (Li et al. 2019). However, the surface urban heat island, like the air urban heat island, relies on the temperature difference between the urban and suburban areas.

In recent years, the modeling has been significantly developed with the techniques such as regression model (e.g. Guo et al. 2015), spatial regression (e.g. Chun et al. 2014), geographically weighted regression (e.g. Su et al. 2012), Gaussian volume model (e.g. Quan et al. 2014), mesoscale model, WRF model (e.g. Chen et al. 2014), and artificial neural networks model (e.g. Bozorgi et al. 2018) for the study of UHI spatial-temporal variations.

Many indices have been proposed for quantifying the urban heat island using the remote sensing data (Schwarz et al. 2011, Li et al. 2018, 2019). Although these indices allow to calculate the heat island intensity in a uniform manner around the world, the credibility of these methods should be established for the cities in the arid regions (Jin, 2012, Li et al. 2018). This is mainly resulted from the fact that in the arid regions, there is a remarkable difference in water budget between the urban areas and the surroundings deserts. Therefore, in order to investigate the heat island in Isfahan, which is located in the arid biome, it is necessary to define a proper index based on the background climate characteristics of the metropolitan area of Isfahan. This is the main purpose of this research.

2.1. Study site

The Isfahan metropolis is the third largest city of Iran. The area of the city is about 231 Km2 and a population of more than 2 million people. The Isfahan metropolis located in central part of Iran and lies between 32.60°N to 32.75°N and 51.58°E to 51.75°E (Atlas of Isfahan metropolis, 2015). The climate of Isfahan province ranges from cold and rainy mountainous climate to the hot and dry climate, and the climate of Isfahan metropolis is warm and dry. However, the dominant background climate of Isfahan is hot arid climate (Masoodian, 2011).

As the goal of this research is investigation of Isfahan metropolis heat island, among the entire database an area with 54\(\times\)84 pixels (50051.68m\(\times\)77858.16m) was extracted, being Isfahan metropolis as its center (Fig. 1).

2.2. Data

Two categories of data were used in this study. First, to distinguish between the urban and non-urban areas, the MODIS "land cover type" product (MCD12Q1) was used. This product provides the surface land cover type with the 500 m spatial resolution and the annual temporal resolution. In this product, the surface land cover is ranked in 17 groups (Wang et al. 2007). Second, the land surface temperature (LST) data of MODIS sensor that is installed on two Terra/Aqua satellites. The MODIS sensor installed on this satellites have many products with different temporal and spatial resolution capabilities. The Terra satellite passes Iran once between 10:00 to 12:00 during daytime and once between 21:00 to 23:30 during nighttime. The Aqua satellite passes Iran once between 12:00 to 14:00 during daytime and once between 1:00 to 3:00 during nighttime.

Among various temperature products of MODIS, the MOD11A1 and MYD11A1 due to having the proper technical specifications was selected from the 2000 to 2016. The projection system of this product is the Sinusoidal Equivalent. The provided data are in the form of tiles with 10 geographical degrees (1200\(\times\)1200 pixels) with HDF¹ format and with a resolution of approximately 1000 m.

To explore the city impact on temperature, it is first necessary to separate the urban and non-urban areas. The urban areas refers to parts of the land that are reported as urban and built-up in the MODIS land cover type product (13 code). The non-urban areas means all the lands that are not reported as built-up. On the urban cluster scale, a range around Isfahan with the approximate dimensions of 50 × 80 km (Fig. 1). Here, the boundary of the city defined as all the constructed areas on the latest Google earth images.

2.3. Method

In this research, the urban heat island index is defined in terms of LST difference between the representative urban and non-urban pixels. To identify the representative pixel, the mean spatial correlation method was applied to the LST data. The representative pixel in the non-urban areas is a pixel where the average correlation coefficient of the LST in that pixel with all other pixels within the non-urban region is maximum. The representative pixel of urban areas is a pixel for which the average correlation coefficient of LST with all other pixels within the urban areas is maximum.

The urban heat island intensity index was calculated by the following formula:

(1)\(SUHII={LST}_{UrbanRP}-{LST}_{Non-urbanRP}\)

where \(SUHII\) is the surface urban heat island index, \({LST}_{UrbanRP}\) is the land surface temperature of the representative pixel of urban areas, and \({LST}_{Non-urbanRP}\) is the land surface temperature of the representative pixel of non-urban areas.

1. Hierarchical Data Format

3.1. Spatial analysis

The calculation of spatial long-term mean of the LST during the nighttime showed that in the administrative areas of the city, due to the difference in land cover, the LST shows significant spatial changes. At night, parts of the city are warmer so that the core of heat island in the metropolitan area of Isfahan is extended over the downtown where the oldest part of the city is located (Fig. 2).

3.2. Temporal analysis

The LST difference between night and day was separately calculated for the urban and non-urban areas. The highest frequency of LST difference between night and day in the urban areas (red curve) is 25° K and in the non-urban areas (blue curve) is 30° K. (Fig. 3). The daytime LST is higher in the dry lands around the city than the urban areas. In contrast, at night, the urban area traps the long wave radiation and is warmer than the surrounding deserts. As a result, the daily range of LST in the urban areas is smaller than the non-urban areas, and the difference between the daytime and nighttime LST in the urban areas is much less than that of the non-urban areas (Fig. 3).

3.3. Urban heat island index

Given the temperature difference between the urban and non-urban areas during the day and night, the effect of night heating can be assessed (Fig. 3). To investigate the heat island of Isfahan, we define the heat island index as: temperature difference at night between a representative pixel in the urban area and the representative pixel of the non-urban areas (Fig. 4). Although, occasionally, at some nights, the urban temperature is lower than the non-urban temperature, but the heat island at night is nearly the dominant feature of metropolitan area of Isfahan. In other words, for more than 98% of the cases, the urban temperature at night is higher than the non-urban temperature. In 2007, the nighttime heat island intensity was maximum. Considering that winter 2007 was very cold and snowy (Keikhosravi Kiany, 2016), the existence of anthropogenic thermal resources leads to the faster melting of snow in the city and further lasting in the countryside, resulting in the increased albedo and decreased temperature of the countryside. Hence the stronger nighttime heat island intensity in this year shows the role of albedo and also the indirect impact of anthropogenic thermal resources in the formation of the heat island.

From the climate perspective, the question that should be answered about this heat island index is whether the obtained index is a stable one and whether the statistical period is long enough to achieve a stable image of the heat island. The analysis of the floating mean shows that the ten-year data is sufficient to achieve a stable index of the nighttime heat island in the metropolitan area of Isfahan. In this case, given that the length of the available statistical period is one and a half times more than the sufficient length, the length of the statistical period is not a factor that can limit the validity of this index. The floating mean is calculated by the following formula:

(2)\({FM}_{i}=\frac{1}{i} \sum _{j=1}^{i}{LST}_{j}\)

where \({FM}_{i}\) is the ith floating mean value and \({LST}_{j}\) is the land surface temperature at time j.

The frequency distribution of the nighttime heat island index is normal in Isfahan metropolitan area. The index ranges from − 9 to 14 K. (Fig. 5). The median of distribution index is 3.6 and the mean is 3.4 K. In other words, although in some rare cases (2% of nights), the city is colder than its surroundings (Fig. 6), the temperature of the metropolitan area of Isfahan is often higher at night from 3.5 to 4.0 K than the temperature of suburban areas.

In ninety percent of the nights, the city temperature at night is at least 2 K higher than its surroundings. Since there is no down-scaled data from the city and the surroundings, it is not easy to judge the mechanism of the emergence of the nighttime heat island. However, the evidences suggest, on the one hand, that the soil moisture and atmosphere humidity, and, on the other hand, the layout of urban structures contribute to the entrapment of long-wave radiation energy and the emergence of nighttime heat island in the metropolitan area of Isfahan.

During the investigation period, the 365-day smoothed mean of the nighttime heat island index in the metropolitan area of Isfahan fluctuated between 2.5 and 4 °K. (Fig. 7). These changes can be attributed to the increased population and expansion of the metropolitan area of Isfahan. The recent studies have shown that the heat island buffer in the metropolitan area of Isfahan has increased from 3 km in 1985 to 10 km in 2015 (Madanian et al. 2018, a) and the heat island ratio has increased from 0.16 in 1999 to 0.3 in 2016(Shirani-bidabadi et al. 2019). Also, the impervious surfaces have increased from 15% in 1985 to 30% in 2015 (Madanian et al. 2018, b).

The monthly long-term mean of the nighttime heat island index (Fig. 8) shows that the seasonal effect means the weakened heat island during the warm season and the strengthened one during the cold season of the year. The cause of seasonal variations can be attributed to the biophysical factors such as reduced solar energy and vegetation activity such as evapotranspiration, increase of cloudiness and albedo, and also socio-economic factors such as the increased energy consumption and air pollution in the winter season.

The urban heat island study is based on measuring the air temperature of the weather station within the urban areas and the station in the rural or suburban areas. Since there is no temperature measurement station in Isfahan, the urban land surface method was used to study the urban heat island. The studies have shown that the MODIS land surface temperature is highly correlated (r = 0.97) with the air temperature in the Iranian meteorological stations (Moradi, 2016). Also, the studies on the gridded data of station and MODIS land surface temperature data have shown that the average land surface temperature of Isfahan at noon was 39.7°C and the air temperature was 31.1°C. The occurrence of higher temperatures on the land surface than on the upper layers is reasonable, because the mechanisms affecting the surface temperature and air temperature are different. The land surface directly receives the solar energy, while the 2-m air temperature is controlled by the thermal energy transfer of the land surface.

Due to the relationship between the air temperature and the land temperature, the satellite land temperature data can be used to calculate the urban heat island index if no air temperature data is available. However, using the satellite data to calculate the urban heat island does not mean the absolute superiority of the surface urban heat land method over the air urban heat island method.

In the previous studies, different methods have been introduced to calculate the urban heat island index, including the average land surface temperature difference of urban areas with farm and areas, average temperature difference of urban areas with rural areas, and average LST difference of urban areas with all other areas (Schwarz, 2011). To calculate the indices for the metropolitan area of Isfahan, the region was first subdivided into several areas based on the land cover type, including: built-up, urban garden, suburban farm areas, urban farm areas, river margin areas, urban cluster, and background areas (Fig. 1). Then, the long-term mean pixels of each area were calculated (Table 1).

Table 1

Long term mean of LST at night based on surface cover type in Isfahan metropolis
	Land cover type	Long Term Mean of LST at night
1	Built-up	10.7
2	Urban garden	7.6
3	River margin areas	9.4
4	Farm areas outside of the city (rural)	7.4
5	Farm areas inside of the city	9.8
6	Urban cluster	8.5
7	Background areas	7.1

The built-up areas encompasses the urban areas from which the vegetation such as gardens, farms, parks, green spaces and barren lands is deducted. The background mainly covers the arid and desert lands and is the entire context of the areas from which the urban cluster areas is deducted.

This study shows that the urban built-up area is about 3.5°C warmer at night than the background (non-urban) areas. The calculation of urban heat island index based on the representative pixels of the urban and non-urban areas shows a difference between 3.5 and 4°C. Therefore, it can be concluded that there is no significant difference between the results of the previously introduced methods and the representative pixel method, except that in the previous methods based on the average pixels of each area, the data of all pixels are always needed. However, in the representative pixel method based on the correlation matrix, the pixel that has the highest correlation coefficient with other pixels in the urban areas is selected as the representative pixel of urban areas. Also, the pixel that has the highest correlation coefficient with other suburban pixels is selected as the representative pixel of non-urban areas. Finally, only the data of two pixels in the urban and suburban areas are compared. Therefore, only the data of two pixels are needed to check the heat island index time series.

Based on the spatial distribution map of the average nighttime LST correlation coefficient in the urban areas (Fig. 9), a pixel near the Kaveh passenger terminal represents the nighttime temperature of the urban areas. The long distance to Zayandehrood River; proximity to the interurban passenger terminal, subway station and the non-level intersection of the two main north-south and east-west highways of the city; and dense texture and high density of tall buildings make this point one of the busiest places in Isfahan. Therefore, the placement of representative pixel in this area of the city seems reasonable. About 99% of the average temperature variability of the urban areas can only be explained by having the LST time series in this pixel (Fig. 10).

The spatial distribution map of the average nighttime LST correlation coefficient (Fig. 11) shows that a pixel in the northwest of Shahin Shahr represents the nighttime temperature of non-urban areas. This pixel is located on the dry lands outside the urban areas where the vegetation cover is very sparse. These physical properties cause the arid lands to easily absorb the daytime heat and reflect it to the atmosphere in the nighttime as long wave radiation and to rapidly cool down the land.

About 98% of the average temperature variability of the non-urban areas can only be explained by having the LST time series at this point. Therefore, this point is a good representative of the climate of non-urban areas (Fig. 12).

When there is a strong relationship between these two representatives of the urban and the non-urban areas, it is more likely to achieve a more reliable and consistent urban heat island index (Fig. 13).

The advantage of the representative pixel method is that if the heat island index is calculated based on the average of pixels, the data of all pixels are always needed. If the representative pixel is selected, first, it represents an area and, second, it requires only the data of one pixel to calculate the heat island index and no other pixels are required. If we want to use all pixels to calculate the heat island, as it is very likely that cloud cover prevent from recording the data for some pixels, the chance of calculating the heat island is missed. As consistency is one of the features of every climate index, one cannot calculate the heat island index one day with a set of pixels and another day with another set of pixels. Thus, the use of all pixels results in the decreased consistency of the index, while the representative pixel method ensures the index consistency. Besides, no data would be transmitted if the satellite mission ends. Therefore, if we only rely on the data of a single pixel to calculate the heat island, it is still possible to extract the data from this single pixel and continue monitoring the heat island.

The nighttime LST difference between the urban and the non-urban representative pixels can be referred to as the heat island index. The nighttime heat island is almost the dominant characteristic of the metropolitan area of Isfahan, as in more than 98% of the cases, the urban LST at night is higher than the non-urban LST. In 2007, the intensity of heat island was maximum. Winter 2007 was cold and snowy, and hence, the intensity of heat island could be affected by albedo and anthropogenic heat sources.

The floating mean showed that the ten-year data can be used to achieve a stable index of the nighttime heat island in the metropolitan area of Isfahan. Therefore, the length of the statistical period is not a factor that can limit the validity of this index.

The frequency distribution of the nighttime heat island index in the metropolitan area of Isfahan follows a normal distribution, and at night, it is mostly 3.5-4 K higher than the surrounding areas.

Temporal analysis shows that the monthly long-term mean of heat island index has seasonal effect, as it intensifies in January and weakens in July. The 365-day smoothed mean of the nighttime heat island index ranged between 2.5 and 4 K.

Due to the difference in the land use, the LST has spatial variability within the administrative boundaries of the city. During the nighttime, parts of the city are warmer and parts of the city experience lower temperatures. Understanding these differences is essential for the smart city management. The spatial long-term mean of the LST shows that the core of the heat island is located in the downtown and old parts of the city.

Availability of data and material: Not applicable

Competing interests: This manuscript is the result of my joint research with my colleague Professor Masoodian, and Professor Xulin Guo. There is no conflict of interest.

Funding: Not applicable

Authors' contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Seyed Abolfazl Masoodian and Majid Montazeri. The first draft of the manuscript was written by Majid Montazeri & Xulin Guo and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgements, Not applicable

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Evaluation of surface urban heat island intensity in arid environments (case study: Isfahan metropolitan area)

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Abstract

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1. Introduction

2. Data & Methods

2.1. Study site

2.2. Data

2.3. Method

3. Results

3.1. Spatial analysis

3.2. Temporal analysis

3.3. Urban heat island index

4. Discussion

5. Conclusions

Declarations

References

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