Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Mapping and Analyzing the Park Cooling Intensity in Mitigation of Urban Heat Island Effect in Lahore, Pakistan

  • Hanif, Aysha (Department of Geography, Lahore College for Women University) ;
  • Nasar-u-Minallah, Muhammad (Department of Geography, Govt. Graduate College) ;
  • Zia, Sahar (Department of Geography, Lahore College for Women University) ;
  • Ashraf, Iqra (Department of Geography, Lahore College for Women University)
  • Received : 2022.01.23
  • Accepted : 2022.02.23
  • Published : 2022.02.28
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Abstract

Urban Heat Island (UHI) effect has been widely studied as a global concern of the 21st century. Heat generation from urban built-up structures and anthropogenic heat sources are the main factors to create UHIs. Unfortunately, both factors are expanding rapidly in Lahore and accelerating UHI effects. The effects of UHI are expanding with the expansion of impermeable surfaces towards urban green areas. Therefore, this study was arranged to analyze the role of urban cooling intensity in reducing urban heat island effects. For this purpose, 15 parks were selected to analyze their effects on the land surface temperature (LST) of Lahore. The study obtained two images of Landsat-8 based on seasons: the first of June-2018 for summer and the second of November-2018 for winter. The LST of the study area was calculated using the radiative transfer equation (RTE) method. The results show that the theme parks have the largest cooling effect while the linear parks have the lowest. The mean park LST and PCI of the samples are also positively correlated with the fractional vegetation cover (FVC) and normalized difference water index (NDWI). So, it is concluded that urban parks play a positive role in reducing and mitigating LST and UHI effects. Therefore, it is suggested that the increase of vegetation cover should be used to develop impervious surfaces and sustainable landscape planning.

Keywords

1. Introduction

According to the UN, more than 60% population of the world will be residing in cities by 2050 (Zlotnik, 2017; UN, 2018; Elgendy and Abaza, 2020). The urbanization process leads to the land transformation and degradation of the natural environment. The urban heat island (UHI) effect is also a particular phenomenon related to the increasing trend of urbanization (Zhou and Chen, 2018; Yao et al., 2021, Ravanelli et al., 2018). The perceived increase of temperature in urban areas as compared to the adjacent rural areas is known as Urban Heat Island. Thus, a temperature difference has resulted in urban and rural climates (Oke, 2011; Zhao et al., 2018; Manoli et al., 2019; Li et al., 2019).

According to the United States environmental protection agency, chief factors for generating urban heat islands includes reduced natural landscapes such as trees, grass, vegetation, and water bodies; Conventional human-made materials such as pavements and roof with more heat absorption human-generated heat sources and urban city morphology (Oke, 1995; Weifeng and Xiaoke, 2007; Yow, 2007; Naeem et al., 2021). The outcome of these factors is irreversible. It not only catalyzes the demand for air conditioning and energy consumption but also affects human well-being, health, and the degree of the livability of cities (Li et al., 2011; Yow, 2007; Gago et al., 2013; Leal Filho et al., 2018). Some studies suggested that UHI can boost the mortality rate in cities, energy consumption, and contamination in the atmosphere (Shahmohamadi et al., 2011; Heaviside et al., 2017; Singh et al., 2020). Several findings identified that the atmospheric temperature and water cycle are adversely affected by the process of urbanization and industrialization (Shepherd et al., 2010; Zipper et al., 2017; Susca and Pomponi, 2020; Bilal et al., 2021). The increase in temperature in urban areas and the concept of UHIs have been studied for more than 60 years and opened new research directions to propose mitigation plans. Several studies present different mitigation strategies, such as using reflected materials and present different mitigation strategies such as the usage of reflected materials and present different mitigation strategies such as the usage of reflected materials and blue and green infrastructure either in the cities or in the surrounding areas to reduce the impact of UHI in cities (Gull et al., 2021)

The most effective method to minimize the UHI impact is the plantation of trees. An increase in vegetation is found significant for regulating land surface temperature. Several previous research studies have taken three vegetation types including street parks, theme parks, and urban parks. Some researchers also highlighted that parks could reduce the UHI effect by up to 12.5°C depending on the season, part of the day, and measurement method by up to 12.5°C depending on the season, part of the day, and measurement method (Algretawee et al., 2019). Regarding previous studies, one of the most noticeable challenges in UHI is land surface temperature. LST is the perceived temperature of the surface. Previously, it was to obtain data based on LST from a particular weather station to analyze UHI. But the limited number of meteorological stations could not properly distribute temperature data. Thus, this issue is resolved by using remote sensing technology such as satellite images. Its values can be calculated through satellite images in °C. Satellite remotely sensed thermal infrared imagery is used to derive LSTs. They are vital in understanding the impacts of land use/land cover changes (LULC) on urban heat distribution (Soydan, 2020). LST derived from satellite images can be utilized for heat balance, climate modeling, and worldwide climate change studies (Wang and Siqi, 2020). Studies highlighted that urban ecology and climatology are two important fields to address the issues to mitigate the adverse effects of urban heat. In this context, urban green spaces like parks, forests, playgrounds, and open spaces play a vital role and gain a prominent place in urban planning. They contribute to reducing thermal discomfort in cities. Thus, leading to the provision of significant ecosystem services to improve the physical and mental health of people (Yang et al., 2017a). Urban greenery also contributes to improving the quality of life of people in cities by increasing the comfort level. Literature showed that due to the presence of vegetation, the cooling effect of urban parks might increase from 1° to 7°C (Xiao et al., 2018). This paper aims to identify the effect of parks on the land surface temperature in Lahore by calculating the parks cooling intensity (PCI).

2. Materials and Methods

1) Study Area

Lahore was selected as the study area to conduct the research. As the whole city is urbanized having an 11.12 million population (GOP, 2017; Nasar-u-Minallah et al., 2021) with 329 square miles of built-up land area (853 square kilometers), 33452 per square mile population density, 12916 per square kilometer (June 2020). Lahore is the capital of Punjab province and the second-largest city of Pakistan. It is located in the northeast part of the country, along the left bank of river Ravi. It lies between 31°-15′ and 31°-43′ north latitude, 74°-10′ and 74°-39′ east longitude (Nasar-u-Minallah, 2020; Zia et al., 2021). Lahore experiences extremes of climate. May, June, and July are the hottest months (Zia et al., 2021a). In these months, the mean maximum and mean minimum temperature variations are recorded from 40.4°C and 27.4°C in these months. Whereas December, January, and February are the coldest months having 22°C mean maximum and 5.9°C mean minimum temperature (Nasar-u-Minallah, 2020a). Lahore is a famous metropolitan famous for its historical buildings and gardens. Several beautiful parks/gardens have been developed in Lahore to adorn the city (Zia et al., 2021b). The total area of parks transferred to PHA is 2, 576 acres. Most of the area is located around and in the southwestern part of the city (NESPAK, 2004; 2010). Parks and Horticulture Authority (PHA) is a government institution established in September 1998 for the maintenance and best utilization of greenbelts, parks, playgrounds, and green spaces. According to PHA, there are 828 total parks in Lahore Pakistan horticulture authority (PHA). Fifteen parks were selected for the ground-truthing purpose only, namely Racecourse, Bagh e Jinnah, Greater Iqbal Park, Gulshan e Iqbal Park, Nasir bagh, Rehmannia park, Fatima Jinnah ladies park, Botanical garden Jallo, Ghulabi Park, FCC park, Jam e Shirin park, National Bank park, Nadra begum park, Family park Samanabad and Shahdara bagh (Noor Jahan’s tomb). On basis of selected criteria of urban, theme, and street parks, these fifteen parks were selected for detailed analysis by convenient sampling technique. Only well-maintained parks having maximum public visits were selected. Furthermore, these parks selected in the research were mostly crowded and provided maximum services to the public. Some of them are identified by PHA as category park-like Jillani Park Jinnah Park Greater Iqbal Park, Gulshan Iqbal Park, etc. Lahore is selected as a case study for assessing ecosystem services because it is the second-most populous city of Pakistan. For developing countries, these research studies are very important to conduct which remained neglected area.

2) Data and its Source

In this work, two Landsat-8 satellite images of Lahore were downloaded from USGS (earthexplorer. usgs.gov; Table 1), a level 1 product used to extract Land Surface Temperature (LST) of Lahore. To get the retrieval of LST, the thermal infrared (TIR) bands 10 were utilized to estimate LST temperature, and Operational land imager (OLI) optical bands 2, 3, 4, and 5 were utilized to produce normalized difference vegetation index (NDVI) of the study area (Fig. 1, Table 1). Thermal constant and rescaling factor values of thermal bands have been provided by the Landsat-8 satellite as mentioned in Table 2, which can be utilized for computing LST algorithms. It is cloud-free and high quality with a spatial resolution of 30 meters, and thermal resolution of 100 meters. Bhatti and Tripathi (2014) mentioned that no atmospheric corrections were executed and the Landsat imagery was cloud-free (Deng and Wu, 2013). The data sets in the study comprised the Universal Transverse Mercator (UTM) projection and the WGS84 datum and zone 43North. In conjunction with the satellite image, we also employed additional base maps obtained from high resolution Google Earth images, low altitude UAV images, and the official urban land use map of Lahore. The vector layer (administrative boundary) of district Lahore and Parks are utilized as a mask for clipping the area of interest (AOI) from the complete scene (Sagris and Sepp, 2017; Zaeemdar and Baycan, 2017; Orusa and Mondino, 2019).

OGCSBN_2022_v38n1_127_f0001.png 이미지

Fig. 1. Location map of district Lahore and selected parks in district Lahore.

Table 1. Characteristics of the Landsat-8 OLI image used in this study

OGCSBN_2022_v38n1_127_t0001.png 이미지

Source: http://earthexplorer.usgs.gov/

3) Retrieval of Land Surface Temperature

Several algorithms have been recognized to compute LST from TIRS bands of Landsat data, such as the mono-window algorithm, radiative transfer equation, and single-channel method (Qin et al., 2001; Jiménez- Muñoz and Sobrino, 2003). The radiative transfer equation (RTE) method of land surface temperature (LST) has been widely recognized (Table 1). In this work, RTE based procedure is used to calculate the LST of Lahore and is generally divided into six steps.

(1) Conversion of the Digital Number (DN) to Spectral Radiance (Lλ)

The DN values of thermal bands (10 and 11) were converted to TOA (Top of Atmospheric) radiance values by using the following equation (1).

Lλ= ML× QCAL+ AL       (1)

Where

Lλ=TOA spectral radiance

ML=Band-specific multiplicative rescaling factor

QCAL= QCALcalibrated and quantized standard pixel values (DN),

AL= Band-specific additive rescaling factor as shown in Table 2.

Table 2. Rescaling Factor of Landsat 8-TIRs

OGCSBN_2022_v38n1_127_t0002.png 이미지

Source: https://landsat.usgs.gov/landsat-8-data-users-handbook

(2) Conversion of Spectral Radiance to Brightness Temperature in Kelvin

The reflectance value was converted into satellite brightness temperature. Thus, LST was obtained in Kelvin (Equation 2).

\(T K=\frac{K 2}{1 n} \times\left(\frac{K 1}{L}+1\right)\)       (2)

Whereas

TK is surface temperature in Kelvin and

K1 refers to the prelaunch calibration of constant 1 in-unit W/ (m2· μm)

K2 is the pre-launch calibration of constant 2 in Kelvin.

(3) Conversion of Kelvin to Celsius

Then temperature from Kelvin was converted into Celsius (°C) by using the following equation, where TB is the brightness surface temperature in Celsius °C (Equation 3).

TB= TK–272.15       (3)

Table 3. Thermal Constant of Landsat 8-TIRs.

OGCSBN_2022_v38n1_127_t0003.png 이미지

Source: https://landsat.usgs.gov/landsat-8-data-users-handbook

(4) Derivation of NDVI

Moreover, NDVI (Normalized difference vegetation index) was calculated to identify a correlation between land-use change and LST (Equation 4).

\(\mathrm{NDVI}=\frac{N I R-R E D}{N I R+R E D}\)       (4)

Normalized difference vegetation index for a given pixel always ranges from -1 to +1. A value close to 0 shows less greenery, while a value close to +1 shows the maximum density of greenery. In the end, Land surface emissivity was extracted by using NDVI. The pixel was divided into three categories according to values of NDVI. If the value was higher than 0.5, the pixel was supposed to be covered with the vegetation cover (Equation 5).

\(P V=\frac{N D V I_{M a x}-N D V I_{M i n}}{N D V I_{M a x}-N D V I_{M i n t}} \times 2\)       (5)

(5) Retrieval of Land Surface Emissivity (LSE)

Then emissivity acquires by using the PV value (Equation 6).

LSE(ε) = 0.004 × PV+ 0.986        (6)

(6) Retrieval of Land Surface Temperature

After getting the value of emissivity, Land surface temperature can be retrieved through this formula (Equation 7).

\(L S T=\left(\frac{T B}{1}+\lambda \times\left(\frac{T B}{\rho}\right) \times \operatorname{Ln}(\varepsilon)\right)\)       (7)

Where

TB= Satellite brightness temperature        (7.1)

λ= Wavelength of emitted radiance(11.5 μm)

\(\rho=h \times \frac{c}{\sigma}\)       (7.2)

Where

h= Planck’s constant having value (6.626 *10-23)

σ= Boltzmann constant (1.38 *108m/s)

C= velocity of light (2.998 *108m/s)

Consequently, obtained average Land surface temperature values were compared with average temperature recorded by Pakistan meteorological department to validate the perceived variations.

4) Comparative analysis of LULC temperatures

Google earth’s high-resolution imagery was used for digitizing the boundaries of 15 selected parks. Based on the size, the parks were categorized into three classes, as presented in Table 4 (Li et al., 2020). To compare LST of parks with other land use classes: (A) built up, (b) bare land, and (c) water bodies. About 50 random points were generated against each class using Google earth’s high-resolution imagery to calculate the average LST of each class.

Table 4. Sample parks by type

OGCSBN_2022_v38n1_127_t0004.png 이미지

The temperature values for each point were extracted in ArcGIS 10.7. A comparative analysis was performed on estimated average values of LST for two seasons, including winter and summer for each class.

5) Parks Cooling Intensity (PCI)

Park Cooling Intensity (PCI) has been chosen as an indicator to explore the impact factors of the park cooling effect. Park Cooling Intensity (PCI) usually calculates the temperature difference between the inside and outside of the park. It can be air temperature or land surface temperature. The PCI (units in degree C) was defined as the mean LST difference in this study (Equation 8).

PCI= ΔT–Tu–Tp       (8)

Where

Tu is the mean LST of an urban area of the 500m buffer zone outside of the park

Tp is the mean LST inside the park.

ΔT is the buffer zone that includes the area around the park, which contains different land cover types:

Buildings, roads, impervious surfaces, trees, and green spaces.

3. Results and Discussion

1) Land Surface Temperature

The LST maps retrieved for June and November from multi-temporal Landsat data sets are shown in Fig. 2. The average LST values of District Lahore have been generated which is 35.5°C for June and 21.5°C for November. The LST ranged from 27.4°C to 44.1°C in June 2019. On the other hand, it ranged from 13.1°C to 30.7°C in November 2019. It also shows the spatial distribution of mean LST values in District Lahore. The central and northern parts of District Lahore are characterized by low LST values. There are high LST zones in the southern and western parts.

OGCSBN_2022_v38n1_127_f0002.png 이미지

Fig. 2. LST for June and November 2018.

2) Relationship between Park Types, LST, and PCI

The LST map based on June and November in 2019 was derived from satellite imageries. Both months were selected to compare seasonal variations of park cooling intensity. LST values of selected parks are lower than the mean temperature of District Lahore for June but identified higher than the mean temperature of District Lahore for November. PCI of all selected parks are analyzed by comparing three types of Parks (Table 4). Results show that cooling effects of all three park categories were found different. The urban parks category was identified with the highest cooling effect with 2.4°C, followed by theme park with 2.1°C than its surroundings in June 2019. In addition, the least cooling effect of the street park has been identified that is 1.9°C. On the other hand, PCI averages calculated for November showed that urban parks have the least cooling effect, followed by theme parks with 0.09°C and 0.07°C respectively than their surroundings. However, the PCI effect of street parks was highest in November, as shown in Table 5. This can be concluded that street parks are playing the least significant role in keeping the local temperature lowest as compared to the theme and urban parks due to their small area size.

OGCSBN_2022_v38n1_127_f0003.png 이미지

Fig. 3. LST Map of June (a) Theme Park; (b) Urban Parks and (c) Street Parks & LST map of November (d) Theme Park; (e) Urban Parks and (f) Street Parks.

Table 5. LST (°C) and average PCI in different park types in June & November 2018

OGCSBN_2022_v38n1_127_t0005.png 이미지

4. Conclusion

This study used a comprehensive method to investigate the park cooling effects in District Lahore for two months, June and November, to compare seasonal variations. June is selected to generalize the PCI effect in the summer and November for the winter season. The mean annual temperature derived from LST was 35.5° C and 21.5° C for June and November 2019, respectively. These derived LST values were slightly higher than the actual atmospheric temperature values because surfaces heated more than air. According to PMD, the recorded average temperature of June was 33.2°C and 19°C for November in the year 2019. This showed that the derived values could be used to analyze PCI in District Lahore further. The results indicate that parks have different cooling intensities for both months, warmer in winter and cooler in summer than their surroundings. The results confirmed that all three selected park types could have different PCI impacts. The urban park category has the highest PCI among the three park types, and the Street Park category has the weakest PCI in summer. The situation found reversed in winters, street parks were found with significant cooling effects, and urban parks were found with the least cooling effect, followed by theme parks. This may be because the street parks are mostly very small in size, and it can be concluded that these street parks are not significantly contributing to regulating this ‘park’s cooling intensity due to its small area size. The finding of the study highlights the importance of green spaces especially large size because their effects are more significant than small size parks as previously stated by (Yang et al., 2017b). So, the study called the attention of urban planners and suggested that they include large green spaces by keeping in mind their characteristics in maintaining the homogeneousness of LST, which automatically reduces the UHI effects in the study area.

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