







































Ecology, Economy, and Society–the INSEE Journal 7(1): 137-155, January 2024 

RESEARCH PAPER 

Identifying Urban Hotspots and Cold Spots in Delhi 
Using the Biophysical Landscape Framework 

Rupesh Kumar Gupta* 

Abstract: Urban heat islands (UHIs), which are formed by biophysical landscape 

transformations, have significant adverse effects on environmental quality as well as 

human health, resources, and facilities. Variations in UHI intensity give rise to urban 

hotspots (UHSs) and cold spots in different parts of the city. This study identifies 

such hotspots and cold spots in Delhi by classifying the city into zones of different 

UHI intensities using biophysical landscapes. The data on the selected biophysical 

landscapes were obtained from satellite images and secondary sources. The impact 

of different biophysical landscapes on UHI intensity was calculated using the 

weighted overlay method performed using ArcGIS software. The city of Delhi was 

thus divided into four zones, based on UHI intensity. It was found that UHSs cover 

about 45% of the total area and are mostly located in eastern and central Delhi. While 

built-up areas form the major source landscape, vegetation cover is the major sink 

landscape as per land surface temperature (LST) and UHI intensity. The findings of 

this study will help urban planners and policymakers identify UHSs and adopt 

suitable policies and measures to mitigate UHIs based on the different intensity 

zones. 

Keywords: Biophysical landscape; LST; urban hotspot; Delhi 

1. INTRODUCTION 

The rapid rate of urbanization worldwide—particularly in developing 
countries—has resulted in massive changes in the biophysical landscape and 
thermal environment, which has led to significant changes in the land surface 
temperature (LST) (Liu et al. 2012; Traore et al. 2021). This has resulted in the 

 
* Associate Professor, Department of Continuing Education and Extension, Faculty of Social 

Science, University of Delhi, Delhi, India, gisrs2004@gmail.com  

Copyright © Gupta 2024. Released under Creative Commons Attribution © NonCommercial 
4.0 International licence (CC BY-NC 4.0) by the author.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581–6152 (print); 2581–6101 (web).  

DOI: https://doi.org/10.37773/ees.v7i1.954  

 

mailto:gisrs2004@gmail.com
https://doi.org/10.37773/ees.v7i1.954


 Ecology, Economy and Society–the INSEE Journal [138] 

formation of urban heat islands (UHIs) (Balew and Korme 2020; Ranagalage 
et al. 2019; Roberts et al. 2015; Simwanda et al. 2019). UHIs have a negative 
impact on the microclimate and daily weather of cities, human health and 
behaviour, electricity consumption, water consumption, and the use of other 
resources and facilities (Chan 2011; Mohan and Kandya 2015; NDMA 2016; 
Shi and Zhang 2018). However, the intensity and effect of UHIs are not 
distributed uniformly over the city, as different biophysical landscapes 
respond differently to heat radiation, absorption, and storage, which, in turn, 
influences the LST (Priyankara et al. 2019; Yang et al. 2020). This leads to the 
generation of urban hotspots (UHSs) wherein urban heating is highest and 
necessitates monitoring and planning.  

The spatial pattern of UHI intensity depends on several factors including 
vegetation cover, availability of water bodies and open spaces, built-up areas, 
and the presence of industries, transportation, and other anthropogenic 
activities (Estoque, Murayama, and Myint 2017; Hang and Rahman 2018; Li 
et al. 2017; Traore et al. 2021; Vani and Prasad 2019). Therefore, the impact 
of the different biophysical landscapes of the city on UHI intensity and the 
creation of UHSs and cold spots must be studied to adopt suitable mitigation 
policies and to optimize the use of resources. 

UHIs are areas that experience an increase in temperature as a result of 
anthropogenic activities such that there is a warm landmass between cool 
areas (a natural landscape with lower temperatures). The mixed effects of 
anthropogenic heat discharge, the increase in impervious surfaces, and a 
decline in vegetation density contribute to UHI intensity (Ali and 
Nitivattananon 2012; Bonafoni, Baldinelli, and Verducci 2017; Hang and 
Rahman 2018). The contributions of different features of the biophysical 
landscape to UHI intensity differ and vary over space, as they could work as 
either a source landscape or a sink landscape (Pramanik and Punia 2020). 
Source landscapes intensify the LST and the UHI effect. Some examples are 
built-up areas, industries, transportation facilities, air-conditioned 
environments (Puppala and Singh 2021), bare soil, and others (Hang and 
Rahman 2018; Pramanik and Punia 2020; Puppala and Singh 2021; Toy and 
Yilmaz 2010; Zhu et al. 2017). Sink landscapes decrease the LST and UHI 
intensity and comprise vegetation cover, water bodies, open spaces, cropland, 
and others (Hang and Rahman 2018; Pramanik and Punia 2020; Mathew, 
Khandelwal, and Kaul 2017; Sannigrahi et al. 2017). These factors control the 
intensity and impact of UHIs and their spatial variations in almost all cities 
around the world. 

The impacts of UHIs on microclimates, environmental quality, and quality 
of life are so profound that the phenomenon and its relationship with natural 
and anthropogenic factors have been explored in numerous studies in almost 



[139] Gupta 

all major cities in recent decades. These include Athens (Katsoulis and 
Theoharatos 1985), Tokyo (Saitoh, Shimada, and Hoshi 1996), Singapore and 
Kuala Lumpur (Tso 1996), Shanghai (Chow 1992), Washington, DC (Kim 
1992), London (Jones and Lister 2009), Dhaka (Molla et al. 2014), Faisalabad 
(Shabana, Bashir, and Ali 2015), and Bangkok (Ali, Pumijumnong, and Cui 
2018). In India, the number of studies dealing with the thermal environments 
of cities and their relationships with various biophysical factors has increased 
during the past decade (Chakraborty, Kant, and Mitra 2015; Gupta and 
Parashar 2020; Hang and Rahman 2018; Mallick, Rahman, and Singh 2013; 
Mathew et al. 2016; Pandey et al. 2012; Pramanik and Punia 2020; Sannigrahi 
et al. 2018). These cities include Jaipur (Gupta 2012; Mathew, Khandelwal, 
and Kaul 2017), Greater Hyderabad (Sannigrahi et al. 2018), Vijayawada 
(Puppala and Singh 2021), Guwahati (Borbora and Das 2014), Lucknow 
(Singh, Kikon, and Verma 2017), Bhubaneshwar (Swain et al. 2017), 
Ahmedabad (Mathew et al. 2016), and Delhi NCR (Gupta and Parashar 2020; 
Hang and Rahman 2018; Kant et al. 2009; Mallick, Kant, and Bharath 2008; 
Mallick and Rahman 2012; Mohan and Kandya 2015; Pramanik and Punia 
2020; Sharma and Joshi 2014, 2013).  

Most of these studies examine the correlation between LST and different 
aspects of land use, land cover, and spatial patterns of UHIs (Hang and 
Rahman 2018). Only a few studies classify Indian cities into different UHI 
zones based on the biophysical landscape (Gupta 2020, 2012; Pramanik and 
Punia 2020). Gupta (2012) analyses the spatial pattern of UHIs in Jaipur and 
classifies the city into four UHI zones based on vegetation cover, built-up 
area, population density, industry, traffic congestion, and LST. Pramanik and 
Punia (2020) examine the impact of land cover and land use changes on the 
LST and UHIs and their district-wise and sub-district-wise distribution over 
Delhi. However, the district-wise and sub-district-wise classification of UHI 
intensity does not give a clear picture of the distribution and location of 
UHSs, which is required to draft effective and efficient policies for the 
mitigation of UHI intensity and associated changes in the biophysical 
landscape. 

Analysis of satellite imagery through the assimilation of geographic 
information system (GIS) and remote sensing techniques helps scholars 
calculate the correlation between the biophysical landscape and UHIs and 
their spatial patterns (Gupta 2012; Hang and Rahman 2018; Mallick, Rahman, 
and Singh 2013; Mathew et al. 2016; Pandey et al. 2012; Pramanik and Punia 
2020; Sannigrahi et al. 2017). Remote sensing and the GIS technique are 
suitable for mapping, monitoring, and measuring the LST and UHI intensity 
over a large area (Gluch, Quattrochi, and Luvall 2005). With the help of 
satellite imagery, we can obtain the spatial pattern of the LST, which can be 



 Ecology, Economy and Society–the INSEE Journal [140] 

used to examine the UHI intensity and associated factors (Puppala and Singh 
2021).  

This study classifies the city of Delhi into different UHI zones, identifies 
UHSs, and discovers their association with different features of the 
biophysical landscape comprising built-up areas, industries, thermal power 
plants, traffic congestion, vegetation cover, and LST using satellite imagery 
and the GIS technique. 

2. MATERIAL AND METHODS 

2.1. Study Area 

Delhi is the second-largest megacity in the world and one of the fastest-
growing cities in India, with about 17 million people residing in an area of 
1,483 sq km. It is situated between 28º24'17" N and 28º53'00" N and 
longitudes 76°45'30" E and 77º21'30" E.. The altitude of the city ranges 
between 213 m and 290 m. The city is largely a plain area except for two main 
relief features—the Delhi Ridge and the Yamuna River—which act as 
cooling agents and thermal moderators for the climate. It has an extreme 

continental-type climate where the annual temperature varies between 3C 

and 45C and the average annual rainfall ranges from 400 mm to 600 mm. 
Thorny scrub-type vegetation—typical of semi-arid environments—covers 
about 20% of Delhi. The population of the city grew from 1.7 million in 1901 
to 17 million in 2011 (Census of India 2011). The rapid increase in 
urbanization has accelerated the landscape transformation of Delhi from a 
mainly rural area to an urban area, which has influenced the thermal 
environment of the city. 

Figure 1: Methodology for Identification of UHI Intensity Zones in Delhi 

 

Source: Prepared by the author 



[141] Gupta 

2.2. Database and Methods 

The data was collected from satellite maps and other secondary sources. The 
data on LST, obtained from MODIS (MOD11A1) satellite data for March 
2020, was converted to degrees Celsius. Google Earth data from March 2020 
was used to extract the selected landscape features—comprising built-up 
areas, industries, thermal power plants, areas with traffic congestion, and 
vegetation cover—which were verified through data collected from 
secondary sources. The weighted overlay method was used to identify UHI 
intensity zones, UHSs, and urban cold spots, as shown in Figure 1. 

2.3. Estimation of LST 

LST was used as the reference to classify the city into different UHI intensity 
zones. The data on LST was extracted from the analysis of MODIS satellite 
imagery. The computation of LST from satellite images has been described 
extensively in the literature and involves three steps. First, the top-of-
atmosphere (TOA) reflectance values are obtained by converting the digital 
numbers (the brightness value of a pixel) of the thermal infrared bands using 
Equation 1: 

𝐿𝜆 = 𝑀𝐿 × 𝐷𝑁 + 𝐴𝐿 (1) 

where 𝐿𝜆 denotes the TOA radiance, ML refers to the multiplicative rescaling 
factor of a particular band, DN represents the standard values of pixels, and 
AL is the additive rescaling factor for the specific band. Next, the brightness 
temperature is calculated using the TOA reflectance of each pixel, using 
Equation 2: 

T = K2 ln ((K1/L) + 1) (2) 

where T represents the brightness temperature measured in Celsius, L is the 
TOA reflectance, and K1 and K2 represent the constants for thermal 
conversions of the specific band. Finally, the LST is corrected for the 
emissivity of different urban landscapes using Equation 3: 

LST = 𝑇/1 + (𝜆𝑇/𝑝) 𝑙𝑛 𝜀 (3) 

where  is the wavelength at the centre of the thermal infrared band and 𝜀 is 
the emissivity. 

2.4. Computation of Normalized Differential Vegetation Index 
(NDVI) 

Normalized differential vegetation index (NDVI) is a graphical indicator of 
the quantity of green biomass, which is estimated to identify the impact of 
vegetation on temperature; as the vegetation index increases, the LST 



 Ecology, Economy and Society–the INSEE Journal [142] 

decreases. The vegetation index was calculated using the red and near-
infrared bands of Landsat-8 imagery, which helps in monitoring vegetation 
cover. NDVI is calculated using Equation 4: 

NDVI =  (𝑁𝐼𝑅 − 𝑅𝐸𝐷)/(𝑁𝐼𝑅 + 𝑅𝐸𝐷) (4) 

where RED and NIR represent the reflectance in the red band and near-

infrared band, respectively. The NDVI varies between +1 and −1, which 
represent the highest and lowest greenness, respectively. 

2.5. Identification of UHI Intensity Zones and UHSs 

Weights were assigned to each selected landscape feature based on its impact 
on UHI intensity, as described in Table 1. Several parameters have been 
incorporated for the analysis of UHIs using the multi-criteria analysis 
method. The built-up area is one of the most important factors in heat island 
phenomena in cities (Han et al. 2022; Choi, Suh, and Park 2014; Oke 1982; 
Gupta 2012; Khandelwal, Goyal, and Kaul 2010; Saaroni et al. 2000). 
Accordingly, it has been assigned the highest weightage (30%) under a three-
point Likert Scale based on density (high, moderate, or low). LST estimation 
is very useful in determining the UHI (Bokaie et al. 2016; Schwarz, 
Lautenbach, and Seppelt 2011; Sundarakumar 2012). It was grouped as per 
the three-point Likert Scale and was given a weightage of 25%.  

Next, the presence of industries and thermal power plants (Gupta 2012; Gao 
et al. 2022; Phelan et al. 2015; Zhang et al. 2017) was marked on a two-point 
Likert Scale, with 20% and 15% weightage, respectively. Finally, traffic 
congestion, which has a significant effect on UHI intensity (Zhu et al. 2017; 
Husni et al. 2022; Louiza, Zeroual, and Djamel 2015), was also classified as 
high or low using a two-point Likert Scale. The UHI intensity zones and the 
UHSs and cold spots were identified by overlaying each biophysical 
landscape in the ArcGIS using the LST map as the base. 

3. RESULTS AND DISCUSSION 

3.1. Spatial Pattern of LST 

The areas with the maximum number of LST pixels were found inside the 
city, and those with the minimum number of LST pixels were found on the 
fringe or northwest and southwest boundaries of the fringe area, as shown in 
Figure 2. The data shows that the LST in different parts of the city varied 

between 27C and 35C—a difference of 8C between UHSs and cold 
spots—in March 2020. Thus, the maximum LST occurred in the core of the 
city, which has the largest impervious area and the lowest vegetation cover. 
In contrast, the LST was low in the northwest and southwest boundaries of 
the study area, where the impervious area is low and vegetation cover is high. 



[143] Gupta 

Table 1: Weightages of Different Biophysical Landscapes Responsible for UHI 
Intensity 

Parameters Sub-parameter Scale Value 
Impact 

(%) 

Density of built-up area  

High  5 

30 Moderate  3 

Low  1 

Land surface temperature 

High  5 

25 Moderate  3 

Low  1 

Major industries 
Yes 5 

20 
No  1 

Thermal power plants 
Yes 5 

15 
No 1 

Traffic congestion 
High  5 

10 
Low  1 

Source: Multi-criteria analysis method (Gupta 2012; Mirzaei and Haghighat 2010) 

3.2. Spatial Distribution of Vegetation Cover 

Vegetation cover is a major sink landscape feature that helps in minimizing 
the LST and UHI intensity. Hence, the NDVI of Delhi was estimated using 
Equation 4 for four months—December, March, June, and September—
across four different seasons in the period 2019–2020, as shown in Figure 3. 
The results indicate that Delhi witnessed a seasonal variation in the vegetation 

cover. In December, the NDVI for the city varied from −0.092 to 0.45. In 
March, the NDVI ranged between 0.21 and 0.74, after which it showed a 
decline in June and September. The seasonal variation in the NDVI in the 
city may be attributed to the presence of clouds and fog, as a value of NDVI 
below 0 indicates the presence of clouds and snow. Therefore, calculating 
NDVI in March would give the best results for vegetation cover due to the 
lack of clouds and fog in the city. 

The NDVI maps help us understand vegetation patterns in the city and show 
that there was healthy vegetation where the UHI intensity was low and built-
up areas where the intensity was high. The spatial pattern of vegetation cover 
shows that the central part of the city was devoid of vegetation except for a 
few patches of green. Most of the vegetation was found along the Yamuna 
bank and in the western and eastern outskirts of the city. The pattern of 
vegetation cover in the city highlights the impact of built-up areas because 
the areas devoid of vegetation cover were those with a high built-up area 
density.  



 Ecology, Economy and Society–the INSEE Journal [144] 

Figure 2: Land Surface Temperature in Delhi 

 

Source: Prepared by the author using MODIS data 

Figure 3: NDVI Images of Delhi for (A) December, (B) March, (C) June, and (D) 
September in 2019–2020

 

35℃ 

27℃ 



[145] Gupta 

Source: Prepared by the author using open source satellite data 

Figure 4: Built-up Area–Density, Industries, Thermal Power Plants, and Traffic 
Congestion in Delhi 

 

Source: Prepared by the author using Google Earth data 

3.3. Spatial Pattern of the Source Landscape 

The source landscapes included in the study were built-up areas, industrial 
areas, and areas with thermal power plants and traffic congestion, as shown 
in Figure 4. As per the literature, these are the major contributors to LST and 
UHI intensity, as these landscapes have different heat absorption, reflection, 
and storage properties. Some of these landscapes, such as industries and 
transportation, also release air pollutants and particulate matter into the 
atmosphere, which trap short-wave terrestrial radiation, further increasing 
the LST and intensifying the UHI effect. The density of built-up areas is the 
most important determinant of UHI intensity.  

The findings of this study indicate that there was a high density of built-up 
areas in the central, eastern, and south-eastern parts of the city. There were 
also some patches with a high density of built-up areas in the northern and 
western parts of the city. Most of the industries were located in the northern, 



 Ecology, Economy and Society–the INSEE Journal [146] 

eastern, and central parts of the city. The thermal power stations were mostly 
located in the eastern part of the city where traffic congestion was also high. 

3.4. UHI Intensity Zones and Location of Urban Hotspots and Cold 
Spots 

The weighted overlaying of different biophysical landscape features in the 
ArcGIS classified the city into four UHI intensity zones—high, medium, low, 
and very-low-intensity zones—as shown in Figure 5. While the areas with 
high and moderate UHI intensity are termed UHSs, the areas with low and 
very low intensity are considered cold spots. UHSs covered about 45% of the 
area of the city, and the rest of the areas were cold spots. UHSs were mostly 
located in the areas around the central and eastern parts of the city, but a few 
small pockets were also located in the northern, western, and southern parts 
of the city. Urban cold spots were mostly found in the outskirts of the city, 
with some pockets also located in hotspot zones. The high UHI intensity 
zone covered almost 15% of the area, including the localities of Kashmiri 
Gate, Kamla Nagar, Wazirpur, Lajpat Nagar, Greater Kailash, Badarpur, 
Patparganj, Anand Vihar, Dilshad Garden, and Shahdara, as displayed in 
Table 2. The moderate UHI intensity zone covered about 30% of the area 
and included localities such as New Delhi, Shakarpur, Laxmi Nagar, Nawada, 
Kirari, Rohini, Jahangirpuri, Shalimar Bagh, and others. 

The low UHI intensity zone covered an area of about 34% and was mostly 
located on the outskirts of the city. However, some patches of low UHI 
intensity were also found in central and north Delhi, which may be due to 
the presence of open spaces and vegetation cover. The UHI intensity of the 
areas located in the vicinity of the Yamuna River was usually low because of 
the presence of a relatively larger area under scrub vegetation as well as open 
spaces. The Yamuna, apart from the monsoon months, does not have much 
water due to a number of anthropogenic causes such as pollution, discharge 
of effluents, and so on. Therefore, it does not play a critical role in 
ameliorating heat islands in Delhi. Moreover, Delhi sits astride the Yamuna, 
that is, only the north-eastern part of Delhi can be influenced by the river. 
Thus, it does not play a prominent part in modifying the LSTs of the whole 
of Delhi. The localities of Delhi with low UHI intensity included Kakrola, 
DDA Colony, Sainik Farm, Rangpuri, and others, as shown in Table 2. The 
remaining areas came under the very low intensity zone and included the 
localities that lie outside the city, covering only about 21% of the area of the 
city. 

 

 



[147] Gupta 

Figure 5: Urban Heat Island Intensity Zones in Delhi 

 

Source: Compiled by the author using GIS tools 

Table 2: Areas and Localities of Delhi under Different UHI Intensity Zones 

UHI 
Intensity 
Zone 

Area 
(sq km) 

Area 
(%) 

Names of Localities of Delhi 

High 222.5 15.0 

IP Extension, Patparganj, Shahdara, Sarojini 
Nagar, Dilshad Garden, Greater Kailash, 
Kamla Nagar, Badarpur, Kashmiri Gate, 
Janakpuri, Tis Hazari, Tilak Nagar, Paschim 
Vihar, Punjabi Bagh, Mangolpuri, Azadpur, 
Wazirpur, Lajpat Nagar, Naraina, Sarai 
Rohilla 

Moderate 440.5 30.0 

Sunder Nagri, New Mandoli, Shakarpur, 
Yamuna Bank, Laxmi Nagar, Indraprastha, 
Bindapur, New Delhi, Nawada, Rohini, 
Kirari Suleman Nagar, Shalimar Bagh, 
Narela, Nathupura, Bawana, Jahangirpuri, 
Samaypur 

Low 504.0 34.0 

Gopal Nagar Extension I, Kakrola, 
Kanjhawala, Shahpur Garhi, DDA Colony, 
Shampur Khampur, Todapur, Mayur Vihar I 
Extension, JJ Colony, Mayur Vihar Phase II, 
Tughlakabad Extension, Fatehpur Beri, 
Sainik Farm, Rangpuri 



 Ecology, Economy and Society–the INSEE Journal [148] 

UHI 
Intensity 
Zone 

Area 
(sq km) 

Area 
(%) 

Names of Localities of Delhi 

Very low 318.0 21.0 
Jaffarpur Kalan, Badarpur Majra, Nangal 
Thakran, Fatehpur Jat, Barwala, outskirt 
croplands 

Total  1,484.86 100  

Source: Compiled by the Author using GIS Tools 

The spatial pattern of UHI intensity and location of hotspots and cold spots 
reveal that built-up area density, industrialization, vegetation cover, open 
spaces, and traffic congestion determine UHI intensity. However, findings 
reveal that built-up area density and vegetation cover are the major 
determinants of UHI intensity, which is similar to the findings of earlier 
studies (Pramanik and Punia 2020; Puppala and Singh 2021; Sannigrahi et al. 
2017). While built-up area is the major source landscape of UHI intensity, 
vegetation cover works as the major sink landscape for the same. The impact 
of industries, thermal power plants, and traffic congestion on UHI intensity 
is minor, but they play a crucial role in increasing the intensity of UHIs in 
built-up areas, as they hinder free air circulation.  

4. CONCLUSION 

This study aimed to identify UHI intensity zones in the city of Delhi by 
analysing the impact of different biophysical factors. The factors employed 
in the study for the identification of UHI intensity zones included LST, 
vegetation, density of residential and commercial buildings, location of 
industries and thermal power plants, and traffic congestion. A strong 
correlation between built-up area density and UHI intensity was found, as 
most of the hotspots featured dense built-up areas. Similarly, vegetation 
cover had a positive impact on the UHI intensity, as most of the hotspots 
and cold spots were in areas where vegetation cover was sparse and high, 
respectively. Therefore, increasing the density of vegetation cover with the 
help of plants, green walls, and rooftop vegetation is an effective solution to 
minimize the impact of UHI intensity. Besides the density of built-up areas 
and vegetation cover, industries, thermal power plants, and traffic congestion 
also influence UHI intensity by releasing heat and pollutants into the 
surroundings, which helps trap heat in the terrestrial atmosphere, thus 
increasing the LST. Therefore, urban planners should formulate policies 
targeted at reducing the impact of air pollution and traffic congestion. 

This study includes only a few biophysical landscapes responsible for UHIs, 
which is one of its limitations. UHI intensity is also influenced by wind speed, 
humidity, the width and angle of streets, and so on. Therefore, future studies 



[149] Gupta 

could include all source and sink urban landscapes while demarcating UHI 
intensity zones and their impact on the location of hotspots and cold spots.  

We conclude with the suggestion that policymakers should focus on 
measures to diminish the contribution of built-up areas to UHIs by using 
suitable materials, providing unpaved surfaces, and improving vegetation 
cover with the help of plants, green walls, green rooftops, and other suitable 
measures. 

ACKNOWLEDGEMENT 
The author is grateful to the Institute of Eminence, University of Delhi, for 
providing the financial support to complete this study. 

Ethics Statement: I hereby confirm that this study complies with 
requirements of ethical approvals from the institutional ethics committee for 
the conduct of this research.  

Data Availability statement: The data used to support this research is 

available in a repository and the hyperlinks and persistent identifiers (e.g. 

DOI or accession number) are stated in the paper. 

Conflict of Interest Statement: No potential conflict of interest was 

reported by the author. 

REFERENCES 

Ali, Ghaffar, and Vilas Nitivattananon. 2012. “Exercising Multidisciplinary 
Approach to Assess Interrelationship between Energy Use, Carbon Emission and 
Land Use Change in a Metropolitan City of Pakistan.” Renewable and Sustainable Energy 
Review 16 (1): 775–786. https://doi.org/10.1016/j.rser.2011.09.003  

Ali, Ghaffar, Nathsuda Pumijumnong, and Shenghui Cui. 2018. “Valuation and 
Validation of Carbon Sources and Sinks through Land Cover/Use Change Analysis: 
The Case of Bangkok Metropolitan Area.” Land Use Policy 70: 471–478. 
https://doi.org/10.1016/j.landusepol.2017.11.003  

Balew, Abel, and Tesfaye Korme. 2020. “Monitoring Land Surface Temperature in 
Bahir Dar City and Its Surrounding using Landsat Images.” Egyptian Journal of Remote 
Sensing and Space Science 23 (3): 371–386. https://doi.org/10.1016/j.ejrs.2020.02.001  

Bokaie, Mehdi, Mirmasoud Kheirkhah Zarkesh, Peyman Daneshkar Arasteh, and Ali 
Hosseini. 2016. “Assessment of Urban Heat Island Based on the Relationship 
between Land Surface Temperature and Land Use/Land Cover in Tehran.” 
Sustainable Cities and Society 23: 94–104. https://doi.org/10.1016/j.scs.2016.03.009.  

Bonafoni, Stefania, Giorgio Baldinelli, and Paolo Verducci. 2017. “Sustainable 
Strategies for Smart Cities: Analysis of the Town Development Effect on Surface 
Urban Heat Island through Remote Sensing Methodologies.” Sustainable Cities and 
Society 29: 211–218. https://doi.org/10.1016/j.scs.2016.11.005  

https://doi.org/10.1016/j.rser.2011.09.003
https://doi.org/10.1016/j.landusepol.2017.11.003
https://doi.org/10.1016/j.ejrs.2020.02.001
https://doi.org/10.1016/j.scs.2016.03.009
https://doi.org/10.1016/j.scs.2016.11.005


 Ecology, Economy and Society–the INSEE Journal [150] 

Borbora, Juri, and Apurba Kumar Das. 2014. “Summertime Urban Heat Island Study 
for Guwahati City, India.” Sustainable Cities and Society 11: 61–66. 
https://doi.org/10.1016/j.scs.2013.12.001  

Census of India. 2011. Delhi Census Handbook—2011. Delhi: Directorate of Census 
Operations.  

Chakraborty, Surya Deb, Yogesh Kant, and Debashis Mitra. 2015. “Assessment of 
Land Surface Temperature and Heat Fluxes over Delhi Using Remote Sensing Data.” 
Journal of Environmental Management 148: 143–152. 
https://doi.org/10.1016/j.jenvman.2013.11.034  

Chan, ALS. 2011. “Developing a Modified Typical Meteorological Year Weather File 
for Hong Kong Taking into Account the Urban Heat Island Effect.” Building and 
Environment 46 (12): 2434–2441. https://doi.org/10.1016/j.buildenv.2011.04.038  

Choi, Youn-Young, Myoung-Seok Suh, and Ki-Hong Park. 2014. “Assessment of 
Surface Urban Heat Islands over Three Megacities in East Asia Using Land Surface 
Temperature Data Retrieved from COMS.” Remote Sensing 6 (6): 5852–5867. 
https://doi.org/10.3390/rs6065852  

Chow, Shun Djen. 1992. “The Urban Climate of Shanghai.” Atmospheric Environment, 
Part B. Urban Atmosphere 26 (1): 9–15. https://doi.org/10.1016/0957-1272(92)90033-
O  

Estoque, Ronald C, Yuji Murayama, and Soe W Myint. 2017. “Effects of Landscape 
Composition and Pattern on Land Surface Temperature: An Urban Heat Island 
Study in the Megacities of Southeast Asia.” Science of the Total Environment 577: 349–
359. https://doi.org/10.1016/j.scitotenv.2016.10.195  

Gao, Jianfeng, Qingyan Meng, Linlin Zhang, and Die Hu. 2022. “How Does the 
Ambient Environment Respond to the Industrial Heat Island Effects? An Innovative 
and Comprehensive Methodological Paradigm for Quantifying the Varied Cooling 
Effects of Different Landscapes.” GIScience & Remote Sensing 59 (1): 1643–1659. 
https://doi.org/10.1080/15481603.2022.2127463.  

Gluch, Renee, Dale Quattrochi, and Jeffrey C Luvall. 2005. “A Multiscale Approach 
to Urban Thermal Analysis.” Remote Sensing of Environment 104 (2): 123–132. 
https://doi.org/10.1016/j.rse.2006.01.025.  

Gupta, Rupesh. 2012. “Temporal and Spatial Variations of Urban Heat Island Effect 
in Jaipur City Using Satellite Data.” Environment and Urbanization Asia 3 (2): 359–374. 
https://doi.org/10.1177/0975425312473232.  

Gupta, Rupesh, and Deepanshu Parashar. 2020. “Estimation of Land Surface 
Temperature in the Urbanized Environment Using Multi-Resolution Satellite Data.” 
Uttar Pradesh Geographical Journal 25: 15–28. 

Han, Wenchao, Zhuolin Tao, Zhanqing Li, Miaomiao Cheng, Hao Fan, Maureen 
Cribb, and Qi Wang. 2022. “Effect of Urban Built-Up Area Expansion on the Urban 
Heat Islands in Different Seasons in 34 Metropolitan Regions across China.” Remote 
Sensing 15 (1): 248. https://doi.org/10.3390/rs15010248.  

Hang, Hong Thi, and Atiqur Rahman. 2018. “Characterization of Thermal 
Environment Over Heterogeneous Surface of National Capital Region (NCR), India 

https://doi.org/10.1016/j.scs.2013.12.001
https://doi.org/10.1016/j.jenvman.2013.11.034
https://doi.org/10.1016/j.buildenv.2011.04.038
https://doi.org/10.3390/rs6065852
https://doi.org/10.1016/0957-1272(92)90033-O
https://doi.org/10.1016/0957-1272(92)90033-O
https://doi.org/10.1016/j.scitotenv.2016.10.195
https://doi.org/10.1080/15481603.2022.2127463
https://doi.org/10.1016/j.rse.2006.01.025
https://doi.org/10.1177/0975425312473232
https://doi.org/10.3390/rs15010248


[151] Gupta 

Using LANDSAT-8 Sensor for Regional Planning Studies.” Urban Climate 24: 1–18. 
https://doi.org/10.1016/j.uclim.2018.01.001  

Husni, Emir, Galang Adira Prayoga, Josua Dion Tamba, Yulia Retnowati, Fachri 
Imam Fauzandi, Rahadian Yusuf, and Bernardo Nugroho Yahya. 2022. 
“Microclimate Investigation of Vehicular Traffic on the Urban Heat Island through 
IoT-Based Device.” Heliyon 8 (11): e11739. 
https://doi.org/10.1016/j.heliyon.2022.e11739.  

Jones, Philip D, and David H Lister. 2009. “The Urban Heat Island in Central 
London and Urban-Related Warming Trends in Central London since 1900.” Weather 
64 (12): 323–327. https://doi.org/10.1002/wea.432.  

Kant, Yogesh, BD Bharath, Javed Mallick, Clement Atzberger, and Norman Kerle. 
2009. “Satellite-Based Analysis of the Role of Land Use/Land Cover and Vegetation 
Density on Surface Temperature Regime of Delhi, India.” Journal of the Indian Society 
of Remote Sensing 37: 201–214. https://doi.org/10.1007/s12524-009-0030-x. 

Katsoulis, BD, and GA Theoharatos. 1985. “Indications of the Urban Heat Island 
in Athens, Greece.” Journal of Climate and Applied Meteorology 24 (12): 1296–1302. 
https://doi.org/10.1175/1520-0450(1985)024<1296:IOTUHI>2.0.CO;2. 

Khandelwal, Sumit, Rohit, Goyal, and Nivedita Kaul. 2010. “Study of Seasonal and 
Spatial Pattern of Urban Heat Island of Jaipur City and Its Relationship with 
Enhanced Vegetation Index.” Paper presented at the 13th Annual International 
Conference and Exhibition on Geospatial Information Technology and Applications Map India, 
Gurgaon, January 19–21, 2010. 

Kim, HH. 1992. “Urban Heat Island.” International Journal of Remote Sensing 13 (12): 
2319–2336. https://doi.org/10.1080/01431169208904271.  

Kusuma, Sundarakumar. 2012. “Estimation of Land Surface Temperature to Study 
Urban Heat Island Effect Using LANDSAT ETM+ Image.” International Journal of 
Engineering Science and Technology 4 (2): 807–814. 

Li, Weifeng, Qiwen Cao, Kun Lang, and Jainsheng Wu. 2017. “Linking Potential 
Heat Source and Sink to Urban Heat Island: Heterogeneous Effects of Landscape 
Pattern on Land Surface Temperature.” Science of the Total Environment 586: 457–465. 
https://doi.org/10.1016/j.scitotenv.2017.01.191.  

Liu, Yue, Goto Shintaro, Dafang Zhuang, and Wenhui Kuang. 2012. “Urban Surface 
Heat Fluxes Infrared Remote Sensing Inversion and Their Relationship with Land 
Use Types.” Journal of Geographical Science 22: 699–715. 
https://doi.org/10.1007/s11442-012-0957-7.  

Louiza, Haddad, Aouachria Zeroual, and Haddad Djamel. 2015. “Impact of the 
Transport on the Urban Heat Island.” International Journal for Traffic and Transport 
Engineering 5 (3): 252–263. https://doi.org/10.7708/ijtte.2015.5(3).03.  

Mallick, Javed, Yogesh Kant, and BD Bharath. 2008. “Estimation of Land Surface 
Temperature over Delhi Using Landsat ETM+.” Journal of Indian Geophysics Union 12 
(3): 131–140. 

https://doi.org/10.1016/j.uclim.2018.01.001
https://doi.org/10.1016/j.heliyon.2022.e11739
https://doi.org/10.1002/wea.432
https://doi.org/10.1175/1520-0450(1985)024%3C1296:IOTUHI%3E2.0.CO;2
https://doi.org/10.1080/01431169208904271
https://doi.org/10.1016/j.scitotenv.2017.01.191
https://doi.org/10.1007/s11442-012-0957-7
https://doi.org/10.7708/ijtte.2015.5(3).03


 Ecology, Economy and Society–the INSEE Journal [152] 

Mallick, Javed, and Atiqur Rahman. 2012. “Impact of Population Density on the 
Surface Temperature and Micro-Climate of Delhi.” Current Science 102 (12): 1708–
1713. https://www.jstor.org/stable/24084829.  

Mallick, Javed, Atiqur Rahman, and Chander Kumar Singh. 2013. “Modeling Urban 
Heat Islands in Heterogeneous Land Surface and Its Correlation with Impervious 
Surface Area by Using Nighttime ASTER Satellite Data in Highly Urbanizing City, 
Delhi-India.” Advances in Space Research 52 (4): 639–655. 
https://doi.org/10.1016/j.asr.2013.04.025.  

Mathew, Aneesh, Sumit Khandelwal, and Nivedita Kaul. 2017. “Investigating Spatial 
and Seasonal Variations of Urban Heat Island Effect over Jaipur City and Its 
Relationship with Vegetation, Urbanization and Elevation Parameters.” Sustainable 
Cities and Society 35: 157–177. https://doi.org/10.1016/j.scs.2017.07.013.  

Mathew, Aneesh, Sreenu Sreekumar, Sumit Khandelwal, Nivedita Kaul, and Rajesh 
Kumar. 2016. “Prediction of Surface Temperatures for the Assessment of Urban 
Heat Island Effect over Ahmedabad City Using Linear Time Series Model.” Energy 
and Buildings 128: 605–616. https://doi.org/10.1016/j.enbuild.2016.07.004.  

Mirzaei, Parham A, and Fariborz Haghighat. 2010. “Approaches to Study Urban 
Heat Island—Abilities and Limitations.” Building and Environment 45 (10): 2192–2201. 
https://doi.org/10.1016/j.buildenv.2010.04.001.  

Mohan, Manju, and Anurag Kandya. 2015. “Impact of Urbanization and Land-
Use/Land-Cover Change on Diurnal Temperature Range: A Case Study of Tropical 
Urban Airshed of India Using Remote Sensing Data.” Science of the Total Environment 
506: 453–465. https://doi.org/10.1016/j.scitotenv.2014.11.006.  

Molla, Neelima Afroz, Kabirul Ahsan Mollah, Ghaffar Ali, Wijitr Fungladda, OV 
Shipin, Waranya Wongwit, and Hoshiko Tomomi. 2014. “Quantifying Disease 
Burden among Climate Refugees Using Multidisciplinary Approach: A Case of 
Dhaka, Bangladesh.” Urban Climate 8: 126–137. 
https://doi.org/10.1016/j.uclim.2014.02.003.  

NDMA. 2016. Guidelines for Preparation of Action Plan—Prevention and Management of 
Heat Wave. New Delhi: National Disaster Management Authority, Government of 
India. 

Nina Schwarz, Sven Lautenbach, and Ralf Seppelt. 2011. “Exploring Indicators for 
Quantifying Surface Urban Heat Islands of European Cities with MODIS Land 
Surface Temperatures.” Remote Sensing of Environment 115 (12): 3175–3186. 
https://doi.org/10.1016/j.rse.2011.07.003.  

Oke, TR. 1982. “The Energetic Basis of the Urban Heat Island.” Quarterly Journal of 
the Royal Meteorological Society 108 (455): 1–24. 
https://doi.org/10.1002/qj.49710845502  

Pandey, Puneeta, Dinesh Kumar, Amit Prakash, Jamson Masih, Manoj Singh, 
Surendra Kumar, Vinod Kumar Jain, and Krishan Kumar. 2012. “A Study of Urban 
Heat Island and Its Association with Particulate Matter during Winter Months over 
Delhi.” Science of the Total Environment 414 (1): 494–507. 
https://doi.org/10.1016/j.scitotenv.2011.10.043.  

https://www.jstor.org/stable/24084829
https://doi.org/10.1016/j.asr.2013.04.025
https://doi.org/10.1016/j.scs.2017.07.013
https://doi.org/10.1016/j.enbuild.2016.07.004
https://doi.org/10.1016/j.buildenv.2010.04.001
https://doi.org/10.1016/j.scitotenv.2014.11.006
https://doi.org/10.1016/j.uclim.2014.02.003
https://doi.org/10.1016/j.rse.2011.07.003
https://doi.org/10.1002/qj.49710845502
https://doi.org/10.1016/j.scitotenv.2011.10.043


[153] Gupta 

Phelan, Patrick E, Kamil Kaloush, Mark Miner, Jay Golden, Bernadette Phelan, 
Humberto Silva, and Robert A Taylor. 2015. “Urban Heat Island: Mechanisms, 
Implications, and Possible Remedies.” Annual Review of Environment and Resources 40 
(1): 285–307. https://doi.org/10.1146/annurev-environ-102014-021155. 

Pramanik S, and Punia M. 2020. “Land Use/Land Cover Change and Surface Urban 
Heat Island Intensity: Source–Sink Landscape-based Study in Delhi, India. 
Environment Development and Sustainability 22 (8): 7331–7356. 
https://doi.org/10.1007/s10668-019-00515-0.  

Priyankara, Prabath, Manjula Ranagalage, DMSLB Dissanayake, Takehiro 
Morimoto, and Yuji Murayama. 2019. “Spatial Process of Surface Urban Heat Island 
in Rapidly Growing Seoul Metropolitan Area for Sustainable Urban Planning Using 
Landsat Data (1996–2017).” Climate 7 (9): 110. https://doi.org/10.3390/cli7090110.  

Puppala, Harish, and Ajit Pratap Singh. 2021. “Analysis of Urban Heat Island Effect 
in Visakhapatnam, India, Using Multi-Temporal Satellite Imagery: Causes and 
Possible Remedies.” Environment, Development and Sustainability 23: 11475–11493. 
https://doi.org/10.1007/s10668-020-01122-0.  

Ranagalage, Manjula, Yuji Murayama, DMSLB Dissanayake, and Matamyo 
Simwanda. 2019. “The Impacts of Landscape Changes on Annual Mean Land 
Surface Temperature in the Tropical Mountain City of Sri Lanka: A Case Study of 
Nuwara Eliya (1996–2017).” Sustainability 11 (19): 5517. 
https://doi.org/10.3390/su11195517.  

Roberts, Dar A, Philip E Dennison, Keely L Roth, Kenneth Dudley, and Glynn 
Hulley. 2015. “Relationships between Dominant Plant Species, Fractional Cover and 
Land Surface Temperature in a Mediterranean Ecosystem.” Remote Sensing of 
Environment 167: 152–167. https://doi.org/10.1016/j.rse.2015.01.026  

Saaroni, Hadas, Eyal Ben-Dor, Arieh Bitan, and Oded Potchter. 2000. “Spatial 
Distribution and Microscale Characteristics of the Urban Heat Island in Tel-Aviv, 
Israel.” Landscape and Urban Planning 48 (1–2): 1–18. https://doi.org/10.1016/S0169-
2046(99)00075-4.  

Saitoh, TS, T Shimada, and H Hoshi. 1996. “Modeling and Simulation of the Tokyo 
Urban Heat Island.” Atmospheric Environment 30 (20): 3431–3442. 
https://doi.org/10.1016/1352-2310(95)00489-0.  

Sannigrahi, Srikanta, Sandeep Bhatt, Shahid Rahmat, Bhumika Uniyal, Sayndeep 
Banerjee, Suman Chakraborti, Shouvik Jha, Sivaji Lahiri, Krishanu Santra, and Anand 
Bhatt. 2018. “Analyzing the Role of Biophysical Compositions in Minimizing Urban 
Land Surface Temperature and Urban Heating.” Urban Climate 24: 803–819. 
https://doi.org/10.1016/j.uclim.2017.10.002.  

Sannigrahi, Srikanta, Shahid Rahmat, Suman Chakraborti, Sandeep Bhatt, and 
Shouvik Jha. 2017. “Changing Dynamics of Urban Biophysical Composition and Its 
Impact on Urban Heat Island Intensity and Thermal Characteristics: The Case of 
Hyderabad City, India.” Modeling Earth Systems and Environment 3 (2017): 647–667. 
https://doi.org/10.1007/s40808-017-0324-x  

Shabana, Ghaffar Ali, Muhammad Khalid Bashir, and Hassan Ali. 2015. “Housing 
Valuation of Different Towns Using the Hedonic Model: A Case of Faisalabad City, 

https://doi.org/10.1146/annurev-environ-102014-021155
https://doi.org/10.1007/s10668-019-00515-0
https://doi.org/10.3390/cli7090110
https://doi.org/10.1007/s10668-020-01122-0
https://doi.org/10.3390/su11195517
https://doi.org/10.1016/j.rse.2015.01.026
https://doi.org/10.1016/S0169-2046(99)00075-4
https://doi.org/10.1016/S0169-2046(99)00075-4
https://doi.org/10.1016/1352-2310(95)00489-0
https://doi.org/10.1016/j.uclim.2017.10.002
https://doi.org/10.1007/s40808-017-0324-x


 Ecology, Economy and Society–the INSEE Journal [154] 

Pakistan.” Habitat International 50: 240–249. 
https://doi.org/10.1016/j.habitatint.2015.08.036.  

Sharma, Richa, and PK Joshi. 2013. “Monitoring Urban Landscape Dynamics over 
Delhi (India) Using Remote Sensing (1998–2011) Inputs.” Journal of Indian Society of 
Remote Sensing 41: 641–650. https://doi.org/10.1007/s12524-012-0248-x.  

Sharma, Richa, and PK Joshi. 2014. “Identifying Seasonal Heat Islands in Urban 
Settings of Delhi (India) Using Remotely Sensed Data—An Anomaly Based 
Approach.” Urban Climate 9: 19–34. https://doi.org/10.1016/j.uclim.2014.05.003.  

Shi, Yurong, and Yufeng Zhang. 2018. “Remote Sensing Retrieval of Urban Land 
Surface Temperature in Hot-Humid Region.” Urban Climate 24: 299–310. 
https://doi.org/10.1016/j.uclim.2017.01.001  

Simwanda, Matamyo, Manjula Ranagalage, Ronald C Estoque, and Yuji Murayama. 
2019. “Spatial Analysis of Surface Urban Heat Islands in Four Rapidly Growing 
African Cities.” Remote Sensing 11 (14): 1645. https://doi.org/10.3390/rs11141645    

Singh, Prafull, Noyingbeni Kikon, and Pradipika Verma. 2017. “Impact of Land Use 
Change and Urbanization on Urban Heat Island in Lucknow City, Central India. A 
Remote Sensing Based Estimate.” Sustainable Cities and Society 32: 100–114. 
https://doi.org/10.1016/j.scs.2017.02.018.  

Swain, David, GJ Roberts, Jadunandan Dash, K Lekshmi, V Vinoj, and S Tripathy. 
2017. “Impact of Rapid Urbanization on the City of Bhubaneswar, India.” Proceedings 
of the National Academy of Sciences, India Section A Physical Sciences 87 (4): 845–853. 
https://doi.org/10.1007/s40010-017-0453-7.  

Toy, Süleyman, and Sevgi Yilmaz. 2010. “Thermal Sensation of People Performing 
Recreational Activities in Shadowy Environment: A Case Study from Turkey.” 
Theoretical and Applied Climatology 101 (3–4): 329–343. 
https://doi.org/10.1007/s00704-009-0220-z.  

Traore, Mamadou, Mai Son Lee, Azad Rasul, and Abel Balew. 2021. “Assessment of 
Land Use/Land Cover Changes and Their Impacts on Land Surface Temperature in 
Bangui (the Capital of Central African Republic).” Environmental Challenges 4: 100114. 
https://doi.org/10.1016/j.envc.2021.100114.  

Tso, CP. 1996. “A Survey of Urban Heat Island Studies in Two Tropical Cities.” 
Atmospheric Environment 30 (3): 507–519. https://doi.org/10.1016/1352-
2310(95)00083-6.  

Vani, M, and P Rama Chandra Prasad. 2019. “Assessment of Spatio-temporal 
Changes in Land Use and Land Cover, Urban Sprawl, and Land Surface Temperature 
in and around Vijayawada City, India.” Environment, Development and Sustainability 22: 
3079–3095. https://doi.org/10.1007/s10668-019-00335-2.  

Yang, Zhiyuan, Chandi Witharana, James Hurd, Kao Wang, Runmei Hao, and Siqin 
Tong. 2020. “Using Landsat 8 Data to Compare Percent Impervious Surface Area 
and Normalized Difference Vegetation Index as Indicators of Urban Heat Island 
Effects in Connecticut, USA.” Environmental Earth Sciences 79 (18): 1–13. 
https://doi.org/10.1007/s12665-020-09159-0  

https://doi.org/10.1016/j.habitatint.2015.08.036
https://doi.org/10.1007/s12524-012-0248-x
https://doi.org/10.1016/j.uclim.2014.05.003
https://doi.org/10.1016/j.uclim.2017.01.001
https://doi.org/10.3390/rs11141645
https://doi.org/10.1016/j.scs.2017.02.018
https://doi.org/10.1007/s40010-017-0453-7
https://doi.org/10.1007/s00704-009-0220-z
https://doi.org/10.1016/j.envc.2021.100114
https://doi.org/10.1016/1352-2310(95)00083-6
https://doi.org/10.1016/1352-2310(95)00083-6
https://doi.org/10.1007/s10668-019-00335-2
https://doi.org/10.1007/s12665-020-09159-0


[155] Gupta 

Zhang, Linlin, Qingyan Meng, Zhenhui Sun, and Yunxiao Sun. 2017. “Spatial and 
Temporal Analysis of the Mitigating Effects of Industrial Relocation on the Surface 
Urban Heat Island over China.” International Journal of Geo-Information 6 (4): 121. 
https://doi.org/10.3390/ijgi6040121  

Zhu, Rui, Man Sing Wong, Éric Guilbert, and Pak-Wai Chan. 2017. “Understanding 
Heat Pattern Produced by Produced by Vehicular Flows in Urban Areas.” Scientific 
Reports 7: 16309. https://doi.org/10.1038/s41598-017-15869-6   

 

https://doi.org/10.3390/ijgi6040121
https://doi.org/10.1038/s41598-017-15869-6

