Article

Environment and Urbanization Asia Monitoring Spatial Patterns of 7(1) 1–17 © 2016 National Institute Land Surface Temperature and Urban of Urban Affairs (NIUA) SAGE Publications Heat Island for Sustainable Megacity: sagepub.in/home.nav DOI: 10.1177/0975425315619722 A Case Study of , Using http://eua.sagepub.com Landsat TM Data

Aakriti Grover1 RB Singh2

Abstract Land use/cover change has brought major changes on land surface, including urban heat budget, tem- perature regimes, urban hydrology and others, leading to unsustainable environments. Remote sensing has played important role in monitoring these changes across globe especially where instrumental data is not available for such observations. The study uses Landsat 5 TM remote sensing data (26 October 2010) to monitor spatial patterns of land surface temperature and urban heat island in city of Mumbai, India. The thermal data was corrected and converted to top of the atmosphere radiance values and further to temperature in degrees Celsius from Kelvin scale using Erdas Imagine 9.2. The study reveals that urban areas have the highest temperature, ranging from 28°C for low built-up density and 34°C for high built-up and high-rise urban areas. Relatively, lower temperature is associated with forests and water bodies. The temperature varies with the vegetation density and depth of water. High vegetation density is related with low temperature (23°C) and low density with high temperature (26°C). Higher temperature (25°C) is recorded for shallow water and lower temperature (21°C) for deep water. The rocky bare surface record temperature, ranges between 26°C and 28°C. The east–west and north– south profile of surface temperature, confirm the heat island phenomena in Mumbai. The central and southern parts of the city have the highest temperature (34°C) whereas the city outskirts have lower temperatures (23°C). For megacities to be sustainable, the spatial spread and composition of urban growth needs to be regulated and constantly monitored.

针对特大城市可持续性的地表温度和城市热岛的空间分布监测：以印度孟买为例，利用 Landsat TM数据 土地利用/覆盖的变化已经对地表产生了重大变化，包括城市热预算、温度状况、城市水文情况 等，导致了环境的不可持续。遥感技术已经在监测全球类似变化，特别是在仪器产生的数据不 适用于此类观测时，发挥了重要作用。本研究采用的是Landsat 5 TM遥感数据（2010年10月26

1 Assistant Professor, Department of Geography, Swami Shraddhanand College, University of , India. 2 Associate Professor, Department of Geography, Delhi School of Economics, University of Delhi, India.

Corresponding author: Aakriti Grover, Assistant Professor, Department of Geography, Swami Shraddhanand College, University of Delhi, Delhi- 110007, India. E-mail: [email protected] 2 Environment and Urbanization Asia 7(1)

日），它监测了印度孟买市地表温度与城市热岛的空间分布。热数据被校正并转换到大气辐射 值的顶部，并进一步通过Erdas Imagine 9.2从开氏温标转换成摄氏温度。研究表明，城市地区 具有最高温度，分布从低密度建成区的28℃到高密度和高层建成区的34℃等。相对而言，较低 的温度与森林和水体有关。温度随植被的密度和水的深度发生变化。高密度植物与较低的温度 有关（23℃），而低密度植物与较高温度有关（26℃）。浅水温度较高（25℃），深水则较低 （21℃）。裸露的岩石地表的温度范围在26-28℃之间。东西和南北剖面上的地表温度，证实了 孟买的热岛现象。城市中心和南部地区拥有最高气温（34℃），而城市边缘温度较低（23℃）。 对于特大城市来说要实现可持续发展，城市发展的空间蔓延和构成需要进行控制并持续监测。

Keywords UHI, NDVI, NDBI, LST, Mumbai, India

Introduction

The cities around the world have increased in size and population concentration due to urbanization. According to the United Nations (UN, 2011), the number of cities in the world with populations greater than one million significantly increased from 75 in 1950 to 447 in 2011 and it is projected that there will be 527 such cities by 2020. The world’s urban population has also grown phenomenally from 746 million (30 per cent) in 1950 to 3.9 billion (54 per cent) in 2014 and about 66 per cent population is projected to dwell in urban areas 2050 (UN, 2014). Presently, about 53 per cent of the world’s urban population resides in Asia and about 48 per cent of the total population of Asia is urban. The rate of urbanization is currently more rapid in Asia than any other region in the world (UN, 2014). The total estimated urban population of India was 286.1 million in 2001 and 377.1 million in 2011, with a decadal growth rates of 27.81 per cent (1991–2001) and 31.80 per cent (2001–2011) (Census of India, 2011), which is projected to further increase to 463 million by 2020 (UN, 2011). The number of million plus cities in India has increased from 12 in 1981 to 23 in 1991, 35 in 2001 and 53 in 2011. The rapid urbanization and growth of cities in recent decades has drastically altered the natural envi- ronment with the replacement of natural landscapes by artificial settings, for example, concrete surfaces, high-rise building landscapes, degraded forest cover and water bodies, etc. The pervious soil and vegeta- tion surfaces have been substituted by impervious urban built-up materials like concrete, metal and asphalt (Lo & Quattrochi, 2003; Zhang, Yiyun, Qing & Jiang, 2012). The change in surface material has a number of implications on micro land–atmosphere energy processes, albedo and land surface tempera- tures (LST). Generally, a higher surface temperature is observed in the cities due to built-up-impervious surfaces and high-rise buildings constructed though heavy use of metal as compared to surrounding rural landscapes characterized by forest–tree cover and pervious surfaces. The phenomenon is known as an urban heat island (UHI), which is a manifestation of this land use/cover modification (Xiao & Weng, 2007). The alteration of land use/cover in urban centres is associated with increasing concretization, traf- fic congestion and diminishing vegetation covers in the cities, thereby producing more elevated tempera- tures than the greener peripheral areas. The UHI analysis is closely associated with the spatial patterns of LST, which primarily depends on the physical properties of land use/cover (Lo & Quattrochi, 2003; Oke, 1995; Singh, Grover & Zhan, 2014). The elevated LST in cities and resultant UHI have significant influence on human health and energy consumption in cities. During the summer months, when the temperature is already high, the phenomenon of UHI can lead to various health hazards, including heat strokes, fainting and even deaths in extreme cases. The combination of UHI and high air pollution can aggravate respiratory problems such as asthma Grover and Singh 3 and bronchitis, dermatological illness including skin allergies and various other health problems such as eye–nose irritation. Therefore, there is an urgent need of enquiry on UHI and LST. The development of spatial information technology and readily available remote sensing data has provided impetus to the research on patterns of LST and formation of UHI (Singh & Grover, 2014; Zhang et al., 2012). Remote sensing satellite data have been extensively used in recent years to ana- lyze LST in association with vegetation and built-up indexes for cities across the world (Grover & Singh, 2015; Muttittanon, & Tripathi, 2005; Torres-Vera et al., 2007). The various satellite data, for example, Landsat TM (Li et al., 2012; Singh et al., 2014), ETM+ (Zhang & Wang 2008), MODIS (Benali et al., 2012; Pu et al., 2006; Wang & Liang, 2009), ASTER (Wang & Liang, 2009) and AVHRR (Pu et al., 2006) images have been widely analyzed to assess LST and UHI (Ding & Shi, 2013; Singh et al., 2014). Various facets of LST have been studied using remote sensing satellite data, for example, factors responsible for UHI creation by Oke (1995); spatio-temporal changes in UHI by Zhang and Weng (2008); whereas the impacts of land use/cover on LST are examined by Jiang and Tian (2010) and Weng, Zengshang and Jacquelyn (2004). There exist intricate relationships between UHI, LST and land use/ cover that are extensively studied for various cities of the world. Some recent studies include Li et al. (2011) on Shanghai, Mallick, Kant and Bharath (2008) on Delhi, and Beijing by Zhang, Wu and Chen (2010). Abundant researches on UHI in relation to NDVI and NDBI have been conducted. Yuan and Bauer (2007) correlated the impervious surfaces with UHI phenomena and NDVI. Others like Kawashima (1994), Yue, Xu, Tan and Xu (2007), Weng et al. (2004) and Zhang et al. (2012) interlinked LST with UHI and NDVI. To examine the intensity of UHI, NDBI is also studied by scholars like Xu (2007) and Liu and Zhang (2011). The negative impacts of UHI creation on urban environment is analyzed by Weng and Yang (2004) and Lo and Quattrochi (2003). Indian cities, except Delhi, are rarely studied for LST and UHI phenomena (Singh & Grover, 2014). Mallick et al. (2012), Mallick et al. (2008) and Singh et al. (2014) analyzed Landsat images for LST and UHI, while Pandey, Kumar, Prakash, Kumar and Jain (2009) used MODIS data for UHI in Delhi. Faris and Reddy (2010) discussed the relationship between LST, land use/cover and UHI with respect to Chennai using Landsat image. The LST and UHI researches on Mumbai are rare. Grover and Singh (2015) and Dwivedi, Khire and Mohan (2015) have visually analyzed UHI and LST patterns with NDVI and other underlying factors in Mumbai. These studies confirm the presence of UHI but for different seasons, for example, Grover and Singh (2015) for April 2009 and Dwivedi et al. (2015) for December 2009. Both the studies have not analyzed and quantified the degree of influence of various factors of UHI and LST such as vegetation cover, built-up land, water bodies, etc. Therefore, the present article analyzes (a) spatial patterns of LST in Mumbai (b) the presence of UHI along four profile lines (three west–east and one north–south); and (c) relationships between LST with vegetation (NDVI) and impervious and concrete surface (NDBI).

Study Area

Mumbai is one of the most populous city of India with total population of about 12 million and second largest metropolitan region with about 20.7 million populations (Census of India, 2011). It is capital of the state of and located on the western coast ( coast), between 18°53′ and 19°19′ northern latitudes and 72°45′ and 73° eastern longitudes (Figure 1). The present Mumbai is divided into two revenue districts, Mumbai City District, that is, the island city in the south and the that includes the western and eastern suburbs. The island city and the suburbs together form the Greater Mumbai or Brihan-Mumbai or Municipal Corporation of Greater Mumbai (MCGM) also referred as Mumbai. Mumbai is approximately 11 m above the mean sea level covering a total area of 437.71 sq. km is under MCGM and rest under different authorities like the Port Trust, Ministry of Defence, Atomic 4 Environment and Urbanization Asia 7(1)

Figure 1. Location of Study Area in India. The Background Image is Landsat 5 TM

Energy Commission and Sanjay Gandhi National Park, etc. (Bhagat & Jones, 2013; Grover and Parthasarathy, 2012). The population density of Mumbai city has been estimated to be over 20,038 persons per sq. km, while the population density of Mumbai suburban district is 20,925 persons per sq. km (Census of India, 2011). Total growth of population during last decade has been estimated as –5.75 in Mumbai district and 8.01 for the suburbs (Census of India, 2011). The total forest cover in the Mumbai city is merely 2 sq. km while it is 120 sq. km in Mumbai subur- ban district (FSI, 2011). Most of this is open forest and a minimal area is under moderate dense forest. As per Dwivedi et al. (2015), 70 per cent of total area of Mumbai is built-up land, 20 per cent covered with vegetation, and 5 per cent each is barren or occupied with water bodies. The study area is surrounded by Arabian Sea on the south and west, district and creek on the north, on the east and southeast. Climatologically, it is a tropical coastal city. Grover and Singh 5

The average temperature is 27.2°C and average annual rainfall is about 2146.6 mm (Dwivedi et al., 2015). There are number of big lakes in the study area such as Powai, Vihar and Tulsi. The northern side of the study area is well characterized by the tropical vegetation. The Brihan-Mumbai Electric Supply Transport (BEST) buses operate 3,587 buses and carry 43 lakh passengers daily (MCGM, 2010). The total number of vehicles plying on the road were 1,767,798 in Greater Mumbai in 2010; that increased from 1,393,647 in 2006 (GoM, 2010–2011).

Data Source and Methods

Landsat 5 TM satellite images (26 October 2010) have been primarily used in the present study, which were acquired from www.earthexplorer.usgs.gov. Landsat 5 TM acquires the satellite images in seven spectral bands; of which three are obtained in the visible range, that is, blue, green and red (1, 2 and 3), one in the near infrared (band 4, NIR) region, two in the middle infrared (band 5 and 7) and one thermal infrared (TIR) band (band 6) (Table 1). The spatial resolution of the sixth band is 120 m, while of all the other bands 30 m. For estimating the LST and UHI and their relationships with factors (vegetation and built-up land), the satellite images were first radiometrically corrected, thereafter LST, NDVI and NDBI were calculated (Chen et al., 2006; Xu, 2007).

Estimation of LST

The sixth band (TIR) of TM5 has been extensively used for mapping and estimating LST and UHI in many studies (Lo & Quattrochi, 2003; Singh et al., 2014). It involves three steps for estimating the LST from Landsat thermal data, namely, (a) convert raw Digital Number (DN) of the sixth band to spectral radiance; (b) convert spectral radiance to temperature (Kelvin scale) image; and (c) conversion of tem- perature in Kelvin to degree Celsius scale (Singh et al., 2014). These steps are mentioned in detail in Murayama and Lwin (http://giswin.geo.tsukuba.ac.jp), which particularly deals with LST estimation using Landsat 5 TM thermal data. Chander and Markham (2003) also describe these methods with spe- cial reference to Landsat 5 TM. Chander et al. (2009) mentioned the process for all the Landsat thermal datasets (4, 5 and 7), whereas the Landsat 7 Science Data Users Handbook for Landsat ETM+ also explains these processes.

Table 1. Details of Thematic Mapper 5 Satellite Image Used

Spectral Spatial Bands Range (µm) Resolution (m) Bands Gain Offset/Bias 1 0.450–0.515 30 Blue 0.762823529 –1.520000000 2 0.525–0.605 30 Green 1.442509804 –2.840000000 3 0.630–0.690 30 Red 1.039882353 –1.170000000 4 0.760–0.900 30 Near IR 0.872588235 –1.510000000 5 1.550–1.750 30 Mid IR 0.116980392 0.370000000 6 10.40–12.5 120 Thermal 0.055156863 1.238000000 7 2.080–2.35 30 Mid IR 0.064117647 0.150000000 Sources: Chander et al. (2009); http://landsat.usgs.gov/ 6 Environment and Urbanization Asia 7(1)

Step 1. Conversion of the Digital Number (DN) to Spectral Radiance (L)

Ll = LMIN + (LMAX – LMIN) × DN / 255 (1) where Ll = spectral radiance, LMIN = 1.238, LMAX = 15.600 and DN = digital number Step 2. Conversion of Spectral Radiance to Temperature in Kelvin

TB = K2 / ln{(K1/Ll) + 1} (2) where K1= calibration constant 1 (607.76) and K2 = calibration constant 2 (1260.56) for thermal band of

TM data, TB = surface temperature Step 3. Conversion of Kelvin to Celsius

TB = TB – 273 (3) The temperature values thus obtained are referred to as black body temperature, thus needs correction for spectral emissivity according to the nature of land cover (Lo & Quattrochi, 2003; Yue et al., 2007). Lo and Quattrochi (2003) further suggest that the temperature remains almost the same even after emis- sivity correction, thus there is no need for correcting the black body temperature for emissivity. We have therefore calculated the LST based on Lo and Quattrochi (2003).

Estimation of NDVI

NDVI is an important indicator of vegetation health, stress and greenness or biomass. The values of NDVI ranges between –1 to +1, where higher values indicate healthy vegetation cover and negative values represent land surface devoid of vegetation cover (built-up, water, barren, snow regions, etc) (Lo & Quattrochi, 2003; Yuan & Bauer, 2007). The NDVI of Landsat 4, 5 and 7 sensors is estimated using NIR (fourth band) and IR (third band) (Amiri et al., 2009), since wavelengths of required spectral channels are identical Chander et al. (2009). However, certain corrections are needed on the specific bands to be used to estimate NDVI (Bruce and Hilbert, 2006). The specific bands (third and fourth bands) were first converted to spectral radiance using the equation one, which were then converted to the atmosphere reflectance using following equation (based on Chander & Markham, 2003; Chander et al., 2009). rl p 2 q = d Ll/E0lcos s (4) where rl = reflectance d = Earth–Sun distance (astronomical units) Ll = radiance E0l = mean solar exo-atmospheric irradiance p = 3.14159 qs = angle of solar zenith (degrees) NDVI for the TM satellite data is calculated using following equation (Amiri et al., 2009; Bruce & Hilbert, 2006; Weng et al., 2004; Yue et al., 2007) (NIR – R) / (NIR + R) (5) where NIR = Band 4, R = Band 3 Grover and Singh 7

Estimation of NDBI

NDBI is also considered an important factor and indicator of LST. NDBI is sensitive to the built-up land (Chen et al., 2006). Its values also range between –1 to +1. Positive values represent highly built-up land and negative values indicate other land cover types. Prior to estimation of NDBI, Equations 1 and 4 are applied on required bands (4 [NIR] and 5 [MIR] bands); thereafter following equation is applied for estimating NDBI (Chen et al., 2006; Xu, 2007). NDBI for the TM satellite data is calculated as

(MIR – NIR) / (MIR + NIR) (6) where MIR = Band 5, NIR = Band 4 The surface patterns of LST, NDVI and NDBI have been visually correlated to understand the pat- terns of LST, estimation of UHI and its relationships with NDVI and NDBI. Pixel samples were taken along four profile lines, namely, A–a (564 pixels), B–b (510 pixels), C–c (194 pixels) and D–d (1286 pixels) across the study area. The D–d line is the north–south profile, whereas others are west–east pro- files (Figure 2). Three west–east profile lines were drawn because of the shape of the study area. The

Figure 2. Location of Profile Lines (North–South and East–West) for Comparison of LST, NDVI and NDBI 8 Environment and Urbanization Asia 7(1) profiles of LST, NDVI and NDBI have been plotted to analyze their interrelationships. The scatter plots of LST vs NDVI and LST vs NDBI have been further drawn with trend line and r2 values were deter- mined to statistically understand degree of influence and relationships.

Results and Discussion Spatial Pattern of LST The spatial distribution of LST reveals that there is higher temperature in the city than its periphery (Figure 3a). The LST in Mumbai ranges from a high of 35°C to the lowest of 24°C. The mean LST is estimated to be about 25.31°C (s 1.87°C). The city is covered with multiple land use/cover types ranging from the water bodies (lakes and rivers) to forest cover (Sanjay Gandhi National Park [SGNP] also known as Borivilli National Park) and high-density high-rise buildings in the city centre, interspersed with large areas covered with slums. Large urban built-up and concrete surfaces exhibit highest tempera- tures (35°C) in the city. The densely built-up urban areas with high temperature are found in most of the study region. The – urban complex near bay records a high of 32°C. The LST soars to a maximum (35°C) in Central Mumbai and . The eastern and along the Borivilli National park record moderate temperatures with patches of hotspots. In the western suburbs, the region coinciding with high-density urban uses tend to have LST ranging between 29°C to 31°C. In the midst of these hotspots, the Indian Institute of Technology, Bombay campus, bordered by Powai Lake and Borivilli National Park is greener and therefore the temperature ranges between 26°C and 28°C. Gorai, adjacent to the Manori Creek has a maximum temperature of 28°C, but largely it is not more than 26°C owing to the presence of mangroves and green areas. The pervious zones along the coastal areas covered with mangroves show moderate temperature ranges (26°C–27°C), whereas the lakes and rivers have lower temperatures of 24°C–25°C and 25°C–26°C.

Spatial Pattern of NDVI and NDBI The NDVI measures the greenness of environment (Lo & Quattrochi, 2003). NDVI in Mumbai varies from a maximum of 0.768 to a minimum of –0.527 (Figure 3b). The greenest areas in Mumbai are the coastal areas covered with mangroves and the SGNP in the Mumbai suburbs. The NDVI is estimated highest in dense vegetation areas of SGNP. Most parts in the national park have an NDVI of more than 0.5, though a few patches have lower NDVI indicating sparse vegetation. Thane Creek, Malad Creek, Gorai and Mahim Bay have moderate NDVI values (0.3 to 0.6). These green patches absorb the heat and therefore act as heat moderators. The NDVI however is mostly negative in the built-up area, especially in Mumbai city. The tem- perature hotspots coincide with the low and negative NDVI values. These are the dense urban built-up areas and the transportation lines. The southernmost point, Point exhibits higher NDVI values (0.55) along the coast but reduces to 0.06 towards the urban areas. Similarly, the Malabar Point NDVI ranges from 0.52 to 0.11 depending on the land use/cover type. The International Airport has very low NDVI values (0.01) with patches of tree cover possessing higher NDVI values (0.4). Mumbai city is more densely occupied with buildings compared to the suburbs and the maximum NDVI for these regions is 0.2. The NDVI dips along the roads and highways to 0.07 confirming the absence of tree cover in the city. Grover and Singh 9

Figure 3. Spatial Distribution of a. LST, b. NDVI and c. NDBI in Mumbai 10 Environment and Urbanization Asia 7(1)

The NDBI is commonly used indicator for extraction of built-up land from the urban area (Liu & Zhang, 2011). The spatial pattern of NDBI in Mumbai city and suburb is depicted in Figure 3c. The maximum and minimum NDBI values are 0.439 and –1 respectively. Mumbai city, except the coastal areas, possesses very high NDBI values (0.44 to 0.23). This includes the colonies of Colaba, , , , Worli, , Sion and Mahim. Mumbai suburbs, on the other hand, have large areas under lakes, creeks and vegetation. However, high NDBI values are recorded in various parts of the suburbs including Bandra, Kurla, Andheri, Vile Parle, Santa Cruz, Ghatkopar, Chembur and Bhandup (0.22–0.36). The Powai lake region has very low NDBI values (0.05), whereas Gorai, Goregaon and Mulund in the north suburb are relatively less urbanized with lower NDBI (0.1). The spatial patterns of built-up areas hence illustrate decreasing NDBI trend towards the north Mumbai. The areas covered with vegetation, possess negative NDBI values.

Relationship between LST, NDVI and NDBI

The existance of UHI can be interpreted with the help of LST and validated using the NDVI and NDBI values. The three indicators are closely related (Figures 3 and 4). The areas characterized by higher tem- perature are also with the higher NDBI values and lower NDVI values, thus there is positive relationship with LST and NDBI and negative relationship between LST and NDVI. Based on the four transect lines (Figure 2), these relationships and the degree of corelation among LST, NDVI and NDBI are clearly represented through various profile lines in Figure 4. The northermost profile line (Figures 2 A–a and 4A) passes through the northern Mumbai Suburban district cutting across the mangroves and marshy lands in the west, urban areas in the centre and vegeta- tion areas (SGNP) in the east. In the western mangroves, the temperature observed is very low (24°C– 25°C) as compared to very high temperature (32°C–32°C) in central urban built-up areas (Gorai, Borivilli and Khandiwali) and very low temperature in eastern vegetation areas (SGNP). The patterns of LST, NDVI and NDBI are clearly interlinked. The western half of the profiles has peaks of LST and NDBI corresponding to the urban areas, wheras the second half that crosses through the vegetation cover park has NDVI as high as 0.66, LST reaching a low of 24°C and NDBI of 0.38. This clearly reflects on the role of vegetation and mangroves in maintaining the city’s temperatures. The central profile line (Figures 2 B–b and 4B) running from west to east passes through southern part of Mumbai suburban district. It passes through the dense urban built-up settlements of Santa Cruz, Kurla and Ghatkopar in the west, International airport and Mithi river in the central profile area and mangrove- covered areas are found on the eastern side of profile indicating the eastern coast. The LST profile lines present apparent peaks and troughs. The very high LST and NDBI values and negative NDVI values correspond well to urban built-up settlements. The LST reaches a high of 33°C at the International Airport, 32°C at Kurla and nearly 30°C at Ghatkopar, which are also characterized with lowest NDVI and highest NDBI. Some prominent lows of LST and NDBI are tree cover zones, gardens, Mithi River. The LST dips to 25°C–26°C in the extreme east, which is covered with mangroves and trees along the Thane Creek. This region corresponds to highest NDVI and lowest NDBI values. The southernmost transect (Figures 2 C–c and 4C) represents the main Mumbai district that is densely covered with concrete, stone and metal having negligible natural vegetation cover. The profile lines primalry cross the urban built-up areas, for example, Cumballa Hill, Mahalaxmi, Mumbai Central, Byculla and Mazagaon from west to east is indicated by NDVI values in the range between 0.1 to 0.5. Most of the built-up areas record high LST (up to 31°C) and NDBI (0.1) values. Two highs Grover and Singh 11

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Figure 4. Spatial Relationships of LST, NDVI and NDBI. (A) West–East Profile (North), (B) West–East Profile (Central), (C) West–East Profile (South) (D) North–South Profile of LST can be seen near Cumballa Hill in West Mumbai. Overall, high urbanization has led to high NDBI values and high LST. NDBI crosses 0.1 near Mumbai Central and twice in Mazagaon. There is sudden increase of temperature at Mumbai Central that is also representative of high density road and railway network. Another peak is found near Mazagaon in the east associated with high NDBI and NDVI lower that 0. 12 Environment and Urbanization Asia 7(1)

The north–south profile line cuts across the Mumbai suburbs in the north and the city in the south, dividing it into two parts (Figures 2 D–d and 4D). The line passes mainly through built-up zones, except the extreme north representing the mangroves and marshy areas and the extreme south cover- ing the coastal area. The extreme north and south regions have the lowest temperatures. The peaks in the LST graph correspond to dense urbanized surfaces (Figure 4D). Owing to high calorific capacity, these regions record LST as high as 33°C, whereas lower temperatures of 24°C correspond to man- grove-covered areas. To better understand the relationship between LST, NDVI amd NDBI, simple linear regression was carried out (Figures 5 and 6). The regression lines for relationships between LST and NDVI for the four transect lines are shown in Figure 5. The horizontal axis shows the abundance of vegetation through NDVI and vertical axis demonstrate the LST in °C. Except for the southern profile line (Figure 5A), negative relationships between LST and NDVI are observed. In all the cases, the observed LST are well explained by NDVI (more than 30 per cent). The LST and NDBI have positive relationships in case of all profile lines (Figure 6), where the x-axis represents NDBI and the y-axis, LST. The weakest relationship is visible in the north–south profile line (r2 0.30), whereas it is fairly strong in the case of all the west–east profiles. The NDBI very well corre- spond to and explain the LST (r2 0.68). The UHI phenomenon is visible for Mumbai but unlike many cities of the world, like Shanghai, Hong Kong, Beijing, the intensity of UHI is not very strong for Mumbai. This is mainly on account of the impact of the coast, large areas under mangroves and relatively lower heights of buildings. However, in comparison to Delhi, Mumbai has a higher UHI (Singh et al., 2014). This is because the tree cover along the roads, railway lines and in other open spaces is too low. Despite a fairly good public transport net- work, private vehicles are on the rise in Mumbai (Pacione, 2006). The in-migrations, population increase, and vehicular and industrial pollution are main contributors to UHI creation. This however, can be con- trolled by moving towards low-carbon use in industrial and transport sector. The preservation of existing water bodies and mangroves is vital to maintain the balance between population needs and resources available in the city (Kamini et al., 2006). People’s participation with the public and private sector on programmes related to afforestation, green roofs and terrace gardens needs to be promoted to uphold the liveability and sustainability of Mumbai city.

Conclusion

The examination of the linkages between UHI based of LST, NDVI and NDBI from satellite-derived data proves to be informative and useful in achieving targets. Mumbai district is densely populated with low buffer zones and tree cover. On the other hand, the Mumbai suburbs are experiencing fast rate of replacement of natural land cover with urban paved-land use. The mangroves and marshy areas are being replaced by concrete zones; the lakes are shrinking and the rivers are rapidly converted to nalas with increased waste disposal and low rate of replenishment. Owing to the massive demand of land for residential purposes and less available land, the urbanization in the suburbs is experiencing a massive vertical growth. There are high-rise buildings being constructed at a fast rate. Hence, both the districts show high LST resulting in the formation of strong UHI in the region that correspond to built-up areas in Mumbai, that may be aggravated to cause serious damage to human health in the future. The habitability of the Mumbai megacity relies upon meticulous planning and its diligent implementation. Please provide Editable Image

Figure 5. Scatter Plot Linear Regression of LST and NDVI. (A) West–East Profile (North), (B) West–East Profile (Central), (C) West–East Profile (South), (D) North–South Profile Please provide Editable Image

Figure 6. Scatter Plot Linear Regression of Lst and Ndbi. (A) West–East Profile (North), (B) West–East Rofile (Central), (C) West–East Profile (South), (D) North–South Profile Grover and Singh 15

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