seasonal, annual and diurnal distributions of uhi in...

Seasonal, Annual and Diurnal Distributions of UHI in Delhi

“Nature gives to every time and season some beauties of its own; and from morning to night, as from the cradle to the grave, it is but a succession of changes so gentle and easy that we can scarcely mark their progress”.

- Charles Dickens (1839)

6.1. Introduction

The previous chapter established the formation of Urban Heat Island in Delhi. It detailed

the progression of urbanization in the city, its related impacts on environment quality,

and manifestation of such impacts in form of UHI. This chapter shall discuss the

seasonal behaviour of UHI. The chapter will address the questions whether the UHI

pattern change with season or not; if yes then how? The diurnal behaviour of UHI in the

city will be also focussed upon.

For quantitative analysis of UHI, measuring UHI intensity becomes imperative.

Researchers have used various methods for UHI quantification. Keramitsoglou et al.

(2011) have used difference between LST and reference LST (RLST) to assess UHI

intensity. Zhang et al. (2009) calculated LST differences between different impervious

surface area categories and water as an estimate for UHI intensity. Zhang and Wang

(2008) proposed hot island area (HIA) as UHI intensity estimate that is based on

standard deviation segmentation of LST image.

6.2. Objectives

The focus of this chapter is to delve into the temporal variation of the spatial expanse

and intensity of UHI in Delhi. The temporal scale considered in this study ranges from

diurnal through seasonal to annual. Such an analysis would add on to the existing

knowledge of UHI behaviour in cities of tropical developing countries, with special

emphasis on the semi-arid conditions prevailing in Delhi. The secondary and a bit

implicit objective of the study is to identify the UHI susceptible locations in the city.

6.3. Data and methods

For the temporal analysis of UHI, seasonal and diurnal cycles of change were analyzed,

based on Urban Heat Island Intensity (UHI-I). UHI-I is the measure of strength or

magnitude of urban heat island. It is a well-known indicator of urban heating that was

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TERI University–Ph.D. Thesis, 2013

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employed here to study temporal patterns of UHI in Delhi. UHI patterns across the city

were studied for different months of the year understanding the seasonal behaviour; and

during day/night to comprehend the diurnal patterns of UHI as well as to identify

locations within city that are more prone to UHI effects through the course of day and

night or throughout the year. Moreover, such studies hold high importance in UHI

research because; UHI behaves very differently during day and night hours. Such

analysis holds strong implication for mitigation strategies and policy aspects.

Table 6. 1: Satellite data used and the corresponding meteorological conditions

Months Seasons/ Time

Acquisition Date

Air Temperature (°C) RH (%)

Min. Max. Mean

January Winter 29/1/2010 14 26 20 44

February Winter 14/2/2010 10 22 16 67

March Spring 4/3/2011 14 22 18 64

April Summer 3/4/2010 25 37 31 18

May Summer 5/5/2010 22 41 32 29

June Summer 22/6/2010 35 44 40 32

September Monsoon 26/9/2010 23 32 28 66

October Post-monsoon 28/10/2010 15 29 22 42

November Winter 29/11/2010 12 24 18 53

October Day 1/10/2002 21 36 28 52

October Night 14/10/2002 22 32 27 63

LST images following Qin et al.’s monowindow algorithm (explained in previous

chapter) were retrieved from images procured for different seasons. Cloud free Landsat

TM satellite images used for seasonal analysis were for January, March, April,

September and October months (Table 6. 1). An important consideration while selecting

images was to avoid days that witnessed precipitation or at least two days preceding it.

September month imagery was an exception to this condition due to two major reasons.

The first reason is that September falls in the monsoon season, and Delhi receives


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majority of its precipitation during monsoon months, so availability of cloud free and

precipitation free days was a constraint during these months (except 26th September

2010 data, that was cloud free). Secondly, as we were analyzing seasonal behavior of

UHI, considering rainy season itself calls for use of image with some precipitation.

Figure 6.1: The methodological framework

6.3.1. UHI-I measurement

As discussed above, UHI-I was used as an indicative of UHI in Delhi. Different methods

to identify and quantify UHI have been reported in literature. Most of these methods

utilize the descriptive statistics for LST such as (a) the mean, in form of difference

between mean LST of urban and rural (Imhoff et al., 2010; Rajasekar and Weng, 2009;

P. Zhang et al., 2010) or urban and all other area (Dousset and Gourmelon, 2003; Gallo

et al., 1993; Roth et al., 1989; Tomlinson et al., 2012; Zhou et al., 2010) or urban and

water surface (Chen et al., 2006); (b) standard deviation (Zhang and Wang, 2008); and

Gaussian area or magnitude (Streutker, 2003, 2002; Tran et al., 2006). In the present

study, three different approaches centred around measures of central tendency (mean in

this case) and variability (minimum, maximum, and standard deviation) of the LST


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images were used to estimate the UHI-I. Figure 6.1 summarizes the methodology

followed in this chapter. The three UHI-I estimation methods used were;

a) Anomaly based approach –

Anomaly was computer at spatial seasonal (AST) scale and annual (AAT) scale. Seasonal

anomalies helped in quantifying UHI on monthly basis to study its seasonal variation.

∑−==

=j

ni

ijjijiST nLSTLSTA

1

Where AST is the seasonal temperature anomaly, ASTji is seasonal temperature anomaly

for ith pixel of any season j, LSTji is the land surface temperature for ith pixel of any

season j, nj is the total number of pixels in the image and j is the season. Thus, a spatial

thermal anomaly map for each season was generated to identify locations that tend to

possess more heat in surface with respect to other locations in the city.

Annual UHI-I was estimated based on annual thermal anomaly computed using

equation 9. A prerequisite for this was generation of an annual mean LST (MLST), which

was accomplished as per equation (10). Spatial thermal anomaly of surface temperature

in the city was computed. This helped in identification of locations that are relatively

more susceptible to UHI. The areas that have high MLST are indicative of experiencing

more heat throughout the year. A high annual thermal anomaly is evocative of tendency

of these areas to remain hot throughout the year and therefore, they appear to be UHI

susceptible spots of annual UHI or UHI in general.

∑−==

=nMLSTMLSTA

ni

iiiAT

1

Where AAT is the annual temperature anomaly, AATi is annual temperature anomaly for

any pixel i, MLSTi is the annual mean land surface temperature for any pixel i, n is the

total number of pixels in the image. MLSTi is computed as given below:

nLSTMLSTn

jiji ∑

=

=1


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Where MLSTi is the annual mean land surface temperature for any pixel i, j is any given

season from the total number of seasons under study.

These anomalies in LST were used to determine UHI intensity by categorizing these into

three classes; (i) Urban Cool Island or UCI with AST or AAT < 0, (ii) Urban Heat Island or

UHI with AST or AAT >0 and (ii) heat neutral where AST or AAT is equal to zero.

b) Standard Deviation (σ) based approach –

Another approach followed to map UHI intensity variation was to segment LST image

into 5 categories based on the standard deviation values. The categories were; Very Cold

Island (less than μ - 2σ), Cold Island (greater than μ - 2σ but less than μ - σ), Neutral

(between μ-σ to μ+σ), Hot Island (greater than μ+σ but less than μ+ 2σ) and Very Hot

Island (greater than μ+2σ). This method was attempted for both seasonal and annual

mean LST maps to identify UHI susceptible spots in the city.

c) Normalization based approach –

The third way employed to quantify UHI was to normalize LST images (equation 4) to

get UHI intensity maps.

−−

−=minmax

imaxLSTLSTLSTLSTUHII 1

Where; UHI-I is the intensity for UHI, LSTmax and LSTmin are maximum and minimum

LST values for the image, LSTi is the LST value of any pixel i.

6.3.2. Analysis

The LST for nine months was computed using Qin’s monowindow algorithm (discussed

in Chapter 5) which were used for studying seasonal and annual distribution of UHI in

the city. Analysis of LST was performed under three sub-headings of seasonal, annual

and diurnal analysis.

6.3.2.1. Seasonal analysis

The UHI-I images obtained using three methods, were employed to study seasonal

rhythms of spatial distribution and intensity patterns of UHI in Delhi. Image of January


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was used for winter season, March for spring season, April for summer season,

September for monsoon season, and October for post-monsoon season.

6.3.2.2. Annual analysis

Annual UHI estimation and analysis was performed using mean LST computed for all

the nine months. This investigation proved helpful when trying to identify areas that

tend to remain hot throughout various seasons and thus bear high vulnerability for UHI

impacts.

6.3.2.3. Diurnal analysis

For diurnal pattern analysis ASTER (for night) and Landsat Data (for day) were used. A

north-south transect for day and night times were compared to study diurnal variations

in UHI with respect to varying land use and land covers. The two datasets were used to

compute apparent thermal inertia which explains the diurnal LST variations in urban

and rural lands.

6.4. Results

LST mapping for the nine months is done to study thermal variations across the city for

different time periods of the year (Figure 6. 2). Of all the months, the winter months of

January, February and November displayed least variation in LST viz., 14º, 17º and 13º

C, respectively. Highest maximum temperature was observed for September and lowest

for January. Highest and lowest minimum temperature was again exhibited by

September and January, respectively.

Spatial patterns of UHI were studied using LST maps of the city. As shown in the figure,

high temperature area (UHI influenced) during winter months are located in and around

built-up areas of the city which are mainly located towards interior parts. But during

summer months of April to June, UHI pattern was skewed towards south-western parts

of the city. This is because peripheral northern, western and south-western parts of the

city are agricultural lands and during summer months, these tend to lay fallow. Such

fallow lands have an inclination to exhibit high surface temperatures.

Winter season (January, February and November) shows lowest values for all minimum,

maximum and mean LST (Figure 6. 2). This season receives lowest solar radiation which

explains the minimum observations for LST statistics. Maximum values are observed in

monsoon (September) and summer (April-June) seasons, when incoming solar radiation


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is very high. The highest LST values are observed during monsoon season. Similar

results have also been reported by Cui and Foy (2012) for Mexico city, where MODIS

data was used to study seasonal UHI and found that UHI for wet season is higher than

dry seasons. A detailed analysis of monsoon image reveals that very few pixels have very

high LST and these too are clustered in airport exposed area. In spring (March) and

post-monsoon (October), LST statistics were intermediary as these are also the

transition seasons.

The LST images were further processed to get UHI-I maps for all the months, as the

atmospheric conditions vary across the multiple dates. Thus, it is not appropriate to

directly compare the LST through multiple time periods.

6.4.1. Seasonal analysis

Out of nine months, one representative month was selected for each season, based on

meteorological information about the city. January, March and April represented winter,

spring and summer, while monsoon and post-monsoon were represented by September

and October months. Seasonal analysis explains the results of Anomaly, Standard

Deviation and Normalization based UHI-I mapping on seasonal basis.

6.4.1.1. Anomaly based approach

Maximum UHI-I is found to vary in order of summer > monsoon > spring > post-

monsoon > winter. Apart from the variation in intensity, UHI also varies with respect to

distribution in space throughout the city. It is dominant in the city centre during spring

and monsoon, while it shifts to south-west part in summers and post-monsoon

harvesting seasons. Largest UHI-I (=16.2°C) was recorded for summer season when

solar radiation is very high and most of the agricultural fields are fallow. Fallow land

tends to heat up easily due to its low thermal capacity. The winter season experienced

lowest UHI-I (=7.4°C) as this is the season when incoming solar radiation is low as well

the agricultural land is covered with crops and is rich in moisture. This results in

increased thermal capacity of the surface and thus heating gets slowed down.


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Figure 6. 2: LST for different months; (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) September, (h) October


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Figure 6. 2: (i) November

Cool island intensity was found to be strongest for post-monsoon (=-12.4ºC). This is

attributed to harvested agricultural lands where the fallow results in higher mean LST,

but the overall incoming solar radiation is not high and thus the heating up of other land

surface is less. The resulting difference between the mean and the minimum LST is

higher for post-monsoon period which shoots up the UCII for this season. UCII was

weakest for winter season (=-6.4ºC) due to less heating up of the surface as the surface

doesn’t receive much of the solar radiation. There is not much variation in overall

distribution of LST during winters, which minimizes both UHI and UCI intensities for

this season.

Winter season is represented by data for the month of January. Maximum UHI-I

observed for this season was around 7ºC and maximum UCII was found to be around

6ºC. The patches of urban heat island are randomly distributed in the entire area (Figure

6.3). However, a set of islands are linearly distributed along the north-south axis of the

city, starting from Bawana-Narela in north to Airport area in south. The north-west parts

of Delhi (encompassing Rohini, Nangloi, Mundka, Badli, and Jehangirpuri), Dwarka in

south-west, and Okhla in south-east were found to be the prime regions under the

influence of UHI in the winter month. Winter UCI was observed in agricultural areas of


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Bhaktawarpur, Auchandi, and Jaffarpur Kalan located towards the city peripheries that

were under Rabi crop.

March month image represented the spring season. UHI-I for spring season increased to

12ºC from 7ºC in winters. The areas that experienced maximum UHI during winter

continued to experience maximum UHI during spring season as well. The heat island

appeared to be spread out to other parts of the city as well. Notably in eastern Delhi, a

2ºC (approx.) winter UCI was replaced with 2-4ºC UHI during spring season (Figure

6.3). In contrast to winter UHI pattern, when scattered random patches of UHI were

seen across the city, spring exhibited more smooth and continuous UHI surface covering

built-up areas of the city.

UHI intensity reached its maximum (=16ºC) during summers. Summer UHI appears in

extreme south-west Delhi, the area that is majorly occupied by agricultural land and is

left fallow during summers (Figure 6.3). The exposed fallow land with low heat capacity

gets heated up easily. This heating up of agricultural fallows is further supported by the

high amount of incoming solar radiation during this season. Summer UHI-UCI maps

present a distinct pattern, where urban areas appeared to be cool islands while

agricultural (or rural) areas emerge as heat island sites. This is attributed to open fallow

land which exhibit lower thermal capability. Such UHI inversion was also observed in

Abu Dhabi by Lazzarini et al. (2013) during the summer months.

Monsoon exhibited the second highest UHI intensity (=13.8ºC), despite the fact that

monsoon season receives maximum rains. But spatially, high UHI-I values are restricted

to industrial and commercial regions across the city (Bawan-Narela in extreme north,

Mundka in west, Azadpur and Wazirpur industrial sites extending from north-west to

Old Delhi in centre, Narayana-Mayapuri from centre to Najafgarh in south-west, Okhla-

Badarpur in south) and airport area. The city otherwise remains cool or neutral in other

parts (Figure 6. 4). This is because industrial areas are concrete and asbestos dominated

surfaces do not retain moisture for long. During monsoons when most of city land has

abundant moisture, the industrial and airport impervious areas are still largely devoid of

moisture and such surfaces emerge as UHI areas with high intensities.

Post-monsoon season UHI pattern is similar to that of summer season as UHI

dominance is observed in periphery of the city (Figure 6. 4). But post-monsoonal UHI-I

is relatively smaller than that of summer. The city displays low UHI but highest UCI


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intensities. UCI-I (=6ºC) is observed around the central ridge and along the river. Due to

prevalence of fallow lands, UHI-I is high but when compared with summer season, the

incoming radiation is comparatively less. This brings UHI-I values down to 10.5°C.

Fallow lands drag the mean LST to higher side, but due to less heating of other land

surfaces, UCI tends to be high during post-monsoons. Similar results have also been

reported by Buyantuyev and Wu (2010) for October daytime data for Phoenix, Arizona

where city behaved as a cool island against the hot surrounding desert area.

Thus, as the city progresses from winters to summers both heat and cool island

intensities keep on increasing. Shifts in season from summers to winters via post-

monsoon causes heat island intensity to fall down with an exception of monsoon season.

Simultaneously, the cool island also becomes milder with change in season from winter

to summer.


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Figure 6.3: Anomaly maps for different seasons; (a) winter, (b) spring, (c) summer, (d) monsoon, and (e) post-monsoon seasons


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Figure 6. 4: UCI and UHI distribution during different seasons; (a) winter, (b) spring, (c) summer, (d) monsoon, and (e) post-monsoon seasons


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6.4.1.2. Standard Deviation approach for UHI-I estimation

The nine UHI-I maps were prepared to complement UHI pattern study and to quantify

UHI (Figure 6. 5). Very cold island class is least visible and is restricted only to water

bodies (i.e. along the Yamuna river). Hot island area is more or less scattered through

the built-up areas. Very hot island pixels show maximum variation in space through

different months. These tend to be concentrated towards interior regions of the city

during January, February, March and September months. But for May, June and

October, these are dominant in the agricultural areas due to abundance of harvested

fallow lands. April and November are two months when the very hot island class is well

scattered in built-up as well as agricultural areas.

The area for each of UHI-I class was quantified to study variation in different UHI-I

categories at different times of the year (Figure 6. 6). The figure shows that very cold

island category is least dominant with considerable area coverage during April, May and

June months. February and March are two months when cold island showed its highest

coverage percentage (~5% of total area). Neutral UHI-I category ranges from 86% in

October to 80% in April and September. Overall this class remained most dominant

UHI-I class. Hot island dominated the months of September and April the most, the two

months when neutral UHI-I reached its lowest. Very hot island category was highest in

coverage during harvesting months of May, June and October and lowest during

February, March and September. For each month, range for each category was; very cold

island- 0 to 0.5%, cold island- 0.8 to 4.6%, neutral- 80-85%, hot island- 8 to 16% and

very hot island- 0.5 to 6%.


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Figure 6. 5: SD based UHI-I; (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) September, (h) October, (i) November


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Figure 6. 5: (i) November

Figure 6. 6: Seasonal distribution of SD based UHI-I classes

6.4.1.3. Normalization based approach for UHI-I estimation

Winter season highest UHI (UHI-I of 0.8 to1) is found to be concentrated in southern

parts of city mainly in and around airport area, parts of peripheral city in south, south-

west (Dwarka and Najafgarh), western and north-western region including residential

areas of Mundka and Rohini. Along with these, commercial and industrial areas of

Narayana-Mayapuri, Anand Parbat, Udyog Nagar, Wazirpur, Rajasthani Udyog Nagar,


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Badli, Narela and Bawana also exhibited high UHI-I of order of 0.7 and above. Similar

results were observed by Dobrovolný (2013) in cold climatic conditions of Brno city in

Czech Republic. Though the UHI-I values were of order 6-7°C which was due to extreme

cold conditions in the city.

With the advent of spring, high UHI-I areas stay confined to some parts of the airport

and the industrial zones with values ranging from 0.5 to 0.8. As the city approached

summer season, the high UHI-I moves to the outer parts, which were covered with

agricultural fallows. The April UHI-I map (Figure 6. 7) shows that maximum UHI-I

values attained are 0.8 and stay restricted to external areas of Delhi including Bawana,

Narela in north, Jharoda Kalan, Jaffarpur Kalan, Auchandi, and Jhangola villages in

west and south-west.


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Figure 6. 7: Seasonal UHI-I for Normalization based approach; (a) winter, (b) spring, (c) summer, (d) monsoon, (e) post-monsoon


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With the arrival of monsoon, UHI-I moved back to central areas of Delhi ranging from

0.5 to 0.6, while touching upper range of 0.7-0.8 at some hotspot locations, such as

airport, commercial centres of Old Delhi and the industrial locations mentioned above.

The typical UHI profile, as often mentioned in literature was observed during monsoon

season. Post-monsoon witnessed the second reversal phase in UHI distribution with

peripheral fallow lands of village fields displaying highest UHI-I values that were above

0.7 and reached 1 at places and majority parts of the city experiencing UHI-I of above 0.5

as compared to other seasons when major portions of city were well under 0.4 UHI-I.

This observation is attributed to the leaf-off phase of vegetation during post-monsoonal

months.

6.4.2. Annual analysis

Major portions of the city experienced a mean LST of 35°C or greater except the land

under water, or vegetation such as cultivation, and forest. Water covered areas

illustrated LST of range 25°C to 30°C. Vegetation areas experienced surface hotness of

the measure of 30-35°C. Extremely bare or exposed lands of airport and various

industrial locations of the city demonstrated very high LST that exceeded 40°C.

The annual mean LST was analyzed by the three UHI-I estimation methods (Figure 6. 8).

Each method had its own advantage over the other. The anomaly and normalization

methods were more effective at qualitative analysis of UHI with anomaly method being

more proficient at UHI detection and normalization being good at identifying UCI. The

SD based method had the advantage of quantitative analysis of UHI.


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Figure 6. 8: Annual mean LST

6.4.2.1. Anomaly based approach for annual UHI-I estimation

The anomaly computation performed for the mean LST layer was found to be more

efficient in highlighting annual heat island spots in the city (Figure 6. 9). Based on

spatial distribution of anomaly values, ten locations with very high anomalies were

identified as UHI susceptible areas. Six of these are industrial areas, viz., Narela-

Bawana, Samaipur-Badli, Wazirpur, Zakhira-Anand Parbat, Mayapuri and Okhla. The

reason for these areas behaving as UHI will be explained in the later section. The vast

expanse of airport is another area that exhibits higher UHI-I values. Relatively huge

stretch of land, largely open or concrete and lack of vegetation canopy makes the airport

highly prone to UHI. The airport thus as vast surface exposed to radiation and being

concrete in nature, it gets heated up easily and contributes towards development of UHI.

Rest three locations were residential areas of Rohini, Najafgarh-Dwarka area and


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Mangolpuri and Nangloi colonies. Rohini and Najafgarh are areas where urban sprawl is

still in action with many new and rapid constructions coming up in the area. Vast

stretches of land are under-construction stage that is exposed lands, which are result in

excessive heating. Nangloi colony is a slum dominated area, most houses have asbestos

roofs. The colony is densely packed with almost nil vegetation cover. Due to these two

factors this area develops a tendency for UHI phenomenon.

Figure 6. 9: Annual Mean Anomaly (Locations: (1) Narela & Bawana, (2) Samaipur-Badli Industrial Area, (3) Rohini, (4) Wazirpur Industrial Area, (5) Zakhira & Anand Parbat, (6)

Mundka & Nangloi, (7) Mayapuri Industrial Area, (8) Najafgarh & Dwarka, (9) Airport, (10) Okhla Industrial Area)

Very high annual UHI intensities were observed over Samaipur-Badli industrial area

(anomaly ≈ 7ºC), Nangloi (anomaly ≈ 7ºC), Kondli and Kondli-extension (anomaly ≈

6.7ºC), and Airport (anomaly ≈ 6.5ºC). Relatively lower intensities were recorded for

Dwarka (anomaly ≈ 5.6ºC), Wazirpur (anomaly ≈ 5ºC), Jehangirpuri-Azadpur (anomaly

≈ 5ºC), Sadar Bazar and Chandni Chownk (anomaly ≈ 5ºC), Mayapuri (anomaly ≈

4.5ºC) and Rohini (anomaly ≈ 4.5ºC). Areas such as Narela-Bawana (anomaly ≈ 4ºC),


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Zakhira (anomaly ≈ 4ºC), Ghazipur (anomaly ≈ 3.8ºC), Najafgarh road industrial area

(anomaly ≈ 3.5ºC) and Paharganj (anomaly ≈ 3ºC) display lowest intensity of UHI.

Figure 6. 10: SD based annual UHI-I estimation

6.4.2.2. Standard Deviation approach for annual UHI-I estimation

SD based method helped quantifying the UHI into different UHI classes (Figure 6. 10).

Very hot island class of UHI majorly comprised of exposed area (33%), sparse built-up

(24%) and dense built-up (23%). This suggests the dominance of high temperatures in

built-up and exposed lands throughout the year. Similarly hot island areas were

predominantly covered by exposed area (23%), sparse built-up (19%) and dense built-up

(11%). Hot island category was surprisingly also dominated by agricultural classes of rabi

(10%) and kharif (15%) crops and by scrub (15%) land use. Rabi and kharif crop lands

and scrub vegetation covered areas tend to remain fallow during extremely hot and dry

months of May and June, when solar insolation is extremely high. This tends to drag up

the mean LST for these lands, which contributes to hot island category. Double crop


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lands formed major proportion of cold (42%) and very cold (34%) islands. Forest is

another class that mainly contributed to only cold (12%) and very cold (10%) categories.

Two other classes that contributed to very cold island formation were water (17%) and

plantation (14%).

Considering heat island wise composition of different land use classes, it was found that

all the classes except water were nearly half or more neutral. Therefore the analysis was

done based on next most abundant category for each land use. Double crop was majorly

considered as the cold island class (45%). Rabi crop was well distributed in both cold

(17%) and hot (11%) island categories. Kharif crop was majorly hot island area (21%).

Rabi crop land remains fallow in hot dry summer months bringing their annual mean

higher but they are cropped during winter months which tend to drag the mean on the

lower side. In contrast to this, kharif cropped lands are left fallow during extreme

summers and are harvested by winters. Thus, they tend to receive more heat both during

summer and winter fallow months, which raise their mean annual LST. Therefore, rabi

crop are composed more of cold island while kharif crop had more of hot island

percentage. 28% of zaid crop and 17% of plantation lands fall under cold island category.

The only category that majorly comprised of very cold island class was water with 87% of

its cover falling under very cold category.

6.4.2.3. Normalization based approach for annual UHI-I estimation

Normalization method for UHI-I estimation seems to be more effective in studying

overall UHI distribution with respect to its intensity. This is more suitable for

identification of cold island areas in the city. The normalization based approach

identified heat islands similar to that form anomaly approach. Most of the heat island

sites identified were found to be in industrial zones which mainly comprise of high

density built-up and are mostly devoid of green cover. Concrete structures are

abundant in the industrial environment where they perform a variety of functions. Due

to their thermal properties and low emissivity, these structures absorb more radiations

and emit less. In addition to this, the exposed vegetation-devoid lands of industrial set-

up lack moisture and contribute to the warming effect. Dominance of asbestos roofs in

such areas further increases susceptibility towards UHI. As a cumulative effect of all

these factors, heat gets accumulated in the area gradually converting them into heat

islands. Other locations were either areas with high commercial activities such as

Paharganj and Chandni Chowk or residential lands with dense built-up that exhibit poor


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thermal insolations. The cool islands were seen over the agricultural lands of Jharoda

Kalan, Jaffarpur Kalan, Auchandi, and Jhangola villages and forests of central ridge,

Cantonment area and Asola Bhati Wildlife Sanctuary areas demonstrating the fact that

vegetated areas tend to be cooler than surrounding built-up lands. As these have more

shade and moisture, such land surfaces absorb lesser heat and also emit more due to

evaporation. The normalization method helped identify the urban cold island (UCI)

spots which could not be visualised by anomaly approach. The cold island spots were

confined to extreme north, areas along the river and other water bodies and to the forest

patches in the city. The names of these locations are given with description of Figure 6.

11. Four out of six cold island locations were agricultural fields and other two were forest

patches present inside the city.

Figure 6. 11:Annual Normalization based UHI-I; Locations: (1) to (10) are same as that of Anomaly method (Figure); (A) Agricultural fields of Lampur, Ghoga and Hareoli villages, (B) Agricultural fields of Alipur, (C) Usmanpur agricultural land, (D) Agricultural patch of Mundka Extension, (E) Central ridge, (F) Hauz Khas forest patch.


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6.4.3. Diurnal analysis

UHI patterns were found to vary diurnally (Figure 6. 12). The typical UHI profile is

visible at night with high LST in city centres in contrast to low rural surface temperatures

(difference ≈5ºC). Urban areas remain hot (≈4ºC), while rural areas (fallow lands) with

very high day time LST become cold at night (≈10ºC). Variable thermal properties of

urban and rural land uses contribute to variable patterns of day and night LST.

The fallow lands are exposed dry soil areas. These tend to heat up more due to low

thermal capacity of fallow land. Since the area is characterized by sandy soils, which have

very low thermal capacity and such areas tends to appear very hot during day times. But

at night, when there is no source of heat, these lands tend to loose away their heat owing

to low thermal inertia. Thus, rural area in the city which appears hottest during day time

cools down low at night time. In contrast to this, urban areas (at the centre on each side

of the river), are hot during day time due to solar heating, but at night these do not allow

heat to escape on account of their high thermal inertia.

Figure 6. 12: Day and night LST distribution

For comparing the two LST variations in the two time periods, standard deviation based

method was applied for UHI-I estimation for day and night. Other two methods

(anomaly based and normalization based) efficiently illustrate the spatial distribution of


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UHI and identify the locations under UHI effect. These methods also could be

expeditiously employed for estimating the strength of UHI. Standard deviation method

has the added advantage of being capable of quantifying the area under influence of UHI

in addition to estimating its intensiveness. Thus, for quantitative comparison of the day

and night LST images, standard deviation method was followed.

Figure 6. 13: (a) SD based UHI-I distribution according to land use during day

Figure 6. 13: (b) SD based UHI-I distribution according to land use during night


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Daytime UHI-I distribution with respect to land use and land cover patterns was

analyzed (Figure 6. 13). Major portion of all land use classes fall under normal category

except long fallow as 40% of this land use forms very hot heat island. Ignoring normal

category for each class, it was observed that next dominant group for agriculture, forest,

plantation, scrub and water is cold; for exposed land, fallow and sparse built-up is hot

heat island. At night the most dominant was the hot category that majorly composed of

dense built-up.

Figure 6. 14 evidently illustrates that areas dominated by bare open lands such as

exposed area, fallow, long fallow and sparse built-up lands strongly exhibited very hot

heat island effect. 54% of very hot island category was constituted by long fallow; rest

20%, 11% and 9% was contributed by fallow, sparse built-up and exposed area. Hot

category of UHI comprised of 23% as agriculture, 20% as sparse built-up, 19% and 18%

as long fallow and fallow lands. Contrary to this, cold category has 25% as forest, 19% as

agriculture, and 18% as plantation. Water formed more than half (53%) of very cold

category indicating that water acts as cold island during day. Other relatively smaller

contributions to this group were made by forest (15%), and plantation (10%).

Very Cold Cold Hot Very Hot

Day

Night

Figure 6. 14: Contribution of LULC categories to diurnal and nocturnal UHI-I

At night, a tremendous change in contribution of differential land use to different UHI

categories is observed. At night the very hot island category of which 82% was

constituted of dense built-up this during day formed only 1% of this category. Similarly


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hot island was 61% dense built-up and 20% sparse built-up during night. Other

interesting classes are fallow and long fallow; as these were mainly hot and very hot

during day while at night they formed 18% and 50% of colder categories. Thus, open

lands that were pervious such as fallow and long fallow were more cooling than

anthropogenic impervious lands (built-up areas). Forest formed 4% and 7% of very cold

and cold categories. Natural vegetation (forest and scrub) had more cooling effect at

night as compared to anthropogenic vegetation (plantation). This is supported by the

fact that forest during night was 16% cold island, scrub was 18% in contrast to plantation

that was 20% hot island.

The stark contrast in day and night LST for urban and rural parts is illustrated in Figure

6. 15. The figure shows north-south transect values of LST for day and night. During day,

highest LST was observed for open area and lowest for water. Built-up LST was higher

than agriculture but lower than open areas. Analyzing night time LST for same transect

demonstrates a typical UHI profile, with cliff and plateau areas. Overall LST for all the

land use classes fell up to 10°C during night time. However, a comparative analysis of

agriculture (rural) and built-up (urban) shows a huge difference in LST as high as 5°C.

Agriculture and open area in peripheral parts represented the cliff, while built-up formed

the plateau parts of UHI profile. Similar contrast in day and night time UHI pattern was

observed for Madrid (Spain) by Sobrino et al. (2013) where typical UHI pattern was

observed during night, while during day the city behaved as negative heat island.

Figure 6. 15: Day and night transect for LST from north to south


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The reason for differential thermal behavior of land use and land cover classes during

day and night could be explained by an important property named ‘Thermal Inertia’.

Thermal inertia is the tendency of a material to resist change in temperature. However,

accurate estimation of thermal inertia is not possible with remote sensing, thus an

approximation of thermal inertia is often calculated. This approximate impedance to

temperature change by the material is termed as its Apparent Thermal Inertia (ATI = (1-

A)/∆T). A is the albedo or reflectance during daytime, and ΔT is the day-night LST

difference. Inputs from ATI, helped in understanding the homogeneity and

heterogeneity of diurnal LST variations over the city. The higher the ATI more will be the

material’s tendency to resist change in temperature. Water for instance is cold during

day time, due to its low thermal conductivity, it absorbs slowly. Conversely at night,

water appears to be hotter due to its high ATI (Figure 6. 16).

Figure 6. 16: Apparent Thermal Inertia (ATI) map of Delhi (October 2002)


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Other class with relatively higher ATI is built-up category (both sparse and dense)

predominantly covering central parts of the city. Though both water and built-up lands

have high ATI, but water is still not a causal agent for UHI, while built-up is. This is due

to differential heating of water and built-up during day. Daytime differential heating of

water and built-up is explained by their emissivity values. Due to high emissivity values,

water tends to reradiate the maximum energy that it has absorbed while contrary to this

built-up lands with low emissivity tend to store more heat and become hotter during

daytime. During day time hours, built-up lands tend to absorb more heat while water

tends to remain cool during day absorbing very little heat. With high heat absorption and

high ATI, the cumulative effect of these two makes built-up lands the hotspot of UHI

effect. Water on the contrary has high ATI but since it absorbs less, it is comparatively

cold at night times and does not cause UHI. Fallow lands were found to have high

daytime LST but still appear cold at night. This is attributed to their low ATI. Due to low

ATI, fallow lands easily loose the heat that they had absorbed during day and appear

much cooler than built-up areas.

6.5. Conclusion

UHI is majorly and strongly manifested mainly in predominantly urban/built-up areas

of the city. These areas due to thermal properties of surface materials tend to remain hot

even during night times. This indicates that UHI are more prominent and visible at night

time.

Comparison of UHI across space and/or time, calls for an efficient measurement of the

phenomenon through UHI Intensity (UHI-I) estimation. This study proposed three

methods for UHI-I estimation. Each method has its own advantage and disadvantage

over the other (Table 6. 2). The discretion on use of any one method depends on the

objective of the study. For mere mapping and identification of heat island locations in

the city, the anomaly based approach was found to be better than SD and normalization

based approaches. The method also has the advantage of comparative investigation of

the intensiveness of UHI at two or more heat island locations within the city. But this

method is not good enough when comparing the location from two different cities. For

such a case, SD based or normalization method would be better. SD method is efficient

for quantitative analysis of UHI between two cities. This method is even good at relating

UHI effect with LULC patterns. Normalization based approach is best for identifying the


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cool island or to spot the locations that offer mitigation solution to the city’s UHI

phenomenon. The method being based on normalization technique can be used for

across city or across time comparison. But the absolute values in terms of temperature

cannot be assessed which are still possible in anomaly based method. With UHI-I

measurement it could be concluded that UHI is not only a phenomenon of urban areas

being hot, but is collective manifestation of hot urban/built-up areas surrounded or

interspersed by cool rural/vegetation dominant areas. Given this conclusion, the various

techniques suggested for UHI-I estimation need to be explored further, in particular

Anomaly based approach where the reference locations are not static that may result in

difficulties for inter-season comparison of UHI.

Table 6. 2: Summary of different methods employed for UHI analysis at different spatio-temporal scales (seasonal, annual and diurnal)

Anomaly based SD based Normalization

based

Method µ−x

σ×±µ x

−−

−MinMax

xMax1

Statistical parameters involved

Central tendency (Mean)

Central tendency and variability

Variability (Maximum and Minimum)

Classes

Urban Cool Island or UCI with AST or AAT < 0

Urban Heat Island or UHI with AST or AAT >0

Heat Neutral where AST or AAT is equal to zero

Very Cold Island (less than μ - 2σ)

Cold Island (greater than μ - 2σ but less than μ - σ)

Neutral (between μ-σ to μ+σ)

Hot Island (greater than μ+σ but less than μ+ 2σ)

Very Hot Island (greater than μ+2σ).

No thematic analysis, but continuous data

Advantages

Inter-city analysis

Mapping and identification of UHI locations within the city

Absolute values of UHI-I available

Intra-city analysis

Quantitative analysis of UHI between two cities.

Good for relating UHI with LULC

Inter-city and across time analysis

Identifying cool islands, locations that offer mitigation solution to the city’s UHI phenomenon

Disadvantages

Not good for intra-city comparison; Moving reference locations for UHI-I estimation

Absolute UHI-I values not available

Absolute UHI-I values not available


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It is evident from the study that UHI is not a static phenomenon but keeps displaying its

dynamicity through temporal variations. The scale for these temporal variations could be

a large one such as seasonal or monthly scale or could be as low as diurnal (day and

night) range. UHI extensively varies in spatial domain during different seasons. It

exhibits seasonality moving from outskirts (long fallow agricultural lands) in summer

(May-June) to central parts of the city during monsoons (July-September). UHI then

moves back to city peripheries in next fallow season of post-monsoon (October) only to

come back to city centers during winter (December-February) and spring (March)

seasons.

An annual analysis of UHI was also attempted using annual mean LST layer. The

locations within the city were identified that have a higher tendency to remain under

UHI. Some of these locations were Samaipur-Badli industrial area, Nangloi, Kondli and

Kondli-extension, Airport, Dwarka, Wazirpur, Jehangirpuri-Azadpur, Sadar Bazar,

Chandni Chownk, and Mayapuri to name but a few.

Variability in LST distribution and UHI intensity was also observed during day and

night. This differential thermal response of various land uses during day and night is

found to be a function of a number of factors such as emissivity, thermal conductivity,

thermal capacity and ATI. Thus, there is no direct relationship between LST or its

diurnal variation with any of these factors. In general, built-up areas tend to heat up

during day and store that heat for longer making these areas an island of heat.


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seasonal, annual and diurnal distributions of uhi in...

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