Delineation & Comparison of Urban Heat Islands (UHI) in Tamilnadu - PPT

V.Dhivya Lakshmi (BTG-12-006)

P.R.Selva Akshaya (BTG-12-029)

S.Vignesh (BTG-12-037)

D.Vishnu Ram (BTG-12-040)

PROJECT GUIDE: Dr. Balaji Kannan, Asst.Prof (RS & GIS)

DELINEATION AND COMPARISON OF

URBAN HEAT ISLANDS IN

TAMILNADU

DELINEATION AND COMPARISON OF

URBAN HEAT ISLANDS IN TAMILNADU

• Introduction – Urban Heat Islands & It’s Effects

• Project Objective

• Project Description

• Selection of a relevant & suitable satellite and timeline

• Project Process Chart

• Data Acquisition

• Methodology

• Results & Discussion

• Conclusion and Recommendations.

INTRODUCTION

URBAN HEAT ISLANDS & IT’S EFFECTS

• An urban heat island is a city or metropolitan area that

is significantly warmer than its surrounded rural areas

due to human activities.

• Whereas, the term heat island refers to any area,

populated or not, which is consistently hotter than the

surrounding area.

INCREASED TEMPERATURE IN URBAN AREA

SOURCE - OKE, T.R. 1997. URBAN CLIMATES AND GLOBAL ENVIRONMENTAL CHANGE

SOURCE - OKE, T.R. 1997. URBAN CLIMATES AND GLOBAL ENVIRONMENTAL CHANGE

PROJECT OBJECTIVE

• Conversion of thermal band data of LANDSAT 5 & 8 (Satellites)

into Temperature contours in order to isolate and compare the

Urban Heat Islands (UHI) of Tamil Nadu over a decade (i.e.,

2005 & 2015) and over the different seasons of an year (2014

• Identification of the factors responsible for UHI formation with

reference to Land use

• Intensity of the UHI formed

• Suggestion of Mitigation Measures

PROJECT DESCRIPTION

• The thermal band data for Tamil Nadu covered by 11 tiles

constituting the paths 142,143,144 and rows 51,52,53,54 are

obtained from USGS (United States Geological Survey) website.

These 11 tiles are to be integrated into a single image by mosaicing

(Mosaicking) it using ArcGIS 10.1. These data acquired from satellite

Landsat 5&8 stores the reflectance value as Digital Number (DN) in

each pixel of the image. The DN images are to be converted into Top

of Atmosphere radiance, which in turn is converted into Land Surface

Temperature. Temperature contours are drawn for the obtained Land

surface temperatures, the contours are classified for isolation of UHI.

• Selection of a relevant

and suitable satellite

and Timeline for

delineation of UHI

Satellite Landsat 1 Landsat 2 Landsat 3 Landsat 4 Landsat 5 Landsat 6 Landsat 7 Landsat 8

Duration

2 years,

11 months

and 15 days

2 years,

10 month

s and

17 days

5 years

and

26 days

11 years,

4 months

and

28 days

29 years,

3 months

and

4 days 0 days

16 years,

1 month and

4 days

2 years,

3 months and

8 days

2015 jan landsat 6 Landsat 7 Landsat 8

2014 failed to reach orbit Landsat 7 Landsat 8

2013 5-Jun failed to reach orbit Landsat 7 11-Feb->13-Apr

2012 Landsat 5 failed to reach orbit Landsat 7













1999 Landsat 5 failed to reach orbit 15-Apr-> 12-Aug

1998 Landsat 5 failed to reach orbit




1994 Landsat 5 landsat 6

1993 14-Dec Landsat 5 5-Oct

1992 Landsat 4 Landsat 5








1984 Landsat 4 1-Mar

1983 31-Mar Landsat 4

1982 25-Feb Landsat 3 16-Jul




1978 6-Jan Landsat 2 5-Mar



1975 Landsat 1 22-Jan

1974 Landsat 1

1973 Landsat 1

1972 23-Jul

Path no.

Row no. 51 52 53 54 51 52 53 54 51 52 53 54

SEP 7 5 5 5 5

SEP 14 5 5 5

SEP 16 5 5 5 5

JAN 19 8 8 8

JAN 26 8 8 8

JAN 28 8 8 8 8

APR 16 8 8 8

APR 18 8 8 8 8

APR 25 8 8 8 8

JUL 21 8 8 8

JUL 23 8 8 8 8

JUL 30 8 8 8 8

OCT 2 8 8 8 8

OCT 9 8 8 8 8

OCT 27 8 8 8 8

JAN 22 8 8 8 8

JAN 29 8 8 8

JAN 31 8 8 8 8

144 143 142

2014

2015

2005

SET Satellite Month Date Year

1

2

3

4

5

6

LAN

DSA

T 5

LAN

DSA

T 8

Data Selection

PROJECT PROCESS CHART

DATA AQUISITION

• The thermal band data for Tamil Nadu covered by 11 tiles constituting the paths 142,143,144 and rows 51,52,53,54 are obtained from USGS (United States Geological Survey) website

MOSAICING

• These 11 tiles were integrated into a single image by mosaicing (Mosaicking) it using ArcGIS 10.1

DN to TAR• The Digital Number images were converted into Top of Atmosphere Radiance

TAR TO SURFACE

TEMP

• TAR was converted into Land Surface Temperature

CONTOUR MAKING

• Temperature contours were drawn for the obtained Land surface Temperature values

UHI ISOLATION

• UHIs were isolated using the contours

CAUSES & REMEDIATI

ON

• The causes for the formation of UHIs were identified

DATA ACQUISITION

DATA COLLECTION – FROM WEBSITES

USGS – EARTH EXPLORER

TAMIL NADU

TILES TO BE CONCENTRATED FOR TAMIL NADU

3 PATHS & 4 ROWS

(I.E.) 4 ROWS , 3 COLUMNS

FILE NAME DESCRIPTION

• LE71430522000325SGS01

• LE7 143 052 2000 325 SGS01

• The file name , most significant feature gives information like, the image was

taken from LANDSAT 7 satellite, it belongs to the row 143 & path 52 and the

date of acquisition was 20th November 2000 and the ground station is

SGS01, located near china.

LANDSAT 7 ‘S

SATTELLITE

DATA

PATH

NUMBERROW

NUMBER

SATTELLITE

IMAGE

ACQUISITION

DATE

DAY OF

THE

YEAR

GROUND

STATION

METHODOLOGY

MOSAICING

• These 11 tiles are to be integrated into a single image by mosaicing (Mosaicking) it using ArcGIS 10.1

DN to TAR• The Digital Number images are to be converted into Top of Atmosphere radiance

TAR TO SURFACE

TEMP

• TAR is converted into Land Surface Temperature in Celsius and kelvin

CONTOUR MAKING

• Temperature contours are to be drawn for the obtained Land surface Temperature values

MOSAICED DATASET

LANDSAT 8-B10 JULY 2014LANDSAT 5-SEP 2005

LANDSAT 8-B11 JULY 2014

Similarly, We mosaicked datasets of :

LANDSAT 8-BAND10 & BAND11 JAN 2014

LANDSAT 8-BAND10 & BAND11 APR 2014

LANDSAT 8-BAND10 & BAND11 OCT 2014

LANDSAT 8-BAND10 & BAND11 JAN 2015

DIGITAL NUMBER TO TOP ATMOSPHERIC RADIANCE

• Digital Numbers ranging from 0-255 (in case of landsat-5) Or 0-

65535 (in case of landsat-8) are converted into Absolute

Radiance(Top of Atmosphere Radiance) (W/m2)

i. Gain & Bias Method

ii. Spectral Radiance Scaling Method

DIGITAL NUMBER

The formulae to convert DN to radiance

1. Gain & Bias Method

• CV R1 =gain * DN + bias

2. Spectral Radiance Scaling Method

• CVRI =((LMAXλ -LMIN λ)/(QCALMAX-QCALMIN))*(QCAL-QCALMIN)+LMIN λ

• We found Second method more accurate than the first one, thus we followed thesecond one,

CVR1 is the cell value as radiance

QCAL = digital number

LMINλ = spectral radiance scales to QCALMIN

LMAXλ = spectral radiance scales to QCALMAX

QCALMIN = the minimum quantized calibrated pixel value

QCALMAX = the maximum quantized calibrated pixel value

TOP ATMOSPHERIC RADIANCE IS CONVERTED

INTO LAND SURFACE TEMPERATURE

T=K2

ln K1 ∗εCVRI

+1

• T Temperature in degree Kelvin

• CVR1 is cell value as radiance

• ε is Emissivity (taking atmospheric emissivity as 0.95)

• K1 & K2 Values are taken from meta data file(USGS)

Kelvin Image will be converted into Celsius image (K-273),οC

TEMPERATURE CONTOURS

• To understand atmospheric circulations, we must be able to

understand how variables (temperature, pressure, winds, humidity,

clouds, salinity) are changing in time and how they are changing

with respect to one another.

• A contour line of a function of two variables is a curve along which

the function has a constant value. A contour map is

a map illustrated with contour lines.

• The contour interval of a contour map is the difference in

elevation between successive contour lines.

• We took 10˚C interval contour for the study.

Mosaic Radiation kelvin Celcius Positive Contour

SEPTEMBER 2005 DATA

MOSAIC RADIATION KELVIN CELCIUS POSITIVE CONTOUR

OCTOBER 2014 DATA ( BAND10)

SIMILARLY, WE CONTOURED DATASETS OF

• LANDSAT 8-BAND10 & BAND11 JAN 2014

• LANDSAT 8-BAND10 & BAND11 JUL 2014

• LANDSAT 8-BAND10 & BAND11 APR 2014

• LANDSAT 8-BAND10 & BAND11 OCT 2014

• LANDSAT 8-BAND10 & BAND11 JAN 2015

2014 JAN

2014 APR

2014 JULY

2015 JAN

RESULTS & FINDINGS

COMPUTATION OF ZONAL

STATISTICS-Jan 2015

District Name Population Max. Temp (˚C)

Chennai 4,343,645.00 45.04

Coimbatore 4,271,856.00 31.74

Cuddalore 2,285,395.00 38.44

Dharmapuri 2,856,300.00 56.90

Dindigul 1,923,014.00 33.74

Erode 2,581,500.00 35.74

Kancheepuram 2,877,468.00 38.85

Kanniyakumari 1,676,034.00 30.35

Madurai 2,578,201.00 35.31

Nagapattinam 1,488,839.00 34.30

Namakkal 1,493,462.00 30.90

Perambalur 493,646.00 34.30

Pudukkottai 1,459,601.00 40.87

Ramanathapuram 1,187,604.00 40.07

Salem 3,016,346.00 31.74

Sivaganga 1,155,356.00 36.80

Thanjavur 2,216,138.00 34.72

The Nilgiris 762,141.00 20.50

Theni 1,093,950.00 31.74

Thiruvallur 2,754,756.00 38.85

Thiruvarur 1,169,474.00 32.61

Thoothukkudi 1,572,273.00 26.79

Tiruchirappalli 2,418,366.00 35.12

Tirunelveli 2,723,988.00 31.74

Tiruvannamalai 2,186,125.00

Vellore 3,477,317.00 38.44

Viluppuram 2,960,373.00 34.72

Virudhunagar 1,751,301.00 38.44

Population Data obtained from - Directorate of census Operation Tamilnadu

Population and

Temperature Data

(2005)

Districts of Kaur, Ariyalur is

not included due to non

availability of data

TEMPERATURE & POPULATION OF 2005

0.00

0.50

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henn

ai

Coi

mba

tore

Cud

dalo

re

Dha

rmap

uri

Din

digu

l

Ero

de

Kan

chee

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m

Kan

niya

kum

ari

Mad

urai

Nag

apat

tinam

Nam

akka

l

Per

amba

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Pud

ukko

ttai

Ram

anat

hapu

ram

Sal

em

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agan

ga

Tha

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ur

The

Nilg

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The

ni

Thi

ruva

llur

Thi

ruva

rur

Tho

othu

kkud

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Tiru

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ppal

li

Tiru

nelv

eli

Tiru

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Tiru

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ai

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MIL

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AT

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E

DISTRICTS

2005 DATA

Demographic Map Maximum Temp Map

Population and

Temperature Data

(2015)

Districts of Kaur, Ariyalur is

not included due to non

availability of data

District Name Population Max. Temp (˚C)

Chennai 4,681,087.00 36.35

Coimbatore 3,472,578.00 40.64

Cuddalore 2,600,880.00 38.22

Dharmapuri 3386631.00 40.37

Dindigul 2,161,367.00 41.27

Erode 2,259,608.00 39.40

Kancheepuram 3,990,897.00 42.08

Kanniyakumari 1,863,174.00 40.72

Madurai 3,041,038.00 43.24

Nagapattinam 1,614,069.00 32.42

Namakkal 1,721,179.00 43.80

Perambalur 564,511.00 38.68

Pudukkottai 1,618,725.00 37.14

Ramanathapuram 1,337,560.00 38.42

Salem 3,480,008.00 45.81

Sivaganga 1,341,250.00 38.63

Thanjavur 2,402,781.00 37.29

The Nilgiris 735,071.00 39.97

Theni 1,243,684.00 41.64

Thiruvallur 3,725,697.00 39.11

Thiruvarur 1,268,094.00 31.29

Thoothukkudi 1,738,376.00 39.70

Tiruchirappalli 2,713,858.00 53.93

Tirunelveli 3,072,880.00 42.04

Tiruppur 2,471,222.00 40.28

Tiruvannamalai 2,468,965.00 39.11

Vellore 3,928,106.00 39.53

Viluppuram 3,463,284.00 38.96

Virudhunagar 1,943,309.00 43.02

Population Data obtained from - Directorate of census Operation Tamilnadu

2015 DATA

Demographic Map Max. Temp Map

TEMPERATURE & POPULATION OF 2015

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60.00C

henn

ai

Coi

mba

tore

Cud

dalo

re

Dha

rmap

uri

Din

digu

l

Ero

de

Kan

chee

pura

m

Kan

niya

kum

ari

Mad

urai

Nag

apat

tinam

Nam

akka

l

Per

amba

lur

Pud

ukko

ttai

Ram

anat

hapu

ram

Sal

em

Siv

agan

ga

Tha

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ur

The

Nilg

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The

ni

Thi

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Thi

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Tho

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PU

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MIL

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MP

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E

DISTRICTS

Temperature Population

RESULTS AND DISCUSSION:

• The correlations are very less, which are indicative that the Population rise influences the

temperature at a very lesser rate.

• There may be two Reasons for this Result

1) The data obtained from the satellite may contain little variations

2) The influence of other factors like Land use/Land cover , Reduced spacing of

buildings etc.., could have dominated over the population rise factor.

• The factor of Landuse and Landcover is to be used as a factor for further studies.

ZONAL STATISTICS TABLE-LANDUSE

Contd.,

• Similarly , the Zonal Statistics was computed using the Land use for

2005 September

2014 January

2014 April

2014 July

2014 October , and

2015 January.

AVERAGE TEMPERATURE DATA OF TAMIL

NADU BASED ON LAND USE

Year 2005 2014 JAN 2014 APR 2014 JULY 2014 OCT 2015

Agricultural Land 21.47228 20.00418 30.439847 16.72242 20.52887 23.13809

Waterbodies 22.29618 19.33708 30.004355 16.32722 16.57789 26.12562

Forest 16.35451 18.91619 27.22696091 14.7095 18.34383 20.90721

Wastelands 23.86727 19.04837 29.14875273 17.42403 21.86814 29.09692

Built Up (Urban) 25.92222 18.84035 32.731394 12.40358 16.6293 23.03187

Built Up - Mining /

Industrial area 23.6232 22.02785 31.03650667 11.68106 15.14985 20.88321

Wetlands 25.38803 20.08572 33.6979825 11.64225 12.64309 23.83304

0

5

10

15

20

25

30

Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining /Industrial area

Wetlands

TE

MP

ER

AT

UR

E(°

c)

LANDUSE

2005 -AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

INFERENCE:

• In 2005 September, on comparing the average temperatures of various land use parameters, it was found that the forest region, agricultural land and water bodies has significantly lower temperatures compared to the urban lands resulting from mining, industrial processes and built-up areas.

• Thus, built-up urban and industrial/ mining areas contributes to higher temperature, which contributes to the formation of urban heat islands.

17

18

19

20

21

22

23


Wetlands

TE

MP

ER

AT

UR

E(˚

C)

LANDUSE

2014 JAN- AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

INFERENCE:

• It was observed that there is an increased temperature of 20°C in agricultural areas during January

2014 due to the prevalence of dry season and subsequently the agricultural activities are reduced and

land gets heated .

• It is clear that temperature difference & UHI formation can be easily found in winter season.

0

5

10

15

20

25

30

35

40


Wetlands

TE

MP

ER

AT

UR

E(°

c)

LANDUSE

2014 APR-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

INFERENCE:

• Due to April being summer season, there was an overall temperature increase all over Tamil Nadu during 2014 .Increase in temperature of Agricultural land about 30°C and the water bodies and forest experience significantly lower temperature.

• The wastelands has much higher temperature because of the land being barren and the lack of vegetation resulted in acquiring more temperature.

• The built-up urban and industrial/mining areas gets heated and experience highest temperature of about 35°C and it contributes to the formation of urban heat island.

0

2

4

6

8

10

12

14

16

18

20


Wetlands

TE

MP

ER

AT

UR

E(°

C)

Land use

2014 JULY-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

2014 JULY

• The presence of

maximum cloud cover in

the path 144 and rows

051,052,053 during July

2014 has lead to the

deviation of results of the

average temperature

analysis from normal.

• Hence, the land use in

these areas could not

have contributed to the

average temperature.

INFERENCE:

• Due to Rainy Season Built-up Areas, wetlands, forest areas, agricultural lands, holds more

water and became cooler than wasteland which leaches out water in a faster rate.

0

5

10

15

20

25


Wetlands

TE

MP

ER

AT

UR

E(°

c)

Land use

2014 OCT-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

0

5

10

15

20

25

30

35


Wetlands

TE

MP

ER

AT

UR

E(°

C)

LAND USE

2015-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

2015 JAN

• As it has accounted for

large amount of clouds in

path 144 due to it being

winter during January

2015 which has lead to

the deviation of results of

the average temperature

analysis from normal.

• Hence, the land use in

these areas could not

have contributed to the

average temperature.

RESULTS AND DISCUSSION:

• Thus on the whole, it was observed that the Urban area contributed to the

maximum temperature during 2005 , January 2014 and April 2014, though

the wasteland showed significantly higher temperature during July 2014 and

January 2015 due to insufficient availability of data because of cloud cover.

• It is indicative from the average temperature graphs that Urban areas (both

built-up and mining) has lead to the formation of UHI in Tamil Nadu.

EFFECT OF LANDUSE POLYGON SIZE

CORRELATION BETWEEN TEMPERATURE AND LANDUSE

2005-2015

Year 2005 2014jan 2014apr 2014july 2014oct 2015 Average

Agricultural Land -0.10594 -0.34349 -0.17328 -0.2526 -0.15003 -0.03939 -0.17745

Waterbodies -0.18038 -0.21142 -0.14368 -0.17549 -0.06275 -0.01951 -0.1322

Forest -0.58463 -0.34976 -0.60076 -0.01855 -0.22513 0.137597 -0.27354

Wastelands -0.21992 0.188311 0.030989 -0.09467 -0.07126 -0.7184 -0.14749

Built Up (Urban) 0.25673 0.173409 0.210887 0.5770120.204108 0.207302 0.271575

Built Up - Mining / Industrial area 0.30283 0.223932 0.107899 0.9205120.648294 0.663338 0.477805

Wetlands -0.13655 -0.25723 -0.34259 0.0011560.084292 -0.98511 -0.27267

INFERENCE:

• The land area of the Land use Polygon positively correlated

with surface temperature for commercial, built up areas

(r=0.27) and for mining and industrial areas(r=0.48), as the area

of these polygons increases ,the temperature also tends to

increase

• On the other hand, Agricultural land(r=-0.18), Waterbodies(r=-

0.13), Forest areas(r=-0.27),wetlands(r=-0.27) showed a

statistically significant negative relationship. As the area of the

waterbody or agricultural land increases the temperature

decreases.

UHI FORMATIONS

Chennai Kodungayur Landfill in 2005,

having temperatures less than 30oC

Chennai Kodungayur Landfill in

2015, having 30oC contours

UHI FORMATIONS

UHI FORMATIONS

Water Bodies that

escape from contour

Heat Absorbing Plain

Sea shore Land

Vegetation that

escape from the

contour

White Roofs that

escape from the

contour

Chennai urban area

UHI FORMATIONS

2005 2015

2015: Areas inside the contour are less than 30 and all other are greater than 30

Water Bodies that

are encapsulated

into the contour

Heat Absorbing Plain

Sea shore Land

2005: Areas inside the contour are greater than 30 and all other are less than 30

UHI INTENSITY-1

The temperature varies 10oC in 620 ft (or) 189 m, therefore UHI Index is 0.05 oC/m

o620 ft

UHI INTENSITY-2

The temperature varies 10oC in 97 ft (or) 29.5 m, therefore UHI Index is 0.33 oC/m

o

97 ft

UHI INTENSITY-3

90 ft

The temperature varies 10oC in 90 ft (or) 27.5 m, therefore UHI Index is 0.36 oC/m

o

UHI INTENSITY-4


o

180 ft

UHI INTENSITY-5


o

155 ft

COMPARISON OF UHI INTENSITY

ANALYSIS AND RESULTS

UHI Place Type of land UHI Index (oC/m) Ranking Mitigation Measures

1 Iyerpadi Hill Rocks

Surrounded

by Forest

10oC / 189m

= 0.05

5 Minor effect that could

be neglected

2 Coimbatore

Outer – 1

Barren Land

Surrounded

by Vegetation

20oC / 55m

= 0.33

4 Agriculture /

Afforestation is

encouraged

3 Coimbatore

Outer - 2

Barren Land

Surrounded

by Vegetation

20oC / 47m

= 0.36

3 Agriculture /

Afforestation is

encouraged

4 Vadugapalay

am

Urban Area

Surrounded

by Vegetation

10oC / 29.5m

= 0.36

2 Vegetation in between

the buildings & White

roofs are encouraged

5 Jeeva Nagar Urban Area

Surrounded

by Vegetation

10oC / 27.5m

= 0.42

1 Vegetation in between

the buildings & White

roofs are encouraged

CONCLUSION & RECOMMENDATIONS

• The dark surfaces and lack of vegetation can be mitigated by

increasing reflective surfaces and increasing vegetative surfaces.

• In surfaces, the higher the reflectivity (ALBEDO) and emissivity of

a material, the less likely it is to store heat and radiate it back into

the atmosphere or into the building through the walls and roof.

• The reflectivity of a surface determines its ability to reflect solar

radiation. Albedo is represented on a scale of 0 to 1.

EMISSIVITY & ALBEDO FACTORS OF MATERIALS

Material Emissivity factor Albedo factor

Polished alumium 0.1 0.9

Dirty concrete 0.9 0.2

Dark wood 0.95 0.15

Red brick 0.9 0.3

Tamished copper 0.4 0.4

White marble 0.9 0.6

White paint 0.9 0.8

Plaster 0.9 0.9

SOURCE – UHI EFFECTS - FISCHETTI, 2008; LIU AND BASS, 2005

• All vegetation has the potential to provide these ecosystem services

and co-benefits to a city and surrounding areas: storm water filtration,

groundwater recharge, reduced stress on combined sewer overflow

systems, improved public recreation space, and increased urban

habitat.

• Potential vegetation actions include installing green roofs, planting

trees to shade the south and west of homes, and using grass pavers

where possible. Green walls offer a building decreased heat

absorption.

• On a sunny, 26°C day, a dark roof can reach a temperature of up to

80°C, a white roof, 45°C, and a green roof, 29°C

Thank You

Delineation & Comparison of Urban Heat Islands (UHI) in Tamilnadu - PPT

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Transcript of Delineation & Comparison of Urban Heat Islands (UHI) in Tamilnadu - PPT