Delineation and Comparison of Urban Heat Islands in Tamilnadu

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


5 years and 26 days


29 years, 3 months and 4 days 0 days

16 years, 1 month and 4 days


2015 jan landsat 6 Landsat 7 Landsat 82014 failed to reach orbit Landsat 7 Landsat 82013 5-Jun failed to reach orbit Landsat 7 11-Feb->13-Apr2012 Landsat 5 failed to reach orbit Landsat 72011 Landsat 5 failed to reach orbit Landsat 72010 Landsat 5 failed to reach orbit Landsat 72009 Landsat 5 failed to reach orbit Landsat 72008 Landsat 5 failed to reach orbit Landsat 72007 Landsat 5 failed to reach orbit Landsat 72006 Landsat 5 failed to reach orbit Landsat 72005 Landsat 5 failed to reach orbit Landsat 72004 Landsat 5 failed to reach orbit Landsat 72003 Landsat 5 failed to reach orbit Landsat 72002 Landsat 5 failed to reach orbit Landsat 72001 Landsat 5 failed to reach orbit Landsat 72000 Landsat 5 failed to reach orbit Landsat 71999 Landsat 5 failed to reach orbit 15-Apr-> 12-Aug1998 Landsat 5 failed to reach orbit1997 Landsat 5 failed to reach orbit1996 Landsat 5 failed to reach orbit1995 Landsat 5 failed to reach orbit1994 Landsat 5 landsat 61993 14-Dec Landsat 5 5-Oct1992 Landsat 4 Landsat 51991 Landsat 4 Landsat 51990 Landsat 4 Landsat 51989 Landsat 4 Landsat 51988 Landsat 4 Landsat 51987 Landsat 4 Landsat 51986 Landsat 4 Landsat 51985 Landsat 4 Landsat 51984 Landsat 4 1-Mar1983 31-Mar Landsat 41982 25-Feb Landsat 3 16-Jul1981 Landsat 2 Landsat 31980 Landsat 2 Landsat 31979 Landsat 2 Landsat 31978 6-Jan Landsat 2 5-Mar1977 Landsat 1 Landsat 21976 Landsat 1 Landsat 21975 Landsat 1 22-Jan1974 Landsat 11973 Landsat 11972 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

DSAT

5LA

NDS

AT 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 & REMEDIATIO

N

• The causes for the formation of UHIs were identified

DATA ACQUISITIONDATA 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 NUMBER

ROW 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 2014LANDSAT 8-BAND10 & BAND11 APR 2014LANDSAT 8-BAND10 & BAND11 OCT 2014LANDSAT 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 the second 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• 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 ContourSEPTEMBER 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

1

Demog

raphic

Correla

tion

1

Demog

raphic

Correla

tion

District Name Population Max. Temp (˚C)

Chennai 4,343,645.00 45.04Coimbatore 4,271,856.00 31.74Cuddalore 2,285,395.00 38.44Dharmapuri 2,856,300.00 56.90Dindigul 1,923,014.00 33.74Erode 2,581,500.00 35.74Kancheepuram 2,877,468.00 38.85Kanniyakumari 1,676,034.00 30.35Madurai 2,578,201.00 35.31Nagapattinam 1,488,839.00 34.30Namakkal 1,493,462.00 30.90Perambalur 493,646.00 34.30Pudukkottai 1,459,601.00 40.87Ramanathapuram 1,187,604.00 40.07Salem 3,016,346.00 31.74Sivaganga 1,155,356.00 36.80Thanjavur 2,216,138.00 34.72The Nilgiris 762,141.00 20.50Theni 1,093,950.00 31.74Thiruvallur 2,754,756.00 38.85Thiruvarur 1,169,474.00 32.61Thoothukkudi 1,572,273.00 26.79Tiruchirappalli 2,418,366.00 35.12Tirunelveli 2,723,988.00 31.74Tiruvannamalai 2,186,125.00Vellore 3,477,317.00 38.44Viluppuram 2,960,373.00 34.72Virudhunagar 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

1

Demog

raphic

Correla

tion

TEMPERATURE & POPULATION OF 2005

Chenn

ai

Cudda

lore

Dindigu

l

Kanch

eepu

ram

Madura

i

Namak

kal

Puduk

kotta

iSale

m

Thanja

vur

Theni

Thiruv

arur

Tiruch

irapp

alli

Tirupp

ur

Vellore

Virudh

unag

ar0.00

10.00

20.00

30.00

40.00

50.00

60.00

0.00

500,000.00

1,000,000.00

1,500,000.00

2,000,000.00

2,500,000.00

3,000,000.00

3,500,000.00

4,000,000.00

4,500,000.00

5,000,000.00

Districts

Tem

pera

ture

Popu

latio

n

1

Demog

raphic

Correla

tion

2005 DATADemographic Map Maximum Temp Map

1

Demog

raphic

Correla

tion

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 36.35Coimbatore 3,472,578.00 40.64Cuddalore 2,600,880.00 38.22Dharmapuri 3386631.00 40.37Dindigul 2,161,367.00 41.27Erode 2,259,608.00 39.40Kancheepuram 3,990,897.00 42.08Kanniyakumari 1,863,174.00 40.72Madurai 3,041,038.00 43.24Nagapattinam 1,614,069.00 32.42Namakkal 1,721,179.00 43.80Perambalur 564,511.00 38.68Pudukkottai 1,618,725.00 37.14Ramanathapuram 1,337,560.00 38.42Salem 3,480,008.00 45.81Sivaganga 1,341,250.00 38.63Thanjavur 2,402,781.00 37.29The Nilgiris 735,071.00 39.97Theni 1,243,684.00 41.64Thiruvallur 3,725,697.00 39.11Thiruvarur 1,268,094.00 31.29Thoothukkudi 1,738,376.00 39.70Tiruchirappalli 2,713,858.00 53.93Tirunelveli 3,072,880.00 42.04Tiruppur 2,471,222.00 40.28Tiruvannamalai 2,468,965.00 39.11Vellore 3,928,106.00 39.53Viluppuram 3,463,284.00 38.96Virudhunagar 1,943,309.00 43.02

Population Data obtained from - Directorate of census Operation Tamilnadu

1

Demog

raphic

Correla

tion

2015 DATADemographic Map Max. Temp Map

1

Demog

raphic

Correla

tion

TEMPERATURE & POPULATION OF 2015

Chenn

ai

Cudda

lore

Dindigu

l

Kanch

eepu

ram

Madura

i

Namak

kal

Puduk

kotta

iSale

m

Thanja

vur

Theni

Thiruv

arur

Tiruch

irapp

alli

Tirupp

ur

Vellore

Virudh

unag

ar0.00

10.00

20.00

30.00

40.00

50.00

60.00

0.00

500,000.00

1,000,000.00

1,500,000.00

2,000,000.00

2,500,000.00

3,000,000.00

3,500,000.00

4,000,000.00

4,500,000.00

5,000,000.00

Temperature Population

Districts

Tem

pera

ture

Popu

latio

n

1

Demog

raphic

Correla

tion

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.

1

Demog

raphic

Correla

tion

ZONAL STATISTICS TABLE-LANDUSE2

Averag

e

Tempe

rature

Vs Lan

duse

2

Averag

e

Tempe

rature

Vs Lan

duse

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.

2

Averag

e

Tempe

rature

Vs Lan

duse

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

2

Averag

e

Tempe

rature

Vs Lan

duse

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

Wetlands0

5

10

15

20

25

30

2005 -AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

LANDUSE

TEM

PERA

TURE

(°c)

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.

2

Averag

e

Tempe

rature

Vs Lan

duse


Wetlands17

18

19

20

21

22

23

2014 JAN- AVERAGE TEMPERATURE PLOT OF LANDUSE PARAM-ETERS

LANDUSE

TEM

PERA

TURE

(˚C)

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.

2

Averag

e

Tempe

rature

Vs Lan

duse


Wetlands0

5

10

15

20

25

30

35

40

2014 APR-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAM-ETERS

LANDUSE

TEM

PERA

TURE

(°c)

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.

2

Averag

e

Tempe

rature

Vs Lan

duse


Wetlands0

2

4

6

8

10

12

14

16

18

20

2014 JULY-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

Land use

TEM

PERA

TURE

(°C)

2

Averag

e

Tempe

rature

Vs Lan

duse

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.

2

Averag

e

Tempe

rature

Vs Lan

duse

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.


Wetlands0

5

10

15

20

25

2014 OCT-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

Land use

TEM

PERA

TURE

(°c)

2

Averag

e

Tempe

rature

Vs Lan

duse

Agricu

ltural

Land

Waterbo

dies

Forest

Wastel

ands

Built Up (

Urban)

Built Up -

Minin

g / In

dustr

ial are

a

Wetlan

ds0

5

10

15

20

25

30

35

2015-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS

LAND USE

TEM

PERA

TURE

(°C)

2

Averag

e

Tempe

rature

Vs Lan

duse

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.

2

Averag

e

Tempe

rature

Vs Lan

duse

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.

2

Averag

e

Tempe

rature

Vs Lan

duse

EFFECT OF LANDUSE POLYGON SIZE

3

Effect o

f

Land

use Poly

gon S

ize

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

3

Effect o

f

Land

use Poly

gon S

ize

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.

3

Effect o

f

Land

use Poly

gon S

ize

UHI FORMATIONS

Chennai Kodungayur Landfill in 2005, having temperatures less than 30oC

Chennai Kodungayur Landfill in 2015, having 30oC contours

4

UHI

Index

& Inten

sity

UHI FORMATIONS4

UHI

Index

& Inten

sity

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

4

UHI

Index

& Inten

sity

UHI FORMATIONS2005 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

4

UHI

Index

& Inten

sity

UHI INTENSITY-1

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

o620 ft

4

UHI

Index

& Inten

sity

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

4

UHI

Index

& Inten

sity

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

4

UHI

Index

& Inten

sity

UHI INTENSITY-4


o

180 ft

4

UHI

Index

& Inten

sity

UHI INTENSITY-5


o

155 ft

4

UHI

Index

& Inten

sity

COMPARISON OF UHI INTENSITY ANALYSIS AND RESULTS

UHI Place Type of land UHI Index (oC/m) Ranking Mitigation Measures1 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

encouraged3 Coimbatore

Outer - 2Barren Land Surrounded by Vegetation

20oC / 47m = 0.36

3 Agriculture / Afforestation is

encouraged4 Vadugapalay

amUrban 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

4

UHI

Index

& Inten

sity

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 and Comparison of Urban Heat Islands in Tamilnadu

Engineering

Transcript of Delineation and Comparison of Urban Heat Islands in Tamilnadu