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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
2011 Landsat 5 failed to reach orbit Landsat 7
2010 Landsat 5 failed to reach orbit Landsat 7
2009 Landsat 5 failed to reach orbit Landsat 7
2008 Landsat 5 failed to reach orbit Landsat 7
2007 Landsat 5 failed to reach orbit Landsat 7
2006 Landsat 5 failed to reach orbit Landsat 7
2005 Landsat 5 failed to reach orbit Landsat 7
2004 Landsat 5 failed to reach orbit Landsat 7
2003 Landsat 5 failed to reach orbit Landsat 7
2002 Landsat 5 failed to reach orbit Landsat 7
2001 Landsat 5 failed to reach orbit Landsat 7
2000 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
1997 Landsat 5 failed to reach orbit
1996 Landsat 5 failed to reach orbit
1995 Landsat 5 failed to reach orbit
1994 Landsat 5 landsat 6
1993 14-Dec Landsat 5 5-Oct
1992 Landsat 4 Landsat 5
1991 Landsat 4 Landsat 5
1990 Landsat 4 Landsat 5
1989 Landsat 4 Landsat 5
1988 Landsat 4 Landsat 5
1987 Landsat 4 Landsat 5
1986 Landsat 4 Landsat 5
1985 Landsat 4 Landsat 5
1984 Landsat 4 1-Mar
1983 31-Mar Landsat 4
1982 25-Feb Landsat 3 16-Jul
1981 Landsat 2 Landsat 3
1980 Landsat 2 Landsat 3
1979 Landsat 2 Landsat 3
1978 6-Jan Landsat 2 5-Mar
1977 Landsat 1 Landsat 2
1976 Landsat 1 Landsat 2
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
201420152005
SET Satellite Month Date Year
1
2
3
4
5
6
LANDSAT5LANDSAT8
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
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 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 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=
K2
ln K1 ∗ε
CVRI
+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
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00 Chennai
Coimbatore
Cuddalore
Dharmapuri
Dindigul
Erode
Kancheepuram
Kanniyakumari
Madurai
Nagapattinam
Namakkal
Perambalur
Pudukkottai
Ramanathapuram
Salem
Sivaganga
Thanjavur
TheNilgiris
Theni
Thiruvallur
Thiruvarur
Thoothukkudi
Tiruchirappalli
Tirunelveli
Tiruppur
Tiruvannamalai
Vellore
Viluppuram
Virudhunagar
POPULATION
MILLIONS
TEMPERATURE
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
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00
Chennai
Coimbatore
Cuddalore
Dharmapuri
Dindigul
Erode
Kancheepuram
Kanniyakumari
Madurai
Nagapattinam
Namakkal
Perambalur
Pudukkottai
Ramanathapuram
Salem
Sivaganga
Thanjavur
TheNilgiris
Theni
Thiruvallur
Thiruvarur
Thoothukkudi
Tiruchirappalli
Tirunelveli
Tiruppur
Tiruvannamalai
Vellore
Viluppuram
Virudhunagar
POPULATION
MILLIONS
TEMPERATURE
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
TEMPERATURE(°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
Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining /
Industrial area
Wetlands
TEMPERATURE(˚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
Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining /
Industrial area
Wetlands
TEMPERATURE(°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
Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining /
Industrial area
Wetlands
TEMPERATURE(°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
Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining /
Industrial area
Wetlands
TEMPERATURE(°c)
Land use
2014 OCT-AVERAGE TEMPERATURE PLOT OF LANDUSE
PARAMETERS
0
5
10
15
20
25
30
35
Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining /
Industrial area
Wetlands
TEMPERATURE(°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 -0.225130.137597 -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 0.5770120.2041080.207302 0.271575
Built Up - Mining /
Industrial area 0.30283 0.223932 0.107899 0.9205120.6482940.663338 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
o
620 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
The temperature varies 20oC in 180 ft (or) 55 m, therefore UHI Index is 0.36 oC/m
o
180 ft
UHI INTENSITY-5
The temperature varies 20oC in 155 ft (or) 47 m, therefore UHI Index is 0.42 oC/m
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

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Delineation and Comparison of Urban Heat Islands in Tamilnadu

  • 1. 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
  • 2. 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.
  • 3.
  • 4. 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.
  • 5. INCREASED TEMPERATURE IN URBAN AREA SOURCE - OKE, T.R. 1997. URBAN CLIMATES AND GLOBAL ENVIRONMENTAL CHANGE
  • 6. SOURCE - OKE, T.R. 1997. URBAN CLIMATES AND GLOBAL ENVIRONMENTAL CHANGE
  • 7. 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
  • 8. 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.
  • 9. • 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 2011 Landsat 5 failed to reach orbit Landsat 7 2010 Landsat 5 failed to reach orbit Landsat 7 2009 Landsat 5 failed to reach orbit Landsat 7 2008 Landsat 5 failed to reach orbit Landsat 7 2007 Landsat 5 failed to reach orbit Landsat 7 2006 Landsat 5 failed to reach orbit Landsat 7 2005 Landsat 5 failed to reach orbit Landsat 7 2004 Landsat 5 failed to reach orbit Landsat 7 2003 Landsat 5 failed to reach orbit Landsat 7 2002 Landsat 5 failed to reach orbit Landsat 7 2001 Landsat 5 failed to reach orbit Landsat 7 2000 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 1997 Landsat 5 failed to reach orbit 1996 Landsat 5 failed to reach orbit 1995 Landsat 5 failed to reach orbit 1994 Landsat 5 landsat 6 1993 14-Dec Landsat 5 5-Oct 1992 Landsat 4 Landsat 5 1991 Landsat 4 Landsat 5 1990 Landsat 4 Landsat 5 1989 Landsat 4 Landsat 5 1988 Landsat 4 Landsat 5 1987 Landsat 4 Landsat 5 1986 Landsat 4 Landsat 5 1985 Landsat 4 Landsat 5 1984 Landsat 4 1-Mar 1983 31-Mar Landsat 4 1982 25-Feb Landsat 3 16-Jul 1981 Landsat 2 Landsat 3 1980 Landsat 2 Landsat 3 1979 Landsat 2 Landsat 3 1978 6-Jan Landsat 2 5-Mar 1977 Landsat 1 Landsat 2 1976 Landsat 1 Landsat 2 1975 Landsat 1 22-Jan 1974 Landsat 1 1973 Landsat 1 1972 23-Jul
  • 10. 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 201420152005 SET Satellite Month Date Year 1 2 3 4 5 6 LANDSAT5LANDSAT8 Data Selection
  • 11. 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
  • 12. DATA ACQUISITION DATA COLLECTION – FROM WEBSITES
  • 13. USGS – EARTH EXPLORER
  • 15. TILES TO BE CONCENTRATED FOR TAMIL NADU
  • 16. TILES TO BE CONCENTRATED FOR TAMIL NADU
  • 17. 3 PATHS & 4 ROWS (I.E.) 4 ROWS , 3 COLUMNS
  • 18. 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
  • 19. 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
  • 20. 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
  • 21. 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
  • 23. 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
  • 24. TOP ATMOSPHERIC RADIANCE IS CONVERTED INTO LAND SURFACE TEMPERATURE T= K2 ln K1 ∗ε CVRI +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
  • 25. 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.
  • 26. Mosaic Radiation kelvin Celcius Positive Contour SEPTEMBER 2005 DATA
  • 27. MOSAIC RADIATION KELVIN CELCIUS POSITIVE CONTOUR OCTOBER 2014 DATA ( BAND10)
  • 28. 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
  • 35.
  • 36. 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
  • 37. TEMPERATURE & POPULATION OF 2005 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 0.00 10.00 20.00 30.00 40.00 50.00 60.00 Chennai Coimbatore Cuddalore Dharmapuri Dindigul Erode Kancheepuram Kanniyakumari Madurai Nagapattinam Namakkal Perambalur Pudukkottai Ramanathapuram Salem Sivaganga Thanjavur TheNilgiris Theni Thiruvallur Thiruvarur Thoothukkudi Tiruchirappalli Tirunelveli Tiruppur Tiruvannamalai Vellore Viluppuram Virudhunagar POPULATION MILLIONS TEMPERATURE DISTRICTS
  • 38. 2005 DATA Demographic Map Maximum Temp Map
  • 39. 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
  • 40. 2015 DATA Demographic Map Max. Temp Map
  • 41. TEMPERATURE & POPULATION OF 2015 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 0.00 10.00 20.00 30.00 40.00 50.00 60.00 Chennai Coimbatore Cuddalore Dharmapuri Dindigul Erode Kancheepuram Kanniyakumari Madurai Nagapattinam Namakkal Perambalur Pudukkottai Ramanathapuram Salem Sivaganga Thanjavur TheNilgiris Theni Thiruvallur Thiruvarur Thoothukkudi Tiruchirappalli Tirunelveli Tiruppur Tiruvannamalai Vellore Viluppuram Virudhunagar POPULATION MILLIONS TEMPERATURE DISTRICTS Temperature Population
  • 42. 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.
  • 44.
  • 45. 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.
  • 46. 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
  • 47. 0 5 10 15 20 25 30 Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining / Industrial area Wetlands TEMPERATURE(°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.
  • 48. 17 18 19 20 21 22 23 Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining / Industrial area Wetlands TEMPERATURE(˚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.
  • 49. 0 5 10 15 20 25 30 35 40 Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining / Industrial area Wetlands TEMPERATURE(°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.
  • 50. 0 2 4 6 8 10 12 14 16 18 20 Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining / Industrial area Wetlands TEMPERATURE(°C) Land use 2014 JULY-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS
  • 51. 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.
  • 52. 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 Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining / Industrial area Wetlands TEMPERATURE(°c) Land use 2014 OCT-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS
  • 53. 0 5 10 15 20 25 30 35 Agricultural Land Waterbodies Forest Wastelands Built Up (Urban) Built Up - Mining / Industrial area Wetlands TEMPERATURE(°C) LAND USE 2015-AVERAGE TEMPERATURE PLOT OF LANDUSE PARAMETERS
  • 54. 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.
  • 55. 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.
  • 57. 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 -0.225130.137597 -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 0.5770120.2041080.207302 0.271575 Built Up - Mining / Industrial area 0.30283 0.223932 0.107899 0.9205120.6482940.663338 0.477805 Wetlands -0.13655 -0.25723 -0.34259 0.0011560.084292 -0.98511 -0.27267
  • 58. 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.
  • 59. UHI FORMATIONS Chennai Kodungayur Landfill in 2005, having temperatures less than 30oC Chennai Kodungayur Landfill in 2015, having 30oC contours
  • 61. 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
  • 62. 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
  • 63. UHI INTENSITY-1 The temperature varies 10oC in 620 ft (or) 189 m, therefore UHI Index is 0.05 oC/m o 620 ft
  • 64. 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
  • 65. 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
  • 66. UHI INTENSITY-4 The temperature varies 20oC in 180 ft (or) 55 m, therefore UHI Index is 0.36 oC/m o 180 ft
  • 67. UHI INTENSITY-5 The temperature varies 20oC in 155 ft (or) 47 m, therefore UHI Index is 0.42 oC/m o 155 ft
  • 68. 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
  • 69. 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.
  • 70. 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
  • 71. • 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