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Quantitative estimation of Land Surface Temperature and its
relationship with Land Use/Cover around Mahan Essar
Thermal Power Plant in Singrauli District, Madhya Pradesh,
India
Maya Kumari*#, Kiranmay Sarma*, Richa Sharma# and Soubhik Karmakar$
* School of Environmental Management, Block 'A' Guru Gobind Singh Indraprastha University, New Delhi, India
#Amity School of Natural Resources & Sustainable Development, Amity University, Noida, Uttar Pradesh. India
$Amity Institute of Geoinformatics and Remote Sensing, Amity University, Noida, Uttar Pradesh. India
[*email id: maya.84s@gmail.com]
Abstract
The relationship between Land Surface Temperature (LST) and Normalized differential vegetation index
(NDVI) over various landuse/cover (LULC) was studied for the Mahan Essar thermal power plant covering
an area of 10 km2 for three years - 2005, 2010 and 2015, respectively. Chronologically, the year 2005 is
the phase of pre-establishment of the thermal power plant; the year 2010 specifies the construction phase
and 2015 is the operational phase. Landsat ETM and Landsat 8 data of remote sensing system were
used for this study. The LULC study showed a significant decrease in the forest cover, and a dramatic
expansion in vegetation and barren land. These changes in LULC have together broadened the range of
Land Surface Temperature. The change in LST can be attributed to change in construction materials in
the urban area and vegetation abundance both in the urban and rural areas. The study reveals that there
is an overall rise of 90C in LST from the year 2005 to 2015. Deforestation, mining and other
anthropogenic activities are few of the main factors which have influenced the increased LST over time.
The results of Pearson’s correlation analysis illustrate an inverse co-relationship between LST and NDVI
over various LULC in the study area. Therefore, LST and its relationship with NDVI over LULC are
important parameters to study the urban ecosystem and this can be used as an effective tool for
assessing environmental effect on ecological unit.
Introduction
Land cover is an important feature on the earth
surface which is influenced various factors such
as geologic, hydrologic, climatic, atmospheric,
and land use processes that occur at a range of
spatial and temporal scales. In recent years, the
anthropogenic impact on land cover changes
has unprecedentedly accelerated due to
technological development and increase in
human population (Findell et al., 2007). Land
cover changes can have positive and negative
impacts on human well-being (DeFries and
Belward, 2000).
The study of the relationship between Land
Surface Temperature and Land Use/Cover has
immense importance in understanding the
changing dynamics of an environment. There
are several factors that contribute to land
surface temperature ranging from direct to
indirect. Land use/ land cover changes are
complex processes involving numerous driving
forces that are location specific (Nduati et al.,
2013). Land use/ land cover changes are also
spatially and temporally dynamic. Changing land
cover modifies the underlying heat fluxes that
exist within and between the earths’s surface
and vegetation thus influencing land surface-
atmosphere interactions (Yang, 2004).
The anthropogenic activities affects the natural
balance of an ecosystem in an unusual way
leading to increase in surface temperature and
many more such as erratic rainfall pattern and
melting of glaciers & polar sheets in polar
regions . LST is a property of land surface and is
affected by change in LULC. Previously, the
meteorological temperature was obtained from
weather stations through conventional method
which was non-continuous and requiring
extrapolation. But, now days the LULC change
is measurable continuously over space and time
using remote sensing techniques (Perez, 2014).
Remote sensing is a relatively cost effective and
quick method to acquire up-to-date information
over a large geographical area, providing
information about remote areas that wouldn’t be
possible to access.
The focus of this study is to examine the
relationship between LULC and LST defined by
the temporal and spatial variations. The total
study area comprises an area of 10 sq km
around Mahan Essar thermal power plant. The
study area comprises of seven LULC such as
forest, barren rocky area, fallow/ agricultural
area, marshy, sandy vegetation and water body.
A quantitative assessment of relationship
between LULC and LST reveals the cause-
effects analysis and the relationship between
these two parameters statistically. The outcome
of the study explains that how land surface
temperature rises with predevelopment,
construction and operational phase of the
thermal power plant. It also explains that how
LST correlates with different Land Use/Cover
indicating the significant impacts of
establishment of power plant on LULC dynamics
and microclimate
Study Area
The study area for the present work is Singrauli.
It is the 50th district of the state of Madhya
Pradesh in India. The status of a district was
given to it on 24 May 2008, with its headquarters
at Waidhan. It was formed after dividing it from
Sidhi district. It has three tehsils namely
Singrauli, Deosar and Chitrangi. Singrauli town
is a Municipal Corporation with a population of
about two lakhs. The population of Singrauli
district is about 11 lakhs (1.1 million).
Fig.1 Map of Study Area
The eastern part of the state of Madhya Pradesh
along with the adjoining southern part of
Sonebhadra district of Uttar Pradesh together
constitutes Singrauli, formerly also called
Shringavali. Dense and unnavigable forests
covered the entire district and wild animals
inhabited the area. Today, it is fast emerging as
the energy hub of India and would soon be the
India's new energy capital. The total installed
capacity of all thermal power plants at Singrauli
is around 10% of total installed capacity of India.
The locals also call it as ‘Urjanchal’ i.e. the land
of energy (Figure. 1)
At present, 17 mega power projects have
already started their work of setting up plants
and industries in various parts of Singrauli
district resulting in a large-scale displacement of
land as well as property. One of the power
plants is Mahan Essar power plant. The power
plant is located in Singrauli (earlier Sidhi) district
of Madhya Pradesh State. The site is near to the
Govind Ballabh Pant Sagar, and gets its water
supply from this reservoir.
Mahan Super Thermal Power Project is a
1,800-megawatt coal-fired power station under
development by Essar Energy in Madhya
Pradesh. It received environmental clearance in
2007. Captive coal mines provide enhanced
reliability of fuel supply but the power station has
faced delays in construction and operation due
to difficulties in securing mining approval for the
Mahan Coal Block, the captive mine for the
Mahan Power station and Mahan Aluminum
power station The baseline land use/cover and
vegetation - Land Surface Temperature
relationship of the study area of 10 km radius
from the project site is identified through
buffering method.
Methodology
Derivation of LST
The methodology for derivation of LST is in
accordance to LANDSAT 7 Science Data Users
Handbook.
Digital numbers (DN) in thermal band of the
image were converted to Spectral Radiance (Lλ)
Where Lλ is spectral radiance; gain is slope of
the radiance/DN conversion function; DN is
digital number of a given pixel and offset is the
intercept of the radiance/DN conversion function
(Landsat Project Science office, 2002). The
metadata accompanying each Landsat image
supplies the gain and offset values. The primary
image processing and analysis tools used are
ERDAS Imagine 14 and ArcGIS10.
Subsequently, the spectral radiance was
converted to Temperature in Kelvin
The conversion formula is:
Where TB is the surface temperature (K); K2 is
the calibration constant 2 (1282.71); K1 is the
calibration constant 1 (666.09) and Lλ is the
spectral radiance of thermal band pixels [7].
The temperature values obtained from the
above equation are referenced to a black body.
Therefore, according to the type of land cover
the corrections for spectral emissivity (e)
become necessary. The content, chemical
composition, structure and roughness of the
land are the factors that affect the emissivity of a
surface. For vegetated surfaces, emissivity can
vary significantly with the type of plant species,
areal density and its growth stage [14].
According to the type of land surface the
corrections in the emissivity were applied.
Vegetated areas and non-vegetated areas were
given a value of 0.95 and 0.92, respectively [8].
The emissivity-corrected Land Surface
Temperature (LST) was computed using the
equation below [4, 21]
Where λ is the wavelength of the emitted
radiance (for which the peak response and the
average of the limiting wavelengths λ=11.5 will
be used); ρ = h x c/σ (1.438 x 10-2 Mk); σ is
Boltzmann’s constant (1.38 x 10-23JK-1); h is
Planck’s constant (6.626 x 10-34Js), and c is the
velocity of light (2.998 x 108ms-1)
Derivation of NDVI
The Normalized Difference Vegetation Index
(NDVI) is an most commonly used vegetation
index which is an indicator of “greenness” or
photosynthetic activity of a plant. NDVI provides
an approximation of vegetation health and
serves as a method of monitoring changes in
vegetation over time in multispectral remote
sensing data.
The NDVI ratio is normalized difference between
Near-infra red (NIR) and red color band which
can be calculated using the formula below:
Result and Discussion
For analyzing the changes that have occurred in
the present study area from 2005 to 2015, a
LULC map was prepared. The image
classification was carried out using the method
of maximum likelihood-based supervised
classification. The algorithm assumes that the
statistics of training data for each class in each
band lie under Gaussian distribution i.e.
normally distributed. (Kumari and Sarma, 2017).
The southern part of the study area is covered
with dense forest area extending towards north-
west encompassing 20.62 percent of the total
area in the year 2005. It sharply declined from
the year 2005 to 2015 from 20.62 percent to
13.71 percent of total area (Figure. 2a, 2b and
2c). The establishment of power plant in the
study area can be one of the reasons of the
decline. Due to conversion of forest into other
land uses such as cultivable/fallow land and
plantation, the area of vegetation has increased
from the year 2005 to 2015 by 12.60 percent.
Huge areas of marshy lands (22.75%) can be
observed on the eastern side of the project site.
Conversion of landcover into marshy area during
2010 can be because of constructional activities
and later in the year 2015 due to disposal of ash
slurry in the form of ash dyke. There are some
surface water bodies in the study area. The total
area of water body in the year 2005 is 0.31
percent of the total area. Waterbodies have
shown marginal variation in the area in the
successive years.
(a) (b) (c)
Fig. 2 LULC (a) 2005 (b) 2010 (c) 2015
Difference in mean LST and mean NDVI by
land-use/cover types
The changes in LULC resulted in changes in
LST, especially in the areas around the thermal
power plant. Since 2010, many infrastructure
and amenities expanded dramatically (Figure.
3). The increased temperature in the study
areas was mainly due to rapid expansion of
industry during 2010 to 2015. In addition to
setting-up of the power plant, there have been
significant changes to the other LULC. A great
amount of land in the study area has been
converted from forest to fallow /agricultural area.
In the past, forest areas could provide a buffer
zone to absorb excess heat generated by
automobiles and industries.
The mean LST and NDVI associated with seven
classes of LULC were derived using zonal
statistics in GIS environment. The result of the
GIS zonal statistics is shown in Table 1..
The derived statistical value represents
significant changes in both land surface
temperature and NDVI.
Table 1. Temporal variation of NDVI and LST
over different LULC
Landuse
2005
2010
2015
ND
VI
LS
T
ND
VI
LS
T
ND
VI
LS
T
Barren
land
0.2
1
19.
74
-
0.1
5
26.
27
-
0.0
1
27.
36
Fallow
land/Agric
ultural land
0.2
5
19.
35
0.2
7
24.
39
0.0
2
25.
13
Forest
Area
0.3
2
18.
77
0.4
0
21.
63
0.3
6
23.
76
Marshy
land
0.2
0
19.
79
0.1
9
25.
80
0.0
4
26.
80
Sandy
Area
-
0.1
6
20.
12
-
0.0
7
26.
82
-
0.0
5
29.
61
Vegetation
0.2
6
19.
43
0.4
0
23.
90
0.4
3
25.
45
Water
Body
0.2
3
19.
59
0.0
3
24.
52
-
0.0
1
26.
25
(a) (b) (c)
Fig.3 LST map of study area (a) 2005 (b) 2010 (c) 2015
(a) (b) (c)
Fig. 4 NDVI map of study area (a) 2005 (b) 2010 (c) 2015
The maximum LST is depicted in sandy area
ranging 20.12⁰C to 29.61⁰C with mean surface
value of 25.522⁰C mainly because of clear scalp
of the soil and the minimum LST has been
recorded over forest area and water bodies with
a mean of 21.380C and 23.450C. It is found that
the barren rocky area, which is usually
composed of bare land and developing land,
had high mean LST of 24.46⁰C. It was followed
by marshy area, fallow land/agricultural land and
vegetation. Overall, the upsurge in the number
of thermal power plants and other related
infrastructure in the area have resulted in the
rise of temperature and thereby increasing
urban heat island effect.
The figure 4 shows the changes in the mean
NDVI values of predevelopment, construction
and operational phase of thermal plant that is
during 2005, 2010, and 2015. The mean NDVI
value is depicting an actual scenario of changing
environmental phenomena and the effect of
Thermal Plant over environment. NDVI
effectively differentiates between the vegetation
and non-vegetation land cover classes. The
NDVI values for barren land and sandy area are
negative ranging between 0.21 to – 0.01 and -
0.16 to - 0.05 respectively.
On the other hand the NDVI values for
vegetation and forest area are positive and
range between 0.26 to 0.43 and 0.32 to 0.46
respectively. NDVI values are sensitive to
vegetation abundance, density of foliage and
growth stage of crops. Correspondingly all NDVI
values have been significantly decreased during
2015 which clearly reflects the adverse effect of
Thermal Plant on Vegetation. Conversion of
agriculture and forested land to open bare one is
another cause of degrading NDVI values.
The relationship between LST and NDVI by
land-use/cover types
To apprehend the relationship between LST and
vegetation abundance indicators (NDVI), the
thermal environment of each land-use/cover
type must be investigated. To assess the effect
of LULC exactly, a regression analysis between
LST and NDVI with respect to LULC was
performed. The correlation coefficient is
significant at the 0.01 level (2-tailed). The
comparison of coefficients of regression function
discloses that moderate negative association
between NDVI and LST. Due to increasing
activities of the thermal plant the overall land
surface temperature is increasing which in turn
making a negative impact on the land use types
and are justified by the statistical output.
Table 2. Temporal variation of Pearson’s co-
relation coefficient between NDVI and LST over
different LULC
Y
ea
r
Landus
e
n
r
R2
Regre
ssion
Equat
ion
Signifi
cance
Level
Vegetati
5
-
0.3
y = -
0.01
20
05
on
1
0.
62
8
91
2
0.085
x +
1.944
Forest
Area
7
2
0
-
0.
47
8
0.2
28
y = -
0.045
5x +
1.178
7
0.01
Marshy
land
1
9
9
-
0.
53
8
0.2
89
1
y = -
0.055
8x +
1.3
0.01
Barren
land
2
8
3
-
0.
50
4
0.2
53
7
y = -
0.065
8x +
1.508
7
0.01
Water
Body
4
2
0.
13
7
0.5
42
7
y = -
0.063
1x +
1.481
5
0.01
Fallow
land/Agr
icultural
land
4
3
0
-
0.
40
1
0.1
60
5
y = -
0.042
7x +
1.079
9
0.01
Sandy
Area
7
2
-
0.
57
2
0.3
26
9
y = -
0.051
7x +
1.180
1
0.01
20
10
Vegetati
on
7
2
-
0.
31
9
0.1
01
9
y = -
0.020
5x +
0.890
6
0.01
Forest
Area
7
2
7
-
0.
21
9
0.0
47
9
y = -
0.008
9x +
0.595
9
0.01
Marshy
land
3
0
0
-
0.
40
3
0.1
62
y = -
0.019
5x +
0.71
0.01
Barren
Rocky
Area
2
5
4
-
0.
38
9
0.1
51
5
y = -
0.013
8x +
0.521
6
0.01
Water
Body
6
3
0.
18
6
0.0
34
7
y = -
0.021
6x +
0.549
0.05
4
Fallow
land/Agr
icultural
land
3
4
8
-
0.
48
9
0.2
39
4
y = -
0.015
9x +
0.659
4
0.01
Sandy
Area
1
0
9
-
0.
25
9
0.0
66
9
y = -
0.006
2x +
0.238
3
0.01
20
15
Vegetati
on
4
1
6
-
0.
24
6
0.0
60
6
y = -
0.016
x +
0.721
8
0.01
Forest
Area
1
3
8
-
0.
07
9
0.0
06
3
y = -
0.001
8x +
0.307
0.05
Marshy
land
4
6
6
-
0.
47
5
0.2
25
4
y = -
0.016
7x +
0.671
6
0.01
Barren
Rocky
Area
1
8
9
-
0.
07
2
0.0
05
1
y = -
0.001
6x +
0.225
9
0.05
Water
Body
9
3
0.
22
0.0
00
5
y = -
0.002
x +
0.272
1
0.05
Fallow
land/Agr
icultural
land
4
7
7
-
0.
23
3
0.0
54
2
y = -
0.005
7x +
0.356
3
0.01
Sandy
Area
3
7
-
0.
35
1
0.1
23
1
y = -
0.003
5x +
0.234
1
0.05
Table 2 shows the correlation between LST and
NDVI for selected LULC types. It is observed
that NDVI values tend to be negatively
correlated with LST of vegetation type cover and
positively correlated with water bodies. Overall,
in all three time period the strongest negative
correlation between LST and NDVI values is
found over vegetation followed by barren rocky
area and sandy area.
It is suggested that more vegetation cover
(greater NDVI) in some land-use/cover areas
can cause correspondingly higher rates of
evapotranspiration and promote the latent and
sensible heat exchange between the land
surface and atmosphere when compared with
little vegetation cover, such as in developed
areas. The distribution of green lands around
these land-use/cover types might reduce
temperature and improve the thermal
environment more effectively.
Conclusion
This study has examined LULC changes around
Essar Mahan thermal power plant from 2005 to
2015. The results indicated that industry areas
and supporting infrastructure has expanded
dramatically, while forest area has declined.
Barren land increased, mainly in the boundary
areas between forest and fallow land. The
observed changes in LULC were largely
attributed to rapidly growing infrastructure and
poor land use planning. Changes in LULC were
accompanied by changes in LST.
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