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International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
www.ijsr.net
Licensed Under Creative Commons Attribution CC BY
Spatial Shrinkage of Vembanad Lake, South West
India during 1973-2015 using NDWI and MNDWI
Parvathy K. Nair1, D. S. Suresh Babu2
National Centre for Earth Science Studies (NCESS), Ministry of Earth Sciences (MoES), Thiruvananthapuram 695031, India
Abstract: Vembanad Lake is the largest estuarine- Lagoon system in Kerala. Considering its ecological significance and high
biodiversity, it had been designated as the Ramsar site by the UNESCO in the Iranian city of Ramsar in1981 and also classified as an
Ecologically Sensitive Zone by the Ministry of Environment and Forests, India. In recent years, this estuary had undergone shrinkage
due to various developmental and agricultural activities, which has been computed using remote sensing and GIS techniques coupled
with field validation. A set of four Landsat satellite images that were acquired between 1973 and 2015 was employed to map the change
in surface area of the Vembanad Lake using the water index methods. In the present study Normalised Difference Water Index (NDWI)
and Modified Normalised Difference Water Index (MNDWI) methods were used to qualify the changes in the water area of the
Vembanad Lake. The estuarine area were mapped using visual interpretation and ArcGIS 10.2.1 environment. The present study shows
that the estuarine area is declining due industrial and agricultural developmental activities. The total shrinkage of the estuary during
the study period was found to be 12.28sq.km (6.93%).
Keywords: Estuary, Water index methods, NDWI, MNDWI, Vembanad Lake
1. Introduction
Vemaband Lake has been subjected to multifarious studies
by many authors. Its evolution, water-sediment chemistry,
biology, pollution indices, siltation, tourism potential etc
have been addressed in several publications over the years
(Mallik and Suchindan, 1984; Menon et al., 2000; Narayana
et al., 2006; Selvam et al., 2012 and Padmalal et al., 2014).
The decline of spatial coverage and reclamation of water
body in many sectors have also been reported (Dipson et al.,
2015). This study highlights the spatial shrinkage of the lake
assessed by water index method for improving the
management of the lake. This method can provide data about
the shrinkage and thus help in formulating necessary
measures for the protection of the lake which is an important
water resource of the region.
Estuaries are unique systems with an unquestionable
economical, ecological and recreational values (Taborda,
2009) which are partially enclosed coastal wetlands with one
or more rivers or streams debouching into it. They possess a
direct link to the open sea and hence are subjected to strong
seasonal changes in chemical composition, flow patterns,
sedimentation rate etc. (Dipson et al., 2015; Boschker et al.,
2005). They are subjected to marine influences such as tides,
waves, incursion of saline water as well as riverine
influences like influx of fresh water and sediments (Nayak et
al., 2002). However, in recent years many lakes around the
world are changing rapidly mainly by the climatic change
and human activity (Coe and Foley, 2001 and zhang et al.,
2015).
Remotely sensed images can be used as a tool to map
ecosystems and to detect, monitor and evaluated changes
within them thereby supporting the development of
resources management strategies. Satellite and airborne
systems offer major opportunities for monitoring large scale,
earth surface characteristics and provide a data base for
change detection studies (Ahamed et al,. 2009). Generally
the near infrared (NIR) and the middle infrared (MIR) bands
have higher potential for detecting water bodies (Lillesand
and Kiefer,1994) ,therefore this would be useful in wetness
detection and monitoring. There are many techniques used
for delineating the water body boundaries. Some of them are
image classification, Principal component analysis (PCA).
Tasselled cap (TC) transformations, Normalised difference
Water Index (NDWI) and analysis of digital elevation
models (DEM) (Ahmed et al., 2009 and Fei Zhang et al.,
2015).
Ahamed et al., (2009) analysed the environmental change of
North African coastal lagoons using remote sensing
techniques. Song et al., (2012) studied wetland shrinkage in
Muleng-Xingkai Plain, China based on landscape metrics
and the land use changes transition matrix. Joao et al.,
(2012) evaluates the performances of NDWI NIR/MIR,
NDWIG/NIR, and NDWI G/MIR for mapping seasonal and
permanent water in Sahara-Sahel Transition zone. Kumar
andLakshman (2015) have identified the hydrologically
active areas using Modified Normalised Differential Water
Index (MNDWI) from remote sensing data and a Soil
Topographic Index (STI) derived from topographic data in
Upper Cauvery Basin, India. Vivek et al., (2015) conducted
a study to detect the changes in surface water dynamics in
Bangalore using various methods like Water Ratio Index
(WRI), Normalised Difference Water Index (NDWI),
Modified Normalised Difference Water Index (MNDWI),
supervised classification and wetness component of K-T
transformation. Sajeeva and Subramanian (2003) have
studied the land use/ land cover (LULC) changes in
Ashtamudi wetland region in Kerala, from 1967-1997 using
remote sensing data and GIS techniques. Rao et al., (1999)
have monitored the spatial extent of Sunder bans Delta,
India and analysed the wetland changes between 1973 and
1993 using the remote sensing data. Chithra et al., (2015)
mapped the change detection of built up impervious surfaces
in and around Cochin area, Kerala using the GIS techniques.
The main objective of the present study is to estimate the
surface area change in the Vembanad Lake over the past four
Paper ID: ART2016471
1394
International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
www.ijsr.net
Licensed Under Creative Commons Attribution CC BY
decades from Landsat satellite images using water indices
and GIS techniques.
2. Materials and methods
2.1 Study Area
Vembanad Lake is the longest lake in India, largest lake in
Kerala state and it is the largest lagoon – backwater system
on the South West coast of India. This Lake had been
designated as Ramsar site in 1981 by the UNESCO in the
Iranian city of Ramsar. This lake is a part of a Vembanad–
Kol wetland system. It was declared as “Ecologically
sensitive zone” as per the Environment Protection Act 1985
of Govt. of India. This is an oxbow shaped lake extended for
a distance of 96 km, from Azheekode in the North to
Alappuzha in the South with a NW-SE orientation. This lake
is connected to the Arabian Sea at two places one at Cochin
and other at Munanbam. Minimala, Meenachil, Pamba and
Achankovil rivers flow into the lake in the southern side of
Thannermukom bund and Muvattupuzha, Periyar and
Chalakudy rivers flow north of the bund. These rivers carry
annually 732560MT of sediments into the lagoon (Narayana
et al., 2006). The study area map is given in Fig.1.
2.2 Data used
Landsat satellite data acquired by US Geological Survey
(USGS) and Global Land Cover Facility (GLCF) were
downloaded. Satellite images pertaining to the years 1973,
1992, 2005 and 2015 were used for this study. The details of
the satellite data and toposheets are given in the Table 1 and
Table 2.
Table 1: Satellite data used for the present study
Sensor
Acquisition Date
Source
Resolution (m)
Path/Row
MSS
10-02-1973
GLCF
60
155/53
TM
31-12-1992
GLCF
30
144/53
ETM+
10-02-2005
USGS
30
144/53
ETM+
22-02-2015
USGS
30
144/53
Table 2: Toposheets used for the present study
Toposheets No
Year
Scale
Source
58C/6,58C/5,58B/8 58B/4
1968
1:50,000
Survey of India
Figure 1: Location Map of the study area.
2.3 Image Preprocessing
In the present study the Erdas image and ArcGIS software
were used for the processing of satellite data. The
information stored in satellite imagery is not in real spectral
indices but it is in the form of digital numbers (DN).The
radiance was calculated from these digital number, from
which reflectance was calculated. In the present study, the
DN values were converted as top of the atmosphere (TOA)
radiance using the following equation of Wilson and Rocha
(2012).
Where, Lλ is spectral radiance received at the sensor in watts
per metre squared * ster * μm (Wm−2 sr−1 μm−1), Gain is
rescaled gain contained in the image product header file
(Wm−2 sr−1 μm−1), Qcal is quantised calibrated pixel
values in DN, Bias (or offset) is the rescaled bias contained
in the image product header file (W m−2 sr−1 μm−1),
QcalMAX is the maximum quantised calibrated pixel value
(corresponding to LMAXλ) in DN, QcalMIN is the
minimum quantised calibrated pixel value (corresponding to
LMINλ) in DN, LMAXλ is spectral radiance that is scaled to
QcalMAX (W m−2 sr−1 μm−1), and LMINλ is spectral
radiance that is scaled to QcalMIN (W m−2 sr−1 μm−1).
The atmosphere has a significant impact on satellite data,
Such as information loss caused by scattering by the
Paper ID: ART2016471
1395
International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
www.ijsr.net
Licensed Under Creative Commons Attribution CC BY
atmospheric constituents and aerosol. Atmospheric
correction for the scattering of aerosols can be assumed to be
of the order of one percentage of the total reflectance of
ground resolution cell. L1% was estimated using the equation
given below (Pacheo et al., 2014).
Where Esunλi is the exo-atmospheric solar irradiance for band
λi(Wm−2 μm−1), and d is the Earth–Sun distance (in
astronomical units).
The surface reflectance was estimated from the TOA
radiance reduced for aerosol scattering by using the
following equation. (Nazeer, M., 2014).
Where Lhazeλ is the path radiance for band λ (W m–2 sr–1
μm–1), Esunλ is the Exoatmospheric solar irradiance for
band λ (Wm–2 μm–1), and d is the Earth–Sun distance
(astronomical units).
2.4 Method of water body information extraction
Different pairs of band combinations are used to calculate
the wetness index (Kumar et al 2015). In general, green and
near infrared (NIR) (McFeteers, 1996; Leiji, 2009; Joao,
2012,; Feizhange et al. 2015), NIR and Middle infrared
(MIR) (Willson and Sader, 2005), Green and MIR (Xu,
2006; Li, et al., 2011; Hezham, et al., 2013; Kumar and
Lakshman, 2015; Gautan, et al., 2015), were used to
calculate wetness index. In the present study, NDWI and
MNDWI were used to extract water body information. The
NDWI is expressed as follows
NDWI= (Green-NIR)/ (Green + NIR) (McFeeters, 1996).
Where green is the green band, such as in MSS it is band 4
and band 2 in TM and NIR is the near infrared band, such as
band 6 in MSS, band 4 in TM.
NDWI is computed using the green and NIR bands of the
spectral band. Using this index water features have positive
values while the vegetation and soil usually have zero or
negative values. However, the application of NDWI in water
region with a built-up land background doesn't achieve its
goal as expected. The extracted water information in those
regions was often mixed with built up land noise. This
means that many built up land features also have positive
values in NDWI images. As, a result the computation of the
NDWI also produce a positive value for built up land just as
for water. However, a detailed examination of the signatures
revealed that the average digital number of the TM band 5
representing MIR radiation, is much greater than that of TM
band 2 (green). Therefore, if MIR band is used instead of the
NIR band in the NDWI, the built up land should have
negative value. Based on this assumption, the NDWI is
modified by substituting the MIR bands for the NIR band.
MNDWI can be expressed as follows (Tebbs et al., 2013 and
Feizhang et al., 2015).
MNDWI= (Green –MIR)/ (Green + MIR)
In MNDWI index water will have greater positive values
than the NDWI, because it absorbs more MIR light than NIR
lights, the built-up land, soil and vegetation will have
negative values in this wetness index.
MNDWI was applied only to 2005 and 2015 satellite
images, because the MSS images of the year 1973 and 1992
were lacking the MIR band. So NDWI was applied only for
those images which lacked MIR band. Theses calculation
was done using the spatial analyst extension in ArcGIS.
After calculating the NDWI and MNDWI index for
corresponding satellite images, the estuary area was
manually digitized using Arc GIS software and area was
calculated using the same.
2.5 Dynamic Degree of the lake area
In this study, the estuary area of different study periods was
derived using following equation (Feizhang, 2015 and Li et
al., 2009). K= (Ub - Ua) / Ua× 1/T ×100
Where, K is the dynamic indicator for lake area, Ua and U
are the areas of the lake at start date and at the end date and
T is the time scale under consideration.
3. Results and Discussion
In this paper, the author adopted NDWI to extract water
body information for 1973, 1992 images and MNDWI to
extract water body information for 2005 and 2015 images.
As shown in Fig: 2 the estuarine areas have changed in each
of the years for which study was undertaken. The result
shows that the overall decrease of the estuarine area during
the study period is 12.28 sq km (6.93%).
The change in the Vembanad Lake surface area is shown in
Fig: 3, which reveals that there occurred a noticeable
shrinkageduring the study period (42 years). In 1973 estuary
had 177.29 sq km area and in 1992 the area was reduced to
169.22 sq km. The estuary continued to shrink to 166.29 in
2005 and 165.01 in 2015. The total areal loss of the estuary
was 12.28 sq km between 1972 and 2015.
From the Table: 3 during the period 1973-1992 the Lake
area is decreased by 8.07 sq km and the dynamic degree is -
0.25 %. ;during 1992- 2005 the estuarine area had declined
by 2.93 sq km and the dynamic degree is -0.14% ; during
2005-2015 the estuarine area decreased by 1.28 sq km and
the dynamic degree is -0.18%. This indicates a declining
trend. Using the ArcGIS software the area of the estuary is
delineated and analysed. The overall decline of estuary
during the study period is 6.93% and which is shown in Fig:
4.
Paper ID: ART2016471
1396
International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
www.ijsr.net
Licensed Under Creative Commons Attribution CC BY
Figure 2: The 1973 and 1992 image shows the water body (blue colour) as extracted from NDWI and 2005 and 2015
Image (blue colour) shows the water body estimated from MNDWI
Paper ID: ART2016471
1397
International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
www.ijsr.net
Licensed Under Creative Commons Attribution CC BY
Figure 3: Surface area change of Vembanad Lake between
1973 and 2015.
Table 3: Change in estuarine area from 1973 to 2015.
Period
Lake area decreases
in sq km
Dynamic degree (K)
%
1973-1992
8.07
-0.25
1992-2005
2.93
-0.15
2005-2015
1.28
-0.08
1973-2015
12.28
-0.18
Figure 4: Spatial changes of Estuary during 1973-2015
Figure 5 (a): Lake area reclaimed for Willington Island
development.
Figure 5 (b): Lake area reclaimed for paddy cultivation in
Southern part of the lake.
During 1973 -1992 the estuary has decreased by an area of
4.45%, where 1.15 sq km estuary near R and H blocks were
reclaimed and 0.46 sq km of estuary area was reclaimed for
the extension of Willington Island. The field verification of
the relevant study is given in Fig: 5a and 5b. The spatial
changes during this period is given in Fig: 6a. During 1992-
2005 the estuarine area was apparently declined by 1.73%
among which a big portion of the estuary (0.61 sq km) was
reclaimed for the development of Willington Island and 0.32
sq km area in the North Eastern arm of the estuary is
reclaimed for paddy cultivation. Spatial changes during this
time are shown in Fig: 6b. In 2005-2015 periods 1.16 sq
kmof estuary area is reclaimed for cultivation and
plantations which is shown in Fig: 6c.
4. Conclusion
The pace of developmental activities focusaround the
Vembanad Lake area has eventually led to the decline of the
estuarine area. The developmental activities include
expansion of island area and agricultural processes. Firstly
the expansion of the port and container terminal area of the
Willington Island has made a decrease in the area of
northern part of the estuary. Secondly the conversion of a
portion of southern part of the estuary for agricultural
purposes, mainly for the implementation of reclamation of
lake waters into paddy fields. These factors have gradually
reduced the area of the lake during this period as evident
from the study conducted. The combined use of satellite
images and NDWI & MNDWI techniques has proved to be
flawless in the estimation of differences in area of the
Vembanad Lake. The study also warrants ceasing
Paper ID: ART2016471
1398
International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
www.ijsr.net
Licensed Under Creative Commons Attribution CC BY
unauthorised encroachment of lake area and seeks
intervention of authorities to implement necessary regulation
and better estuary protection protocols for Vembanad Lake.
Figure 6 (a): Spatial change during 1973-1992
Figure 6 (b): Spatial change during 1992 – 2005
Figure 6 (c): Spatial change during 2005 - 2015
5. Acknowledgement
The authors gratefully acknowledge The Kerala State
Council for Science, Technology and Environment
(KSCSTE), India for financial support. We also thank the
Director, National Centre for Earth Science Studies
(NCESS), Thiruvananthapuram for the facilities and the
support.
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International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
www.ijsr.net
Licensed Under Creative Commons Attribution CC BY
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Author Profile
Parvathy K. Nair received the BSc degree in Geology
from University of Kerala in 2010 and M.Sc. degree in
Geology from Mahatma Gandhi University in 2012.
She is now doing research in the field of Groundwater
studies in National Centre for Earth Science studies
Thiruvananthapuram, Kerala, India as KSCSTE Research Fellow.
Paper ID: ART2016471
1400
International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
www.ijsr.net
Licensed Under Creative Commons Attribution CC BY
Dr. D.S. Suresh Babu, received the M. Sc. degree in
Geology fromUniversity of Kerala in 1984 and Ph.D.
degree inGeologic-Mineralogical Sciencesfrom
Ministry of Higher Education, Moscow, Russiain
1990. He is a Scientist in Coastal Processes (CoP) in
National Centrefor Earth Science studies. Thiruvananthapuram,
Kerala, India.
Paper ID: ART2016471
1401