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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%).
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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
Scale
Source
58C/6,58C/5,58B/8 58B/4
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, is spectral radiance received at the sensor in watts
per metre squared * ster * μm (Wm2 sr1 μm1), Gain is
rescaled gain contained in the image product header file
(Wm2 sr1 μm1), Qcal is quantised calibrated pixel
values in DN, Bias (or offset) is the rescaled bias contained
in the image product header file (W m2 sr1 μm1),
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 m2 sr1 μm1), and LMINλ is spectral
radiance that is scaled to QcalMIN (W m2 sr1 μm1).
The atmosphere has a significant impact on satellite data,
Such as information loss caused by scattering by the
Paper ID: ART2016471
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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
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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(Wm2 μm1), and d is the EarthSun 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 m2 sr1
μm1), Esunλ is the Exoatmospheric solar irradiance for
band λ (Wm2 μm1), and d is the EarthSun 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
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ISSN (Online): 2319-7064
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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
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ISSN (Online): 2319-7064
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Volume 5 Issue 7, July 2016
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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
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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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Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
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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
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Index Copernicus Value (2013): 6.14 | Impact Factor (2015): 6.39
Volume 5 Issue 7, July 2016
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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
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... VKW is the longest river, stretching 96.5 km [20], and the largest brackish, humid tropical wetland ecosystem on the southwest coast of India, nourished by 10 rivers [13]. It has an oxbow shape, extending from Azheekode in the north to Alappuzha in the south, with a northwest-southeast orientation [38]. VKW is known for its rich biodiversity, with over 90 species of resident birds, 50 species of migratory birds, and a wide range of flora, including hydrophytes, mangroves, and associated species [41]. ...
... The wetlands also serve as a hub for retting coconut husks and the production of coir, a significant cottage industry in the region [5]. The Vembanad Lake was declared an "ecologically sensitive zone" by the Government of India under the Environment Protection Act of 1985 before being designated as a Ramsar site [38]. WATER ...
... These changes are primarily driven by the transformation of coastal wetlands for transportation and tourism purposes, as well as the discharge of untreated sewage and industrial waste, and encroachment for construction [33]. Numerous researchers have studied VKW using multi-temporal remote sensing satellite images and field-based studies, highlighting the declining dynamics of the wetland [33,38,49]. Gopalan et al. [21] provided a detailed historical overview of the vertical and horizontal reduction of VKW, with all studies reporting significant geoscape changes. ...
... Fieldwork was carried out in Vembanad Lake, Kumarakom (9.605N, 76.418E) in Kerala State, Southwestern India. Details about the ecophysiographic features of the Vembanad wetland are available in Ramamirtham et al. (1986), Rajan et al. (2011), andNair &Babu (2016). Only those snakes that were inadvertently killed by local farmers during their routine tilling procedures were collected and vouchered as wet-persevered, entire specimens. ...
... Fieldwork was carried out in Vembanad Lake, Kumarakom (9.605N, 76.418E) in Kerala State, Southwestern India. Details about the ecophysiographic features of the Vembanad wetland are available in Ramamirtham et al. (1986), Rajan et al. (2011), andNair &Babu (2016). Only those snakes that were inadvertently killed by local farmers during their routine tilling procedures were collected and vouchered as wet-persevered, entire specimens. ...
Article
We present new findings on Dussumier’s Mud Snake Dieurostus dussumierii based on recent fieldwork conducted in and around Vembanad Lake (Kumarakom) in Kerala, Southwest India. We describe a series of 10 voucher specimens, eight females and two males, ranging from juveniles (207 mm) to adults (835 mm). We report new data on microhabitat associations, fossorial haunts, sympatric aquatic snakes (Fowlea cf. piscator, Cerberus rynchops), and intraspecific morphological variations in this species. We also illustrate and describe an overlooked, historical, non-type specimen of this species collected over a century ago. This work assembles the largest dataset of preserved voucher specimens used to characterize D. dussumierii, since its description 170 years ago.
... Lake reclamation, driven by various human activities, has led to significant land use changes over the past few decades (Nair and Babu, 2016;Dipson et al., 2015). Wetlands and adjacent lakes, known as 'kayal,' have been subjected to reclamation for purposes such as agricultural expansion, aquaculture, harbour expansion, urban development, inland navigation and tourism. ...
... The variation in size of different water bodies, which undergo changes due to various factors, has been analyzed over multiple periods, using remote sensing and Geographical Information System (GIS) techniques in conjunction with field validation. Generally, the normalized difference water Index (NDWI) is used to rate changes in the water area [3]. ...
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The accuracy assessment of three different Normalized Difference Water indices (NDWIs) was performed in La Salada, a typical lake in the Pampean region. Data were gathered during April 2019, a period in which floods occurred in a large area in the Southwest of the Buenos Aires Province (Argentina). The accuracy of the estimations using spaceborne medium-resolution multi-spectral imaging and the reliability of three NDWIs to highlight shallow water features in satellite images were evaluated using a high-resolution airbone imagery as ground truth. We show that these indices computed using Landsat-8 and Sentinel-2 imagery are only loosely correlated to the actual flooded area in shallow waters. Indeed, NDWI values vary significantly depending on the satellite mission used and the type of index computed.
... The most cost-effective method is using remote sensing and GIS techniques together with field validation. In most cases, the Normalized Difference Water Index (NDWI) is used to rate changes in the water area [3]. Furthermore, several studies have been conducted using remote sensing data to detect spatial and temporal changes in flooded areas, study their changes, and assess actual or potential flood damage in urban areas. ...
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We performed the accuracy assessment of three different Normalized Difference Water Indices (NDWIs) in water bodies during April 2019, a period in which floods occurred in a large proportion of the Southwest of the Buenos Aires Province (Argentina). The accuracy of the estimations using spaceborne medium-resolution multi-spectral imaging, and the reliability of three NDWIs to highlight shallow water features in satellite images, was evaluated using a high resolution airbone imagery as ground-truth. It is shown that these indices computed using Landsat 8 and Sentinel-2 imagery are only loosely correlated to the actual flooded area in shallow waters. Indeed, NDWI values vary significantly depending on the satellite mission used and the type of index computed.
... Several studies have used NDWI (Lahay & Koem, 2021), a combination of NDWI-NDVI (Lahay & Koem, 2022), AWEInsh (Eraku et al., 2019), a combination of SVM classification with NDWIMNDWI-AWEI (Sarp & Ozcelik, 2017), MSTNDWI combination (Y. Zhou et al., 2018), MNDWI (Yue et al., 2017), NDWI-MNDWI combination (Nair & Babu, 2016), multispectral classification (Koto et al., 2016), combination NDVI-MNDWI (W. Zhou et al., 2015) and manual delineation (Trisakti et al., 2014) as methods for monitoring/mapping lake area. ...
Conference Paper
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The available water on the earth's surface consists of rivers, swamps, and lakes whose positions can be in the highlands to the lowlands. The lake area can be mapped using remote sensing data. Remote sensing has now become one of the important sources of information for data analysis on surface water. The AWEIsh index is one of the water indices that can be used as a method to map the area of a lake. AWEIsh has advantages over other water indices such as NDWI and MNDWI because it can produce higher accuracy values and can reduce shadow disturbances and dark building surfaces so that non-water pixels can be removed. This study aims to map surface water using medium-resolution optical images. The data used are Landsat 8 OLI image path/row 113/59 and path/row 112/59. Both images were recorded on April 10, 2021, and July 24, 2021. The data acquisition and processing method were carried out using the google earth engine platform. The method is based on cloud computing which can process large data (big data) and storage that is cloud computing. The first step in data processing is image preprocessing. Image preprocessing involves searching for minimal cloud images in the research area that have been corrected for atmospheric (TOA), and cutting the research area (Limboto and Tondano Lake). The next stage is processing, namely image data processing by reducing lake area information based on the AWEIsh index. The results showed that the AWEIsh index can be used as a reference for mapping the area of the lake, where a positive reflection indicates a water object and a negative value is a non-water object.
... During the last three decades, the Kerala coast has lost a significant proportion of its mangrove forests (Sreelekshmi et al., 2020), the destruction is much higher in the Ernakulam district with remaining mangrove and natural estuarine areas located mostly around the Vembanad lake (Rani et al., 2018;Sreelekshmi et al., 2020). During the period between 1973 and 2015, there were losses of 6.93% (~12.28 km²) (Parvathy & Babu, 2016). Accordingly, our current observations not only update the distribution status of B. × rhynchopetala, but they also emphasise the great need for urgent conservation of these valued but threatened ecosystems of south-western India. ...
Article
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Bruguiera × rhynchopetala (W.C.Ko) N.C.Duke & X.J.Ge, a natural hybrid between mangrove species, B. gymnorhiza (L.) Lam. and B. sexangula (Lour.) Poir., is newly reported for India. This is the first time a Bruguiera hybrid has been reported outside the south-east Asian, east Asian and western Pacific region. The intermediate nature of this new entity for India was confirmed using morphometric analyses. It was observed within the reportedly threatened mangrove area of Vembanad lake which is the largest Ramsar site on the South West coast of India. These regions were noted for the co-occurrence of their putative parental taxa. Our report updates the currently existing distribution of B. × rhynchopetala and further challenges prior assumptions that populations of B. gymnorhiza and B. sexangula at their western limit of overlap show a greater degree of genetic separation between parent species. Also, it emphasises the urgency of conserving the Vembanad ecosystem and the biological diversity at risk.
... The most commonly used water indices are the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) (Xu, 2006). Both of these methods were applied in the previous studies (Dinka, 2012;Donia, 2019;Nair & Babu, 2016;Zheng et al., 2019). Therefore, NDWI and MNDWI were used as the primary methods in this study. ...
Article
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Lakes are of great value to human beings and important for various reasons like regulating the flow of river water, to maintain the eco-system and storage of water during the dry seasons. Lake Haramaya, which is situated at 14 km Northwest of Harer town (UNESCO Site) is one of the famous and beautiful lakes of Ethiopia. It acts as a source of life for human beings and animals. The over exploitation of the lake haramaya for water supply and agricultural purposes has led to its extinction in the last two decades. This study attempts to identify the fluctuations in surface area of Lake Haramaya between 1995 to 2020 by using multi-temporal satellite data. The Landsat 5TM images of 1995, 2000 and 2010, Landsat 7ETM+ image of 2005 and Landsat 8 OLI TIRS images of 2015 and 2020 are analyzed using the Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) methods. These methods are used to quantify the changes in surface area and compared to each other for identifying the suitable method for detecting water bodies. The present study shows that, between 1995 to 2010 the lake lost up to 2.3238 sq.km and almost dried. But, during 2011 to 2020 the lake surface area increased by 2.6946 sq.km. The study states that the lake surface area is fluctuating and MNDWI method is highly reliable in extracting water bodies.
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Eutrophication, the biological response to the excess input of nutrients into a water body, arises rarely under natural conditions, but commonly recognized because of human activities (Moss, 1988). Changes in the aquatic environment accompanying anthropogenic pollution are a cause of growing concern and require monitoring of the surface waters and the organisms inhabiting them (Vandysh, 2004). Polluted water also represents a potential hazard to the aquatic environment (Rauf and Javed, 2007). Problems occur when the quantity of organic matter discharged exceeds the carrying capacity of the ecosystem and/or when its dispersion is constrained within coastal waters. The different plant operations of the seafood processing industry generate large quantities of effluents. The characteristics of the waste water produced are defined and conditioned by the operations and production lines of the factory. Some factories work practically the whole year round and produce only one type of product (Veiga, 1989) such as tuna processing industries, others possess different Vidya V* and G. Prasad The seafood processing units found along the coast of Vembanad Lake are posing serious threat to water bodies by unloading their waste without any treatment. The present study was conducted in Cherthala-Aroor-Edakochi coastal belt, where most seafood processing plants are functioning. The water samples were collected from ten different stations for a period of two years on a monthly basis. The stations, S1-S9 were closely associated with the seafood processing discharge outlets and the S10, was kept as a reference site, which is free from the seafood processing discharge. The physico-chemical parameters such as atmospheric and water temperature, TDS, pH, EC, salinity, DO, BOD, alkalinity, COD, hardness and nutrients such as nitrate, phosphate, ammonia and silica. The present study was formulated to create thematic maps to understand the stress zones using the physicochemical parameters in and around the collection sites of this wetland. Parameters like TDS, alkalinity, BOD, COD and hardness exceeded the permissible limits in polluted stations. EC, TDS, hardness, alkalinity, BOD, COD and nitrate are found to be high during monsoon whereas high phosphate, ammonia, and silicate are reported in post monsoon season. Hypereutrophic status is high in the interconnected channels than the main water body.The increased levels of free CO2, BOD, phosphate, nitrate, and ammonia in the selected stations confirms degrading lake water because of seafood processing effluents. High content of nutrients namely, nitrate and ammonia is associated with eutrophication. The thematic map prepared supported the laboratory analysis.
Chapter
Assam, a state in northeastern India, experiences intensive and frequent floods every year owing to Brahmaputra River as well as dynamic and extreme atmospheric circulation. In May 2020, cyclone Amphan hit northeast India including Assam and caused a severe emergency. Therefore, continuous and precise monitoring of floods through remote sensing techniques has received attention for monitoring and analyzing such frequent events. Synthetic aperture radar (SAR) data has proved to be the best source for monitoring and analyzing floods because it functions day and night and is independent of weather extremes. This study used Sentinel-1 SAR and Sentinel-2 Multispectral Instrument (MSI) data and the Modified and Normalized Difference Water Indices (MNDWI and NDWI) to study floods in the Kopili River basin in Assam. SAR data (VV polarization) and false color composites of Sentinel MSI data were classified using a support vector machine algorithm to find the proportion and extent of flood water in the area. The classified map was postprocessed and validated with 98% and 94% overall accuracy and 0.97 and 0.89 kappa coefficients, respectively. The results show that SAR-based flood maps give more accurate results than do optical data. SAR data show that almost 15% of the area had a flooding emergency in the study area. Moreover, MNDWI values provide better representation of water compared with NDWI.
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Ashtamudi estuary, the second largest wetland ecosystem of Kerala is the deepest among all the estuaries of Kerala. This wetland is under severe environmental stress due to large-scale land use / land cover conversions that occurred in and around it for the past several decades. Ashtamudi is one of the 21 notified wetlands of paramount importance in India needing special conservation measures. In the present study, land use/ land cover conversions in Ashtamudi wetland region from 1967 to 1997 is quantified using the technique of Remote Sensing and Geographic Information System (GIS). Survey of India topographic map, hard copies of IRS-IA LISS II, IRS-IC LISS III images are used in the present work. The study shows that increasing population density, change in family system, extensive coconut husk retting and deposition of husk waste along the margin of the estuary, solid waste deposition from factories, reclamation of the estuary by local population and low profit obtained from paddy cultivation are mainly responsible for the large-scale land use/ land cover conversions in the wetland region. The study tries to correlate unscientific land use/ land cover conversions in the wetland region with its environmental degradation.
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The normalized difference water index (NDWI) has been successfully used to delineate surface water features. However, two major problems have been often encountered: (a) NDWIs calculated from different band combinations [visible, near-infrared, or shortwave-infrared (SWIR)] can generate different results, and (b) NDWI thresholds vary depending on the proportions of subpixel water/non-water components. We need to evaluate all the NDWIs for determining the best performing index and to establish appropriate thresholds for clearly identifying water features. We used the spectral data obtained from a spectral library to simulate the satellite sensors Landsat ETM+, SPOT-5, ASTER, and MODIS, and calculated the simulated NDWI in different forms. We found that the NDWI calculated from (green – SWIR)/(green + SWIR), where SWIR is the shorter wavelength region (1.2 to 1.8 mm), has the most stable threshold. We recommend this NDWI be employed for mapping water, but adjustment of the threshold based on actual situations is necessary.
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Information on the spatial distribution of Hydrologically Active Areas (HAAs) in a watershed is an important input for many applications, such as hydrological modeling, water resource planning and flood estimation. HAAs can be delineated using a wetness index derived from either a Digital Elevation Model (DEM) or from satellite data. The purpose of this study was to develop and apply a methodology to delineate the HAAs in the Harangi (535 km 2) and Hemavathy (2974 km 2) watersheds located in Karnataka, India. Spatial distributions of HAAs derived from the DEM and from satellite data (Landsat 7 ETM+ sensor) were compared. It was found that wetness index obtained from satellite data was better able to capture the HAAs in comparison to the use of DEM. The delineated HAAs will be useful in identifying runoff generation areas and improve process representation in distributed hydrological modeling of the watershed.
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The study area, located in the western side of Kerala State, South India, is a part of Vembanad-Kol wetlands - the largest estuary in India's western coastal wetland system and one of the Ramsar Sites of Kerala. Major portion of this estuary comes under the Ernakulam district which includes the Cochin City - the business and Industrial hub of Kerala, which has seen fast urbanization since independence (1947). Recently, this region is subjected to a characteristic fast urban sprawl, whereas, the estuarine zone is subjected to tremendous land use/land cover changes (LULC). Periodic monitoring of the estuary is essential for the formulation of viable management options for the sustainable utilization of this vital environmental resource. Remote sensing coupled with GIS applications has proved to be a useful tool in monitoring wetland changes. In the present study, the changes this estuarine region have undergone from 1944 to 2009 have been monitored with the help of multi-temporal satellite data. Estuarine areas were mapped with the help of Landsat MSS (1973), Landsat ETM (1990) and IRS LISS-III (1998 and 2009) using visual interpretation and digitization techniques in ArcGIS 9.3 Environment. The study shows a progressive decrease in the estuarine area, the reasons of which are identified chronologically.