INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES
Volume 6, No 5, 2016
© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0
Research article
ISSN 0976 – 4402
Normalised difference water index based water surface area delineation
and assessment
Mohit Singh, S.N.Mohapatra and Monika Sharma
Centre of Remote Sensing and GIS, School of Studies in Earth Science,
Jiwaji University, Gwalior-474011, India
[email protected]
doi: 10.6088/ijes.6060
ABSTRACT
In the present paper, the geospatial technology has been extensively used to map and assess
the changes of water surface area of Tighra da m situated near Gwalior city of Madhya
Pradesh.. The change of water surfaces area has been examined for last one decade period.
The LANDSAT digital satellite data from different sensors for the year 2004, 2009 and 2014
have been used for the present study. All the satellite data used are of the month June of
respective years. The Normalised Difference Water Index (NDWI) was derived by using the
NIR and SWIR-1 band. The NDWI along with the SWIR-2 band has been used to delineate
the water surface area. In order to delineate the water surface, a threshold NDWI value has
been assigned to separate it from other classes. The possible causes for the changes in the
water surface area over the said years have been discussed. The climatic conditions
particularly the rainfall over the study period has been studied and correlated with the
fluctuation of the water surface area of the dam It has been concluded that the changes in
open water features of the area is related to climate change and other societal development
activities. However, it is observed that the resolution of the images is one of the constraints,
and high-resolution image can provide more accurate and precise result.
Keywords: Water Surface Area, Water Resources, LANDSAT, GIS, NDWI.
1. Introduction
Water plays such a significant role in our day to day life, that it has been inevitable to manage
and monitor water resources.. But the availability of water and its amount continuously
fluctuate whole year. So, monitoring of this fluctuation of water availability over a large area
is required to avail the water to us without interruption. Dams are the engineering structures,
built to create a reservoir to meet the demands of water for human needs. But monitoring
these resources using conventional techniques is a tough task. However, field measurements
of the dam level are still overpriced and involve a lot of effort and time (Yunus and Fidelia,
2012).
In this era of geospatial technology, we can estimate, monitor and map the surface water
bodies’ occurrences and its extent with the availability of suitable satellite data. By acquiring
synoptic view of the earths’ surface using remote sensing technologies helps to detect not
only changes but also provides cheap and quick information about the status of the water in
reservoir/dams and the land use/land cover (Mustafa et al., 2012; Verma et al., 2013). In the
present study, the open water surface area of the Tighra reservoir has been estimated using
Normalized Difference Water Index (NDWI) and multispectral LANDSAT satellite data of
Received on December 2015 Published on March 2016
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Normalised difference water index based water surface area delineation and assessment
the year 2004, 2009 and 2014. This study further helps in prioritizing the surface water body
for conservation purposes.
2. Study area
Tighra Dam is situated in the west of Gwalior City at 26.12°N latitude and 78.18°E
longitudes on the Sank River. The climate of Gwalior is extremely hot and cold during
summers and winters respectively (Figure 1). It experiences south-western monsoon rains in
June-September with an annual rainfall of around 766 mm.
Figure 1: Climate data for Gwalior from year 1951-2000: Source IMD.
Figure 2: Location of Study Area.
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Normalised difference water index based water surface area delineation and assessment
Tighra reservoir forms the main source of water for Gwalior city (Figure 2). The water of the
reservoir is also dependent on the Tighra-Kaketo system. The supplies from Tighra dam are
supplemented by supply from another reservoir of the Kaketo dam on Narver River. Water is
supplied from the dam to the city after treatment of water from Moti Jheel plant. A significant
volume of this water is supplied to Gwalior city from the Tighra dam. Thus water levels in
the dam are checked on a daily basis for assuring that there should be no shortfall in supplies
for next coming days.
3. Methodology
3.1 Data Used
In this study, LANDSAT satellite data, topographic sheet and other ancillary data such as
weather and climatic condition of the study area has been used.. Any change detection
analysis requires at least two data of different time period. Here we used LANDSAT sensors
data (Table 1) of the month June of the year 2004, 2009 and 2014. Details characteristics of
these data are discussed below.
Table 1: Summarised detail of Landsat sensor data used in this work
S.No.
1
2
3
Acquisition
Satellite
Month
June – 2004
June – 2009
June – 2014
Landsat
7
Sensor
ETM+
No. of Band Used for NDWI /
Bands
Resolution (meter)
Band4/30m
NIR
Band5/30m
SWIR-1
Band4/30m
NIR
Band5/30m
SWIR-1
Band5/30m
NIR
Band6/30m
SWIR-1
8
Landsat
5
TM
Landsat
8
OLI and
11
TIRS
7
3.1.1 Landsat 7 ETM+
The Landsat Enhanced Thematic Mapper Plus (ETM+) is the sensor of the Landsat 7 satellite.
The images of earth have been acquired nearly continuously since July 1999, with a 16-day
repeat cycle. The images of Landsat 7 are referenced to the Worldwide Reference System-2.
The Scan Line Corrector (SLC) on the instrument failed in May of 2003
(www.explorationconnect.com.au). This caused all Landsat 7 data acquired after this date to
have line gaps. Landsat 7 ETM+ images have eight spectral bands with a spatial resolution of
30 meters for bands 1 to 7. The panchromatic band 8 has a resolution of 15 meters. All the 7
bands collect one of the two gain settings (high or low) for increased radiometric sensitivity
and dynamic range. Whereas, Band 6 collects both high and low gain for all scenes.
Approximate scene size is 170 km north-south by 183 km east-west. The acquired data of 22Mohit Singh, Mohapatra S.N. and Monika Sharma
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Normalised difference water index based water surface area delineation and assessment
June-2004 of path 145 and row 042 has been used for this study. The data is affected by the
SLC-off error, and it is filled by local histogram matching and focal analysis method using
image processing techniques.
3.1.2 Landsat 5 TM
The Landsat Thematic Mapper (TM) sensor of Landsat 4 and 5 was operated from July 1982
to May 2012 with a 16-day repeat cycle. The images were referenced to the Worldwide
Reference System-1. Very few images were acquired from November 2011 to May 2012.
The satellite began decommissioning activities in January 2013. Landsat 4-5 TM image data
files consist of seven spectral bands. The resolution is 30 meters for bands 1 to 7. (Thermal
infrared band 6 was collected at 120 meters but was resampled to 30 meters
(www.explorationconnect.com.au). The approximate scene size is 170 km north-south by 183
km east-west. In this study, the has been acquired data of path 145 and row 042 of dated 12June-2009
3.1.3 Landsat 8 OLI and TIRS
The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are instruments
onboard for the Landsat 8 satellite, which was launched in February of 2013. The satellite
collects images of the Earth with a 16-day repeat cycle, referenced to the Worldwide
Reference System-2 (www.usgs.gov). The satellite’s acquisitions are in an 8-day offset to
Landsat 7. The approximate scene size is 170 km north-south by 183 km east-west.
The spectral bands of the OLI sensor are similar to that of Landsat 7 ETM+ sensor which
provides enhancements except two new spectral bands: a deep blue visible channel (band 1)
specifically designed for water resources and coastal zone investigation, and a new infrared
channel (band 9) for the detection of cirrus clouds. Two thermal bands (TIRS) capture data
with a minimum of 100-meter resolution but are registered to and delivered with the OLI data
product. Landsat 8 file sizes are larger than Landsat 7 data, due to new bands and improved
16-bit data product. In this study, we have acquired the data of 10-June-2014 for 145/042
(path/row).
3.2 Methods
The flowchart of methodology is shown in the (Figure 3). The major steps are shown in the
flowchart but there are also some intermediate steps which will be carried out during the
tenure of study are not shown.
3.2.1 Geometric Correction
Landsat data of three different years are georeferenced with Survey of India topographic
sheet which is having scale 1:50000 and re-projected to UTM Zone 44 North considering the
16-GCPs for the image to map registration using first order polynomial transformation and
nearest neighbor sampling method.
3.2.2 Filling Gap of Landsat 7 ETM+ Data
Due to the failure of instrument “Scan Line Corrector” in Landsat 7 ETM+ sensor imagery
are affected by line gap. These gaps are filled by applying linear histogram matching
methodology to the filling image to adjust it based on the standard deviation and mean values
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of each band. In this digital image processing techniques, LANDSAT 5 TM scene considered
as the master scene for filling the gap.
Figure 3: Methodology
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3.2.3 Atmospheric Correction
Many atmospheric correction methods are widely used for pre-processing satellite data to
enhance the imagery in terms of visual interpretation. But there is dark object subtraction
which is often useful when we perform band-ratios. The dark object represents the area of
zero reflectance values in the image including reflection from water in NIR, shadow,
scattering and electrical gain in the sensor. In this work, dark object subtraction was applied
by verifying the dark object greatest in reflected band and with comprises less in the other
band.
3.2.4 NDWI and Thresholding
There are many band rationing technique which are proposed for use with multispectral
satellite data. Some are more complex ratios involving the sums of and differences between
the various spectral band, from one of them, is Normalized Difference Water Index (NDWI)
has been widely used for estimating water of vegetation using remote sensing. NDWI is
defined as (ρ(0.86 µm) - ρ(1.24 µm))/(ρ(0.86 µm) + ρ(1.24 µm)), where ρ represents the
radiance in reflectance units(Gao, 1996).
Figure 4: NDWI for year (a) 2004, (b) 2009 and (c) 2014.
The value of NDWI ranges from -1 to +1.
𝑁𝐷𝑊𝐼 =
𝑁𝐼𝑅 − 𝑆𝑊𝐼𝑅 1
𝑁𝐼𝑅 + 𝑆𝑊𝐼𝑅 1
In the resultant NDWI images has been shown in Figure 4, the pixel which is having higher
water content is appears brighter than the pixel having lower water content. The NDWI value
for the year 2004 ranged from -0.15 to +0.15, 2009 ranged from -0.22 to +0.44 and 2014
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Normalised difference water index based water surface area delineation and assessment
ranged from 0.7 to 0.5. For the delineation of surface water extent, we estimated and apply
thresholding to the range of NDWI by relating the range of brightness values of SWIR-2
band of these Landsat sensors where water bodies appeared nearly black in grey scale.
Subsequently, the water surface area of Tighra reservoir (Figure 5) is estimated after applying
thresholding to the NDWI ranges.
Figure 5: Estimated water surface area for year– 2004, 2009 and 2014
4. Results and discussion
In this study, there is a decrease in water surface area of Tighra reservoir which is estimated
from the year 2004 to 2014 is -1.02 sq.km. (Figure 6). Water surface area of the Tighra
reservoir in the year 2004, 2009 and 2014 was 13.17, 12.94, 12.15 sq.km. respectively (Table
2). The average estimated surface area of Tighra reservoir for the period of 2004-2014 is
12.75 sq.km.
Figure 6: Estimated water surface area for the period of 2004-2014.
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Figure 7: Change in water surface extent in Tighra reservoir from 2004-2014.
Meteorological data has been correlated with the resultant change in water surface area.
There are changes in the surface water extent of the Tighra reservoirs from 2004
subsequently in ten years are shown in Figure 7. During this period 2004-2014, we have
noticed that there is a little increment in average total rainfall comparatively to a single year
and also rise in temperature. Total monthly precipitation (Figure 8) of the Tighra reservoir
region is considered according to the year of satellite data used in this study. Total rainfall in
Tighra reservoir region for the year 2004, 2009 and 2014 was 735.6, 516.6 and 717.6 mm
respectively which has been given in Table 2. When the minimum, maximum and average
rainfalls for the five-year are considered it is observed that even after good rainfall condition
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water surface extent tends to decrease in the reservoir. This increase in rainfall does not bring
trend up and increase the water surface area rapidly, but gradually it takes the time to reflect
the change in water surface area of the reservoir.
Table 2: Yearly and periodical analysis of data for total rainfall, average temperature and
estimated water surface area
Year/Period
Rainfall
(mm.)
Temperature
(°C)
Estimated Water
Surface Area (sq.km.)
2004
735.6
25.62
13.17
2009
516.6
26.40
12.94
2014
717.6
26.41
12.15
2004-2014
738.41
25.82
12.75
Figure 8: Total monthly rainfall for year 2004, 2009 and 2014.
The average rainfall for a period of ten years is 738.41 mm in the Tighra reservoir region.
The total rainfall for the year 2004 is 735.6 mm which is lower than the average rainfall.
Similarly, the total rainfall recorded in the year 2009 is 516.6 mm and in the year 2014 is
717.6 mm which is also lower than the average rainfall. Thus, lower rainfall in the region can
be considered leading cause of the decrease in water surface area of Tighra reservoir. Annual
average temperature variation for the period 2004-2014 is shown in Figure 9. On examining
the mean temperature data for the Tighra reservoir region it was 25.62°C in the year 2004,
26.40°C in the year 2009 and 26.41°C in the year 2014 (Table 2). The average temperature of
the Tighra dam region for the period 2004-2014 was 25.82°C. Except in the year 2004, the
mean temperature of the year 2009 and 2014 were higher than the mean temperature for the
period 2004-2014. Thus rise in temperature causes increase in evaporation rate to decrease
water surface area of the Tighra reservoir. In Figure 10 average annual temperature of each
year from the period 2004-2014 and its trend which is went down are observed during
analysis. Similarly while carrying out the statistical analysis of meteorological data as a
minimum, maximum and the average temperature for the period of 2004-2009 and 20092014 shown in Table 3, there is a decrease in mean temperature values are observed. The
relationship between rainfall, temperature and water surface area extent are shown in Figure
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Normalised difference water index based water surface area delineation and assessment
10. Thus, a decrease in water surface area cannot be directly co-related to these
meteorological data except for rainfall. There are other factors such as supply and demand of
water for irrigation, industrial and domestic purposes causing changes in water surface area
of the reservoir.
Figure 9: Annual average temperature.
Table 3: Five-year maximum, minimum, average of rainfall, temperature and estimated
change in water surface area
Period
Max.
Min.
Avg.
2004-2009
768.8
516.6
637.48
2009-2014
1027.4
516.6
802.37
2004-2009
26.48
25.29
25.95
2009-2014
26.41
24.96
25.78
Estimated
Change
in 2004-2009
Wate r
Surface
Area
(sq.km.)
2009-2014
-0.23
Rainfall (mm.)
Temperature (°C)
-0.79
Figure 10: Relationship between Rainfall, Temperature and Water Surface Area Extent.
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5. Conclusions
Monitoring the changes of water in the reservoir is important in terms of ecological and
historical consideration. In this study, ten year period was investigated easily with the help of
Remote Sensing and GIS technology. Landsat sensors data of the year 2004, 2009, 2014 and
meteorological data was considered, and it is concluded that there is a reduced open water
surface area from 13.17 to 12.15sq.km in ten years from 2004 to 2015. The reason for this
declined in water surface area is due to change in climatic condition. The rainfall is a major
factor which is responsible for increase/decrease of water in the Tighra reservoir and also the
supply and demand scenario of consumption of water for irrigation, industrial and domestic
purposes. The major part of Gwalior city which mainly depend upon the supply of water from
Tighra reservoir are now facing a shortage of water supply. The Municipal Corporation,
Gwalior provides water on alternate days from the dam to ensure the water should be
available in next coming days and hope the reservoir will be filled in next upcoming rainy
season. Thus, continuous monitoring of water in the reservoir is essential in order to take
necessary action.
Field measurements of the dam level are still very expensive and requires a lot of effort and
time. This study shows that the synoptic view of Satellite data and the use of geospatial
technology help to provide information on the larger surface area of the earth without
conducting a field survey. NDWI is less affected by atmospheric scattering. NDWI does not
remove completely background soil reflectance effects, but it minimizes the effect of their
reflectance which makes it easier to delineate and estimate the surface area of water bodies on
the Earth’s surface (www.modis.gsfc.nasa.gov). There are other factors such as the resolution
of remote sensing data which are the main constraints for more accurate and precise work.
Delineating the water surface area from satellite imagery has its own advantages over the
conventional techniques. With the help of remotely sensed data, we can easily interpret the
water bodies in the larger area of the earth surface. In the perspective of remote sensing and
geographical information system (GIS), the spectral and spatial characteristics of these
satellite data helped in wetland mapping, estimation of moisture condition of soil and
vegetation with the aid of digital image processing techniques. Remote Sensing data not only
helps in the study of the larger surface area of the earth but also helps in temporal change
analysis with ease if there is no previous dated data is available. Thus, it can play an
important role in monitoring changes in water resources and help to take necessary action
6. References
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