ABSTRACT Secchi disk transparency (SDT) is the simplest and the most efficient measure to monitor water quality of the reservoir. The nutrients from the agriculture fields, sewage from human settlements and industries drain into reservoirs and lakes. It contributes to the increasing level of suspended particles, algal growth and coloring of water and reduces transparency of water. These changes can be detectable by remote sensors. In this study LISS III sensor of Resourcesat-2 used to model SDT of Warasgaon reservoir, Pune. The green (r = 0.75), red (r = 0.79) and NIR (r = 0.75) bands of LISS III showed good correlation with observed SDT, while band ratios Red/NIR (r = 0.81) and NIR/SWIR (r = 0.81) showed significant correlations. Several linear and multiple linear regression models developed from the in-situ measurements of SDT and the radiance value of LISS III image. The multiple linear regression model based on green, red, NIR and red/SWIR found to be the best fit (r = 0.88) to the in-situ data. The results showed that the Warasgaon reservoir was oligotrophic in condition during the December 2012

Lakshmi N Kantakumar

Priti Neelamsetti

Sangeeta V Pandit

CiteSeerX

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES   Volume 5, No 4, 2015 
 
© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 
 
Research article                                                                                                        ISSN   0976 – 4402  
                                                                                                                      
Received on November 2014 Published on January 2015                                                                                 794                          
Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by 
using LISS III sensor 
Priti Neelamsetti1, Sangeeta V. Pandit1, Lakshmi N. Kantakumar2 
1-Department of Zoology, University of Pune, Pune- 411007. 
2-Institute of Environment Education and Research, Bharati Vidyapeeth University, Pune-
411043. 
drpanditsv@unipune.ac.in  
doi: 10.6088/ijes.2014050100074 
 
ABSTRACT 
 
Secchi disk transparency (SDT) is the simplest and the most efficient measure to monitor 
water quality of the reservoir. The nutrients from the agriculture fields, sewage from human 
settlements and industries drain into reservoirs and lakes. It contributes to the increasing level 
of suspended particles, algal growth and coloring of water and reduces transparency of water. 
These changes can be detectable by remote sensors. In this study LISS III sensor of 
Resourcesat-2 used to model SDT of Warasgaon reservoir, Pune. The green (r = 0.75), red (r 
= 0.79) and NIR (r = 0.75) bands of LISS III showed good correlation with observed SDT, 
while band ratios Red/NIR (r = 0.81) and NIR/SWIR (r = 0.81) showed significant 
correlations. Several linear and multiple linear regression models developed from the in-situ 
measurements of SDT and the radiance value of LISS III image. The multiple linear 
regression model based on green, red, NIR and red/SWIR found to be the best fit (r = 0.88) to 
the in-situ data. The results showed that the Warasgaon reservoir was oligotrophic in 
condition during the December 2012. 
 
Keywords: Secchi disk transparency (SDT), Liss III, regression model. 
 
1. Introduction 
 
Surface water is an essential natural resource to the living beings. Its quality directly affects 
the ecosystems, human health, and economic activities. Lakes, reservoirs, rivers and ocean 
are the main sources of surface water. Generally, reservoirs are built to store massive amount 
of surface water for drinking, irrigation and generate electric energy. The nutrients from the 
agriculture fields, sewage from human settlements and industries drain into reservoirs and 
lakes. It contributes to the increasing level of suspended particles, algae growth, and coloring 
of water and reduces dissolved oxygen levels that deplete the quality of water and threat the 
ecosystem of the water body. The increased level of suspended particles and growth of Algae 
reduces the sunlight penetration into the water (Choubey 1994). The sunlight penetration into 
the water can be measured in terms of transparency. It decreases with the increase of turbidity 
and biological activity in the water. Therefore, transparency of the water can be used as a 
measure to monitor the water quality of the reservoir. Secchi disk is a simple and economic 
device used to measure the transparency of water. It is made of a circular metal disk of 8 inch 
in diameter painted black and white in alternating quadrants and attached to a measurement 
thread. The disk is slowly lowered into the water until it is no longer visible. The depth of the 
water where the disk disappeared is termed as Secchi Disk Transparency (SDT), A reservoir 
can be classified into four categories (Table 1), i.e, oligotrophic, mesotrophic, eutrophic and 
hyper-eutrophic based on SDT values (Carlson and Simpson 1996). The nutrient levels in the 
Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor 
Priti Neelamsetti et al., 
International Journal of Environmental Sciences Volume 5 No.4, 2015 
795
water increases from oligotrophic to hyper-eutrophic, while the water quality and 
transparency decreases. The aquatic plants and algae presence are abounded in the eutrophic 
and hyper eutrophic reservoirs. 
 
Table 1:  Lake tropic states and classification range for SDT (Carlson and Simpson 1996) 
 
Classification SDT  (m) Water Calrity 
Oligotrophic >8 - 4 Clear water, Salmonid Fisheries dominate 
Mesotrophic 4 - 2 Moderate clear water 
Eutrophic 2 - 0.5 
Less Clear water, Warm water fisheries dominates, 
Nuisance macrophytes, algal scums 
Hypereutrophic 
0.5 - 0.25 
> 
Dense algae and macrophytes 
 
SDT sampling requires in-situ measurements. These in-situ measurements are accurate, and 
require a lot of time and money to enumerate the entire reservoir.  Remote sensing can be 
used as a contemporary tool to overcome this difficulty. Several researchers used remote 
seining sensors in combination of in-situ measurements of SDT to monitor the water quality 
of lakes (Choubey 1994; Harris et al. 1976; Sheela et al. 2011; Thiemann and Kaufmann 
2000). Regression equation based on observed SDT and radiance values of satellite imagery 
often used to estimate SDT at un-sampled locations (Baban 1993; Olmanson et al. 2001). 
Landsat Multi Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic 
Mapper plus (ETM+) data was widely used to estimate the SDT of water bodies (Baban 
1993; Giardino et al. 2001; Harris et al. 1976; Olmanson et al. 2008). Indian remote sensing 
sensors viz., LISS-I, III and IV are also used to estimate the water quality of the reservoirs 
(Choubey 1994; Mabwoga et al. 2010; Sheela et al. 2011; Thiemann and Kaufmann 2000).  
In this study Resourcesat-2’s LISS III (Linear Imaging Self Scanner) imagery acquired from 
National Remote Sensing Centre (NRSC) of India used to monitor the SDT of Warasgaon 
reservoir. 
 
2. Material and methods 
 
2.1 Study area 
 
Warasgaon reservoir on Mose River is situated south west to the Pune city. Also, it is known 
as Veer Pasalkar dam. The reservoir extended approximately 20 km in length from west to 
east and about 5 km wide in north to south direction. The reservoir was constructed to cope 
up with drinking water needs of the Pune city. The reservoir water is used also for recreation, 
fishing and irrigation. Warasgaon reservoir (Figure 1) bounded between coordinates 18° 21' 
to 18° 25' north of equator and 73° 25' to 73° 37' east of Greenwich meridian.  
 
2.2 Secchi disk measurements 
 
Secchi disk transparency (SDT) is a commonly used, economic parameter measures the water 
quality (Guan et al., 2011). SDT values measured at 24 sampling locations on 24-Dec-2012 
and selected on the basis of systematic random sampling nearly 500 meters apart (figure 2), 
In order to avoid the reflection from the surrounding vegetation classes the sample locations 
are selected along the central cross section of the reservoir. The location of each sampling 
point is measured by using Garmin 24 channel hand held GPS. 
Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor 
Priti Neelamsetti et al., 
International Journal of Environmental Sciences Volume 5 No.4, 2015 
796
 
Figure1: Location map of the Warasgoan reservoir 
 
 
Figure 2: Map showing sampling location and water affected by cloud over the reservoir. 
 
3. Satellite image processing 
 
The clear water allows high transmittance and less absorption of wavelength below 0.6 µm 
and large amount of suspended sediments from soil erosion contributes to the reflectance of 
Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor 
Priti Neelamsetti et al., 
International Journal of Environmental Sciences Volume 5 No.4, 2015 
797
higher wavelength in visible range (Li and Li 2004).  The increasing level of nutrients in 
water causes growth of algae and increases the water reflectance in green wavelength. Further, 
the transmittance of water decreases with the increase of sediments and algae growth. The 
level suspended sediments are strongly correlated with the Near Infra-Red (NIR) radiation 
(Mabwoga et al. 2010; Onderka and Pekárová 2008). These optical properties of water can be 
detectable by the satellite sensors. Once the relationship established between the observed 
SDT values with corresponding radiance values in satellite image, one can estimate the SDT 
at unsampled locations. Therefore, remote sensing data provides a contemporary solution to 
overcome the difficulty to predict water quality of unsampled location (Fuller et al. 2004). 
This study used NRSC’s LISS-III sensor dated 20-Dec-2012 to monitor the water clarity of 
the study area. Table 2 presents the specification of LISS III sensor boarded on Resourcesat-2. 
The acquired satellite imagery was geo-referenced with the help of Survey of India’s Open 
Series Map (OSM) toposheets E43H11 and E43H07 to UTM 43N projection on WGS 84 
datum. 
Table 2: Specification of LISS III sensor boarded on Resourcesat-2 
 
IGFOV (across track) (m) 23.5 
Spectral Bands (microns) 
B2(Green): 0.52 - 0.59 
B3(Red): 0.62 - 0.68 
B4(NIR): 0.77 - 0.86 
B5(SWIR): 1.55 - 1.70 
Swath (km) 141 
Bands Saturation radiance (mW cm-2 
sr-1 µm-1) 
B2:  50.0973, B3:  46.79, B4: 31.5,  
B5:5.9862 
Quantizationb(bits) 
10 (7bit data is transmitted to BDH 
after DPCM) 
 
No. of gains Single 
Source: RESOURCESAT-2 Data Users’ Handbook, December 2011, NRSC and Meta data 
file 
 
Water only image is necessary to prepare the pixel level map of SDT based on the best fitted 
regression model. An unsupervised classification method used to extract the water only 
image from the satellite data. ISODATA clustering algorithm implemented on the LISS-III 
image to classify into 150 landcover classes. Due to the presence of cloud over the westren 
part of the reservoir, first land cover class has been identified as water not effected by cloud 
and from second to fifth land cover classes have been identified as water under cloud. The 
water land cover image of the reservoir used as mask to extract the water only image from the 
satellite data. In order to avoid ambiguity in the regression, the water not under fog has been 
used for analysis (figure 2), The Digital Numbers (DN) of each pixel in each band of water 
only satellite image converted to their corresponding physical measures, i.e., radiance values 
by using the following formulae (Markham and Barker 1987). 
 
         … (1) 
 = spectral radiance (mW cm-2 sr-1 µm-1) 
 = maximum spectral radiance 
 = minimum spectral radiance 
 = maximum digital number 
= digital number 
Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor 
Priti Neelamsetti et al., 
International Journal of Environmental Sciences Volume 5 No.4, 2015 
798
The maximum and minimum spectral radiance values of the LISS III sensor of Resourcesat-2 
can be found in metadata file supplied by the data provider. The values used for the 
conversion of DN to the radiance are presented in table 3. 
 
Table 3: Maximum and minimum spectral radiance and  values of Resourcesat-2’s 
LISS-III image. 
 
BAND    
B2: Green 52 0 1023 
B3: Red 47 0 1023 
B4: NIR 31.5 0 1023 
B5: SWIR 7.5 0 1023 
 
4. Regression analysis 
 
The relationship between remotely sensed imagery and water quality parameters are usually 
examined through linear regression of the in-situ measurements and the spectral radiance 
values retrieved from the sensor (Kloiber et al. 2002; Nelson et al. 2003; Onderka and 
Pekárová 2008). Regression analysis is a statistical method used to investigate the 
relationship between two or more variables. It is generally used to understand or predict a 
dependent variable with the help of independent variables. Simple linear regression predicts 
the value of a dependent variable based on one independent variable, whereas multiple liner 
regression predicts the value of a dependent variable based on several independent variables. 
In this study, SDT of sampling locations is treated as dependent variable and the average 
radiance value of 3*3 window of image bands and band ratios of the corresponding locations 
used as independent variables. Data analysis extension of Microsoft excel has been used to 
perform linear and multi linear regressions. 
 
Table4: List of good fitted regression models with observed SDT. 
 
S. No. Regression Model 
Correlation coefficient 
(r) and Coefficient of 
Determination (r2) 
Signification 
(P) 
1 
SDT = - 5.047 + 
3.107(B3/B5) 
r = 0.81 and r2 = 0.66 p = 0.0002 
2 
SDT = 27.424 – 6.444 (B2) + 
6.299 (B3) + 7.516 (B4) – 
38.936 (B5) 
r = 0.88 and r2 = 0.77 p = 0.003 
3 
SDT = 7.503 – 6.388 (B2) – 
5.915 (B3) +7.490 (B4) + 
6.201 (B3/B5) 
r = 0.88 and r2 = 0.78 p = 0.003 
4 
SDT = 4.510 – 5.087 (B2) – 
4.344 (B4) + 3.831 (B3/B5) 
r = 0.86 and r2 = 0.75 p = 0.001 
 
The radiance values of band 2: Green (r = 0.75), band 3: Red (r = 0.79) and band 4: NIR (r = 
0.75) were correlated well with the SDT observations, while the band ratios B3/B5 (r = 0.81) 
and B4/B5 (r = 0.81) are shown a significant correlation with SDT observations of 24-Dec-
2012. Several regression models, including linear regression and multiple linear regression 
were developed using SDT as dependent variable and band radiance or/and Band ratios as 
independent variables. Table 4 shows the best fitted linear and multiple regression models. 
From the table, one can conclude that, the regression model with radiance values of band 2, 3, 
Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor 
Priti Neelamsetti et al., 
International Journal of Environmental Sciences Volume 5 No.4, 2015 
799
4 and band ratio of 3 to 5 is the best fitted model. Therefore, it was used to estimate the SDT 
of un-sampled locations to prepare a pixel level SDT map of Warasgaon reservoir (figure 3), 
 
Figure 3: Map showing the modeled SDT based on the best fitted regression model of 
Warasgoan reservoir. 
5. Results and conclusion 
 
The map of modelled SDT based remote sensing and in-situ measurements revealed that most 
of the part of Warasgaon reservoir comes under Oligotrophic condition in the month of 
December 2012. This shows the quality of water in the reservoir was good with less nutrient 
levels.  Since the cloud over western part of the reservoir, the study not accounted the spatial 
variation SDT in western part of the reservoir. However, it was observed the water 
transparency increased from west to east in direction. All bands of LISS III expect SWIR 
band shown good correlation and band ratios B3/B5 and B4/5 shown significant correlation 
with in-situ SDT observations. The multiple linear regression model based on B2, B3, B4 and 
B3/B5 found best fitted model of SDT of Warasgaon reservoir. The study revealed the use of 
LISS III sensor to model the spatial distribution of water transparency of the reservoirs. The 
absence of the blue wavelength band might be a drawback of LISS III sensor to monitor the 
water clarity, because clear water reflects most of the incident radiation in blue wavelength. 
This study showed the potential use of remote sensing to monitor the water quality of the 
reservoir with minimum number of in-situ observations.  The method is cost effective and 
easy to adapt to other inland water bodies. The study showed LISS III sensor data can be used 
to model and monitor the water quality of the study area.  
 
Acknowledgement 
 
We greatly acknowledge ISRO-UoP fund grant no: GOI-A-337(B) (87) and the Department 
of Zoology, University of Pune for providing necessary facilities to conduct the research.  
 
 
 
 
Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor 
Priti Neelamsetti et al., 
International Journal of Environmental Sciences Volume 5 No.4, 2015 
800
6. References 
 
1. Baban, S. M. J. (1993), Detecting water quality parameters in the Norfolk Broads, 
U.K., using Landsat imagery. International Journal of Remote Sensing, 14(7), pp 
1247–1267.  
 
2. Carlson, R. E., and Simpson, J. (1996), A Coordinator’s Guide to Volunteer Lake 
Monitoring Methods. North American Lake Management Society. 
 
3. Choubey, V. K. (1994), Monitoring water quality in reservoirs with IRS-1A-LISS-I. 
Water Resources Management, 8(2), pp 121–136.  
 
4. Fuller, L. M., Aichele, S. S., and Minnerick, R. J. (2004), Predicting water quality by 
relating Secchi-disk transparency and chlorophyll a measurements to satellite imagery 
for Michigan inland lakes, August 2002. US Department of the Interior, US 
Geological Survey. 
 
5. Giardino, C., Pepe, M., Brivio, P. A., Ghezzi, P., and Zilioli, E. (2001), Detecting 
chlorophyll, Secchi disk depth and surface temperature in a sub-alpine lake using 
Landsat imagery. Science of the Total Environment, 268(1–3), pp 19–29.  
 
6. Guan, X., Li, J., and Booty, W. G. (2011), Monitoring Lake Simcoe Water Clarity 
Using Landsat-5 TM Images. Water Resources Management, 25(8), pp 2015–2033.  
 
7. Harris, G. P., Bruton, J. E., and Bukata, R. P. (1976), Satellite Observations of Water 
Quality. Transportation Engineering Journal, 102(3), pp 537–554 
 
8. Kloiber, S. M., Brezonik, P. L., Olmanson, L. G., and Bauer, M. E. (2002), A 
procedure for regional lake water clarity assessment using Landsat multispectral data. 
Remote Sensing of Environment, 82(1), pp 38–47.  
 
9. Li, R., and Li, J. (2004), Satellite remote sensing technology for lake water clarity 
monitoring: an overview. Environmental Informatics Archives, 2, pp 893–901. 
 
 
10. Mabwoga, S. O., Chawla, A., and Thukral, A. K. (2010), Assessment of water quality 
parameters of the Harike wetland in India, a Ramsar site, using IRS LISS IV satellite 
data. Environmental Monitoring and Assessment, 170(1-4), pp 117–128.  
 
11. Markham, B. L., and Barker, J. L. (1987), Thematic Mapper bandpass solar 
exoatmospheric irradiances. International Journal of Remote Sensing, 8(3), pp 517–
523.  
 
12. Nelson, S. A. C., Soranno, P. A., Cheruvelil, K. S., Batzli, S. A., and Skole, D. L. 
(2003), Regional assessment of lake water clarity using satellite remote sensing. 
Journal of Limnology, 62(1s). 
Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor 
Priti Neelamsetti et al., 
International Journal of Environmental Sciences Volume 5 No.4, 2015 
801
13. Olmanson, L. G., Bauer, M. E., and Brezonik, P. L. (2008), A 20-year Landsat water 
clarity census of Minnesota’s 10,000 lakes. Remote Sensing of Environment, 112(11), 
pp 4086–4097.  
 
14. Olmanson, L. G., Kloiber, S. M., Bauer, M. E., and Brezonik, P. L. (2001), Image 
processing protocol for regional assessments of lake water quality. Water Resources 
Center Technical Report, 14. 
 
15. Onderka, M., and Pekárová, P. (2008), Retrieval of suspended particulate matter 
concentrations in the Danube River from Landsat ETM data. Science of the Total 
Environment, 397(1–3), pp 238–243.  
 
16. Sheela, A. M., Letha, J., Joseph, S., Ramachandran, K. K., and P, S. S. (2011), 
Trophic state index of a lake system using IRS (P6-LISS III) satellite imagery. 
Environmental Monitoring and Assessment, 177(1-4), pp 575–592.  
 
17. Thiemann, S., and Kaufmann, H. (2000), Determination of Chlorophyll Content and 
Trophic State of Lakes Using Field Spectrometer and IRS-1C Satellite Data in the 
Mecklenburg Lake District, Germany. Remote Sensing of Environment, 73(2), pp 
227–235.  
 
   


Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=92A82646313FC2AEC10BC74623E523BB?doi=10.1.1.1048.539&rep=rep1&type=pdf

Monitoring Secchi disk transparency of Warasgaon reservoir of Pune by using LISS III sensor

Abstract

Similar works

Full text

Available Versions

CiteSeerX