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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 3, No 1, 2012

Research article ISSN 0976 – 4402

Integrated remote sensing and GIS approach for water quality analysis of
Gomti river, Uttar Pradesh
Somvanshi.S1, Kunwar.P2, Singh.N.B3, Shukla.S.P3, Pathak.V3
1- Amity Institute of Environmental Sciences, Amity University, Sector-125, Noida, India.
2- Forest Resources and Ecology Division, Remote Sensing Applications Centre, U.P.,
Sector-G, Jankipurm, Kursi Road, Lucknow, Uttar Pradesh, India
3- Department of Civil Engineering, Institute of Engineering and Technology, Gautam Budha
Technical University, Lucknow, Uttar Pradesh, India.
shivangi.842003@gmail.com
doi:10.6088/ijes.2012030131008

ABSTRACT

This paper deals with development of decision making tool for mapping of water quality
parameters of Gomti River in parts of Lucknow, Sitapur and Barabanki districts of Uttar
Pradesh, India. Mapping was done using IRS LISS III data combined with measurement of
selected sample points. Water quality data was collected for both pre-monsoon and post-
monsoon seasons. Radiance value of each band of IRS LISS III data has been calculated and
observed radiance value on those sample points of each band along with band ratios and
principal components were compared with in situ measurements of water quality parameters.
The water quality parameters included, TS, DS, SS, pH, COD, BOD, DO, Chloride and TH.
Using radiance data of pre-monsoon images and in situ measurement data of water quality
parameters correlation and multiple linear regression models were developed and selected
most appropriate band combinations which were having highest R2 value. With the help of
these multiple linear regression water quality parameters were predicted which were then
compared with the values obtained through laboratory analysis of water quality. These
appropriate band combinations and principal components of pre-monsoon satellite data were
used in estimation of water quality parameters. The all these water quality parameters were
significantly correlated with LISS III radiance data except SS. The same band combinations
of post-monsoon satellite data were also used for estimation of water quality parameters in
post-monsoon. Subsequently, multiple linear regression equations models were used in
estimation of water quality parameters and preparation of digital cartographic maps depicting
the water quality over the entire study area for both the seasons respectively.

Keywords: Gomti River, Water Quality Parameters, Remote Sensing, Satellite Data, GPS,
LISS III, PC, Regression Models, Water Quality Maps.

1. Introduction

Gomti River is an important tributary of Ganga River and a perennial river of Awadh plain
runs across the major parts of Uttar Pradesh, India, covering nine districts and a distance of
approximately 940 km. During its course, Gomti River receives huge quantities of untreated
sewage agricultural runoffs brings lot of pesticides, fertilizers, street washouts bringing oil,
asphalt, sediments; industrial wastes all of which significantly alter the physico-chemical
characteristics of its water. Before reaching in the Lucknow city, the Gomti River receives
wastes from sugar and distillery industries of Sitapur district. In Lucknow city, various
industries like distillery, defence, milk dairy, vegetable, oil, carbon etc are pouring effluents

Received on May 2012 Published on July 2012 62
Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh

directly into the Gomti River. Besides the industrial effluents, domestic wastewaters are also
discharged into the Gomti River. In Lucknow city, from Gaughat upstream to Gomti barrage,
19 drains are discharging about 200 MLD wastewaters into the Gomti River. Thus, the Gomti
River (at Lucknow) water gets polluted right from Gaughat to Gomti barrage (Singh, et al.,
2005).

Gomti River is the only source of surface water for the nearby communities. Due to increased
pollution levels water quality of Gomti River is deteriorating continuously. Water quality
refers to the physical, chemical and biological properties of water. It may be degraded by the
presence of wastes, nutrients, microorganisms, pesticides, heavy metals and sediments.
Different water quality standards have been developed in order to keep check on the extent of
water pollution, and in order to maintain these quality standards. Water quality assessment
and apportionment of pollution sources of river is been done using in situ laboratory analysis
and multivariate statistical techniques. These traditional techniques were time consuming,
costly and reference to sample site only. In contrast, using remote sensing technique is an
economical way to monitor water quality, because it can monitor large areas in a short time
on a repetitive basis. It is also easy to update water quality parameters using remote sensing
data, which allows continuous monitoring of water quality. Several investigators have studied
the applicability of remote sensing technique in determining and monitoring water quality
(Johnson and Harriss, 1980; Khorram and Cheshire, 1985; Verdin, 1985; Tassan, 1993; Braga
and Setzer, 1993; Arenz, et al., 1996; Dewidar and Khedr, 2005).

The present study demonstrates the ability of remote sensing technique to monitor water
quality in Gomti River. Pollution due to increase in population, industrialization and other
anthropological activities is effecting the physical, chemical and biological parameters of
river water. In this study, water quality status and quantification of different water quality
parameters in Gomti River has been monitored using remote sensing technique. This study
aimed to find out the appropriate regression models to establish and analyzed the remote
sensing methods to retrieve water quality parameters, and to show the possibility of
performing routine water quality monitoring.

2. Study area

The study area covers Gomti River in part of Lucknow, Sitapur and Barabanki districts of
Uttar Pradesh having an area of 815.10 ha and lies between 27012'13"to 27043'19"N latitude
800 46'40" to 81012' 59" E longitude. The study area covers parts of 63A/16, 63B/13 and
63F/02 topographical maps of Survey of India on 1:50,000 scale. The location map of study
area and sample sites location is shown in Figure 1.

3. Materials and methods

3.1 Collection and laboratory analysis of water quality samples

The five sites were selected for the water quality parameters in Gomti River. Samples from 3
points (1/4, 1/2, & 3/4) across the river width, from each sampling site was collected in the
each sites of the Gomti River, which covers the 15 representative sampling points.
Coordinates of each sample point locations were recorded in field through handset GPS.
These sample point locations have been shown in Table-1.

Somvanshi. S, Kunwar. P, Singh. N.B, Shukla. S.P, Pathak. V 63
International Journal of Environmental Sciences Volume 3 No.1, 2012
Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh

Figure 1: Sampling sites of Gomti river

Table 1: Location of Sampling Points

Sampling Sampling Location
Sampling Site Point No. Position Latitude Longitude
Bhatpur 1 4-Jan 27˚11'19.02"N 80˚48'05.47"E
2 2-Jan 27˚11'19.22"N 80˚48'05.89"E
3 4-Mar 27˚11'19.41"N 80˚48'06.32"E
Gaughat 4 4-Jan 26˚53'11.76"N 80˚54'01.35"E
5 2-Jan 26˚53'12.56"N 80˚54'01.73"E
6 4-Mar 26˚53'13.37"N 80˚54'02.08"E
Mohan 7 4-Jan 26˚51'49.84"N 80˚55'43.39"E
Meakins 8 2-Jan 26˚51'50.58"N 80˚55'44.48"E
9 4-Mar 26˚51'51.27"N 80˚55'45.61"E
Pipraghat 10 4-Jan 26˚50'01.72"N 80˚58'03.41"E
11 2-Jan 26˚50'02.01"N 80˚58'04.01"E
12 4-Mar 26˚50'02.34"N 80˚58'04.59"E
Gangaganj 13 4-Jan 26˚43'37.36"N 81˚12'18.37"E
14 2-Jan 26˚43'37.69"N 81˚12'18.76"E
15 4-Mar 26˚43'38.03"N 81˚12'19.16"E

The samplings were done in post-monsoon (6 October, 2006) and pre-monsoon (10 May,
2007). The water quality parameters included Total Solids (TS), Dissolved Solids (DS),
Suspended Solids (SS), pH, Chemical Oxygen Demand (COD), Biochemical Oxygen
Demand (BOD), Dissolved Oxygen (DO), Chloride and Total Hardness (TH) in this study.
These water quality parameters of each sample were analysed in laboratory of Institute of
Industrial Toxicology Research (IITR), Lucknow using appropriate standard methods.

Somvanshi. S, Kunwar. P, Singh. N.B, Shukla. S.P, Pathak. V 64
International Journal of Environmental Sciences Volume 3 No.1, 2012
Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh

Table 2: Results of Water Quality Parameters of Pre-Monsoon on Sample Points in Gomti
River
Sampling
Point No. TS DS SS pH COD BOD DO Chloride TH
1 264 250 14 8.41 14.9 3.8 7.2 9 182
2 282 274 8 8.48 15.6 3.9 7.1 8 190
3 274 260 14 8.45 16.4 4.2 6.9 8 184
4 286 260 26 8.47 13.6 3.1 7.2 9 196
5 292 270 22 8.54 13.2 3 7.3 10 198
6 306 282 24 8.54 14 3.4 7.1 7 198
7 312 284 28 8.39 24 13 2 14 204
8 318 294 24 8.33 20.8 11.5 2.8 13 194
9 324 304 20 8.27 22.4 12 2.4 15 200
10 366 338 28 8.19 27.2 16 1.8 20 212
11 380 344 36 8.17 25.6 14.5 1.7 20 212
12 374 350 24 8.16 28 16.5 1.3 21 208
13 282 264 18 8.16 18.4 9.5 4.6 17 178
14 298 272 26 8.15 19.2 10 4.5 18 170
15 294 270 24 8.14 20.9 11 4.3 19 170

Table 3: Results of Water Quality Parameters of Post-Monsoon on Sample Points in Gomti
River
Sampling
Point No. TS DS SS pH COD BOD DO Chloride TH
1 444 288 2 8.72 16.4 4 6.7 10 198
2 502 236 26 8.71 17.2 4.1 6.5 8 214
3 392 294 12 8.75 17.6 4.25 6.4 7 202
4 396 288 24 8.73 14.8 3.4 6.8 6 196
5 316 290 10 8.72 15.2 3 6.9 6 192
6 378 280 10 8.76 14.4 3.3 6.7 6 202
7 536 374 4 8.57 23 13.5 0 17 246
8 418 334 24 8.52 21.6 12 2.3 20 214
9 370 312 10 8.57 20.8 11.5 2.5 8 214
10 388 360 12 8.55 21.6 12 2.2 12 216
11 414 350 20 8.56 22.4 11.5 2.1 11 210
12 406 334 14 8.54 20.6 13 2.4 12 204
13 374 342 64 8.56 18.4 11 3 14 232
14 474 356 34 8.51 19.2 11.5 2.9 17 228
15 432 356 70 8.54 20 12.5 2.8 16 218

3.2 Analysis of the satellite image data

Satellite data of Indian Remote Sensing Satellite (IRS) P6 LISS III (Path - 100, Row - 52, 53)
acquired on 10th May 2007 (pre-monsoon) and 6th October 2006 (post-monsoon) were
procured from National Remote Sensing Centre, Hyderabad with a multispectral resolution in
two visible (band 1 = Green, band 2 = Red), near infra-red (band 3 = NIR) and middle infra-
red (band 4 = MIR) bands..

Somvanshi. S, Kunwar. P, Singh. N.B, Shukla. S.P, Pathak. V 65
International Journal of Environmental Sciences Volume 3 No.1, 2012
Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh

The several well distributed ground control points obtained from 1:50,000 scale topographic
maps and coordinates recorded through handset GPS were used to calculate the geometric
transform. Image rectification and projection was performed on the above mentioned images
using Erdas Imagine 9.1 software. Complete rectification and projection was done in UTM
projection WGS 84 datum using geometric correction method. The river channel of Gomti
River having water spread area was extracted from the images so that only water would be
analyzed.

The geometrically corrected images having radiance value were further processed with band
ratioing and principal component analysis to obtain in different values of radiance. Ratioing
is effective in enhancing or revealing latent information when there is an inverse relationship
between two spectral responses to the same biophysical phenomenon. Most useful bands for
the study of water bodies are red, green and near infrared. Band ratios which have been used
in the present study are R/G, R/NIR, NIR/G and MIR/G. these ratios were done using the
modular of the Erdas Imagine 9.1 software. Principal component analysis (PCA) is a linear
transformation which rotates the axes of image space along lines of maximum variance. The
rotation is based on the orthogonal eigenvectors of the covariance matrix generated from a
sample of image data from the input channels. The output from this transformation is a new
set of image channels. The PCA1 axis is most widely stretched and its values are highest and
retain nearly two thirds of the information contained in the sum of these four bands, axes 2,
axes3 and axes4 of PCA 2, PCA3 and PCA4 are followed successively. Radiance values of
bands, band ratios and principal components have been used in the present study.

Table 4: Spectral Radiance Value of Satellite Data on Sample Points in Pre-Monsoon
Sampling
Points
No. B1 B2 B3 B4 PC1 PC2 PC3 PC4 B2/B1 B2/B3 B3/B1 B4/B1
1 6 4 4 1 7.703 -2.156 -2.222 0.292 0.667 1 0.667 0.167
2 6 5 5 1 8.699 -2.866 -1.75 -0.238 0.833 1 0.833 0.167
3 6 5 7 1 9.215 -4.787 -1.724 -0.445 0.833 0.714 1.167 0.167
4 6 4 7 1 8.477 -5.037 -2.183 -0.019 0.667 0.571 1.167 0.167
5 7 5 7 1 9.798 -4.668 -2.485 -0.187 0.714 0.714 1 0.143
6 7 5 7 1 9.798 -4.668 -2.485 -0.187 0.714 0.714 1 0.143
7 5 4 4 0 6.898 -2.247 -1.919 -0.827 0.8 1 0.8 0
8 5 3 4 0 6.16 -2.498 -2.377 -0.4 0.6 0.75 0.8 0
9 5 3 3 0 5.902 -1.537 -2.39 -0.297 0.6 1 0.6 0
10 5 4 3 0 6.64 -1.287 -1.932 -0.723 0.8 1.333 0.6 0
11 5 4 3 0 6.64 -1.287 -1.932 -0.723 0.8 1.333 0.6 0
12 6 4 4 1 7.703 -2.156 -2.222 0.292 0.667 1 0.6 0.167
13 5 4 4 1 7.12 -2.274 -1.46 0.033 0.8 1 0.8 0.2
14 5 4 4 1 7.12 -2.274 -1.46 0.033 0.8 1 0.8 0.2
15 6 4 4 1 7.703 -2.156 -2.222 0.292 0.667 1 0.667 0.167

Table 5: Spectral Radiance Value of Satellite Data on Sample Points in Post-Monsoon
Sampling
Points
No. B1 B2 B3 B4 PC1 PC2 PC3 PC4 B2/B1 B2/B3 B3/B1 B4/B1
1 5 3 4 0 5.305 -4.095 -2.145 -0.699 0.6 0.75 0.8 0
2 5 3 4 0 5.305 -4.095 -2.145 -0.699 0.6 0.75 0.8 0
3 5 4 3 1 6.264 -3.158 -1.336 0.041 0.8 1.333 0.6 0.2
4 5 3 3 0 5.321 -3.101 -2.172 -0.599 0.6 1 0.6 0
Somvanshi. S, Kunwar. P, Singh. N.B, Shukla. S.P, Pathak. V 66
International Journal of Environmental Sciences Volume 3 No.1, 2012
Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh

5 5 3 2 0 5.337 -2.107 -2.199 -0.498 0.6 1.5 0.4 0
6 5 3 2 0 5.337 -2.107 -2.119 -0.498 0.6 1.5 0.4 0
7 4 3 3 0 4.712 -3.058 -1.389 -0.72 0.75 1 0.75 0
8 4 3 2 0 4.728 -2.063 -1.416 -0.619 0.75 1.5 0.5 0
9 4 3 2 0 4.728 -2.063 -1.416 -0.619 0.75 1.5 0.5 0
10 5 3 4 0 5.305 -4.095 -2.145 -0.699 0.6 0.75 0.8 0
11 4 3 3 0 4.712 -3.058 -1.389 -0.72 0.75 1 0.75 0
12 5 3 3 0 4.712 -3.058 -1.389 -0.72 0.6 1 0.6 0
13 4 3 4 0 4.696 -4.052 -1.362 -0.82 0.75 0.75 1 0
14 4 2 3 0 3.937 -3.091 -1.943 -0.419 0.5 0.667 0.75 0
15 4 3 3 0 4.712 -3.058 -1.389 -0.72 0.75 1 0.75 0

2.3 Development of regression models for estimation water quality parameters from
satellite data

The statistical analysis was done using STATISTICA 6.0 software. Multiple Linear
regressions were used to explore the relationship between the water quality parameters
(dependent variables) and LISS III radiance data (independent variables). The multiple
correlation coefficients (R2) were estimated in each combination of independent variables
with dependent variables. The maximum value of R2 was considered in the regression
equation. In time of analysis have been also considered 95 percent confidence level and other
statistical parameters viz. Multiple R, Adjusted R, F, P, standard error etc. The independent
variables were selected in order to maximum R2 value with respect of dependent variables.
The independent variables selection was based on commission and omission technique to
eliminate the insignificant independent variables; only those independent variables
combination having highest R2 value with dependent variables were selected for the equation
and model to estimate water quality parameters. Whitlock et al., 1982 and Wilkinson, 1997
were also established regression models using statistical significance of multiple correlation
coefficients (R2), the standard error of the mean Y estimate (SE (Y)), F-ratio values, and
probability (P) at 95 percent confidence level.

Table 6: Water Quality Parameter Wise R2 Value, and Coefficient Value of Regression
Equation in Pre-Monsoon and Post-Monsoon
Water R2 Value Regression Equation: Y = a + bX1 + cX2 + d X3
Qualit Coefficient of Pre- Coefficient of Post-
y Pre- Post- monsoon monsoon
Param mons mons X
eter oon oon 1 X2 X3 a b c d a b c d
P - - - -
0.54 0.22 C B3/ B4/ 437. 19. 193 214 320 7.7 120. 61.1
TS 69 12 2 B1 B1 07 212 .91 .77 .71 97 23 1
- - - -
0.56 0.46 B B3/ B4/ 381. 14. 175 183 293 67. 343. 14.5
DS 32 98 3 B1 B1 464 587 .02 .2 .16 4 27 5
P - - - -
0.32 0.23 C PC B2/ 12.6 6.3 8.9 35. 36. 25. 22.6 39.7
SS 95 79 1 2 B3 29 5 64 732 091 17 78 62
P - - -
0.73 0.70 C B3/ B4/ 0.0 0.3 0.8 7.8 0.1 0.77 0.48
pH 9 82 1 B1 B1 7.49 88 13 9 22 71 3 6
0.69 0.28 P PC B3/ 37.4 - - - 28. - - 3.12
COD 82 68 C 4 B1 24 0.7 3.9 15. 941 2.4 0.09 4

Somvanshi. S, Kunwar. P, Singh. N.B, Shukla. S.P, Pathak. V 67
International Journal of Environmental Sciences Volume 3 No.1, 2012
Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh

1 74 91 709 37 9
- - - - -
0.72 0.48 B B3/ B4/ 22.1 1.7 4.0 13. 5.9 7.1 36.6 10.7
BOD 66 93 3 B1 B1 52 83 71 479 17 64 36 7
- - -
0.76 0.56 B B3/ B4/ 6.74 1.0 5.2 9.1 8.9 3.0 1.67 4.47
DO 01 63 1 B1 B1 7 57 98 46 15 85 4 5
P - - -
Chlori 0.65 0.69 C PC B2/ 14.7 1.5 3.2 12. 44. 6.0 1.56
de 61 33 1 4 B3 4 97 44 855 556 83 1.82 1
- - -
0.64 0.64 B B3/ B4/ 213. 11. 69. 145 188 24. 144.
TH 65 73 3 B1 B1 627 086 328 .43 .57 16 55 -4.09

2.4 Application of the regression models to the study area

The Multiple Linear Regression Models, developed between the water quality parameters and
the radiance values of LISS III bands for 15 sample sites, were extended to the entire study
area for mapping the water quality parameters for both pre and post-monsoon season using
Eardas Imagine 9.1 software. The extension of these models to the study area was
accomplished by using a simple linear discriminate function. Applying this function to each
pixel in the study area and then grouping these continuous water quality variables into
discrete classes accomplished the classification. A unique function for each water quality
parameter was applied to the LISS III data, producing nine water quality parameter maps of
pre and post-monsoon season. The results of these classifications are displayed as water
quality maps.

3. Results and discussion

The laboratory analyses of the water quality samples of the 15 sample sites dated 10th May
2007 and 06th October 2006. The results of water quality parameters of each sample points in
pre and post-monsoon are represented in Table-2 and Table-3 respectively. The spectral
radiance of each bands were calculated from satellite image. The spectral radiance values of
bands, band ratios and principal components have been used in the present study. On each
sample points spectral radiance value in pre-monsoon and post-monsoon are represented in
Table-4 and Table-5 respectively. The Multiple Linear Regressions were used to establish the
relationship between the water quality parameters (dependent variables) and LISS III
radiance data (independent variables). Spectral radiance data of pre-monsoon images and in
situ measurement data of water quality parameters correlation and multiple linear regression
models were developed and selected most appropriate band combinations which were having
highest R2 value. The Multiple Linear Regression equations were developed on TS, DS, SS,
pH, COD, BOD, DO, Chloride and TH water quality parameter. Using these multiple linear
regression equations of appropriate band combinations and principal components of pre-
monsoon satellite data were used in estimation of water quality parameters. The R2 value and
coefficient value of regression equation in each water quality parameter of pre-monsoon and
post-monsoon are represented in Table-6. The R2 value were estimated 0.5469, 0.5632,
0.3295, 0.7390, 0.6982, 0.7266, 0.7601, 0.6561 and 0.6465 of TS, DS, SS, pH, COD, BOD,
DO, Chloride and TH respectively in pre-monsoon. The all these water quality parameters
were significantly correlated with LISS III radiance data except SS. All nine dependent
variables were selected to generate cartographic maps and estimation water quality in entire
study area into different classes.

Somvanshi. S, Kunwar. P, Singh. N.B, Shukla. S.P, Pathak. V 68
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Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh

Figure 2: Water quality maps of different parameter in pre-monsoon

Figure 3: Water quality maps of different parameter in post-monsoon

These same band combinations and principal components of pre-monsoon satellite data were
also used for estimation of R2 value of post-monsoon satellite data. The R2 value and
coefficient value of regression equation in each water quality parameter in pre-monsoon and
post-monsoon is shown in Table-6. The maps generated using regression equations in Erdas

Somvanshi. S, Kunwar. P, Singh. N.B, Shukla. S.P, Pathak. V 69
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Imagine environment for pre-monsoon and post-monsoon are represented in Figure 2 and
Figure 3 respectively. The maximum area covered in each water quality parameter class in
pre- monsoon and post-monsoon were estimated and represented in Table 7.

Table 7: Maximum Area Covered in Water Quality Parameters Class in Pre-Monsoon and
Post-Monsoon
Water Pre-monsoon Post-monsoon
Quality Class Range Area Area (%) Class Range Area (ha) Area (%)
Parameter (ha)
280 - 300 320 - 340
TS mg/l 242.96 29.81 mg/l 384.6 47.18
250 - 300 250 - 300
DS mg/l 511.95 62.81 mg/l 379.98 46.62
SS 20 - 25 mg/l 353.67 43.39 10 - 15 mg/l 152.55 18.72
pH 8.3 - 8.5 252.9 31.03 8.3 - 8.5 444.36 54.52
C OD 18 - 21mg/l 237.37 29.12 18 - 21 mg/l 331.42 40.66
BOD 7 - 11 mg/l 262.14 32.16 3 - 7 mg/l 379.98 46.62
DO 4 -6 mg/l 281.31 34.51 4 - 6 mg/l 371.16 45.54
Chloride 16 - 20 mg/l 281.45 34.53 8 - 12 mg/l 292.37 35.87
180 - 200 200 - 220
TH mg/l 463.39 56.85 mg/l 333.09 40.87

This study demonstrated that all the bands of IRS P6 LISS III data contribute to the water
quality variables deriving. Single band data have been widely used in water quality study.
However, attempts have been made to find combinations of LISS III bands which would
provide more information about water quality variables than were available in single band
(Lavery et al., 1993). The results of this study also indicated that it was essential to select
feasible combinations of bands in the Multiple Linear Regressions analysis. Although
algorithms were quite different in the selected bands when compared with those used in other
studies (Bilge et al., 2003; Wang et al., 2001; Lavery et al., 1993).

The Multiple Linear Regression Models were generated according to statistical analysis
performed between water quality parameters and the values of the corresponding LISS III
radiance data. The independent variables selection was based on commission and omission
technique to eliminate the insignificant independent variables; only those independent
variables combination having high values of correlation coefficient (R2), high value of F-ratio
and low values of standard error. Results of the LISS III data analysis produce (1) a series of
models for predicting water quality parameters; and (2) a series of colour-coded maps of
Gomti River, each pertaining to a water quality parameter of interest. The TS, DS, SS pH,
COD, BOD, DO, Chloride and TH models were selected to represent the statistical
relationship between the water quality measurements obtained from reference surface and the
values of the corresponding IRS P6 LISS III radiance data.

Lavery et al. (1993) developed regression models for predicting surface water quality
parameters from TM data. The significance of regression models indicated that there existed
a statistical perfect correlation for water quality variables including The TS, DS, pH, COD,
BOD, DO, Chloride and TH water quality parameters having R2 value >0.5 in pre-monsoon
seasons, while SS could not be retrieved within an acceptable R2 value in this study. The
relationship established between the surface measurements and the LISS III radiance data
were extended to the entire study area (Gomti River), producing a series of class maps which

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were grouped and colour-coded to represent the distribution of water quality parameters. The
distribution of these surface water quality parameters throughout the river is shown on these
maps. These results are in agreement with expected and reported values of these parameters
in similar geographic regions (Abdel Moneim, et al., 1990; Shakweer, et al., 1998).

The results obtained as above in pre-monsoon, the models of all nine water quality
parameters in post-monsoon was also analysied and mapped based on samples from 15
sampling points on October 06, 2006 and LISS III data.

In the study area all the water quality parameters showed a wide variation in space and time.
Temporal variations were due to seasonal influences mainly, the effect of rainfall. The data
analyses revealed that most of the parameters showed a substantial decrease after monsoon.
However, concentration of total hardness (TH) and total solids (TS) increased during post-
monsoon periods. Domestic and industrial discharges into the river are probably responsible
for the observed high concentration values of TH (Adedokun et al., 2008). The increase in
hardness may be due to the domestic activities like washing clothes, animals, vehicles etc.
done at the river site (Prasad and Patil, 2008). TS was comparatively higher in post-monsoon
and lowest in pre-monsoon, may be due to increase siltation after monsoon. The pollution of
water bodies from pollutant transport through surface runoff and uncontrolled discharge of
untreated and partially treated sewage has been reported by Inoue et al., (1991); Inanc et al.,
(1998) & Martin et al., (1998).

Biochemical Oxygen Demand (BOD) model indicated that the range of BOD in pre-monsoon
period is 7-11 mg/l in most of the river parts whereas during post-monsoon period the range
of BOD is 3-7 mg/l. This shows that the average concentration of BOD was high in pre-
monsoon and low in post-monsoon (Pathak, 1991). This may be due to the effect of increased
dilution and the treatment of wastewaters of the drains. As an indication of deteriorating
quality of river water, BOD obtained was high in pre-monsoon period. It means higher
amount of oxygen from river water was getting used in degeneration of organic waste. Since
the volume of waste dumped in water is increasing, BOD is also going up. The results from
this study reveal that suspended solid (SS) are generally higher during the dry season. This
suggests that the run-offs have only a diluting effect on this parameter.

During pre-monsoon period, the range of chloride in maximum part of the river lies between
16-20 mg/l while during post-monsoon period it was 8-12 mg/l this could be due to the
occurrence of more anthropogenic pollution during the pre-monsoon period which was
diluted in post-monsoon period. Inorganic constituent like chloride exhibited higher values in
pre- monsoon and lowest in post-monsoon may be due to local quality of soil, as the main
source of chloride in river water is the application of chemical fertilizers and run off from
agricultural field (Venkatesharaju, 2009).

This study showed that all nine water quality variables had satisfied retrieval results
according to the mean relative error, which is one of the most important indicators for
practical application of water quality monitoring through remote sensing. This study also
proved that IRS P6 LISS III data has capabilities in modelling of river water quality as Brivio
et al. (2001) did using Landsat – 5 TM data. The purpose of surface water quality retrievals
from IRS P6 LISS III data, the parameters used in this study were significantly estimated
using the multiple regression algorithms. Therefore, the study demonstrated that remote
sensing is a valuable tool in obtaining information on the processes taking place in surface
water quality monitoring (Zhang, 2002).

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Integrated remote sensing and GIS approach for water quality analysis of Gomti river, Uttar Pradesh

4. Conclusion

This study showed that there existed a statistical significant correlation between each selected
water quality variable and remote sensing data in the Gomti River. The nine water quality
variables i.e. TS, DS, SS, pH, COD, BOD, DO, Chloride and TH considered for The Multiple
Linear Regression Analysis in the Gomti River, eight of them obtained the correlation with
acceptable accuracies except SS, and the radiance values of LISS III data with limited sample
data were tested to be the best method for mapping and area estimation of water quality
parameters. The comparing the maximum ranges of the water quality parameters across the
river, it can be concluded that parameters like Chloride, BOD and suspended solids are
clearly higher in the pre-monsoon compared to post-monsoon. On the other hand some
parameters like Total Hardness and Total Solids are lower in the pre-monsoon than in the
post-monsoon period.

The IRS P6 LISS III radiance data can be successfully used to map some surface water
quality parameters for Gomti River. The visible bands and infra-red bands with principal
components show significant relationship between water quality parameters and their
radiance value in Multiple Linear Regression Equation Models which were developed for this
study. In additional studies are needed at different times of the year and under different flow
conditions in order to develop generalized models. Repetitive remote-sensed data may be
considered by agencies as having the potential to provide an alternative method for gathering
and processing surface water quality information.

The findings of the study also indicate the need for proper water resource planning,
management and the safe disposal of industrial and urban waste, which would otherwise lead
to severe environmental degradation.

Acknowledgments

Authors are grateful to Director, Remote Sensing Applications Centre, Uttar Pradesh,
Lucknow and head Department of Civil Engineering, Institute of Engineering and
Technology, Gautam Budha Technical University, Lucknow for providing necessary
facilities and support in carrying out this study. We profusely thank Dr. Kunwar K.P. Singh
IITR, Lucknow for providing necessary analytical data.

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