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1. Application Of Remote Sensing And GIS In Water Quality
Assessment - Review Paper
Alina Nero*1
and Saju Simon S.G 2
*1
Assistant Professor ,Department of Civil Engineering, ACE College Of Engineering,
Thiruvallom,Trivandrum,Kerala,India – 695027, alinanero88@gmail.com
2
Assistant Professor, Department of BME,Noorul Islam University,Kumaracoil-629480,KK
district,Tamil Nadu,India , sajusimon.nta@gmail.com
Abstract
Water quality assessment and management has attained significant
importance over the years owing to the growing concern and awareness on
environment and health related impacts.This review paper highlights on how
the different Remote sensing and Geographic Information System(GIS)
techniques has been applied for the water quality assessment.Satellite images
used for tracing the organic contamination in surface water through different
models like WQI models,GIS based spatial regression models,export
coefficient model,GPZM groundwater pollution zone model.Pollution
mapping techniques through GIS helps in identifying the extent of pollution
and the overlying with different thematic layers highlights the source of
pollution.High resolution satellite data such as hyperion were used for
mapping water quality degradation components using biooptical model.The
wavelength and band selection of the remote sensed data is of importance for
the identification of particular contaminants in water body.Relating to health
aspects ,GIS is being used for evaluating and controlling the water quality in
the distribution system.It is not only the assessment but also managing the
non point sources to improve water resources .Integration of Remote sensed
data and visualization capability of GIS along with different models help in
better decision making in the field of water resource .
Index Terms : Water quality,Remote sensing,GIS,models.
1.Introduction
At the united nations conference at Mar del plata in 1977 which launched the
international drinking water supply and sanitation decade this philosophy was
adopted :- ―All peoples ,whatever their stage of development and social and
economic condition have the right to have access to drinking water in quantities and
of a quality equal to their basic needs‖. Our water resources are of major
environmental, social and economic value. If water quality becomes degraded this
resource will become degraded. Water quality is important not only to protect public
health but provides ecosystem habitats, used for farming, fishing, mining and
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2. contributes to tourism and recreation. If water quality is not properly maintained, the
environment as well as the commercial and recreational value of our water resources
will also diminish. Since 1980’s, satellite Remote Sensing represents an opportunity
for synoptic and multi temporal viewing of water quality (Claudia 2006). Remotely
sensed satellite images may be used either independently or integrated with GIS
techniques.
2 .Materials And Methods
2.1 Water quality parameter modelling and mapping techniques
Mapping of chlorophyll-a and trip ton concentration from hyper spectral image using
forward and inverse bio-optical modelling (C.Giardino et al,2006).The Matrix
Inversion Method(MIM) (Brando and Dekker ,2003) provides range of chlorophyll-a
concentrations comparable to in situ data collected the day of the satellite overpass
the area. The bio-optical model is similar to the lake water model (Pierson and
Strombeck, 2001).
Assessment of ground water vulnerability by DRASTIC index through GIS analysis
and geo-processing framework (S.Saidi,2007) .DRASTIC stands for the seven
parameters:- Depth of water, Recharge, Aquifer media, Soil media, Topography,
Impact of the vadose zone and the Hydraulic Conductivity of the aquifer. Maps are
generated using modified DRASTIC index.fig2.1.
The Export coefficient model (N.M.Mattikalli, 1996) is used to estimate the nutrient
loss from a watershed to surface water for all possible sources of nitrogen and
phosphorus by defining appropriate export coefficients for these nutrients. It utilizes
information on both the areal extents of various types of land use and their export
coefficients to estimate solute losses. It calculates the areal external nutrient load
upon a water body by summing outputs from all potential sources. In the case of a
watershed, the model calculates the solute load delivered at its outlet. The data was
obtained through remote sensing and computations involved in the export coefficient
model were performed in the GIS. The integration in a GIS of multiple variables
related to pollution risk, in different thematic layers, has defined a static index
introduced as the Groundwater Pollution Zone Model (GPZM) (Prashanth K.S,
2012).
A model that predicts the Water Quality Index (WQI) (A.Vignolo, 2005) of surface
waters using linear regression analysis and the model has been validated using a data
set of 12 physicochemical parameters obtained during the last 3 years. Here satellite
images have been used to trace organic contamination in surface waters.
WQI = KƩ (CiPi)/Ʃ Pi ..................................... (2.1)
where Ci is the percent value assigned for the parameters, Pi is the weight assigned to
each parameter, K is a constant that takes different values according to the following:
1.00 for clear water, 0.75 for waters with light colour, turbidity and foam, 0.50 for
water with the appearance of contamination and high odour and 0.25 for dark waters.
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3. Fig 2.1 Groundwater Vulnerability Map Of an Aquifer, Using a Modified
DRASTIC Index
Two step multivariate cluster analysis(TSMCA) classifies homogeneous subgroups
of cases in a population by minimizing the within-group and maximizing between-
group variations(M.Shaban et al,2009).It can then be visualized in GIS by the
decision makers to estimate the areas of increased monitoring requirement.
2.2 Remote Sensing In Water Quality Parameter (WQP) Extraction
Using remote sensing, information can be extracted about those components in the
water column that interact with the natural light field (A.G.Dekker, 1996)
2.2.1 Empirical Method
The basis of this method is the use of statistical relationships derived between
measured spectral values and measured water parameters. The relationship between
remotely sensed radiance (Lrs) and the WQP are established by means of regression
techniques; the results have no multi temporal validity. Estimates by empirical
models will always need in situ data because many parameters may change between
remote sensing missions, such as the down welling irradiance field and the state of
the water surface.
2.2.2 Semi-Empirical Method
Spectral characteristics of the parameters are known and this knowledge is included
in the statistical analysis by focusing on well chosen spectral areas whereby
appropriate combinations of wavebands are used as correlates. Lrs is first
recalculated to above surface or subsurface irradiance reflectance and then, through
regression techniques, related to the WQP. Relationships between reflectance and the
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4. WQP have multi temporal validity because of stability under varying solar angles,
atmospheric conditions and states of the water surface. However, validity can only be
assumed within the range of optical water quality data from which the algorithms
were developed, which is usually small. This is the most commonly used method.
2.2.3 Analytical Method
The inherent optical properties such as the scattering coefficient, absorption
coefficient and the volume scattering function and apparent optical properties such as
the diffuse attenuation coefficient for down welling irradiance and the irradiance
reflectance are used to model the reflectance and vice versa. Relationships are
derived between the WQP, the underwater light field and the remotely sensed
radiance.
Table1 .Algorithms For WQP Estimation for Remote Sensing Data (CAESAR
and CASI) Inland Water Mode
WQP ALGORITHM
Chlorophyll-a -59.0 + 78.9R(0-)B8/R(0-)B7
Cyanophycocyanin
-28.1 + 15904[0.5 {R(0-)B3 + R(0-)B5} –
R(0-)b4]
Seston dry weight 0.71 + 406R(0-)B8
Vertical attenuation coefficient:-
All water -0.7950+ 1.9984R(0-)b8/R(0-)b7
Shallow water 0.0492 + 1.5950R(0-)b8/R(0-)B7
Deep water -0.4045 + 1.3600R(0-)B8/R(0-)B7
Secchi depth:-
All water 5.05 + 1.616R(0-)B8/R(0-)B7
Shallow lake 4.89 + 1.464R(0-)B8/8(0-)B7
Deep lake 5.51 + 2.038R(0-)B8/R(0-)B7
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5. 3. Results and Discussion
The Hyperion derived CHL-a was in good agreement with in situ point data, showing
a correlation coefficient (r) of 0.77, a determination coefficient (R2) of 0.59, a RMSE
of 0.36 mg m−3 (relative RMSE 20%), a bias of 0.12 mg m−3 (relative bias 6%), and
being close to the 1:1 line. Fig 2.1
Fig 2.1 Scatter Plots Of Hyperion-Derived Products And In Situ Concentrations
Measured In Point Stations: On Left For Chlorophyll-A, On Right For Tripton.
Dot Lines Indicate The 1:1 Relation.
From the analysis of potential vulnerability maps obtained by DRASTIC index the
reasons and the further decision can be taken. Multi linear regression analysis gives a
very good prediction of WQI. Predicted values (WQIP) were obtained using the
expression:-
WQIP = 19.310 BAND1 + 5.410 BAND2 -196.95................................................. (1)
With p < 0.000085 and R2 = 0.82.
The best correlation of WQI was found with BAND 1 and BAND 2 of Land sat 7
TM.
The empirical, semi-empirical and analytic water quality assessment methods were
compared using a similarity index in order to investigate the role of remote sensing as
a source of additional, uniquely spatial, information.
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6. Fig 2.2 Observed Vs Predicted Water Quality Indexes Obtained From Multi-
linear Regression Analysis
4. Conclusion
The review paper highlights the different techniques in which water quality can be
assessed with the help of remote sensing and GIS.GIS is an effective tool not only for
collection, storage, management and retrieval of a multitude of spatial and non spatial
data but also for spatial analysis and integration of these data to derive useful outputs
and modelling. Without GIS capabilities the vulnerability and sensitivity analysis
could not have been rigorously demonstrated. Remote sensing gives valuable
additional information compared to the other water quality assessment tools. It has
also been proved to be a powerful tool to trace the impact of contaminated water.
.
References
[1] Claudia G , Vittorio EB , Arnold GD ,Niklas S , Gabriele C (2006)
Assessment of water quality in Lake Garda (Italy) using Hyperion, Remote
Sensing of Environment ,109 (2007) 183–195.
[2] Dekker AG, Zamurovicnenad Z, Hoogenboom HJ , Peters SWM (2009)
Remote sensing, ecological water quality modelling and in situ
measurements: a case study in shallow lakes, Hydrological Sciences Journal
,4l(4) August 1996.
[3] Salwa S, Salem B , Hamed BD (2011) Sensitivity analysis in groundwater
vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer,
Tunisia: a validation study, Hydrological Sciences Journal, 56(2) 2011.
[4] Prashant KS , Dawei H , Manika G, Saumitra M (2012) Integrated
framework for monitoring groundwater pollution using a geographical
information system and multivariate analysis, Hydrological Sciences Journal,
57(7) 2012.
International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.59 (2015)
© Research India Publications; httpwww.ripublication.comijaer.htm
65

7. [5] Alicia V, Alberto P, Daniel C (2005) Water quality assessment using
remote sensing techniques: Medrano Creek, Argentina, Journal of
Environmental Management, 81 (2006) 429–433.
[6] Kari K, Jouni P and Pasi Y (2005) MERIS, MODIS and ETM+ Channel
Configurations in the Estimation of Lake Water Quality from Subsurface
Reflectance Using Semianalytical and Empirical Algorithms, Geophysica ,
41(1-2), 31-55.
[7] Xiaoying Y , Wei J (2010) GIS-based spatial regression and prediction of
water quality in river networks: A case study in Iowa, Journal of
Environmental Management, 91 (2010) 1943e1951.
[8] Jerry AG (2001) Geographic techniques and recent applications of remote
sensing to landscape-water quality studies Water, air, and soil pollution ,138:
181–197, 2002.
[9] Foster JA , McDonald AT (2000) Assessing pollution risks to water
supply intakes using geographical information systems (GIS), Environmental
Modelling & Software , 15 (2000) 225–234.
[10] Shaban M,Urban B , ElSaadi A ,Faisal M (2010) Detection and mapping
of water pollution variation in the Nile Delta using multivariate clustering and
GIS techniques, Journal of Environmental Management, 91,1785-1793.
[11] Nandish MM and Keith SR (1996) Estimation of Surface Water Quality
Changes in Response to Land Use Change: Application of The Export
Coefficient Model Using Remote Sensing and Geographical Information
System, Journal of Environmental Management , 48, 263–282.
[12] Sandow MY , Bruce BY , Abdul SA , Thomas MA (2012) Groundwater
quality in some Voltaian and Birimian aquifers in northern Ghana—
application of mulitvariate statistical methods and geographic information
systems, Hydrological Sciences Journal, 57(6) 2012.
[13] Meijerink AMJ (1997) Remote sensing applications to hydrology:
groundwater, Hydrological Sciences,41(4),August 1997.
[14] Boggs GS ,Delaney JL, Conacher A (2001) Using Digital Spatial Data
and CIS Tools to Examine Water Quality Variability in the Upper Talbot
Brook Catchment, WA, CartographyVol. 30, No. 2.
[15] Abdullah CK, Fehiman C, Selcuk T, Huseyin S, Burcu A (2010) The
Geographical Information System (GIS) based water quality assessment of a
International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.59 (2015)
© Research India Publications; httpwww.ripublication.comijaer.htm
66

8. drinking water distribution system in the Denizli City , Desalination and
Water Treatment 19 (2010) 318–324.
[16] Chang H (2008) Spatial analysis of water quality trends in the Han River
basin, South Korea ,Water Research ,42, 3285e3304.
[17] Coats R , Larsen M, Heyvaert A, Thomas J , Luck M, Reuter J (2008)
Nutrient and sediment production, watershed characteristics, and land use in
the Tahoe Basin, California Nevada , Journal of the American Water
Resources Association, 44 (3), 754e770.
[18]Keller PA (2001) Comparison of two inversion techniques of a semi
analytical model for the determination of lake water constituents using
imaging spectrometry data, Science of the Total Environment, 268, 189−196.
International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.59 (2015)
© Research India Publications; httpwww.ripublication.comijaer.htm
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