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Research Articletgis_1234 811..832
Remote Sensing of Turbidity andPhosphate in Creeks and Coast ofMumbai: An Effect of Organic Matter
Deepty Ranjan SatapathyEnvironment and SustainabilityDivisionInstitute of Minerals and MaterialsTechnology
Ritesh VijayEnvironmental System Design andModeling DivisionNational EnvironmentalEngineering Research Institute
Swapnil R. KambleEnvironmental System Design andModeling DivisionNational EnvironmentalEngineering Research Institute
Rajiv A. SohonyEnvironmental System Design andModeling DivisionNational EnvironmentalEngineering Research Institute
AbstractGeospatial approaches to monitoring and mapping water quality over a wide rangeof temporal and spatial scales have the potential to save field and laboratory efforts.The present study depicts the estimation of water quality parameters, namely tur-bidity and phosphate, through regression analysis using the reflectance derived fromremote sensing data on the west coast of Mumbai, India. The predetermined coastalwater samples were collected using the global positioning system (GPS) and weremeasured concurrently with satellite imagery acquisition. To study the influence ofwastewater, the linear correlations were established between water quality param-eters and reflectance of visible bands for either set of imagery for the study area,which was divided into three zones: creek water, shore-line water and coastal water.Turbidity and phosphate have the correlation coefficients in the range 0.75–0.94 and0.78–0.98, respectively, for the study area. Negative correlation was observed forcreek water owing to high organic content caused by the discharges of domesticwastewater from treatment facilities and non-point sources. Based on the leastsquare method, equations are formulated to estimate turbidity and phosphate, to
Address for Correspondence: Deepty Ranjan Satapathy, E & S Division, IMMT, Bhubaneswar – 13,India. E-mail: drsatapathy@immt.res.in; drsatapathy@gmail.com
Transactions in GIS, 2010, 14(6): 811–832
© 2010 Blackwell Publishing Ltddoi: 10.1111/j.1467-9671.2010.01234.x
map the spatial variation on the GIS platform from simulated points. The applica-bility of satellite imagery for water quality pattern on the coast is verified for efficientplanning and management.
1 Introduction
Protection and maintenance of water quality is a primary objective for any regulatoryagency and organization. The quality of water, and thus its suitability for recreation andother activities, is affected by the type and the quantity of various suspended anddissolved substances, including sediments. Increase of turbidity and phosphate is symp-tomatic of eutrophic conditions (Shafique et al. 2001) and serves as a carrier and storageagent for nitrogen, phosphorous and organic compounds that can be indicators ofpollution (Jensen 2000). The water quality parameters that can be estimated with opticalremote sensing methods include phytoplankton (chlorophyll), turbidity, water tempera-ture, suspended inorganic material, e.g. sand, dust and clay, colored dissolved organicmatter, etc. (Allee and Johnson 1999, Forster et al. 1993, Fraser 1998, Kondratyev et al.1998, Parada and Canton 1998, Pattiaratchi et al. 1994, Rimmer et al. 1987, Tassan1998). Some of the substances found in water contribute to the way in which opticalradiation interacts with water bodies through scattering and absorption of electromag-netic radiation (EMR). Since these optically significant substances change the color ofwater, remote sensing instruments using spectral bands in the optical region of thespectrum can detect the changes and estimate the amount of optically significant sub-stances. Remote sensing satellites measure the amount of solar radiation at variouswavelengths reflected by surface water (Ekercin 2007). Remote sensing techniques havethe potential to overcome the limitation of traditional methods of water quality studiesby providing an alternative means of studying and monitoring water quality over a widerange of both temporal and spatial scales. Water turbidity is widely accepted as arepresentative parameter for the littoral dynamic study investigated by diverse authorsfrom digital images at different spatial (scale) and spectral resolutions, e.g. ATM(Pattiaratchi et al. 1986), SPOT HVR (Lathrop and Lillesand 1989), Landsat TM(Pattiaratchi et al. 1994).
Water quality assessment of ocean and inland waters using satellite data has beencarried out since the first remote sensing satellite Landsat MSS was operational(Thiemann and Kaufmann 2000). Today, there are many satellites with high resolutionfor use in water quality monitoring studies. Among these satellites, IKONOS, despite therestrictive spectral range of its multispectral imagery (up to 800 nm), is suitable for waterquality mapping because of its high spatial resolution (Li and Li 2004, Ritchie et al.1990). Most of the previous studies focused on the discovery of the relationship betweenremote sensing data and in-situ measurements. Moreover, integrating a geographicinformation system (GIS) into the system, rather than using traditional numerical tech-niques, aids the study of spatial variation of monitoring data results. Water quality withinsurface water bodies can exhibit significant spatial variability and the estimation andcharacterization of these patterns will help in planning and management.
The primary aim of the study was to estimate turbidity and phosphorus using remotesensing data in order to produce thematic maps complementing experimental informa-tion and to interpret the spatial distribution of water quality parameters. Based on in situmeasurements and reflectance data from satellite imagery during the same period,
812 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
correlation coefficients for the abovementioned water quality parameters were calcu-lated. Two different numerical approaches were used, namely traditional multiple regres-sion and geostatistical techniques. Finally, thematic maps resulting from theseapplications were integrated into a GIS, together with other information, with the goalof achieving more efficient planning in water quality management.
2 Study Area
Maharashtra, one of the coastal states located on the west coast of India, is endowedwith a coastline of 720 km. Mumbai is one of the densely populated metropolitan citieslocated on the west coast, 26 km along its western edge. The coastline is indented withlarge and small creeks. These creeks or coastal waterways are tide dominant, andtherefore naturally turbid because of the strong tidal currents, which mobilize thesuspended particles. The study covers an area from longitude 72°42′45″ to 72°50′00″and latitude 19°06′47″ to 19°14′52″ including Malad creek, Manori creek, seashore andcoast (see Figure 1). The study area is divided into three zones, coast, creek and seashore,because the interference of organic matter through wastewater varies the relationshipbetween turbidity and phosphate with satellite derived reflectance.
3 Methodology
Broadly, the methodology consisted of the following steps:
1. conversion of satellite image digital number values to unit-less surface reflectance toeliminate the effects of high local variability on remote sensing observation values;
2. atmospheric and geometric correction of the satellite image;3. regression analysis between the pixel (5 ¥ 5 pixel configuration array) reflectance
values and the water quality parameters;4. estimating water quality through simulation;5. mapping turbidity and phosphate from the satellite image.
3.1 Estimation of Water Quality Parameters: Field Data
3.1.1 Sampling
The sampling was performed by a boat at the predetermined sampling location fixed withthe help of a Trimble Juno global positioning system unit and synoptic coverage ofsatellite imagery and its variance in digital number (DN) values. The position of eachsampling location was constrained by differential global positioning system. In all, 32sampling locations were fixed to cover the entire study area divided into three differentstudy zones (see Figure 1). Sampling locations S1-S16 are categorized as coast, S17–S21,S23 as seashore and S22, S24, S25, and S26–32 as creek.
3.1.2 Chemical analysis
The turbidity of sea water samples is determined in the laboratory with the help of aturbidity meter by measuring the scattering of light from the water sample. The intensity
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 813
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
70°0
'0"E
70°0
'0"E
80°0
'0"E
80°0
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90°0
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814 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
of light scattered at 90 degrees as the beam of light passes through a water sample.Turbidity measured in NTU (Nephelometric Turbidity Units) was estimated per thestandard method APHA (2002). Similarly, phosphate in sea water samples is determinedby molybdate reactive orthophosphate with the calorimetric stannous chloride estima-tion method. The intensity of the blue coloured complex is measured using a spectro-photometer, which is directly proportional to the concentration of phosphate present inthe sample in mg/L.
3.2 Image Analysis
The satellite imageries used for this case study consisted of: (1) IKONOS (SpaceImaging) 1-m color product, IKONOS Geo-processing level, which was created bypan-sharpening multispectral (red: 0.632–0.698 mm, green: 0.506–0.595 mm, blue:0.445–0.516 mm) imagery at a spatial resolution of 4 m using panchromatic imageryat a spatial resolution of 1 m; (2) Landsat 7 ETM+ consisting of visible and infraredbands at a spatial resolution of 30 m. The scenes used in this study were collectedcoincident with the water survey for quality parameters. The image is cloud-free overthe water and no wind lines are visible on the water surface.
The digital number values in the satellite image bands were converted to surfacereflectance values as described below. This conversion is necessary for studies regardingreflectance of water surfaces because the raw digital numbers of a Landsat 7 ETM+/IKONOS image are not only dependent on the reflectance characteristics of the specificscene, but also contain noise and digital number value offsets that are a result of theviewing geometry of the satellite, the angle of the sun’s incoming radiation, atmosphericdepth due to viewing angle and the design characteristics of the sensor. Conversion of thedata to radiance removes the voltage bias and gains from the satellite sensor usingequations (1) and (3). The radiance values are then further converted to satellite reflec-tance according to equations (2) and (4). This conversion accounts for the varying sunangle due to differences in latitude, season, and time of day, and the variation in thedistance between the Earth and Sun.
3.2.1 Image standardization
Image standardization is the process of normalizing image pixel values for differencesin sun illumination geometry, atmospheric effects and instrument calibration. In thefield of remote sensing the most commonly used measure of radiant energy is radiance(L). Radiance is defined as the amount of radiant energy per unit time per unit solidangle (i.e. towards a certain direction) per unit of projected area of the source. Spectralradiance is the radiance per unit wavelength interval at a given wavelength (unit W m-2
sr-1 mm-1).
(i) DN-to-Reflectance Conversion: Landsat ETM+
The conversion of DN values to radiance has been carried out using the followingequation (1) using different characteristics of the imagery (see Table 1).
LL L
Q QDN Q LMax Min
Cal CalCal Minλ = −
−⎛⎝⎜
⎞⎠⎟
× −( ) +max min
min (1)
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 815
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Tabl
e1
Co
nve
rsio
no
fD
Nto
refl
ecta
nce
usi
ng
Lan
dsa
t7
ETM
+
Wes
tC
oas
to
fM
um
bai
,In
dia
Lan
dsa
t7
ETM
+W
ater
qu
alit
yp
aram
eter
DN
-Dig
ital
nu
mb
erL-
Spec
tral
rad
ian
ceR
-Su
rfac
ere
flec
tan
ce
Spec
tral
ban
d
Spec
tral
ban
d,
QC
ALm
ax=
255,
QC
ALm
in=
1
Spec
tral
Ban
d,
d=
0.99
25,q
=38
.406
3539
Sam
plin
glo
cati
on
Co
ord
inat
es,U
TMzo
ne
43
Turb
idit
y(N
TU)
PO4
(mg/
L)V
isb
lue
Vis
gree
nV
isre
dV
isb
lue
Vis
gree
nV
isre
d
Vis
blu
eV
isgr
een
Vis
red
XY
ESU
Nl
1969
ESU
Nl
1840
ESU
Nl
1551
S172
°48
′50″
19°
6′0″
9.6
0.09
9170
6064
.18
48.7
231
.68
0.12
870.
1045
0.08
06S2
72°
46′3
7″19
°7′
59″
7.5
0.08
9069
5763
.11
48.1
629
.81
0.12
650.
1033
0.07
59S3
72°
46′3
2″19
°10
′32″
9.1
0.09
9169
5663
.89
47.9
229
.19
0.12
810.
1028
0.07
43S4
72°
46′8
″19
°13
′1″
120.
193
7468
65.1
551
.91
36.6
50.
1306
0.11
140.
0933
S572
°47
′49″
19°
6′0″
4.5
0.06
8967
5162
.21
46.3
226
.08
0.12
470.
0994
0.06
64S6
72°
45′3
2″19
°7′
59″
5.8
0.07
8966
5062
.33
45.5
225
.46
0.12
490.
0977
0.06
48S7
72°
46′5
″19
°10
′34″
5.6
0.07
8867
5161
.55
46.3
226
.08
0.12
340.
0994
0.06
64S8
72°
44′5
3″19
°13
′1″
7.1
0.08
9069
5063
.11
47.9
225
.46
0.12
650.
1028
0.06
48S9
72°
46′4
8″19
°6′
0″2.
20.
0687
6451
60.8
543
.93
26.0
80.
1220
0.09
420.
0664
S10
72°
44′4
6″19
°7′
59″
4.7
0.07
8864
5261
.40
43.9
326
.70
0.12
310.
0942
0.06
80S1
172
°45
′7″
19°
10′3
4″4.
80.
0788
6750
61.1
746
.32
25.4
60.
1226
0.09
940.
0648
S12
72°
44′1
4″19
°13
′2″
5.1
0.07
8868
5161
.55
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226
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0.12
340.
1011
0.06
64S1
372
°45
′40″
19°
5′53
″1.
30.
0685
6148
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.53
24.2
20.
1187
0.08
910.
0616
S14
72°
43′3
4″19
°7′
52″
1.7
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8661
4759
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41.5
323
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030.
0891
0.06
00S1
572
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′59″
19°
10′2
3″1.
30.
0587
6345
60.7
743
.13
22.3
50.
1218
0.09
250.
0569
S16
72°
43′4
″19
°12
′52″
1.2
087
6346
60.7
743
.13
22.9
70.
1218
0.09
250.
0585
S17
72°
49′1
5″19
°6′
11″
300.
2593
7464
65.4
452
.16
33.9
30.
1312
0.11
190.
0863
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72°
48′4
3″19
°7′
46″
190.
0789
7056
62.3
348
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28.9
10.
1249
0.10
410.
0736
S19
72°
47′1
1″19
°9′
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093
7465
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1312
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180.
0879
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72°
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2″19
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230.
1591
7262
63.8
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830.
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72°
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7″19
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200.
1290
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63.1
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640.
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S22
72°
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3″19
°11
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420.
2480
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55.3
235
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23.6
60.
1109
0.07
670.
0602
S23
72°
46′4
6 ″19
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250.
1891
7362
63.8
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.89
32.8
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1281
0.10
920.
0835
S24
72°
48′2
″19
°12
′26″
460.
3478
5245
53.7
634
.58
22.0
50.
1078
0.07
420.
0561
S25
72°
48′2
9″19
°13
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390.
2380
5448
55.3
235
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23.9
10.
1109
0.07
710.
0609
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72°
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9″19
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6″30
0.16
8155
4856
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36.6
524
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0.11
250.
0786
0.06
11S2
772
°48
′2″
19°
8′20
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0.18
8255
4856
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36.6
924
.46
0.11
400.
0787
0.06
22S2
872
°48
′8″
19°
8′35
″48
0.39
7853
4353
.76
34.8
020
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0.10
780.
0747
0.05
34S2
972
°48
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19°
8′54
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0.42
7852
4453
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34.6
021
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0.10
780.
0742
0.05
50S3
072
°48
′26″
19°
9′11
″65
0.42
7650
4152
.21
32.7
419
.87
0.10
470.
0702
0.05
06S3
172
°48
′52″
19°
9′35
″45
0.3
7953
4554
.54
34.9
022
.05
0.10
930.
0749
0.05
61S3
272
°49
′23″
19°
10′8
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7651
4252
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33.9
320
.27
0.10
470.
0728
0.05
16
816 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
where Ll is the radiance for spectral band 1 at the sensor’s aperture (W/m2/mm/sr), DNl
is the Digital Number of each pixel in the image, LMAX and LMIN are the calibrationconstants, and QCALMAX and QCALMIN are the highest and the lowest points of the range ofrescaled radiance in DN.
(ii) Spectral Reflectance Calculation, Landsat ETM+
The reflectance for band l is computed by the following equation (Markham and Barker1986, NASA 2002):
ρ πθλ
λ
λ= × ×
×L d
ESUN
2
cos(2)
where Ll is the spectral radiance, which is the outgoing radiation energy of the bandobserved at the top of atmosphere by the satellite, d is the Earth-Sun distance inastronomical units, ESUNl is the mean solar exo-atmospheric irradiances for the band l,and Cosq is the cosine of the solar incident angle. Supposing a horizontal land surface isflat, the cosine of solar incident angle (cosq) can be calculated from the Sun Elevation cos(90-SunElevation). The Zenith angle for the image is 38.40° (obtained from the headerfile of the imagery).
(iii) DN-to-Reflectance Conversion: IKONOS Data
The pixel value in the 11-bit IKONOS DNs in different bands are transformed to spectralradiance value (W sr-1 cm-2) using different characteristic features of the sensor (seeTable 2) using equation (3) (Space Imaging Eurasia 2006):
L DN CalCoef Bandwidthλ λ λ λ= ∗( ) ∗( )104 (3)
where Ll is the radiance for spectral band 1 at the sensor’s aperture (W/m2/mm/sr), DNl
is the digital value for spectral band 1, CalCoefl is the radiometric calibration coefficient[DN/(mW/cm2-sr)], Bandwidthl is the bandwidth of spectral band 1 (nm).
The three IKONOS bands were converted to at-satellite reflectance (albedo) r
ρ πθλ
λ
λ= × ×
×L d
ESUN
2
cos(4)
where r is the at-sensor reflectance, Ll is the radiance for spectral band 1 at the sensor’saperture (W/m2/mm/sr), ESUNl is the band dependent mean solar exoatmospheric irra-diance and d is the Earth-sun distance, in astronomical units, and qs is solar zenith angleand was found to be 27.29 (Fleming 2003).
4 Correlation of in situ Water Quality Parameter with Reflectance
An attempt was made to establish the relationship between the average reflectance valuesof 5 ¥ 5 pixels with measured concentration of turbidity and phosphate in three visiblebands of satellite imagery (Ekercin 2007). The spectral signature of the waterbodyindicates that it has reflectance only in visible red and green bands but absorbance in thenear-infrared band. The regression model was calibrated by in situ samples. Differentlinear regression models are formulated for different study zones using different satellite
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 817
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Tabl
e2
Co
nve
rsio
no
fD
Nto
refl
ecta
nce
usi
ng
IKO
NO
S
Wes
tC
oas
to
fM
um
bai
,In
dia
IKO
NO
SW
ater
qu
alit
yp
aram
eter
DN
-Dig
ital
nu
mb
erL-
Spec
tral
rad
ian
ceR
-Su
rfac
ere
flec
tan
ce
Spec
tral
ban
dSp
ectr
alb
and
Spec
tral
ban
d,d
=0.
9925
,q=
27.2
959
Sam
plin
glo
cati
on
Co
ord
inat
es,U
TMzo
ne
43
Turb
idit
y(N
TU)
PO4
(mg/
L)V
isb
lue
Vis
gree
nV
isre
d
Vis
blu
eV
isgr
een
Vis
red
Vis
blu
eV
isgr
een
Vis
red
XY
Cal
ib.C
oef
f.
ESU
Nl
1930
.9ES
UN
l18
54.8
ESU
Nl
1556
.5
728
727
949
Ban
dW
idth
(nm
)
71.3
88.6
65.8
S172
°48
′50″
19°
6′0″
9.6
0.09
664
482
276
127.
9274
.78
44.1
80.
2306
0.14
030.
0988
S272
°46
′37″
19°
7′59
″7.
50.
0865
347
327
112
5.90
73.5
043
.43
0.22
690.
1379
0.09
71S3
72°
46′3
2″19
°10
′32″
9.1
0.09
659
479
293
127.
0274
.41
46.9
90.
2290
0.13
960.
1051
S472
°46
′8″
19°
13′1
″12
0.1
676
539
352
130.
1883
.67
56.3
50.
2347
0.15
700.
1260
S572
°47
′49″
19°
6′0″
4.5
0.06
629
419
217
121.
1764
.97
34.8
20.
2184
0.12
190.
0779
S672
°45
′32″
19°
7′59
″5.
80.
0764
946
124
512
5.00
71.5
139
.31
0.22
530.
1342
0.08
79S7
72°
46′5
″19
°10
′34″
5.6
0.07
638
457
255
122.
9770
.96
40.8
10.
2217
0.13
320.
0913
S872
°44
′53″
19°
13′1
″7.
10.
0865
146
226
012
5.45
71.6
941
.56
0.22
610.
1345
0.09
29S9
72°
46′4
8″19
°6′
0″2.
20.
0662
841
921
512
0.99
64.9
734
.45
0.21
810.
1219
0.07
70S1
072
°44
′46″
19°
7′59
″4.
70.
0762
942
824
412
1.17
66.4
339
.13
0.21
840.
1247
0.08
75S1
172
°45
′7″
19°
10′3
4″4.
80.
0763
144
123
712
1.62
68.4
238
.00
0.21
920.
1284
0.08
50S1
272
°44
′14″
19°
13′2
″5.
10.
0763
845
524
412
2.97
70.6
039
.13
0.22
170.
1325
0.08
75S1
372
°45
′40″
19°
5′53
″1.
30.
0662
340
719
212
0.04
63.1
630
.70
0.21
640.
1185
0.06
87S1
472
°43
′34″
19°
7′52
″1.
70.
0562
741
221
412
0.72
63.8
934
.26
0.21
760.
1199
0.07
66S1
572
°43
′59″
19°
10′2
3″1.
30.
0560
937
120
011
7.34
57.5
332
.01
0.21
150.
1080
0.07
16S1
672
°43
′4″
19°
12′5
2″1.
20
586
357
150
112.
8455
.36
23.9
60.
2034
0.10
390.
0536
818 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Tabl
e2
Co
nti
nu
ed
Wes
tC
oas
to
fM
um
bai
,In
dia
IKO
NO
SW
ater
qu
alit
yp
aram
eter
DN
-Dig
ital
nu
mb
erL-
Spec
tral
rad
ian
ceR
-Su
rfac
ere
flec
tan
ce
Spec
tral
ban
dSp
ectr
alb
and
Spec
tral
ban
d,d
=0.
9925
,q=
27.2
959
Sam
plin
glo
cati
on
Co
ord
inat
es,U
TMzo
ne
43
Turb
idit
y(N
TU)
PO4
(mg/
L)V
isb
lue
Vis
gree
nV
isre
d
Vis
blu
eV
isgr
een
Vis
red
Vis
blu
eV
isgr
een
Vis
red
XY
Cal
ib.C
oef
f.
ESU
Nl
1930
.9ES
UN
l18
54.8
ESU
Nl
1556
.5
728
727
949
Ban
dW
idth
(nm
)
71.3
88.6
65.8
S17
72°
49′1
5″19
°6′
11″
300.
2564
748
634
812
4.55
75.5
055
.79
0.22
450.
1417
0.12
48S1
872
°48
′43″
19°
7′46
″19
0.07
635
451
270
122.
2970
.06
43.2
50.
2204
0.13
150.
0967
S19
72°
47′1
1″19
°9′
1″34
064
949
237
112
5.00
76.4
159
.35
0.22
530.
1434
0.13
27S2
072
°47
′22″
19°
10′2
5″23
0.15
639
475
333
123.
2073
.70
53.3
60.
2221
0.13
830.
1193
S21
72°
46′5
7″19
°9′
41″
200.
1263
746
430
212
2.74
72.0
548
.30
0.22
130.
1352
0.10
80S2
272
°47
′43″
19°
11′5
4″42
0.24
617
437
334
118.
9267
.88
53.5
40.
2144
0.12
740.
1197
S23
72°
46′4
6″19
°13
′1″
250.
1864
648
033
912
4.38
74.5
954
.29
0.22
420.
1400
0.12
14S2
472
°48
′2″
19°
12′2
6″46
0.34
611
413
305
117.
7964
.07
48.8
60.
2123
0.12
020.
1093
S25
72°
48′2
9″19
°13
′1″
390.
2362
546
536
412
0.49
72.2
358
.22
0.21
720.
1356
0.13
02S2
672
°47
′59″
19°
8′6″
300.
1666
350
939
012
7.70
78.9
562
.53
0.23
020.
1482
0.13
98S2
772
°48
′2″
19°
8′20
″38
0.18
631
484
336
121.
6275
.14
53.7
30.
2192
0.14
100.
1202
S28
72°
48′8
″19
°8′
35″
480.
3960
741
230
411
6.89
63.8
948
.68
0.21
070.
1199
0.10
88S2
972
°48
′14″
19°
8′54
″52
0.42
587
401
257
113.
0662
.25
41.1
90.
2038
0.11
680.
0921
S30
72°
48′2
6″19
°9′
11″
650.
4257
939
926
111
1.48
61.8
941
.75
0.20
100.
1161
0.09
34S3
172
°48
′52″
19°
9′35
″45
0.3
614
434
312
118.
2467
.33
49.9
90.
2131
0.12
640.
1118
S32
72°
49′2
3″19
°10
′8″
680.
4853
933
827
810
3.83
52.4
544
.56
0.18
720.
0984
0.09
96
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 819
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
imagery for turbidity and phosphate. Spatial variation of water quality parameters areplotted as contours using the spline interpolation method on the GIS platform based onin-situ values of turbidity and phosphate.
5 Retrieval of Water Quality Parameters using Remote Data
The linear least squares fitting technique is the simplest and most commonly applied formof linear regression and provides a solution to the problem of finding the best fittingstraight line through a set of points. Regression equations were developed for twodifferent datasets (Bilge et al. 2003). A mathematical procedure was utilized for findingthe best-fitting curve to a given set of points by minimizing the sum of the squares of theoffsets (the residuals) of the points from the curve. The sum of the squares of the offsetsis used instead of the offset absolute values because this allows the residuals to be treatedas a continuous differentiable quantity (Farebrother 1999). The correlation coefficientand the standard error are calculated as in linear regression. Based on the least squaremethod, constants are derived using Solver in MS Excel to be best fitted to N data points.Finally, equations are formulated as illustrated below in order to estimate the selectedwater quality parameter.
Y A A X A A X= + ∗ + ∗ + ∗0 1 1 2 2 3 3χ (5)
Equations derived for Y, Turbidity (NTU)/ Phosphate (mg/L) through regression analysis,for x, the pixel reflectance in the vis blue, green and red bands using satellite imagery andA0, A1, A2 and A3 are derived constants. The turbidity and phosphate water qualityparameters are estimated at numerous locations other than the in situ sampling locationsfrom reflectance of satellite imagery falling under three different study zones using theequations derived based on the least square method (see Table 3). These simulated valuesare plotted as contours and compared with the in-situ contours.
6 Results
The turbidity and phosphate in creek water and seashore are high as compared to coastalwater based on in situ measurements, due to wastewater discharges emanating from theanthropogenic activities of a densely populated city like Mumbai as visualized fromsatellite imagery. Spatial variability of in situ water quality parameters is depicted fromthe contours superimposed upon the satellite imagery (see Figures 2a–d).
Before finding any relationship between one of the water quality parameters andreflectance in the satellite imagery, an attempt was made to establish a relationship ofreflectance among various bands. It was found that there is a strong relationship betweenvis-blue and green bands of either set of imagery, which is conducive for the study ofwater quality in both the bands (see Table 4).
Goward et al. (2003) found radiometric differences between the two sensors butwhen the Ikonos sensor is aggregated to 30 m spatial resolution, the difference isminimized. However, correlations of turbidity and phosphate with reflectance wereexplored in different visible bands, namely vis-blue, green and red of IKONOS (seeFigures 3a–f) and Landsat 7 ETM+ (see Figures 4a–f) in order to formulate equations toderive water quality parameters.
820 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
The regression models were found to be statistically significant with correlationcoefficients in the range of 0.76–0.94 for turbidity and 0.78–0.98 for phosphate for threedifferent study zones using either set of imagery (see Table 5).
The best correlation was found in blue reflectance, which is inconsistent with theresults of Fraser (1998) in which equal correlation was found for red, green and bluebands. Lathrop and Lillesand (1986) found it for the Vis Red band. The coefficients in theabove equations could vary with different weather and illumination conditions. Turbidityand phosphate were found to be positively correlated as far as coast and seashore zoneswere concerned, which is consistent with many case studies using satellite imagery. Butin the case of the creek zone, a strongly negative correlation was found between turbidityand reflectance because of the high quantity of organic matter present (Chen et al. 2009).The presence of organic matter in the creek water is evident from biochemical oxygendemand (BOD) values ranging from 15–40 mg/L due to discharges of wastewater fromthe nearby sewage treatment facilities and non-point sources. In order to derive the bestfit model to estimate turbidity and phosphate, measured and estimated values arecompared (see Figures 5a–d and Table 6).
However it has been found that water quality parameters are highly correlated withreflectance values of satellite imagery (0.98, P = 0.005). The comparison relationship wastested on simulation points by estimating the water quality. Water quality parameterswere estimated on simulation points using different equations for different study zonesand plotted as contours for both sets of imagery to interpret the spatial variability overthe study area (see Figures 6a–d).
The spatial variability of simulated values is shown as contours over the region.Based on the error percentage between in situ and simulated values, the spatial dif-ference is also indicated as contours between in situ and simulated values. Similarly,
Table 3 Equations formulated to estimate water quality parameters
Study areaEquation derived using LandsatETM+ Equation derived using IKONOS
TurbidityCoast Y = 153.693 ¥ 1 + 156.910 ¥ 2 +
204.0245 ¥ 3 - 43.605Y = - 495.524 ¥ 1 + 280.2783 ¥ 2 +
165.660 ¥ 3 + 63.886Creek Y = 278.872 ¥ 1 - 3894.9785 ¥ 2 -
253.105 ¥ 3 + 323.418Y = - 783 ¥ 1 - 21.922 ¥ 2 -
101.052 ¥ 3 + 226.2856Seashore Y = 3036.154 ¥ 1 - 1119.35 ¥ 2 +
203.366 ¥ 3 - 259.711Y = 1647.558 ¥ 1 + 842.290 ¥ 2 -
151.833 ¥ 3 - 441.188
PhosphateCoast Y = 0.323 ¥ 1 + 0.918 ¥ 2 + 0.887 ¥
3 - 0.125Y = 0.858 ¥ 1 + 0.209 ¥ 2 + 0.464 ¥
3 - 0.188Creek Y = - 7.86 ¥ 1 - 13.38 ¥ 2 - 11.58 ¥
3 + 2.83Y = 3.122 ¥ 1 - 7.97 ¥ 2 - 1.47 ¥ 3 +
0.812Seashore Y = 2.96 ¥ 1 + 26.97 ¥ 2 - 4.08 ¥ 3 -
2.8Y = - 6.184 ¥ 1 + 49.06 ¥ 2 -
11.032 ¥ 3 - 3.95
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 821
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Figu
re2
(a)C
on
tou
rso
ftu
rbid
ity
(in
situ
)su
per
imp
ose
do
ver
Iko
no
sim
ager
y;(b
)Co
nto
urs
oft
urb
idit
y(i
nsi
tu)s
up
erim
po
sed
ove
rLa
nd
sat
7ET
M+
imag
ery;
(c)
Co
nto
urs
of
ph
osp
hat
e(i
nsi
tu)
sup
erim
po
sed
ove
rIK
ON
OS
imag
ery;
and
(d)
Co
nto
urs
of
ph
osp
hat
e(i
nsi
tu)
sup
erim
po
sed
ove
rLa
nd
sat
ETM
+im
ager
y
822 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Figu
re2
Co
nti
nu
ed
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 823
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
the difference in spatial extent of the simulated values between Landsat ETM+ andIKONOS vary due to reflectance estimated based on the empirical relationship.
It is also important to evaluate the effect of pixel size on water quality assessment.There are many possible sources of errors for estimation of the water quality param-eters as far as this study is concerned. The spatial resolution of the LandsatETM+ data may result in an overestimation of water quality parameters comparedwith IKONOS. Even though the 30 m resolution is low in comparison to theIKONOS, 1 m with a coverage of 30 ¥ 30 m represents the same value. In reality,however, within the small coverage of 1 ¥ 1 m the water quality parameter is nothomogeneous. Another source of error is geometric error carried with the satelliteimagery. Hence, care has been taken while performing geometric correction of theimagery to study the sampling locations coherently, with the help of DGPS. Selectionof sampling sites may have been insufficient, resulting in a high spatial variability ofthe water quality parameter. This presents another source of error in estimating waterquality using satellite data. Sources of error in measuring water quality using remotesensing data will be there for 1 m2 (IKONOS sensor) as well as more for 900 m2
(Landsat ETM+). Bearing in mind that the studied parameters, turbidity and phos-phate, dilute over a large spatial extent of sea region, a 5 ¥ 5 pixel array is consideredfor the study.
7 Conclusions
In the present study, an attempt has been made to establish the relationship betweenwater quality parameters, such as turbidity and phosphate, with high and moderateresolution satellite imageries such as IKONOS and Landsat 7 ETM+ in a specific studyarea on the west coast of Mumbai, India. The applicability of satellite imagery to thiskind of study show strong evidence of turbidity and phosphate, as discussed above.Multiple regression results give significantly consistent information with very highdetermination coefficients for both of the water quality parameters. In the creek zone,strong negative correlation was found reflecting high turbidity and low reflectance dueto the presence of a large quantity of organic matter. Another aspect of the study was
Table 4 Correlation matrix of reflectance between differ-ent bands of Landsat ETM+ and IKONOS imageries
Blue Green Red
Landsat ETM+Blue 1Green 0.966 1Red 0.723 0.772 1
IKONOSBlue 1Green 0.795 1Red 0.126 0.465 1
824 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
to check the feasibility of IKONOS, high resolution satellite imagery for retrieval ofwater quality parameters. It has been found that the high resolution satellite imageryalso provides good results in comparison with moderate resolution satellite imagery,and hence are suitable for water quality mapping. The finding of the case study infersthat there is an interference of organic matter through wastewater discharges in creekwater, because of which a negative trend was found between the water quality param-eter and satellite derived reflectance as evident from BOD values of sampling locationsin the creek. Spatial distribution of water quality in the form of contour maps over thewater bodies also shows water quality conditions over the whole region. This type of
Turbidity Vs Reflectance (Interior Coast) using Ikonos
0
2
4
6
8
10
12
14
0.05 0.1 0.15 0.2 0.25
Reflectance
Tu
rbid
ity (
NT
U)
Blue Green Red
Turbidity Vs Reflectance (Creek) using Ikonos
20
30
40
50
60
70
80
0.05 0.1 0.15 0.2 0.25
Reflectance
Tur
bid
ity (
NT
U)
Blue Green Red
Turbidity Vs Reflectance (Shore Line) using Ikonos
15
20
25
30
35
0.05 0.1 0.15 0.2 0.25
Reflectance
Tu
rbid
ity
(NT
U)
Blue Green Red
Phosphate Vs Reflectance (Interior Coast) using Ikonos
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.05 0.1 0.15 0.2 0.25
Reflectance
Pho
sph
ate
(mg
/L)
Blue Green Red
Phosphate Vs Reflectance (Shore Line) using Ikonos
0.05
0.1
0.15
0.2
0.25
0.3
0.05 0.1 0.15 0.2 0.25
Reflectance
Pho
spha
te (
mg/
L)
Blue Green Red
Phosphate Vs Reflectance (Creek) using Ikonos
0.1
0.2
0.3
0.4
0.5
0.05 0.1 0.15 0.2 0.25
Reflectance
Pho
spha
te (
mg
/L)
Blue Green Red
a b
c d
e f
Figure 3 (a) Correlation of Turbidity and Reflectance in Vis Blue, Green and Red Band ofIKONOS (Coast); (b) Correlation of PO4 and Reflectance in Vis Blue, Green and Red Bandof IKONOS (Coast); (c) Correlation of Turbidity and Reflectance in Vis Blue, Green andRed Band of IKONOS (Creek); (d) Correlation of PO4 and Reflectance in Vis Blue, Greenand Red Band of IKONOS (Creek); (e) Correlation of Turbidity and Reflectance in VisBlue, Green and Red Band of IKONOS (SeaShore); and (f) Correlation of PO4 andReflectance in Vis Blue, Green and Red Band of IKONOS (Seashore)
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 825
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
study is essential for the planning and management of water pollution in coastal areassurrounding densely populated cities like Mumbai.
Acknowledgements
We would like to thank the Director of the National Environmental EngineeringResearch Institute (NEERI), Nagpur for his support of this study. We would also like toshow our appreciation to Mr P. S. Kelkar and Mrs S. Dhage for their help in the field datacollection and chemical analysis.
Turbidity Vs Reflectance (Interior Coast) using Landsat7 ETM+
0
2
4
6
8
10
12
14
0.05 0.07 0.09 0.11 0.13 0.15
Reflectance
Tur
bidi
ty (
NT
U)
Blue Green Red
Turbidity Vs Reflectance (Creek) using Landsat7 ETM+
20
30
40
50
60
70
80
0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12
Reflectance
Tur
bidi
ty (
NT
U)
Blue Green Red
Turbidity Vs Reflectance (Shore line) usingLandsat7 ETM+
15
20
25
30
35
0.05 0.07 0.09 0.11 0.13 0.15
Reflectance
Tu
rbid
ity (
NT
U)
Blue Green Red
Phosphate Vs Reflectance (Interior Coast) using Landsat7+ ETM
0.04
0.06
0.08
0.1
0.12
0.05 0.07 0.09 0.11 0.13 0.15
Reflectance
Pho
spha
te (
mg/
L)
Blue Green Red
Phosphate Vs Reflectance (Shore Line) usingLandsat7+ ETM
0.05
0.1
0.15
0.2
0.25
0.3
0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14
Reflectance
Phos
phat
e (m
g/L
)
Blue Green Red
Phosphate Vs Reflectance (Creek) using Landsat7+ ETM
0.1
0.2
0.3
0.4
0.5
0.04 0.06 0.08 0.1 0.12
Reflectance
Phos
phat
e (m
g/L
)
Blue Green Red
a b
c d
e f
Figure 4 (a) Correlation of Turbidity and Reflectance in Vis Blue, Green and Red Band ofLandsat 7 ETM+ (Coast); (b) Correlation of PO4 and Reflectance in Vis Blue, Green andRed Band of Landsat 7 ETM+ (Coast); (c) Correlation of Turbidity and Reflectance in VisBlue, Green and Red Band of Landsat 7 ETM+ (Creek); (d) Correlation of PO4 andReflectance in Vis Blue, Green and Red Band of Landsat 7 ETM+ (Creek); (e) Correlationof Turbidity and Reflectance in Vis Blue, Green and Red Band of Landsat 7 ETM+ (Sea-shore); and (f) Correlation of PO4 and Reflectance in Vis Blue, Green and Red Band ofLandsat 7 ETM+ (Seashore)
826 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Measured Vs. Estimated (Landsat ETM+)
y = 0.9719x + 0.1376R2 = 0.9812
0
10
20
30
40
50
60
70
0 20 40 60 80
Measured Turbidity (NTU)
Est
imat
ed T
urb
idity
(N
TU
)
Measured Vs Estimated (IKO NO S)
y = 0.9886x - 0.1869R2 = 0.9894
0
10
20
30
40
50
60
70
80
0 20 40 60 80Measured Turbidity (NTU)
Est
imat
ed T
urbid
ity
(NT
U)
Measured Vs. Estimated using Landsat ETM+
y = 0.9613x + 0.004R2 = 0.9742
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.1 0.2 0.3 0.4 0.5 0.6Measured Phosphate (mg/L)
Est
imat
ed P
hosp
hate
(m
g/L
)
Measured Vs. Estimated using IKO NO S
y = 0.9458x + 0.0063R2 = 0.9839
0
0.1
0.2
0.3
0.4
0.5
0 0.1 0.2 0.3 0.4 0.5 0.6Measured Phosphate (mg/L)
Est
imat
ed P
hosp
hate
mg/
L)
a b
c d
Figure 5 (a) Correlation of Measured and Estimated Turbidity using Landsat 7 ETM+; (b)Correlation of Measured and Estimated Turbidity using IKONOS; (c) Correlation ofMeasured and Estimated Phosphate using Landsat 7 ETM+; and (d) Correlation of Mea-sured and Estimated Phosphate using IKONOS
Table 5 Correlation of in situ data and satellite derived reflectance through regressionanalysis
Category ofwaterbody
No. ofobservation Satellite/sensor
Spectralband
Turbidity Phosphate
Multiple R R Square Multiple R R Square
Coast 16 Landsat ETM+ B 0.956 0.913 0.904 0.817G 0.943 0.890 0.897 0.804R 0.901 0.812 0.886 0.785
Creek 10 Landsat ETM+ B 0.925 0.855 0.950 0.903G 0.913 0.833 0.895 0.801R 0.906 0.821 0.947 0.897
Seashore 6 Landsat ETM+ B 0.958 0.918 0.987 0.975G 0.939 0.882 0.994 0.988R 0.915 0.838 0.935 0.874
Coast 16 IKONOS B 0.970 0.940 0.947 0.896G 0.945 0.893 0.940 0.883R 0.943 0.890 0.937 0.878
Creek 10 IKONOS B 0.964 0.930 0.917 0.840G 0.926 0.857 0.954 0.910R 0.870 0.757 0.904 0.817
Sea Shore 6 IKONOS B 0.940 0.884 0.939 0.881G 0.928 0.861 0.959 0.920R 0.912 0.831 0.919 0.845
Min = 0.757Max = 0.94
Min = 0.78Max = 0.98
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 827
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Tabl
e6
Stan
dar
der
ror
of
the
esti
mat
e
Sam
plin
g
loca
tio
n
Turb
idit
yPh
osp
hat
e
Insi
tum
easu
rem
ent
(NTU
)
Lan
dsa
tET
M+
IKO
NO
S
Insi
tum
easu
rem
ent
(mg/
L)
Lan
dsa
tET
M+
IKO
NO
S
Esti
mat
ed%
of
Erro
rEs
tim
ated
%o
fEr
ror
Esti
mat
ed%
Erro
rEs
tim
ated
%Er
ror
S19.
69.
015
6.09
15.
316
44.6
240.
090.
088
2.09
20.
085
5.92
6S2
7.5
7.52
8-0
.379
6.18
117
.586
0.08
0.08
2-2
.572
0.08
0-0
.321
S39.
17.
364
19.0
726.
970
23.4
050.
090.
081
10.3
490.
086
4.39
6S4
1212
.971
-8.0
9312
.490
-4.0
810.
10.
106
-6.4
720.
104
-4.2
62S5
4.5
4.69
6-4
.360
2.72
939
.357
0.06
0.06
9-1
5.51
20.
061
-1.1
02S6
5.8
4.14
128
.598
4.41
023
.967
0.07
0.06
65.
194
0.07
4-5
.439
S75.
64.
493
19.7
676.
489
-15.
874
0.07
0.06
91.
620
0.07
2-2
.875
S87.
15.
188
26.9
314.
935
30.4
890.
080.
072
10.3
210.
077
3.86
8S9
2.2
3.47
2-5
7.82
32.
749
-24.
957
0.06
0.06
4-5
.904
0.06
00.
004
S10
4.7
3.96
215
.699
5.08
8-8
.249
0.07
0.06
56.
701
0.06
66.
146
S11
4.8
4.05
415
.547
5.31
9-1
0.81
80.
070.
067
3.98
60.
066
5.69
7S1
25.
14.
762
6.62
95.
674
-11.
252
0.07
0.07
0-0
.694
0.07
0-0
.175
S13
1.3
1.19
28.
338
1.25
43.
504
0.06
0.05
311
.041
0.05
410
.108
S14
1.7
1.10
934
.779
2.35
0-3
8.26
30.
050.
052
-4.9
860.
059
-17.
910
S15
1.3
1.24
14.
546
1.19
58.
091
0.05
0.05
3-6
.891
0.04
92.
186
S16
1.2
1.56
4-3
0.30
61.
091
9.11
5S1
730
30.9
25-3
.084
29.1
182.
942
0.25
0.24
71.
394
0.23
65.
616
S18
1918
.135
4.55
218
.062
4.93
80.
070.
069
1.50
80.
069
0.98
7S1
934
31.3
507.
795
30.6
629.
817
S20
2324
.688
-7.3
3923
.061
-0.2
630.
150.
153
-2.0
830.
145
3.25
6S2
120
20.8
86-4
.428
20.8
39-4
.194
0.12
0.12
4-3
.190
0.12
3-2
.823
S22
4240
.346
3.93
943
.492
-3.5
520.
240.
235
2.19
60.
290
-20.
905
S23
2523
.910
4.36
227
.688
-10.
750
0.18
0.17
62.
468
0.19
1-6
.253
S24
4650
.333
-9.4
2046
.296
-0.6
440.
340.
340
-0.1
140.
356
-4.7
70S2
539
38.6
570.
880
40.0
29-2
.639
0.23
0.22
23.
455
0.21
94.
991
S26
3033
.117
-10.
390
28.6
044.
653
0.16
0.18
6-1
6.49
70.
145
9.68
6S2
738
32.8
7813
.479
39.3
35-3
.513
0.18
0.16
011
.211
0.19
6-8
.997
S28
4849
.204
-2.5
0847
.618
0.79
60.
390.
366
6.14
70.
354
9.10
9S2
952
50.4
742.
935
54.7
83-5
.353
0.42
0.35
216
.111
0.38
29.
056
S30
6566
.232
-1.8
9556
.897
12.4
660.
420.
482
-14.
779
0.37
710
.316
S31
4548
.119
-6.9
3245
.272
-0.6
050.
30.
319
-6.3
270.
306
-2.0
67S3
268
56.0
2117
.617
67.4
630.
790
0.48
0.43
69.
187
0.46
53.
022
SD=
17.2
16SD
=17
.01
SD=
8.08
SD=
7.54
828 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Figu
re6
(a)
Co
nto
urs
of
Turb
idit
y(s
imu
late
d)
sup
erim
po
sed
ove
rIk
on
os
imag
ery;
(b)
Co
nto
urs
of
Turb
idit
y(s
imu
late
d)
sup
erim
po
sed
ove
rLa
nd
sat
7ET
M+
imag
ery;
(c)
Co
nto
urs
of
Pho
sph
ate
(sim
ula
ted
)su
per
imp
ose
do
ver
Iko
no
sim
ager
y;an
d(d
)C
on
tou
rso
fPh
osp
hat
e(s
imu
late
d)
sup
erim
po
sed
ove
rLa
nd
sat
7ET
M+
imag
ery
Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai 829
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
Figu
re6
Co
nti
nu
ed
830 D R Satapathy, R Vijay, S R Kamble and R A Sohony
© 2010 Blackwell Publishing LtdTransactions in GIS, 2010, 14(6)
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