Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in...

Water Quality Retrievals from High Resolution IkonosMultispectral Imagery: A Case Study in Istanbul, Turkey

Semih Ekercin

Received: 23 August 2006 /Accepted: 16 February 2007 / Published online: 15 March 2007# Springer Science + Business Media B.V. 2007

Abstract This paper presents an application of highresolution satellite remote sensing data for mappingwater quality in the Goldon Horn, Istanbul. It is anapplied research emphasizing the present waterquality conditions in this region for water qualityparameters; secchi disc depth (SDD), chlorophyl-a(chl-a) and total suspended sediment (TSS) concen-tration. The study also examines the retrievals ofthese parameters through high resolution IKONOSmultispectral data supported by in situ measure-ments. Image processing procedure involving radio-metric correction is carried out for conversion fromdigital numbers (DNs) to spectral radiance tocorrelate water quality parameters and satellite databy using multiple regression technique. The retrievedand verified results show that the measured andestimated values of water quality parameters in goodagreement (R2>0.97). The spatial distribution mapsare developed by using multiple regression algorithmbelonging to water quality parameters. These mapspresent apparent spatial variations of selected param-eters and inform the decision makers of water qualityvariations in a large water region in the Istanbulmetropolitan area.

Keywords Satellite remote sensing .Water quality .

In-situ .Multiple regression .

IKONOSmultispectral data

1 Introduction

Water quality is the general term that describeswhether or not the surrounding environment may beendangered by pollutants in the water. From ahistorical perspective, poor water quality has not beena perceived problem before the industrial revolutionand the rapid growth of cities. Since about the turn ofthe century, attempts at water quality control haveconcentrated on sewage and industrial discharges(Engman and Gurney 1991). These attempts requirea number of water sampling from a set of conve-niently distributed locations which are time consum-ing and require a lot of field and laboratory efforts inaddition to budget restriction problems. At this point,it can be mentioned that remote sensing has animportant and effective role in water quality manage-ment, processing time reduction and requirement ofseveral ground measurements (Gan et al. 2004;Kondratyev et al. 1999; Pozdnyakov et al. 2005;Sváb et al. 2005).

Remote sensing techniques can be used to assessseveral water quality parameters (i.e., suspendedsediments, turbidity, chlorophyll, temperature). Theseoptical and thermal sensors on boats, aircraft, and

Water Air Soil Pollut (2007) 183:239–251DOI 10.1007/s11270-007-9373-5

S. Ekercin (*)Department of Geodesy and Photogrammetry,Aksaray University,68100 Aksaray, Turkeye-mail: [email protected]

satellites provide both spatial and temporal informa-tion needed to understand changes in water qualityparameters necessary for developing better manage-ment practices to improve water quality (Ellis 1999;Jensen 2000).

Remote sensing satellites measure the amountof solar radiation at various wavelengths reflectedby surface water. In this procedure, the opticalproperties (i.e. reflectance) of water depend on theconcentration and character of water quality parame-ters. In water quality studies, measured solar radi-ation and in-situ measurements are correlated todevelop water quality models by using different meth-ods such as multiple regression technique. It isan effective and economic approach for estimationof water quality over large areas, especially whenthe spatial coverage of satellite image is consid-ered. Hence, it is possible to reduce the number ofin-situ measurements needed and increases the spa-tial and temporal coverage of the estimates (D’saet al. 2000; Hellweger et al. 2004; Pozdnyakov et al.2003).

Today, there are many satellites which have highenough resolution for use in water quality monitoringstudies. Even if Landsat-TM, IKONOS, SPOT HVR,or Linear Imaging Self-Scanning Sensor (LISS-III)satellite data could not be analysed using specificabsorption characteristics due to the broad band-widths, they offer the advantage of large area cover-age and multitemporal use (Bilge et al. 2003). Amongthese satellites, despite the restrictive spectral rangeof multispectral IKONOS imagery (up to 800 nm),its high spatial resolution makes it suitable forwater quality mapping (Li and Li 2004; Ritchie andCooper 2001).

This study is an applied research emphasizing thepresent water quality conditions in this region forwater quality parameters; secchi disc depth (SDD),chlorophyl-a (chl-a) and total suspended sediment(TSS) concentration. The study also examines theretrievals of these parameters through high resolutionIKONOS multispectral data supported by in-situmeasurements. This is the first one involving the useof remote sensing data for water quality mapping inthe region and gives valuable information to interpretwater quality variations over a large water area in theIstanbul metropolitan with the help of high resolutionsatellite image data.

2 Methodology

2.1 Study Area

For this study, the Halic Region in Istanbul, Turkey(as known Golden Horn) was selected as the test site.This region is located in the southern part of Istanbuland covers an area of approximately 28 km2 from41°00′43.10″ N to 41°04′06.08″ N in latitude, and28°55′56.04″ E to 28°59′13.85″ E in longitude. Asthe sun sets in Istanbul, its yellow light blazes on thewaters of the Golden Horn, giving this freshwaterestuary its romantic name. The Haliç is merely thebody of water which separates the “old” and “new”parts of European Istanbul (Turkey Travel Planner2006). Geographical location of the study area ispresented in Fig. 1.

2.2 Image Processing

The remotely sensed data used for this study was a highresolution IKONOS-2 multispectral image acquiredon June 04, 2005 (08:57 GMT). This georeferencedimage was ordered from the Space Imaging Eurasia(INTA Space Systems, Inc.) with the ID number of2005060408574270000011117273. Geometric accu-racy of the multispectral imagery was checked bycomparing with IKONOS panchromatic data (04/06/2005) having 1 m spatial resolution and georefer-enced using topographic maps in the scale of 1:5,000.After the preparation of data set by merging spectralbands, the image was radiometrically corrected toconvert DNs to spectral radiance and to minimizeatmospheric effects (Chavez 1996; Karpouzli andMalthus 2003; Lillesand et al. 2004; Mather 1999).Following equation was used for this process (SpaceImaging Eurasia 2006):

Ll ¼ 104 � DNl

CalCoef l � Bandwidthl ð1Þ

where;

& L1 : Radiance for spectral band 1 at the sensor’saperture (W/m2/μm/sr),

& DN1 : Digital value for spectral band 1,& CalCoef1: Radiometric calibration coefficient

[DN/(mW/cm2-sr)],& Bandwidth1 : Bandwidth of spectral band 1 (nm).

240 Water Air Soil Pollut (2007) 183:239–251

Fig. 1 Location of the study area

Water Air Soil Pollut (2007) 183:239–251 241

Tab

le1

Detailsof

atmosph

eric

correctio

nprocessforIK

ONOSmultispectralim

agedata

GOLDON

HORN

Istanb

ul,TURKEY

IKONOS-2

04/06/20

05(U

TM,Zon

e35

)DN

–Digitalnu

mber

L–SpectralRadiance

L=DN1/(CalCoef 1*B

anwidth

1)

R–Surface

reflectance

Spectralband

Spectralband

Spectralband

Blue

Green

Red

NIR

Blue

Green

Red

NIR

Blue

Green

Red

NIR

Statio

nname

Coo

rdinates

(m)

Calib.Coeff.

728

727

949

843

d=1.01

439

04/06/20

05Julianday,

155

R¼

p�d2�L

lESUN

l�cos

q s

XY

Bandwidth

(nm)

71.3

88.6

65.8

95.4

ESUN1

1930

.918

54.8

1556

.51156

.9

GK

4542

912

6658

8432

727

812

582

62.96

43.09

19.95

10.25

0.1145

0.08

160.04

500.03

11UK

4543

336

6651

8629

325

612

385

56.49

39.78

19.70

10.56

0.10

270.07

530.04

440.03

20KP

4543

984

6652

7828

824

612

591

55.44

38.19

19.95

11.37

0.10

080.07

230.04

500.03

45CA

4544

111

6644

2028

023

7110

6853

.99

36.74

17.67

8.48

0.09

820.06

950.03

990.02

57VS

4544

349

6641

9927

623

8111

7753

.17

36.92

17.79

9.53

0.09

670.06

990.04

010.02

89HK

4545

399

6632

8127

824

1115

7653

.64

37.38

18.43

9.42

0.09

750.07

080.04

160.02

86ES

4545

944

6629

1227

924

3115

7953

.71

37.66

18.40

9.86

0.09

770.07

130.04

150.02

99AA

4546

534

6631

4528

325

112

697

54.56

38.97

20.14

12.02

0.09

920.07

380.04

540.03

65AS

4547

130

6636

5427

724

812

788

53.41

38.57

20.41

10.89

0.09

710.07

300.04

600.03

30

Sun

Zenith

(θs)=90

-Sun

Elevatio

n.


CalCoef1 and Bandwidth1 for the IKONOS bandsare reported by Space Imaging (2006). Since theIKONOS image used in this study was acquired afterFebruary 22, 2001, the values of CalCoef1 used were728 for band 1, 727 for band 2, 949 for band 3, and843 for band 4. Coefficients are for the 11-bit IKONOSproducts (Thenkabail et al. 2000). The details ofradiometric correction process are given in Table 1.

In the study, four bands of IKONOS multispectralimage data were used as input file to develop thematicmaps presenting water quality over the study area foreach parameter. In the study, water quality mappingprocedure including radiometric correction was per-formed by usingModeler algorithm of ERDAS Imagine.The created new diagram is presented in Fig. 2.

2.3 Correlating Remotely Sensed Data and WaterQuality Parameters

In the study, multiple regression algorithm was usedto explore the relationships between water quality

parameters and IKONOS data in the study area. Thisalgorithm can be expressed as;

WQPs ¼ A0 þXk

i¼1

A�i ðIKONOSiÞ ð2Þ

where; IKONOSi is the spectral radiances of fourIKONOS bands; k is the IKONOS band number from1 to 4; A0 and Ai are the empirical regressioncoefficients derived using the observations from theground truth points (Zhang et al. 2003). In the study,the level of significance, p=0.05, was used to denotestatistical significances of statistical data analyses.Microsoft Excel was used for this process.

The independent variables used in the multipleregression are spectral bands of IKONOS multispec-tral imagery; blue (B1): 0.45–0.53 μm, green (B2):0.52–0.61 μm, red (B3): 0.64–0.72 μm) and nearinfrared (B4): 0.77–0.88 μm. In order to remove(probable) errors resulting from GPS (Global Posi-tioning System) measurements in the field work and

Fig. 2 Flow chart showing the process of water quality mapping by using Modeler algorithm of ERDAS Imagine image processingsoftware


to determine optimal pixel (window) size for corre-lating IKONOS data with in-situ measurements, 3×3,5×5, 7×7 and 10×10 size options were tested(Table 2). The average digital number of pixels (5x5array) surrounding the sample pixel was used tocorrelate water quality parameters with the IKONOSmultispectral imagery having 4 m spatial resolution.

The Istanbul Water and Sewerage AdministrationAuthority (İSKİ) is responsible to maintain thewater quality in Halic region which is located southpart of Istanbul, Turkey. Therefore, water qualityconditions have been monitoring by İSKİ withseasonal measurements. Within the Halic region, atotal of nine permanent stations (Fig. 1) have beeninstalled and operated since June 23, 2000, togetherwith many temporary observatories. In this study,secchi disc depth-SDD (m), total suspended sediment-TSS (mg/l), and chlorophyll-a (μg/l) were selected aswater quality parameters because these parametersoptically active in terms of estimation from satelliteremote sensing image data (Bilge et al. 2003). Theywere taken from 9 permanent stations during theIKONOS-2 overpass of the June 2005. One of them(UK-Unkapanı) was used to check the developedalgorithm. During the field work, a handheld GPS(Magellan 315, accuracy < ±10 m) was used todetermine ground coordinates of water samplingpoints. Another important matter needs to bementioned is the evaluation of meteorological databelonging to IKONOS image acquisition day.

Meteorological data collected by Kirecburnu Meteo-rological Station (17061, Istanbul) was analyzedtaking into account possible effects of rainfall andrunoff phenomena. It is clearly detected that there isno rainfall record in the period between 03/06/2005and 07/06/2005 which includes the acquired day ofIKONOS image (05/06/2005).

3 Results and Discussion

In clear waters, the transmission of solar incidentenergy is greater than the reflected energy. Thismeans that the brightness values at pixels are lowerin clean water regions than polluted parts. Waterquality studies, as summary, are based on the effectsof water quality parameters such as suspendedsediment on the spectral reflectance of clear water.Suspended sediment plays an important role in waterquality management since it is related to total primaryproduction and fluxes of heavy metals and micropollutants. Synoptic information on suspended matterat a regular frequency is difficult to obtain from aroutine in situ monitoring network since suspendedmatter is a spatially inhomogeneous parameter. Thisproblem can be avoided by integrated use of remotelysensed data, in situ data and water quality models(Dekker et al. 2002).

The correlation between different IKONOS bandsranged from 0.122 to 0.924 (Table 3). The high

Water Quality Parameter(WQP)

The size of pixel configuration(window)

Determination coefficient(R2)

SDD Center 0.93113×3 0.94665×5 0.98937×7 0.936210×10 0.9342

Chl-a Center 0.91543×3 0.94735×5 0.99247×7 0.932910×10 0.8828

TSS Center 0.83253×3 0.95125×5 0.97247×7 0.835410×10 0.8445

Table 2 Determination ofoptimal pixel size for corre-lating quality parameterswith the IKONOS data


correlation between IKONOS bands means that thesebands measure similar properties in the study.

Examination of the correlations (R2) between waterquality parameters are also presented in Table 3. Thecorrelation of secchi disc depth (SDD) with chloro-phyll-a (chl-a) concentration is negative and up to0.746. This means that SDD is significantly related toorganic matter in the region. On the other hand, SDDhas a negative correlation of 0.617 with total sus-pended sediment concentration (TSS). This indicatesthat the increase in the amount of suspended sedi-ment concentration will naturally reduce the visibilityin the water. Thus, it will negatively affect secchi discdepth (SDD) in the area.

In the study area, as mentioned before, there are ninepermanent stations operated by The Istanbul Water andSewerage Administration Authority (İSKİ). Waterquality measurement taken from UK (Unkapanı-UK,

see Fig. 1 and Table 4) was used to check the multipleregression results.

3.1 Secchi Disc Depth (SDD)

Secchi disc depth (SDD) is an optical measurement ofwater quality. This very quick and easy measurementis often well correlated with various other waterquality measurements (e.g., concentration of chloro-phyll-a or suspended materials in the water)(Lillesand et al. 2004). Since SDD has become awidely accepted tool to measure water transparency,there have been many efforts to map this variablefrom satellite remote sensing data (Zhang et al. 2003).

When SDD is the dependent variable and the fourbands of IKONOS are the independent variables,regression analysis indicate that blue, green and redbands significantly explain 98.93% of the variance inSDD (Fig. 3). Although, the relationship betweenblue, red bands and SDD is positive, between greenband and SDD is negative. The R2 change of nearinfrared (NIR) band is 0.06% and the change isnot significant. Therefore, only blue, green and red(visible bands of IKONOS) can be used to relate SDDand water radiance. At this point, it can be remem-bered that in-situ measurements of SDD are performedvisually (by using visible range of electromagneticspectrum).

The results indicate that the accuracy of SDDestimation by applying multiple regression algorithmis very high (R2=98.93). Retrieved results for SDDare presented in Table 4 and Fig. 3.

Table 3 Correlation (R2) matrix of IKONOS-2 bands and ofwater quality parameters

Blue Green Red NIR

Blue 1Green 0.912 1Red 0.221 0.525 1NIR 0.122 0.407 0.924 1

SDD Chl-a TSS

SDD 1Chl-a −0.746 1TSS −0.617 0.684 1

Table 4 Comparison of in-situ measurements and estimated values using IKONOS

Station name Secchi Disc Depth (SDD) (m) Chlorophyll-a (Chl-a) (μg/l) Total Suspended Sediment (TSS) (mg/l)

In-situ Estimated In-situ Estimated In-situ Estimated

GK 6,500 6,517 2,316 2,271 23,800 23,832KP 3,000 2,903 8,912 8,634 28,000 28,701CA 2,500 2,814 10,598 11,376 25,200 24,247VS 2,500 2,418 15,288 13,686 23,400 23,508HK 2,000 1,770 23,021 23,055 29,000 28,487ES 2,000 1,843 22,681 23,563 25,200 26,870AA 1,000 1,045 53,769 58,455 33,000 31,670AS 0,800 0,998 51,376 50,074 40,300 40,535UKa 3,000 3,210 3,750 4,204 22,400 24,301

a External check point.


3.2 Chlorophyll-A (Chl-a)

Chlorophyll-a (chl-a) is present in all oxygenicphotosynthesizers, and as such is used to moni-tor phytoplankton in aquatic environments. Charac-terized by well-defined and specific optical propertiesin the visible range, chl-a is suitable for directobservation with optical instruments (Kirk 1994; Iluzet al. 2003).

In the study, a t-test of multi regression of chl-aas the dependent variable shows that all used bands(blue, green, red and NIR bands) significantlycontribute to the variance in chl-a (R2=0.9924).Therefore, all bands of IKONOS image data wereselected as the independent variable to relate chl-a

and spectral radiance. Blue and red bands havepositive, green and NIR bands have negative rela-tionship with chl-a. This is meaningful and expecteddue to the higher reflectance characteristics of greenalgae containing chlorophyll in the green and NIRregion of the electromagnetic spectrum.

Retrieved results for chl-a by applying multipleregression technique are presented in Table 4 andFig. 3.

3.3 Total Suspended Sediment (TSS)

Total suspended sediment concentration (TSS)is a measure of the weight of inorganic particulates

Fig. 3 Retrieved results for water quality parameters using multiple regression technique in the study area


suspended in the water column (Harrington andSchiebe 1992). TSS is one of the most successfullymeasured parameters by means of remote sensing.Theoretically, a suspension of inorganic matter of a

homogenous fraction should give the most accuratemodel results of all substances in a water mass as thismaterial has well-defined physical reflectance properties(Zhang et al. 2003).

Fig. 4 Distribution of chl-a concentration over the study area on 04 June 2005 (The base image is 04/06/2005 dated IKONOSPan data)


Multi regression of dependent variable TSS whichis the last parameter selected for this study shows thatall spectral bands contribute to the variance in TSS.Therefore, all spectral bands were used to develop a

final model to predict TSS (Table 4). Blue and NIRbands have negative, green and red bands havepositive relationship with TSS. All bands significantlycontribute 97.24% of the variance in TSS. Figure 3

Fig. 5 Distribution of secchi disc depth over the study area on 04 June 2005 (The base image is 04/06/2005 dated IKONOS Pan data)


presents the retrieved results for total suspendedsediment-TSS concentration (in addition to theretrievals of other parameters) by using multipleregression technique.

3.4 Spatial Distribution of Water Quality Parameters

The Halic region, Istanbul has been confronted withthe problems of industrialization and urbanization for

Fig. 6 Distribution of TSS over the study area on 04 June 2005 (The base image is 04/06/2005 dated IKONOS Pan data)


last three decades. The region is highly affected bythe inputs coming from settlement and industrial areaswhich discharge high concentration of suspendedparticles and nutrients. In the study, therefore,thematic maps were developed to visualize and detectthe spatial distribution of water quality parameters(secchi disk depth, chlorophyll-a and total suspendedsediment). This process provided opportunity for theinterpretation of water quality variations over thestudy area. The obtained water quality image mapsfor the parameters are presented in Figs. 4, 5 and 6,respectively.

The multiple regression algorithms which havebeen developed from four bands of IKONOS (onlyfirst three bands for SDD) were applied to showpatterns of water quality by applying the function toeach pixel and contouring these values into classes(Bilge et al. 2003). The flow chart of this process isgiven Fig. 2.

Figure 4 presents the spatial distribution of secchidisc depth (SDD) in the Halic region. This resultantmap indicates that SDD in the northern part of thearea lower (mostly <2 m) than that of the southernpart (>4 m) connecting to the Istanbul Strait. Theresulting thematic SDD map generally shows a similarpattern with the bathymetric map of the Halic region.It is well known fact that spectral radiance of deep andshallow water is different and SDD is directly relatedto the water transparency (or turbidity).

The interpretation results of chlorophyll-a (chl-a)map given in Fig. 5 show that the amount of chl-aconcentration in the southern part of the studyarea close to the Istanbul Strait and Marmara Seais mostly lower than 5 μg/l. On the contrary, inthe Upper Halic Region (northern part), it is mostlyhigher than 20 μg/l (in a large area >40 μg/l). TheHalic region receives wastewater directly from themunicipal sewage treatment and industrial sources.These discharges load nutrients which contribute tothe growth of phytoplankton (micro-algae). Chloro-phyll is used as an indication of the intensity of algaegrowth (Bilge et al. 2003). Additionally, relativelyhigh chl-a concentration along the coastline indicatethe discharges in the area.

Figure 6 presents the spatial distribution of totalsuspended sediment (TSS) concentration over thestudy area. The results indicate that the TSS concen-tration in the northern part of the area higher than25 mg/l (in some parts >40 mg/l). In the southern part

connecting to the Istanbul Strait, the TSS valuesremain under 25 mg/l. Here, it should be mentionedthat wastewater also loads high concentrations ofsuspended particles increasing turbidity in the water.Additionally, TSS concentration is directly related tothe bottom topography and water depth. Taking intoaccount this for the study area, it can be said that thespatial distribution map of TSS gives meaningfulresults, as parallel to the bathymetric data.

4 Conclusions

In this paper, water quality retrievals from highresolution IKONOS multispectral imagery were ex-amined in a case study. Multiple regression resultsgive significantly consistent information with veryhigh determination coefficients (R2=0.9893, 0.9924and 0.9724 for secchi disc depth, chlorophyll-a andtotal suspended sediment, respectively).

Also, the produced water quality image mapsenable for the interpretation of water quality con-ditions in the whole study area. This is essential formanagement of water pollution risk especially for abig city such as Istanbul metropolis. As general, waterquality condition in the northern part of the Halicregion which is relatively shallower than the southernpart is poor. Another words, water close to theIstanbul Strait has better quality. The results showsimilar pattern with the bathymetric maps.

Another important result of this study is onthe feasibility of high resolution IKONOS multispec-tral imagery over narrow (<150 m for case study)and long water bodies in densely built areas. Despitethe restrictive spectral resolution of multispectralIKONOS imagery, its high spatial resolution (4 m)makes it suitable for water quality mapping in thistype of areas.

However, further studies are needed for examina-tion of retrievals from multispectral IKONOS dataover the study area. In this study, only nine samplepoints (permanent station located in the Halic region)were used, so further work using more data frommore sample points may be useful to strengthen theseconclusions.

Acknowledgements The author appreciates the support pro-vided by The Engineering and Consultancy Services Cooper-ation (BIMTAS) and The Water-Sewerage Administration


Authority (İSKİ) of Istanbul. Ground and satellite data used inthis study was provided by BİMTAŞ and İSKİ. The author alsothanks the anonymous reviewers for their helpful commentsand scientific suggestions through the manuscript.

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http://www.spaceimaging.com/products/ikonos/spectral.htm

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Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in...

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