Water quality assessment of ocean and inland waters using satellite data has been carried out since the first r

satellite Landsat-MSS has been operational (Thiemann and Kaufmann, 2000)

Abdullah, K.

Bakar, M.N.A.

Lim, H.S.

Mat Jafri, M.Z.

English

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Establishing a global algorithm for water quality mapping from multi-dates images Page I of82?TflA"b-stra€t- |Establishing a global algorithm for water quality mapping from multi-dates images?vrg*r'*l a) tf,"* M/tf ktq.eP a?v>, /\ "/*" 6d' *t'rYbl,c'L 'H. S. Lim, M. Z. MatJafri, K. Abdullah and M. N. A. BakarSchool of PhysicsUniversiti Sains Malaysia,11800 PenanglntroductionWater quality assessment of ocean and inland waters using satellite data has been carried out since the first rsatellite Landsat-MSS has been operational (Thiemann and Kaufmann, 2000). Many researchers used satelliteinvestigations [Allee, et al., (1999), Forster, et al., (1993) and Ritchie, et al., (1990)]. However, in this study weemote sensing. A digital camera was used as a sensor to capture the images at altitude of 8000 feet. The main rresent study is to update our proposed algorithm for mapping total suspended solids in marine environmencamera images from previous study (MatJafri, et a1.,2002). We also attempted to develop a simple correction te,airborne images acquired from different dates, locations, and different flying altitudes. Data from seven imagrcombined in the present analysis. This study also proposed a cheaper and economical alternative to overcomeobtaining cloud-free scenes in the Equatorial region.Study Areaf n this study, a Kodak DC290 digital camera was used as a sensor and a Cessna 172Q aircraft was used a:capture images of the study areas. The study areas were the Prai, Muda, and Merbok river estuaries, located witl22'N to 5o 24'N and longitudes 10Oo 21'Eto 100o 23'E;5o 34'N to 5o 36'N and longitudes 100o 19'E to 100o 2N to 5o 41'N and longitudes 100o 20'E to 100o 24'E, respectively. The images were captured from the altitude olOctober 2001 and 8,000ft on 9 March 2002 for Prai River estuary, 8,000ft on 20 January 2002 and 9 March :River estuary, and also 8,000 ft on 5 May 2002, 25 October 2002 and 22 March 2003 for Merbok River estuary. Tare shown in Figure 1. Water samples were collected from a small boat within the areas covered by the sceneswith the airborne image acquisition and later analyzed in the laboratory.(Source: Macrosoft Corp., 2OO1.)Figure 1. Study areaOptical model of waterA physical model relating radiance from the water column and the concentrations of the water quality constituenmost effective way of analyzing remotely sensed data for water quality studies. Reflectance is particularlyinherent optical properties: the absorption coefficient and the backscattering coefficient. The irradiance reflectathe water surface, R(l), is given by Kirk (1984) as,Irv' .l.hiruaL{'tn'fi le ://D :\Environmental Plannine\5 5.htmR(l') = 0.33b(i.)/a(i.) (1)rU22/2004Establishing a global algorithm for water quality mapping from multi-dates images Page 2 of 8where l. = the spectralwavelengthb = the backscattering coefficienta = the absorption coefficientThe inherent optical properties are determined by the contents of the water. The contributions of the individualthe overall properties are strictly additive (Gallegos and Correl, 1990). For a case involving two water quality ccchlorophyll, C, and suspended sediment, P, the simultaneous equations for the two channels given by Gallie andcan be expressed asIt(tr I - Rr =n..'"{0^5bo*iL' )+b-'(i' )L'+lr*'{i'lPl {3sl{irw{&} +il**{,}.1 gl+ 4*-qh, }P}R1)",) - R= =',,'-.ijfri.LlklllliJ;:ji'i'' rrbrwherebb*(i) = backscattering coefficient of waterbo.* = specific backscattering coefficients of chlorophyllboo = specific backscattering coefficients of sedimenta*(i) = absorption coefficient of watera.* = specific absorption coefficients of chlorophyllao* = specific absorption coefficients of sedimentC = chlorophyllP = suspended sedimentRegression AlgorithmTSS concentration can be obtained by solving the two simultaneous equations to get the series of terms R1 and fas$r *'at*r*l$1,'t-u*&+xrRrRsc' &fh!l aslts;*. ffi1 sl{at. *rK1 }Rf -* s*Krtlta:+"". t3}where aj, j = O, 1 ,2, ... are the coefficient for equation (3) that can be solved empirically using multiple regressiorequation can also be extended to the three-band method given asP,"rln-a'ittldl*"qfu+*3Rrr t'+Rrlhr crllrllir; *sR$ru+ erltl:;,6p1ut,soR u (4)where the coefficient ej, j = 0, 1, 2, ... can also be solve empirically.Data Analysis And ResultSeven sets of the colour images were selected for calibration analysis. Figure 2 shows the images that were usecfile ://D :\Environmental Planning\5 5.htm 1U2212004Establishing a global algorithm for water quality mapping from multi-dates images Page 3 of8tetFigure 2. Images of the study ares: (a) the oblique image of the Prai River estuary captured on 28 October 2001 from altitude ofoblique image cif the Muda River estuary captured on 20 January 2002 from altitude of 8,000ft, (c) the vertical image of the Praicaptured on 9 March 2002 from altitude of 8,000ft. (d) the vertical image of the Muda River estuary captured on 9 March 2002 f8'000ft, (e) the oblique image of the Merbok River estuary captured on 5 May 2002 from altitude of 8,000ft, (f) the vertical imagrtslfile://D:\Environmental Planning\5 5.htm tU22t2004Establishing a global algorithm for water quality mapping from multi-dates images Page 4 of8River estuary captured on 26 october 2002 from altitude :iSitPr:h1lfl,lrr)",ffJ,;tb.r?l imase of the Merbok River estuary capturThe colour images were separated into three bands, namely, red, green and blue bands for multispectral analysisFigure 2(a) was taken at an altitude of 3,000ft, while the rest were taken from altitude of 8,000ft. The imageFigure 2(b) and Figure 2(e) were taken obliquely. The view angle correction was first performed to the oblique imfor the angular dependence of image brightness. In this study, a contour map of the image brightness was plotteangle effect was removed based on the map. Then, the multi-date data were corrected to remove the differenceeffects between scenes using radiometric normalization technique. The vertical image of Figure 5(c) was s,reference image and the average brightness of the chosen target; in this case, grass vegetation was noted. Wrreflectance of these targets did not change with time. This assumption is in accordance with the methods propc(1990). The average brightness values of grass in other images were then recorded. The difference from the nwas used to correct for each scene. All the brightness values of the other five images of Figure 5 (a), (b), (d),were adjusted using this normalization technique. This normalization technique forced the images to h:atmospheric conditions and the effects due to different camera altitudes have also been removed. The correctethen regressed with the sea-truth data to obtain all the coefficients of equation (4) in the proposed multi-date, rmulti-altitude analysis. lmage rectification was performed using second order polynomial tranformation equation.The DN values corresponding to the water sample locations were extracted from all the images. The relationshipLnd DN of the data set is shown in Figure 3. The coefficients values are listed in Table 1. Figure 4 showsalgorithm produced high correlation coefficient (R) and low root-mean-square (RMS).The TSS maps were generated using the proposed calibrated algorithm. The generated maps were then filtered Ipixels average for removing random noise. Finally, the generated TSS maps were colour-code for visual interpretiin Figure 5. This indicates the reliability of the calibrated proposed algorithm for TSS mapping using digital cameriable 1. Gorrelation coefficients of equationCoefficients ao a1 a2 a3 a4 a5 aO a7 a8 a9Values 43.794 -2.021 -9.964 10.710 0.121 -7.278x10-2 0.279 1.640x10-2 -o.141 -0.15i .*q"'1"< . flq-.; l"t- ' ilt- aC' '- : I *-osItt*' ", ,'t-* .tt4'I atl{r I#l1*r'i!s,&.igtaat^ g*?E i'**k *dPs$ilx .wrSag-It.lst; l*iieiwii *'i*e:@3 4x.l'd{eqaeje,E*e 1i * d||w*.&{F4!' * i:|ffirff*i Lra4e;-tI r hrtr**l*1$'a*1,6+ Etal|tl|llFfir* X-r-idr Ki- !*'i * M@ktlKil&.i * qtlE!#{8.{{.sr}:r;I ; FMd*f&r*{N: x P{.*fe!trirSs.}t !ii* k*tiletx{tsr&ii' * wee|!*lFtI.r Ww{*gs*l!I a e{Fdrye,'t{t{*qNiI i Wliel*F,soa,F0r.I *,n**d!M**{ii*r6'S&I * *f+$tr{{***drxtI . tt@rit.rtfttmtei*r &Gbnaf|ltrl|'- Lublsnl.li-"",,".,,."..,..,.-,,",.,--.1..*Figure. 3 TSS concentration versus digital number (DN).file ://D :\Environmental Planning\5 5.htm rr/22/2004Establishing a global algorithm for water quality mapping from multi-dates imagesRE{{*4ffS&l(S* {f,$finnrat*'tf.,, *|at.F au,#lF.p$ t*'+.l t- llrlrrnt."$al?Page 5 of8t.g'lltl| *tt#,j!,*ftri- .ta if;tt${ ,i*:tbtilf,i-*---^-': I5f r@ rEd as, ffi rde|sssurF$r@{ru$TFigure 4. Measured versus estimated TSS concentration{.s.1fi le://D :\Environmental Planning\5 5.htm It/22/2004Establishing a global algorithm for water quality mapping from multi-dates imagesn r*"l**I Wm 6s.1*r"lrorqsrt W *r:l*-!rrom*!t IPage 6 of 8(cJ{dr$$i.'3ar. -:sqgflfl*"*ns, ! '*i I ***",.*Figure 5. TSS map for the study area estimated using the proposed algorithm. Colour code:Verification analysisFor the verification analysis, sea truth data were divided into two groups, half of the numbers of water samplrselected for algorithm calibration and the another half of the numbers of water samples were radom selectedanalysis. The calibrated algorithm was produced high accuracy with R value of 0.9685 and RSM value of 13verification analysis. Figure 6 shows the relationship of the measured TSS versus estimated TSS concentratiocalibration analysis. Figure 7 shows the relationship of the measured TSS versus estimated TSS concentrationanalysis.file ://D :\Environmental Planning\5 5.htm rt/22/2004Establishing a global algorithm for water quality mapping from multi-dates images Page 7 of8str tfi! 1* @ Elo m'rSS&*rffio.TffiJnglnFigure 6, Measured TSS versus estimated TSS concentration for algorithm calibration analysisFisure 7. trreasured rss versus estamated JiSffiiltr!3" a, verification anarysis conctusionThis study gives a cheaper way to overcome the problem of difficulty of obtaining cloudfree scenes at the EqrTraditional water quality monitoring method based on water sample collection is time consuming and requires acost. lt is good for determined the water pollution for real time. The proposed algorithm is considered superioralgorithms based on the values of the correlation coefficient, R=0.97 and root-mean-square error, RMS=15mg/1.that the TSS maps can be generated using digital camera imagery with the proposed algorithm.AcknowledgementThis project was carried out using the Malaysian Government IRPA grant no.08-02-05- 6011 and USM sl'FPP20011130. We would like to thank the technical staff and research officers who participated in this proje,extended to USM for support and encouragement.Referencess;'IsIgt,i! ,srilvt't-3t0BE;g1. ?rob(1..n.o>?m&160&r 0,ffiS9fi*|e = t1.t$ hqrlfile://D :\Environmental Planning\5 5.htm Ir/2212004Establishing a global algorithm for water quality mapping from multi-dates images Page 8 of8o Allee, R.J., and Johnson, J.E., (1999). 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Determination of chlorophyll content and trophic state of lalspectrometer dan IRS-1C satellite data in the Mecklenburg Lake District, Germany. Remote Sensing of Er227 235.aaaaafile://D:\Environmental Plarmine\5 5.htm rt/22t2004

A multi{emporal study of chlorophyll-a concentration in the Albufera lagoo Spain, Using Thematic Mapper data.

Algorithm For Tc Solids Mapping Using Digital Camera lmages. Proceeding in SPIE's Third InternationalAsia-Pacific Enviro Sensing Symposium - Remote Sensing Of The Atmosphere, Ocean, Environment, and Space: Ocean Rr Dan Applications,

Dependence of relationship between inherent and apparent optical properties of altitude.

Modeling spectral diffuse attenuation, absorption and scattering c turbid estuary.

Remote sensing of water quality parameters usin lnternationaf

s ;'Is I g t, i! ,s ril vt 't3t0 B E; g1. ?ro b (1. .n .o >?m & &r 0,ffiS9 fi*|e = t1.t$ hqrl file://D :\Environmental Planning\5 5.htm Ir/2212004Establishing a global algorithm for water quality mapping from multi-dates images

Specific absorption and backscattering spectra for suspended chlorophyll-a in Chilko Lake, British Columbia. Remote Sensing of

The relationship of MSS and TM digital data rrr sediment, chlorophyll and temperature in Moon Lake, Mississippi. Remote Sensing of environment,

Establishing A Global Algorithm For Water Quality Mapping From Multi-Dates Images.

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Establishing A Global Algorithm For Water Quality Mapping From Multi-Dates Images.

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