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Estuarine, Coastal and Shelf Science 89 (2010) 89e96

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Estuarine, Coastal and Shelf Science

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Remote sensing of water depths in shallow waters via artificial neural networks

Özçelik Ceyhun*, Arısoy YalçınDepartment of Civil Engineering, Dokuz Eylul University, 35160 Izmir, Turkey

a r t i c l e i n f o

Article history:Received 25 November 2009Accepted 26 May 2010Available online 2 June 2010

Keywords:water depthbathymetryremote sensingartificial neural networks

* Corresponding author.E-mail address: ceyhun.ozcelik@deu.edu.tr (Ö. Cey

0272-7714/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.ecss.2010.05.015

a b s t r a c t

Determination of the water depths in coastal zones is a common requirement for the majority of coastalengineering and coastal science applications. However, production of high quality bathymetric mapsrequires expensive field survey, high technology equipment and expert personnel. Remotely sensedimages can be conveniently used to reduce the cost and labor needed for bathymetric measurements andto overcome the difficulties in spatial and temporal depth provision. An Artificial Neural Network (ANN)methodology is introduced in this study to derive bathymetric maps in shallow waters via remotesensing images and sample depth measurements. This methodology provides fast and practical solutionfor depth estimation in shallow waters, coupling temporal and spatial capabilities of remote sensingimagery with modeling flexibility of ANN. Its main advantage in practice is that it enables to directly useimage reflectance values in depth estimations, without refining depth-caused scatterings from otherenvironmental factors (e.g. bottom material and vegetation). Its function-free structure allows evaluatingnonlinear relationships between multi-band images and in-situ depth measurements, therefore leadsmore reliable depth estimations than classical regressive approaches. The west coast of the Foca, Izmir/Turkey was used as a test bed. Aster first three band images and Quickbird pan-sharpened images wereused to derive ANN based bathymetric maps of this study area. In-situ depth measurements weresupplied from the General Command of Mapping, Turkey (HGK). Two models were set, one for Aster andone for Quickbird image inputs. Bathymetric maps relying solely on in-situ depth measurements wereused to evaluate resultant derived bathymetric maps. The efficiency of the methodology was discussed atthe end of the paper. It is concluded that the proposed methodology could decrease spatial and repetitivedepth measurement requirements in bathymetric mapping especially for preliminary engineeringapplication.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Bathymetric surveying of shallowwaters has a great importancefor coastal engineering and coastal science applications as well asshipping safety (Leu and Chang, 2005; Grilli, 1998). Especially incoastal zones intensive sediment transportation due to tidalmovements, wave propagation, bottom currents, tributaries etc.cause significant temporal and spatial changes on sea bottom andmade recursive surveying necessary (Zanial, 1994; Spiter and Dirks,1987; Lyzenga, 1985, 1978). Therefore, a requirement for a practicaldepth estimation method arises especially for preliminaryapplications.

The shipboard echo sounder having been used conventionally tomeasure water depths gives quite accurate results for pointmeasurements (Leu and Chang, 2005; Lyzenga, 1985). However,

hun).

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since it needs intensive labor, time and money, remote sensingtechniques including airborne lidar measurements and opticalremote sensing are preferably used in practical coastal engineeringand coastal science applications. (Mas, 2004; Martin, 1993; Lyonet al., 1992). Lidar has a high accuracy in depth measurements,provided that the altitude of the measurement platform is knownaccurately. Its coverage is limited by maximum altitude, scan angleand position of the platform (Lyzenga, 1985, 1981). Optical remotesensing relies on passive multispectral scanner data. It uses opticalcharacteristics of the water column to estimate water depths(Fonstad and Marcus, 2005; Lyzenga, 1978). Since multispectralscanner images can be easily obtained for different time and loca-tion, it is favored for practical bathymetric data requirements(Louchard et al., 2003).

Various depth estimation methods based on the optical remotesensing developed in the literature. For instance, Lyzenga (1978)proposed a modified exponential depth model for clear shallowwaters, ignoring the internal reflection in the water column;Louchard et al. (2003) realized radiative transfer calculations to

Ö. Ceyhun, A. Yalçın / Estuarine, Coastal and Shelf Science 89 (2010) 89e9690

generate a spectral library of remote sensing reflectance and thus toclassify obtained reflectance according to bottom type and waterdepth; Leu and Chang (2005) used two dimensional wave spec-trums to estimate water depths based on the principle that whilewaves propagate toward shoreline, their wave lengths decrease dueto decrements in water depth; Fonstad and Marcus (2005)combined remote sensing imagery and open channel flow princi-pals to estimate water depths in clear rivers. Most of these methodsare either difficult to apply or valid for only some specific condi-tions. Therefore, linear regression models on spectral reflectancevalues are generally more attractive for practical purposes becausethey are easy to handle as well as they can be calibrated for any areaand time images.

The Single BandAlgorithm (SBA) is the simplest andmostwidelyused linear regression approach (Martin, 1993). The SBA assumesthat log transformed reflectance of the pixels on a single band imageare linearly correlated with water depths on those pixels. PrincipalComponent Algorithm (PCA) or Multi-band Approach is anothermethod that evaluates the relationship between in-situ depthmeasurements and log transformed reflectance values. It can eval-uate multi-band images together to get better depth estimations.The PCA firstly finds the principal components (PC) of the logtransformed reflectance, and then assumes that the scores of the PCare linearly correlated with water depths (Martin, 1993). Althoughthese models provide temporal and spatial freedom and easiness inmodeling, it is generally not possible to have significant linearrelationships between PC scores (or single band image reflectance)and water depths, especially for the regions where environmentalparameters affecting the reflectance from water column changeunevenly (Leu and Chang, 2005; Fonstad and Marcus, 2005;Lyzenga, 1981). It is very complex to refine the reflectancesourced solely by depth changes from the effects of environmentalfactors such as bottom material, vegetation, pollution, algae cover,turbulence, tributary etc. (Hengel and Spitzer, 1991; Lyzenga, 1985).

Fig. 1. The study are

Few researchers developed different methods to able to modeleffects of these environmental factors (Härmä et al., 2001; Zanial,1994; Lyon et al., 1992; Spiter and Dirks, 1987), however, there isno unique reliable method that is simple and practically applied. Onthe other hand, black box models, that don’t need to focus oninterior processes, stands as a reasonable alternative of regressivemodels for depth estimations via optical remote sensing.

The methodology proposed in this study use Artificial NeuralNetworks (ANN) to estimate water depths in shallowwaters. ANN’snonlinear, sample based and model free structure allows themethodology to consider nonlinear multi-parameter relationshipbetween reflectance from different spectral bands and waterdepths. Its main advantage in practice is that it can provide depthestimations without eliminating problems in optical remotesensing associated with environmental factors such as bottommaterial and vegetation. The proposed methodology reducesspatial and repetitive depth measurement requirement. Technical,logistical and economical difficulties on field surveys and on clas-sical optical remote sensing make it useful especially for prelimi-nary engineering applications. The Foca bay/Turkey was used asa test bed. Two ANN models were built. Each of these models wascalibrated (trained) using Aster and Quickbird images respectively.Bathymetric maps were derived based on these models’ estima-tions. Accuracies of results were evaluated based on two referencemaps relying on different number of in-situ depth measurements.Finally, the effects of the number of the depth measurements anddifferent band satellite images on model estimations wereinvestigated.

2. Study area and data

The proposed ANN methodology is applied for the Foca bay,Izmir, Turkey (Fig. 1). Themain economic activities in the region arefishing and tourism. Navigation is realized via the Foca port. Due to

a, Foca district.

Ö. Ceyhun, A. Yalçın / Estuarine, Coastal and Shelf Science 89 (2010) 89e96 91

heavy sediment transport throughout Foca shoreline, sea bottomhave exposed to level changes, and thus navigation and othercoastal activities such as fishing and recreation have affectednegatively.

In-situ depth measurements were supplied from the surveys ofthe General Command of Mapping, Turkey (HGK). Aster first threeband images and also Quickbird pan-sharpened first four bandimages were used to build depth models. Both depth measure-ments and satellite images were acquired in same period JuneeJuly2005. Two reference maps were generated based on all (300)/half(150) of the depthmeasurements supplied, in order to see effects ofthe number of the depth measurements on bathymetric mapping.The distance-weighted average method was used for bathymetricmap generation. The resultant maps are provided in 5 m precisionto facilitate interpretations (Fig. 2).

3. Methodology

3.1. ANN modeling

ANN having a flexible information transferring structure hasattracted large interest during the last years (Mas, 2004; Atkinsonand Tatnall, 1997; Jain et al., 1996; Lek et al., 1996). The ability tohandle nonlinear functions, estimate for unseen inputs and useobserved data have made it popular (Lek et al., 1996; Civco, 1993;Freeman and Skapura, 1991). It has found wide range of applica-tion especially in environmental science, image processing,medicine and molecular biology (Mas, 2004; Atkinson and Tatnall,1997).

Connection pattern of an artificial neural network is generallyconsidered in two categories, feed forward and feedback (recur-rent) networks (Hagan et al., 1996; Haykin, 1994). In this studymulti-layer feed forward neural network (MFN) is used for depthestimations because of its simplicity in practice. In-situ depthmeasurements are considered as the expected output vector, andthe reflectance values on each spectral band are accounted asinputs vectors. Fig. 3 shows the general structure of the ANNmodelproposed. Where i, j and p are, respectively, layer, node andobservation numbers; n, m and N are, respectively, the numbers oflayers, nodes and observations; w[;j are the network weights

Fig. 2. Bathymetric maps generated using (a)

between the [th node of the (i� 1) th layer and jth node of the ithlayer; bj are the biases; Ij(p) is jth input vector (reflectance from jthband); T(p) is the calculated output vector for each epoch; O(p) isthe expected output vector (in-situ depth measurements). For thismodel, net inflow netj to the jth node of the ith layer is defined asfollows (Hagan and Menhaj, 1994; Freeman and Skapura, 1991).

netj ¼Xm[¼1

�w[; j � o[

�þ bj (1)

where o[ is the output of the [th node of the (i-1) th layer andequals to pth element of the input vectors for the first layer and to f(netj) for the other layers. f(netj) is a transfer function that trans-forms the net input netj into the node outputs o[ (Hagan et al.,1996). It is taken here as the log sigmoid function given in Eq. (2).

f�netj

� ¼ 1=�1þ exp

��netj��

(2)

Model weights are calculated by using the typical performancefunction in Eq. (3) (Atkinson and Tatnall, 1997; Hagan and Menhaj,1994).

RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPN

p¼1ðTðpÞ � OðpÞÞ2N

s(3)

If the root of mean square errors (i.e. RMSE) between calculatedand observed outputs is low enough to ignore, then initial (orcalculated) weights and biases are assumed as the resultantweights and biases, else the calculated error is distributed viaa training algorithm to find out new weights and biases. Thisprocedure is repeated until an acceptable RMSE is reached. Lev-enbergeMarquard training algorithm given in Eq. (4) is used in thisstudy (Hagan and Menhaj, 1994).

xkþ1 ¼ xk �hJTJþ mI

i�1JT3k (4)

where xk is a vector of current weights and biases; 3 and J are,respectively, the vector and Jacobean matrix of the network errors;m is a scalar which indicates how to use thememory and calculationspeed of the Jacobean matrix; k is iteration number; I is the unitmatrix, and superscript T indicates transposition.

150, (b) 300 in-situ depth measurements.

Fig. 3. Typical ANN architecture used in depth estimations.

Ö. Ceyhun, A. Yalçın / Estuarine, Coastal and Shelf Science 89 (2010) 89e9692

3.2. Depth modeling and simulation

Preparation of model inputs and determination of modelarchitecture and parameters are two important steps to model andsimulate water depths in shallow waters via the ANN modelproposed in Fig. 3. Model inputs, reflectance from different spectralbands and in-situ measurements, should be processed before usedin the modeling. Filtering, rectifying of the input images andscaling/rescaling of the model inputs and outputs can be regardedin this manner. Model architecture is constrained by some factorssuch as problem to be solved, number of input factors and numberof outputs required (Hagan et al., 1996). The number of the nodes inthe input layer equals to the number of input spectral bands, andthe number of the nodes in the output layer is naturally one sincethere is only one output (i.e. water depths). The number of the innerlayers and nodes are determined experimentally (Freeman andSkapura, 1991) to have the best performance model. Modelparameters are obtained as a result of training and testingprocesses. Following the preparation of model inputs and deter-mination of model architecture and parameters, water depthsimulations are made for multispectral reflectance values ofrequired coastal points. The methodology proposed is summarizedin Fig. 4.

According to Fig. 4, each band image should be first filtered toboth discard the land area, clouds and ships, and decrease noise andscattering effects on input images. The land area, clouds and shipscan be discarded by using a boolean filter (referred also as a binaryor logical filter) while noises and scattering effects can be reducedby applying pixel based filters such as mean (low pass), Gaussian,minimum, median filters, etc. (Jain, 1989). After in-situ depthmeasurements and input images are transformed into same coor-dinate system, reflectance values corresponding to the pointswhere there are available depth measurements (reference points)and where depth simulations will be made (key points) areextracted from input multispectral images. The key points are soselected that they do not have an individual behavior and areevenly distributed throughout considered region. To get modelinputs, the input reflectance on the reference and key points anddepth measurements (expected outputs) on the reference points

are scaled so as to always fall within a specified range that is usablewith the transfer function (see Eq. (2)). The scaled data of referencepoints are separated into two parts as training and testing data set.The scaled data of the key points are used in depth simulations.

Once the model inputs are prepared, model architecture andparameters can be readily determined using principles in theSection 3.1. For determined network architecture, model trainingand testing are conducted until an acceptable error is reached. Theeventual ANN model tested is used for depth simulations onselected key and reference (if required) points. The depth estima-tions are obtained by rescaling network outputs. These estimatesare used to interpolate a contour map or to generate a digitalelevation model.

4. Application and results

Single Band Algorithm (SBA) and Principal Component Algo-rithm (PCA) did not provide reliable depth estimations in the studyarea since the bottom vegetation highly affected the reflectancefromwater column. Table 1 shows the coefficients of determinationR2 of the SBA and PCA models. Where R2 designates the varianceratio of in-situ depth measurements explained by the SBA and PCAmodels. SBA and PCA models set for Aster images provided morereliable result than those for the Quickbird images. SBA models setfor the first and third band Aster images are more reliable than themodel set for second band Aster image, and also the PCA model setfor the first PC is the only efficient model. The SBAmodel set for theQuickbird’s third band image seems preferable among the modelsset for the Quickbird images. The PCA model set for the first PC ofthe Quickbird images is the only PCA model having high R2. Inoverall evaluation, though some of the SBA and PCAmodels may beused for a course evaluation of the bathymetric structure of theregion, they are not adequate for a reliable bathymetric mappingbecause they are not able to explain the variance of the depthvariations sufficiently (see Table 1).

The ANN methodology was applied apropos to flow chart inFig. 4. Boolean filter was used, for all band images, to discard thepixels representing the land area, clouds and vessels and thus toobtain images only from the marine environment. The Gaussian

In-situ depth measurements Image acquisition (Band images)

Extraction of reflectance on the reference points for each band

Image filtering Coordinatetransformation

Selection of the ANN structure

Initialweights

and biases

Training of the ANN

Training data (Scaled reflectance and in-situ

depth measurements)

ANN outputs and

output weights

Decision for expectable error between expected and calculated outputs

(Is the RMSE acceptable?)

Scaling

Testing ofthe ANN

Rescaling

Contouring and Digital Elevation Map

Selection of the key points on which depth estimations will be made; Extraction of reflectance on these points

Scaling

Re-training

Simulation (ANN outputs on the key/reference points)

ANN outputs and output weights

No

Yes

Testing data (Scaled reflectance and in-situ

depth measurements)

Fig. 4. Depth estimation using remote sensed images and ANN.

Table 1The coefficients of determination for the SBA and PCA models.

Aster Quickbird

Single bandalgorithm

Principalcomponentalgorithm

Single bandalgorithm

Principalcomponentalgorithm

Bandnumber

R2 P.C. No R2 Bandnumber

R2 P.C. No. R2

1 0.75 1 0.74 1 0.29 1 0.662 0.67 2 0.02 2 0.58 2 0.013 0.76 3 0.02 3 0.66 3 0.02

4 0.51 4 0.01

Ö. Ceyhun, A. Yalçın / Estuarine, Coastal and Shelf Science 89 (2010) 89e96 93

5� 5 filter for the Aster images and 7� 7 filter for the Quickbirdimages were used to reduce scattering effects, noise, and problemscaused by the pixels having individual reflective properties. Afterthe filtered images and depth measurements were transformedinto same reference system (UTM 35 North, WGS 84), 300 keypoints for the Aster model and 450 key points for the Quickbirdmodel were specified for simulations. 300 reference points wereconsidered in the analyses. The reflectance values on the reference/key points were extracted for all band images via a computerprogram written in the Visual Basic environment. Depthmeasurements on reference points and corresponding reflectancevalues were arranged into two (training and testing) data sets.

Ö. Ceyhun, A. Yalçın / Estuarine, Coastal and Shelf Science 89 (2010) 89e9694

These data sets include input vectors consist of reflectance inconsidered spectral bands and expected output vector consist of in-situ depth measurements. Reflectance values on the key points fordifferent spectral bands were arranged into input vectors ofsimulation data All input and expected output vectors constitutedwere scaled by 0.8� (Rp� Rmin)/(Rmax� Rmin)þ 0.1 to make themusable for the ANN analyses. Where Rp designates pth observationof the input or output vectors; Rmin and Rmax are the minimum andmaximum observations in the vector considered, respectively.

Two ANN models, one for the Aster image inputs and the otherfor Quickbird image inputs were set. These models will be calledhereafter as Aster and Quickbird ANN models, respectively. Thearchitectures of the Neural Networks were determined so that thenumbers of nodes and layers as well as the RMSE between calcu-lated andmeasured depths would be a minimum. Two inner layers,each of which has four nodes, were used in the eventual ANNmodels. Since only the Aster first three band images and Quickbirdfirst four band images have significant difference in their reflec-tance values on the sea surface, three and four neurons were usedin the input layers respectively for Aster and Quickbird models.LevenbergeMarquardt algorithm was used to train the networksconstituted. Initial weights were given randomly, and log sigmoidfunction was used as a transfer function. The significant epochnumber, the epoch after which RMSE values begin to be level offwas used to avoid over-training errors. It was calculated as 3 and 5for Aster and Quickbird ANN models, respectively. The modelstrained were run for the testing data set. Regarding the Aster andQuickbird ANN models, the depth estimations obtained for thetesting and training data sets were depicted against to the depthmeasurements (Fig. 5).

Training data set

05

1015202530354045

0 5 10 15 20 25 30 35 40 45

Measured depths (m)

)m( shtpe

D detamits

E

Training data set

05

1015202530354045

0 5 10 15 20 25 30 35 40 45

Measured depths (m)

)m( shtpe

D detamits

E

y = 0.9004x + 1.7331 R2=0.9004

y = 0.9187x + 0.0259 R2=0.9184

a

b

Fig. 5. Measured vs. estimated water depth

According to the results in Fig. 5(a) and (b), the calculated vari-ance ratios explained by ANN models were accepted satisfactory.Therefore, constituted ANNmodels were used in depth simulations,taking the simulation data set as model input. The distance-weighted averages of the simulated depths were used to producebathymetric maps. Resultant maps were provided in 5 m precisionto facilitate comparison. Fig. 6 shows the bathymetric maps derivedfor the Aster and Quickbird images.

Finally, different band image combinations were used inmodeling to evaluate the effects of the different bands on modelperformances. In these analyses, simulations were made for 15epoch. Resultant depth estimations were compared with in-situdepth measurements. Table 2 shows the R2 and RMSE betweenestimated and measured depths for each band combination used.

5. Discussion

For many coastal engineering and coastal science applica-tions, it is very important to know bathymetry of study area.However, it is generally very difficult to get detailed informationfor reliable bathymetric mapping. Implementing bathymetricsurvey for whole study area is more expensive and time/laborconsuming process. The proposed methodology presents a prac-tical solution to estimate water depths in shallow waters viaArtificial Neural Networks (ANN), using remote sensing imagesand sample depth measurements. Nonlinear, model free struc-ture of ANN enables the proposed methodology to produce moreaccurate estimations than classical regressive methods. Areal andspatial flexibility of remote sensing images coupled withnonlinear estimation capability of ANN account for the proposed

Testing data set

05

1015202530354045

0 5 10 15 20 25 30 35 40 45

Measured depths (m)

)m( shtpe

D detamits

E

Testing data set

05

1015202530354045

0 5 10 15 20 25 30 35 40 45

Measured depths (m)

)m( shtpe

D detamits

E

y = 0.8968x + 2.1102 R2=0.8997

y = 0.9198x + 0.0172 R2=0.9143

s, (a) Aster, (b) Quickbird ANN models.

Fig. 6. Bathymetric maps derived for (a) Aster, (b) Quickbird ANN models.

Ö. Ceyhun, A. Yalçın / Estuarine, Coastal and Shelf Science 89 (2010) 89e96 95

methodology’s prevailing advantages especially for preliminarycoastal engineering studies as well as for practical scientificapplications. Main advantages of the proposed methodology canbe listed as below.

� provides depth estimations using raw reflectance values,without refining scattering caused by environmental factorssuch as bottom material and vegetation.

� allows considering nonlinear multi-parameter relationshipbetween reflectance from different spectral bands and waterdepths.

� reduces spatial and repetitive depth measurement requirement.� provides fast and practical bathymetric information.

For the analyses implemented for the study area, SBA and PCAmodels produced low accurate depth estimations for Aster imagesThis may be explained by the fact that since Foca bay is subject toextensive bottom vegetation, the reflectance from water column ismainlycomposedof the reflectance sourced fromdepthchanges andthe reflectance sourced from bottom material. Aster and QuickbirdANNmodels providedmore efficient estimations. Aster images havelower resolutione15 m� 15 me and fewerpixels therefore there isno possibility to estimates water depths for regions smaller than225 m2. However, when precise depth measurements can not beprovided, Aster images can be conveniently used by regarding thatreflectance of Aster’s large pixels may produce average estimations.Aster sensor’s digitations is limited by 256 values, therefore it maynot account for high reliable depth estimations especially for theregions having dense bottom vegetation and turbidity. It should benoted that 256 is the full resolution of Aster’s sensors, the range onthe sea surface is much lower than those on the land area andchanges dependingon thephysical and chemical properties ofwatercolumn. The cleaner water will result in larger range and thus in

Table 2Effects of band numbers on depth estimation performances.

Aster Quickbird

Bands RMSE R2 Bands RMSE R2

3,2,1 0.006 0.90 4,3,2,1 0.005 0.922,1 0.008 0.85 3,2,1 0.007 0.901 0.010 0.82 2,1 0.011 0.83

1 0.021 0.67

accurate estimations (Leu and Chang, 2005; Louchard et al., 2003).Quickbird pan-sharpened images have 0.61 m� 0.61 m pixel size.Even local depth changes can be theoretically estimated. However,since the number of the pixels in a study area is larger for theQuicbird images, training and testing data sets should be larger toget reliable models, this means that more in-situ measurement isneeded for themodels set forQuickbird images. Correctdata couplesmay not be easily provided for the Quickbird models since the pixelsize is too small to match depth measurements to the reflectancevalues. On the other hand, 11 bit digitation of the Quickbird imagesmake possible to evaluate small changes on reflective properties ofthe water column; therefore more precise estimations can be madeby the Quickbird ANN models.

The number of depth measurements affects remarkably theaccuracy of the bathymetric mapping. This effect can be easily seenvisually comparing the maps generated using different numbers ofin-situ depth measurements (Fig. 2). Different depth values espe-cially near the coast line and on the deepwaters are clear. Some zerodepth regions appear both on Fig. 2(a) and (b). The area of such zerodepth regions is lower for Fig. 2(b) than for Fig. 2(a). There are somelocal depth regions on Fig. 2(b). These regionsmight be generated asonly numerical products during map generation. Mentioned effectsof the number of depth measurements on bathymetric mappingcould be reduced by means of the proposed methodology. Bathy-metricmapsderived for theAster andQuickbirdANNmodels (Fig. 6)presentedmoreaccurate anddetailedbathymetric information thangenerated maps (i.e. Fig. 2). Due to the number and structure of thedata used in bathymetric mapping, the map derived for the Quick-bird image inputs (i.e. themap in Fig. 6(b)) has higher precision thanthemap derived for the Aster image inputs (i.e. themap in Fig. 6(a)).The area of zero depth regions is much lower for Fig. 6(b) than forFig. 6(a). Different depth regions are clear in Fig. 6(a). The local depthregions appearing in Fig. 2(b) seems tobe smoothed in Fig. 6(b). Notethat reliabilities of estimations on these local regions depend highlyon sampling data used in training and testing. It is seen from theFig. 5 that depth estimations lose their effectiveness for the higherdepths than 40 m since the reflectance on sea surface does notchange significantly for deeper regions. This depth value can bedifferent for other study areas, regarding water clarity.

To reveal efficiency of ANN based bathymetric mapping, thederived maps (i.e. Fig. 6(a) and (b)) and generated maps (i.e. Fig. 2(a) and (b)) may be differenced. In-situ measurements are point

Ö. Ceyhun, A. Yalçın / Estuarine, Coastal and Shelf Science 89 (2010) 89e9696

measurements contrary to the proposed methodology estimatingan average depth value corresponding to the mean reflectance ofthe considered pixel. Unless there is no available high resolutionbathymetric map for the study area, differencing may not beinformative especially when images with large pixel are used indepth estimations. In this study, investigation of the effectivenessof the proposedmethodology is confined with the evaluation of thetraining/testing results and the visual comparison of the derivedand generated maps since there is no available high resolutiondigital bathymetric map for the region.

In the practice, bottom material, vegetation or pollution affectthe reflectance from the water column, remarkably. Therefore, theinput data should be representative and concurrent to build a reli-able model. It is natural that the more changing properties of watercolumn, the less reliable depth estimates; however, reliabilities ofthe model can be increased using large, representative data sets.Although, theoretically, simultaneous depth measurements andimage acquisition are needed for modeling, data from differentdated images and depth measurements may also be used under theassumption that bathymetric changes are small or homogeneous onall reference points during the date difference. However, accuraciesof the results will be depended on how much depth chances haveoccurred on reference points between the dates of image acquisi-tion and depth measurements. Accordingly, temporal flexibility inbathymetric mapping can be provided when depth measurementsfrom locations where there is no significant bottom currentchanging bathymetry are used in modeling. Short term localpollutants will affect themodel performance negatively if referencepoints used in training and testing and key points used in simula-tions are close to or under the effect of the source of pollutants.Model performance is also affected by the number of input bandsused. The infrared band has no contribution for Aster model andlittle contribution for Quickbird model. Water depths may be esti-mated with moderate accuracy even using only first two bands.

6. Conclusion

Bathymetric map generation process highly depends on thenumber of in-situ depth measurements. Depth models providepractical solutions to increase reliability of generated bathymetricmaps. However, changing bottom vegetation and material makesdifficult to use classical, regressive, optic remote sensing baseddepth estimation methods such as SBA and PCA. The methodologyproposed herein provides a useful tool for bathymetric mapping forpractical purposes. Its nonlinear model free structure allowsconsidering nonlinear relationships between multi-band imagereflectance andwater depths, thusmore accurate depth estimationscould be obtained. Since the ANNmodels e a black box model e donot have to consider the factors affecting the reflective properties ofwater column anddo not have to necessarily be trainedbynewer in-situ measurements, the methodology can be practically appliedwithout handling anycomplex reflectance separationprocess, and itcan be reliably used for repetitive bathymetric mapping providedthat there is available representative input data set.

Since the study area is subject to intensive bottom vegetation,SBA and PCA models did not provide reliable depth estimations.The proposed methodology produced quite accurate estimates.Even first two bands of both satellite images could model thebathymetry by a determination coefficient R2 over 80%. Low dig-itation and spatial resolution of the Aster images facilitated datamanipulation andmap generation procedure. The model set for theAster images did not provide as much reliable estimates as themodel set for the Quickbird images and could not reveal precise andlocal depth changes; however it can be used properly when there is

no need for detailed mapping. On the other hand, high resolution ofthe Quickbird images permitted to evaluate local depth changes.High digitation of the quickbird images made possible more precisedepth estimations.

It is concluded in the study that the Artificial Neural Networkscan be effectively used for bathymetric modeling. The proposedmethodology can produce efficient depth estimations up to40e45 m depending on water clarity and regardless the portion ofthe reflectance sourced from environmental factors. Time depen-dency of the bathymetric mapping may be decreased by trainingANN models with newer images. Thus, the cost, labor and timespend for detailed and repeated depth measurements could besignificantly reduced.

Acknowledgements

We would like to thank Dr Hüsnü ERONAT from Dokuz EylulUniversity The Institute of Marine Sciences and Technology for hiskind helps on data provision.

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