Utility of Remote Sensing Technology for Determining Characteristics of Sandy Beaches

Sinasi Kaya1

Dursun Z. Seker1

Nilay E. Yasa1

Sedat Kabdasli1

Aysegul Tanik1

1Istanbul Technical University (ITU), CivilEngineering Faculty, Istanbul, Turkey.

Research Article

Utility of Remote Sensing Technology forDetermining Characteristics of Sandy Beaches

In this study, four sandy beaches located on the western part of the Turkish Black Seacoast were selected to determine the characteristics of sand. Hence, field studies wereconducted and remotely sensed data taken simultaneously were utilized.A hand spec-troradiometer was used to measure reflectance values in the beaches. Various moni-toring stations for spectral reflectance measurements and sand sampling stations formoisture content analyses were selected on each of the beaches. The sand sampleswere further sieved in the laboratory to obtain the grain size distribution. The spec-tral reflectance measurements of the beaches and the grain size distributions werecompared with the classified IKONOS satellite images obtained simultaneously withthe field surveys. Reflectance values showed similarities between the beaches. Classifi-cation done by using the grain size distributions put forth the areal-based informa-tion on sand size characteristics.

Keywords: Coastal morphology; IKONOS; Moisture; Remote sensing; Spectroradiometer; Sandybeaches;

Received: August 20, 2007; revised: October 1, 2007; accepted: October 18, 2007

DOI: 10.1002/clen.200700116

1 Introduction

Sandy beaches, forming an essential part of the coastal morphology,bear vital importance and their preservation is highly stressed. Sev-eral problems such as erosion of sandy beaches and dunes andchanges in the characteristics of the sediments that form thebeaches occur due to uncontrolled use and overuse of rivers. Suchsensitive areas are decreasing drastically, requiring permanent solu-tions that should be systematically examined. The determinationand inspection of such vulnerable areas, which usually cover largeareas, by means of conventional measurement and evaluation tech-niques are costly and time consuming due to data acquisition. Oneof the modern tools for investigating such large areas of land isremote sensing (RS) using satellite imagery.

RS is the science and art of obtaining information about anobject, area, or phenomenon through the analysis of data acquiredby a device that is not in contact with the object, area, or phenom-enon under investigation [1]. Earth observation by RS is the interpre-tation and understanding of measurements of electromagneticenergy that is reflected from or emitted by the Earth's surface oratmosphere, and the establishment of relationships between thesemeasurements and the nature and distribution phenomena on theEarth's surface or within the atmosphere [2]. It can be used for appli-cations in several different areas, including geology and mineralexploration, hazard assessment, oceanography, agriculture and for-

estry, land degradation and environmental monitoring. Differentsatellite images taken from different satellites have various sensors.Each sensor is designed for a specific purpose. With optical sensors,the design focuses mainly on the spectral bands to be collected [3 –5].

RS is also used for sediment characterization. For example, it ispossible to obtain the reflectance (a measurement of how muchelectromagnetic radiation is reflected out of a surface) of sandybeaches using a spectroradiometer. These data integrated with sat-ellite imagery of high spatial resolution aid to monitor sands anddefine sediment composition and other specific characteristicssuch as grain size distribution. For the naked eye, the grain compo-sition of the sand is correlated with its color [6]. Based on this fact, itis expected that differences in the reflectance curve (produced bythe differences in color) are related with the composition of thesand. The data will help monitoring of beaches, allowing rapid anal-yses of sand composition. Remotely sensed data can be used toobtain reliable information about the coastal morphology. Sandcomposition provides important clues about the origin of the indi-vidual grains [7]. Determination of the structural characteristics ofthe beaches reveals the general structure of the natural balance of aregion. As mentioned, analyzing beach characteristics of the entireregion may be time and money consuming by traditional methods.References [1, 8, 9] are a few examples of the recent researches con-ducted on the subject of concern.

In this study, four sandy beaches that are affected by the Filyosstream, which ends in the Turkish part of the Black Sea, are exam-ined. Remotely sensed data is an important data set utilized in theevaluation of sediment sources as they are transported from broadareas. The sediments are transported from an area of 13 km2 of theFilyos basin. The physical characteristics of the sand accumulated infour sandy beaches through sediment transportation by the Filyos

Correspondence: Prof. S. Kaya, Istanbul Technical University (ITU), CivilEngineering Faculty, 34469 Maslak, Istanbul, Turkey.E-mail: [email protected]

Abbreviations: GPS, global positioning system; RS, remote sensing

i 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com

654 Clean 2007, 35 (6), 654 –659

Clean 2007, 35 (6), 654 – 659 Sediment Characteristics 655

stream were analyzed by using the IKONOS satellite images and byfield studies such as spectroradiometer measurements.

Multispectral (4 m spatial resolution) and panchromatic (1 m spa-tial resolution) IKONOS satellite images were used as remotelysensed data. Sand samples were collected from two or three differ-ent locations at each sandy beach. The coordinates of each samplingstation were recorded by the global positioning system (GPS). Addi-tionally, reflectance values were also measured by spectroradiome-ter, and moisture measurements were done. GPS and spectroradi-ometer measurements were performed simultaneously with satel-lite image acquisition time. The correlation between the satelliteimages and the field surveys was examined.

This study aims to present an RS approach for retrieving sandcharacteristics of sandy beaches along the Black Sea coast of Turkey.

2 The Study Area

The Filyos stream is geographically located in the western Black Searegion of Turkey, with the coordinates of 408369– 418369 and308149– 338 429. Its watershed is approximately 13 305 km2 and cov-ers almost 46 % of the West Black Sea watershed [9]. The majority ofthe watershed is covered by mountains and plates. There exist highmountains especially in the eastern and southern part of the area;approximately 85 % of the watershed has elevations between 500and 2000 m, indicating that it is under erosion risk with high occur-rence of sediment transfer. The main reason for selecting the Filyosriver as the study area is that it is known to be the only river of Tur-key compared to similar others – in size and length – on whichthere are no man-made structures like dams. The river feeds onlyfour sandy beaches, namely Filyos, Kizilkum, Mugada and Inkum,which were selected for further investigation (see Fig. 1).

3 Methodology Used

3.1 Field Studies

In this study, 1 day was spent on all the four beaches to obtain a gen-eral idea about their appearance and to plan the location of the sam-pling stations for taking sand samples and of the monitoring sta-tions for spectroradiometer and moisture analyses. It is importantto note here that the locations of the sand samples required fordetermining the grain size distribution were selected by consider-ing their representative characteristics. Attention was paid to col-lecting samples of different sizes. Moreover, sampling stations forspectroradiometer and moisture analyses were selected in an opti-mized manner so as to represent the entire beach as much as possi-ble. The site-specific field surveys were conducted during 10 –13 August 2006, with 1 day spent on each beach. During the sur-veys, the meteorological conditions did not vary considerably, andthe satellite imagery retrieval belonged to 09 and 11 August 2006.

The initial measurements were conducted on the Inkum beachwhere reflectance values and moisture contents were recordedfrom 15 points whose spatial locations were determined by GPS.Besides, sand samples from three points were taken, presenting dif-ferent characteristics for further granulometric analysis. For therest of the beaches, similar site-specific studies were realized. Thenumber of monitoring and sampling stations was 15 in Mugada, 17in Kizilkum and 16 on the Filyos beaches. Various sand sampleswere also collected from each of the beaches.

In the moisture content analysis, a KS-D1 moisture-measuringinstrument (DELMHORST) was used. During the measurements, theprobes of the instrument were inserted only 5 cm deep into thesand in order to record the moisture content near the surface. Themeasurements were recorded after waiting for 0.5 h. It was neces-


Figure 1. The geographical location of the study area.

656 S. Kaya et al. Clean 2007, 35 (6), 654 –659

sary to convert the recorded KS-D1 values to % moisture content insand by utilizing a calibration curve drawn between the KS-D1 meas-urements. During sampling, the samples were taken in a cylinderwith care so as not to disturb the sand media. The bottom of the cyl-inder was covered by a permeable cloth, and the samples werebrought to the laboratory where the cylinder was placed in a waterbath to saturate the sand with water. The samples were then filteredfor a certain period of time to get rid of excess water. Every day themoisture content of the sample was measured together with itsweight. This procedure was repeated till the weight of the sampleremained constant. Then the sample was dried in a furnace toobtain the final weight. This weight, the measured weight rangeand the KS-D1 values were used to draw the calibration curve of thesample. Therefore, the % moisture content values were obtained byutilizing the calibration curve. Figure 2 presents the % moisturevalues for each monitoring station of the four beaches.

It can be seen that the moisture contents of the various scatteredmonitoring stations on the beaches vary between 0 and 23%. Themoisture content fluctuates a lot on every beach, and no typicalvalue could be achieved.

3.2 Spectroradiometer measurements and RemoteSensing Data

A hand spectroradiometer (Analytical Spectral Devices – ADS) wasused in this study. The spectral measurement range of the instru-ment varied between 325 and 1075 nm, whereas the spectral resolu-tion was 3.5 nm at 700 nm and the error in the wavelength wasl1 nm.

During the measurements, a 108 lens was used to obtain the spec-tral properties of sand distributed in a wide area. The measure-ments were realized by holding the lens at a distance of 30 – 40 cmfrom the object. The device was calibrated at each of the sandybeaches prior to measurements. Standard deviations of the meas-urements were calculated in order to check whether the values liewithin the acceptable range. Ten measurements were conducted ateach station and each measurement was recorded automaticallyevery 5 s. Thus, 150 measurements were done at Inkum andMugada, 170 measurements in 17 stations at Kizilkum and 160measurements in 16 stations in Filyos. Figure 3 illustrates the loca-tion of the monitoring stations at the Mugada and Kizilkumbeaches. As an example, the spectroradiometer measurements ofone of the monitoring stations on the Inkum beach are given inFig. 4.

The white target measurements used at the calibration stagemust be near to 1.00 and the impact of noise prior to 400 nm andafter 900 nm is clearly observed from Fig. 4.

During the field studies, the IKONOS satellite sensor data was alsoobtained simultaneously. These data were converted to the UTMcoordinate system by means of control points produced by means ofthe GPS measurements. Besides, the IKONOS data were converted toreflectance values. A classification process was carried out by utiliz-ing converted reflectance values.

4 Results and Discussion

4.1 The Spectral Reflectance Values

The spectral reflectance values obtained for all the inspectedbeaches are shown in Fig. 5. The band ranges of the IKONOS detectorare defined as blue band between 450 and 520 nm, green bandbetween 520 and 600 nm, red band between 630 and 690 nm, andinfrared band between 760 and 900 nm. The illustrative figure indi-cates that the blue and green bands may be used to investigate thephysical properties of the sand.

The reflectance curves of each sandy beach indicate that similar-ities exist among them as the different beaches reflect almost iden-tical trends throughout the different wavelengths.


Figure 2. % Moisture values from each station on the four beaches.

Figure 3. Point distribution of the Mugada and Kizilkum beaches.


4.2 Grain Size Distribution of Sand Samples

Granulometric analyses of the sand samples taken were conductedto determine the distribution of particle size. The diameters of thesieves used were 2.8, 2, 1.4, 1.0, 0.75, 0.5, 0.250, 0.180, 0.125 and0.063 mm.

The results of the analyses are given in the form of cumulative dis-tribution curves. The coordinates of any point on the curve displaythe weight ratio of the particles in the soil that have diameters lessthan a certain diameter. D50 is the particle size that presents theaverage particle size of the system. The D10 value, on the other hand,is the effective grain size where 10% of the particles have diametersless than the effective diameter and 90% have diameters higherthan the effective diameter. The types of soil according to their par-ticle sizes are given in Tab. 1. The soil uniformity coefficient, Cu, andthe compression coefficient, Cc, can be identified through readingthree typical diameter values on the particle size distribution curve[10]. Cu and Cc are expressed as:

CU ¼D60

D10ð1Þ

CC ¼D2

30

D106D60ð2Þ

If all the particles are of identical size in a mixture of sand andgravel like Cu l 1, the soils are classified as “uniform soil”. The soilcan only have continuous particle sizes under conditions where 1 a

Cc a 3 [10]. The characteristics of the grain sizes for the collectedsand samples from all four beaches are given in Tab. 1.

When the grain size distribution analysis results are compared, itcan be stated that the samples taken have particle sizes within therange of 1.0 – 0.2 mm, and the distribution of the particle sizes isalmost uniform with the medium and coarse sand properties. In allthe sand samples, the Cc values lie within the range of 1 – 3, indicat-ing that all the samples have a continuous particle distribution. As


Figure 4. Spectroradiometer meas-urements in one of the monitoringstations of Inkum beach.

Figure 5. Presentation of the spec-tral range of the IKONOS satelliteimage on sand reflectance valuesand average measurements takenfrom the Inkum, Mugoda, Kizilkumand Filyos sandy beaches.

658 S. Kaya et al. Clean 2007, 35 (6), 654 –659

an example, the granulometric distribution of the sand obtainedfrom the Mugada beach through sieve analysis is given in Fig. 6.

The sand samples were analyzed under the microscope, and thefindings indicate that all beaches have similar characteristics incompliance with the spectroradiometer measurements.

The sand samples taken during the field studies were sievedunder laboratory conditions and the grain size distributions weredetermined for all the four beaches. The coordinates of each sandsampling station were selected as test regions. The IKONOS multi-spectral data of 4 m resolution were used to determine the overallgrain size distribution of the beaches and were classified by usingsupervisor classification and maximum likelihood methods. Threerepresentative stations (F1, F2, F3) were selected as test regionsalong the Filyos, as shown in Fig. 7. Three classes were determinedaccording to the classification procedure and they were found tocorrespond to sizes of 0.35, 0.54 and 0.75, respectively. All thebeaches were then classified according to these data.

Three classes were determined on the Filyos beaches correspond-ing to grain sizes of 0.35, 0.54 and 0.75, whereas two classes werefound on the Kizilkum beach, one with a grain size of 0.37 andanother one within the range of 0.51 – 0.62. Similarly, two classes ofsizes of 0.35 and 0.51 were defined in Mugada, and of sizes of 0.60and within the range of 0.40 – 0.42 in Inkum.

A limited number of sand samples were taken during the fieldsurveys. According to the results, the grain size of 0.75 was notdetected on any other sandy beach apart from Filyos. Grain sizeswithin the range of 0.40 – 0.62 were determined as a single class onall the four beaches. The classes within the range of 0.35 – 0.37 wereobserved at the inner parts of the beaches. This area with grain sizesof 0.35 – 0.37 continues at the inner parts of the beaches in a decreas-ing manner from west towards east. This fact is probably due towind effects. It is considered that northern winds lead to accumula-tion of fine sand in the inner parts of the beaches. This circumstanceis also verified by the field studies. Fine sand also exists along theFilyos riverbed, accumulated by wave effects, and fine sedimenttransfer to the inner parts occurs through wind effects.

When the IKONOS classification results are evaluated, it is seenthat sandy areas of grain size 0.35 cover an area of 63.8 ha in Filyos,the 0.54 grain size occupies an area of 3.9 ha whereas the 0.75 grainsize constitutes 16.5 ha. Similar calculations were repeated for theother sandy beaches: The 0.37 grain size occupies 21.6 ha, grainsizes of the range 0.51 – 0.62 constitute 34.8 ha in Kizilkum, whereasthe 0.35 grain size covers an area of 7.9 ha, and the 0.51 grain sizean area of 13.2 ha, and 8.1 ha is covered by sand in the range of0.40 – 042. An area of 6.3 ha is covered with sand of grain size 0.60. Itis recognized that, most probably, sand with grain sizes greater


Figure 6. Granulometric distribu-tions of the sand obtained fromsieve analysis (sample belongs tothe Mugada beach).

Table 1. Typical characteristics of soil samples.

Samples Taken D10 D30 D50 D60 Cu Cc

Cylinder No: 1A (Mugada) 0.25 0.30 0.35 0.36 1.44 1.00Cylinder No: 2A (Kizilkum) 0.45 0.55 0.60 0.65 1.44 1.03Cylinder No: 3A (Kizilkum) 0.48 0.55 0.51 0.54 1.13 1.17Cylinder No: 4A (Inkumu, far from the sea) 0.42 0.80 0.85 0.88 2.10 1.73Cylinder No: 5A (Inkumu, near the sea) 0.45 0.55 0.60 0.65 1.44 1.03Cylinder No: 1B (Mugada) 0.25 0.31 0.35 0.39 1.56 0.99Cylinder No: 2B (Mugada) 0.26 0.39 0.51 0.56 2.15 1.04Cylinder No: 3B (Kizilkum) 0.52 0.58 0.62 0.65 1.25 1.00Cylinder No: 4B (Kizilkum) 0.26 0.37 0.50 0.55 2.12 0.96Cylinder No: 5B (Kizilkum) 0.26 0.39 0.51 0.54 2.08 1.08Cylinder No: 6B (Filyos) 0.23 0.28 0.35 0.35 1.52 0.97CylinderNo: 7B (Inkumu) 0.26 0.33 0.42 0.47 1.81 0.89Cylinder No: 8B (Filyos) 0.27 0.41 0.54 0.58 2.15 1.07CylinderNo: 9B (Filyos) 0.44 0.64 0.75 0.80 1.82 1.16Cylinder No: 10B (Inkumu) 0.25 0.33 0.40 0.45 1.80 0.97


than 0.60 have not been transported towards the east direction. Fig-ure 7 illustrates the overall distribution of the sandy beaches inves-tigated according to their grain sizes.

When the reflectance values within 450 and 520 nm for the blueband of the spectroradiometer measurements are evaluated, it isseen that as grain size increases, the reflectance values decrease.Therefore, a reciprocal ratio appears between grain size and reflec-tance value. A result can be derived from such a correlation.Reflected light on fine sand has occurred comparatively less due tosurface roughness. Therefore, more light is reflected.

When the moisture content of the sands is correlated with satel-lite imagery, a considerable relationship is not found. The main rea-son for this fact may be due to sudden loss of moisture on the sandsurface, and thus, the optical data do not satisfactorily reflect thesurface.

5 Conclusions and Recommendations

This study forms an example of a multi-disciplinary study con-ducted to identify the sandy beach characteristics of large-scaleareas where RS technology is used as a tool along with ground truthmeasurements and analyses. The aim of the study was to utilizeremotely sensed data to investigate the sand characteristics of foursandy beaches on the western part of the Turkish Black Sea coast.Ground truth measurements included collection of sand samplesfrom the beaches and measuring the moisture content of the sand.In parallel to the field studies, sprectroradiometer measurements

were also conducted to determine the reflectance values of the sandtypes. The grain size distribution of the sand samples was correlatedwith the reflectance values and IKONOS satellite data to find theoverall distribution of sand on the large-scale beaches.

The authors have experienced certain difficulties during the fieldsurveys, basically due to the largeness of the beaches studied. Theselection of an optimized number of sand samples and monitoringstations for determining the moisture content and for the spectror-adiometer measurements required expertise and funding. Similarstudies are rarely conducted in Turkey. Therefore, it is recom-mended to conduct such studies on coastal morphology in thefuture to promote researches on the coastal zones of the country.

Acknowledgements

The authors thank TUBITAK (The Scientific and TechnologicalResearch Council of Turkey) for financially supporting the Project“Analysis of General Characteristics of Filyos River Basin by Meansof Satellite Imagery and Digital Elevation Models” referred to as104Y296.

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Figure 7. Classification of sandy beaches according to grain size distribu-tion.

Utility of Remote Sensing Technology for Determining Characteristics of Sandy Beaches

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