Coastline Change Assessment at the Aegean Sea Coast in Turkey Using Multitemporal Landsat Imagery

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Journal of Coastal Research 23 3 691–698 West Palm Beach, Florida May 2007

Coastline Change Assessment at the Aegean Sea Coasts inTurkey Using Multitemporal Landsat Imagery

Semih Ekercin

Istanbul Technical UniversityRemote Sensing Division

Ayazaga Campus/Maslak

Istanbul, Sariyer 34469 Turkey

[email protected]

ABSTRACT

EKERCIN, S., 2007. Coastline change assessment at the Aegean Sea Coasts in Turkey using multitemporal Landsatimagery Journal of Coastal Research, 23(3), 691–698. West Palm Beach (Florida), ISSN 0749-0208.

This paper focuses mainly on the coastline movements at the northeast coasts of the Aegean Sea in Turkey. The Aegean Sea is a semiclosed sea that has unique geographical features and covers an area of 191,000 km2. The studyarea includes the coastal zone located between the southeastern part of the Meric River mouth and the Dalyan Lakecoasts. The Meric Delta has accreted toward the Aegean Sea as a result of sediment discharge and transport. Incontrast to this, the width of the natural land barrier between the Aegean Sea and the Dalyan Lake has decreasedover the years because of coastal erosion. These processes have caused the morphological changes of coastline along some parts of the northeast coasts of the Aegean Sea. In this study, these changes were examined by using satellitedata from Landsat MSS, TM, and ETM collected between 1975 and 2001. In the image processing step, registration,ISODATA classification, and temporal image ratioing techniques were used to carry out coastline change assessment.

At the end of the study, significant coastline movements (in some parts more than 200 m) were detected for a 26-year period.

ADDITIONAL INDEX WORDS: Coastal zone management, change detection, digital image processing, multitemporalimage data.

INTRODUCTION

The coast is a difficult place to manage, involving a dynam-

ic natural system that has been increasingly settled and pres-

surized by expanding socioeconomic systems (TURNER, 2000).

It is currently estimated that in excess of 37% of the globalpopulation (over 2.1 billion people) lives in coastal areas (V I-

TOUSEK and MOONEY , 1997). Human activities are modifying

the patterns of water runoff and the delivery of nutrients and

sediments to coastal waters (LI et al., 2003).

On the other hand, remote sensing studies on coastal areas

have been carried out by many scientists for many years (EU-

RICO and RICHARD, 2003; W ALKER and HUDSON, 2003). Re-

mote sensing offers great opportunities for obtaining infor-

mation about this type of application. The repetitive acqui-

sition and synoptic capabilities of remote sensing systems can

especially be exploited to provide timely spatial data (WHITE

and EL-A SMAR, 1999). The main purpose of remote sensing

projects, including coastal applications, is to detect and mon-

itor coastline movements. The rates of erosion and depositionin a coastal zone, for example, can be easily detected by using

multitemporal satellite data at low cost.

Some of the most important factors that result in coastline

movements are coastal erosion, sediment transport, and de-

position. It is well known that coastal movements appear at

very dynamic coastlines such as some sections of the Aegean

DOI:10.2112/04-0398.1 received 31 October 2004; accepted in revision 20 May 2005.

Sea, which is a semiclosed sea and covers an area of 191,000

km2 (Figure 1A).

In this study, the coastal zone located between the south-

eastern side of the Meric River mouth and the Dalyan Lake

coasts (here, the northeast coasts of the Aegean Sea very nearto Dalyan Lake is called as the Dalyan Lake coasts) was ex-

amined. The Meric Delta has accreted toward the Aegean Sea

as a result of the sediment discharge and transport that occur

especially at the lower water discharge condition. In contrast

to this, the width of natural land barrier between the Aegean

Sea and Dalyan Lake has decreased over the years because

of coastal erosion. These processes were analyzed with the

use of multemporal Landsat data for a 26-year period.

STUDY SITE

The Aegean Sea is surrounded by the western coasts of

Anatolia from the east, the southern coasts of Thrace and

eastern Macedonia from the north, the eastern coasts of Thessaly and Peloponnesus peninsula from the west, and the

islands of Crete and Rhodes from the south (Figure 1A). The

Aegean Sea is a semiclosed sea that has unique geographical

features and covers an area of 191,000 km2 (GOKSEL et al.,

1999). The area selected for this study is located between the

southeastern part of the Meric River mouth and the Dalyan

Lake coasts (260047 and 260406 E; 404147 and

404424 N) and covers an area of 4 by 5 km. The geographic

location of the study site is presented in Figure 1.


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Journal of Coastal Research, Vol. 23, No. 3, 2007

Figure 1. Location of the study area with the panchromatic band from

Landsat-7 ETM, 2001.

Table 1. Specifications of Landsat data used in the study.

Sensor Date Band (m)

Spatial

Resolution

(m)

Pixel Size

after Resampling

(m) Path/Row

Landsat-2 MSS

Landsat-2 MSS

Landsat-4 MSS

01.06.1975

21.05.1977

01.09.1982

4: 0.50–0.60

5: 0.60–0.70

6: 0.70–0.80

7: 0.80–1.10

80 80 195/32

182/32

Landsat-5 TM 21.07.1987

11.07.1992

1: 0.45–0.52

2: 0.52–0.60

30 80 182/32

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Landsat-7 ETM* 20.08.2001 3: 0.63–0.69

4: 0.76–0.90

5: 1.55–1.75

7: 2.08–2.35

Pan*: 0.52–0.90 15

182/32

MATERIALS AND METHODS

Materials Used

Landsat image data have been widely used in coastal re-

search for many years. This is because of its widespread

availability and unrivaled length of record (since 1972), but

also because the grain, extent, and multispectral features

make Landsat suitable for a variety of environmental appli-

cations at regional and global scales (HUDAK et al., 2002). In

this study, three MSS images acquired in 1975, 1977, 1982,

two TM images from 1987 and 1992, and one ETM image

from 2001 were used as satellite sensor data. Further infor-

mation about the specifications of satellite data used in the

study is given in Table 1. Additionally, 1:25,000-scale topo-graphic maps produced by General Command of Mapping

were used to gather ground control points (GCPs) for geore-

gistration operations. These maps were compiled to NATO

level A standards (TURKER and G ACEMER, 2004). Also, 1:

50,000-scale bathymetric maps were another data source for

evaluation of the coastal zone in the study. Image processing

and interpretation processes were performed in the labora-

tory of the Remote Sensing Division of Istanbul Technical

University.

Methods

Registration

The geometric registration process was carried out by using

Erdas Imagine 8.5 image analysis software. The finest reso-lution (Landsat-7 ETM Pan) image was used as the base im-

age (S AITO et al., 2003). For this purpose, ETM, 2001 Pan

data were first referenced to the UTM (zone 35) projection by

using 20 map-derived ground control points (EKERCIN, 2003;

ORMECI and EKERCIN, 2001; TURKER and G ACEMER, 2004).

The MSS (1975, 1977, 1982) and TM images, (1987, 1992)

were then registered to the geocorrected ETM, 2001 Pan im-

age by means of image-to-image registration (EL-A SMAR and

WHITE, 1997). The number of GCPs used for these processes

was approximately 40 for each image (for a 4 by 5 km study

area). Experience suggests that 10 to 15 control points will

give acceptable results for a small image area and a first-

order fit (M ATHER, 1987). The nearest neighbor resampling

technique and a first-order polynomial transformation wereused to create the output images with 80-m ground resolution

because the study area presents an almost flat topography,

especially along the coastline (METTERNICHT and ZINCK ,

1998).

The requirement of registration accuracy may vary from

one task to the other. For example, it has been reported that

a registration accuracy of less than one-fifth of a pixel is re-

quired to achieve a change detection error within 10% (CHEN,

A RORA , and V ARSHNEY , 2003; D AI and K HORRAM, 1998). In

this study, the root mean square error of the transformations

was not permitted to exceed 0.2 pixel for each process and

the accuracy of registration was evaluated on test points.

Image Enhancement

Spatial Filtering. The type of filter used mainly depends on

the requirements of the problem concerned. Filters suppress

(de-emphasize) certain frequencies and pass (emphasize) oth-

ers. High pass filters pass high frequencies and emphasize

fine detail and edges. Low pass filters, which suppress high

frequencies, are useful in smoothing an image and may re-


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693Coastline Changes at the Aegean Sea Coasts


Figure 2. Accretion in the Meric Delta in the period 1975 to 2001. (A) (B) (C) The near infrared band (0.80 to 1.10 m) from Landsat MSS, 1975, 1977,and 1982. (D) (E) (F) The near infrared band (0.76 to 0.90 m) from Landsat TM, 1987, 1992, and Landsat ETM, 2001.

duce or eliminate noise. A low pass (mean) filter tends togeneralize the image. Furthermore, edge detection filtering

is used to delineate edges surrounding objects and to high-

light abrupt discontinuities (FINKL, BENEDET, and A N-

DREWS, 2004).

In this study, low pass filtering and edge detection filtering

were used to enhance the images and to delineate land–water

boundaries. Experimental results show that the use of 3 3

low pass and then edge detection filtering give the best re-

sults, especially for enhancement of historical satellite data

(MSS, 1975).

Spectral Rationing. Ratio images are used in remote sens-

ing projects for several purposes, such as reduction of scene

illumination, differentiation of areas of the stressed and un-

stressed vegetation, and detection of temporal changes. Theuse of temporal difference and temporal ratio images might

be especially useful for distinction and interpretation of

changes in an area of interest. Procedure is to simply register

two images and prepare a temporal difference image by sub-

tracting (or a temporal ratio image by dividing) the digital

numbers (DNs) for one date from those of the other (LILLE-

SAND and K IEFER, 1987). Areas of change will have higher

or lower ratio values for ratio images and negative or positive

values for difference images. In this study, temporal differ-

ence images and temporal ratio images were used to detectthe coastline change at the Aegean Sea coasts for a 26-year

period. In order to delineate the land–sea boundary, band-4

(for MSS and ETM comparison) and band-7 (for TM and ETM

comparison) were used for establishing temporal difference

and ratio images. All processes were performed by using the

operation algorithm of ERDAS Imagine 8.5.

Classification and Coastline Determination

There are many techniques used for coastline determina-

tion, such as image segmentation, level slicing, and vectori-

zation of classified image. Here, iterative self-organizing data

analysis (ISODATA) classification and raster-to-vector con-

version processes were used to obtain the land–sea boundary(A RMENAKIS et al., 2003).

The unsupervised classification approach involves minimal

user input (no user specified training data) and requires the

computer to determine spectrally separable classes (FINKL,

2000). Shorter wavelengths allow some penetration through

water, giving a more gradational effect and making exact de-

lineation of the coastline difficult. Problems of high reflec-

tance offshore due to surf in the breaker zone are also ame-

liorated by using shortwave infrared data (EL-A SMAR and


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694 Ekercin


Figure 3. Position of coastlines mapped in 1975, 1987, and 2001. (A) Location of the sample points on the near infrared band from ETM, 2001. (B) (C)

(D) (E) Vector maps showing changes at the position of coastline between the Meric Delta and the Dalyan Lake coasts.


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Figure 4. (A) Coastline change in the period 1975 to 2001 (m) sampled

at 250-m intervals along the coast. Negative values indicate erosion, pos-

itive values indicate accretion. (B) Acceleration of rates of coastline

change (m y2) comparing the period 1975 to 1987 to the period 1987 to

2001. Positive values indicate acceleration of rates of change; negative

values indicate deceleration of rates of change.

Table 2. Rates of coastline changes measured from satellite data at 20 sample points.

Number of

Points

UTM Coordinates (Zone 35)

X (m) Y (m)

Coastline Change

1975–1987

(m)

Coastline Change

1987–2001

(m)

Total Change

1975–2001

(m)

1

2

3

4

5

4509927.07

4509716.84

4509543.39

4509362.06

4509204.38

417618.53

417760.44

417931.26

418117.84

418317.57

0

137166110

185

252

954235

23

252

232208145

2086

7

89

10

4509049.33

4508852.24

4508644.384508448.66

4508250.28

418530.44

418727.54

418844.46418995.22

419148.63

6231

2618

0

3960

166040

10129

104240

11

12

13

14

15

4508051.91

4507829.73

4507637.14

4507402.72

4507178.72

419309.97

419413.12

419461.61

419552.78

419654.36

0

330

1141

220

47

7552

223347

6493

16

17

18

19

20

4506949.50

4506693.83

4506425.64

4506157.45

4505889.25

419761.15

419789.26

419836.59

419899.70

419947.03

46

729290

90

35

945714

28

81

166149104

62

Positive values indicate seaward accretion of coastline; negative values indicate erosion.

WHITE, 2002; FROUIN, SCHWINDLING, and DESCHAMPS,

1996; WILSON, 1997). For this purpose, band-4 for MSS and

band-7 for TM and ETM (near infrared and shortwave infra-

red regions of the electromagnetic spectrum) were selected as

input data for the classification process. Firstly, an unsuper-

vised classification was performed for 1975, 1987, and 2001

registered satellite images. A 40-class ISODATA classifica-

tion was performed to a 95% convergence threshold by using the ISODATA algorithm of ERDAS Imagine 8.5. The result-

ing spectral signatures were then recoded as land and water

(REES, WELLIAMS, and V ITEBSKY 2003). After the classifi-

cation process, the boundaries of the classified regions are

subsequently vectorized by using the raster to feature con-

version algorithm of the ArcMap GIS software.

RESULTS

To perform coastline change analysis, coastline movements

were evaluated in terms of accretion of coastline and coastal

erosion. The results outlined below address the types and

rates of coastline movements that occurred at the region and

the reasons why.

Coastal Accretion and Erosion

The results of this study show the complex pattern of

change along the coastline between the mouth of the Meric

River and the Dalyan Lake coasts. The resulting coastline

vector maps indicate that the Meric Delta accretes toward

the Aegean Sea because of the sediment discharge while the

sandy beach area near the Dalyan Lake coasts is eroded be-

cause of the significant wave direction (SW) of the coast and

sediment transport. These processes have caused the mor-

phological changes of coastline along the northeast coasts of

the Aegean Sea.

Few published rates of coastal research exist for the Dal-

yan because this region includes the border between Turkeyand Greece. One of the most important studies was carried


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696 Ekercin


Figure 5. Detection of coastline changes with the help of temporal ratio and difference images derived from Landsat MSS, TM, and ETM collected in

1975, 1987, and 2001, respectively. (A) MSS4-ETM4. (B) TM7-ETM7. (C) MSS4/ETM4. (D) TM7/ETM7.

out by M AKTAV and K APDASLI (1995). They stated that sand

bar formation along the coastline is seen almost in sequence.

This kind of formation can only occur at coasts that have very

strong longshore and onshore–offshore sediment transport.

The impact of the dynamic structure of coastline is evident

in Figure 2. The images indicate that an intensive sediment

transport has occurred for years around the Meric River

mouth, which results in coastline advance toward to the Ae-

gean Sea.

Taking all these results into account, we compared the vec-

tor maps extracted from satellite data at 250-m intervals for

determination of rapid changes; this was found to be an ap-

propriate resolution to detect detailed patterns of change. To

identify which of these changes are most problematic, we cal-

culated changes in two periods (1975 to 1987 and 1987 to

2001) between the three image acquisitions. The quantities

of change between coastlines were measured manually on the

digital integrated vector map at every sample point and per-


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pendicular direction to the coastline (Figure 3A). At this

point, the feasibility of Landsat data for coastal applications

should be discussed. WHITE and EL-A SMAR (1999), as an ex-

ample, demonstrated that Landsat data can be used to map

changes of dynamic coastal landforms.

Figure 3 presents the location of sample points and vector

maps showing changes along the coastline. The vector maps

indicate that the most rapid areas of erosion are at the Dal-yan Lake coasts (Figures 3E and 4A), although other areas

show less dramatic changes. The rate of change in this region

is more than 100 m along the coast. Conversely, the area

around the southern mouth of the Meric River shows an im-

portant coastline advance (more than 200 m) toward to the

Aegean Sea (Figures 3B and 4A). The eroded sand is trans-

ported northward and deposited at the southern side of the

Meric River mouth.

The results also indicate that decreasing sediment dis-

charge leads to an observed deceleration of coastline advance

at the Meric River mouth. This case can be seen in Figure

4B by the deceleration of rates of change at sample points

from 1 to 4. In general, a complex pattern of sediment redis-

tribution is apparent along the coast. The accreting sectionsare adjacent to eroding sections. The rates of change for all

sample points are given in Table 2.

Change Detection with Ratio Images

In a ratio image, the black and white extremes of the gray

scale represent pixels with the greatest difference in reflec-

tivity between the two spectral bands. The darkest signa-

tures are areas where the denominator of the ratio is greater

than the numerator. Conversely the numerator is greater

than the denominator for the brightest signatures. Where de-

nominator and numerator are the same, there is no difference

between the two bands (S ABINS, 1996). It can be said that

this is basic information for interpretation of ratio images.

We used this technique to interpret temporal ratio and dif-

ference images.

The resulting ratio images verify the coastline changes ob-

tained from the vector maps. Figure 5 illustrates four ratio

images generated from the near infrared (MSS4-ETM4,

MSS4/ETM4) and shortwave infrared (TM7-ETM7, TM7/

ETM7) bands of multitemporal Landsat data by subtracting

and dividing. As may be seen in Figure 5, especially at the

Dalyan Lake coasts, very bright signatures indicating coast-

line change are systematically apparent along the coastline.

In addition to this, darker tones that also indicate coastline

change appear around the Meric Delta.

DISCUSSION AND CONCLUSIONS

The results of this study demonstrate that coastal move-

ments such as erosion and deposition have caused the mor-

phological changes at the northeast coasts of the Aegean Sea.

Coastal accretion is most significant at the southern side of

the Meric River mouth because of sediment deposition, with

the maximum coastline advance about 250 m. Also, coastal

erosion is dramatically apparent at the Dalyan Lake coasts.

Another essential point is that there is no scientific re-

search on coastal inventory that includes all the coasts of

Turkey, which is surrounded by three seas, the Aegean Sea,

the Black Sea and the Mediterranean Sea. For this purpose,

an inventory database should be constituted immediately. At

the same time, coastline movements should be analyzed in

terms of morphologic conditions periodically (at least yearly)

by using the synergy of the integration of remote sensing and

geographic information systems and the results should be

evaluated in a database.One of the most important results of this study is the im-

portance of using historical satellite data. It is most probable

that they are the most important data sources for coastal

studies dealing with multitemporal change detection.

ACKNOWLEDGMENTS

The author would like to thank Prof. Dr. Cankut Ormeci

and Dr. Cigdem Goksel for their generous support. Also, the

helpful comments of two reviewers are gratefully acknowl-

edged.

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Coastline Change Assessment at the Aegean Sea Coast in Turkey Using Multitemporal Landsat Imagery

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