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Change detection of land cover andland use using remote sensing and GIS:a case study in Kemer, TurkeyIşin Onur a , Derya Maktav b , Mustafa Sari c & N. Kemal Sönmez a

a Remote Sensing Research & Application Center , 07059, Antalya,Turkeyb Istanbul Technical University , Civil Engineering Faculty ,Department of Geodesy and Photogrametry Engineering , RemoteSensing Division , 34469, Ayazağa, İstanbul, Turkeyc Akdeniz University Faculty of Agriculture , Department of SoilScience , 07059, Antalya, TurkeyPublished online: 30 Apr 2009.

To cite this article: Işin Onur , Derya Maktav , Mustafa Sari & N. Kemal Sönmez (2009) Changedetection of land cover and land use using remote sensing and GIS: a case study in Kemer, Turkey,International Journal of Remote Sensing, 30:7, 1749-1757, DOI: 10.1080/01431160802639665

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Change detection of land cover and land use using remote sensing andGIS: a case study in Kemer, Turkey

ISIN ONUR*{, DERYA MAKTAV{, MUSTAFA SARI§ and

N. KEMAL SONMEZ{

{Remote Sensing Research & Application Center, 07059, Antalya, Turkey

{Istanbul Technical University, Civil Engineering Faculty, Department of Geodesy and

Photogrametry Engineering, Remote Sensing Division, 34469, Ayazaga, Istanbul, Turkey

§Akdeniz University Faculty of Agriculture, Department of Soil Science, 07059, Antalya,

Turkey

Suitable climate conditions as well as rare natural and cultural resources in the

Mediterranean region of Turkey have made it a centre of attraction for two

conflicting interests: agricultural production and tourism activity. In recent years,

the natural appeal of the area and economic interests have dominated tourism

over agriculture, forestry and wildlife and led to significant urban sprawl. The

objective of this study was to investigate the dimensions of the land cover/use

conversion of a quiet, small village (Kemer) into an internationally popular

touristic destination. In the scope of this study, land cover and land use changes

were analysed over approximately 30 years using Landsat Multispectral Scanner

(MSS) data (1975), and Landsat Thematic Mapper (TM) data (1987, 1995 and

2003) by image classification techniques. In the land use hierarchy, the

Coordination of Information on the Environment (CORINE) methodology was

used as a base. Data organization and collection stages were achieved in a

geographical information system (GIS) environment. Finally, the results indicate

that, from 1975 to 2003, permanent crops decreased by 75% and most of these

areas were structured. Throughout the same years a 55% decrease was determined

to arise in heterogeneous agricultural areas. From 1975 to 2003, there had been

no serious change in forests. The main reason for this is the accommodation of

the Olympos-Bey Mountains national park in the region.

1. Introduction

In recent years, the development of the tourism sector led to an extensive expansion

of hotel and resort construction especially along the sea shore and population

growth increased significantly in the settlement areas of the Mediterranean region of

Turkey.

One of the most attractive areas subject to such a situation is the municipality of

Kemer, located in the western half of the province of Antalya. In addition, within the

scope of the South Antalya Tourism Project, Kemer developed rapidly by gaining

roads and other infrastructure. The opening of the highway in the 1980s, linking

Kemer to the primary urban hub in the province, in particular has increased demand

for not only land, but also natural and human resources to support the tourism

*Corresponding author. Email: isinonur9@gmail.com

International Journal of Remote Sensing

Vol. 30, No. 7, 10 April 2009, 1749–1757

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2009 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160802639665

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sector. These pressures and the resultant urbanization have led to observable

changes and conversions of the land use.

Up until 1987, local government exercised a planned and controlled urban growth

strategy and the South Antalya Tourism Project’s principles conformed to local

objectives for the development. But with a revision made in 1987 on the project plan,

zoning for some of the agricultural land and camping areas was changed to allow

hotel and resort construction, and areas that were previously allocated for public

accommodation were converted to other usage areas such as temporary private

accommodation and day-use recreation. Beldibi, Goynuk, Kızıltepe, Tekerlektepe,

Camyuva, and Tekirova districts of Kemer were re-designated as organized tourism

development areas and pre-existing urbanization plans were ignored. This created a

permissive policy environment for unchecked urban growth with little consideration

for environmental impacts (TC Governorship of Antalya and Dictorate of Tourism

1991).

Within this scope, a strong need appeared for monitoring this region from past to

present to determine the problematic zones and effects. This research was conducted

using Landsat data from Multispectral Scanner (MSS) and Thematic Mapper (TM)

sensors (1975 MSS, 1987 TM, 1995 TM and 2003 TM images). Besides stereo pairs

of aerial images and IKONOS image were also used as ancillary data. A GIS system

is a good aid as a decision making tool for selecting suitable ‘areas of interest’ in this

study. Land cover and land use conditions were evaluated separately for each image

and a comparative analysis was conducted for each study period (1975–1987, 1987–

1995, 1995–2003). The Coordination of Information on the Environment (CORINE)

system, which is used in this study, has a hierarchical level system starting from one

to three. CORINE was established by the European Union as a land cover/use

definition hierarchy. One of the main purposes of the program is to bring

standardization to the studies of the environment and its change that have been

carried out over the years at different levels (international, within the European

Union, national and regional) (Heymann et al. 1994).

Relatively low spatial but high spectral resolution Landsat images were classified

with the maximum likelihood method (Lillesand and Kiefer 2000) and change

detection was carried out by post classification comparisons (Dwivedi et al. 2005).

2. Study area

Kemer was first established as a Lycian city. In 1910, there was a small settlement

area called Eski Koy (meaning old village). Subsequently the local community called

their region Kemer (meaning aqueduct) after building a 23 km stone wall, which

evokes this new name, to protect their area from flooding. Its location is along

Turkey’s Mediterranean coast, at the base of the western Taurus mountains and lies

43 km west of Antalya city. It covers 52 km of shoreline and includes the ancient

cities such as Olympos and Phaselis, which have historical, cultural, and natural

value. The study area is between 36u45970.908 and 36u26910.168 north latitudes and

27u229230.947 and 27u359170.331 east longitudes. Figure 1 presents a satellite image

of the province of Antalya with jurisdictional boundaries superimposed. Kemer is

highlighted in red while districts (or sub-provinces) within the province of Antalya

are identified in lower case with white boundary lines. Names of the neighbouring

provinces of Antalya are indicated in capital letters with their boundaries in black.

To support the visualization of the study area, a 3-dimensional model was produced

using an ASTER satellite image (figure 2).

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3. Data and methodology

This study aims to detect land cover and land use with supervised classification by

means of unsupervised classification and visual interpretation of ancillary data, then

to analyse changes over time with post classification comparisions. Technical details

about the base data, which were obtained at approximately 10-year intervals, are

presented in table 1.

The ancillary data include black and white stereo aerial photographs from 1981

(scale 1 : 20 000) and from 1992 (scale 1 : 40 000); in addition an IKONOS image from

2004 with pan-sharpened RGB bands that cover the coastal zone of Kemer were

used to determine the classes. Demographic data were also associated with the

Figure 1. Province of Antalya and its sub-provinces on Landsat-7 mosaic image.

Figure 2. 2001 image of Kemer overlaid on DEM generated by ASTER.

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classification results and were obtained from the Turkish Statistical Institute from

1975 to the present (table 2).

A 3-dimensional model was established to reveal the topography of the region. A

15 m resolution image was overlaid with DEM data acquired in 2001 from the

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

satellite. The digital elevation model (DEM) data were obtained from photogram-

metric interpretations of stereoscopic nadir and backwards data (visible near infared

(VNIR) 3N and 3B) (Nik Insaat Tic. Ltd Sti. 2007, http://www.nik.com.tr/new/

yazilimlar/uydular/aster_specf_TUR.pdf).

The study was conducted in several consecutive phases. Initially, satellite data

were pre-processed. Due to the weather conditions in the coastal Mediterranean

region in summer, both of the images were free of haze and cloud, therefore no

atmospheric corrections were required. The pre-processing methods performed in

this study include image enhancement and georeferencing. In this phase, histogram

equalization, standard deviation stretch and necessary brightness/contrast adjust-

ments were performed on each image in order to facilitate image interpretation.

Furthermore, satellite images and aerial photographs georeferenced using

topographic maps and resampled according to the second order polynomial

transformation method and the frames of the data were mosaicked.

Land use classes were determined by visual interpretation from the aerial

photographs (3-dimensional) and IKONOS images. They were stored in vector

format and queried for their aerial extent in the geographical information system

(GIS) environment. Unsupervised classifications with ISODATA method were

applied to Landsat images and information obtained was evaluated before

Table 2. Demographic data for Kemer, Turkey.

Years Population

1975 6 2761980 8 1551985 11 0311990 23 2631997 33 6662000 55 092*2001 58 019*2002 62 595*2003 67 457*2004 72 606*2005 78 064*2006 83 843*

*Estimated records from the Turkish Statistical Institute.

Table 1. Properties of the data used in the study.

Satellite Sensor Date acquired Spatial resolution (m) Spectral bands

Landsat-3 MSS 16 June 1975 60 VNIRLandsat-5 TM 26 August 1987 30 VNIR-SWIR-TIRLandsat-5 TM 31 July 1995 30 VNIR-SWIR-TIRLandsat-5 TM 22 August 2003 30 VNIR-SWIR-TIR

MSS, Multi Spectral Scanner; TM, Thematic Mapper.VNIR, Visible Near Infrared; SWIR, Short Wave Infrared; TIR, Thermal Infrared.

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supervised classification. Based on the composition of the land, the Landsat MSSimage had the most influence on determining the classification level in order to

ensure conformity with the higher resolution Landsat TM images, and land cover

types up to the second level of the CORINE system were found to be appropriate.

Table 3 represents land use classes used in second level of this study.

In order to monitor the region, different change detection methods were

investigated. According to Singh (1989), there are two basic approaches for land

cover/use change detection: (1) post-classification comparisons; and (2) simultaneous

analysis of multi-temporal data. Both approaches have their advantages anddisadvantages. According to Aspinall and Hill (1997), the first approach has some

sources of uncertainty. These include locational inaccuracy in the different

classifications and the problems derived from classification errors. According to

Singh (1989), this approach requires very good accuracy in the classification because

the accuracy of the change map is the product of the accuracies of the individual

classifications. In the case of the second approach, several procedures have been

developed, such as multi-date classification, image differencing, vegetation index

differencing, principal component analysis and change vector analysis (Fung andLeDrew 1987). In these procedures, the basic premise is that changes in the land use/

cover must result in changes in reflectance values, which must be larger than those

caused by other factors such as differences in atmospheric conditions, sun angle, soil

moisture or precise sensor calibration. Selecting image acquisition dates as close as

possible to the different years used minimizes problems related to the sun position

and vegetation phenology (Pilon et al. 1988). Nevertheless, there are some problems

related to this second approach: (1) most of these procedures provide little

information about the specific nature of land use/cover changes; (2) the thresholdtechnique used to differentiate changes from no change is usually not clear (Smits

and Annoni 2000); and (3) the number of bands and their wavelengths (spectral

information) is different (Fung 1992) as well as the sensitivity of the sensors. While

this is also a problem in the first approach, it is more critical in the second approach

(Mundia and Aniya 2005).

The data used in this study were acquired on different days of the summer season.

One of the datasets has a different spatial resolution, acquired from a different

sensor of the same satellite series, therefore post-classification comparisons werefound to be the more appropriate change detection method. This method, according

to Mundia and Aniya (2005), is also the more commonly used method in change

detection studies.

In order to realize the post-classification comparisons process, supervised

classification was performed using the maximum likelihood algorithm. The

maximum likelihood decision rule is based on the probability that a pixel belongs

to a particular class. The basic equation assumes that these probabilities are equal

Table 3. Levels and land cover/use classes subject to classification.

Level 1 Level 2

1. Artificial surfaces 1.1 Urban fabric2. Agricultural areas 2.1 Permanent crops

2.2 Heterogeneous agricultural areas3. Forests and semi-natural areas 3.1 Forests

3.2 Open spaces with little or no vegetation4. Water bodies 4.1 Inland waters

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for all classes, and that the input bands have normal distributions (Erdas LLC 2002).

In addition, according to Demirbuken, Ay, Nuransoy (1993) the maximum

likelihood method is the most reliable among other classification methods because

classes are not only generated due to the pixel’s reflectance values but also due to its

variance-covariance matrix values (Kuscu 2005).

An important step in supervised classification is the determination of classes. The

number of classes that will be used and how these classes will be defined need to be

determined for the area subject to classification. In this study, the differentiability of

the classes was examined via the images classified with the unsupervised method. In the

latter phase of the study, supervised classification tests were taken into account with

those examinations under the guidance of the CORINE land cover/use program and

consequently the classes were identified and labelled. Vector data gained from the

interpretation of aerial photographs and the IKONOS image were overlaid with

suitable (close date) satellite images before determining the control areas in the GIS

environment. When the supervised classification was initially performed, urban

structures remained confused with areas covered with snow and other white spots on

the land. Therefore, urban settlements in the 1987, 1995, and 2003 images were

assigned as ‘regions of interest’ by using vector boundaries and analysed separately in

the GIS. In the 1975 image, it was not possible to identify urban structures due to the

small area coverage of the settlements, as well as the low resolution of the image.

However, trustable results can be provided with the help of close date ancillary data.

4. Results and discussion

The classes that were used in the study are urban fabric (UrF), heterogeneous

agricultural lands (HtA), permanent crops (PrC), forests (Fr), open spaces with little

or no vegetation (OpV), and inland waters (InW). Classified Landsat images and

land cover/use areal extents are presented in figure 3 and table 4, respectively.

The land cover/use results indicate that forest covered 61% of the total study area

in 1975, constituting the majority of the land cover. Despite the decrease of this ratio

to 55% in 1987, forests continued to constitute the largest portion of land cover.

Land cover/use changes from 1975 to 1987 were: 10% decrease in forests, 52%

increase in open areas with little or no vegetation, 46% decreases in heterogeneous

agricultural areas, 34% decrease in permanent crops. Heterogeneous agricultural

areas imply annual crops like wheat, barley, oats, sesame and tomato.

Between 1975 and 1987, urban fabric increased from approximately 1 ha to

462.88 ha. It should be noted that the dispersed settlement in 1975 combined with the

low resolution of the 1975 image led to a lack of clear identification of the areal

extent of urban settlement in Kemer in 1975 without the ancillary data. This

increased urban growth can be seen as the direct result of the highway that was

opened between Antalya and Kemer in the 1980s, which played an important role in

the ‘discovery’ and accessibility of Kemer. Subsequently, trade and tourism activities

spread rapidly. These activities became the major factor of urbanization in Kemer.

In 1987, forests constituted 51% of the total area. When the data from 1995 was

compared with 1987, 8% decrease in forests, 8% increase in open areas with little or

no vegetation, 23% increase in heterogeneous agricultural areas, 7% decrease in

permanent crops, and 64% decrease in inland waters was observed due to

misclassification. This decrease in inland waters, which was obtained by visual

interpretations, is anomalous. In order to achieve a better quality result, control

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areas were increased; however this resulted in river beds being confused with open

areas with little or no vegetation. As a result, this practice was abandoned. Finally, a

159% increase in urban fabric was observed between 1987 and 1995. It can be seen

that the urbanization trend continued and tourism activities spread.

In the year 2003, urban fabric is increased by 44% compared to the 1995 image.

Forests decreased by 5%, open spaces with little or no vegetation increased by 16%,

heterogeneous agricultural areas decreased by 54%, permanent crops decreased by

39% and inland waters increased by 180%.

These changes to the percentages in the areal extent of the land use over 30 years

were obtained from satellite images acquired at certain periods. These change

percentages obtained for Kemer due to urbanization are also found to be useful for

Figure 3. Supervised classification results of (a) Landsat 1975 MSS, (b) Landsat 1987 TM,(c) Landsat 1995 TM, and (d) Landsat 2003 TM images.

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comparing with the demographic data achieved from the DIE (Turkish Statistical

Institute) (table 2). While the population increased by 205%, urban settlements

increased by 159% between 1985 and 1997. While urban area increased due topopulation increase, it can be linked with migration due to tourism, which became

the majority source of income for local people. Similarly, while the population from

1997 to 2003 increased by approximately 100%, urbanization increased 44% between

1995 to 2003. While these different components (population and area usage) both

have a comparable increase between 1987 and 1995, they do not show a parallel

increase between 1997 and 2003. This is because the population increase in the latter

period was accommodated by an increase in density within the existing urban areas

rather than expansion into adjacent areas.

When examining land use types other than urban (especially agricultural lands),

data collected from the local TC (Turkish Republic) Governorship of Kemer andDictorate of Tourism was also used in addition to the information gained from

satellite images. It was found that citrus trees, in particular, were reduced by 63% from

1975 to 2003. Many citrus orchards along the coast in Kemer were removed and these

areas were transformed to hotels and holiday villages. In addition, a decrease of 70% in

the mixed agricultural lands was determined. Products such as tomato, eggplant,

pepper, and sesame, which were widespread in the past, are not so prevalent today and

the number of farmers living in local communities has also decreased significantly.

Classification accuracy analysis was performed after each classification process

with error matrix and Kappa analysis. In the analysis, 50 sample points, which are

independent of sample classes (training areas), were used and the total accuracy was

89.20% in 1975, 93.60% in 1987, 89.20% in 1995 and 90.80% in 2003. In addition,Kappa coefficients are 0.8784, 0.9268, 0.8724 and 0.8968 in 1975, 1987, 1995 and

2003, respectively.

5. Conclusions

A similar study was carried out on another sub district of Turkey, in Istanbul/

Buyukcekmece. Buyukcekmece was popular with summer vacationers of Istanbul

and people from surrounding cities. Monitoring studies introduced a 1000%

explosion in population in some areas after many high apartment houses were built

during the period 1985–1997. This attractive summer resort was dramaticallyinfluenced by increased immigration and became a part of Istanbul metropolitan city

(Maktav and Erbek 2005).

Table 4. Land cover/use change results and their areal extents.

Land use/cover

1975 1987 1995 2003

(ha) (%) (ha) (%) (ha) (%) (ha) (%)

Urban fabric 1 0 462.87 1 1200 3 1728.21 4Forests 29414.95 61 26485.88 55 24274.46 51 23080.68 48Open spaces withlittle orno vegetation

10672.81 22 16226.47 34 17567.91 37 20333.99 42

Heterogeneousagricultural areas

3950.24 8 2117.54 4 2601.25 5 1188.63 3

Permanent crops 3666.41 8 2409.79 5 2248.40 5 1366.74 3Inland waters 295.59 0.6 297.45 0.6 107.98 0.2 301.75 0.6Total 48000.00 100 48000.00 100 48000.00 100 48000.00 100

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This study intends to offer objective information on land cover/use changes inKemer from its beginning as a ‘quiet village’. It is expected to support land use

decision making, particularly with respect to tourism, urbanization, agriculture, and

natural resources in the area. In this context, Kemer’s land cover and land use were

comparatively analysed between 1975 and 2003.

The results of this study profile and highlight the urban and rural change

dynamics in Kemer. The intention is to promote the protection of existing natural

environments as much as possible and facilitate a healthier urban development. In

the future, developing a wider and more integrated GIS system of the district anddetermining the trend of population growth, estimating the direction of the urban

spread and other multi-discipline analyses based on this research are possible and

would be advantageous for the municipality and other governmental authorities for

different kinds of planning activities.

Acknowledgement

The authors would like to thank the Scientific Studies Management Unit of Akdeniz

University in Antalya, Turkey.

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