land cover

SYMPOSIUM

PROGRAM

28 June 2011, Tuesday, (Day 1) 08.00 Departure to the Venue from Hotel 09.30-10.00 Registration 10.00-10.30 Opening Ceremony - Prof. Dr. Zafer Ayvaz, Chairman - Prof. Dr. Seyfullah Çevik, Rector, Gediz University - Ahmet Özyanık, President, Turkish Environmental Protection Agency for Special Areas 10.30-12.00 Session

1

Chairpersons: - Prof. Dr. Chansheng He, Western Michigan University, USA - Prof. Dr. Bahattin Tanyolaç, Ege University, Izmir, Turkey 1. Using Geospatial Technology on the Internet to Promote Transparency in the Federal Government (USA) the U.S. Environmental Protection Agency’s (EPA) Recovery Mapper. (Lawrence Thornton*) *

NJ Department of Environmental Protection, Bureau of GIS, USA

2. Developing A Governance Model to Enhance Coordination of GIS Users Statewide in New Jersey USA. (Lawrence Thornton*) *


3. Simulation of Soil Texture/Rainfall Effect on Zayandehrood River Basin Pollution Using GIS/ANN. (Sayyed-Hassan Tabatabaei1, Kamran Mohsenifar2, Ebrahim Pazira2, Payam Najafi2) 1

Shahrekord University, Faculty of Agriculture, Department of Water Engineering, IRAN Islamic Azad University, Faculty of Agriculture, Department of Soil Science, , IRAN

2

4. Preparation of Disaster Information Systems of Kutahya Province by Using Geographic Information Systems. (Can Aday1, Yasar Kibic2, Canan Güngör2) 1

Anadolu University, Faculty of Engineering, Department of Geology, TURKEY Dumlupınar University, Department of Geology, TURKEY

2

5. Changes In the Coastline of the Burdur Lake Between 1975 and 2010. (Ünal Yıldırım1, Murat Uysal2) 1

Afyon Kocatepe University, Department of Geography, TURKEY Afyon Kocatepe University, Department of Surveying, TURKEY

2

1

6. Datum Transformation by Artificial Neural Networks for Geographic Information Systems Applications. (Mevlut Gullu1, Mustafa Yilmaz2, Ibrahim Yilmaz1, Bayram Turgut1) 1 2

Afyon Kocatepe University, Department of Geodesy and Photogrammetry, Faculty of Engineering, TURKEY Afyon Kocatepe University , Directorate of Construction and Technical Works, TURKEY

7. Assessment of Soil Erosion at the Değirmen Creek Watershed Area, Afyonkarahisar. (Ünal Yıldırım*) * Afyon Kocatepe University, Department of Geography, TURKEY)

12.00-14.00 Lunch 14.00-15.30 Session

2

Chairpersons: - Prof. Dr. Turan Batar, Dokuz Eylül University, Izmir, Turkey - Assoc. Prof. Dr. Anya Z. Butt, Central College, Iowa, USA 1. NJ-Geoweb: Building Spatial Intelligence On The Internet For Environmental Decision-Making: Combining Database Media Reports With GIS Layers In New Jersey (USA). (Lawrence Thornton*) *


2. Using GIS to Assess Salt Marsh Submergence in the New York Metropolitan Region. (Alice Benzecry*) *Fairleigh Dickinson University, New Jersey, USA

3. NJ-Geoweb, GIS For Watershed Management. (Joan Leder*) *Fairleigh Dickinson University, New Jersey, USA.

4. Building a Statewide National Hydro Database (NHD) Network. (Lawrence Thornton*) *


5. Hydrological Resource Sheds and Water Quality Management In North America’s Great Lakes Watersheds. (Chansheng He*) *Western Michigan University, Department of Geography, USA

6. Application of GIS in Derivation of Hybrid Model Parameters Based on Watershed Geomorphologic Characteristics. (Nasim Fazel Modares*) *Tebriz University, Department of Water Resource Engineering, IRAN

7. Water Quality Monitoring in Potable Water Reservoirs of Cities by Remote Sensing and GIS Techniques. (Erhan Alpaslan*) * TUBITAK MRC Earth And Marine Science Institute, TURKEY

15.30-16.00 Coffee Break

2

16.00-17.30 Session

3

Chairpersons: - Assoc. Prof. Dr. Bahri Başaran, Ege University, Turkey - Joan Leder, Petrocelli College, New Jersey, USA 1. Four Iterations (1986-2007) of Statewide, Detailed Land Use Land Cover for New Jersey. (Lawrence Thornton*) *


2. Using GIS to Create a Plan for Smart Growth in New Jersey. (Lawrence Thornton*) *


3. A Proposal for GIS Based Evaluation of Urban Plans in Terms of Environmental Sustainability. (Ilgi Atay Kaya1, Nursen Kaya Erol1) 1

Izmir Institute of Technology, Department of City and Regional Planning, TURKEY

4. Modeling Urban Growth and Land Use/Land Cover Change in Bornova District of Izmir Metropolitan Area From 2009 to 2040 (Hakan OĞUZ1, Birsen KESGİN ATAK2, Hakan DOYGUN1, Engin NURLU3) 1 2

Kahramanmaraş Sütçü İmam University, Faculty of Forestry, Department of Landscape Architecture, TURKEY Adnan Menderes University, Faculty of Agriculture, Department of Landscape Architecture, TURKEY

3

Ege University, Faculty of Agriculture, Department of Landscape Architecture, TURKEY

5. Using GIS for Brownfields Redevelopment in the City of Garfield, New Jersey USA. (Mehmet Seçilmiş*) * GIS Specialist, Dewberry, USA

6. Using GIS for Transportation Alternative Analysis to Minimize Environmental Impacts. (Mehmet Seçilmiş*) * GIS Specialist, Dewberry, USA

7. Environmental Potential Evaluation of Anzan Area in Sabalam Mountain for Tourism Development Using GIS. (Ebrahim Fataei1, Sayeh Meftahpoor1, Akram Ojaghi2) 1

Department of Environmental Engineering, Ardabil Branch, Islamic Azad University, Ardabil, IRAN

2

Education Organization of Ardabil Province, Ardabil Department, IRAN

20.00-22.00 Dinner: Şifa University, Bornova, Izmir.

3

29 June 2011, Wednesday, Day 2 08.00 Departure to the Venue from Hotel 09.30-11.00 Session

4

Chairpersons: - Assoc. Prof. Dr. Yunus Doğan, Dokuz Eylul University, Izmir, Turkey - Lawrence Thornton, NJ Department of Environmental Protection, USA 1. Land-Use Planning of the Northern Zone of the Nile Delta Coast in Using Earth Observation and Geographic Information. (Mahmoud H. Ahmed, Hesham M. El-Asmar, Elham M. Ali*) * Suez Canal University, Department of Environmental Management, EGYPT

2. Developing a Campus GIS to Support Sustainability Efforts. (Anya Z. Butt*) *Central College, USA

3. Environmental Resources Inventory Using GIS: City of Englewood, New Jersey, USA. (Mehmet Seçilmiş*) * GIS Specialist, Dewberry, USA

4. The Effect of Urban Land Cover Change Around the Observation Sites on Temperature. (Ismail Çınar1,2, Ceylan Yozgatlıgil1, İnci Batmaz1) 1

Middle East Technical University,Department of Statistics, TURKEY

2

Muğla University, Department of Landscape Architecture, TURKEY

5. Analysis of Terrain Usage in Kastamonu-Ilgaz Mountain Natural Park. (Duran Aydınözü1, Ünal İbret1, Miraç Aydın2) 1

Kastamonu University, Faculty of Education, TURKEY

2

Kastamonu University, Faculty of Forestry, Department of Watershed Management, TURKEY

6. A GIS-Based Method for Regeneration Strategies in Historic Urban Quarters: A Case Study of Konya, Turkey. (Mahmut Serhat Yenice*) *Selçuk University, Faculty of Architecture and Engineering, Department of City and Regional Planning, TURKEY

7. Determination of the Impact of Population Change on the Terrain Usage via GIS (Kastamonu-Küre Case) Park. (Türkan Aydın1, Miraç Aydın2) 1

Artvin Çoruh University, Faculty of Forestry, Department of Forest Economic, TURKEY

2

Kastamonu University, Faculty of Forestry, Department of Watershed Management, TURKEY

11.00-11.30 Coffee Break

4

11.30-13.00 Session

5

Chairpersons: - Prof. Dr. Yavuz Akbaş, Ege University, Izmir, Turkey - Assoc. Prof. Dr. M. Kirami Ölgen, Ege University, Izmir, Turkey 1. Forecasting of the Evaporative Losses From Open Water Mass of Lake Nasser, Egypt Using Remotely Sensed Data. (Islam Abou El-Magd1, Elham M. Ali2) 1

NARSS, Environment Department, EGYPT

2

Suez Canal University, Department of Environmental Management, EGYPT

2. Investigating Geospatial Patterns in the Location of Toxic Waste Facilities to Determine Environmental Equity: A Case Study. (Anya Z. Butt*) * Central College, USA

3. Spatial Distribution of Heavy Metals in the Epiphytic Lichen, Xanthoria Parietina Using Geostatistical Techniques: Examples On Vicinity of Yatağan Coal-Fired Power Plant. (Kirami Ölgen*) *

Ege University, Department of Geography, TURKEY

4. A GIS Based Geo-Relationship Between Environment and Cancer Cases: Izmir Case. (Çiğdem Tarhan1, S. Pelin Özkan1, Pınar Altan1, Sultan Eser2, Cankut Yakut2, Ömür Saygın1) 1

Izmir Institute of Technology, Department of City and Regional Planning, TURKEY

2

Izmir Cancer Registry,TURKEY

5. Structure Inventory Using GIS for Flood Assessments: Cases from New York and New Jersey. (Mehmet Seçilmiş*) * GIS Specialist, Dewberry, USA

13.00-14.00 Lunch 14.00-15.30 Session 6: Special Session for the Turkish Ministry of Environment and Environmental Protection Agency for Special Areas Chairpersons: - Prof. Dr. Zafer Ayvaz, Ege University, Turkey - Mehmet Menengiç, Turkish Environmental Protection Agency for Special Areas 1.

Determination of Biological Diversity of Coastal and Marine Areas in Fethiye-Göcek Special Environmental Protection Area (SEPA). (Emrah Manap*) * Turkish Environmental Protection Agency for Special Areas (EPASA) TURKEY

5

2. Determination of Carrying Capacity for Sea Vehicles in Gocek Gulf. (Levent Keskin*) * Turkish Environmental Protection Agency for Special Areas (EPASA) TURKEY

3. Opportunity Cost Analysis for Drip Irrigation Agricultural Products in Lake Tuz Special Environment Protection Area (Eyüp Yüksel*) * Turkish Environmental Protection Agency for Special Areas (EPASA) TURKEY

4. The Study of Determination of Rural Settlements and Enviromental Master Plan Revision Based on Geographical Information System In Datça- Bozburun Special Enviromental Protected Area. (Nesrin Reyhan*) * Turkish Environmental Protection Agency for Special Areas (EPASA) TURKEY

5. Preparation of Decisions on Area Management and Master Plans Based on GIS Support (Golbasi Sepa Case). (Sibel Meriç*) * Turkish Environmental Protection Agency for Special Areas (EPASA) TURKEY

6. GIS Database Design and Practices for the Geographical Data About Special Environmental Protection Areas Within A Context of Environmental Information System. (Ahmet Çömlekçi1, Aygün Erdoğan2) 1

Turkish Environmental Protection Agency for Special Areas (EPASA) TURKEY

2

Karadeniz Technical University, Department of City and Regional Planning, TURKEY

7. Strengthening the System of the Marine and Coastal Protected Areas System of Turkey (Güner Ergün1, Melek Derya Güler1, Gülden Atkın1, Harun Güçlüsoy1) 1

Turkish Environmental Protection Agency for Special Areas (EPASA) TURKEY

2

United Nations Development Program (UNDP) TURKEY

8. National Biodiversity Database and Systematic Conservation Plan Efforts. (Erdal Özüdoğru*) * Turkish Ministry of Environment and Forestry, TURKEY

9. Turkish Land Cover Database and Geographic Data Portal. (Kamile Kalaycı) * Turkish Ministry of Environment and Forestry, TURKEY

15.30-16.00 Closing Remarks 16.00-20.30 Excursion: Boat Tour (Dinner included) in Foça Special Environmental Protection Area

6

Modeling Urban Growth and Land Use/Land Cover Change in Bornova District of Izmir Metropolitan Area from 2009 to 2040 Hakan OĞUZ*,1, Birsen KESGİN ATAK2, Hakan DOYGUN1, Engin NURLU3 Kahramanmaraş Sütçü İmam Üniversitesi Orman Fakültesi Peyzaj Mimarlığı Böl., Kahramanmaraş 2 Adnan Menderes Üniversitesi Ziraat Fakültesi Peyzaj Mimarlığı Böl. Aydın 3 Ege Üniversitesi Ziraat Fakültesi Peyzaj Mimarlığı Böl., İzmir * E-mail: [email protected]

1

Abstract City planners and policy makers are increasingly becoming more concerned about urbanization and urban growth since trends and patterns of urbanization have huge effect on socio-economic development. During the past decades, both the scale and pattern of urban growth have been transformed with increasing rapidity. Increasing urban growth through the world has aroused concerns over the degradation of environment. Therefore, understanding the dynamics of urban systems and evaluating the impacts of urban growth on the environment are needed and they involve modeling. Models used in land use change analysis are indispensable because these models are important tools that support spatial urban planning for sustainable development. The aim of this study was to predict and analyze the impact of urban growth and land use/land cover (LULC) change using SLEUTH Model for Bornova District of Izmir Metropolitan Area. In this aspect, SLEUTH Model, a spatially explicit cellular automata model, was used to simulate future (2009-2040) urban expansion and LULC change in Bornova, the fastest growing district of Izmir Metropolitan Area. The model was calibrated with historical data for the period 1984-2009 that are extracted from a time series of Landsat 5 TM images. The dataset consists of historical urban extents (1984, 1990, 2000, 2009), land use (1984, 2009), transportation (1984, 1990, 2000, 2009), slope, hillshade, and excluded layers. Future growth patterns were predicted based on growth coefficients derived during the calibration phase. After calibrating the model successfully, the spatial pattern of urban growth for the period from 1984 to 2040 was predicted. In order to predict the future development and change, three scenarios were developed based on no protection, current trends and managed growth. The scenario predictions were discussed in detail. Using models that project future urban growth and land use/land cover change such as SLEUTH has become a necessity in order to take appropriate measures to ensure nature conservation and succeed in physical planning studies. Keywords: SLEUTH modeling, urban growth, land use/land cover change, Bornova, Remote Sensing, GIS.

INTRODUCTION City planners and policy makers are increasingly becoming more concerned about urbanization and urban growth since trends and patterns of urbanization have huge effect on socio-economic development (Antrop, 2000). During the past decades, both the scale and pattern of urban growth have been transformed with increasing rapidity. Increasing urban growth through the world has aroused concerns over the degradation of environment. Therefore, understanding the dynamics of urban systems and evaluating the impacts of urban growth on the environment are needed and they involve modeling (Antrop, 2004). Models used in land use change analysis are indispensable because these models are important tools that support spatial urban planning for sustainable development (Nurlu et al. 2008; Erdogan and Nurlu, 2010). It is well-known that urban growth has altered world landscape especially during the last two centuries. Throughout the world, urban areas have had larger spatial extent in order to accommodate larger population. During the last 200 years, the world’s urban population has increased over 100 times while the world’s total population has multiplied only six times (Stalker, 2000). Masser (2001) has stated that urban growth was inevitable at least for the next two decades and most of this growth would occur in the developing countries. Turkey is also one of the developing countries which are subjected to urban growth during the past century. Urban population of Turkey has doubled the rural population by 2011 and is expected to quadruple in the next three decades (Fig. 1).

Fig. 1. Population increase in Turkey (Source: UN, 2007)

SLEUTH is a cellular automaton (CA) model designed for modeling urban growth and land use/land cover change and developed by Keith C. Clarke at University of California at Santa Barbara in collaboration with the US Geological Survey (Gigalopolis 2011), which has been used worldwide (Oguz, in press; Oguz et al. 2010; Jantz et al. 2010; Rafiee et al. 2009; Oguz et al. 2008; Oguz et al. 2007; Oguz et al. 2004; Yang and Lo 2003; Silva and Clarke, 2002; Clarke and Gaydos 1998; Clarke et al. 1997). In this study, an existing cellular automaton model, SLEUTH, was applied to Bornova District, located on the Izmir metropolitan area, which has experienced rapid land-use change in recent decades. Future growth was projected out to 2040 assuming three different policy scenarios: 1) Scenario 1: No protection over natural and semi-natural areas, 2) Scenario 2: Management with partial protection, and 3) Scenario 3: Management with maximum protection.

MATERİALS AND METHODS Study Area Izmir is located along the outlying waters of the Gulf of Izmir on the eastern shoreline of the Aegean Sea. It is the third most populous and the second largest port city in Turkey (Wikipedia, 2011). The city of Izmir is composed of several districts, of which, Bornova has experienced rapid growth in terms of population and spatial extent especially during the last decades (Fig. 2).

Fig. 2. The study area: Bornova district of Izmir Metropolitan Area

The district of Bornova covers an area of approximately 21400 ha and altitudes ranging from 3 to 1090 m above sea level. The area enjoys typical Mediterranean climate with mild rainy winters and hot dry summers. The average annual temperature is 17.5 ºC and mean annual temperatures rise to 28 ºC in July and August and decrease to 8 ºC in January. Mean annual precipitation is about 600 mm, most of which occur in December (TSMS, 2010). The study area is dominated by typical Mediterranean flora. The area is predominantly covered with Mediterranean vegetation consisting of Red pine (Pinus brutia) and maquis (Quercus coccifera, Myrtus communis and Laurus nobilis). The topography of the area is generally characterized by rolling hills and open plains, with grasslands, croplands, forests, urban zones, and scattered water bodies as the predominant land cover types. Topography is relatively flat with 43% of the study area ranges in 0-6% slope. On the other hand, 11.5% of the study area has a slope greater than 30%. Bornova's vast plain has been a preferred location since more than a century for Izmir's industrial base. The choice of Bornova by numerous official institutions as their regional headquarters, combined with the services industry, with medical and legal services especially standing out, as well as other professions, all contribute to the pace of the district (Fig 3). Agricultural production is comparatively very modest in added value. Bornova was chosen based on the preliminary investigation where urban sprawl is prevalent. Because of the population and economic growth, the region has experienced signiﬁcant alteration of its natural landscapes as its urban built-up land increases.

Fig. 3. The population change of Bornova District (TSI, 2010)

The Model and Data This research has employed CA-based model, SLEUTH, which is capable of predicting the urban growth and land use/land cover change at regional, continental and even global scales. The name of the model is an acronym derived from the initials of the model’s input layers; Slope, Landuse, Excluded, Urban, Transportation and Hillshade (Fig. 4). The input layers to predict future urban growth and land use/land cover change using SLEUTH are shown in table 1 below.

Fig. 4.Input layers for the model

Table – 1: Input dataset Number of Data Type Layers

Data Years

Data Source

1984, 1990, 2000, 2009

Landsat 5 TM

1984, 2009

Landsat 5 TM

4

Urban extent

2

Land use

4

Transportation

1

Excluded

NA

NA

1

Slope

NA

DEM

1

Hillshade

NA

DEM

1984, 1990, 2000, 2009

Digitized from topo/Landsat

The model is implemented in three phases: preparation of input layers, calibration phase where historic growth patterns are simulated, and prediction phase, where those historic growth patterns are projected into the future. Input layers, as shown in table 1 above, were compiled using remote sensing (RS) and geographic information systems (GIS) techniques. The model requires that all the input layers must be in same extent, same projection and same spatial resolution. Thus, all data were rectified with an acceptable root mean square error (RMSE) and resampled into 30 meter using nearest neighbor algorithm. Then, study area, Bornova District, was clipped out from the Landsat 5 TM satellite images. 1984 and 2009 land use/land cover layers were derived using 1984 and 2009 clipped Landsat 5 TM images, which were then classified into 7 different classes using both unsupervised and supervised classification techniques: 1- urban, 2- agriculture, 3- forest, 4- maquis/herb. 5- salines, 6- water body, 7- others. The urban classes of these land use layers were extracted to be used as 1984 and 2009 urban extents. 1990 and 2000 clipped Landsat TM images were then classified into only 2

classes: 1- urban, and 2- nonurban. Transportation layers, on the other hand, were prepared by digitizing only the major highways of the study area from Landsat TM and topographic map, and then converted into raster format. Percent slope and hillshade images were created from the digital elevation model (DEM). Various scenarios can be prepared and applied to the model with excluded layer, which indicates areas that are partially or completely excluded from development (Jantz et al. 2010). After preparing all the input layers as shown in Fig. 4, they were converted into 8-bit grayscale graphics interchange format (GIF) image format.

Fig. 5. The flowchart of SLEUTH model

Method The model’s flowchart is illustrated in Fig. 5 above. Future urban growth and land use/land cover change were predicted in SLEUTH, which employs four growth types: spontaneous growth, which simulates the random urbanization of land; new spreading centers, which simulates the development of new urban areas; edge growth, which simulates growth occurring on edges of already urbanized land; and road-influenced growth, which simulates the influence of transportation network on development. These growth types are controlled by five growth coefficients; diffusion, breed, spread, slope resistance, and road gravity, as shown in table 2.

Table 2. Relationship between growth rules and growth coefficients. Growth Types Growth Coefficients Spontaneous growth

Diffusion

New spreading centers growth

Breed

Edge growth

Spread Slope resistance Diffusion

Road-influenced growth

Breed Slope resistance Road gravity

Before starting calibration phase, the model was first tested with the prepared input 8-bit grayscale gif images before proceeding exhaustive and rigorous calibration phase. The model was calibrated in three phases: Coarse, where the spatial resolution of all input layers was reduced to ¼; Fine, where the resolution was reduced to ½; and Final, where input layers were used in original resolution. The product of selected metrics by Dietzel and Clarke (2007) was employed during calibration. After rigorous calibration phase, the parameter values to be used in

prediction phase were retrieved. These values were used in prediction phase to predict the future urban growth and land use/land cover change (Fig. 6).

Fig. 6. The growth coefficients used in prediction phase

Three different policy scenarios have been developed using three different excluded layers (Fig. 7): 1- Scenario 1: No protection over natural/semi-natural areas, 2- Scenario 2: Management with partial protection, and 3- Scenario 3: Management with maximum protection.

The first scenario assumes no protection over all natural/semi-natural areas such as agricultural lands, forests, parks, maquis and herbaceous. On the other hand, the second scenario assumes partial protection over natural and semi-natural lands reflecting current trends. Finally, the third scenario, however, reflects a strong commitment to resource protection (Table 3).

Table 3. Exclusion (from development) levels for each scenario

Exclusion levels (in percent) LULC Classes

Scenario 1

Scenario 2

Scenario 3

1st degree protected natural area

0

100

100

2nd degree protected natural area

0

80

80

3rd degree protected natural area

0

60

60

Urban protected area

0

80

80

Agriculture

0

50

80

Forest

0

50

90

Maquis and Herbaceous

0

50

80

Salines

0

0

0

100

100

100

0

0

0

Water body Others

Fig. 7. Exclusion levels for each scenario RESULTS

The model’s prediction phase has been executed after growth coefficients have been retrieved over a rigorous calibration phase. According to the results, the first scenario predictions show the highest dispersed development patterns due to the lack of protection over natural/seminatural areas as occurring mostly in and around existing urban centers (Figs. 8-10) as expected.

Fig. 8. Predicted lulc layer for the year 2040 in scenario 1



The impact assessment has also been done for each scenario as shown in Fig. 11 below. According to this assessment, forest land was the most affected from development with a loss close to 1,500 ha in scenario 1 by 2040, and agricultural land was found to be the least affected class after salines (Fig. 11).

Fig. 11. Impact assessment for the three scenarios

DISCUSSION A realistic modeling system is critical in urban growth and land use/land cover change studies to simulate the potential impacts of incentive policies. CA-based models represent feasible approach since they are capable of predicting the complex behavior of urban systems. The visualization tool is the SLEUTH model’s best feature since the visualization of potential land use change makes the model powerful for raising public awareness. SLEUTH model could be used to guide more localized modeling efforts after evaluating the findings from this study. Even though excluded layer is ideal for simulating the future development, the SLEUTH model does not have an adequate mechanism to simulate the potential impacts of incentive policies. Furthermore, using different data sources to prepare input layers may compromise the accuracy of the model. Therefore, consistent and reliable data derived from RS imageries were employed in this study. The model may have some weaknesses but it may still be a useful tool for assessing the impacts of alternative policy scenarios.

ACKNOWLEDGMENTS This study is part of the research funded by the Scientific and Technological Research Council of Turkey (TUBITAK) (Project No: 109Y210).

REFERENCES Antrop M (2000) Changing patterns in the urbanized countryside of Western Europe. Landscape Ecology, 15 (3): 257-270. Antrop M (2004) Landscape change and the urbanization process in Europe. Landscape and Urban Planning, 67: 9-26. Clarke KC, Hoppen S, Gaydos L (1997) A self-modifying cellular automata model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24, 247–61. Clarke KC, Gaydos LJ (1998) Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12: 699-714. Dietzel C, Clarke K (2007) Toward optimal calibration of the SLEUTH Land Use Change Model Transactions in GIS, 11(1), 29-45. Erdoğan N, Nurlu E (2010) Alan kullanım değişim modellerinin sürdürülebilir planlama aracı olarak kullanılabilirliğine yönelik bir araştırma. Peyzaj Mimarlığı IV. Kongresi Bildiri Özetleri Kitabı, sayfa: 36-37, İzmir, 21-24 Ekim. (In Turkish). Gigalopolis (2011) Project Gigalopolis. (Accessed on 05.10.2011). Jantz CA, Goetz SJ, Donato D, Claggett P (2010) Designing and implementing a regional urban modeling system using the SLEUTH cellular urban model. Computers, Environment and Urban Systems, 34 (1), 1-16. Masser I (2001) Managing Our Urban Future: The Role of Remote Sensing and Geographic Information System. Habitat International, 25: 503-512. Nurlu E, Erdem U, Ozturk M, Guvensen A, Turk T (2008) Landscape, demographic developments, biodiversity and sustainable land use strategy: A case study on Karaburun Peninsula, Izmir, Turkey, Use of Landscape Sciences for the Assessment of Environmental Security. pp.357-368. Petrosillo, I., Müller, F., Jones, K.B., Zurlini, G., Krauze, K., Victorov, S., Li, B.L., Kepner, W.G. (Eds.), 497 p. ISBN 978-1-4020-6588-0, Springer, The Netherlands.

Oguz H (In Press) Simulating Future Urban Growth in the City of Kahramanmaras, Turkey from 2009 to 2040. Journal of Environmental Biology. Oguz H, Klein AG, Srinivasan R (2004) Modeling urban growth and land use/land cover change in the Houston metropolitan area from 2002-2030. Texas A&M University, PhD Thesis. Oguz H, Klein AG, Srinivasan R (2007) Using the Sleuth urban growth model to simulate the impacts of future policy scenarios on urban land use in the Houston-Galveston-Brazoria CMSA, Research Journal of Social Sciences, 2: 72-82. Oguz H, Klein AG, Srinivasan R. (2008) Predicting Urban Growth in a US Metropolitan Area with No Zoning Regulation, International Journal of Natural and Engineering Sciences, 2 (1), 9-19. Oguz H, Kesgin B, Nurlu E, Doygun H (2010) Narlıdere-Balçova örneğinde Sleuth Modeli yardımıyla kentleşme senaryolarının geliştirilmesi. I. Ulusal Planlamada Sayısal Modeller Sempozyumu Bildiriler Kitabı, sayfa: 473-486, İstanbul, 24-26 Kasım, (In Turkish). Rafiee R, Mahiny AS, Khorasani N, Darvishsefat AA, Danekar A (2009) Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM), Cities, 26, 19–26. Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Spain. Computers, Environment and Urban Systems, 26: 525-552. Stalker P (2000) Handbook of World. Oxford University Press, New York. TSI (2010) Turkish Statistical Institute (Accessed on 05.08.2011). TSMS (2010) Turkish State Meteorological Service. (Accessed on 05.07.2011). UN, (United Nations) (2007) Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Urbanization Prospects. (Accessed on 05.08.2011). Wikipedia (2011) The Free Encyclopedia. (Accessed on 05.05.2011). Yang X, Lo CP (2003) Modelling urban growth and landscape change in the Atlanta metropolitan area. International Journal of Geographical Information Science, 17, 463– 88.

MODELING URBAN GROWTH AND LAND USE / LAND COVER CHANGE IN BORNOVA DISTRICT OF IZMIR METROPOLITAN AREA FROM 2009 TO 2040

Hakan OĞUZ* - Birsen KESGİN ATAK** - Hakan DOYGUN* - Engin NURLU***

*

Kahramanmaraş Sütçü İmam University, Faculty of Forestry, Dept. of Landscape Architecture, KAHRAMANMARAŞ

** Adnan Menderes University, Faculty of Agriculture, Department of Landscape Architecture, AYDIN *** Ege University, Faculty of Agriculture, Dept. of Landscape Architecture & Centre for Environmental Studies, IZMIR 28-29 June 2011 Gediz University İZMİR

INTRODUCTION Planners and policy makers are increasingly becoming more concerned about urbanization and urban growth since trends and patterns of urbanization have huge effect on socio-economic development. During the past decades, both the scale and pattern of urban growth have been transformed with increasing rapidity. Increasing urban growth through the world has aroused concerns over the degradation of environment.

Therefore, understanding the dynamics of urban systems and evaluating the impacts of urban growth on the environment are needed and they involve modeling. Models used in land use change analysis are indispensable because these models are important tools that support spatial urban planning for sustainable development.

H. OĞUZ , B. KESGİN ATAK, H. DOYGUN, E. NURLU

28-29 June 2011 Gediz University İZMİR

INTRODUCTION It is well-known that urban growth has altered world landscape especially during the last two centuries. Throughout the world, urban areas have had larger spatial extent in order to accommodate larger population. During the last 200 years, the world’s urban population has increased over 100 times while the world’s total population has multiplied only six times.



INTRODUCTION Masser (2001) has stated that urban growth was inevitable at least for the next two decades and most of this growth would occur in the developing countries. Turkey is also one of the developing countries which are subjected to urban growth during the past century. Urban population of Turkey has doubled the rural population by 2011 and is expected to quadruple in the next three decades.

Population increase in Turkey (UN, 2007) H. OĞUZ , B. KESGİN ATAK, H. DOYGUN, E. NURLU


AIM The aim of this study was to predict and analyze the impact of urban growth and land use/land cover (LULC) change using SLEUTH Model for Bornova District of Izmir Metropolitan Area.

Future growth was projected out to 2040 assuming three different policy scenarios:

Scenario 1

Scenario 2

Scenario 3

no protection over natural/semi-natural areas

management with partial protection

management with maximum protection



STUDY AREA Izmir is located along the outlying waters of the Gulf of Izmir on the eastern shoreline of the Aegean Sea. It is the third most populous and the second largest port city in Turkey. The city of Izmir is composed of several districts, of which, Bornova has

experienced

rapid

growth

in

terms of population and spatial extent especially during the last decades.



THE MODEL AND DATA

This research has employed CACAbased Model, odel, SLEUTH, SLEUTH which is capable of predicting the urban growth and land

L

use/land URBAN EXTENT

cover

change

at

regional,

continental and even global scales.

A

The name of the model is an acronym derived from the initials of the model’s

Y

input layers;

LAND USE

The input layers to predict future urban growth and land use/land cover

E

change using SLEUTH; SLEUTH; TRANSPORTATION

R S

Number of Layers

Data Type

Data Years

Data Source

4

Urban extent

1984, 1990, 2000, 2009

Landsat 5 TM

T A

2

Land use

1984, 2009

Landsat 5 TM

4

Transportation

S E

1

Excluded

NA

NA

1

Slope

NA

DEM

T

1

Hillshade

NA

DEM

D A EXCLUDED

SLOPE HILLSHADE H. OĞUZ , B. KESGİN ATAK, H. DOYGUN, E. NURLU

1984, 1990, 2000, 2009

Digitized from topo/Landsat


SLEUTH MODEL



SLEUTH MODEL



METHOD Future urban growth and land use/land cover change were predicted in SLEUTH, SLEUTH which employs four growth types:

spontaneous growth, which simulates the random urbanization of land; new spreading centers, which simulates the development of new urban areas; edge growth, which simulates growth occurring on edges of already urbanized land; and road-influenced growth, which simulates the influence of transportation network on development. These growth types are controlled by five growth coefficients;

diffusion, breed, spread, slope resistance, and road gravity. Growth Types

Growth Coefficients

Spontaneous growth

Diffusion

New spreading centers growth

Breed

Edge growth

Spread Slope resistance Diffusion

Road-influenced growth

Breed Slope resistance Road gravity

Relationship between growth rules and growth coefficients



METHOD

The growth coefficients used in prediction phase



METHOD Scenario 1

Scenario 2

Scenario 3

no protection over natural/semi-natural areas

management with partial protection

management with maximum protection

Exclusion levels (in percent) LULC Classes

Scenario 1

Scenario 2

Scenario 3

1st degree protected natural area

0

100

100

2nddegree protected natural area

0

80

80

3rddegree protected natural area

0

60

60

Urban protected area

0

80

80

Agriculture

0

50

80

Forest

0

50

90

Maquis and Herbaceous

0

50

80

Salines

0

0

0

100

100

100

0

0

0

Water body Others

Exclusion (from development) levels for each scenario H. OĞUZ , B. KESGİN ATAK, H. DOYGUN, E. NURLU


METHOD



RESULTS

Predicted LULC Layer for the year 2040 H. OĞUZ , B. KESGİN ATAK, H. DOYGUN, E. NURLU


RESULTS



RESULTS



RESULTS

Impact assessment for the three scenarios



RESULTS

This study is part of the research funded by the Scientific and Technological Research Council of Turkey (TUBITAK) (Project No: 109Y210).

