SLEUTH İle Arazi Örtüsü Değişimi Simülasyon Modelinin Oluşturulması, İstanbul İli Örneği

Year 2021, Volume: 3 Issue: 1, 40 - 47, 28.05.2021

Ahmet Emir Yakup İsmail Ercüment Ayazlı

Abstract

Nüfus artışı kentlerin büyümesine ve doğal yapıdaki alanların tahrip olmasına yol açmaktadır. Kentsel büyüme arazi örtüsündeki değişimleri tetiklemektedir. Arazi örtüsündeki değişim etkilerinin belirlenmesi sürdürülebilir politikalar için gereklidir. Bu nedenle benzetim uygulamaları planlama çalışmalarında yoğun olarak kullanılmaktadır. Kentsel büyümenin neden olduğu arazi örtüsü/kullanımı değişimlerini araştırmak için sıklıkla hücresel otomat (HO) yöntemi tercih edilmektedir. Bu çalışmada, HO tabanlı SLEUTH modeli kullanılarak İstanbul’un süregelen kentsel büyüme eğilimleri doğrultusunda 2040 yılı için benzetim modeli oluşturulması amaçlanmıştır. Modelin ihtiyaç duyduğu zamansal veriler 2000, 2006, 2012 ve 2018 yıllarına ait CORINE verilerinden üretilmiştir. Oluşturulan benzetim modeli ile İstanbul’daki olası kentsel büyüme ve arazi örtüsü değişimine etkileri araştırılmıştır. Üretilen benzetim modeline göre; tarım arazilerinin %25’inin, sulak alanların %2’sinin ve ormanların %14’ünün yerleşim alanlarına dönüşebileceği belirlenmiştir. İstanbul’da 2018 ile 2040 yılları arasında %24 oranında kentsel büyümenin olabileceği kestirilmiştir. Elde edilen sonuçlar İstanbul ilinin yoğun bir kentleşme baskısı altında olduğunu göstermiştir.

Keywords

CBS, Arazi Örtüsü Değişimi, Kentsel Büyüme, Hücresel Otomat, Simülasyon

Creating a Land Cover Change Simulation Model with SLEUTH, the Case of Istanbul Province

Abstract

Population growth leads to the growth of cities and the destruction of natural areas. Urban growth triggers changes in land cover. Determining the effects of change in land cover is necessary for sustainable urban management. For this reason, simulation applications are used extensively in planning studies. The cellular automata (CA) based simulation methods are often preferred to investigate land cover/use changes caused by urban growth. In this study, it is aimed to create a simulation model for the year 2040 in line with the ongoing urban growth trends of Istanbul by using CA-based SLEUTH model. The temporal data required by the model are generated from CORINE Land Cover data for the years 2000, 2006, 2012 and 2018. With the simulation model created, the effects of possible urban growth and land cover change in Istanbul were investigated. According to the simulation model produced, it was determined that 25% of Agricultural Land, 2% of wetlands and 14% of forests could be turned into residential areas. It is estimated that there may be 24% urban growth in Istanbul between 2018 and 2040. The results showed that the province of Istanbul is under intense urbanization pressure.

Keywords

GIS, Land Cover Change, Urban Growth, Cellular Automata, Simulation

References

• AÇA. (2016). Urban Sprawl in Europe: Joint EEA-FOEN.
• AÇA. (2020). CORINE Land Cover. Avrupa Çevre Ajansı. https://land.copernicus.eu/pan-european/corine-land-cover
• Angel, S., Parent, J., Civco, D., & Blei, A. (2011). Making Room for a Planet of Cities Making Room for a Planet of Cities.
• Ayazli, I.E. (2011). Simulation Model of Urban Driven By Transportation Networks: 3rd Bosphorus Bridge Example. Yildiz Technical University.
• Ayazli, I.E., Batuk, F., & Kleinschmit, B. (2010). Simulating landuse changes driven by a 3rd bosphorus bridge. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
• Ayazli, I.E., Kilic, F., & Demir, H. (2014). A simulation model of urban growth driven by the bosphorus bridges. Lecture Notes in Geoinformation and Cartography. https://doi.org/10.1007/978-3-642-31833-7_15
• Ayazli, I. E., Kilic, F., Lauf, S., Demir, H., & Kleinschmit, B. (2015). Simulating urban growth driven by transportation networks: A case study of the Istanbul third bridge. Land Use Policy, 49, 332–340. https://doi.org/https://doi.org/10.1016/j.landusepol.2015.08.016
• Batty, M. (2008). The Size, Scale, and Shape of Cities. Science (New York, N.Y.), 319, 769–771. https://doi.org/10.1126/science.1151419
• Batty, M. (2009). Urban Modeling. Içinde International Encyclopedia of Human Geography (ss. 51–58). https://doi.org/10.1016/B978-008044910-4.01092-0
• Benenson, I., & Torrens, P. (2004). Geosimulation: Automata-based modeling of urban phenomena. John Wiley & Sons.
• Bihamta, N., Soffianian, A., Fakheran, S., & Gholamalifard, M. (2015). Using the SLEUTH Urban Growth Model to Simulate Future Urban Expansion of the Isfahan Metropolitan Area, Iran. Journal of the Indian Society of Remote Sensing, 43(2), 407–414. https://doi.org/10.1007/s12524-014-0402-8
• BM. (2019). World Urbanization Prospects: The 2018 Revision. https://doi.org/10.18356/b9e995fe-en
• Candau, J., & Clarke, K. C. (2000). Probabilistic Land Cover Transition Modeling Using Deltatrons. 2000 URISA Annual Conference, Orlando.
• Chaudhuri, G., & Clarke, K. C. (2013). The SLEUTH Land Use Change Model : A Review. The International Journal of Environmental Resources Research, 1(1), 88–104.
• Cheng, J. (2003). Modelling Spatial and Temporal Urban Growth.
• Clarke, K., & Gaydos, L. (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(7), 699–714. https://doi.org/10.1080/136588198241617
• Clarke, K., Hoppen, S., & Gaydos, L. (1997). A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area. Environment and Planning B: Planning and Design, 24(2), 247–261. https://doi.org/10.1068/b240247
• Clewlow, L. (1989). Cellular automata and dynamical systems.
• Dennunzio, A., Formenti, E., & Kurka, P. (2012). Cellular Automata Dynamical Systems.
• Dezhkam, S., Jabbarian Amiri, B., Darvishsefat, A., & Sakieh, Y. (2013). Simulating the urban growth dimensions and scenario prediction through sleuth model: a case study of Rasht County, Guilan, Iran. GeoJournal, 79. https://doi.org/10.1007/s10708-013-9515-9
• Di Lena, P., & Margara, L. (2008). Computational complexity of dynamical systems: The case of cellular automata. Inf. Comput., 206, 1104–1116.
• Dietzel, C., & Clarke, K. (2007). Toward Optimal Calibration of the SLEUTH Land Use Change Model. T. GIS, 11, 29–45. https://doi.org/10.1111/j.1467-9671.2007.01031.x
• Dyson, T. (2011). The Role of the Demographic Transition in the Process of Urbanization. Population and Development Review, 37(s1), 34–54. https://doi.org/https://doi.org/10.1111/j.1728-4457.2011.00377.x
• Foot, D. (2017). Linear urban models (ss. 137–173). https://doi.org/10.4324/9781315105307-6
• Gigalopolis. (2020). Project Gigalopolis Web Page. USGS. http://www.ncgia.ucsb.edu/projects/gig/
• Goldstein, N. C., Candau, J. T., & Clarke, K. C. (2004). Approaches to simulating the “March of Bricks and Mortar”. Computers, Environment and Urban Systems, 28(1), 125–147. https://doi.org/https://doi.org/10.1016/S0198-9715(02)00046-7
• Han, H., Hwang, Y., Ha, S., & byung sik, K. (2015). Modeling Future Land Use Scenarios in South Korea: Applying the IPCC Special Report on Emissions Scenarios and the SLEUTH Model on a Local Scale. Environmental management, 55. https://doi.org/10.1007/s00267-015-0446-8
• Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sensing of Environment, 86(3), 286–302. https://doi.org/https://doi.org/10.1016/S0034-4257(03)00075-0
• Jantz, C. A., Goetz, S. J., & Shelley, M. K. (2004). Using the Sleuth Urban Growth Model to Simulate the Impacts of Future Policy Scenarios on Urban Land Use in the Baltimore-Washington Metropolitan Area. Environment and Planning B: Planning and Design, 31(2), 251–271. https://doi.org/10.1068/b2983
• Mestav Sarica, G., Zhu, T., & Pan, T. C. (2020). Spatio-temporal dynamics in seismic exposure of Asian megacities: Past, present and future. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ababc7
• Nigussie, T. A., & Altunkaynak, A. (2017a). Modeling the effects of project canal istanbul on the urban extent and hydrological response of Ayamama Watershed, Istanbul. World Environmental and Water Resources Congress 2017: Watershed Management, Irrigation and Drainage, and Water Resources Planning and Management - Selected Papers from the World Environmental and Water Resources Congress 2017. https://doi.org/10.1061/9780784480601.001
• Nigussie, T. A., & Altunkaynak, A. (2017b). Modeling Urbanization of Istanbul under Different Scenarios Using SLEUTH Urban Growth Model. Journal of Urban Planning and Development. https://doi.org/10.1061/(asce)up.1943-5444.0000369
• Saadani, S., Laajaj, R., Maanan, M., Rhinane, H., & Aaroud, A. (2020). Simulating spatial–temporal urban growth of a Moroccan metropolitan using CA–Markov model. Spatial Information Research. https://doi.org/10.1007/s41324-020-00322-0
• Silva, E. A., & Clarke, K. C. (2005). Complexity, emergence and cellular urban models: lessons learned from applying SLEUTH to two Portuguese metropolitan areas. European Planning Studies, 13(1), 93–115. https://doi.org/10.1080/0965431042000312424
• Silva, E., & Clarke, K. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26, 525–552. https://doi.org/10.1016/S0198-9715(01)00014-X
• Siri, J. G., Brown, Z., & Spielauer, M. (2010). Simulation modeling of the long-term evolution of local malaria transmission and acquired immunity in the context of urban growth and urban-rural travel. Malaria Journal, 9(2), P47. https://doi.org/10.1186/1475-2875-9-S2-P47
• Stecklov, G. (2018). The Components of Urban Growth in Developing Countries. https://doi.org/10.31235/osf.io/4zk5b
• TOB. (2020). CORINE Projesi. Türkiye Cumhuriyeti Tarım ve Orman Bakanlığı. https://corine.tarimorman.gov.tr/corineportal/nedir.html
• Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234–240. https://doi.org/10.2307/143141
• Torrens, P.M. (2000) How cellular models of urban systems work (1. theory). Working paper. CASA Working Papers (28). Centre for Advanced Spatial Analysis (UCL), London, UK.
• TÜİK. (2020). Türkiye İstatistik Kurumu Web Sayfası. https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr
• Verburg, P. H., van de Steeg, J., Veldkamp, A., & Willemen, L. (2009). From land cover change to land function dynamics: A major challenge to improve land characterization. Içinde Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2008.08.005
• Vliet, J. van, White, R., & Dragicevic, S. (2009). Modeling urban growth using a variable grid cellular automaton. Computers, Environment and Urban Systems, 33(1), 35–43. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2008.06.006
• White, R., & Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems, 24(5), 383–400. https://doi.org/https://doi.org/10.1016/S0198-9715(00)00012-0
• Xie, Y., Ma, A., & Wang, H. (2010). Lanzhou urban growth prediction based on Cellular Automata. Içinde 2010 18th International Conference on Geoinformatics, Geoinformatics 2010. https://doi.org/10.1109/GEOINFORMATICS.2010.5567556
• Yi, W., & He, B. (2009). Applying SLEUTH for simulating urban expansion of Beijing. Içinde Proceedings - 2009 International Forum on Information Technology and Applications, IFITA 2009 (C. 2). https://doi.org/10.1109/IFITA.2009.543
• Yiğit, A., & Hayır-kanat, M. (2017). İstanbul Şehrinde Ağırlıklı Nüfus Merkezinin Değişimi ve Nedenleri: 1990-2010 Dönemi (C. 6, ss. 114–123). Şahin ORUÇ.
• Zhang, Z., Jiang, L., Peng, R., & Yin, Y. (2010). The spatiotemporal change of urban form in Nanjing, China: Based on SLEUTH and spatial metrics analysis. 2010 18th International Conference on Geoinformatics, 1–5. https://doi.org/10.1109/GEOINFORMATICS.2010.5567753