Investigating changes in land cover in high-density settlement areas by protected scenario

Abstract

Uncontrolled urban growth is one of the most prominent problems in modern urbanism and planning. Rapid urbanization and population growth cause changes in land cover. In addition, determining the effects of these changes is essential in terms of sustainable urban management policies. Urban growth is a complex, dynamic structure that initiates changes in land cover. For this reason, simulation models are used extensively in planning studies. In this study, land cover simulation of the Sancaktepe district in Istanbul was carried out with the SLEUTH model based on cellular automata (CA). The study aims to identify the damage caused by uncontrolled urbanization. In this context, a scenario was created based on the assumption that forests will be protected based on the changes in land cover that occurred between 1961-2014. The data used in the model were generated from cadastral maps on a parcel basis. For this purpose, four-period data sets (1961-1992-2001-2014) were prepared between 1961-2014. According to the simulation results, 82% of agricultural land, 2% of forest land and 84% of open land will probably be converted into residential use. According to the results, it has been determined that almost all of the open and agricultural land in the towns of Pasakoy and Samandira in the district of Sancaktepe have been converted into residential areas. According to the prediction that the changes in current land cover will continue, no change is expected in forests, while it has been determined that the potential to transform agricultural land and open land into settlement areas is quite high.

Keywords

• GIS
• Urban Growth
• SLEUTH
• Cellular Automata
• Land Management

References

1. Akin A, Clarke K C & Berberoglu S (2014). The impact of historical exclusion on the calibration of the SLEUTH urban growth model. International Journal of Applied Earth Observation and Geoinformation, 27(PARTB), 156–168. https://doi.org/10.1016/j.jag.2013.10.002
2. Al-Awadhi T (2007). Monitoring and Modeling Urban Expansion Using GIS & RS: Case Study from Muscat, Oman. https://doi.org/10.1109/URS.2007.371790
3. Ayazli I E (2019). An Empirical Study Investigating the Relationship between Land Prices and Urban Geometry. ISPRS International Journal of Geo-Information, 8, 457. https://doi.org/10.3390/ijgi8100457
4. Ayazli I E & Baslik S (2016). Creating Simulation Model of the Relationship between the Ownership Pattern and Urban Growth; Project Report.
5. Ayazli I E, Gul F K, Baslik S, Yakup A E & Kotay D (2019). Extracting an Urban Growth Model’s Land Cover Layer from Spatio-Temporal Cadastral Database and Simulation Application. Polish Journal of Environmental Studies, 28(3), 1063–1069. https://doi.org/10.15244/pjoes/89506
6. 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
7. Aydin B (2010). Identification of ecological criteria for the greenfield development and adaptation of these criteria within current city planning policies: Ömerli waterbasin-Sancaktepe case study. Istanbul Technical University.
8. Batty M (2009). Urban Modeling. In International Encyclopedia of Human Geography (pp. 51–58). https://doi.org/10.1016/B978-008044910-4.01092-0

9. Benenson I & Torrens P (2004). Geosimulation: Automata-based modeling of urban phenomena. John Wiley & Sons.
10. Bihamta N, Soffianian A, Fakheran S & Gholamalifard M (2014). 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. https://doi.org/10.1007/s12524-014-0402-8
11. Candau J & Clarke K C (2000). Probabilistic Land Cover Transition Modeling Using Deltatrons. 2000 URISA Annual Conference, Orlando.
12. 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.
13. 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
14. 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
15. Dennunzio A, Formenti E & Kurka P (2012). Cellular Automata Dynamical Systems.
16. Di Lena P & Margara L (2008). Computational complexity of dynamical systems: The case of cellular automata. Inf. Comput., 206, 1104–1116.
17. EEA (2016). Urban Sprawl in Europe: Joint EEA-FOEN.
18. Foot D (2017). Linear urban models (pp. 137–173). https://doi.org/10.4324/9781315105307-6
19. Gandhi S & Suresh V (2012). Prediction of Urban Sprawl in Hyderabad City using Spatial Model, Remote Sensing and GIS Techniques. International Journal of Scientific Research, 1, 80–81. https://doi.org/10.15373/22778179/JUL2012/25
20. Gigalopolis (2020). Project Gigalopolis Web Page. USGS. http://www.ncgia.ucsb.edu/projects/gig/
21. 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
22. Grimm V, Revilla E, Berger U, Jeltsch F, Mooij W M, Railsback S F, Thulke H H, Weiner J, Wiegand T & DeAngelis D L (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. In Science. https://doi.org/10.1126/science.1116681
23. 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
24. Huanga J, Zhangb J & Luc X (2008). Applying SLEUTH For Simulating and Assessing Urban Growth Scenario Based on Time Series TM Images : Referencing to a Case Study of Chongqing, China.
25. 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
26. 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
27. Sancaktepe (2010). 1/1000 Scale Sancaktepe Implementary Development Plan Sancaktepe Municipality.
28. Sancaktepe (2020). Sancaktepe Municipality Web Page. http://www.sancaktepe.bel.tr/tr/cografi-durumu
29. Sandamali S, Kantakumar L & Sivanantharajah S (2018). Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning. Remote Sensing Data and SLEUTH Urban Growth Model, 28, 274–286. https://doi.org/10.1007/s11769-018-0946-6
30. Satterthwaite D (2005). The Scale of Urban Change Worldwide 1950-2000 and Its Underpinnings. International Institute for Environment and Development. https://books.google.com.tr/books?id=M47aajCtKr8C
31. 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
32. 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
33. 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
34. TUIK (2020). Turkish Statistical Institute Web Page. Turkish Statistical Institute. https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr
35. Verburg P H (2006). Simulating feedbacks in land use and land cover change models. Landscape Ecology, 21(8), 1171–1183. https://doi.org/10.1007/s10980-006-0029-4
36. Watkiss B M (2008). The SLEUTH urban growth model as forecasting and decision-making tool. Stellenbosch University.
37. Xie Y, Ma A & Wang H (2010). Lanzhou urban growth prediction based on Cellular Automata. In 2010 18th International Conference on Geoinformatics, Geoinformatics 2010. https://doi.org/10.1109/GEOINFORMATICS.2010.5567556
38. Yakup A E (2018). Research the relationship between the ownership pattern and urban growth. Sivas Cumhuriyet University.
39. Yi W & He B (2009). Applying SLEUTH for simulating urban expansion of Beijing. In Proceedings - 2009 International Forum on Information Technology and Applications, IFITA 2009 (Vol. 2). https://doi.org/10.1109/IFITA.2009.543
40. 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