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

Year 2022, Volume: 7 Issue: 1, 1 - 8, 15.02.2022

Ahmet Emir Yakup , İsmail Ercüment Ayazlı

https://doi.org/10.26833/ijeg.850247
Cited By: 6

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

Supporting Institution

TUBITAK

Project Number

112K469

References

  • 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
  • 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
  • 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
  • Ayazli I E & Baslik S (2016). Creating Simulation Model of the Relationship between the Ownership Pattern and Urban Growth; Project Report.
  • 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
  • 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
  • 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.
  • Batty M (2009). Urban Modeling. In International Encyclopedia of Human Geography (pp. 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 (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
  • 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.
  • 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
  • Dennunzio A, Formenti E & Kurka P (2012). Cellular Automata Dynamical Systems.
  • Di Lena P & Margara L (2008). Computational complexity of dynamical systems: The case of cellular automata. Inf. Comput., 206, 1104–1116.
  • EEA (2016). Urban Sprawl in Europe: Joint EEA-FOEN.
  • Foot D (2017). Linear urban models (pp. 137–173). https://doi.org/10.4324/9781315105307-6
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • Sancaktepe (2010). 1/1000 Scale Sancaktepe Implementary Development Plan Sancaktepe Municipality.
  • Sancaktepe (2020). Sancaktepe Municipality Web Page. http://www.sancaktepe.bel.tr/tr/cografi-durumu
  • 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
  • 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
  • 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
  • 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
  • TUIK (2020). Turkish Statistical Institute Web Page. Turkish Statistical Institute. https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr
  • 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
  • Watkiss B M (2008). The SLEUTH urban growth model as forecasting and decision-making tool. Stellenbosch University.
  • 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
  • Yakup A E (2018). Research the relationship between the ownership pattern and urban growth. Sivas Cumhuriyet University.
  • 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
  • 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

Year 2022, Volume: 7 Issue: 1, 1 - 8, 15.02.2022

Ahmet Emir Yakup , İsmail Ercüment Ayazlı

https://doi.org/10.26833/ijeg.850247
Cited By: 6

Abstract

Project Number

112K469

References

  • 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
  • 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
  • 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
  • Ayazli I E & Baslik S (2016). Creating Simulation Model of the Relationship between the Ownership Pattern and Urban Growth; Project Report.
  • 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
  • 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
  • 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.
  • Batty M (2009). Urban Modeling. In International Encyclopedia of Human Geography (pp. 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 (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
  • 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.
  • 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
  • Dennunzio A, Formenti E & Kurka P (2012). Cellular Automata Dynamical Systems.
  • Di Lena P & Margara L (2008). Computational complexity of dynamical systems: The case of cellular automata. Inf. Comput., 206, 1104–1116.
  • EEA (2016). Urban Sprawl in Europe: Joint EEA-FOEN.
  • Foot D (2017). Linear urban models (pp. 137–173). https://doi.org/10.4324/9781315105307-6
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • Sancaktepe (2010). 1/1000 Scale Sancaktepe Implementary Development Plan Sancaktepe Municipality.
  • Sancaktepe (2020). Sancaktepe Municipality Web Page. http://www.sancaktepe.bel.tr/tr/cografi-durumu
  • 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
  • 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
  • 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
  • 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
  • TUIK (2020). Turkish Statistical Institute Web Page. Turkish Statistical Institute. https://biruni.tuik.gov.tr/medas/?kn=95&locale=tr
  • 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
  • Watkiss B M (2008). The SLEUTH urban growth model as forecasting and decision-making tool. Stellenbosch University.
  • 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
  • Yakup A E (2018). Research the relationship between the ownership pattern and urban growth. Sivas Cumhuriyet University.
  • 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
  • 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
There are 40 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ahmet Emir Yakup 0000-0002-1789-4448

İsmail Ercüment Ayazlı 0000-0003-0782-5366

Project Number 112K469
Publication Date February 15, 2022
Published in Issue Year 2022 Volume: 7 Issue: 1

Cite

APA Yakup, A. E., & Ayazlı, İ. E. (2022). Investigating changes in land cover in high-density settlement areas by protected scenario. International Journal of Engineering and Geosciences, 7(1), 1-8. https://doi.org/10.26833/ijeg.850247
AMA Yakup AE, Ayazlı İE. Investigating changes in land cover in high-density settlement areas by protected scenario. IJEG. February 2022;7(1):1-8. doi:10.26833/ijeg.850247
Chicago Yakup, Ahmet Emir, and İsmail Ercüment Ayazlı. “Investigating Changes in Land Cover in High-Density Settlement Areas by Protected Scenario”. International Journal of Engineering and Geosciences 7, no. 1 (February 2022): 1-8. https://doi.org/10.26833/ijeg.850247.
EndNote Yakup AE, Ayazlı İE (February 1, 2022) Investigating changes in land cover in high-density settlement areas by protected scenario. International Journal of Engineering and Geosciences 7 1 1–8.
IEEE A. E. Yakup and İ. E. Ayazlı, “Investigating changes in land cover in high-density settlement areas by protected scenario”, IJEG, vol. 7, no. 1, pp. 1–8, 2022, doi: 10.26833/ijeg.850247.
ISNAD Yakup, Ahmet Emir - Ayazlı, İsmail Ercüment. “Investigating Changes in Land Cover in High-Density Settlement Areas by Protected Scenario”. International Journal of Engineering and Geosciences 7/1 (February 2022), 1-8. https://doi.org/10.26833/ijeg.850247.
JAMA Yakup AE, Ayazlı İE. Investigating changes in land cover in high-density settlement areas by protected scenario. IJEG. 2022;7:1–8.
MLA Yakup, Ahmet Emir and İsmail Ercüment Ayazlı. “Investigating Changes in Land Cover in High-Density Settlement Areas by Protected Scenario”. International Journal of Engineering and Geosciences, vol. 7, no. 1, 2022, pp. 1-8, doi:10.26833/ijeg.850247.
Vancouver Yakup AE, Ayazlı İE. Investigating changes in land cover in high-density settlement areas by protected scenario. IJEG. 2022;7(1):1-8.

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