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Modeling Land Cover Change with CORINE Database: Urban Growth Prediction of Ankara City

Year 2023, , 54 - 60, 28.02.2023

Öznur Işınkaralar

https://doi.org/10.51664/artium.1196926

Abstract

Land use land cover change studies are very effective in decision-making processes related to cities. In the research, the future change of land cover in Ankara is predicted by using Coordination of Information on the Environment (CORINE) data for the years 1990, 2012, and 2018. The obtained data were analyzed using Geographic Information Systems. The cellular automata and Markov chain methods were applied and integrated into the production of forecast maps, and the growth of structural areas for the year 2056 was modeled as spatial and temporal. The suitability of the applied modeling approach has been proven by analysing the reference and forecast maps for 2018 with the Kappa statistical value (Klocation: 0.9744). The areal change between 2018-2056 reveals the loss of agricultural lands, wetlands, and water bodies in contrast to the increase in artificial areas. The results reveal the speed of land cover change and especially the west, northwest, and southwest growth pressure of the city.

Keywords

cellular automata Markov chain GIS Spatio-temporal modeling

References

  • Alan İ., Demirörs, Z., Bayar, R. ve Karabacak, K. (2020). Markov Chains based land cover estimation model development: The case of Ankara Province. lnternational Journal of Geography and Geography Education, (42), 650-667.
  • Aricak, B., Kucuk, O., & Enez, K. (2014). Determination of pumper truck intervention ratios in zones with high fire potential by using geographical information system. Journal of Applied Remote Sensing, 8(1), 083598.
  • Aydın, N., ve Polat, E. (2021). Kentin organik dokusunun değişiminin yapılan planlama çalışmaları ile karşılaştırılarak incelenmesi, Isparta Örneği, ;31(3):530–545.
  • Aune-Lundberg, L., & Strand, G. H. (2021). The content and accuracy of the CORINE Land Cover dataset for Norway. International Journal of Applied Earth Observation and Geoinformation, 96, 102266.
  • Bachantourian, M., Chaleplis, K., Gemitzi, A., Kalabokidis, K., Palaiologou, P., & Vasilakos, C. (2022). Evaluation of MODIS, Climate Change Initiative, and CORINE Land Cover Products Based on a Ground Truth Dataset in a Mediterranean Landscape. Land, 11(9), 1453.
  • Bao, W., Yang, Y., & Zou, L. (2021). How to reconcile land use conflicts in mega urban agglomeration? A scenario-based study in the Beijing-Tianjin-Hebei region, China. Journal of Environmental Management, 296, 113168. Baudoux, L., Inglada, J., & Mallet, C. (2021). Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach. Remote Sensing, 13(6), 1060.
  • Bayraktar, E. P., Isinkaralar, O., & Isinkaralar, K. (2022). Usability of several species for monitoring and reducing the heavy metal pollution threatening the public health in urban environment of Ankara. World Journal of Advanced Research and Reviews, 14(3), 276-283.
  • Castro, M. L., Machado, P., Santos, I., Rodriguez-Fernandez, N., Torrente-Patiño, A., & Carballal, A. (2022). Flow Space and the Complexity of Urban Spatial Network State of the Art on Artificial Intelligence in Land-Use Simulation. Complexity, 2022.
  • Cervelli, E., Pindozzi, S., Allevato, E., Saulino, L., Silvestro, R., Scotto di Perta, E., & Saracino, A. (2022). Landscape Planning Integrated Approaches to Support Post-Wildfire Restoration in Natural Protected Areas: The Vesuvius National Park Case Study. Land, 11(7), 1024.
  • Diep, N. T. H., Nguyen, C. T., Diem, P. K., Hoang, N. X., & Kafy, A. A. (2022). Assessment on controlling factors of urbanization possibility in a newly developing city of the Vietnamese Mekong delta using logistic regression analysis. Physics and Chemistry of the Earth, Parts A/B/C, 126, 103065.
  • Geng, J., Shen, S., Cheng, C., & Dai, K. (2022). A hybrid spatiotemporal convolution-based cellular automata model (ST-CA) for land-use/cover change simulation. International Journal of Applied Earth Observation and Geoinformation, 110, 102789.
  • Ghazaryan, G., Rienow, A., Oldenburg, C., Thonfeld, F., Trampnau, B., Sticksel, S., & Jürgens, C. (2021). Monitoring of urban sprawl and densification processes in western Germany in the light of SDG indicator 11.3. 1 based on an automated retrospective classification approach. Remote Sensing, 13(9), 1694.
  • Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., ve Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological modelling, 222(20-22), 3761-3772.
  • Guan, Q., Wang, L., & Clarke, K. C. (2005). An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartography and Geographic Information Science, 32(4), 369-380.
  • Hepburn, C., Qi, Y., Stern, N., Ward, B., Xie, C., & Zenghelis, D. (2021). Towards carbon neutrality and China's 14th Five-Year Plan: Clean energy transition, sustainable urban development, and investment priorities. Environmental Science and Ecotechnology, 8, 100130.
  • Hyandye, C., Mandara, C. G.,ve Safari, J. (2015). GIS and logit regression model applications in land use/land cover change and distribution in Usangu catchment. Am. J. Remote Sens, 3(6).
  • Imbrenda, V., Quaranta, G., Salvia, R., Egidi, G., Salvati, L., Prokopovà, M., ... & Lanfredi, M. (2021). Land degradation and metropolitan expansion in a peri-urban environment. Geomatics, Natural Hazards and Risk, 12(1), 1797-1818.
  • Işınkaralar, Ö., ve Varol, C. (2021). Kent Merkezlerinde Ticaret Birimlerin Mekansal Örüntüsü Üzerine Bir Değerlendirme: Kastamonu Örneği. Journal of Architectural Sciences and Applications, 6(2), 396-403.
  • Isinkaralar, O., Varol, C., & Yilmaz, D. (2022). Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling. Applied Geomatics, 1-11.
  • Isinkaralar, O., & Varol, C. (2023). A cellular automata-based approach for spatio-temporal modeling of the city center as a complex system: The case of Kastamonu, Türkiye. Cities, 132, 104073.
  • Kamusoko, C., Aniya, M., Adi, B., ve Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435-447.
  • Kucsicsa, G., Popovici, E. A., Bălteanu, D., Grigorescu, I., Dumitraşcu, M., ve Mitrică, B. (2019). Future land use/cover changes in Romania: regional simulations based on CLUE-S model and CORINE land cover database. Landscape and ecological engineering, 15(1), 75-90.
  • Li, Q., Feng, Y., Tong, X., Zhou, Y., Wu, P., Xie, H., ... & Wang, C. (2022). Firefly algorithm-based cellular automata for reproducing urban growth and predicting future scenarios. Sustainable Cities and Society, 76, 103444.
  • Lichter, D. T., Brown, D. L., & Parisi, D. (2021). The rural–urban interface: Rural and small town growth at the metropolitan fringe. Population, Space and Place, 27(3), e2415.
  • Liu, J., Xiao, B., Li, Y., Wang, X., Bie, Q., & Jiao, J. (2021). Simulation of dynamic urban expansion under ecological constraints using a long short term memory network model and cellular automata. Remote Sensing, 13(8), 1499.
  • Lv, T., Wang, L., Xie, H., Zhang, X., & Zhang, Y. (2021). Exploring the global research trends of land use planning based on a bibliometric analysis: current status and future prospects. Land, 10(3), 304.
  • Maithani, S. (2009). A neural network based urban growth model of an Indian city. Journal of the Indian Society of Remote Sensing, 37(3), 363-376.
  • Mallick, S. K., Das, P., Maity, B., Rudra, S., Pramanik, M., Pradhan, B., & Sahana, M. (2021). Understanding future urban growth, urban resilience and sustainable development of small cities using prediction-adaptation-resilience (PAR) approach. Sustainable Cities and Society, 74, 103196.
  • Noszczyk, T. (2019). A review of approaches to land use changes modeling. Human and Ecological Risk Assessment: An International Journal, 25(6), 1377-1405.
  • Nuissl, H., & Siedentop, S. (2021). Urbanisation and land use change. In Sustainable Land Management in a European Context (pp. 75-99). Springer, Cham.
  • Önaç A.K., Birişçi T., (2019). Transformation of urban landscape value perceptionover time: a Delphi technique application. EMAS, 191:741
  • Özcan, K. Y. (2019). Ankara’nın Batı Koridorundaki Gelişme Bağlamında Törekent Mahallesi’ndeki Konut Özelliklerinin Konut Fiyatlarına Etkisi. Megaron, 14(2), 279-295.
  • Öztürk ve Işınkaralar (2019). Kastamonu Kent Merkezinde Otopark Sorunsalı: Eleştirel Bir Değerlendirme, Uluslararası Sosyal Araştırmalar Dergisi, 12 (67), 506-511.
  • Prăvălie, R., Patriche, C., Borrelli, P., Panagos, P., Roșca, B., Dumitraşcu, M., ... & Bandoc, G. (2021). Arable lands under the pressure of multiple land degradation processes. A global perspective. Environmental Research, 194, 110697.
  • Salem, M., Bose, A., Bashir, B., Basak, D., Roy, S., Chowdhury, I. R., ... & Tsurusaki, N. (2021). Urban expansion simulation based on various driving factors using a logistic regression model: Delhi as a case study. Sustainability, 13(19), 10805.
  • Sarif, M., & Gupta, R. D. (2022). Spatiotemporal mapping of Land Use/Land Cover dynamics using Remote Sensing and GIS approach: a case study of Prayagraj City, India (1988–2018). Environment, Development and Sustainability, 24(1), 888-920.
  • Sat A., Üçer Z.A. G., Varol Ç., Yenigül S. B., (2017). Sürdürülebilir Kentler İçin Çok Merkezli Gelişme: Ankara Metropoliten Kenti İçin Bir Değerlendirme, Ankara Araştırmaları Dergisi, 5(1), 98-107.
  • Shafizadeh-Moghadam, H., Minaei, M., Pontius Jr, R. G., Asghari, A., & Dadashpoor, H. (2021). Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj Region of Iran. Computers, Environment and Urban Systems, 87, 101595.
  • Tian, P., Li, J., Cao, L., Pu, R., Wang, Z., Zhang, H., ... & Gong, H. (2021). Assessing spatiotemporal characteristics of urban heat islands from the perspective of an urban expansion and green infrastructure. Sustainable Cities and Society, 74, 103208.
  • Wang, S. W., Munkhnasan, L., ve Lee, W. K. (2021). Land use and land cover change detection and prediction in Bhutan's high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges, 2, 100017.
  • Xu, T., Zhou, D., & Li, Y. (2022). Integrating ANNs and Cellular Automata–Markov Chain to Simulate Urban Expansion with Annual Land Use Data. Land, 11(7), 1074.
  • Yayla, E. E., Sevik, H., & Isinkaralar, K. (2022). Detection of landscape species as a low-cost biomonitoring study: Cr, Mn, and Zn pollution in an urban air quality. Environmental Monitoring and Assessment, 194(10), 1-10.
  • Yetişkul E., (2017). Karmaşık Kentler ve Planlamada Karmaşıklık. Planlama, 27(1), 7-15.
  • Yilmaz, D., & Isinkaralar, Ö. (2021). Climate action plans under climate-resilient urban policies. Kastamonu University Journal of Engineering and Sciences, 7(2), 140-147.
  • Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689.
  • Zaldo-Aubanell, Q., Serra, I., Sardanyés, J., Alsedà, L., & Maneja, R. (2021). Reviewing the reliability of Land Use and Land Cover data in studies relating human health to the environment. Environmental Research, 194, 110578.
  • Zhang, B., & Wang, H. (2022). Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods. GIScience & Remote Sensing, 59(1), 71-95.

Arazi Örtüsü Değişiminin CORINE Verisiyle Modellenmesi: Ankara İlinin Kentsel Büyüme Tahmini

Year 2023, , 54 - 60, 28.02.2023

Öznur Işınkaralar

https://doi.org/10.51664/artium.1196926

Abstract

Arazi kullanımı arazi örtüsü değişimi araştırmaları, kentlere ilişkin karar alma süreçlerinde oldukça etkilidir. Araştırmada, Ankara ilinde 1990, 2012 ve 2018 yılları Coordination of Information on the Environment (CORINE) verisi kullanılarak arazi örtüsünün gelecekteki değişimi öngörülmektedir. Elde edilen veriler, Coğrafi Bilgi Sistemleri kullanılarak analiz edilmiştir. Tahmin haritalarının üretilmesinde hücresel özişleme ve Markov zinciri yöntemleri entegre olarak uygulanmış ve 2056 yılı için yapısal alanların büyümesi zamansal-mekânsal olarak modellenmiştir. Uygulanan modelleme yaklaşımının uygunluğu, 2018 yılı için referans ve tahmin haritalarının Kappa istatistiki değeriyle (Klocation: 0,9744) analiz edilmesi yoluyla ispatlanmıştır. 2018-2056 yılları arasındaki alansal değişim, yapay alanlardaki artışa karşılık tarımsal alanlar ile sulak alanlar ve su kütlelerindeki kaybı ortaya koymaktadır. Sonuçlar, arazi örtüsü değişimindeki hızı ve özellikle kentin batı, kuzeybatı ve güneybatı yönlü büyüme baskısını ortaya koymaktadır.

Keywords

Zamansal-mekânsal modelleme Markov zinciri CBS hücresel özişleme

References

  • Alan İ., Demirörs, Z., Bayar, R. ve Karabacak, K. (2020). Markov Chains based land cover estimation model development: The case of Ankara Province. lnternational Journal of Geography and Geography Education, (42), 650-667.
  • Aricak, B., Kucuk, O., & Enez, K. (2014). Determination of pumper truck intervention ratios in zones with high fire potential by using geographical information system. Journal of Applied Remote Sensing, 8(1), 083598.
  • Aydın, N., ve Polat, E. (2021). Kentin organik dokusunun değişiminin yapılan planlama çalışmaları ile karşılaştırılarak incelenmesi, Isparta Örneği, ;31(3):530–545.
  • Aune-Lundberg, L., & Strand, G. H. (2021). The content and accuracy of the CORINE Land Cover dataset for Norway. International Journal of Applied Earth Observation and Geoinformation, 96, 102266.
  • Bachantourian, M., Chaleplis, K., Gemitzi, A., Kalabokidis, K., Palaiologou, P., & Vasilakos, C. (2022). Evaluation of MODIS, Climate Change Initiative, and CORINE Land Cover Products Based on a Ground Truth Dataset in a Mediterranean Landscape. Land, 11(9), 1453.
  • Bao, W., Yang, Y., & Zou, L. (2021). How to reconcile land use conflicts in mega urban agglomeration? A scenario-based study in the Beijing-Tianjin-Hebei region, China. Journal of Environmental Management, 296, 113168. Baudoux, L., Inglada, J., & Mallet, C. (2021). Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach. Remote Sensing, 13(6), 1060.
  • Bayraktar, E. P., Isinkaralar, O., & Isinkaralar, K. (2022). Usability of several species for monitoring and reducing the heavy metal pollution threatening the public health in urban environment of Ankara. World Journal of Advanced Research and Reviews, 14(3), 276-283.
  • Castro, M. L., Machado, P., Santos, I., Rodriguez-Fernandez, N., Torrente-Patiño, A., & Carballal, A. (2022). Flow Space and the Complexity of Urban Spatial Network State of the Art on Artificial Intelligence in Land-Use Simulation. Complexity, 2022.
  • Cervelli, E., Pindozzi, S., Allevato, E., Saulino, L., Silvestro, R., Scotto di Perta, E., & Saracino, A. (2022). Landscape Planning Integrated Approaches to Support Post-Wildfire Restoration in Natural Protected Areas: The Vesuvius National Park Case Study. Land, 11(7), 1024.
  • Diep, N. T. H., Nguyen, C. T., Diem, P. K., Hoang, N. X., & Kafy, A. A. (2022). Assessment on controlling factors of urbanization possibility in a newly developing city of the Vietnamese Mekong delta using logistic regression analysis. Physics and Chemistry of the Earth, Parts A/B/C, 126, 103065.
  • Geng, J., Shen, S., Cheng, C., & Dai, K. (2022). A hybrid spatiotemporal convolution-based cellular automata model (ST-CA) for land-use/cover change simulation. International Journal of Applied Earth Observation and Geoinformation, 110, 102789.
  • Ghazaryan, G., Rienow, A., Oldenburg, C., Thonfeld, F., Trampnau, B., Sticksel, S., & Jürgens, C. (2021). Monitoring of urban sprawl and densification processes in western Germany in the light of SDG indicator 11.3. 1 based on an automated retrospective classification approach. Remote Sensing, 13(9), 1694.
  • Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., ve Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological modelling, 222(20-22), 3761-3772.
  • Guan, Q., Wang, L., & Clarke, K. C. (2005). An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartography and Geographic Information Science, 32(4), 369-380.
  • Hepburn, C., Qi, Y., Stern, N., Ward, B., Xie, C., & Zenghelis, D. (2021). Towards carbon neutrality and China's 14th Five-Year Plan: Clean energy transition, sustainable urban development, and investment priorities. Environmental Science and Ecotechnology, 8, 100130.
  • Hyandye, C., Mandara, C. G.,ve Safari, J. (2015). GIS and logit regression model applications in land use/land cover change and distribution in Usangu catchment. Am. J. Remote Sens, 3(6).
  • Imbrenda, V., Quaranta, G., Salvia, R., Egidi, G., Salvati, L., Prokopovà, M., ... & Lanfredi, M. (2021). Land degradation and metropolitan expansion in a peri-urban environment. Geomatics, Natural Hazards and Risk, 12(1), 1797-1818.
  • Işınkaralar, Ö., ve Varol, C. (2021). Kent Merkezlerinde Ticaret Birimlerin Mekansal Örüntüsü Üzerine Bir Değerlendirme: Kastamonu Örneği. Journal of Architectural Sciences and Applications, 6(2), 396-403.
  • Isinkaralar, O., Varol, C., & Yilmaz, D. (2022). Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling. Applied Geomatics, 1-11.
  • Isinkaralar, O., & Varol, C. (2023). A cellular automata-based approach for spatio-temporal modeling of the city center as a complex system: The case of Kastamonu, Türkiye. Cities, 132, 104073.
  • Kamusoko, C., Aniya, M., Adi, B., ve Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435-447.
  • Kucsicsa, G., Popovici, E. A., Bălteanu, D., Grigorescu, I., Dumitraşcu, M., ve Mitrică, B. (2019). Future land use/cover changes in Romania: regional simulations based on CLUE-S model and CORINE land cover database. Landscape and ecological engineering, 15(1), 75-90.
  • Li, Q., Feng, Y., Tong, X., Zhou, Y., Wu, P., Xie, H., ... & Wang, C. (2022). Firefly algorithm-based cellular automata for reproducing urban growth and predicting future scenarios. Sustainable Cities and Society, 76, 103444.
  • Lichter, D. T., Brown, D. L., & Parisi, D. (2021). The rural–urban interface: Rural and small town growth at the metropolitan fringe. Population, Space and Place, 27(3), e2415.
  • Liu, J., Xiao, B., Li, Y., Wang, X., Bie, Q., & Jiao, J. (2021). Simulation of dynamic urban expansion under ecological constraints using a long short term memory network model and cellular automata. Remote Sensing, 13(8), 1499.
  • Lv, T., Wang, L., Xie, H., Zhang, X., & Zhang, Y. (2021). Exploring the global research trends of land use planning based on a bibliometric analysis: current status and future prospects. Land, 10(3), 304.
  • Maithani, S. (2009). A neural network based urban growth model of an Indian city. Journal of the Indian Society of Remote Sensing, 37(3), 363-376.
  • Mallick, S. K., Das, P., Maity, B., Rudra, S., Pramanik, M., Pradhan, B., & Sahana, M. (2021). Understanding future urban growth, urban resilience and sustainable development of small cities using prediction-adaptation-resilience (PAR) approach. Sustainable Cities and Society, 74, 103196.
  • Noszczyk, T. (2019). A review of approaches to land use changes modeling. Human and Ecological Risk Assessment: An International Journal, 25(6), 1377-1405.
  • Nuissl, H., & Siedentop, S. (2021). Urbanisation and land use change. In Sustainable Land Management in a European Context (pp. 75-99). Springer, Cham.
  • Önaç A.K., Birişçi T., (2019). Transformation of urban landscape value perceptionover time: a Delphi technique application. EMAS, 191:741
  • Özcan, K. Y. (2019). Ankara’nın Batı Koridorundaki Gelişme Bağlamında Törekent Mahallesi’ndeki Konut Özelliklerinin Konut Fiyatlarına Etkisi. Megaron, 14(2), 279-295.
  • Öztürk ve Işınkaralar (2019). Kastamonu Kent Merkezinde Otopark Sorunsalı: Eleştirel Bir Değerlendirme, Uluslararası Sosyal Araştırmalar Dergisi, 12 (67), 506-511.
  • Prăvălie, R., Patriche, C., Borrelli, P., Panagos, P., Roșca, B., Dumitraşcu, M., ... & Bandoc, G. (2021). Arable lands under the pressure of multiple land degradation processes. A global perspective. Environmental Research, 194, 110697.
  • Salem, M., Bose, A., Bashir, B., Basak, D., Roy, S., Chowdhury, I. R., ... & Tsurusaki, N. (2021). Urban expansion simulation based on various driving factors using a logistic regression model: Delhi as a case study. Sustainability, 13(19), 10805.
  • Sarif, M., & Gupta, R. D. (2022). Spatiotemporal mapping of Land Use/Land Cover dynamics using Remote Sensing and GIS approach: a case study of Prayagraj City, India (1988–2018). Environment, Development and Sustainability, 24(1), 888-920.
  • Sat A., Üçer Z.A. G., Varol Ç., Yenigül S. B., (2017). Sürdürülebilir Kentler İçin Çok Merkezli Gelişme: Ankara Metropoliten Kenti İçin Bir Değerlendirme, Ankara Araştırmaları Dergisi, 5(1), 98-107.
  • Shafizadeh-Moghadam, H., Minaei, M., Pontius Jr, R. G., Asghari, A., & Dadashpoor, H. (2021). Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj Region of Iran. Computers, Environment and Urban Systems, 87, 101595.
  • Tian, P., Li, J., Cao, L., Pu, R., Wang, Z., Zhang, H., ... & Gong, H. (2021). Assessing spatiotemporal characteristics of urban heat islands from the perspective of an urban expansion and green infrastructure. Sustainable Cities and Society, 74, 103208.
  • Wang, S. W., Munkhnasan, L., ve Lee, W. K. (2021). Land use and land cover change detection and prediction in Bhutan's high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges, 2, 100017.
  • Xu, T., Zhou, D., & Li, Y. (2022). Integrating ANNs and Cellular Automata–Markov Chain to Simulate Urban Expansion with Annual Land Use Data. Land, 11(7), 1074.
  • Yayla, E. E., Sevik, H., & Isinkaralar, K. (2022). Detection of landscape species as a low-cost biomonitoring study: Cr, Mn, and Zn pollution in an urban air quality. Environmental Monitoring and Assessment, 194(10), 1-10.
  • Yetişkul E., (2017). Karmaşık Kentler ve Planlamada Karmaşıklık. Planlama, 27(1), 7-15.
  • Yilmaz, D., & Isinkaralar, Ö. (2021). Climate action plans under climate-resilient urban policies. Kastamonu University Journal of Engineering and Sciences, 7(2), 140-147.
  • Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689.
  • Zaldo-Aubanell, Q., Serra, I., Sardanyés, J., Alsedà, L., & Maneja, R. (2021). Reviewing the reliability of Land Use and Land Cover data in studies relating human health to the environment. Environmental Research, 194, 110578.
  • Zhang, B., & Wang, H. (2022). Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods. GIScience & Remote Sensing, 59(1), 71-95.
There are 47 citations in total.

Details

Primary Language Turkish
Subjects Urban Policy
Journal Section Articles
Authors

Öznur Işınkaralar 0000-0001-9774-5137

Publication Date February 28, 2023
Acceptance Date December 5, 2022
Published in Issue Year 2023

Cite

APA Işınkaralar, Ö. (2023). Arazi Örtüsü Değişiminin CORINE Verisiyle Modellenmesi: Ankara İlinin Kentsel Büyüme Tahmini. Artium, 11(1), 54-60. https://doi.org/10.51664/artium.1196926

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