ANALYSING THE IMPACT OF URBAN GROWTH ON AGRICULTURAL LANDS USING SLEUTH MODEL AND GOOGLE EARTH ENGINE

Year 2024, Volume: 12 Issue: 4, 1006 - 1021, 01.12.2024

Lütfiye Karasaka , Murat Güneş

https://doi.org/10.36306/konjes.1563738

Abstract

In this study, it is aimed to determine the urban growth in the Selçuklu district of Konya, which is the study area with the SLEUTH model based on cellular automata, which is widely used in the modeling of urban growth and land use, and to examine the effect of urbanization on agricultural areas in the near future. In addition to the simulations carried out for the years 2030 and 2050 starting from 2015, which was determined as the last control year in the model, the simulation results of the year 2022 were compared with the terrain classes obtained from the Google Earth Engine (GEE) controlled classification of the 2022 Landsat satellite image. As a result of the creation of simulation models for the years 2030 and 2050, it was concluded that 10428.75-23747.49 hectares of agricultural land will be destroyed, respectively. The SLEUTH model has modeled a total of 56468.26 hectares of agricultural land for 2022. This corresponds to 95% of the classification result for 2022, which is an important factor in examining the accuracy of the model. This study, which aims to guide decision makers and planners, shows that the use of the SLEUTH model has strong implications for the planned examination of future land use.

Keywords

Agricultural Land, GEE, Remote Sensing, SLEUTH, Urban Growth

References

• Y. Ren, H. Li, L. Shen, Y. Zhang, Y. Chen, and J. Wang, “What Is the Efficiency of Fast Urbanization? A China Study,” Sustainability, vol. 10, no. 9, 2018, doi: 10.3390/su10093180.
• UN, “World Urbanization Prospects 2018,” 2018.
• D. Öztürk and İ. E. Ayazlı, “Tokat İlinde Kentsel Büyümenin SLEUTH Modeli İle Simülasyonu,” in SETSCI-Conference Proceedings, SETSCI-Conference Proceedings, 2018, p. 8.
• C. Uysal, M. Uysal, and M. Uysal, “CBS Temelli Hücresel Özişleme Yaklaşımı ile Kentsel Büyüme Simülasyonu: Afyonkarahisar Örneği,” Türkiye Coğrafi Bilgi Sistemleri Dergisi, vol. 2, no. 1, pp. 26–36, 2020, [Online]. Available: https://dergipark.org.tr/en/pub/tucbis/issue/52936/655063
• F. A. CANPOLAT and D. DAĞLI, “ELAZIĞ İLİ’NDE ARAZİ KULLANIMI DEĞİŞİMİ (2006-2018) VE SİMÜLASYONU (2030),” lnternational Journal of Geography and Geography Education, no. 42, 2020, doi: 10.32003/igge.746668.
• M. Deniz and Ö. Hiç, “İklim değişikliği ve tarımın değişen yüzü: artan riskler, tarımdaki daralmalar ve orman yangınları sonrası politika önerileri,” Biga İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 3, no. 1, pp. 12–22, 2022.
• M. Ateş and D. Erinsel Önder, “‘Akıllı Şehir’kavramı ve dönüşen anlamı bağlamında eleştiriler,” Megaron, 2019.
• B. Dey and P. Sharma, “A comprehensive review of urban growth studies and predictions using the Sleuth model,” The Scientific Temper, vol. 15, no. 02, pp. 2333–2341, 2024.
• K. Dhanaraj and G. V Jain, “Urban growth simulations in a medium-sized city of Mangaluru, India, through CA-based SLEUTH urban growth model,” Journal of the Indian Society of Remote Sensing, vol. 51, no. 3, pp. 497–517, 2023.
• R. N. Jawarneh, “Modeling Past, Present, and Future Urban Growth Impacts on Primary Agricultural Land in Greater Irbid Municipality, Jordan Using SLEUTH (1972–2050),” ISPRS Int J Geoinf, vol. 10, no. 4, 2021, doi: 10.3390/ijgi10040212.
• Y. Sakieh, B. J. Amiri, A. Danekar, J. Feghhi, and S. Dezhkam, “Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran,” Journal of Housing and the Built Environment, vol. 30, no. 4, pp. 591–611, 2015, doi: 10.1007/s10901-014-9432-3.
• C. Dietzel and K. Clarke, “The effect of disaggregating land use categories in cellular automata during model calibration and forecasting,” Comput Environ Urban Syst, vol. 30, no. 1, pp. 78–101, 2006.
• F. E. Tombuş, “Çorum ili ve yakın çevresinin Uzaktan Algılama yöntemleri ile arazi kullanımının değerlendirilmesi,” 2019.
• A. A. Jamali, A. Behnam, S. A. Almodaresi, S. He, and A. Jaafari, “Exploring factors influencing urban sprawl and land-use changes analysis using systematic points and random forest classification,” Environ Dev Sustain, vol. 26, no. 5, pp. 13557–13576, 2024.
• S. Saha, D. Sarkar, and P. Mondal, “Urban Expansion Monitoring Using Machine Learning Algorithms on Google Earth Engine Platform and Cellular Automata Model: A Case Study of Raiganj Municipality, West Bengal, India,” in Advancements in Urban Environmental Studies: Application of Geospatial Technology and Artificial Intelligence in Urban Studies, Springer, 2023, pp. 43–55.
• R. W. Aslam, H. Shu, and A. Yaseen, “Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data,” Ann GIS, vol. 29, no. 3, pp. 355–367, 2023.
• M. N. Khalid, M. N. Ahmad, M. A. Javed, and S. R. Ahmad, “Modeling future urban network capacity and land use/land cover simulation using GEE and remote sensing data,” Arabian Journal of Geosciences, vol. 16, no. 11, p. 628, 2023.
• R. T. Handayanto, S. Samsiana, and H. Herlawati, “Driving Factors Selection and Change Direction of a Land Use/Cover,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, no. 1.5, pp. 243–248, 2019.
• D. Öztürk, \.Ismail Ercüment Ayazl\i, and others, “Kentsel Büyümenin Modellenmesi ve Simülasyon Modelleri,” International Journal of Multidisciplinary Studies and Innovative Technologies, vol. 3, no. 1, pp. 44–47, 2019.
• A. Ilyassova, L. N. Kantakumar, and D. Boyd, “Urban growth analysis and simulations using cellular automata and geo-informatics: comparison between Almaty and Astana in Kazakhstan,” Geocarto Int, vol. 36, no. 5, pp. 520–539, 2021.
• X. Yang and C. P. Lo, “Modelling urban growth and landscape changes in the Atlanta metropolitan area,” International Journal of Geographical Information Science, vol. 17, no. 5, pp. 463–488, Jun. 2003, doi: 10.1080/1365881031000086965.
• G. Manca and K. C. Clarke, “Waiting to know the future: A SLEUTH model forecast of urban growth with real data,” Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 47, no. 4, pp. 250–258, 2012.
• I. S. Serasinghe Pathiranage, L. N. Kantakumar, and S. Sundaramoorthy, “Remote Sensing Data and SLEUTH Urban Growth Model: As Decision Support Tools for Urban Planning,” Chin Geogr Sci, vol. 28, no. 2, pp. 274–286, 2018, doi: 10.1007/s11769-018-0946-6.
• G. Chaudhuri and S. Foley, “DSLEUTH: A distributed version of SLEUTH urban growth model,” in 2019 Spring Simulation Conference (SpringSim), 2019, pp. 1–11.
• Ö. \cSevik, “Application of SLEUTH model in Antalya,” Middle East Technical University, 2006.
• H. Oguz, B. K. Atak, H. Doygun, and E. ve Nurlu, “Modeling urban growth and land use/land cover change in Bornova district of Izmir metropolitan area from 2009 to 2040,” in Int. Symp. on Environmental Protection and Planning: Geographic Information Systems (GIS) and Remote Sensing (RS) Applications (ISEPP), 2011.
• D. Öztürk and İ. E. Ayazlı, “Tokat İlinde Kentsel Büyümenin SLEUTH Modeli İle Simülasyonu,” in SETSCI-Conference Proceedings, SETSCI-Conference Proceedings, 2018, p. 8.
• A. Shelestov, M. Lavreniuk, N. Kussul, A. Novikov, and S. Skakun, “Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping,” Front Earth Sci (Lausanne), vol. 5, p. 232994, 2017.
• K. C. Clarke and J. M. Johnson, “Calibrating SLEUTH with big data: Projecting California’s land use to 2100,” Comput Environ Urban Syst, vol. 83, p. 101525, 2020.
• C. Dietzel and K. C. Clarke, “Toward optimal calibration of the SLEUTH land use change model,” Transactions in GIS, vol. 11, no. 1, pp. 29–45, 2007.