پایش تغییرات کاربری اراضی شهری و شبیه‌سازی توسعۀ آتی شهر (مطالعۀ موردی: شهر کرکوک، عراق)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه جغرافیا و برنامه‌ریزی شهری، دانشگاه مراغه، مراغه، ایران

2 گروه جغرافیا و برنامه‌‏ریزی شهری، دانشگاه مراغه، مراغه، ایران

3 گروه جغرافیا و ‏برنامه ریزی شهری، دانشگاه مراغه، مراغه، ایران

10.22059/jtcp.2025.402308.670522

چکیده

افزایش شتابان شهرنشینی و تغییرات گسترده در الگوهای کاربری اراضی، به‌ویژه در مناطق در حال توسعه، موجب بروز چالش‌هایی در زمینۀ مدیریت پایدار زمین، از بین رفتن منابع طبیعی و اختلال در توازن اکولوژیکی شده است. مطالعۀ حاضر با هدف تحلیل فضایی‌ـ‌ ـزمانی تغییرات کاربری اراضی در شهر کرکوک طی دورۀ ۱۹۹۰ تا ۲۰۲۴ و پیش‌بینی روند آن تا سال ۲۰۳۶ با بهره‌گیری از رویکردهای پیشرفتۀ سنجش از دور و مدل‌سازی انجام گرفته است. برای این منظور، از تصاویر چندزمانۀ ماهواره‌ای لندست 5 و 8 استفاده شد و پس از انجام دادن پیش‌پردازش‌های هندسی و رادیومتریکی، طبقه‌بندی به کمک الگوریتم یادگیری ماشین SVM در محیط نرم‌افزار ENVI اجرا شد. صحت طبقه‌بندی با استفاده از ماتریس خطا، ضریب کاپا و دقت کلّی ارزیابی شد که نتایج به‌ترتیب برابر با 88 درصد (Kappa=0/90) در سال ۱۹۹۰ و 91 درصد (Kappa=0/94) در سال ۲۰۲۴ برآورد شد. در ادامه، با بهره‌گیری از مدل ترکیبی CA– Markov در نرم‌افزار IDRISI، پیش‌بینی تغییرات تا افق ۲۰۳۶ انجام گرفت. نتایج مدل نشان داد که سطح کاربری شهری از 6/8 درصد به بیش از 3/22 درصد خواهد رسید، درحالی‌که اراضی بایر از حدود 88 درصد به کمتر از 74 درصد کاهش خواهد یافت. همچنین، افزایش محدود در کاربری باغی و پهنۀ آبی نیز مشاهده شد. این الگوها حاکی از توسعۀ پراکنده و ناپایدار مناطق شهری در جهت جنوب و غرب شهر است. دستاوردهای این پژوهش بیانگر کارایی بالای ترکیب الگوریتم‌های SVM و CA– Markov در پایش و پیش‌بینی تغییرات کاربری اراضی در مناطق پیچیدۀ شهری است. همچنین، نتایج می‌تواند به ‌عنوان ابزار تصمیم‌یار در تدوین سیاست‌های فضایی، کنترل گسترش بی‌رویة شهری، و مدیریت منابع طبیعی به ‌کار گرفته شود.

کلیدواژه‌ها

موضوعات


Abdallatif, M. I., Omer; D. A., & Noori, A. M. (2024). Monitoring and prediction of land cover changes of Kirkuk City using machine learning and remote sensing data Available to Purchase. The 5th International Conference On Civil And Environmental Engineering Technologies. https://doi.org/10.1063/5.0236482
Ahmed, S. H. (2017). Detection of urban expansion and its impact on land surface temperatures in Kirkuk, Iraq using remote sensing and GIS. BİNGÖL UNIVERSIT, Department of Soil Science Plant Nutrition .
Ajaj, Q. M. (2025). Remote Sensing-based Land Surface Temperature Retrieval in Kirkuk City, Iraq Using GIS and TES Algorithm: A Climatic Concer. Multidisciplinary Research and Growth Evaluation, 6 (4), 475-481.
Alberti, M. (2005). The effects of urban patterns on ecosystem function. Int. Reg. Sci. Rev. 28, 168–192. https://doi.org/10.1177/0160017605275160
Angel, S. (2023). Urban expansion: theory, evidence and practice. Buildings & Cities, 4 (1), 124‑138. https://doi.org/10.5334/bc.348
Arneth, A., Brown, C., & Rounsevell, M. D. A. (2014). Global models of human decision-making for land-based mitigation and adaptation assessment. Nat. Clim. Chang. 4, 550–557. https://doi.org/10.1038/nclimate2250
Bajocco, S., De Angelis, A., Perini, L., Ferrara, A., & Salvati, L. (2012). The impact of land use/land cover changes on landdegradation dynamics: A mediterranean case study. Environmental Management, 49 (5), 980–989. https://doi.org/10.1007/s00267-012-9831-8
Basheer, S., Wang, X., Farooque, A. A., Nawaz, R. A., Liu, K., Adekanmbi, T., & Liu, S. (2022). Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques. Remote Sensing, 14 (19), 4978. https://doi.org/10.3390/rs14194978
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Bhatta, B. (2010). Causes and consequences of urban growth and sprawl. In Analysis of Urban growth and sprawl from remote sensing data. Berlin, Heidelberg: Springer, 17-36.  https://doi.org/10.1007/978-3-642-05299-6
Boulila, W., Ghandorh, H., Khan, M. A., Ahmed, F., & Ahmad, J. (2021). A novel CNN‑LSTM‑based approach to predict urban expansion: Evaluation in Saudi Arabia. ArXiv preprint. https://doi.org/10.48550/arXiv.2103.01695
Cahya, D.L., Martini, E., & Kasikoen, K.M. (2018). Urbanization and land use changes in peri-urban area using spatial analysis methods (case study: Ciawi urban areas, Bogor Regency). In IOP conference series: Earth and environmental science. IOP Publishing Ltd,123, 12035. https://doi.org/10.1088/1755-1315/123/1/012035
Cao, H., Liu, J., Fu, C., Zhang, W., Wang, G., Yang, G., & Luo, L. (2017). Urban Expansion and Its Impact on the Land Use Pattern in Xishuangbanna since the Reform and Opening up of China. Remote Sens. 9(137). https://doi.org/10.3390/rs9020137
Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113 (5), 893–903. https://doi.org/10.1016/j.rse.2009.01.007
Chettry, V. (2022). Geospatial measurement of urban sprawl using multi‑temporal datasets from 1991 to 2021: Case studies of four Indian medium‑sized cities (Lucknow UA, Bhubaneswar UA, Raipur UA, Dehradun UA). Environmental Monitoring and Assessment, 194 (12), 860. https://doi.org/10.1007/s10661-022-10542-6
Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22, 100482. https://doi.org/10.1016/j.rsase.2021.100482
Congalton, R. G., & Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (3rd ed.). CRC Press. https://doi.org/10.1201/9780429052729
DAO. (2014). Kirkuk Masterplan – Dar Al Omran. Retrieved from.
Diaconu, D. C., Peptenatu, D., Gruia, A. K., Grecu, A., Gruia, A. R., Gruia, M. F., Drăghici, C. C., Băloi, A. M., Alexandrescu, M. B., & Sibinescu, R. B. (2025). The Impact of Urban Expansion on Land Use in Emerging Territorial Systems: Case Study Bucharest-Ilfov, Romania. Agriculture, 15, 406. https://doi.org/10.3390/agriculture15040406
Eastman, J. R. (2016). TerrSet Geospatial Monitoring and Modeling System: Manual. Clark Labs, Clark University. https://www.geocarto.com/TerrSet_Brochure.pdf
Elmqvist, T., Fragkias, M., Goodness, J., Güneralp, B., Marcotullio, P. J., McDonald, R. I., Parnell, S., Schewenius, M., Sendstad, M., Seto, K., Wilkinson, C. (2013). Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities: A Global Assessment. Dordrecht Heidelberg New York London: Springer Nature.
Frenkel, A., & Ashkenazi, M. (2008). Measuring urban sprawl: How can we deal with it? Environment and Planning: Planning & Design, 35 (1), 56–79. https://doi.org/10.1068/b32155
Gaur, S., & Singh, R. (2023). A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability, 15, 903. https://doi.org/10.3390/su15020903
Gehad, T.  Y., Omer, N.  Q., & Abdulmajeed, N.  S. (2024). Methodology for using multi‑temporal Landsat images to monitor urban growth of Kirkuk Governorate. Techniques, 6 (2), 113–119. https://doi.org/10.51173/jt.v6i2.1915
Guite, L. T. S. (2019). Assessment of urban sprawl in Bathinda city, India. J. Urban Manag, 8, 195–205. https://doi.org/10.1016/j.jum.2018.12.002
Gumma, M. K., Mohammad, I., Nedumaran, S., Whitbread, A., & Lagerkvist, C. J. (2017). Urban sprawl and adverse impacts on agricultural land: A case study on Hyderabad, India. Remote Sensing, 9 (11), 1–16. https://doi.org/10.3390/rs9111136
Hishe, S., Bewket, W., Nyssen, J., & Lyimo, J. (2020). Analysing past land use land cover change and CA-Markov-based future modelling in the middle Suluh Valley, Northern Ethiopia. Geocarto Int. 35, 225–255. https://doi.org/10.1080/10106049.2018.1516241
Hu, S., Yang, Z., Andres, S., Torres, G., Wang, Z., Han, H., Wada, Y., Wanger, T.C., & Li, L. (2023). Converging trend of global urban land expansion sheds new light on sustainable development. ArXiv. https://doi.org/10.48550/arXiv.2310.02293
Huang, C., Davis, L. S., & Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. Remote Sensing, 23 (4), 725–749. https://doi.org/10.1080/01431160110040323
Islam, M. S., & Ahmed, R. (2012). Land use change predic- tion in Dhaka City using Gis aided markov chain modeling. Life and Earth Science, 6, 81–89. https://doi.org/10.3329/jles.v6i0.9726
Jasim, A., Yassin Ezalden, N., & Ajaj, Q.  M., Hasan, K. (2020). Comparative urban growth among various local jurisdictions of Kirkuk City from 2014 to 2016. JSST, 63 (2).
Khan, Z., Saeed, A., & Bazai, M. H. (2020). Land use/land cover change detection and prediction using the CA-Markov model: A case study of Quetta city, Pakistan. Geography and Social sciences, 2 (2), 164-182
Kilgarriff, P., Lemoy, R., & Caruso, G. (2020). Change in Artificial Land Use over time across European Cities: A rescaled radial perspective. arXiv. https://doi.org/10.48550/arXiv.2010.08401
Lal Shrestha, H., Poude, N. S., Bajracharya, R. M., & Sitaula, B. K.(2019). Mapping and Modelling of Land Use Change in Nepal. Forest and Livelihood, 18 (1). https://doi.org/10.3126/jfl.v18i1.59621
Long, Y., Liu, X., Luo, S., Luo, T., Hu, S., Zheng, Y., Shao, J., & Liu, X. (2023). Evolution and Prediction of Urban Fringe Areas Based on Logistic–CA–Markov Models: The Case of Wuhan City. Land, 12 (10), 1874. https://doi.org/10.3390/land12101874
Millard‑Ball, A., & Barrington‑Leigh, C. (2020). Want to fix urban sprawl? Ditch the cul‑de‑sac, Wired. https://www.wired.com/story/fix-urban-sprawl-ditch-cul-de-sac/
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3), 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Omar, N. Q., & Raheem, A. M. (2016). Determining the suitability trends for settlement based on multi criteria in Kirkuk, Iraq. Open geospatial data, softw. stand. 1, 10. https://doi.org/10.1186/s40965-016-0011-2
Omer, N. Q., & Raheem, A. M. (2016). Determining the suitability trends for settlement based on multi criteria in Kirkuk, Iraq. Open Geospatial Data, 10. https://doi.org/10.1186/s40965-016-0011-2
Pal, M., & Mather, P. M. (2005). Support vector machines for classification in remote sensing. Remote Sensing, 26 (5), 1007–1011. https://doi.org/10.1080/01431160512331314083
Pampoore‑Thampi, O., Varde, A. S., & Yu, D. (2021). Mining GIS data to predict urban sprawl. arXiv. https://arxiv.org/abs/2103.11338
Powers, R. P., & Jetz, W. (2019). Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Chang, 9, 323–329. https://doi.org/10.1038/s41558-019-0406-z
Ribeiro, M. P., Viégas, V. S., de Mello, K., Soares, F. D. S., Valente, R. A., & Chen, D. (2024). Land use/land cover forecast and urban sprawl analysis in a Brazilian city in the Atlantic Forest Biome. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII, 3, 465–470. https://doi.org/ 10.5194/isprs-archives-XLVIII-3-2024-465-2024
Rimal, B., Zhang, L., Keshtkar, H., Haack, B. N., Rijal, S., & Zhang, P. (2018a). Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and markov chain. ISPRS Int. J. Geo Inf, 7, 154. https://doi.org/10.3390/ijgi7040154
Rimal, B., Zhang, L., Stork, N., Sloan, S., & Rijal, S. (2018b). Urban expansion occurred at the expense of agricultural lands in the Tarai region of Nepal from 1989 to 2016. Sustainability, 10, 1341. https://doi.org/10.3390/su10051341
Samardzic-Petrovic, M., Dragicevic, S., Kovačević, M., Bajat, B. (2015). Modeling Urban Land Use Changes Using Support Vector Machines. Transactions in GIS, 25 (5). https://doi.org/10.1111/tgis.12174
Shareef, M.  A., Hassan, N.  D., & Noori, A.  M. (2019). Integrating GIS and fuzzy multi‑criteria method to evaluate land degradation and their impact on the urban growth of Kirkuk city, Iraq. International Journal of Advanced Science and Technology, 28 (15), 800–815.
Siedentop, S., Schmidt, S., & Dunlop, A. (2022). Managing Urban Growth – an Overview of the Literature. Raumforschung und Raumordnung, 80 (6), 659‑677. https://doi.org/10.14512/rur.1653
Simkin, R. D., Seto, K. C., Mcdonald, R. I. & Jetz, W. (2022). Biodiversity impacts and conservation implications of urban land expansion projected to 2050. Sustainability Science, 119 (12), e2117297119. https://doi.org/10.1073/pnas.2117297119
Subasinghe, S., Estoque, R. C., & Murayama, Y. (2016). Spatiotemporal Analysis of Urban Growth Using GIS and Remote Sensing: A Case Study of the Colombo Metropolitan Area, Sri Lanka. ISPRS International Journal of Geo-Information, 5 (11), 197. https://doi.org/10.3390/ijgi5110197
Shah, F., Wei, L., Lashari, A. H., Islam, A., Khattak, L. H., & Rasool, U. (2021). Evaluation of land use and land cover Spatio-temporal change during rapid Urban sprawl from Lahore, Pakistan. Urban Climate, 39, 100931. https://doi.org/10.1016/j.uclim.2021.100931
Subedi, P., Subedi, K., & Thapa, B. (2013). Application of a hybrid cellular automaton – Markov (CA-Markov) model in land-use change prediction: a case study of Saddle Creek Drainage Basin, Florida. Appl. Ecol. Environ. Sci, 1, 126–132. https://doi.org/10.12691/aees-1-6-5
Taha, T. S., & Omar, N. Q. (2024). Urban growth suitability index for Kirkuk City using remote sensing data and AHP-based GIS method. The 5th International Conference On Civil And Environmental Engineering Technologies. https://doi.org/10.1063/5.0238433
Tariq, A., Mumtaz, F., Majeed, M., & Zeng, X. (2022). Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan. Environ. Monit. Assess. 195(1).https://doi.org/10.1007/s10661-022-10738-w
UN-Habitat. (2022). Kirkuk City Profile: Urban Recovery and Reconstruction. United Nations Human Settlements Programme (UN-Habitat), Iraq Programme.
Wang, H., Zheng, B., Liu, Y., & LiU, Y. (2020). Urban expansion patterns and their driving forces based on the center of gravity-GTWR model: A case study of the Beijing-Tianjin-Hebei urban agglomeration. Geographical Sciences, 30 (2), 297-318. https://doi.org/10.1007/s11442-020-1729-4
Wang, S. W., Munkhnasan, L., & 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. Environ. Challenges, 2, 100017. https://doi.org/10.1016/j.envc.2020.100017
Wei, Y. D., & Ewing, R. (2018). Urban expansion, sprawl and inequality. Landscape and Urban Planning, 177, 259‑265.  https://doi.org/10.1016/j.landurbplan.2018.05.021
Winkler, K., Fuchs, R., Rounsevell, M., & Herold, M. (2021). Global land use changes are four times greater than previously estimated. Nature Communications, 12. https://doi.org/10.1038/s41467-021-22702-2
Wu, Y., Li, S., & Yu, S. (2015). Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China: 1979–2013. Environmental Monitoring and Assessment, 188 (1), 54. https://doi.org/10.1007/s10661-015-5069-2
Zhang, J., Hou, Y., Dong, Y., Wang, C., & Chen, W. (2022). Land Use Change Simulation in Rapid Urbanizing Regions: A Case Study of Wuhan Urban Areas.  Environmental Research and Public Health, 19 (14), 8785. https://doi.org/10.3390/ijerph19148785