مدل‌سازی تغییرات کاربری اراضی و ارائه الگوی بهینه برای گسترش شهری، مطالعه موردی: شهر قزوین

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه محیط‌زیست، دانشگاه آزاد اسلامی، واحد تهران شمال، تهران، ایران.

2 دانشیار، گروه برنامه ریزی، مدیریت و آموزش محیط‌زیست، دانشگاه آزاد اسلامی، واحد تهران شمال، تهران، ایران.

3 استادیار، گروه محیط‌زیست، دانشگاه آزاد اسلامی، واحد تهران شمال، تهران، ایران.

4 دانشیار، گروه ژئوتکنیک و حمل‌ونقل، دانشگاه شهید بهشتی، تهران، ایران.

چکیده

شهر قزوین به علت نقش مهم اقتصادی، کشاورزی، ارتباطی و نزدیکی به تهران، در چند سال اخیر رشد بیش از اندازه‌ای را تجربه کرده است. هدف از این مطالعه مدل‌سازی تغییرات کاربری اراضی شهر قزوین و تعیین مکان‌های بهینه برای توسعه شهر می‌باشد. در این پژوهش ابتدا اطلاعات مکانی، تصحیح هندسی و رادیومتریک و نقشه‌های کاربری شهر از سال 1990 تا 2020، به کمک مدل فازی آرت‌مپ تهیه شد. سپس با استفاده از مدل‌های تغییرات کاربری و مدل زنجیره مارکوف و سلول‌های خودکار، تغییرات کاربری اراضی تا سال 2025 بررسی شد. همچنین شاخص‌های مناسب ‌برای تعیین پهنه‌های مناسب توسعه شهری شناسایی و عملیات استانداردسازی و تلفیق لایه‌ها با استفاده از سیستم اطلاعات جغرافیایی و عملیات وزن‌دهی به روش فرایند تحلیل سلسله مراتبی انجام‌ شد. نتایج نشان داد که مساحت اراضی ساخته شده، در این دوره 30 ساله، 43/67 درصد رشد داشته و با ادامه یافتن این روند در سال 2025 به 6/3709 هکتار خواهد رسید. در طی این فرایند نیز به ترتیب 12/69 و 98/172 هکتار فضای سبز و سایر کاربری‌ها به اراضی شهری تبدیل خواهند شد. همچنین مجموع مساحت زمین‌های کشاورزی و فضای سبز با 9/72 هکتار کاهش به 63/5427 هکتار خواهد رسید. نتایج ‌به‌ دست آمده از مکان‌یابی بهینه گسترش شهری نیز نشان داد که 03/10 درصد از سطح منطقه (16/983 هکتار) دارای پتانسیل بالایی برای گسترش شهر هستند که اغلب شامل قسمت‌های شمالی و شرقی می‌شوند. یافته‌های به‌دست ‌آمده از رویکرد پیشنهادی می‌تواند به‌عنوان مدل مناسبی برای شناسایی مکان‌های مستعد توسعه شهری و ابزار مناسبی برای برنامه‌ریزی شهری باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling Land use Changes and Providing an Optimal Model for Urban Expansion, Case Study: Qazvin City

نویسندگان [English]

  • Amir Sadeghi 1
  • Roxana Moogouei 2
  • Saeed Malmasi 3
  • Alireza Gharagozlou 4
1 Ph.D. Candidate, Department of Environment, North Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Associate Professor, Department of Environmental Planning, management and Education, North Tehran Branch, Islamic Azad University, Tehran, Iran.
3 Assistant Professor, Department of Environment, North Tehran Branch, Islamic Azad University, Tehran, Iran.
4 Associate Professor, Department of Geotechnique and Transport, University of Shahid Beheshti, Tehran, Iran.
چکیده [English]

Qazvin city has experienced rapid growth over the last few years, owing to its important economic, agricultural, communication, and proximity to Tehran. This study aimed to model land use changes in Qazvin city and determine optimal places for city development. In this study, spatial information, geometric and radiometric correction, and functional maps from 1990 to 2020 were prepared using the Artmap fuzzy model. Land use changes until 2025 were then analyzed utilizing land use change models, Markov chain models and cellular automata. In addition, appropriate factors were identified to determine suitable areas for urban development, and standardization and integration of layers were carried out using the geographic information system and weighting operations using the Analytical Hierarchy process. According to the results, in these 30 years, the area of built land has grown by 67.43 % and if this trend continues, it will reach 3709.6 hectares in 2025. The conversion of 69.12 and 172.98 hectares of green space and other uses to urban areas will take place during this process. Also, the total area of agricultural land and green space will decrease by 72.9 hectares to 5427.63 hectares. The results of the optimal location of urban expansion also showed that 10.03% of the area (983.16 hectares) has a high potential for city expansion, which often includes the northern and eastern parts. These findings can be used as a suitable model for identifying places prone to urban development and a suitable tool for urban planning.

کلیدواژه‌ها [English]

  • Modeling
  • Integrated Model of Markov Chain and Automatic Cells
  • Urban Development
  • Hierarchical Analysis Process
  • Qazvin City
Aburas, M. M., Ho, Y.-M., Firuz Ramli, M. & Ash’aari, Z.H. (2017) Improving the Capability of an Integrated CA-Markov Model to Simulate Spatio-Temporal Urban Growth Trends Using an Analytical Hierarchy Process and Frequency Ratio. International Journal of Applied Earth Observation and Geoinformation, 59, 65-78. DOI: https://doi.org/10.1016/j.jag.2017.03.006
Acheampong, M., Yu, Q., Enomah, L.D., Anchang, J., & Eduful, M. (2018). Land use/cover change in Ghana’s oil city: Assessing the impact of neoliberal economic policies and implications for sustainable development goal number one-A remote sensing and GIS approach. Land Use Policy, 73, 373-384. DOI:‌ https://doi.org/10.1016/j.landusepol.2018.02.019
Agyemang, F.S.K., Silva, E., & Fox, S. (2023). Modelling and simulating ‘informal urbanization’: An integrated agent-based and cellular automata model of urban residential growth in Ghana. Urban Analytics and City Science, 50(4), 863-877. DOI: https://doi.org/10.1177/23998083211068843
AlFanatseh, A. (2022). Land suitability analysis of urban development in the Aqaba area, Jordan, using a GIS-based analytic hierarchy process. Geo Journal, 87, 4143-4159. DOI: https://doi.org /10.1007/s10708-021-10488-1
Alijani, Z., Hosseinali, F., & Biswas, A. (2020). Spatio-temporal evolution of agricultural land use change drivers: A case study from Chalous region, Iran. Journal of Environmental Management, 262, 110326. DOI: https://doi.org/10.1016/j.jenvman.2020.110326
Arkhi, S., & Esfahani, M. (2019). Forecasting land use changes using multi-temporal images and the Marko chain model (Case Study: Ilam city). Geography and Territorial Spatial Arrangement, 9(30), 95-112. (In Persian). DOI: 10.22111/gaij.2019.4529
Azizi, P., Bagheri, F., Sharifi, Sh., & Mikaeili, M. (2022). An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change. Land, 11, 17-15. DOI: https://doi.org/10.3390/ land11101715
Bamrungkhul, S., & Tanaka, T. (2022). The assessment of land suitability for urban development in the anticipated rapid urbanization area from the Belt and Road Initiative: A case study of Nong Khai City. Thailand. Sustainable Cities and Society, 83, 103988. DOI: https://doi.org/10.1016/j.scs.2022.103988
 Bantider, A., Hurni, H., & Zeleke, G. (2011). Responses of rural households to the impacts of population and land-use changes along the‌ Eastern Escarpment of Wello, Ethiopia. Norsk Geografisk Tidsskrift, 65, 42–53. DOI: https://doi.org/10.1080/00291951.2010.549954
Bewket, W. (2002). Land covers dynamics since the 1950s in Chemoga watershed, Blue Nile basin, Ethiopia. Mountain Research and Development, 22, 263–269. DOI: https://doi.org/10.1659/0276-4741(2002)022[0263:LCDSTI]2.0.CO;2
Derakhsh, M., & Sobhanardakani, S. (2022). Simulation of the Spatial Pattern of Land Use Change in the City of Gachsaran Using Cellular Automata Model. Human and Environment, 20(3), 83 - 97.‌ (In Persian)
Entahabu, H.H., Minale, A.S., & Birhane, E. (2023). Modeling and Predicting Land Use/Land Cover Change Using the Land Change Modeler in the Suluh River Basin, Northern Highlands of Ethiopia. Sustainability, 15, 8202. DOI: https://doi.org/10.3390/su15108202
Farahmand, S., & Akbari, N. (2008). Spatial Analysis of Urban Development in Iran. Iranian Journal of Economic Research, 10(34), 73-98.
Guvel, S.P., Akgul, M.A., & Akkoyunlu, M.F. (2023). Monitoring and Evaluation of 2015 Devrek Zonguldak Landslide within the scope of Flood Risk Assessment by Landsat-8 Satellite Data. Journal of Natural Hazards and Environment, 9(1), 81-89. DOI: https://doi.org/10.21324/dacd.1152670‌
Hassan, M.I. & Elhassan, S.M.M. (2020) Modelling of Urban Growth and Planning: A Critical Review. Journal of Building Construction and Planning Research, 8, 245-262. DOI:10.4236 /jbcpr.2020.84016
Jadawala, S.H., Shukla, S.H., & Tiwari, P.S. (2021). Cellular Automata and Markov Chain Based Urban Growth Prediction, International Journal of Environment and Geoinformatics, 8(3), 337-343. DOI: https://doi.org/10.30897/ijegeo.781574
Kabat, P., Claussen, M., Dirmeyer, P.A., Gash, J.H., de Guenni, L.B., & et al. (Eds.) (2004). Vegetation, Water, Humans and the Climate: A New Perspective on an Internactive System. Springer Science & Business Media, Berlin/Heidelberg, Germany. DOI: https://doi.org/10.1007 /978-3-642-18948-7
Karimzadeh Motlagh, Z., Lotfi, A., Pourmanafi, S., & Ahmadizadeh, S. (2022). Evaluation and Prediction of Land-Use Changes using CA_Markov Model. Geography and Environmental Planning, 33 (2), 1-6. (In Persian) DOI: 10.22108/gep.2022.130601.1458
Khan, F., Das, B., & Mohammad, P. (2022). Urban Growth Modeling and Prediction of Land Use Land Cover Change Over Nagpur City, India Using Cellular Automata Approach. In: Rai, P.K. Mishra, V.N. Singh, P. (eds) Geospatial Technology for Landscape and Environmental Management. Advances in Geographical and Environmental Sciences. DOI: https://doi.org/10. 1007 /978-981-16-7373-3_13
Lia, J., Caoa, Y., Lib, Y., Chua, J., Wanga, Y., & Maa, M. (2023). Using EL-CA Model to Predict Multi-Scenario Land Sustainable Use Simulation and Urban Development. Journal of Experimental Nanoscience, 18(1), 2170352. DOI: https://doi.org/10.1080/17458080.2023.2170352
Liu, J., Hu, C., Kang, X., & Chen, F. (2023). A Loosely Coupled Model for Simulating and Predicting Land Use Changes. Land,12, 189. DOI:‌ https://doi.org/10.3390/land12010189
Mahmoudzadeh, H., Abedini, A., & Aram, F. (2022). Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz). Land, 11, 2162. DOI: https://doi.org/10.3390/land11122162
Mamitimin, Y., Simayi, Z., Mamat, A., Maimaiti, B, & Ma, Y. (2023). FLUS Based Modeling of the Urban LULC in Arid and Semi-Arid Region of Northwest China: A Case Study of Urumqi City. Sustainability,15, 4912. DOI: https://doi.org/10.3390/su15064912
Mansour, S., Ghoneim, E., El-Kersh, A., Said, S., & Abdelnaby, S. (2023). Spatiotemporal Monitoring of Urban Sprawl in a Coastal City Using GIS-Based Markov Chain and Artificial Neural Network (ANN). Remote Sensing, 15, 601. DOI: https://doi.org/10.3390/rs15030601
Maurya, N.K., Rafi, S., & Shamoo, S. (2023). Land use/land cover dynamics study and prediction in jaipur city using CA markov model integrated with road network. GeoJournal, 88, 137–160. DOI: https://doi.org/10.1007/s10708-022-10593-9
Pilehvar, A.A. (2021). Spatial-geographical analysis of urbanization in Iran. Humanities and Social Sciences Communications. 8, 63. DOI: https://doi.org/10.1057/s41599-021-00741-w
Saaty, T. L. 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. DOI: https://doi.org/ 10.1016/0270-0255(87)90473-8
Saeidi, S., Mirkarimi, SH., Mohammadzadeh, M., Salmanmahiny, A., & Arrowsmith, C. (2018). Designing an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes. Geocarto international, 33(12), 1381-1397. DOI: https://doi.org/10.1080/10106049.2017.1353647
Salman Mahini, A.R., & Ghayab, H. (2018). Remote sensing and applied geographic information systems with address software. Tehran, Mehr Mahdis Publications.
Shooshtariana, M.R., Dehghanib, M., Margheritac, F., Conti Geac, O., & Mortezazadehd, S. (2018). Land use change and conversion effects on ground water quality trends: An integration of land change modeler in GIS and a new Ground Water Quality Index developed by fuzzy multi-criteria group decision-making models. Food and Chemical Toxicology, 114, 204–214. DOI: https://doi.org/10.1016/j.fct.2018.02.025
Ullah, N., Siddique, M.A., Ding, M., Grigoryan, S., Khan, I.A., Kang, Z., Tsou, S., Zhang, T., Hu, Y., & Zhang, Y. (2023). The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005–2050) of Tianjin City, China. International Journal of Environmental Research and Public Health, 20, 2642. DOI: https://doi.org/10.3390/ijerph20032642
United Nations (2018). World Urbanization Prospects: The 2018 Revisions.
Walker, R. (2004). Theorizing land-cover and land-use change: the case of tropical deforestation. International regional science review, 27. DOI: https://doi.org/10.1177/0160017604266026
Wang, Y., Tao, S., Chen, X., Huang, F., Xu, X., Liu, X., Liu, Y., & Liu, L. (2022). Method multi-criteria decision-making method for site selection analysis and evaluation of urban integrated energy stations based on geographic information system. Renewable Energy, 194, 273-292. DOI: https://doi.org/10.1016/j.renene.2022.05.087
Wood, E., Tappan, G., & Hadj, A. (2004). Understanding the drivers of agricultural land use change in south-central Senegal. Journal of Arid Environments., 59, 565–582. DOI: https://doi.org/10.1016/j.jaridenv.2004.03.022
Xu, C., Hu, X., Liu, Z., Wang, X., Tian, J., & Zhao, Z. (2023). Predicting the Evolution Trend of Water and Land Resource Carrying Capacity Based on CA–Markov Model in an Arid Region of Northwest China. Sustainability, 15, 1269. DOI: https://doi.org/10.3390/su15021269
Zhang, T. (2000). Land Market Forces and Government’s Role in Sprawl. Cities, 17, 123-135. DOI: https://doi.org/10.1016/S0264-2751(00)00007-X
Zhao, L., Liu, X., Xu, X., Liu, C., & Chen, K. (2022). Three-Dimensional Simulation Model for Synergistically Simulating Urban Horizontal Expansion and Vertical Growth. Remote Sensing, 14, 1503. DOI: https://doi.org/10.3390/rs14061503
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