بهینه‌سازی تغییرات کاربری اراضی آینده با استفاده از مدل CLUE-s در شهرستان رامیان
محورهای موضوعی :
آمایش سرزمین
مجید محمدی
1
(استادیار دانشکده مهندسی منابع طبیعی دانشگاه سمنان، سمنان، ایران. * (مسوول مکاتبات))
جعفر دستورانی
2
(دانش‌آموخته دانشکده منابع طبیعی دانشگاه تهران، تهران، ایران.)
تاریخ دریافت : 1395/03/22
تاریخ پذیرش : 1395/06/27
تاریخ انتشار : 1401/08/01
کلید واژه:
CLUE-s,
شبیه‌سازی کاربری اراضی,
سناریو,
شهرستان رامیان,
چکیده مقاله :
زمینه و هدف: تغییرات کاربری اراضی و تبدیل منابع طبیعی به زمین‌های کشاورزی و مناطق مسکونی مشکل بزرگی در بسیاری از کشورهای جهان است. تغییرات کاربری اراضی عامل تعیین کننده مهمی در فرآیندهای هیدرولوژیکی بوده و متغیرهای هیدرولوژیکی مانند حجم رواناب، فراوانی سیل، جریان پایه، جریان سطحی و زیرسطحی را تحت تاثیر قرار می‌دهد. با توجه به اهمیت کاربری اراضی مدل‌های زیادی به‌منظور تغییرات کاربری اراضی ارائه شده است. هدف اصلی این تحقیق شبیه‌سازی تغییرات کاربری اراضی شهرستان رامیان برای آینده بود.
روش بررسی: در ابتدا با استفاده از روش‌های سنجش از دور و تصاویر ماهواره لندست نقشه کاربری اراضی سال‌های 2000 و 2012 تهیه شد، سپس مدل CLUE-s برای شبیه‌سازی کاربری اراضی سال 2030 و طراحی سناریوها استفاده گردید. پنج سناریــــو با استفاده از مدل CLUE-s و تعریف محدودیت‌های تغییرات کاربری اراضی طراحی شد.
نتایج و بحث: نتایج نشان داد مهمترین تغییر کاربری در شهرستان رامیان تبدیل جنگل‌ها و مراتع به زمین‌های کشاورزی و مناطق مسکونی است. 07/18 کیلومتر مربع از جنگل‌ها و مراتع به زمین‌های کشاورزی و مسکونی تبدیل شده است. البته در سناریوهای طراحی شده تبدیل این طبقات کاربری اراضی در محل‌های خاصی محدود شده بود. شبیه‌سازی کاربری اراضی و طراحی سناریو ابزار مفیدی برای برنامه‌ریزان و مدیران منابع طبیعی خواهد بود.
چکیده انگلیسی:
Background and Objective: land use changes and conversion of natural resources to agricultural and residential area is an important problem in many countries. Land use change is an essential determinant factor in hydrological processes and is known as an affecting factor on hydrological parameters such as runoff volume, flood frequency, base flow, subsurface and surface flow. Regard to importance of land use many models was presented to simulate land use changes. The main objective of this research was to simulate the land use changes of Ramian Township for future as a case study site.
Material and Methodology: The main objective of this research was to simulate the land use changes of Ramian Township as a case study site. At first, land use maps related to years of 2000 and 2012 were prepared using remote sensing techniques and Landsat images, and then CLUE-s model was applied to simulate land use map of 2030 and create scenarios. Four scenarios were designed using CLUE-s model and define some limitation of land use change.
Discussion & Conclusion: the results showed that the main land use change in Ramian Township was the conversion of forest and rangeland areas to agriculture and residential land. 18.07 km2 of forest and range were converted to agriculture and residential area. Of course in the designed scenarios conversion of this land use types were limited at the specific locations. Land use simulation and scenario design can be very useful for programmers and natural resources managers.
منابع و مأخذ:
Mohammady, M., 2014. Predicting Effects of Land Use Changes on Runoff Generation Using CLUE-s and WetSpa models for Management of Baghsalian Watershed in Golestan Province. Phd thesis, Tarbiat Modares University. 108 p. (In Persian)
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Manson, S.M., 2005. Agent-based modeling and genetic programming for modeling land change in the Southern Yucata´n peninsular region of Mexico, Agriculture, Ecosystem and Environment, Vol. 111, pp. 47– 62.
Bolliger, J., 2005. Simulating complex landscapes with a generic model: sensitivity to qualitative and quantitative classifications, Ecological Complexity, Vol. 2, pp. 131–149.
Verburg, P.H., Veldkamp, A., 2004. Projecting land use transitions at forest fringes in the Philippines at two spatial scales, Landscape Ecology, Vol. 19, pp. 77–98.
Agarwal, D.K., Silander, J.A. Jr., Gelfand, A.E., Deward, R.E., Mickelson, J.G. Jr., 2005. Tropical deforestation in Madagascar: analysis using hierarchical, spatially explicit, Bayesian regression models, Ecological Modelling, Vol. 185, pp. 105–131.
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Brandt, J.S., Haynes, M.A., Kuemmerle, T., Waller, D.M., Radeloff, V.C., 2013. Regime shift on the roof of the world: alpine meadows converting to shrublands in the southern Himalayas, Biological Conservation, Vol. 158, pp. 116–127.
Mohammady, M., Morady, H.R., Zeinivand, H., Temme, A.J.A.M, 2014. A Comparison of Supervised, Unsupervised and Synthetic Land use Classification Methods in the North of Iran, International Journal of Environmental Science and Technology, DOI 10.1007/s13762-014-0728-3.
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Schmitt-harsh, M., 2013. Landscape change in Guatemala: driving forces of forest and coffee agroforest expansion and contraction from 1990 to 2010, Applied Geography, Vol. 40, pp. 40-50
Gibreel, T.M., Herrmann, S., Berkhoff, K., Nuppenau, E.A., Rinn, A., 2014. Farm types as an interface between an agroeconomical model and CLUE-Naban land change model: Application for scenario modeling, Ecological Indicators, Vol. 36, pp. 766– 778.
Veldkamp, A., Fresco, L.O., 1996. CLUE-CR: an integrated multi-scale model to simulate land use change scenarios in Costa Rica, Ecological Modelling, Vol. 91, pp. 231–248 .
Chen, Y., Xu, Y., Yin, Y., 2009. Impacts of land use change scenarios on storm-runoff generation in Xitiaoxi basin, China, Quaternary International, Vol. 208, pp. 121-128.
Luo, G., Yin, C., Chen, X., Xu, W., Lu, L., 2010. Combining system dynamic model and CLUE-s model to improve land use scenario analyses at regional scale: A case study of Sangong watershed in Xinjiang, China, Ecological Complexity, Vol. 7, pp. 198-207.
Zhang, Y. M., Zhao, S. D., Verburg, P., 2004. Scenario analysis of land use change in Horqin Desert and its surrounding area, Journal of Natural Resources, Vol. 19, pp. 29–38.
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Wu, Q., Li, H., Wang, R., Paulussen, J., He, Y., Wang, M., Wang, B., Wang, Z., 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS, Landscape and Urban Planning, Vol. 78, pp. 322-333.
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Pourghasemi, H.R, Mohammady, M., Noor, H., Afzali, S.F., 2022. Land Use Change Simulation Using CLUE-s Model in the Watershed of Doroodzan Dam, Journal of watershed management science, Vol. 16, pp. 23–31. (In Persian)
Verburg, P.H., Veldkamp, A., Fresco, L.O, 1999. Simulation of changes in the spatial pattern of land use in China, Applied Geography, Vol. 19, pp. 211–233.
Priess, J.A., De Koning, G.H.J., Veldkamp, A., 2001. Assessment of interactions between land use change and carbon and nutrient fluxes in Ecuador, Agriculture, Ecosystems and Environment, Vol. 85, pp. 269–279.
Verburg, P.H., Schulb, C.J.E., Witte, N., Veldkamp, A., 2006. Downscaling of land use change scenarios to assess the dynamics of European landscapes, Agriculture, Ecosystems and Environment, Vol. 114, pp. 39–56.
Lima, M.L., Zelaya, K., Massone, H., 2011. Groundwater vulnerability assessment combining the drastic and Dyna-Clue model in the Argentine Pampas, Environmental Management, Vol. 47, pp. 828–839.
Zheng, X.Q., Zhao, L., Xiang, W.N., Li, N., Lv, L.N., Yang, X., 2012. Coupled model for simulating spatio-temporal dynamics of land-use change: A case study in Changqing, Jinan, China, Landscape and Urban Planning, Vol. 106, pp. 51– 61.
Mohammady, M., 2021. Land use change optimization using a new ensemble model in Ramian County, Iran, Environmental Earth Sciences, Vol. 80, pp. 1-9.
Aydın, A., Eker, R., 2022. Future land use/land cover scenarios considering natural hazards using Dyna-CLUE in Uzungöl Nature Conservation Area (Trabzon-NE Türkiye), Natural Hazards, Vol. 114, pp. 2683–2707.
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Mohammady, M., 2014. Predicting Effects of Land Use Changes on Runoff Generation Using CLUE-s and WetSpa models for Management of Baghsalian Watershed in Golestan Province. Phd thesis, Tarbiat Modares University. 108 p. (In Persian)
Riebsame, W.E., Meyer, W.B., Turner I.I. B.L., Modeling land use and cover as part of global environmental change, Climate Change, Vol. 28: pp. 45-64 .
Thomson, A.M., Brown, R.A., Rosenberg, N.J., Srinivasan, R., Izaurralde, C., 2005. Climate change impacts for the conterminous USA: an integrated assessment, Climate Change, Vol. 69, pp. 67–88.
Mohammady, M., Moharami, S., 2022. Journal of Environmental Science Studies, Vol. 7, pp. 5711–5721. (In Persian)
Manson, S.M., 2005. Agent-based modeling and genetic programming for modeling land change in the Southern Yucata´n peninsular region of Mexico, Agriculture, Ecosystem and Environment, Vol. 111, pp. 47– 62.
Bolliger, J., 2005. Simulating complex landscapes with a generic model: sensitivity to qualitative and quantitative classifications, Ecological Complexity, Vol. 2, pp. 131–149.
Verburg, P.H., Veldkamp, A., 2004. Projecting land use transitions at forest fringes in the Philippines at two spatial scales, Landscape Ecology, Vol. 19, pp. 77–98.
Agarwal, D.K., Silander, J.A. Jr., Gelfand, A.E., Deward, R.E., Mickelson, J.G. Jr., 2005. Tropical deforestation in Madagascar: analysis using hierarchical, spatially explicit, Bayesian regression models, Ecological Modelling, Vol. 185, pp. 105–131.
Verburg, P., Soepboer, W., Limpiada, R., Espaldon, M., Sharifa, M., Veldkamp, T., 2002. Land use change modelling at the regional scale: the CLUE-S model, Environmental Management, Vol. 30, pp. 391–405.
Brandt, J.S., Haynes, M.A., Kuemmerle, T., Waller, D.M., Radeloff, V.C., 2013. Regime shift on the roof of the world: alpine meadows converting to shrublands in the southern Himalayas, Biological Conservation, Vol. 158, pp. 116–127.
Mohammady, M., Morady, H.R., Zeinivand, H., Temme, A.J.A.M, 2014. A Comparison of Supervised, Unsupervised and Synthetic Land use Classification Methods in the North of Iran, International Journal of Environmental Science and Technology, DOI 10.1007/s13762-014-0728-3.
Hudson, W., Ramm, C., 1987. Correct formula of the kappa coefficient of agreement. Photogramm. Engineering Remote Sensing, Vol. 53(4), pp. 421–422.
Foody, G.M., 2002. Status of land covers classification accuracy assessment, Remote Sensing of Environment, Vol. 80(1), pp. 185-201.
Schmitt-harsh, M., 2013. Landscape change in Guatemala: driving forces of forest and coffee agroforest expansion and contraction from 1990 to 2010, Applied Geography, Vol. 40, pp. 40-50
Gibreel, T.M., Herrmann, S., Berkhoff, K., Nuppenau, E.A., Rinn, A., 2014. Farm types as an interface between an agroeconomical model and CLUE-Naban land change model: Application for scenario modeling, Ecological Indicators, Vol. 36, pp. 766– 778.
Veldkamp, A., Fresco, L.O., 1996. CLUE-CR: an integrated multi-scale model to simulate land use change scenarios in Costa Rica, Ecological Modelling, Vol. 91, pp. 231–248 .
Chen, Y., Xu, Y., Yin, Y., 2009. Impacts of land use change scenarios on storm-runoff generation in Xitiaoxi basin, China, Quaternary International, Vol. 208, pp. 121-128.
Luo, G., Yin, C., Chen, X., Xu, W., Lu, L., 2010. Combining system dynamic model and CLUE-s model to improve land use scenario analyses at regional scale: A case study of Sangong watershed in Xinjiang, China, Ecological Complexity, Vol. 7, pp. 198-207.
Zhang, Y. M., Zhao, S. D., Verburg, P., 2004. Scenario analysis of land use change in Horqin Desert and its surrounding area, Journal of Natural Resources, Vol. 19, pp. 29–38.
20- Zhang, X., Zhao, L., Xiang, W., Li, N., Lv, L., Yang, X., 2012. A coupled model for simulating spatio-temporal dynamics of land-use change: A case study in Changqing, Jinan, China, Landscape and Urban Planning, Vol. 106, pp. 51– 61.
Anderson, J.R., Hardy, E.E., Roach, J.T., Witmer, R.E., 1976. A land use and land cover classification system for use with remote sensor data. Washington, dc: U.S. Geological survey. No. Professional paper 964 .
Mendoza, L., Pena, E., Ramırez, M., Prieto, J., Galicia, L., 2006. Projecting land use change processes in the Sierra Norte of Oaxaca, Mexico, Applied Geography, Vol. 26, pp. 276-290.
Wu, Q., Li, H., Wang, R., Paulussen, J., He, Y., Wang, M., Wang, B., Wang, Z., 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS, Landscape and Urban Planning, Vol. 78, pp. 322-333.
Brinkmann, K., Schumacher, J., Dittrich, A., Kadaore, I., Buerkert, A., 2012. Analysis of landscape transformation processes in and around four West African cities over the last 50 years, Landscape and Urban Planning, Vol. 105, pp. 94–105.
Nazari samani, A., Heravi, H., Panahi, M., JafariShalamzari, M., 2013. The effect of change on land use and precipitation on the sediment in Taleghan basin. Journal of range and watershed management. 66: 157-165. (In Persian)
Pourghasemi, H.R, Mohammady, M., Noor, H., Afzali, S.F., 2022. Land Use Change Simulation Using CLUE-s Model in the Watershed of Doroodzan Dam, Journal of watershed management science, Vol. 16, pp. 23–31. (In Persian)
Verburg, P.H., Veldkamp, A., Fresco, L.O, 1999. Simulation of changes in the spatial pattern of land use in China, Applied Geography, Vol. 19, pp. 211–233.
Priess, J.A., De Koning, G.H.J., Veldkamp, A., 2001. Assessment of interactions between land use change and carbon and nutrient fluxes in Ecuador, Agriculture, Ecosystems and Environment, Vol. 85, pp. 269–279.
Verburg, P.H., Schulb, C.J.E., Witte, N., Veldkamp, A., 2006. Downscaling of land use change scenarios to assess the dynamics of European landscapes, Agriculture, Ecosystems and Environment, Vol. 114, pp. 39–56.
Lima, M.L., Zelaya, K., Massone, H., 2011. Groundwater vulnerability assessment combining the drastic and Dyna-Clue model in the Argentine Pampas, Environmental Management, Vol. 47, pp. 828–839.
Zheng, X.Q., Zhao, L., Xiang, W.N., Li, N., Lv, L.N., Yang, X., 2012. Coupled model for simulating spatio-temporal dynamics of land-use change: A case study in Changqing, Jinan, China, Landscape and Urban Planning, Vol. 106, pp. 51– 61.
Mohammady, M., 2021. Land use change optimization using a new ensemble model in Ramian County, Iran, Environmental Earth Sciences, Vol. 80, pp. 1-9.
Aydın, A., Eker, R., 2022. Future land use/land cover scenarios considering natural hazards using Dyna-CLUE in Uzungöl Nature Conservation Area (Trabzon-NE Türkiye), Natural Hazards, Vol. 114, pp. 2683–2707.
