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شبیه‌سازی آینده تغییرات مکانی و زمانی کاربری و پوشش اراضی در مناطق خشک (مطالعه موردی: دشت میناب) | ||
| تحقیقات مرتع و بیابان ایران | ||
| مقاله 11، دوره 28، شماره 3، مهر 1400، صفحه 520-536 اصل مقاله (1.79 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijrdr.2021.125019 | ||
| نویسندگان | ||
| حامد اسکندری دامنه1؛ غلامرضا زهتابیان2؛ حسن خسروی* 3؛ حسین آذرنیوند2؛ علی اکبر براتی4 | ||
| 1دانشجوی دکترای بیابان‌زدایی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2استاد، گروه احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی دانشگاه تهران، کرج، ایران | ||
| 3دانشیار، استاد، گروه احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی دانشگاه تهران، کرج، ایران، | ||
| 4استادیار، دانشکده اقتصاد و توسعه کشاورزی، دانشگاه تهران، کرج، ایران | ||
| چکیده | ||
| در این تحقیق به شبیه‌سازی کاربریهای موجود در دشت میناب با استفاده از روش ترکیبی CA-Markov پرداخته شد. بدین‌منظور نقشه کاربری اراضی برای سال‌های 1379، 1389 و 1399 با استفاده از تصاویر ماهواره لندست با روش طبقه‌بندی حداکثر احتمال تولید گردید و بعد از ارزیابی مدل، نقشه کاربری برای سال 1409 و 1419 با استفاده از روش ترکیبی CA-Markov پیش‌بینی شد. تجزیه‌وتحلیل الگوهای تغییرات کاربری در دشت میناب نشان داد که در طول دوره آماری 1379-1399در سطح کاربریهای این منطقه تغییرات چشمگیری رخ داده است. به‌طوری‌که در طول این دوره 20 ساله مساحت کاربریهای کشاورزی، مناطق شهری و انسان‌ساخت، شوره‌زار و مراتع و اراضی بایر به‌ترتیب از 91/38، 99/25، 09/20 و 15 درصد در سال 1379 به 75/40، 02/40، 44/12 و 80/6 درصد در سال 1399 رسیده است. ارزیابی مدل با استفاده از شاخص کاپای بالای 90% نشان‌دهنده دقّت بالای مدل برای پیش‌بینی کاربری‌هاست. پیش‌بینی تغییرات در سال 1409 و 1419 نشان می‌دهد که کاربری زمین‌های کشاورزی و مناطق شهری و انسان‌ساخت به‌ترتیب با میزان 05/0 و 39/0 درصد در حال افزایش می‌باشند که از سمت شرق دشت به سمت غرب آن در حال پیشروی هستند؛ این درحالی است که کاربریهای شوره‌زار، مراتع و اراضی بایر در مجموع با میزان 44/0 درصد در حال کاهش است که این کاهش بیشتر در سمت غرب و شمال‌غرب این دشت قابل رؤیت است. در پایان از مهمترین راهکارهای اجرایی برنامه‌ریزان و مسئولان برای جلوگیری از تغییر کاربری و در نهایت تخریب اراضی در این منطقه، می‌توان به اصلاح الگوی کشت، روش‌های نوین آبیاری، تغذیه بستر این دشت و حفظ و احیای پوشش گیاهی بومی اشاره نمود. | ||
| کلیدواژه‌ها | ||
| دشت میناب؛ پیش‌بینی؛ کاربری اراضی؛ ضریب کاپا؛ سلول خودکار-زنجیره مارکوف | ||
| عنوان مقاله [English] | ||
| Simulation of future spatial and temporal changes in land uses and cover in arid areas (Case study: Minab plain) | ||
| نویسندگان [English] | ||
| Hamed Eskandari Damaneh1؛ Gholamreza Zehtabian2؛ Hassan Khosravi3؛ Hosein Azarnivand2؛ Aliakbar Barati4 | ||
| 1PhD Student De-Desertification, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 2Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 3Associate Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 4Assistant Professor, Faculty of Economics and Agricultural Development, University of Tehran, Karaj, Iran | ||
| چکیده [English] | ||
| In the present study, the existing land uses in the Minab plain were simulated using the CA-Markov combined method. For this purpose, land use maps for the years 2000, 2010 and 2020 were generated using Landsat satellite images using the maximum probability classification method and after evaluating the model, the land use map for 2030 and 2040 was predicted using the combined CA-Markov method. Analysis of land use change patterns in Minab plain showed that during the statistical period 2000-2020 in the level of land uses in this area has changed significantly so that during this 20-year period the area of agricultural land use, urban and man-made areas, saline lands and rangelands and barren lands respectively from 38.91, 25.99, 20.09 and 15 % in 2000 to 40.75, 40.02, 12.44 and 6.80 percent in 2020. Evaluation of the model using kappa index above 90% indicates the high accuracy of the model for predicting land uses. Prediction of changes in 2030 and 2040 show that the use of agricultural lands and urban areas and man-made are increasing at a rate of 0.05 and 0.39 %, respectively, which are advancing from the east of the plain to the west; Meanwhile, the uses of saline areas, rangelands and barren lands are decreasing at a rate of 0.44%, which is more evident in the west and northwest of this plain. Finally, one of the most important executive strategies of planners and officials to prevent land use change and ultimately land degradation in this area, can be to improve the cultivation pattern, new irrigation methods, nourish the bed of this plain and maintain and restore native vegetation. | ||
| کلیدواژه‌ها [English] | ||
| Minab plain, prediction, land-use, Kappa coefficient, Cellular Automation-Markov Chain | ||
| مراجع | ||
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Aburas, M. M., Ho, Y. M., Ramli, M. F. and Ash’aari, Z. H., 2016. The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 52: 380-389. Ahmadaali, K., Eskandari Damaneh, H., Ababaei, B. and Eskandari Damaneh, H., 2021. Impacts of droughts on rainfall use efficiency in different climatic zones and land uses in Iran. Arabian Journal of Geosciences, 14(2): 1-15. Akbari, D., Moradizadeh, M. and Akbari, M., 2020. Land use changes and urban development simulation using neural network and markov chain cellular automata. Journal of Urban Research and Planning, 10(39):183-196. Amini, P., Yavari, V. and Nejadi, A., 2020. Analyzing attitudes of local People towards socio-economic impacts of land use change in Arasbaran biosphere reserve. Journal of Environmental Science and Technology, 22(3): 133-143. Anand, J., Gosain, A. K. and Khosa, R., 2018. Prediction of land use changes based on Land Change Modeler and attribution of changes in the water balance of Ganga basin to land use change using the SWAT model. Science of the total environment, 644: 503-519. Ansari, A. and Golabi, M. H., 2019. Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands–A case study: Meighan Wetland, Iran. International Soil and Water Conservation Research, 7(1): 64-70. Arsanjani, J. J., Kainz, W. and Mousivand, A. J., 2011. Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran. International Journal of Image and Data Fusion, 2 (4): 329-345. Asghari Sereskanrood, S. and Ardeshirpey, A.A., 2020. Prediction of Land Use Changes Using CA-Markov: A Case Study of Yasuj City. Town and Country Planning, 12(2): 407-430. Bagheri, R., Mohammadi, S. and Saljooghi, M., 2016. Investigating of land use change effect on some soil physical properties (Case study: Baft city of Kerman province). Iranian Journal of Range and Desert Research, 23(2): 243-231. Bazgir, M., Hydari, M., Zeynali, N. and Kohzadean, M., 2020. Effect of land use change from forest to agriculture and abounded of agriculture on soil physical and chemical properties in Zagros Forest Ecosystem. Journal of Environmental Science and Technology, 22(1): 201-214. Belete, M., Deng, J., Abubakar, G. A., Teshome, M., Wang, K., Woldetsadik, M. and Gudo, A., 2020. Partitioning the impacts of land use/land cover change and climate variability on water supply over the source region of Blue Nile Basin. Land Degradation & Development. 52:152-168. Biro, K., Pradhan, B., Buchroithner, M. and Makeschin, F., 2013. Land use/land cover change analysis and its impact on soil properties in the northern part of Gadarif region, Sudan. Land Degradation & Development, 24 (1): 90-102. Chowdhury, M., Hasan, M. E. and Abdullah-Al-Mamun, M. M., 2020. Land use/land cover change assessment of Halda watershed using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Science, 23(1): 63-75. Dadashpoor, H., Azizi, P. and Moghadasi, M., 2019. Land use change, urbanization, and change in landscape pattern in a metropolitan area. Science of the Total Environment, 655: 707-719. Du, X., Jin, X., Yang, X., Yang, X. and Zhou, Y., 2014. Spatial pattern of land use change and its driving force in Jiangsu province. International Journal of Environment Research Public Health, 11:3215–3232. Eskandari Damaneh, H., Borji, M., Khosravi, H., Nakhaee Nejadfar, S. and Eskandari, H., 2016. Change detection of of bakhtegan and tashk Basin during 2001-2013. International Journal of Forest, Soil and Erosion (IJFSE), 6(2), 67-71. Eskandari Damaneh, H., Zehtabian, G.R., Khosravi, H., Azarnivand, H., and Barati A.k., 2020a. Investigation of vegetation changes trend affected by drought in arid and semi-arid regions using remote sensing technique (Case study: Hormozgan province). The Desert Ecosystem Engineering Journal (DEEJ), 9(28): 25-34. Eskandari Damaneh, H., Zehtabian, G.R., Khosravi, H., Azarnivand, H., and Barati A.k., 2020b. Simulation and prediction of climatic parameters of temperature and precipitation in arid regions (Case study: Minab Plain, Iran), Geography Scientific Research Quarterly and International Geographical Association of Iran, 18(66):110-127. Eskandari Damaneh, H., Zehtabiian, G.R., Salajegheh, A., Ghorbani, M. and Khosravi, H., 2018. Assessing the effect of land use changes on groundwater quality and quantity (Case study: west basin of Jazmoryan wetland). Journal of Range & Watershed Management,71(3): 563-578. Eskandari Dameneh, H., Khosravi, H. and Abolhasani, A., 2019. Assessing the Effect of Land Use Changes on Groundwater Quality of Zarand Plain using Satellite Images and Geostatistical. Journal of Natural Environmental Hazards (JNEH), 8(20): 67-82. Faramarzi, M., Amini, D., Mirzaei, N. and Mosavi, M., 2020. Assessment of relationships among groundwater level, drought and land-use changes (Case Study: Eyvan County, Ilam Province). Journal of Environmental Science and Technology, 13 (1): 25-43. Gondwe, S.V., Muchena, R. and Boys, J., 2018. Detecting land use and land cover and land surface temperature change in Lilongwe City, Malawi. Journal of Remote Sensing & GIS, 9(2): 17-26. Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T. and Hokao, K., 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20-22): 3761-3772. Hamad, R., Balzter, H. and Kolo, K., 2018. Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10): 3421. Hosseini, S.B., Saremi, A., Noori Gheydari, M.H., Sedghi, H. and Firoozfar, A.R., 2020. Land use classification and determining the pattern of changes for 2014-2017, using OLI Sensor’s Data. Journal of Water and Soil, 34(1): 55-71. Islam, K., Rahman, M. F. and Jashimuddin, M., 2018. Modeling land use change using cellular automata and artificial neural network: the case of Chunati Wildlife Sanctuary, Bangladesh. Ecological Indicators, 88 (4): 439-453. Kaei, Z., Faramarzi, M., Karimi, H., and Mehdizadeh, H., 2017. Investigating the effects of land use change on quantitative and qualitative parameters of groundwater (Case Study: Mehran Plain- Ilam). Wetland Ecobiology, 9 (3):15-28. Karimi, H., Jafarnezhad, J., Khaledi, J. and Ahmadi, P., 2018. Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran. Arabian Journal of Geosciences, 11(19): 592- 616. Khenamani, A., Fathizad, H.and Hakimzadeh, M., 2019. Evaluating trend change land use / cover using remote sensing technique and object-oriented classification algorithm (Case study: Bartash Plain in Dehloran, Ilam), Iranian Journal of Range and Desert Research, 25(4): 723-734. Kumar, K. S., Kumari, K. P. and Bhaskar, P. U., 2016. Application of Markov Chain & Cellular Automata based model for prediction of urban transitions. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 12(4): 4007-4014. Molaei Arpnahi, M., Salehi, M. H., Karimian Egbal, M., Mosleh, Z., 2020. Effect of land-use change on some physical and chemical indices of soil quality in the Bazoft Region, (Chaharmahal-Va-Bakhtiari Province), Journal of Water and Soil, 34(3): 707-720. Nakhaee Nezhad Fard, S., Gholami, H., Akbari, D., Telfer, M. and Rezaei, M., 2018. Comparing different methods of land use classification using the thermal band (Case study: Southern Khorasan province). Desert Management, 6(11): 65-81. Nateghi, S., Nohegar, A., Ehsani, A. and Bazrafshan, O., 2017. Evaluating the vegetation changes upon vegetation index by using remote sensing. Iranian Journal of Range and Desert Research, 24(4): 778-790. Reis, S., 2008. Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Journal of Sensors, 8:6188-6202. Shirazi, S. A. and Kazmi, S. J. H., 2020. Analysis of population growth and urban development in Lahore-Pakistan using geospatial techniques: Suggesting some future options. Journal of South Asian Studies, 29(1): 269-280. Singh, N. and Punia, M., 2018. Geospatial Approach for Land Use/Land Cover Change Prediction: A case study of Bhagirathi Basin, Uttarakhand, INDIA. cosp, 42: A3-1. Surabuddin Mondal, M., Sharma, N., Kappas, M. and Garg, P. K., 2013. Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques. Geocarto International, 28 (7): 632-656. Yuan, F., Sawaya, K. E., Loeffelholz, B. C. and Bauer, M. E., 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multi-temporal Landsat remote sensing. Remote Sensing Environ, 98: 317- 330. Zhao, G.X., Lin, G. and Warner, T., 2008. Using thematic mapper data for change detection and sustainable use of cultivated land: a case study in the Yellow River delta, China. International Journal of Remote Sensing, 25(13): 2509-2522. Zhou, L., Dang, X., Sun, Q. and Wang, S., 2020. Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 55:102045. | ||
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آمار تعداد مشاهده مقاله: 489 تعداد دریافت فایل اصل مقاله: 292 |
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