Geographical planning of space quarterly journal

Geographical planning of space quarterly journal

Land use Change Detection and Modeling, Using Remote Sensing data, Markov Chains and Cellular Automata (Case Study: City of Bojnord)

Document Type : Research Paper

Authors
MARYAM YOUSEFI 1
JAVAD MIKANIKI
ALI ASHRAFI 2
Najmeh Neysani Samani 3
1 PhD Student of Environmental Science, Land use Planning, Young Researchers and Elite Club, Birjand Branch, Islamic Azad University, Birjand, Iran.
2 Faculty Member, University of Birjand, and PhD Student of Remote Sensing, University of Tehran, Iran
3 Assistant Professor, Department of RS and GIS. University of Tehran, IRAN
Abstract
Land use Change Detection and Modeling, Using Remote Sensing data, Markov Chains and Cellular Automata (Case Study: City of Bojnord)
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Abstract
Nowadays, land use changes are considered as one of the main issues of research in the field of global environmental change and sustainable development. Currently, land use change detection and modeling using satellite imagery is considered as a useful tool for understanding environmental changes related to human activities. The Landsat images 5 and 8 in 2000 and 2014 respectively were used. Fuzzy supervised classification method was used to classify the images. Finally, the changes in land use were identified for 14 years in the foreseeable future and the changes between the 2000 and 2014 were also detected. The results indicated that irrigated Farming land and dry farming lands will be decreased and the barren lands and urban areas will be increased. The irrigated Farming lands, orchards and dry farming lands included 30.9 percent of the study area in 2000; however, they will reduce to 18.4 percent in 2028, urban and industrial areas included 6.7 percent, but will be increased to about 11.5 percent in 2028.
Keywords
Modeling and Change Detection
land use
Markov Chains
Cellular Automata
City of Bojnord
  1. اصلاح، مهدی. سید علی المدرسی و مهدی مفیدیفر و شاهرخ ملک زاده  بافقی. 1393. بررسی کارایی مدل زنجیره ای مارکوف در برآورد تغییرات کاربری اراضی و پوشش زمین با استفاده از تصاویر ماهواره ای لندست. نخستین همایش ملی کاربرد مدل‌های پیشترفته تحلیل فضایی(سنجش از دور و GIS) در آمایش سرزمین، تهران.
  2. رضا زاده، راضیه. مهرداد میراحمدی. 1388. مدل اتوماسیون سلولی، روشی نوین در شبیه­سازی رشد شهری، نشریه علمی پژوهشی فناوری آموزش، دوره چهارم، شماره یکم، تهران.
  3. سفیانیان، علیرضا و لقمان خداکرمی. 1390. تهیه نقشه کاربری اراضی با استفاده از روش طبقه بندی فازی (مطالعه موردی سه زیر حوزه آبخیزکبودر آهنگ، رزن- قهاوند و خونجین- تلخاب در استان همدان، آمایش سرزمین، دوره سوم، شماره چهارم، تهران.
  4. علیمحمدی سراب، عباس. علی­اکبر متکان و ­بابک میرباقری. 1389. ارزیابی کارایی مدل سلول­های خودکار در شبیه­سازی گسترش اراضی شهری در حومه جنوب­غرب تهران. مدرس علوم انسانی-برنامه­ریزی و آمایش فضا، دوره چهاردهم، شماره دوم، تهران.
  5. قربانی، رسول. محمد رضا پور محمدی و حسن محمودزاده. 1393. رویکرد زیست محیطی در مدل‌سازی تغییرات کاربری اراضی محدوده کلانشهر تبریز با استفاده از تصاویر ماهوار‌های چند زمانه­ای، ارزیابی چند معیاری و سلول­های خودکار زنجیره مارکوف (1363-1417). فصلنامه علمی و پژوهشی مطالعات شهری، دوره دوم، شماره هشتم، سنندج.
  6. مجیدی، حسین.1393. ارزیابی و شبیه‌سازی رشد آتی کلانشهر ارومیه در افق سال 2035 میلادی با مدل سلولهای خودکار و RS . پایان‌نامه کارشناسی‌ارشد. غلامعلی خمر: دانشگاه زابل، دانشکده جفرافیا، گروه جفرافیا.
  7. محمدی، محمود. احسان مالکی پور و علیرضا صاحبقرانی.1393. مدل سازی گسترش شهر در اراضی پیرامونی با استفاده از سلول­های خودکار (CA) و فرآیند تحلیل سلسله مراتبی (AHP) (مطالعه موردی: منطقه 7 اصفهان). مطالعات و پژوهش‌های شهری منطقه ای، دوره پنجم، شماره هجدهم،  دانشگاه اصفهان.
  8.  مخدوم، مجید. 1393. شالوده آمایش سرزمین، نوبت پانزدهم، تهران، انتشارات دانشگاه تهران.
    1. Bhatta, B. Saraswati, S and Bandyopadhyay, D. 2010. Quantifying the degree-of- freedom, degree-of-sprawl, and degree-of-goodness of urban growth from remote sensing data, Applied Geography, 30: 1, Netherlands.
    2. Kamusoko, Courage. Aniya Masamu and Bongo Adi and Munyaradzi Manjoro. 2009. Rural sustainability under threat in Zimbabwe simulation of future land use/cover changes in the Bindura district based on the Markov–cellular automata model, Applied Geography, Vol 29: No 3, Netherlands.
    3. Hu, Zhiyong, and Lo, C.P. 2007. Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31: 6. United Kingdom.
    4. Han, Ji. Hayashi yoshotisugu and Cao Xin and Imura Hidefomi. 2009. Application of an integrated system dynamics and cellular automata model for urban growth assessment: a case study of Shanghai, China, Landscape and Urban Planning, 91: 3, Netherlands.
    5. Houet, Thomas and Laurence Hubert-Moy.2006. Modeling and projecting land-use and land-cover changes with Cellular Automaton in considering landscape trajectories." EARSeL eProceedings, 5: 1, United States
    6. He, Chunyang. Okada Norio and Zhang Qiaofeng and Shi Peijun and Zhang, Jingshui. 2006. Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China, Applied Geography, 26: 3–4, Netherlands.
    7. Guan. Dongjie. Li  Haifeng and Takuro Inohae and  Weici, Su and  Nagaie Tadashi. 2011. Modeling Urban Land Use Change by the Integration of Cellular Automaton and Markov Model, Ecological Modelling, 222: 20–22, Netherlands.
    8. Li, HaiFeng. Inohae Takuro and Su Weici and Nagaie Tadashi and Hokao Kazunori. 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222: 20, Netherlands.
    9. Jensen, J.R. 2005. Introductory digital image processing 3rd edition, In Upper Saddle River: Prentice hall.
    10. Arsanjani, Jamal Jokar. Wolfgang Kainz and Ali Jafar Mousivand. 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, United Kingdom.
    11. López, Erna. Gerardo Bocco and Manuel Mendoza, and Emilio Duhau. 2001, Predicting land-cover and land-use change in the urban fringe, a case in Morelia city, Mexico, Landscape Urban Planning, 55: 4, Netherland.
    12. Li, Xia Yeh, and Antony, G.O. 2004. Data mining of cellular automata’s transition rules, International Journal of Geographical Information Science, 18: 8, United Kingdom.
    13. Li, Zhong, W., Zeng Guang, M. and Zhang Huan. 2007. The integrated eco-environment assessment of the red soil hilly region based on GIS a case study in Changsha City, China, Ecological Modeling. 202: 540–546. Netherland.
    14. Liu, Xiaoping. Li Xia and Shi Xun. Wu Shaokun, K. and Liu, Tao. 2008. Simulating complex urban development using kernel-based non-linear cellular automata. Ecologicl. Modeling, 211: 1-2, Netherland.
    15. Mather, Paul M. 1999. Land Cover Classification Revisited. In: Atkinson. P. M. and Tate, N.J., (Eds.). Advances in Remote Sensing and GIS Analysis, UK: John Wiley and Sons, United States.
    16. Manibhushan, Nilanchal Patel. Gadadhar Sahoo and Anil Kumar Singh. 2013. Image classification for different land use and land covers using fuzzy logic for the improvement of accuracies, Journal of Agricultural Science, 3: 278, United Kingdom.
    17. Patino, Jorge. E. and Duque Juanو C. 2013. A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems, Vol 37: 1-17. United Kingdom.
    18. Moghadam, Hossein Shafizadeh, and Marco Helbich. 2013. Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model”, Applied Geography, 40: 140-149. Netherland.
    19. Sylvertown, Jonthan. Holtier Sennino.1992. Cellular automaton models of inter specific competition for space the effect of pattern on process, Journal of Ecology, 80: 3, United Kingdom.
    20. Soe, Minit. Le Weng. 2006. Multicriteria decision approach for land use and land cover change using Markov chain analysis and a cellular automata approach, Canadian Journal of Remote Sensing, 32: 6, United Kingdom.
    21. Schneider, Annemarie. Woodcock Curtis. 2008. Compact, dispersed,
      fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Studies, 45: 30, United Kingdom.
    22. Taubenböck, Hannes. Thomas Esch and Andreas Felbier and Michael Wiesner and Achim Roth, and Stefan Dech. 2012. Monitoring urbanization in mega cities from space. Remote sensing of Environment, 117, Netherland.
    23. Vaz, Eric. Nijkamp Peter and Painho Marco, and Caetano Mario. 2012. A multi-scenario forecast of urban change: a study on urban growth in the Algarve, Landscape and Urban Planning, 104: 20, Netherland.
    24. Wu, Flong. 2002, Calibration of stochastic cellular automata: the application to rural–urban land conversions. International Journal of Geographical Information Science, 16: 8, United Kingdom.
    25. Weng, Qihao. 2002. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modeling"; Journal of Environmental Management , 64: 3, United state.
    26. Wang, Jing. Yongqi Chen and Xiaomei Shao and Yanyu Zhang, and Yingui Cao. 2012. Land-use changes and policy dimension driving forces in China: Present, trend and future. Land Use Policy, 29, Netherland.
Geographical planning of space quarterly journal
Volume 7, Issue 26
Spring 2018
Pages 1-16