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

Document Type : Research Paper


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


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


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