Department of Environmental Planning and Management, Graduate Faculty of Environment, University of Tehran, Tehran, Iran


Analyzing the process of land use and cover changes during long periods of time and predicting the future changes is highly important and useful for the land use managers. In this study, the land use maps in the Ardabil plain in north-west part of Iran for four periods (1989, 1998, 2009 and 2013) are extracted and analyzed through remote sensing technique, using the land-sat satellite images. Then, the future land use changes are simulated for 2030 using integrated CA-Markov model according to the scenario of continuing current management process. The results show that in the period between 1989 and 2009, i.e. since two-thirds of the plain was declared restricted till all of it was declared thus, the study area has experienced a total of about 58645.08 ha changes. After the whole plain was restricted (since 2009 till 2014), the changes have been estimated to be 22466.88 ha. The prediction also indicates that the changes will equal 8908.83 ha by 2030. Agricultural lands and human-built environment constitute the majority of changes and are increasing continuously. The obtained Kappa values for the model accuracy assessment (higher than 0.8) indicated the model's capability to predict future Land use/cover changes in the study area. Thus, analyzing Land use and cover changes trends from past to near future using CA-Markov model can play a significant role in land use policy making, planning, and managing of the restricted plains especially in the proposed study area.

Graphical Abstract

Land use and land cover spatiotemporal dynamic pattern and predicting changes using integrated CA-Markov model


  • Analyzing LUCCs trends from past to near future using CA-Markov model plays a significant role in land use policy making, planning, and managing of the restricted plains
  • Human-built environment and agricultural land use constitute the main dynamics of changes
  • CA-Markov model capability to predict future LUCCs in the study area


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