Investigation and Prediction of Spatial and Temporal Land Use Changes in New Hashtgerd City by Integrating Remote Sensing Data and Cellular Automata Markov model

Subject Areas :

Sara Soukhtezari ¹ (No.19, kiyan st , shahrdari Blvd , saadatabad,Tehran,iran)

Received: 2020-02-01 Accepted : 2020-01-12 Published : 2020-02-20

Keywords: Prediction, land use, remote sensing, Cellular Automata Markov, Hashtgerd,

Abstract :

Land use changes due to the physical expansion of the city in most cities in Iran are so rapid, that urban planners and managers are facing a dynamic and complex development as they integrate the planning process in these areas. The purpose of this study is to investigate land use changes and physical development of Hashtgerd city during the past 19 years and to predict land use change trends for the future. In this study, Landsat multi-time images were used. Using the support vector classification machine algorithm and the algorithm for Cross-Tab change, land use change trends over the past 19 years was evaluated. Also, using the Cellular Automata Markov prediction model, the process of land use change and physical expansion of the city is predicted for the future. The results of this study indicate the unnecessary expansion of the city over the past 19 years. So that the built-up with 736.56% growth have caused excessive destruction of agricultural and bare lands on the outskirts of the city. Investigations show that with increasing distance from land use changes have significantly reduced the amount of land use. Investigation of changes in land uses showed that 564/166 hectares of waste land has become residential land use. Predicting land use changes for 2028 and 2038 showed that residential land use will continue to increase. This highlights the need for special attention of urban planners and managers to the issue of urban development and its consequences in the region. Finally, the evaluation of the accuracy of the automated cell model showed that the percentage of classes area difference was less than 8%.

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