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 :
1 - No.19, kiyan st , shahrdari Blvd , saadatabad,Tehran,iran
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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Kavzoglu, T., and Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5): pp. 352-359.
Mishra, P. K., Rai, A., & Rai, S. C. (2019). Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. The Egyptian Journal of Remote Sensing and Space Science.
Mountrakis, G., Im, J., and Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3): pp. 247-259.
Pal, S., & Ziaul, S. K. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20 (1), 125-145.
Rahman, M. (3009). Detection of land use/land cover changes and urban sprawl in Al-Khobar, Saudi Arabia: An analysis of multi-temporal remote sensing data. ISPRS International Journal of Geo-Information, 1(3), 01.
Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., and Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1): pp. 91-99.
Serra, P., Pons, X., & Saurí, D. (2008). Land-cover and land-use change in a Mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Applied Geography, 28 (3), 189-209.
Usman, M., Liedl, R., Shahid, M. A., & Abbas, A. (2015). Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. Journal of Geographical sciences, (12), 1479-1506.
Wakode, H. B., Baier, K., Jha, R., & Azzam, R. (2014). Analysis of urban growth using
