analysis of urban development and land use change in urmia city

The two decades the urban growth has been caused in changing land cover in the urban areas, especially in agricultural lands locating in borders of cities. it is necessary for suitable urban planning to achieve suitable urban growth plans, the past evolution of the physical and spatial development of cities in order to predict the appropriate zones for future development in proportion to the ecological conditions of the region. the purpose of this research is to identify, describe and predict land use change for the year 2030 by using land cover change modeling (lcm). for this purpose, landsat satellite images landsat 1995 and 2016 have been used to illustrate user changes and classifying the land cover which uses logistic regression model to study the factors affecting land cover variations and then modeling the transmission potential of the artificial neural network multilayer perceptron model (mlp) with five variables. finally, the allocation of users to each other with the markov chain has been assess. the results in study indicate that the most changes are related to the lands built with 5600 hectares (more than the conversion of agricultural land into built). the land cover predict map also showed that the most development will be in the north, northeast, and northwest in 2030.

analysis of urban development and land use change in urmia city

the two decades the urban growth has been caused in changing land cover in the urban areas, especially in agricultural lands locating in borders of cities. it is necessary for suitable urban planning to achieve suitable urban growth plans, the past evolution of the physical and spatial development of cities in order to predict the appropriate zones for future development in proportion to the ecological conditions of the region. the purpose of this research is to identify, describe and predict land use change for the year 2030 by using land cover change modeling (lcm). for this purpose, landsat satellite images landsat 1995 and 2016 have been used to illustrate user changes and classifying the land cover which uses logistic regression model to study the factors affecting land cover variations and then modeling the transmission potential of the artificial neural network multilayer perceptron model (mlp) with five variables. finally, the allocation of users to each other with the markov chain has been assess. the results in study indicate that the most changes are related to the lands built with 5600 hectares (more than the conversion of agricultural land into built). the land cover predict map also showed that the most development will be in the north, northeast, and northwest in 2030.