Document Type : Research Paper
Authors
1 Department of Natural Engineering, Faculty of Natural Resources, Yasouj University, Yasuj, Iran
2 Ph.D. candidate in Urban Planning, Faculty of Art and Architecture, Yazd University, Yazd, Iran
Abstract
Uncontrolled and scattered urban development in growing cities has created serious challenges for environmental sustainability. This research was conducted with the aim of predicting the spatial development of Yasouj city and the changes in its surrounding land uses during the years 2001 to 2037. Landsat 7 satellite data were used for the years 2001 and 2013, and Landsat 8 data for the year 2025. After performing radiometric and atmospheric corrections on the images using ENVI 5.6 software, supervised classification was performed using the Maximum Likelihood Classification (MLC) method, and its accuracy was validated. To simulate and predict changes, the CA-Markov model was used within the TerrSet 2020 software environment. The results of this study showed that during the studied period, the area of residential and agricultural lands increased. The classification results for all three studied years had acceptable accuracy; therefore, it can be claimed that the model possesses acceptable spatial and quantitative accuracy. The growth of residential and agricultural areas primarily occurred at the expense of the quantitative and qualitative reduction of forests and fertile rangelands surrounding the city. The prediction map for the year 2037 shows an intensification of urban sprawl, extensive expansion of scattered settlements, and further weakening of natural land covers. The findings highlight the necessity of revising urban development patterns in Yasouj and adopting approaches based on compact growth, defining urban growth boundaries, and preserving green belts to ensure a balance between development needs and ecological sustainability. Therefore, it is recommended to utilize the inner capacity of the city (infill development) instead of urban growth in peripheral and suburban areas.
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