Evaluation of the most efficient supervised classification algorithm in monitoring growth changes in Tehran

Subject Areas : landuse

Aida Ashjaee ¹ (PhD. Student of Environmental Science and Engineering, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran.)
Seyed Masoud Monavari ² (Associate Professor, Department of Environmental Science and Engineering, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran. *(Corresponding Author))
Jalil Imany Harsini ³ (Assistant Professor, Department of Environmental Science and Engineering, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran.)
Maryam Robati ⁴ (Assistant Professor, Department of Environmental Science and Engineering, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran.)
Zahra Azizi ⁵ (Associate Professor, Remote Sensing and Geographical Information System, Faculty of Natural resources and Environment, University of Science and Research Branch, Iran.)

Received: 2023-03-07 Accepted : 2023-06-28 Published : 2023-09-23

Keywords: Neural Network, Jajroud protected area, Urban sprawl growth, Maximum Likelihood, Tehran,

Abstract :

Background and Objectives: The urban sprawl is a dynamic and complex phenomenon, and the most effective factor is land use-cover change Coordinated by with the growth of population and economy, and the resulting changes affect vegetation and the functioning of urban ecosystems. In this paper, identification of the most appropriate classification algorithm to investigate the effect of urban sprawl growth in the east of Tehran city in the time period of 1986 to 2016 on land use-cover changes of Jajroud protected area has been studied. Material and Methodology: In this research, the land cover-use changes map was prepared using the supervised classification method and the comparison of three neural network algorithms, minimum distance and maximum likelihood was done in ENVI 5.3.1 software environment. Findings: Land use-cover changes from 1986 to 2016 (period of 30 years) shows the increase of land use-cover area including compact rangelands 58.45%, arid region 91/19%, urban 65/57%, and forest 74/47%. In 2016 compared to 1986. Discussion and Conclusion: By comparing and examining three supervised classification algorithms including neural network, minimum distance, maximum likelihood, the neural network method has been the most suitable algorithm to identify land use-cover changes.

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