Monitoring land use change and measuring urban sprawl based on its spatial forms

Autor: Hadi Khani, Jamileh Tavakoli Nia, Hassan Mohammadian Mosammam, Asghar Teymouri, Mohammad Ali Kazemi
Rok vydání: 2017
Předmět:
Zdroj: Egyptian Journal of Remote Sensing and Space Sciences, Vol 20, Iss 1, Pp 103-116 (2017)
ISSN: 1110-9823
DOI: 10.1016/j.ejrs.2016.08.002
Popis: As a response to the challenge of rapid pace of urbanization and lack of reliable data for environmental and urban planning, especially in the developing countries, this paper evaluates land use/cover change (LCLU) and urban spatial expansion, from 1987 to 2013, in the Qom, Iran, using satellite images, field observations, and socio-economic data. The supervised classification technique by maximum likelihood classifier has been employed to create a classified image and has been assessed based on Kappa index. The urban sprawl was also measured using Shannon’s entropy based on its primary spatial forms. To our knowledge, measuring urban sprawl based on its spatial forms would contribute to prioritizing policies and specific regulations in dealing with its dominant form. Finally, LCLU change and urban growth were simulated for 2022, using CA-Markov model. The results revealed that dramatic growth of built-up areas has led to a significant decrease in the area of agriculture, gardens and wasteland, from 1987 to 2013. The obtained relative entropy values have indicated that the Qom city has experienced increasing urban sprawling over the last three decades. The continuous linear and non-continuous linear developments along the major roads and highways are the dominant forms of sprawl in Qom city. The CA-Markov model estimated that this unsustainable trend will continue in the future and built-up areas will be increased by 10% by 2022 resulting in potential loss of 438.03 ha agriculture land, 638.37 ha wasteland, and 17.01 ha gardens. Those results indicated the necessity of appropriate policies and regulations particularly for limiting linear sprawl along the main roads.
Databáze: OpenAIRE