Assessment of future urban growth impact on landscape pattern using cellular automata model: a case study of Xuzhou city, China. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Li, Cheng; Zhao, Jie
• Is Part Of: Journal of environmental engineering and landscape management. Volume 25:Issue 1(2017)
• Page Start: 23
• Page End: 38
• Publication Date: 2017-01-02
• Subjects: cellular automata model -- urban growth -- calibration -- scenario -- landscape metrics -- landscape pattern; Environmental protection -- Periodicals; Environmental engineering -- Periodicals; Pollution -- Periodicals; Environmental sciences -- Periodicals; Forest landscape management -- Periodicals; Agricultural landscape management -- Periodicals; Agricultural landscape management; Environmental engineering; Environmental protection; Environmental sciences; Forest landscape management; Pollution; Periodicals; 333.705
• DOI: 10.3846/16486897.2016.1187620
• Languages: English
• ISSNs: 1648-6897

Abstract

Abstract: Understanding and predicting of the urban growth process and its impact have become increasingly important for decision making toward sustainable development. In this paper, we presented a cellular automata model to assess the consequence of future urban growth. The hybrid calibration method combining logistic regression with trial-and-error was applied to estimate the parameters. The study proposed the integration method of Multi-Criteria Evaluation and Analytic Hierarchy Process that can be utilized to effectively translate the qualitative descriptions of scenarios into quantitative spatial analysis. Finally, the comparison of the different scenarios provided an insight into the impacts of different urban development strategies on landscape patterns. The result indicates that CA model can be effectively connected with the urban decision making processes. The moderate development scenario could be considered as the best one in achieving the objectives of compact urban form, good residential environment, as well as environmentally and economically efficient development.