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Biblioteca A Partitioned and Heterogeneous Land-Use Simulation Model by Integrating CA and Markov Model

A Partitioned and Heterogeneous Land-Use Simulation Model by Integrating CA and Markov Model

A Partitioned and Heterogeneous Land-Use Simulation Model by Integrating CA and Markov Model

Resource information

Date of publication
Dezembro 2022
Resource Language
ISBN / Resource ID
LP-midp000146

Conversion rule is a key element for a cellular automata (CA) model, and it is a significant and challenging issue for both domestic and international experts. Traditional research regarding CA models often constructs a single conversion rule for the entire study area, without differentiating it on the basis of the unique growth features of each location. On the basis of this, a partitioned and heterogeneous land-use simulation model (PHLUS) is constructed by integrating a CA and Markov model: (1) A general conversion rule is constructed for the entire study area. By establishing a land development potential evaluation index system, the conversion rule is refined and differentiated; (2) By coupling a CA model with a Markov model, PHLUS can realize land-use simulation both in micro and macro scales. A simulation study is conducted for the Pearl River Delta region. The results show that: (1) By transforming the CA model rules to further distinguish zones, the accuracy is improved. Compared with the traditional CA-Markov model, the simulation accuracies for 2010 and 2020 are improved by 11.55% and 7.14%, respectively. For built-up land simulation, the PHLUS simulation errors for 2010 and 2020 are only 0.7% and 0.57%, respectively; and (2) Under land-use simulation for 2030, cultivated land and forest land will transfer to built-up land. The built-up land area will reach 10,919 km2. Guangzhou and Shenzhen have the greatest potential for land development, and the built-up land area for the two cities will reach 2727 km2.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Wang, QihaoLiu, DongyaGao, FeiyaoZheng, XinqiShang, Yiqun

Corporate Author(s)
Geographical focus