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A Multitarget Land Use Change Simulation Model Based on Cellular Automata and Its Application

Author

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  • Jun Yang
  • Fei Chen
  • Jianchao Xi
  • Peng Xie
  • Chuang Li

Abstract

Based on the analysis of the existing land use change simulation model, combined with macroland use change driving factors and microlocal land use competition, and through the application of Python language integrated technical approaches such as CA, GIS, AHP, and Markov, a multitarget land use change simulation model based on cellular automata(CA) is established. This model was applied to conduct scenario simulation of land use/cover change of the Jinzhou New District, based on 1:10000 map scale land use, planning, topography, statistics, and other data collected in the year of 1988, 2003, and 2012. The simulation results indicate the following: (1) this model can simulate the mutual transformation of multiple land use types in a relatively satisfactory way; it takes land use system as a whole and simultaneously takes the land use demand in the macrolevel and the land use suitability in the local scale into account; and (2) the simulation accuracy of the model reaches 72%, presenting higher creditability. The model is capable of providing auxiliary decision-making support for coastal regions with the analysis of the land use change driving mechanism, prediction of land use change tendencies, and establishment of land resource sustainable utilization policies.

Suggested Citation

  • Jun Yang & Fei Chen & Jianchao Xi & Peng Xie & Chuang Li, 2014. "A Multitarget Land Use Change Simulation Model Based on Cellular Automata and Its Application," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-11, June.
  • Handle: RePEc:hin:jnlaaa:375389
    DOI: 10.1155/2014/375389
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    Cited by:

    1. Cheechouyang Faichia & Zhijun Tong & Jiquan Zhang & Xingpeng Liu & Emmanuel Kazuva & Kashif Ullah & Bazel Al-Shaibah, 2020. "Using RS Data-Based CA–Markov Model for Dynamic Simulation of Historical and Future LUCC in Vientiane, Laos," Sustainability, MDPI, vol. 12(20), pages 1-20, October.

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