Skip to main content

Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm

Buy Article:

$63.00 plus tax (Refund Policy)

Abstract:

The landscape pattern of Daqing City, China, has undergone a significant change over the past 20 years, as a result of the rapid urbanization process. To understand how urbanization has influenced the landscape in Daqing City, the largest base of the petrochemical industry in China, we conducted a series of spatial analyses with landscape pattern maps obtained from Landsat images in 1979, 1990 and 2000. Results indicate that a substantial urban area has been extended during the past two decades, along with the shrinking of wetland and woodland. Spatio-temporal optimization is not a trivial task in developing landscape models. In previous studies, the optimization of spatial and temporal factors was achieved separately, because of the difficulty in formulating them together in a single model. In this study, we adapted the traditional Markov model by obtaining model parameters and neighbourhood rules from a modified genetic algorithm (GA). Model performance was evaluated between the empirical landscape map from the Landsat image and the simulated landscape map from the models. Over three simulation runs, the global deviation (GD) for the three models was 1.37, 1.10 and 1.15, respectively. This result shows that the Markov model and the GA together are able to effectively capture the spatio-temporal trend in the landscape pattern associated with urbanization for this region. The future landscape distribution in 2010, 2030 and 2050 was derived using a spatial Markov model (SMM) for further urban change and planning research.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160600962749

Affiliations: Texas Center for Geographic Information Science, Department of Geography, Texas State University, San Marcos, TX 78666

Publication date: 2007-01-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more