Generating a Future Land Use Change Scenario with a Modified Population-Coupled Markov Cellular Automata Model

Abstract

With the population increasing and land use patterns changing, there will be environmental consequences. To solve these impending problems, information on the future land use pattern is needed. This study attempted to develop an enhanced land use model, capable of predicting future conditions. The traditional Markov model was modified by incorporating a Cellular Automata (CA) and a population variable to depict the neighboring effects and the impacts of population growth on urbanization. The performance of this new model was quantitatively assessed by generating the 2001 land use patterns of the East Fork Little Miami River watershed in southwest Ohio with and without the CA and the population variable and compared with the actual 2001 land use imagery. From the comparison, it was apparent that the land use map generated with the CA and population variable was more accurate. To further ascertain its applicability in a larger watershed, the same procedure was used to model the entire Little Miami River watershed. The validation results demonstrated that the performance of the modified CA-Markov model at both watershed scales was acceptable, and the inclusion of the CA and population variable could markedly improve model predictability. Based on these findings, the 2030 land use scenario for the LMR watershed was postulated. The resultant map showed much urban expansion in the western and southern portions of the basin. This information can be useful to planners and resource managers, enhancing their efforts in generating more sustainable future development strategies.

Keywords: Markov, CA-Markov, population growth, land use modeling, urbanization, multi-criteria evaluation