Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

ABSTRACT

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.

KEYWORDS:

• Land use change simulation
• cellular automata
• Markov chain
• spatial scale sensitivity
• response surface method

Acknowledgments

The authors are grateful to Taylor & Francis Editing Services for English-language editing to the manuscript. The authors would like to express special thanks to all the anonymous reviewers and the editor for their thoughtful and constructive comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41671406].

Notes on contributors

Hao Wu

Hao Wu is currently a professor at Central China Normal University. His research interests include GIScience, remote sensing and GNSS, focusing on land use and cover change, spatio-temporal data analysis and mining, and volunteered geographic information.

Zhen Li

Zhen Li is a master student at Wuhan University of Technology and her research interests focus on spatio-temporal analysis, and natural hazards monitoring.

Keith C. Clarke

Keith C. Clarke is currently a professor at University of California Santa Barbara. His research interests are environmental simulation modeling, urban growth using cellular automata, terrain mapping and analysis, and real-time visualization.

Wenzhong Shi

Wenzhong Shi is the Head and Chair Professor in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University. His research interests include GIScience and remote sensing, focusing on uncertainties and quality control of spatial data, satellite images and LiDAR data, 3D modeling, and human dynamics.

Linchuan Fang

Linchuan Fang is currently an associate professor at Northwest A&F University, China. His research interests are land use and cover change, and application of advanced analytical, spectroscopic and microscopic instruments in environmental and soil research.

Anqi Lin

Anqi Lin is a Ph. D. candidate at Central China Normal University and her research interests focus on spatio-temporal data analysis and mining, and volunteered geographic information.

Jie Zhou

Jie Zhou is currently an associate professor at Central China Normal University. His research interests are land use and cover change, satellite images processing, and spatio-temporal data analysis and mining.