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• 收稿日期:2014-12-17 接受日期:2015-02-12 出版日期:2015-07-15 发布日期:2015-06-24

Simulating urban land use change by incorporating an autologistic regression model into a CLUE-S model

Weiguo JIANG1,2(), Zheng CHEN1,2, Xuan LEI3,*(), Kai JIA1,2, Yongfeng WU4

1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2. Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China
3. Tianjin University Research Institute of Urban Planning, Tianjin 300073, China
4. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
• Received:2014-12-17 Accepted:2015-02-12 Online:2015-07-15 Published:2015-06-24
• Contact: Xuan LEI E-mail:jiangweiguo@bnu.edu.cn;lx_aatu@yeah.com
• About author:

Author: Jiang Weiguo (1976-), Associate Professor, specialized in ecological remote sensing and natural hazard and risk analysis. E-mail:jiangweiguo@bnu.edu.cn

• Supported by:
National Natural Science Foundation of China, No.41171318;National Key Technology Support Program, No.2012BAH32B03;No.2012BAH33B05;Special Fund for Forest Scientific Research in the Public Welfare, No.201204201

Abstract:

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model is a widely used method to simulate land use change. An ordinary logistic regression model was integrated into the CLUE-S model to identify explanatory variables without considering the spatial autocorrelation effect. Using image-derived maps of the Changsha- Zhuzhou-Xiangtan urban agglomeration, the CLUE-S model was integrated with the ordinary logistic regression and autologistic regression models in this paper to simulate land use change in 2000, 2005 and 2009 based on an observation map from 1995. Significant positive spatial autocorrelation was detected in residuals of ordinary logistic models. Some variables that were much more significant than they should be were selected. Autologistic regression models, which used autocovariate incorporation, were better able to identify driving factors. The Receiver Operating Characteristic Curve (ROC) values of autologistic regression models were larger than 0.8 and the pseudo R2 values were improved, compared with results of logistic regression model. By overlapping the observation maps, the Kappa values of the ordinary logistic regression model (OL)-CLUE-S and autologistic regression model (AL)-CLUE-S models were larger than 0.75. The results showed that the simulation results were indeed accurate. The Kappa fuzzy (Kfuzzy) values of the AL-CLUE-S models (0.780, 0.773, 0.606) were larger than the values of the OL-CLUE-S models (0.759, 0.760, 0.599) during the three periods. The AL-CLUE-S models performed better than the OL-CLUE-S models in the simulation of land use change. The results showed that it is reasonable to integrate autocovariates into CLUE-S models. However, the Kfuzzy values decreased with prolonged duration of simulation and the maximum range of time was not discussed in this paper.