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 i...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 R^2 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.展开更多
基金National Natural Science Foundation of China,No.41171318National Key Technology Support Program,No.2012BAH32B03+1 种基金No.2012BAH33B05Special Fund for Forest Scientific Research in the Public Welfare,No.201204201
文摘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 R^2 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.
