Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models |
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| Authors: | Yongjiu Feng Qianqian Yang Zhonghua Hong Li Cui |
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| Affiliation: | 1. College of Marine Sciences, Shanghai Ocean University, Shanghai, China;2. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Ministry of Education, Shanghai, China;3. College of Information Technology, Shanghai Ocean University, Shanghai, China |
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| Abstract: | This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern. |
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| Keywords: | Land-use change cellular automata spatial autoregressive (SAR) model spatial autocorrelation landscape metrics |
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