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Land use change in China and analysis of its driving forces using CLUE-S and Dinamica EGO model

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Abstract

In order to analyze the driving mechanism and to predict land use change of China in the future, CLUE-S(the conversion of land use and its effects at small regional extent) and Dinamica EGO(environment for geoprocessing objects) model were used to simulate land use change in China from 2000 to 2020 based on the land use data in 2000 and 2005 from Data Center for Resources and Environmental Sciences Chinese Academy of Sciences (RESDC). With Logistic regression and Bayesian estimation, land use suitability and spatial characters of driving factors of land use change from 2000 to 2005 in China were analyzed. The simulation results in 2005 indicated that, the predictions of LUCC (land use change in China) with CLUE-S and Dinamica EGO matched broadly with actual situation and CLUE-S was better than Dinamica EGO model in overall accuracy. However, the Markov process in Dinamica EGO could precisely predict the amount of land use change and the spatial pattern was consistent with empirical result. The simulation results of land use in 2020 showed that areas of farmland, forest, water and construction land would increase, while grassland would decrease largely. Unused land would increase with CLUE-S model but decrease with Dinamica EGO model. This article serves as the scientific foundation for land resource plan and farmland protection policy in China.

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... In this method, the contributions of climate change and human activities are identified through the cropland pattern simulation. For example, CLUE-S and Environment for Geoprocessing Objects Model (Dinamica EGO) were used to investigate the contribution of each driver of cropland change in China for the period -2005(Gao and Yi, 2012. In addition, some researchers combined models of land use and farm decision-making to emphasize the importance of human decision-making. ...
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... We should realize that the methods are only the tools to understand the complicated relationships between cropland change and its driving forces (Gao and Yi, 2012). In order to discuss the contributions of climate change and human activities to cropland change, the explanation should appeal the co-integration test and Granger causality test analyses to judge the causality firstly. ...
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... The CLUE-S model, which was developed using a visual model for spatial suitability, as well as spatial and dynamic simulations of land-use, was developed by Verburg et al. (2002) from Wageningen University in the Netherlands at the end of the twentieth century. This model consists of two core modules (Gao and Yi, 2012): the non-spatial and spatial modules. ...
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