基于CLUE-S和Dinamica EGO模型的土地利用变化及驱动力分析

为了探究中国土地利用变化驱动机制和未来土地利用状况,该文利用中国科学院资源环境科学数据库中的2000年和2005年土地利用数据,结合区域土地利用变化与影响模型CLUE-S(the conversion of land use and its effects at small regional extent)和面向地理过程动态环境模型Dinamica EGO(environment for geoprocessing objects)模拟2000-2020年中国土地利用状况,并借助于Logistic回归结果和贝叶斯估计结果,探讨了中国2000-2005年土地利用适宜性和土地利用变化的驱动力空间特征。以2005年土地利用数据对模拟结果进行验证表明,CLUE-S模型和Dinamica EGO模型在LUCC预测上与实际结果一致性较好,并且CLUE-S模型在预测总体精度上优于Dinamica EGO模型。但在土地利用变化类型的数量预测上,Dinamica EGO模型的Markov过程可以准确预测,并且Dinamica EGO模拟的土地利用变化在空间分布上与经验结果较一致。从2020年中国土地利用预测结果来看,耕地、林地、水域和建设用地将会增加,草地会出现大面积的缩减,未利用地在CLUE-S模型预测中出现增加,而在Dinamica EGO模型中减少。该文可为国土资源规划和耕地资源保护政策的制定提供科学依据。

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

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.