基于CA-Markov模型和MCE约束的白洋淀流域景观动态研究

CA-Markov模型对景观动态分析的精度受到环境因素的影响，本研究以添加约束条件的MCE-CA-Markov模型为对比，基于白洋淀流域2008、2013年和2018年的景观数据对2023年景观进行预测，比较CA-Markov模型和MCE-CA-Markov模型模拟结果差异。结果表明： 2008、2013、2018年三个时点的耕地和林地面积合计均超过白洋淀流域总面积的75%，始终为基质景观。各景观类型斑块2008—2018年破碎且分散，耕地、林地的优势度逐渐降低，全流域格局多样性和复杂程度明显增强。2018—2023年，白洋淀流域耕地、林地面积减少，其他景观面积增加，各景观斑块主要呈紧实且聚集状态，建设用地优势度逐渐升高，全流域格局趋于多样化和均匀化。经验证，MCE-CA-Markov模拟精度（0.900 7）优于CA-Markov模型，2023年白洋淀景观结构和格局变化幅度整体小于CA-Markov模拟结果，且各景观转化特征更符合社会发展实际。

Dynamic simulation of landscape change in the Baiyangdian basin based on the CA-Markov model and MCE constraints

The accuracy of the CA-Markov model for landscape dynamic analysis is usually affected by environmental factors. In this study, the MCE-CA-Markov model with control conditions was used to predict the Baiyangdian basin landscape in 2023, based on landscape data for 2008, 2013, and 2018, and it was also compared with the simulation results of the CA-Markov model. The results showed that:From 2008 to 2018, the total area of farmland and forestland accounted for over 75% in Baiyangdian basin, which illustrated farmland and forestland were the substrate landscape. Landscape patches were fragmented and scattered, with the dominance of farmland and forestland gradually decreasing, and the diversity and complexity of the whole basin landscape pattern increased during this period. From 2018 to 2023, the farmland and forestland areas would decrease, while the area of other landscapes would increase. Landscape patches would mainly be compact and clustered, and dominance of construction land would gradually increase. The whole basin landscape pattern would tend to be diverse and even. The MCE-CA-Markov simulation accuracy(0.900 7) was higher than that of the CA-Markov simulation. Furthermore, the change range of the Baiyangdian basin landscape structure and pattern in 2023, simulated by the MCE-CA-Markov model, was smaller than that of the CA-Markov simulation, more congruent with social development in landscape transformation characteristics.