基于多模型的黑河中游甘临高地区土地利用情景模拟

混合元胞自动机（Mixed-cell cellular automata，MCCA）模型改进了传统的元胞自动机（Cellular automata，CA）模型，基于现实复杂土地结构引入混合元胞，实现了从定性、静态模拟到定量、动态模拟的跨越。本文首先探究MCCA模型在黑河中游甘临高地区（甘州区、临泽县和高台县）的适用性；之后分别采用多目标线性规划（Multiple-objective programming，MOP）模型、普通线性回归模型预测得到2035年可持续发展（Sustainable development，SUD）情景、基本发展（Basic development，BAD）情景中不同地类面积数值，然后将面积输入MCCA模型中进行不同情景的土地利用空间结构可视化，并开展对比研究。结果表明：各项精度评价指标均表明MCCA模型的模拟精度较高，Kappa系数、混合元胞质量系数（Mixed-cell figure of merit，mcFoM）和平均相对熵（Relative entropy，RE）分别为0.886、0.261和0.508，优于基于纯净元胞的斑块生成土地利用变化模拟（Patch-generating land use simulation model，PLUS）模型，因此MCCA模型适用于研究区土地利用结构模拟。2035年SUD情景中林地范围明显高于BAD情景，生态效益较BAD情景增速快，建设用地和耕地适度扩张，综合效益增速较快。该结果表明耦合MOP和MCCA模型模拟的土地利用优化配置方案能够更好地协调经济与环境的关系，既有利于经济快速发展，又能保护生态环境和维持社会稳定。

Multi-model-based Simulation of Different Landuse Scenarios in Gan-Lin-Gao Area in Middle Reaches of Heihe River

The mixed-cell cellular automata (MCCA) model improves the traditional cellular automata (CA) model, and introduces mixed cells based on the actual complex land structure, realizing the progress from qualitative and static simulation to quantitative and dynamic simulation. The applicability of the MCCA model in the Gan-Lin-Gao area (Ganzhou District, Linze County and Gaotai County) in the middle reaches of the Heihe River was firstly explored;after that, the multiple-objective programming (MOP) model and the ordinary linear regression model were separately used to predict the area values of different land use types in the sustainable development (SUD) scenario and the basic development (BAD) scenario in 2035, and then the area number was input into the MCCA model to visualize the land use spatial structure of different scenarios, and carry out comparative studies. The results showed that all accuracy evaluation indicators indicated that the simulation accuracy of the MCCA model was relatively high. The Kappa coefficient, mixed-cell figure of merit (mcFoM) and mean relative entropy (RE) were 0.886, 0.261 and 0.508, respectively, which was better than the patch-generating land use simulation model (PLUS) based on pure cells, so the MCCA model was suitable for the simulation of land use structure in the study area. In 2035, the scope of forest land in the SUD scenario was significantly higher than that in the BAD scenario, and its ecological benefits increased faster than that of the BAD scenario, construction land and arable land expand moderately, and the comprehensive benefits increased relatively fast. The results showed that the optimal land use allocation scheme simulated by coupling the MOP and MCCA model can better coordinate the relationship between economy and environment, which was not only conducive to rapid economic development, but also protected the ecological environment and maintains social stability.