摘要
基于北京与东京土地利用的对比分析,对北京市土地利用结构进行优化,更好地发挥城市土地的宏观调控功能。根据北京市1992-2012年和东京1982-2011年多期土地利用现状数据,集成多目标优化模型的目标决策功能和元胞自动机-马尔可夫链(CA-Markov)模型的空间布局特点,对北京市2020、2030年的土地利用进行数量结构优化和空间布局模拟。通过对北京与东京在土地利用演变过程的深入分析,归纳总结出北京土地利用的发展定位以及2020、2030年的定量目标,然后对土地利用经济、社会和生态3方面构建多目标模型,最后对模型进行求解,同时应用CA-Markov模型对北京市2020、2030年优化后的土地利用结构进行空间布局模拟,通过运算,北京市农用地、生态用地、建设用地、交通用地、水域、其他土地在2020年、2030年的比例分别为21.00%、50.50%、19.00%、3.00%、4.50%、2.00%和20.00%、50.00%、20.00%、3.50%、4.50%、2.00%。优化后的土地利用结构改变以往剧烈变化的现象,更趋向于稳定状态,优化后的土地利用布局更为集约并兼顾生态保护功能。
Based on the comparison of the evolution of land use structure between Beijing and Tokyo, we try to explore the goals of Beijing in land use in future and the quantitative goals in 2020 and 2030, and then optimize land use structure on amount and simulate spatial distribution, which can better develop the macroeconomic regulation and control function. Based on land use data in Beijing during 1992-2012 and in Tokyo during 1982-2011, we try to analyze the land use characteristics and evolvement rules in Tokyo, and then learn from it to develop the land use in Beijing, such as the direction of land use. Combining the land use and social economic development, we seek the detailed goals of land use in 2020 and 2030, and then establish the multiple-object optimization model that combines the economic, social and ecological goals. The Matlab2009 software is used to solve the relative optimal solution for the land use structure in 2020 and 2030. After that, we focus on yielding the optimized land use transition area matrix and the land use suitability map. First, we use the land use in Beijing in 1992 and 2012 to generate land use transition area matrices with an interval of 8 and 18 years, and then optimize the 2 matrices according to the principles of land use development in future, which pays more attention to reducing the frequency of land use conversion between land use patterns, and controlling the transition from farmland to construction land, and from ecological land to farmland. Second, we choose the natural, social and distance factors to yield the land use suitability map. The natural factor includes altitude, aspect and slope, the social factor contains gross domestic product （GDP） and population, and the distance factor mainly contains the distance to highway, railway, river, road, city, and original land use pattern itself. According to the optimized land use amount, the corresponding optimized land use transition area matrix and the land use suitability map, we employ the CA-Markov model to simulate the space distribution of land use. The research showed the optimized land use structure, and the proportions of farmland, ecological land, construction land, traffic land, water body and other land type are 21.00%, 50.50%, 19.00, 3.00%, 4.50% and 2.00% respectively in 2020 and 20.00%, 50.00%, 20.00%, 3.50%, 4.50% and 2.00% respectively in 2030. Farmland will decline and construction land and traffic land will increase in a reasonable degree, while ecological land, water body and other land type maintain as before. The optimized land use structure is in line with the goals of urban development, intensive utilization of land and ecological protection. In the validation process of the CA-Markov model, we not only verify the land use in amount but also in space distribution based on the Kappa coefficient. The absolute value of relative error on construction land is less than 0.3%, and the Kappa coefficients on amount accuracy validation are all above 0.6. Moreover, the spatial validation on Kappa statistics and a map comparison are also eligible. The model validation indicates that the land use optimization is sufficiently useful to planners and policy makers. From the optimization of land use in 2020 and 2030, the land use structure is gradually becoming stable, which means the changes in amount and space distribution are gradually becoming small, and also, the distribution of optimized land use is more intensive and ecological.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第4期217-227,共11页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自科学基金(41401499)
北京市自科学基金(4154073)
城市空间信息工程北京市重点实验室经费(201605)
