基于CA-Markov模型的土地利用演化模拟预测研究
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北京市耕地复合价值提升关键技术研究与应用项目(Z141100000614001)


Simulation and Forecast Study of Land Use Change Based on CA-Markov Model
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    摘要:

    为了探究城市发展过程中土地利用变化规律及各类驱动力的影响,以北京市顺义区为例,基于2000、2005、2010年3期遥感影像解译数据,分析研究区域的土地利用类型的动态变化程度,并基于Markov模型定量分析了土地利用的转移方向及转化程度。结合GIS分析功能,探讨多种土地利用驱动力对于土地利用类型转移的影响,并采用模糊计算的方法,将各类影像土地利用转移的因子标准化,并利用GIS中加权线性方法将其整合,结合元胞自动机马尔可夫模型,模拟出2010年的土地利用分布,经验证模拟的精度高达81.41%,Kappa指数为0.7769,得到了较为理想的结果,证明了方法的可行性及精度的可靠性。并以2010年的土地利用格局为基础,预测2020年的土地利用状况并分析2010—2020年10a间的土地利用变化。分析结果表明,除草地、林地变化不明显,耕地、水域面积继续保持减少的趋势,但减小的趋势渐缓,建设用地仍在不断增加,但也逐渐趋于饱和的增长趋势。此研究可为当前及未来的土地利用规划、管理和生态恢复提供科学依据。

    Abstract:

    In order to explore the influence of land use change and driving forces in the process of urban development, this paper took Shunyi District, Beijing as an example and analyzed the dynamic changes of land use types in the study area. Meanwhile,the direction and degree of the transformation of land use based on Markov model with three remote sensing images in 2000, 2005 and 2010 were analyzed. Combined with GIS analysis function, the influence of various land use driving forces on land use types was discussed, and the factors of land use transformation were standardized by using fuzzy calculation and intergrated by using weighted linear combination method. CA-Markov model was used to simulate the distribution of land use in 2010. The accuracy of the proposed simulation results obtained was as high as 81.41%, the Kappa index was 0.7769. The feasibility and accuracy of the proposed method were proved. Based on the patterns of land use in 2010, the land use status of the land in 2020 was predicted and land use changes from 2010 to 2020 were analyzed. In addition to grassland and forest, the area of cultivated land and water continues maintaining the trend of reducing, but the trend is slowing down. And construction land is still increasing. This study provides scientific support for the planning and decision making of land use in the present and future.

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赵冬玲,杜萌,杨建宇,李鹏山,何珊珊,朱德海.基于CA-Markov模型的土地利用演化模拟预测研究[J].农业机械学报,2016,47(3):278-285.

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  • 收稿日期:2015-08-31
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  • 在线发布日期: 2016-03-10
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