地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (6): 792-799.doi: 10.3724/SP.J.1047.2017.00792

• 地球信息科学理论与方法 • 上一篇    下一篇

基于自组织映射法的时间序列土地利用变化的时空可视化

齐建超(), 刘慧平*(), 高啸峰   

  1. 1. 北京师范大学 遥感科学国家重点实验室,北京 100875
    2. 北京师范大学 环境遥感与数字城市北京市重点实验室,北京 100875
    3. 北京师范大学地理学与遥感科学学院,北京 100875
  • 收稿日期:2016-10-11 修回日期:2017-03-07 出版日期:2017-06-20 发布日期:2017-06-20
  • 通讯作者: 刘慧平 E-mail:qjc9198@163.com;hpliu@bnu.edu.cn
  • 作者简介:

    作者简介:齐建超(1991-),男,河南信阳人,硕士生,主要从事土地利用时空演变研究。E-mail: qjc9198@163.com

  • 基金资助:
    国家自然科学基金项目(40671127);国土资源部公益性行为科研专项(201411015-03)

Research on the Spatio-temporal Visualization of Multiple Time Series Land Use Change by the Self-organizing Map

QI Jianchao(), LIU Huiping*(), GAO Xiaofeng   

  1. 1. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
    2. Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China
    3. School of Geography and RS, Beijing Normal University, Beijing 100875, China
  • Received:2016-10-11 Revised:2017-03-07 Online:2017-06-20 Published:2017-06-20
  • Contact: LIU Huiping E-mail:qjc9198@163.com;hpliu@bnu.edu.cn

摘要:

精细尺度下多时间序列土地利用时空演变分析是当前研究的一个趋势,本研究基于2005、2007、2009、2011、2013年5期土地利用数据采用自组织映射方法分析了北京市乡镇级多时间序列土地利用的时空演变规律,实现了乡镇尺度下多时间序列土地利用数据的时空一体化表达和对比分析。通过构建自组织映射神经网络,利用其聚类和降维可视化功能对5个监测时期的土地利用数据同时进行训练,在其输出面板可以发现不同土地利用类型的分布聚集模式以及相互之间的结构比例关系,并对输出神经元进行二次聚类以及土地利用变化轨迹分析,展示出北京市乡镇级5个监测时相的土地利用时空演变规律。结果揭示出北京市平原区、山区及二者过渡的山前结合带的各自不同的土地利用时空变化轨迹与模式:北京市平原区向高建设用地比例的土地利用结构方向演变,山区向高林地比例的土地利用结构方向演变,而山前结合带的土地利用时空演变较为复杂。

关键词: 自组织映射, 乡镇土地利用变化, 时空可视化, 轨迹分析, 北京市

Abstract:

Analysis of multiple time series land use of spatio-temporal evolution at fine scale is a hot and important research area currently. In this study, the self-organizing map (SOM) neural network was used to analyze the land use spatio-temporal distribution at township-level in Beijing. The study was based on 5 periods of land use classification data of Beijing in 2005, 2007, 2009, 2011 and 2013. We implemented spatio-temporal integrated expression and comparative analysis of multiple time series land use data at township-level. Through creating and training self-organizing map neural network, we could find out the distribution of different land use types (built-up land, farmland, forest land, grassland, garden, water, and unused land) on the SOM output plane. This represented the proportional relationship of different land use types in land use structure. By second-step clustering and building land use change trajectory, we got the spatio-temporal evolution rules of the land use in township of Beijing. The results revealed that there were five land use change trajectories and three spatio-temporal evolution patterns in Beijing at township level. The plain area is developing to the land use structure of high built-up land proportion. The mountainous area is developing to the land use structure of high forest land proportion, and the land use change of piedmont zone is complex.

Key words: self-organizing map, township-level land use change, spatio-temporal visualization, trajectory analysis, Beijing