地理研究 ›› 2017, Vol. 36 ›› Issue (1): 61-73.doi: 10.11821/dlyj201701005

• 研究论文 • 上一篇    下一篇

基于开发利用与产出视角的区域土地利用隐性形态综合研究——以黄淮海地区为例

曲艺1,2(), 龙花楼1,2()   

  1. 1. 中国科学院地理科学与资源研究所,北京 100101
    2. 中国科学院大学,北京 100049
  • 收稿日期:2016-06-24 修回日期:2016-11-18 出版日期:2017-01-20 发布日期:2017-01-22
  • 作者简介:

    作者简介:曲艺(1986- ),女,山东青岛人,博士研究生,主要从事城乡发展与土地利用转型研究。E-mail: quy.13b@igsnrr.ac.cn

  • 基金资助:
    国家科技支撑计划课题“平原农区空心村整治的关键技术集成示范”(2014BAL01B05)

The integrated research on regional land use recessive morphology from the perspectives of exploitation and output:The case of the Huang-Huai-Hai Region

Yi QU1,2(), Hualou LONG1,2()   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2016-06-24 Revised:2016-11-18 Online:2017-01-20 Published:2017-01-22

摘要:

从土地开发利用与产出视角出发,以黄淮海地区为研究区,开展区域土地利用隐性形态的综合研究。首先,从土地开发利用强度、土地经济发展水平和土地污染排放水平三个维度构建了区域土地利用隐性形态的三维表征模型。其次,引入基于遗传算法的K-means聚类方法(KGA)对研究区不同单元的综合土地利用隐性形态进行了类型划分,发现不同类型单元的综合土地利用隐性形态特征与其所处的区域发展水平紧密相关。第三,引入空间距离和空间自相关检验,提出了一种综合空间与非空间属性信息的空间聚类方法(SKGA),用于研究区综合土地利用隐性形态的分区研究。结果表明:基于SKGA的分区方案既能保留各研究单元非空间属性的分异性,又能兼顾不同单元间的空间邻接性和空间关联性。研究结果可为差别化的土地管理决策提供参考。

关键词: 土地利用转型, 土地利用隐性形态, 遗传算法, 空间自相关, 空间聚类, 黄淮海地区

Abstract:

Land use transition refers to the changes in land use morphology including dominant morphology and recessive morphology of a certain region over a certain period of time. If we take different land use morphologies as the points, then different land use transition processes might be the line connecting different points driven by regional socio-economic change and innovation. So, in-depth research on land use morphology may make great contribution to a better understanding of regional land use transition. This paper paid more attention to the recessive land use morphology, a multi-dimensional conception with multiple spatial and non-spatial properties. Taking the Huang-Huai-Hai region as a case study, this paper studies regional land use recessive morphology in a comprehensive way from the view point of exploitation and output. Firstly, a three-dimensional representative model for land use recessive morphology was built. This model consists of the attributes of land use intensity, land use economic condition and land use emission condition. It was subsequently used in recessive land use morphology analysis of the study area. Secondly, a K-means clustering method based on the Genetic Algorithm (KGA) was introduced into the classification for the units with different comprehensive land use recessive morphologies. This showed that the comprehensive land use recessive morphology types of the unit have close relationship to its economic development level. For example, Beijing may have the highest regional land use intensity, highest land use economic level, but lowest land use emission level for its advanced economic development level, while Zhumadian with the lowest regional land use intensity, land use economic level and land use emission level for its underdeveloped economy. Thirdly, by introducing the spatial distance and spatial autocorrelation into the KGA method, this paper proposed a new spatial clustering method combining spatial and non-spatial properties (spatial K-means clustering method based on the Genetic Algorithm, SKGA). As shown that this method performed well in the zoning analysis of comprehensive land use recessive morphologies, for it can maintain the heterogeneity in non-spatial properties, as well as take full account of spatial contiguity and spatial correlation. Finally, differentiated management measures were proposed based on the features of different comprehensive land use recessive morphology zones to support land use policy decision-making.

Key words: land use transition, land use recessive morphology, the genetic algorithm, spatial autocorrelation, spatial clustering, Huang-Huai-Hai Region