自然资源学报 ›› 2017, Vol. 32 ›› Issue (11): 1930-1941.doi: 10.11849/zrzyxb.20161062

• 资源评价 • 上一篇    下一篇

黑土耕作区土壤含水量空间自相关及农业生产分区

高凤杰1, 单培明1, 马泉来3, 韩文文1, 周军2, *, 鞠铁男1, 吴啸1   

  1. 1. 东北农业大学资源与环境学院,哈尔滨 150030;
    2. 黑龙江省环境科学研究院,哈尔滨 150036;
    3. 武汉市江岸区新村街道办事处,武汉 430012
  • 收稿日期:2016-09-30 修回日期:2017-03-12 出版日期:2017-11-20 发布日期:2017-11-20
  • 通讯作者: 周军(1979- ),男,博士,高级工程师,主要从事土壤污染修复研究。E-mail:zhoujunhky@126.com
  • 作者简介:高凤杰(1981- ),女,河北唐山人,副教授,硕士生导师,研究方向为资源环境遥感。E-mail:gaojiecumt@126.com
  • 基金资助:
    国家重点研发计划课题子课题(2016YFD0201009); 国家自然科学基金项目(41471228); 东北农业大学青年才俊项目(14QC30)

Spatial Autocorrelation of Soil Moisture and Agricultural Zoning in a Mollisol Tillage Area of Northeast China

GAO Feng-jie1, SHAN Pei-ming1, MA Quan-lai3, HAN Wen-wen1, ZHOU Jun2, JU Tie-nan1, WU Xiao1   

  1. 1. College of Resource and Environment, Northeast Agricultural University, Harbin 150030, China;
    2. Research Academy of Environmental Sciences of Heilongjiang Province, Harbin 150036, China;
    3. Xincun Sub-district Administration Office, Jiang'an District, Wuhan 430012, China
  • Received:2016-09-30 Revised:2017-03-12 Online:2017-11-20 Published:2017-11-20
  • Supported by:
    National Key Research and Development Program, No.2016YFD0201009; National Natural Science Foundation of China, No.41471228; Northeast Agricultural University Youth Project, No.14QC30

摘要: 论文以东北黑土耕作区土壤表层(0~20 cm)含水量为研究对象,基于3S技术和Moran指数进行空间自相关分析,掌握黑土区土壤表层含水量的空间自相关类型及其分布格局,划定农业生产中的优先区域,为农业生产中土壤含水量的分区管理、农业设施合理配置提供理论依据。结果表明:海沟河小流域土壤含水量空间差异大,变异程度为中等变异,受人类活动等随机因素的影响较大;全局空间自相关系数为0.417 7,表现出较强的正自相关特征,且不同方向存在较大差异;局部空间自相关系数为0.374 4,局部空间自相关类型主要为H-H型(高-高关联)和L-L型(低-低关联),空间集聚特征明显,H-H型主要分布于研究区西北部地势平坦的地区,形成高含水量且高度空间自相关的格局,耕作优势突出,为农业生产中的优先区域,L-L型分布于东部山地与平原的过渡带,形成低含水量集聚的格局,为农业生产中的一般区域。基于土壤含水量空间自相关分布特征,进行农业生产区域的划定及分区管理具有重要的实践价值。

关键词: 分区管理, 黑土耕作区, 空间自相关, 土壤含水量

Abstract: The paper mainly analyzed the spatial distribution pattern and spatial autocorrelation of surface soil moisture (0-20 cm) in a mollisol tillage area of Northeast China with the Moran index model of global and local spatial autocorrelation indicators. The paper discovered the spatial structure and distribution pattern of surface soil moisture and provided a basis for agricultural zoning and facility allocation. The results show that there is great spatial difference of surface soil moisture with moderate variation in the study area. The spatial variation is mostly caused by random factors such as human activities, tillage practice and so on. The global spatial autocorrelation coefficient is 0.417 7, showing strong positive autocorrelation, and there exists anisotropy of spatial autocorrelation. The local spatial autocorrelation coefficient is 0.374 4, mainly displaying H-H (high-high correlation) and L-L (low-low correlation) clusters, which shows the coexistence pattern of high value agglomeration and low value agglomeration. The H-H agglomerations mainly distribute in the flat area in the northwest of the study area. The H-H area has very good tillage condition and has priority in developing agriculture. When farming in this area, people can take full advantage of the nature to achieve high yield with low cost. The L-L agglomerations mainly distribute in the transition zone of mountain and plain in the east part of the study area where the surface soil moisture content is low. When farming in this area, people should invest more on agricultural irrigation infrastructure. In a word, this research could serve in allocation of regional water resources and agricultural facilities.

Key words: mollisol tillage area, partitioned management, soil moisture, spatial autocorrelation

中图分类号: 

  • S152.7