长江流域资源与环境 >> 2022, Vol. 31 >> Issue (1): 166-178.doi: 10.11870/cjlyzyyhj202201016

• 生态环境 • 上一篇    下一篇

基于Geodetector模型的鄂北岗地土壤有机质空间格局及影响因素分析——以枣阳市为例

高浩然1,2, 周勇1,2*, 王丽1,2, 吴正祥1,2   

  1. (1.华中师范大学地理过程分析与模拟湖北省重点实验室,湖北 武汉 430070;2. 华中师范大学城市与环境科学学院,湖北 武汉 430070)
  • 出版日期:2022-01-20 发布日期:2022-02-09

Spatial Pattern and Influencing Factors of Soil Organic Matter Based on Geodetector Model: Taking Zaoyang City as an Example

GAO Hao-ran1,2,ZHOU Yong1,2, WANG Li1,2, WU Zheng-xiang1,2   

  1. (1.Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province,Central China Normal 
    University,Wuhan 430070,China; 2.College of Urban and Environmental Sciences,
    Central China Normal University,Wuhan 430070,China)
  • Online:2022-01-20 Published:2022-02-09

摘要: 为探明鄂北岗地耕地土壤质量及影响因素情况,以湖北省枣阳市为研究区,选择样点240个,以土壤有机质土壤有机质(SOM)含量为研究对象,运用经典统计学、地统计学对耕地土壤有机质含量空间格局进行分析,引入地理探测器Geodetector模型结合佩尔森(Pearson)相关系数法,得出影响枣阳市土壤有机质含量空间格局的显著性因子。结果表明:(1)枣阳市耕地土壤有机质含量分布空间格局呈弱变异,空间变异模型为球状模型拟合,分布格局呈现四周高,中心向东北向偏低的空间格局;(2)对土壤有机质含量单因子影响最强的5种因子均为降水量、土壤类型、NDVI、地表起伏度、公路距离;(3)各因子交互作用解释力均大于单因子作用解释力,表现为非线性协同作用。该研究将地理信息科学与遥感、统计学以及地理探测器等多学科技术相结合,为探索控制鄂北岗地土壤养分的空间格局及其因素提供方法途径,尝试为水稻种植区及盆地地区进行农业管理、施肥管控及土壤养分空间预测等研究工作提供基础。

Abstract: In order to explore the soil quality and influencing factors of cultivated land in the north of Hubei province, Zaoyang City, Hubei Province was selected as the research area, 240 samples were selected, soil organic matter (SOM) was the research object, and classical statistics and geostatistics were used. Analyzing the spatial pattern of cultivated land SOM, introducing the geodetector Geodetector model combined with the Pearson correlation coefficient method to calculate the significant factors affecting the spatial pattern of SOM content in Zaoyang City. The results showed that: (1)The spatial distribution of SOM content in cultivated land in Zaoyang City showed weak variability, and the spatial variability model was fitted by a spherical model; (2)The distribution pattern presented a spatial pattern of high surroundings and low center to northeast; (3)The five factors that have the strongest impact on SOM single factor are precipitation, soil type, NDVI, surface undulation, and highway distance. The explanatory power of each factor interaction is greater than the explanatory power of the single factor action, showing a nonlinear synergy. This study combines geographic information science with remote sensing, statistics, and geographic detectors, and other multidisciplinary technologies, to provide methods to explore the spatial pattern and factors of soil nutrients in the hilly land of northern Hubei, and try to provide agriculture for rice planting areas and basin areas. Management, fertilization control, and spatial prediction of soil nutrients provide the basis for research work.

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