模糊聚类,目的性采样,pH,巴音布鲁克," /> 模糊聚类,目的性采样,pH,巴音布鲁克,"/> fuzzy clustering, purposive sampling, pH, Bayanbulak,"/> <span style="font-family:宋体;font-size:10.5pt;">基于模糊聚类的新疆典型高寒草原土壤<span>pH</span>值空间制图</span>
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干旱区地理 ›› 2019, Vol. 42 ›› Issue (5): 1115-1123.doi: 10.12118/j.issn.1000-6060.2019.05.17

• 地理信息科学 • 上一篇    下一篇

基于模糊聚类的新疆典型高寒草原土壤pH值空间制图

朱磊1,2，盛建东1,2，贾宏涛1,2   

  1. 1 新疆农业大学草业与环境科学学院，新疆乌鲁木齐830052； 2 新疆土壤与植物生态过程实验室，新疆乌鲁木齐830052
  • 收稿日期:2019-03-10 修回日期:2019-06-11 出版日期:2019-09-25 发布日期:2019-09-19
  • 作者简介:朱磊（1982- ），男，博士，副教授，研究方向地理信息系统与遥感应用、土壤属性空间模拟. E-mail：smartzhulei04@163.com
  • 基金资助:
    国家自然科学基金项目（31560171）；新疆农业大学博士后基金联合资助

 Predictive soil pH mapping based on fuzzy clustering in typical alpine grassland of Xinjiang

ZHU Lei1,2, SHENG Jian-dong1,2, JIA Hong-tao1,2   

  1. 1  College of Grassland and Environment Sciences,Xinjiang Agricultural University,Urumqi 830052,Xinjiang,China； 2 Xinjiang Key Laboratory of Soil and Plant Ecological Processes,Urumqi 830052,Xinjiang,China
  • Received:2019-03-10 Revised:2019-06-11 Online:2019-09-25 Published:2019-09-19

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摘要： 准确、高效地掌握草原土壤属性的空间分布能够为草地资源境管理提供基础信息和参考依据。相比于传统土壤调查方法，基于模糊逻辑的土壤—环境推理能够提高野外采样效率和预测制图精度，被广泛应用于数字土壤制图。但由于土壤自身的空间变异性及其与环境条件间的非线性，现有推理模型的稳定性较低，尚未在高寒草原区进行应用。选择新疆巴音布鲁克典型亚高山草原地区约4 km2区域为研究区，以高程、坡度、坡向、沿剖面曲率、沿等高线曲率、地形湿度指数6个地形因子为土壤环境因子，采用模糊[WTBX]C[WTBZ]均值聚类（Fuzzy C-means Clustering，FCM）方法对环境因子聚类，得到9个环境因子组合，并在隶属度值高的环境因子组合中心共设置18个典型点。运用土壤—环境推理方法模拟研究区表层土壤pH值空间分布，其变化范围在7.170~8.186之间。选取35个独立样本进行精度检验（均匀采样点16个，横截面采样点9个，垂直带采样点10个），模拟结果与实测值基本吻合，且基于模糊聚类和土壤—环境推理方法的模拟精度高于普通克里格法和反距离权重法。通过基于模糊逻辑和土壤—环境推理的数字土壤制图方法在小尺度区域的运用验证，结果表明基于典型点的采样方案能够快速、有效地对区域土壤属性进行空间模拟，该方法对于类似小尺度的研究区同样有效。

关键词: font-size:10.5pt, 模糊聚类')">">模糊聚类, 目的性采样, font-size:10.5pt, pH">pHfont-size:10.5pt, ')">">, 巴音布鲁克

Abstract:  

Obtaining spatial distribution of grassland soil properties accurately and efficiently can provide basic information and reference for grassland resource management.Compared with the traditional soil survey method,the soil environment inference model based on fuzzy logic can improve the efficiency of field sampling and accuracy of predictive mapping,which has been widely used in digital soil mapping.However,due to the soil’s spatial variation and its non-linearity with the environmental conditions,the stability of the existing models are relatively low.The models have not been applied in alpine meadow area.In this study,fuzzy C-means clustering (FCM) was used to predict soil pH in the surface layer of grassland soil within a 4 km2 area in Bayanbulak District,Xinjiang Uyghur Autonomous Region,China.Six terrain factors,including elevation,slope,aspect,planform curvature,profile curvature and topographic wetness index,were clustered．Fuzzy membership of 9 groups of environmental factors were derived to position 18 soil samples in the area with membership larger than 0.9.Then pH distribution was predicted with fuzzy membership model.The pH value of study ranged from 7.170 to 8.186 and was consistent with the measured values.The mapping results reflected continuous changing of soil properties with terrain changing.There were 35 individual soil samples (16 equal-interval sampling points,9 cross-sectional sampling points and 10 sampling points according to altitude) collected as validation data set.The agreement coefficients between observed values and predicted values were high,and the accuracy of FCM model is higher than that of Ordinary Kriging method and Inverse Distance Weighted method.FCM and purposive sampling for digital soil mapping is also suitable for small-scale region.This approach is an efficient digital soil mapping method with satisfactory prediction precision using less samples.It could be possibly applied to the areas with the similar landscape conditions．

Key words: font-size:10.5pt, fuzzy clustering')">">fuzzy clustering, purposive sampling, pH, Bayanbulak