Construction of membership functions for predictive soil mapping under fuzzy logic
Construction of membership functions for predictive soil mapping under fuzzy logic
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DOI：
10.1016/j.geoderma.2009.05.024
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发表时间：
2010-03
期刊：
影响因子：
6.1
通讯作者：
A. Zhu;Lin F. Yang;Baolin Li;C. Qin;T. Pei;Baoyuan Liu
中科院分区：
文献类型：
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作者：
A. Zhu;Lin F. Yang;Baolin Li;C. Qin;T. Pei;Baoyuan Liu
模糊隶属函数是土壤预测制图中表征土壤与环境关系的有效工具。通常，模糊隶属函数的构建需要从当地土壤专家或广泛的实地样本中获得土壤-景观关系的知识。对于没有土壤调查专家和没有广泛的土壤实地观测的地区，有目的的抽样方法可以提供有关关系的描述性知识。然而，量化这种描述性的知识的模糊隶属函数的形式预测土壤制图是一个挑战。提出了一种利用描述性知识构造模糊隶属函数的方法。模糊隶属度函数的构建基于两类知识：1）关于每种土壤类型的典型环境条件的知识和2）关于每种土壤类型如何对应于环境条件变化的知识。这两种类型的知识可以从土壤类型的悬链线序列和相关的环境信息中提取，通过有目的的抽样在几个现场样品收集。在黑龙江省嫩江县鹤山农场的一个小流域进行了试验。一组隶属度函数的构建来表示土壤景观关系的描述性知识，这是来自22个领域的样本，通过目的性抽样方法收集。利用这些隶属函数生成了该地区的土壤亚类图和A层土壤有机质含量图。独立收集了45个实地验证点，以评估两个土壤图。土壤亚类图的准确率达到76%。将基于模糊隶属函数的A层土壤有机质含量图与多元线性回归模型的结果进行了比较。结果表明，基于模糊隶属函数的土壤有机质含量图优于基于线性回归模型的土壤有机质含量图。所提出的方法也可用于从其他来源获得的描述性知识构造隶属函数。
Fuzzy membership function is an effective tool to represent relationship between soil and environment for predictive soil mapping. Usually construction of a fuzzy membership function requires knowledge on soil-landscape relationships obtained from local soil experts or from extensive field samples. For areas with no soil survey experts and no extensive soil field observations, a purposive sampling approach could provide the descriptive knowledge on the relationships. However, quantifying this descriptive knowledge in the form of fuzzy membership functions for predictive soil mapping is a challenge. This paper presents a method to construct fuzzy membership functions using descriptive knowledge. Construction of fuzzy membership functions is accomplished based on two types of knowledge: 1) knowledge on typical environmental conditions of each soil type and 2) knowledge on how each soil type corresponds to changes in environmental conditions. These two types of knowledge can be extracted from catenary sequences of soil types and the associated environment information collected at a few field samples through purposive sampling. The proposed method was tested in a watershed located in Heshan farm of Nenjiang County in Heilongjiang Province of China. A set of membership functions were constructed to represent the descriptive knowledge on soil-landscape relationships, which were derived from 22 field samples collected through a purposive sampling approach. A soil subgroup map and an A-horizon soil organic matter content map for the area were generated using these membership functions. Forty five field validation points were collected independently to evaluate the two soil maps. The soil subgroup map achieved 76% of accuracy. The A-horizon soil organic matter content map based on the derived fuzzy membership functions was compared with that derived from a multiple linear regression model. The comparison showed that the soil organic content map based on fuzzy membership functions performed better than the soil map based on the linear regression model. The proposed method could also be used to construction membership functions from descriptive knowledge obtained from other sources.