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Construction of membership functions for predictive soil mapping under fuzzy logic
Zhu A. X.; Yang L.; Li B. L.; Qin C. Z.; Pei T.; Liu B. Y.
Source PublicationGeoderma
2010
Volume155
Issue3-4
Pages164-174
KeywordFuzzy Membership Function Digital Soil Mapping Purposive Sampling Knowledge On soil-envirOnment relatiOnships Solim Spatial Prediction Information Knowledge Attributes Inference Science Model Maps
AbstractFuzzy 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. (C) 2009 Elsevier B.V. All rights reserved.
Indexed BySCI
ISSN0016-7061
Document TypeSCI/SSCI论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/22221
Collection历年回溯文献
Recommended Citation
GB/T 7714
Zhu A. X.,Yang L.,Li B. L.,et al. Construction of membership functions for predictive soil mapping under fuzzy logic. 2010.
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