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CLC number: S159-3; P283.8; P283.7

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Received: 2003-10-09

Revision Accepted: 2004-03-22

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Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.7 P.782~795

http://doi.org/10.1631/jzus.2004.0782


Automated soil resources mapping based on decision tree and Bayesian predictive modeling


Author(s):  ZHOU Bin, ZHANG Xin-gang, WANG Ren-chao

Affiliation(s):  Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310029, China

Corresponding email(s):   zhoubin@zju.edu.cn

Key Words:  Soil mapping, Decision tree, Bayesian predictive modeling, Knowledge-based classification, Rule extracting


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ZHOU Bin, ZHANG Xin-gang, WANG Ren-chao. Automated soil resources mapping based on decision tree and Bayesian predictive modeling[J]. Journal of Zhejiang University Science A, 2004, 5(7): 782~795.

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Abstract: 
This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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