Journal of Zhejiang University-SCIENCE A

, Volume 5, Issue 7, pp 782–795 | Cite as

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

  • Bin Zhou
  • Xin-gang Zhang
  • Ren-chao Wang
Computer & Information Science


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.

Key words

Soil mapping Decision tree Bayesian predictive modeling Knowledge-based classification Rule extracting 

Document code

CLC number

S159-3 P283.8 P283.7 


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Copyright information

© Zhejiang University Press 2004

Authors and Affiliations

  1. 1.Institute of Agricultural Remote Sensing and Information Technology ApplicationZhejiang UniversityHangzhouChina

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