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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

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.

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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References

  1. Burrough, P.A., 1986. Principles of Geographical Information Systems for Land Resources Assessment. Clarendon Press, Oxford, p. 193.Google Scholar
  2. Cook, S.E., Corner, R.J., Grealish, G.J., Gessler, P.E., Chartres, C.J., 1996. A rule-based system to map soil properties.Soil Science Society America Journal,60:1893–1900.CrossRefGoogle Scholar
  3. Huang, X.Q., Jensen, J.R., 1997. A machine-learning approach to automated knowledge-base building for remote sensing image analysis with GIS data.Photogrammetric Engineering & Remote Sensing,63(10): 1185–1194.Google Scholar
  4. Jenny, H., 1941. Factors of Soil Formation: A System of Quantitative Pedology. McGraw-Hill, New York, p. 281.Google Scholar
  5. Jenny, H., 1980. The Soil Resource: Origin and Behaviour. Springer-Verlag, New York, p. 377.CrossRefGoogle Scholar
  6. Lagacherie, P., Holmes, S., 1997. Addressing geographical data errors in a classification tree for soil unit predictions.Int. J. Geographical Information Science, (11):183–198.CrossRefGoogle Scholar
  7. Luger, G.F., Stubblefield, W.A., 1993. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Second Edition, The Benjamin/Cummings Publishing Company, Inc., Redwood City, California, p. 740.zbMATHGoogle Scholar
  8. Mark, D.M., Csillag, F., 1990. The nature of boundaries on ‘area-class’ maps.Cartographica, (27):65–78.Google Scholar
  9. Quinlan, J.R., 1986. Induction of decision tree.Machine Learning,1(1):81–106.Google Scholar
  10. Quinlan, J.R., 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, California.Google Scholar
  11. Skidmore, A.K., 1989. An expert system classifies eucalypt forest types using thematic mapper data and a digital terrain model.Photogrammetric Engineering and Remote Sensing,55(10):1449–1464.Google Scholar
  12. Skidmore, A.K., Ryan, P.J., Dawes, W., Short, D., O’Loughlin, E., 1991. Use of an expert system to map forest soils from a geographical information system.Int. J. Geographical Information Systems,5(4):431–445.CrossRefGoogle Scholar
  13. Wang, R.C., Wang, S.F., Su, H.P., 1986. The research on soil visual interpretation and mapping technique by using MSS imagery.Journal of Zhejiang Agricultural University,12(2):103–111 (in Chinese).MathSciNetGoogle Scholar

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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