Automated soil resources mapping based on decision tree and Bayesian predictive modeling.
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.
Zhou
Bin
B
Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310029, China. zhoubin@zju.edu.cn
Zhang
Xin-Gang
XG
Wang
Ren-Chao
RC
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003661:Decision Support Techniques; D040362:Geographic Information Systems; D005843:Geography / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D012987:Soil / Q000032:analysis*
D012987:Soil