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

作　　者：周斌 张新刚 王人潮 

机构地区：[1]InstituteofAgriculturalRemoteSensingandInformationTechnologyApplication,ZhejiangUniversity,Hangzhou310029,China

出　　处：《Journal of Zhejiang University Science》2004年第7期782-795,共14页浙江大学学报（自然科学英文版）

基　　金：Project supported by the National Natural Science Foundation ofChina (No. 40101014); and by the Science and technology Committee of Zhejiang Province (No. 001110445); China

摘　　要：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.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 hi-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.

关 键 词：Soil mapping Decision tree Bayesian predictive modeling Knowledge-based classification Rule extracting 

分 类 号：S159[农业科学—土壤学] P283[农业科学—农业基础科学]