Application of a Digital Soil Mapping Method in Producing Soil Orders on Mountain Areas of Hong Kong Based on Legacy Soil Data  

作　　者：SUN Xiao-Lin ZHAO Yu-Guo ZHANG Gan-Lin WU Sheng-Chun MAN Yu-Bon WONG Ming-Hung 

机构地区：[1]Croucher Institute for Environmental Sciences, and Department of Biology, Hong Kong Baptist University, Hong KongSpecial Administrative Region (China) [2]State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing210008 (China) [3]joint Open Laboratory of Soil and Environment, Institute of Soil Science, Chinese Academy of Sciences and Hong KongBaptist University (China)

出　　处：《Pedosphere》2011年第3期339-350,共12页土壤圈（英文版）

基　　金：Supported by the Public Policy Research of the Research Grants Council of Hong Kong, China (No.2002-PPR-3);the Knowledge Innovation Program of the Chinese Academy of Sciences (No.KZCX2-YW-409);the National Natural ScienceFoundation of China (Nos.40625001 and 40771092);the Mini-AOE (Area of Excellence) Fund from the Hong Kong Baptist University,China (No.RC/AOE/08-09/01)

摘　　要：Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods （decision tree analysis and linear discriminant analysis） were used, and their applications were compared. Much more effort was put on selecting soil covariates for modeling. First, analysis of variance （ANOVA） was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods (decision tree analysis and linear discriminant analysis) were used, and their applications were compared. Much more eflort was put on selecting soil covariates for modeling. First, analysis of variance (ANOVA) was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.

关 键 词：decision tree analysis linear discriminant analysis soil covariate selection 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S15[自动化与计算机技术—计算机科学与技术]