A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perfo...A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perform the features extraction. Despite of all studies about this, some features cannot be found by the algorithm or it can extract some pixels unduly. So the current article aims to show the results with the software development that uses the original and reference image to calculate some statistics about the extraction process. Furthermore, the calculated statistics can be used to evaluate the extraction process.展开更多
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 K...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.展开更多
文摘A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perform the features extraction. Despite of all studies about this, some features cannot be found by the algorithm or it can extract some pixels unduly. So the current article aims to show the results with the software development that uses the original and reference image to calculate some statistics about the extraction process. Furthermore, the calculated statistics can be used to evaluate the extraction process.
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)+1 种基金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)
文摘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.
