引用本文:武淑霞,张维理,徐爱国,张认连,雷秋良※.不同分级标准下土壤养分图的整合模型构建[J].中国农业信息,2019,31(5):110-120
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 172次   下载 99 本文二维码信息
码上扫一扫!
分享到: 微信 更多
不同分级标准下土壤养分图的整合模型构建
武淑霞, 张维理, 徐爱国, 张认连, 雷秋良※
中国农业科学院农业资源与农业区划研究所/农业农村部农业面源污染控制重点实验室,北京100081
摘要:
【目的】针对我国土壤普查成果中不同省份或地区、甚至相同省份不同县市之间的土壤养分图分级指标不一致导致的全国土壤养分图整合困难的问题,提出不同分级标准下土壤养分图的整合模型,实现全国高精度土壤养分制图表达。【方法】分析土壤二次普查大、中比例尺多种土壤养分图在数据提取及整合时可能出现的问题,在保留原有图斑的土壤养分分级信息、属性数据、不同制图目的下视觉一致的前提下,采用组件式建模方式构建土壤养分分级体系整合模型。【结果】通过对我国第二次土壤普查成果1∶50 万土壤有机质空间分布图的应用,该模型能够对分区调查的矢量化空间数据库进行提取、整合与表达,实现海量土壤养分制图中人机交互的智能表达,并通过对分级属性数据的提取,实现对不同区域土壤养分图的整合,便于重新制图并进一步生成土壤养分低值、中值、高值图。【结论】土壤养分分级体系整合模型可适用于环境、生态等其他领域的类似数据的分级整合与表达。
关键词:  土壤养分;数据整合模型;制图表达;分级系统
DOI:10.12105/j.issn.1672-0423.20190512
分类号:
基金项目:科技部科技基础性工作专项 (2012FY112100,2006FY120200);中央级公益性科研院所基本科研业务费专项(1610132019028);国家自然科学基金项目(31572208)
Development of model for integrating and mapping of soil nutrient maps with different grading systems
Wu Shuxia, Zhang Weili, Xu Aiguo, Zhang Renlian, Lei Qiuliang※
Key Laboratory of Nonpoint Source Pollution Control,Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China
Abstract:
[Purpose]The soil nutrient grading indexes might be different from different provinces or regions,and even among the counties and cities of the same province,which makes it difficult to integrate these high resolution soil nutrient maps in large range such as provincial or national regions. It is necessary to discriminate and integrate these nutrient grading indexes for mapping.[Method]The work presented a model named“ Soil Nutrition Classification Integration Model(SNCIM)” to integrate and harmonize the different nutrient grading indexes originating from various existing soil polygon maps on the basis of understanding the problems that may occur in the data extraction and integration of multiple soil nutrient maps with large or medium scale,keeping the attribute data of the soil nutrient classification information and achieving visual consistency under different mapping purposes.[Result]As an example, SNCIM was applied to integrate the grading indexes of the 1∶500 000 soil organic matter in 17 provinces from 2nd national soil survey. These maps were mostly scaled at 1∶50 000 to 1∶500 000 and was used as the input data. The model successfully extracted from the attribute database of different provinces,integrated the grading indexes and expressed the new grading system for different regions by human-computer interaction. The results could be used to re-map and generate new soil nutrient maps,including the lowest values map,average values map and highest values map.[Conclusion]SNCIM is a general model and can also be applicable to the environment,ecology and other research areas to resolve the similar problems.
Key words:  soil nutrient;data integration model;mapping;grading system