基于土壤-环境关系的更新传统土壤图研究

传统土壤图是流域管理、生态水文模型所需土壤空间分布信息的主要数据源。然而,受传统制图技术和基础数据质量所限,传统土壤图的空间详细度和属性精确度并不高。随着地理信息技术的发展,如何利用可获取的高质量空间数据和现代空间分析技术来更新传统土壤图显得十分必要。基于传统土壤图中的土壤多边形与通过模糊聚类所得环境因子组合之间存在着对应关系这一假设,本文提出了一种从传统土壤图中提取土壤-环境关系知识并利用该知识更新传统土壤图的方法。该方法包括四个步骤:对环境数据进行模糊c均值聚类获取环境因子组合;利用传统土壤图建立环境因子组合与土壤类型间的对应关系;提取土壤-环境关系知识;进行土壤推理制图。将该方法应用于加拿大New B runsw ick省的W akefield研究区,以更新该区现有的1∶20 000的传统土壤图。应用结果表明:更新后的数字土壤图显示了更详细的空间分布信息;经野外独立验证点验证,所得土壤图(制图单元为土壤组合-排水等级)精度高出原土壤图约20%。因此,该方法是一种有效的更新传统土壤图的方法,可增加土壤图的空间详细度、提高土壤图的属性精确度。

Updating conventional soil maps using knowledge on soil-environment relationships extracted from the maps

Conventional soil maps are the major data source for information on soil spatial distribution which is essential in watershed management and eco-hydrological modeling. Due to the limitations of conventional soil mapping techniques, the level of spatial detail and attribute accuracy in conventional soil maps are not high. With the development of geographic information technology and the accumulation of high quality detailed spatial data on environment, the question about how to update the conventional soil maps using these new techniques and high quality data comes up. Based on the assumption that soil polygons in the conventional soil maps do contain basic relationships between soil types and environment, we have developed a new method to extract knowledge on soil-environment relations embedded in conventional soil maps and then update the soil maps with the knowledge using the detail environmental data. The method consists of the following four steps, 1) Fuzzy c-means clustering of environmental data to obtain the combinations of environmental factors; 2) Relating the combinations of environmental factors to soil types based on conventional soil maps; 3) Extraction of knowledge on the soil-environment relationships; 4) Prediction of soil spatial distribution using the SoLIM (Soil-Land Inference Model). This method was applied in Wakefield, New Brunswick, Canada for updating its 1:20,000 conventional soil map. It was showed that the updated soil map had more spatial details than the original one. And verification at independent validation points in the field indicated that the digital soil map depicting spatial distribution of soil associations with drainage classes was about 20% higher in accuracy than the conventional soil map. It is therefore concluded that the proposed method is an effective way to update conventional soil maps.