土壤模糊隶属度不同数据转换方法及其对空间插值结果的影响  

Different transforms of fuzzy membership values of sampled soils and theirs influences on resulted interpolation prediction

作　　者：檀满枝[1] 陈杰[2,1] 

机构地区：[1]土壤与农业可持续发展国家重点实验室中国科学院南京土壤研究所,南京210008 [2]郑州大学自然资源与生态环境研究所,郑州450001

出　　处：《生态学报》2009年第6期3147-3153,共7页Acta Ecologica Sinica

基　　金：国家自然科学基金资助项目(40571065,40701070);中国科学院南京土壤研究所创新前沿资助项目(ISSASIP0716)

摘　　要：应用模糊c-均值算法对土壤进行连续分类时,其输出的土壤模糊隶属度值具有成分数据的结构特点。直接基于土壤隶属度数据实施普通克里格插值,其空间预测结果缺乏可信度。因此,在进行插值预测之前,必须对土壤模糊隶属度值进行必要的数据转换。研究采用对数正态变换方法、对称对数比转换方法和非对称对数比转换方法对土壤模糊隶属度值进行数据转换,分析了各种数据转换形式对插值结果及其精度的影响。结果表明,对样点土壤模糊隶属度进行简单对数正态转换,其插值结果空间上任意点的土壤对于不同类别的隶属度之和均不为1,因此这样的插值结果理论上缺乏可行性。数据经非对称对数比转换和对称对数比转换后,插值结果均满足各个位置组分之和为1和非负限制,二者相比,后者对区域总体趋势的反映较前者好,且精度较高。因此,在应用对称对数比方法对样点土壤模糊隶属度值进行数据转换的基础上,应用克里格技术实施空间插值可以获得最佳预测结果。Soil fuzzy membership values of the sampled soils resulted from fuzzy c-means algorithm, which would be applied in soil predictive mapping, is a kind of compositional data. Owing to the structural characteristics of compositional data, they could not be directly used in prediction of the fuzzy memberships of unknown sites over space by kriging interpolation. To achieve spatial soil prediction, therefore, the membership values of the sampled soils must be transformed before interpolation. In this study, transform of compositional data by several ways were attempted, and influence of different transform approaches on output and precision of prediction compared and analyzed. The results indicated that, membership values of all the spatial predicted sites didn＇t sum to 1 on condition that known membership values of the sampled soils were simply transformed through logarithm. Obviously, the above predictive result was theoretically unauthentic. Contrarily, membership values of all the spatial predicted sites summed to 1 when the membership values of the known soils were transformed by asymmetry Logratio and symmetry Logratio approaches, demonstrating that two approaches were theoretically accepted. Comparatively, symmetry Logratio transform could lead to a better spatial distribution pattern and higher precision.

关 键 词：土壤 模糊隶属度 成分数据 非对称对数比转换 对称对数比转换 空间预测 

分 类 号：S159[农业科学—土壤学]