INTEGRATING SOIL MAP INFORMATION IN MODELING THE SPATIAL VARIATION OF CONTINUOUS SOIL PROPERTIES
INTEGRATING SOIL MAP INFORMATION IN MODELING THE SPATIAL VARIATION OF CONTINUOUS SOIL PROPERTIES
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DOI：
10.1111/j.1365-2389.1995.tb01336.x
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发表时间：
1995-09-01
影响因子：
4.2
通讯作者：
JOURNEL, AG
中科院分区：
文献类型：
--
作者：
GOOVAERTS, P;JOURNEL, AG
本文提出了两种将土壤图信息整合到连续土壤性质空间变化建模中的指标算法：变均值简单指标克里格法和马尔可夫-贝叶斯算法。这两种方法都用于评估苏格兰边境地区铜和钴缺乏的可能性。结果与多边形方法（Thiessen多边形）和不使用土壤地图信息的指标克里格算法得到的地图进行了比较。考虑土壤地图信息被证明可以改善缺乏区域的划定，特别是在采样稀疏的地方。根据铜或钴的焦点概率分布以及测量高估或低估金属浓度成本的功能，将测试地点划分为缺乏或不缺乏，以便将错误分类的预期成本降至最低。将分类结果与试验点实际铜、钴浓度进行对比，结果表明，两种算法均能显著降低误分类带来的经济损失。
This paper presents two indicator algorithms that integrate soil map information into modelling the spatial variation of continuous soil properties: these are simple indicator kriging with varying means and the Markov-Bayes algorithm. Both methods are used to evaluate probabilities for copper and cobalt deficiencies in the Borders Region of Scotland. Results are compared with maps obtained by the polygonal method (Thiessen polygons) and an indicator kriging algorithm that does not use soil map information. Accounting for soil map information is shown to improve delineation of the deficient areas, especially where the sampling is sparse.Test locations are classified as deficient or not so as to minimize an expected cost of misclassification that is derived from focal probability distributions of copper or cobalt and functions measuring the cost of overestimating or underestimating metal concentrations. The comparison of classification results with actual copper and cobalt concentrations at test locations shows that the two proposed algorithms can decrease substantially the economic loss attached to misclassification.