An overview of pedometric techniques for use in soil survey
An overview of pedometric techniques for use in soil survey
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DOI：
10.1016/s0016-7061(00)00043-4
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发表时间：
2000-09-01
期刊：
影响因子：
6.1
通讯作者：
Shatar, TM
中科院分区：
文献类型：
--
作者：
McBratney, AB;Odeh, IOA;Shatar, TM
土壤调查中空间预测的定量技术正在发展速度。它们通常源自地统计学和现代统计数据。尤其是在非线性方法和所有类型的辅助信息方面，对地统计学的最新发展进行了审查。另外，基于变量的非平稳性和辅助信息的使用分析被证明为包括现代回归技术，包括广义线性模型（GLM），广义添加剂模型（GAM），分类和回归树（RT）和神经网络（ nn）。讨论了三项感兴趣的决议。案例研究用于说明不同的计时技术和各种辅助数据。案例研究的重点是预测不同的土壤特性，并将土壤中的土壤分为土壤类别，定义了先验的土壤类别。不同的技术产生了不同的插值误差。与地统计学库等混合方法提供了强大的空间预测方法，尤其是在流域和区域范围内。结果表明，每种计时技术的使用取决于调查的目的以及最终产品所需的准确性。 （c）2000 Elsevier Science B.V.保留所有权利。
Quantitative techniques for spatial prediction in soil survey are developing apace. They generally derive from geostatistics and modern statistics. The recent developments in geostatistics are reviewed particularly with respect to non-linear methods and the use of all types of ancillary information. Additionally analysis based on non-stationarity of a variable and the use of ancillary information are demonstrated as encompassing modern regression techniques, including generalised linear models (GLM), generalised additive models (GAM), classification and regression trees (RT) and neural networks (NN). Three resolutions of interest are discussed. Case studies are used to illustrate different pedometric techniques, and a variety of ancillary data. The case studies focus on predicting different soil properties and classifying soil in an area into soil classes defined a priori. Different techniques produced different error of interpolation. Hybrid methods such as CLORPT with geostatistics offer powerful spatial prediction methods, especially up to the catchment and regional extent. It is shown that the use of each pedometric technique depends on the purpose of the survey and the accuracy required of the final product. (C) 2000 Elsevier Science B.V. All rights reserved.