Some methods to improve the utility of conditioned Latin hypercube sampling

PeerJ

article

Brendan P. Malone¹  Budiman Minansy²  Colby Brungard³ 

[1] CSIRO, Agriculture and Food;The Sydney Institute of Agriculture, The University of Sydney;Plant and Environmental Sciences, New Mexico State University

关键词: Soil sampling;    Conditioned Latin Hypercube;    Digital soil mapping;    Optimization;    Sampling;    Sample optimization;    Legacy soil data;    Pedometrics;    Soil survey;    Fieldwork;   

DOI  :  10.7717/peerj.6451

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

The conditioned Latin hypercube sampling (cLHS) algorithm is popularly used for planning field sampling surveys in order to understand the spatial behavior of natural phenomena such as soils. This technical note collates, summarizes, and extends existing solutions to problems that field scientists face when using cLHS. These problems include optimizing the sample size, re-locating sites when an original site is deemed inaccessible, and how to account for existing sample data, so that under-sampled areas can be prioritized for sampling. These solutions, which we also share as individual R scripts, will facilitate much wider application of what has been a very useful sampling algorithm for scientific investigation of soil spatial variation.

【 授权许可】

CC BY