Malone Brendan P, Minansy Budiman, Brungard Colby
CSIRO, Agriculture and Food, Canberra, ACT, Australia.
The Sydney Institute of Agriculture, The University of Sydney, Sydney, NSW, Australia.
PeerJ. 2019 Feb 25;7:e6451. doi: 10.7717/peerj.6451. eCollection 2019.
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.
条件拉丁超立方抽样（cLHS）算法常用于规划野外抽样调查，以了解土壤等自然现象的空间行为。本技术说明整理、总结并扩展了野外科学家在使用cLHS时所面临问题的现有解决方案。这些问题包括优化样本量、当原始站点被认为无法到达时重新定位站点，以及如何考虑现有样本数据，以便对抽样不足的区域进行优先抽样。我们还将这些解决方案作为单独的R脚本分享，这将促进这种对土壤空间变异进行科学调查非常有用的抽样算法得到更广泛的应用。
