Some methods to improve the utility of conditioned Latin hypercube sampling.

Brendan P Malone,Budiman Minansy,Colby Brungard

doi:10.7717/peerj.6451

Brendan P Malone, Budiman Minansy + Show 1 more

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https://doi.org/10.7717/peerj.6451

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Journal: PeerJ	Publication Date: Feb 25, 2019
Citations: 23	License type: CC BY 4.0

Affiliation: University of Sydney, New Mexico State University

Abstract

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.

Highlights

The conditioned Latin hypercube sampling algorithm (Minasny & McBratney, 2006) was designed with digital soil mapping (DSM) in mind. cLHS is a random stratified procedure that choses sampling locations based on prior information pertaining to a suite of environmental variables in a given area. cLHS has been used extensively in DSM projects throughout the world with recent examples in the last 5 years including Sun et al (2017) in China, Jeong et al (2017) in Korea, Scarpone et al (2016) in Canada, and Thomas et al (2015) in Australia. cLHS has been used for other purposes and contexts too
Its utility for soil sampling was noted by Minasny & McBratney (2006), but they recognized that some generalization of LHS sampling was required so that selected samples existed in the real world
We note that COOBS means at the pixel level, the count of observations estimated to be similar in terms of the given ancillary data. This technical note provides some solutions to common questions that arise when the cLHS algorithm is used for designing a soil or any other environmental survey

Summary

Introduction

The conditioned Latin hypercube sampling (cLHS) algorithm (Minasny & McBratney, 2006) was designed with digital soil mapping (DSM) in mind. cLHS is a random stratified procedure that choses sampling locations based on prior information pertaining to a suite of environmental variables in a given area. cLHS has been used extensively in DSM projects throughout the world with recent examples in the last 5 years including Sun et al (2017) in China, Jeong et al (2017) in Korea, Scarpone et al (2016) in Canada, and Thomas et al (2015) in Australia. cLHS has been used for other purposes and contexts too. The conditioned Latin hypercube sampling (cLHS) algorithm (Minasny & McBratney, 2006) was designed with digital soil mapping (DSM) in mind. CLHS is a random stratified procedure that choses sampling locations based on prior information pertaining to a suite of environmental variables in a given area. For DSM, the algorithm exploits collections of environmental variables pertaining to soil forming factors and proxies thereof (McBratney, Mendonça Santos & Minasny, 2003; e.g., digital elevation model derivatives, remote sensing imagery of vegetation type and distribution, climatic data, and geological maps) to derive a sample configuration (of specified size), such that the empirical distribution function of each environmental variable is replicated (Clifford et al, 2014). Presuming that soil variation is a function of the chosen environmental variables, it is reasoned that models fitted using data collected.

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