In a multi-dimensional setting, for k variables, X 1, X 2,…, X k, the n random values drawn for variable X 1 are combined randomly (or in some order to maintain its correlation) with the n random values drawn for variable X 2, and so on until n k-tuples are formed, that is, the Latin hypercube sample ( Clifford et al., 2014). LHS is an efficient way to reproduce an empirical distribution function, where the idea is to divide the empirical distribution function of a variable, X, into n equi-probable, non-overlapping strata, and then draw one random value from each stratum. cLHS has its origins in Latin hypercube sampling (LHS) first proposed by McKay, Beckman & Conover (1979). Extended discussions about soil sampling, surveying, and monitoring of natural resources in a broad context can be found in seminal publications such as de Gruijter et al. Materials and Methods A short overview of cLHSĬonditioned Latin hypercube sampling is one of the many environmental surveying tools available for understanding the spatial characteristics of environmental phenomena. We first begin with a brief overview of the cLHS algorithm and then address each question separately. The purpose of this technical note is to describe solutions to each of these questions. How do I account for existing samples when designing a new survey? Where else can I sample when a cLHS location cannot be visited because of difficult terrain, locked gate, safety reasons etc.? However, our own experience, and from personal communication with other researchers and field technicians, a common set of methodological questions arise when using cLHS. Presuming that soil variation is a function of the chosen environmental variables, it is reasoned that models fitted using data collected via cLHS, capture all the soil spatial variability and will be applicable across the whole spatial extent to be mapped. For example, in optimal soil spectral model calibration ( Ramirez-Lopez et al., 2014 Kopačková et al., 2017), understanding the conditions which determine Phytophthora distribution in rainforests ( Scarlett et al., 2015), and assessing the uncertainty of digital elevation models derived from light detection and ranging technology ( Chu et al., 2014).įor 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). cLHS has also been used for other purposes and contexts too. cLHS has been used extensively in DSM projects throughout the world with recent examples in the last 5 years including Sun et al. 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. The conditioned Latin hypercube sampling (cLHS) algorithm ( Minasny & McBratney, 2006) was designed with digital soil mapping (DSM) in mind. Some methods to improve the utility of conditioned Latin hypercube sampling. Cite this article Malone BP, Minansy B, Brungard C. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 3 Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USA DOI 10.7717/peerj.6451 Published Accepted Received Academic Editor Paolo Giordani Subject Areas Agricultural Science, Soil Science, Statistics, Data Science, Spatial and Geographic Information Science Keywords Soil sampling, Conditioned Latin Hypercube, Digital soil mapping, Optimization, Sampling, Sample optimization, Legacy soil data, Pedometrics, Soil survey, Fieldwork Copyright © 2019 Malone et al.
0 Comments
Leave a Reply. |