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.
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