Conditioned Latin hypercube sampling (cLHS) has been proven as an efficient sampling strategy and used widely in digital soil mapping. cLHS samples are randomly selected in each stratum of environmental variable
This suggests that cLHS is able to capture significant soil variation using limited samples, and supports the underlying assumption of cLHS, that Conclusions Conditioned Latin hypercube sampling resulted in sampling sites with high taxonomic variability. Out-of-bag error was between 48.5% and 56.6%, ...
Conditioned Latin Hypercube Sampling (cLHS) is a type of stratified random sampling that accurately represents the variability of environmental covariates in feature space. As the smallest possible sample is important for efficient field work, what is the optimal sample size for digital soil mapping?
Conditioned Latin hypercube sampling (cLHS) was recently proposed as a method for sampling based on covariates obtained from proximal soil sensors. The method provides full coverage of the range of each variable by maximally stratifying the marginal distribution. A modification of cLHS was made so ...
Key environmental variables are then identified using the Boruta and the Variance Inflation Factor method, followed by conditioned Latin hypercube sampling (cLHS) to select training points within each subregion. Finally, the selected training points are combined to form the complete training dataset. A...
Based on the cLHS (conditioned Latin hypercube Sampling) method, a sampling method called scLHS (spatial cLHS) considering all these three aspects with the help of ancillary data is proposed in this article. Its advantage lies in simultaneously improving trend estimation, variogram estimation and ...
In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi‐temporal layers of remotely sensed precipitation measurements for capturing the spatio‐temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for ...
Conditioned Latin Hypercube sampling (Minasny and McBratney, 2006) is the most widely used algorithm. After the cLHS design have been firstly presented, it was developed a group of sampling methods based on different modifications of Latin Hypercube sampling (Clifford et al., 2014; Nketia et al...
Minasny B, McBratney AB (2006) A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences 32, 1378-1388. doi:10.1016/j.cageo.2005.12.009A conditioned Latin hypercube method for sampling in the presence of ancillary information[J] . Budi...
Two types of sampling design for calibrating the prediction models are compared: conditioned Latin hypercube sampling (CLHS) and feature space coverage sampling (FSCS). Simple random sampling (SRS), which does not utilize the environmental features, is added as a reference design. The sample sizes...