sgsR (version 1.5.0)

sample_clhs: Conditioned Latin Hypercube Sampling

Description

Conditioned Latin Hypercube Sampling using clhs functionality.

Usage

sample_clhs(
  mraster,
  nSamp,
  iter = 10000,
  cost = NULL,
  existing = NULL,
  access = NULL,
  buff_inner = NULL,
  buff_outer = NULL,
  plot = FALSE,
  details = FALSE,
  filename = NULL,
  overwrite = FALSE,
  ...
)

Value

An sf object with nSamp stratified samples.

Arguments

mraster

spatRaster. ALS metrics raster.

nSamp

Numeric. Number of desired samples.

iter

Numeric. Value giving the number of iterations within the Metropolis-Hastings process.

cost

Numeric/Character. Index or name of covariate within mraster to be used to constrain cLHS sampling. If default (NULL), a cost constraint is not used.

existing

sf 'POINT'. Existing plot network.

access

sf 'LINESTRING' or 'MULTILINESTRING'. Access network.

buff_inner

Numeric. Inner buffer boundary specifying distance from access where plots cannot be sampled.

buff_outer

Numeric. Outer buffer boundary specifying distance from access where plots can be sampled.

plot

Logical. Plots output strata raster with samples.

details

Logical. If FALSE (default) output is only stratification raster. If TRUE return a list where $details is additional stratification information and $raster is the output stratification spatRaster.

filename

Character. Path to write output samples.

overwrite

Logical. Choice to overwrite existing filename if it exists.

...

Additional arguments for clhs sampling. See clhs.

Author

Tristan R.H. Goodbody

References

Minasny, B. and McBratney, A.B. 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 32:1378-1388.

Minasny, B. and A. B. McBratney, A.B.. 2010. Conditioned Latin Hypercube Sampling for Calibrating Soil Sensor Data to Soil Properties. In: Proximal Soil Sensing, Progress in Soil Science, pages 111-119.

Roudier, P., Beaudette, D.E. and Hewitt, A.E. 2012. A conditioned Latin hypercube sampling algorithm incorporating operational constraints. In: Digital Soil Assessments and Beyond. Proceedings of the 5th Global Workshop on Digital Soil Mapping, Sydney, Australia.

See Also

Other sample functions: sample_ahels(), sample_balanced(), sample_existing(), sample_nc(), sample_srs(), sample_strat(), sample_sys_strat(), sample_systematic()

Examples

#--- Load raster and existing plots---#
r <- system.file("extdata", "mraster.tif", package = "sgsR")
mr <- terra::rast(r)

e <- system.file("extdata", "existing.shp", package = "sgsR")
e <- sf::st_read(e)

a <- system.file("extdata", "access.shp", package = "sgsR")
ac <- sf::st_read(a)

sample_clhs(
  mraster = mr,
  nSamp = 200,
  plot = TRUE,
  iter = 100
)

sample_clhs(
  mraster = mr,
  nSamp = 400,
  existing = e,
  iter = 250,
  details = TRUE
)