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Balanced sampling

Source: R/sample_balanced.R
sample_balanced.Rd

Balanced raster sampling using lcube and lpm2_kdtree methods

Usage

sample_balanced(
  mraster,
  nSamp,
  algorithm = "lpm2_kdtree",
  p = NULL,
  access = NULL,
  buff_inner = NULL,
  buff_outer = NULL,
  plot = FALSE,
  filename = NULL,
  overwrite = FALSE
)

Arguments

mraster

spatRaster. ALS metrics raster.

nSamp

Numeric. Number of desired samples.

algorithm

Character. One of lpm2_kdtree, lcube, lcubestratified.

p

Numeric. Vector with length equal to the number of cells in mraster representing the inclusion probability for each candidate sample. Default = nSamp / N, where N is the number of cells.

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 and visualized strata with boundary dividers.

filename

Character. Path to write stratified raster to disc.

overwrite

Logical. Specify whether filename should be overwritten on disc.

Value

An sf object with nSamp samples.

References

Anton Grafström and Jonathan Lisic (2019). BalancedSampling: Balanced and Spatially Balanced Sampling. R package version 1.5.5. https://CRAN.R-project.org/package=BalancedSampling

Jonathan Lisic and Anton Grafström (2018). SamplingBigData: Sampling Methods for Big Data. R package version 1.0.0. https://CRAN.R-project.org/package=SamplingBigData

Grafström, A. Lisic, J (2018). BalancedSampling: Balanced and Spatially Balanced Sampling. R package version 1.5.4. http://www.antongrafstrom.se/balancedsampling

See also

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

Author

Tristan R.H. Goodbody

Examples

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

sample_balanced(
  mraster = mr,
  nSamp = 200
)
#> Simple feature collection with 200 features and 0 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 431110 ymin: 5337730 xmax: 438490 ymax: 5343230
#> Projected CRS: +proj=utm +zone=17 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
#> First 10 features:
#>                  geometry
#> 1  POINT (435450 5343230)
#> 2  POINT (433270 5343210)
#> 3  POINT (434930 5343210)
#> 4  POINT (435970 5343210)
#> 5  POINT (434790 5343170)
#> 6  POINT (434530 5343150)
#> 7  POINT (431110 5343110)
#> 8  POINT (431510 5343110)
#> 9  POINT (437850 5343110)
#> 10 POINT (435290 5343030)