sits (version 1.5.3)

sits_stratified_sampling: Allocation of sample size to strata

Description

Takes a class cube with different labels and a sampling design with a number of samples per class and allocates a set of locations for each class

Usage

sits_stratified_sampling(
  cube,
  sampling_design,
  alloc = "alloc_prop",
  overhead = 1.2,
  multicores = 2L,
  memsize = 2L,
  shp_file = NULL,
  progress = TRUE
)

Value

samples Point sf object with required samples

Arguments

cube

Classified cube

sampling_design

Result of sits_sampling_design

alloc

Allocation method chosen

overhead

Additional percentage to account for border points

multicores

Number of cores that will be used to sample the images in parallel.

memsize

Memory available for sampling.

shp_file

Name of shapefile to be saved (optional)

progress

Show progress bar? Default is TRUE.

Author

Gilberto Camara,

Felipe Carlos,

Felipe Carvalho,

Examples

if (sits_run_examples()) {
    # create a random forest model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # classify a data cube
    probs_cube <- sits_classify(
        data = cube, ml_model = rfor_model, output_dir = tempdir()
    )
    # label the probability cube
    label_cube <- sits_label_classification(
        probs_cube,
        output_dir = tempdir()
    )
    # estimated UA for classes
    expected_ua <- c(
        Cerrado = 0.95, Forest = 0.95,
        Pasture = 0.95, Soy_Corn = 0.95
    )
    # design sampling
    sampling_design <- sits_sampling_design(label_cube, expected_ua)
    # select samples
    samples <- sits_stratified_sampling(
        label_cube,
        sampling_design, "alloc_prop"
    )
}