Plot probability cubes — plot.probs

plots a probability cube

Usage

# S3 method for class 'probs_cube'
plot(
  x,
  ...,
  tile = x[["tile"]][[1L]],
  roi = NULL,
  labels = NULL,
  palette = "YlGn",
  rev = FALSE,
  quantile = NULL,
  scale = 1,
  max_cog_size = 512L,
  legend_position = "outside",
  legend_title = "probs"
)

Arguments

x: Object of class "probs_cube".
...: Further specifications for plot.
tile: Tile to be plotted.
roi: Spatial extent to plot in WGS 84 - named vector (see notes below)
labels: Labels to plot.
palette: RColorBrewer palette
rev: Reverse order of colors in palette?
quantile: Minimum quantile to plot
scale: Scale to plot map (0.4 to 1.0)
max_cog_size: Maximum size of COG overviews (lines or columns)
legend_position: Where to place the legend (default = "outside")
legend_title: Title of legend (default = "probs")

Value

A plot containing probabilities associated to each class for each pixel.

To define a roi use one of:

A path to a shapefile with polygons;
A sfc or sf object from sf package;
A SpatExtent object from terra package;
A named vector ("lon_min", "lat_min", "lon_max", "lat_max") in WGS84;
A named vector ("xmin", "xmax", "ymin", "ymax") with XY coordinates.

Defining a region of interest using SpatExtent or XY values not in WGS84 requires the crs parameter to be specified. sits_regularize() function will crop the images that contain the region of interest().

Author

Gilberto Camara, gilberto.camara@inpe.br

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()
    )
    # plot the resulting probability cube
    plot(probs_cube)
}