sits_view: View data cubes and samples in leaflet

Description

Uses leaflet to visualize time series, raster cube and classified images

Usage

sits_view(x, ...)
# S3 method for sits
sits_view(x, ..., legend = NULL, palette = "Harmonic")
# S3 method for som_map
sits_view(
  x,
  ...,
  label,
  prob_max = 1,
  prob_min = 0.7,
  legend = NULL,
  palette = "Harmonic"
)
# S3 method for raster_cube
sits_view(
  x,
  ...,
  band = NULL,
  red = NULL,
  green = NULL,
  blue = NULL,
  tiles = NULL,
  dates = NULL,
  class_cube = NULL,
  legend = NULL,
  palette = "default"
)
# S3 method for class_cube
sits_view(x, ..., tiles = NULL, legend = NULL, palette = "default")
# S3 method for probs_cube
sits_view(x, ...)
# S3 method for default
sits_view(x, ...)

Value

A leaflet object containing either samples or data cubes embedded in a global map that can be visualized directly in an RStudio viewer.

Arguments

x: Object of class "sits", "raster_cube" or "classified image".
...: Further specifications for sits_view.
legend: Named vector that associates labels to colors.
palette: Palette provided in the configuration file.
label: Label from the SOM map to be shown.
prob_max: Maximum a posteriori probability for SOM neuron samples to be shown
prob_min: Minimum a posteriori probability for SOM neuron samples to be shown
band: For plotting grey images.
red: Band for red color.
green: Band for green color.
blue: Band for blue color.
tiles: Tiles to be plotted (in case of a multi-tile cube).
dates: Dates to be plotted.
class_cube: Classified cube to be overlayed on top on image.

Author

Gilberto Camara, gilberto.camara@inpe.br

Examples

Run this code

if (sits_run_examples()) {
    sits_view(cerrado_2classes)

    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")

    modis_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6",
        data_dir = data_dir,
        parse_info = c("X1", "tile", "band", "date")
    )
    # get the timeline
    timeline <- sits_timeline(modis_cube)
    # view the data cube
    sits_view(modis_cube,
        band = "NDVI",
        dates = timeline[[1]]
    )
    # train a model
    rf_model <- sits_train(samples_modis_ndvi, sits_rfor())

    modis_probs <- sits_classify(
        data = modis_cube,
        ml_model = rf_model,
        output_dir = tempdir(),
        memsize = 4,
        multicores = 1
    )
    modis_label <- sits_label_classification(modis_probs,
        output_dir = tempdir()
    )

    sits_view(modis_label)

    sits_view(modis_cube,
        band = "NDVI",
        class_cube = modis_label,
        dates = sits_timeline(modis_cube)[[1]]
    )
}