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Plot RGB data cubes

Source: R/sits_plot.R
plot.raster_cube.Rd

Plot RGB raster cube

Usage

# S3 method for class 'raster_cube'
plot(
  x,
  ...,
  band = NULL,
  red = NULL,
  green = NULL,
  blue = NULL,
  tile = x[["tile"]][[1L]],
  dates = NULL,
  roi = NULL,
  palette = "RdYlGn",
  rev = FALSE,
  scale = 1,
  first_quantile = 0.02,
  last_quantile = 0.98,
  max_cog_size = 1024L,
  legend_position = "inside"
)

Arguments

x

Object of class "raster_cube".

...

Further specifications for plot.

band

Band for plotting grey images.

red

Band for red color.

green

Band for green color.

blue

Band for blue color.

tile

Tile to be plotted.

dates

Dates to be plotted

roi

Spatial extent to plot in WGS 84 - named vector with either (lon_min, lon_max, lat_min, lat_max) or (xmin, xmax, ymin, ymax)

palette

An RColorBrewer palette

rev

Reverse the color order in the palette?

scale

Scale to plot map (0.4 to 1.0)

first_quantile

First quantile for stretching images

last_quantile

Last quantile for stretching images

max_cog_size

Maximum size of COG overviews (lines or columns)

legend_position

Where to place the legend (default = "inside")

Value

A plot object with an RGB image or a B/W image on a color scale

Note

Use scale parameter for general output control. The dates parameter indicates the date allows plotting of different dates when a single band and three dates are provided, `sits` will plot a multi-temporal RGB image for a single band (useful in the case of SAR data). For RGB bands with multi-dates, multiple plots will be produced.

If the user does not provide band names for b/w or RGB plots, and also does not provide dates, plot.raster_cube tries to display some reasonable color composites, using the following algorithm:

  1. Each image in sits is associated to a source and a collection (e.g, "MPC" and "SENTINEL-2-L2A").

  2. For each source/collection pair, sits has a set of possible color composites stored in "./extdata/config_colors.yml". For example, the following composites are available for all "SENTINEL-2" images:

    • AGRICULTURE: ("B11", "B08", "B02")

    • AGRICULTURE2: ("B11", "B8A", "B02")

    • SWIR: ("B11", "B08", "B04")

    • SWIR2: ("B12", "B08", "B04")

    • SWIR3: ("B12", "B8A", "B04")

    • RGB: ("B04", "B03", "B02")

    • RGB-FALSE1 : ("B08", "B06", "B04")

    • RGB-FALSE2 : ("B08", "B11", "B04")

  3. sits tries to find if the bands required for one of the color composites are part of the cube. If they exist, that RGB composite is selected. Otherwise, the first available band is chosen.

  4. After selecting the bands, the algorithm looks for the date with the smallest percentage of cloud cover and selects that date to be displayed.

. The following optional parameters are available to allow for detailed control over the plot output:

  • graticules_labels_size: size of coord labels (default = 0.7)

  • legend_title_size: size of legend title (default = 0.7)

  • legend_text_size: size of legend text (default = 0.7)

  • legend_bg_color: color of legend background (default = "white")

  • legend_bg_alpha: legend opacity (default = 0.3)

Author

Gilberto Camara, gilberto.camara@inpe.br

Examples

if (sits_run_examples()) {
    # 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
    )
    # plot NDVI band of the least cloud cover date
    plot(cube)
}