sits_view: View data cubes and samples in leaflet

In e-sensing/sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

View data cubes and samples in leaflet

Description

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

To show a false color image, use "band" to chose one of the bands, "tiles" to select tiles, "first_quantile" and "last_quantile" to set the cutoff points. Choose only one date in the "dates" parameter. The color scheme is defined by either "palette" (use an available color scheme) or legend (user-defined color scheme). To see which palettes are pre-defined, use cols4all::g4a_gui or select any ColorBrewer name. The "rev" parameter reverts the order of colors in the palette.

To show an RGB composite, select "red", "green" and "blue" bands, "tiles", "dates", "opacity", "first_quantile" and "last_quantile". One can also get an RGB composite, by selecting one band and three dates. In this case, the first date will be shown in red, the second in green and third in blue.

Probability cubes are shown in false color. The parameter "labels" controls which labels are shown. If left blank, only the first map is shown. For color control, use "palette", "legend", and "rev" (as described above).

Vector cubes have both a vector and a raster component. The vector part are the segments produced by sits_segment. Their visual output is controlled by "seg_color" and "line_width" parameters. The raster output works in the same way as the false color and RGB views described above.

Classified cubes need information on how to render each class. There are three options: (a) the classes are part of an existing color scheme; (b) the user provides a legend which associates each class to a color; (c) use a generic palette (such as "Spectral") and allocate colors based on this palette. To find out how to create a customized color scheme, read the chapter "Data Visualisation in sits" in the sits book.

To compare different classifications, use the "version" parameter to distinguish between the different maps that are shown.

Vector classified cubes are displayed as classified cubes, with the segments overlaid on top of the class map, controlled by "seg_color" and "line_width".

Samples are shown on the map based on their geographical locations and on the color of their classes assigned in their color scheme. Users can also assign a legend or a palette to choose colors. See information above on the display of classified cubes.

For all types of data cubes, the following parameters apply:

• opacity: controls the transparency of the map.

• max_cog_size: For COG data, controls the level of aggregation to be used for display, measured in pixels, e.g., a value of 512 will select a 512 x 512 aggregated image. Small values are faster to show, at a loss of visual quality.

• leaflet_megabytes: maximum size of leaflet to be shown associated to the map (in megabytes). Bigger values use more memory.

• add: controls whether a new visualisation will be overlaid on top of an existing one. Default is FALSE.

Usage

sits_view(x, ...)

## S3 method for class 'sits'
sits_view(x, ..., legend = NULL, palette = "Set3", radius = 5, add = FALSE)

## S3 method for class 'data.frame'
sits_view(x, ..., legend = NULL, palette = "Harmonic", add = FALSE)

## S3 method for class 'som_map'
sits_view(
  x,
  ...,
  id_neurons,
  legend = NULL,
  palette = "Harmonic",
  radius = 5,
  add = FALSE
)

## S3 method for class 'raster_cube'
sits_view(
  x,
  ...,
  band = NULL,
  red = NULL,
  green = NULL,
  blue = NULL,
  tiles = x[["tile"]][[1]],
  dates = NULL,
  palette = "RdYlGn",
  rev = FALSE,
  opacity = 0.85,
  max_cog_size = 2048,
  first_quantile = 0.02,
  last_quantile = 0.98,
  leaflet_megabytes = 64,
  add = FALSE
)

## S3 method for class 'uncertainty_cube'
sits_view(
  x,
  ...,
  tiles = x[["tile"]][[1]],
  legend = NULL,
  palette = "RdYlGn",
  rev = FALSE,
  opacity = 0.85,
  max_cog_size = 2048,
  first_quantile = 0.02,
  last_quantile = 0.98,
  leaflet_megabytes = 64,
  add = FALSE
)

## S3 method for class 'class_cube'
sits_view(
  x,
  ...,
  tiles = x[["tile"]],
  legend = NULL,
  palette = "Set3",
  version = NULL,
  opacity = 0.85,
  max_cog_size = 2048,
  leaflet_megabytes = 32,
  add = FALSE
)

## S3 method for class 'probs_cube'
sits_view(
  x,
  ...,
  tiles = x[["tile"]][[1]],
  label = x[["labels"]][[1]][[1]],
  legend = NULL,
  palette = "YlGn",
  rev = FALSE,
  opacity = 0.85,
  max_cog_size = 2048,
  first_quantile = 0.02,
  last_quantile = 0.98,
  leaflet_megabytes = 64,
  add = FALSE
)

## S3 method for class 'vector_cube'
sits_view(
  x,
  ...,
  tiles = x[["tile"]][[1]],
  seg_color = "yellow",
  line_width = 0.5,
  add = FALSE
)

## S3 method for class 'class_vector_cube'
sits_view(
  x,
  ...,
  tiles = x[["tile"]][[1]],
  seg_color = "yellow",
  line_width = 0.2,
  version = NULL,
  legend = NULL,
  palette = "Set3",
  opacity = 0.85,
  add = FALSE
)

## Default S3 method:
sits_view(x, ...)

Arguments

x	Object of class "sits", "data.frame", "som_map", "raster_cube", "probs_cube", "vector_cube", or "class cube".
...	Further specifications for sits_view.
legend	Named vector that associates labels to colors.
palette	Color palette from RColorBrewer
radius	Radius of circle markers
add	Add image to current leaflet
id_neurons	Neurons from the SOM map to be shown.
band	Single band for viewing false color 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.
rev	Revert color palette?
opacity	Opacity of segment fill or class cube
max_cog_size	Maximum size of COG overviews (lines or columns)
first_quantile	First quantile for stretching images
last_quantile	Last quantile for stretching images
leaflet_megabytes	Maximum size for leaflet (in MB)
version	Version name (to compare different classifications)
label	Label to be plotted (in case of probs cube)
seg_color	Color for segment boundaries
line_width	Line width for segments (in pixels)

Value

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

Author(s)

Gilberto Camara, [e-mail]

Examples

if (sits_run_examples()) {
    # view samples
    sits_view(cerrado_2classes)
    # create a local data cube
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    modis_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # view the data cube
    sits_view(modis_cube,
        band = "NDVI"
    )
    # train a model
    rf_model <- sits_train(samples_modis_ndvi, sits_rfor())
    # classify the cube
    modis_probs <- sits_classify(
        data = modis_cube,
        ml_model = rf_model,
        output_dir = tempdir()
    )
    # generate a map
    modis_label <- sits_label_classification(
        modis_probs,
        output_dir = tempdir()
    )
    # view the classified map
    sits_view(modis_label)
    # view the classified map with the B/W image
    sits_view(modis_cube,
        band = "NDVI",
        class_cube = modis_label,
        dates = sits_timeline(modis_cube)[[1]]
    )
    # view the classified map with the RGB image
    sits_view(modis_cube,
        red = "NDVI", green = "NDVI", blue = "NDVI",
        class_cube = modis_label,
        dates = sits_timeline(modis_cube)[[1]]
    )
    # create an uncertainty cube
    modis_uncert <- sits_uncertainty(
        cube = modis_probs,
        output_dir = tempdir()
    )
    # view the uncertainty cube
    sits_view(modis_uncert, rev = TRUE)
}

e-sensing/sits documentation built on Feb. 13, 2025, 2:22 a.m.