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Mosaic classified cubes

Source: R/sits_mosaic.R
sits_mosaic.Rd

Creates a mosaic of all tiles of a sits cube. Mosaics can be created from both regularized ARD images or from classified maps. In the case of ARD images, a mosaic will be produce for each band/date combination. It is better to first regularize the data cubes and then use sits_mosaic.

Usage

sits_mosaic(
  cube,
  crs = "EPSG:3857",
  roi = NULL,
  multicores = 2L,
  output_dir,
  version = "v1",
  progress = TRUE
)

Arguments

cube

A sits data cube.

crs

A target coordinate reference system of raster mosaic. The provided crs could be a string (e.g, "EPSG:4326" or a proj4string), or an EPSG code number (e.g. 4326). Default is "EPSG:3857" - WGS 84 / Pseudo-Mercator.

roi

Region of interest (see below).

multicores

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

output_dir

Directory for output images.

version

Version of resulting image (in the case of multiple tests)

progress

Show progress bar? Default is TRUE.

Value

a sits cube with only one tile.

Note

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.

The user should specify the CRS of the mosaic. We use "EPSG:3857" (Pseudo-Mercator) as the default.

Author

Felipe Carvalho, felipe.carvalho@inpe.br

Rolf Simoes, rolfsimoes@gmail.com

Felipe Carlos, efelipecarlos@gmail.com

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()
    )
    # smooth the probability cube using Bayesian statistics
    bayes_cube <- sits_smooth(probs_cube, output_dir = tempdir())
    # label the probability cube
    label_cube <- sits_label_classification(
        bayes_cube,
        output_dir = tempdir()
    )
    # create roi
    roi <- sf::st_sfc(
        sf::st_polygon(
            list(rbind(
                c(-55.64768, -11.68649),
                c(-55.69654, -11.66455),
                c(-55.62973, -11.61519),
                c(-55.64768, -11.68649)
            ))
        ),
        crs = "EPSG:4326"
    )
    # crop and mosaic classified image
    mosaic_cube <- sits_mosaic(
        cube = label_cube,
        roi = roi,
        crs = "EPSG:4326",
        output_dir = tempdir()
    )
}