sits_mosaic: Mosaic classified cubes

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

Mosaic classified cubes

Description

Creates a mosaic of all tiles of a sits cube. Mosaics can be created from EO cubes and derived cubes. In sits EO cubes, the mosaic will be generated for each band and date. It is recommended to filter the image with the less cloud cover to create a mosaic for the EO cubes. It is possible to provide a roi to crop the mosaic.

Usage

sits_mosaic(
  cube,
  crs = "EPSG:3857",
  roi = NULL,
  multicores = 2,
  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

The "roi" parameter defines a region of interest. It can be an sf_object, a shapefile, or a bounding box vector with named XY values (xmin, xmax, ymin, ymax) or named lat/long values (lon_min, lon_max, lat_min, lat_max).

The user should specify the crs of the mosaic since in many cases the input images will be in different coordinate systems. For example, when mosaicking Sentinel-2 images the inputs will be in general in different UTM grid zones.

Author(s)

Felipe Carvalho, [e-mail]

Rolf Simoes, [e-mail]

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",
        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()
    )
}

e-sensing/sits documentation built on Jan. 28, 2024, 6:05 a.m.