Create data cubes from image collections

Creates a data cube based on spatial and temporal restrictions in collections available in cloud services or local repositories. Available options are:

To create data cubes from providers which support the STAC protocol, use sits_cube.stac_cube.
To create raster data cubes from local image files, use sits_cube.local_cube.
To create vector data cubes from local image and vector files, use sits_cube.vector_cube.
To create raster data cubes from local image files which have been classified or labelled, use sits_cube.results_cube.

Usage

sits_cube(source, collection, ...)

Arguments

source: Data source: one of "AWS", "BDC", "CDSE", "DEAFRICA", "DEAUSTRALIA", "HLS", "PLANETSCOPE", "MPC", "SDC" or "USGS".
collection: Image collection in data source. To find out the supported collections, use sits_list_collections()).
...: Other parameters to be passed for specific types.

Value

A tibble describing the contents of a data cube.

Note

The main sits classification workflow has the following steps:

sits_cube: selects a ARD image collection from a cloud provider.
sits_cube_copy: copies an ARD image collection from a cloud provider to a local directory for faster processing.
sits_regularize: create a regular data cube from an ARD image collection.
sits_apply: create new indices by combining bands of a regular data cube (optional).
sits_get_data: extract time series from a regular data cube based on user-provided labelled samples.
sits_train: train a machine learning model based on image time series.
sits_classify: classify a data cube using a machine learning model and obtain a probability cube.
sits_smooth: post-process a probability cube using a spatial smoother to remove outliers and increase spatial consistency.
sits_label_classification: produce a classified map by selecting the label with the highest probability from a smoothed cube.

The following cloud providers are supported, based on the STAC protocol: Amazon Web Services (AWS), Brazil Data Cube (BDC), Copernicus Data Space Ecosystem (CDSE), Digital Earth Africa (DEAFRICA), Digital Earth Australia (DEAUSTRALIA), Microsoft Planetary Computer (MPC), Nasa Harmonized Landsat/Sentinel (HLS), Swiss Data Cube (SDC), TERRASCOPE and USGS Landsat (USGS). Data cubes can also be created using local files.

In sits, a data cube is represented as a tibble with metadata describing a set of image files obtained from cloud providers. It contains information about each individual file.

A data cube in sits is:

A set of images organized in tiles of a grid system (e.g., MGRS).
Each tile contains single-band images in a unique zone of the coordinate system (e.g, tile 20LMR in MGRS grid) covering the period between start_date and end_date.
Each image of a tile is associated to a unique temporal interval. All intervals share the same spectral bands.
Different tiles may cover different zones of the same grid system.

A regular data cube is a data cube where:

All tiles share the same set of regular temporal intervals.
All tiles share the same spectral bands and indices.
All images have the same spatial resolution.
Each location in a tile is associated a set of multi-band time series.
For each tile, interval and band, the cube is reduce to a 2D image.

Author

Felipe Carlos, efelipecarlos@gmail.com

Felipe Carvalho, felipe.carvalho@inpe.br

Gilberto Camara, gilberto.camara@inpe.br

Rolf Simoes, rolfsimoes@gmail.com

Examples

if (sits_run_examples()) {
    # --- Access to the Brazil Data Cube
    # create a raster cube file based on the information in the BDC
    cbers_tile <- sits_cube(
        source = "BDC",
        collection = "CBERS-WFI-16D",
        bands = c("NDVI", "EVI"),
        tiles = "007004",
        start_date = "2018-09-01",
        end_date = "2019-08-28"
    )
    # --- Access to Digital Earth Africa
    # create a raster cube file based on the information about the files
    # DEAFRICA does not support definition of tiles
    cube_deafrica <- sits_cube(
        source = "DEAFRICA",
        collection = "SENTINEL-2-L2A",
        bands = c("B04", "B08"),
        roi = c(
            "lat_min" = 17.379,
            "lon_min" = 1.1573,
            "lat_max" = 17.410,
            "lon_max" = 1.1910
        ),
        start_date = "2019-01-01",
        end_date = "2019-10-28"
    )
    # --- Create a cube based on a local MODIS data
    # MODIS local files have names such as
    # "TERRA_MODIS_012010_NDVI_2013-09-14.jp2"
    # see the parse info parameter as an example on how to
    # decode local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    modis_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir,
        parse_info = c("satellite", "sensor", "tile", "band", "date")
    )
}