gdalcubes_selection: Subsetting data cubes

In gdalcubes: Earth Observation Data Cubes from Satellite Image Collections

Subsetting data cubes

Description

Subset data cube dimensions and bands / variables.

Usage

## S3 method for class 'cube'
x$name

## S3 method for class 'cube'
x[ib = TRUE, it = TRUE, iy = TRUE, ix = TRUE, ...]

Arguments

x	source data cube
name	character; name of selected band
ib	first selector (optional), object of type character, list, Date, POSIXt, numeric, st_bbox, or st_sfc, see Details and examples
it	second selector (optional), see ib
iy	third selector (optional), see ib
ix	fourth selector (optional), see ib
...	further arguments, not used

Details

The [] operator allows for flexible subsetting of data cubes by date, datetime, bounding box, spatial points, and band names. Depending on the arguments, it supports slicing (selecting one element of a dimension), cropping (selecting a subinterval of a dimension) and combinations thereof (e.g., selecting a spatial window and a temporal slice). Dimension subsets can be specified by integer indexes or coordinates / datetime values. Arguments are matched by type and order. For example, if the first argument is a length-two vector of type Date, the function will understand to subset the time dimension. Otherwise, arguments are treated in the order band, time, y, x.

Note

This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.

Examples

# create image collection from example Landsat data only 
# if not already done in other examples
if (!file.exists(file.path(tempdir(), "L8.db"))) {
  L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
                         ".TIF", recursive = TRUE, full.names = TRUE)
  create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE) 
}
L8.col = image_collection(file.path(tempdir(), "L8.db"))
v = cube_view(extent=list(left=388941.2, right=766552.4, 
              bottom=4345299, top=4744931, t0="2018-01", t1="2018-12"),
              srs="EPSG:32618", nx = 497, ny=526, dt="P3M", aggregation = "median")
L8.cube = raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))
L8.red = L8.cube$B04


plot(L8.red)


v = cube_view(extent=list(left=388941.2, right=766552.4,
                          bottom=4345299, top=4744931, t0="2018-01-01", t1="2018-12-31"),
              srs="EPSG:32618", nx = 497, ny=526, dt="P1D", aggregation = "median")
L8.cube = raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))

L8.cube[c("B05","B04")] # select bands
L8.cube[as.Date(c("2018-01-10", "2018-01-20"))] # crop by time
L8.cube[as.Date("2018-01-10")] # slice by time
L8.cube["B05", "2018-01-10"] # select bands and slice by time
L8.cube["B05", c("2018-01-10","2018-01-17")] # select bands and crop by time
L8.cube[, c("2018-01-10","2018-01-17")] # crop by time

# crop by space (coordinates and integer indexes respectively)
L8.cube[list(left=388941.2 + 1e5, right=766552.4 - 1e5, bottom=4345299 + 1e5, top=4744931 - 1e5)]
L8.cube[,,c(1,100), c(1,100)] 

L8.cube[,c(1,2),,] # crop by time (integer indexes)

# subset by spatial point or bounding box
if (requireNamespace("sf", quietly = TRUE)) {
  s = sf::st_sfc(sf::st_point(c(500000, 4500000)), crs = "EPSG:32618")
  L8.cube[s]

  bbox =  sf::st_bbox(c(xmin = 388941.2 + 1e5, xmax = 766552.4 - 1e5,
                   ymax = 4744931 - 1e5, ymin = 4345299 + 1e5), crs = sf::st_crs(32618))
  L8.cube[bbox]
}

gdalcubes documentation built on May 29, 2024, 5:11 a.m.