Construct the base VRT object for composing VRT pipelines.

Usage

vrt_collect(x, config_opts, bands, band_descriptions, datetimes)

# S3 method for class 'character'
vrt_collect(
  x,
  config_opts = gdal_config_opts(),
  bands = NULL,
  band_descriptions = NULL,
  datetimes = rep("", length(x))
)

# S3 method for class 'doc_items'
vrt_collect(x, config_opts = gdal_config_opts(), ...)

# S3 method for class 'vrt_collection'
print(x, xml = FALSE, pixfun = FALSE, maskfun = FALSE, blocks = FALSE, ...)

# S3 method for class 'vrt_collection'
c(x, ...)

Arguments

x: An object to be used to create a vrt_x object see details.
config_opts: A named character vector of GDAL configuration options.
bands: A numeric vector of band indices to include in the VRT collection
band_descriptions: A character vector of band descriptions.
datetimes: A character vector of datetimes.
...: In the case of c, additional vrt_collection objects to concatenate to x. Otherwise, additional arguments to pass to the method or unused.
xml: logical indicating whether to print the XML of the VRT collection.
pixfun: logical indicating whether to print the pixel function.
maskfun: logical indicating whether to print the mask function.
blocks: A logical indicating whether to print the blocks instead of the collection summary.

Value

A vrt_collection object.

Details

The main way to create a vrt_collection object, which forms the basis of the vrrt-based pipelines in vrtility is using a doc_items object from the rstac package. For more info on how to create a doc_items object see stac_query(). To build a vrt_stack object a vrt_collection is required first. The vrt_collection object is essentially a list of VRT files. At this stage no alignment is carried out - and the rasters are virtualised as-is. In this state, we can apply masks, for example and when summarisation is required we can use vrt_stack - however, in order to create a stack the collection must contain images from a single spatial reference system (SRS). If there are mutliple SRS values, use vrt_warp() to unify the projection of the collection (This is almost always a good idea anyway).

We can also create a VRT collection from a set of files. This is useful when we have the data on disk or as a downstream step after first processing a stac collection.

You can use the c method to combine multiple vrt_collection objects. All collections must have the same number of bands.

Examples

s2files <- fs::dir_ls(system.file("s2-data", package = "vrtility"))
vrt_collect(s2files)
#> → <VRT Collection>
#> 
#>  VRT SRS: 
#> PROJCS["unknown",GEOGCS["unknown",DATUM["Unknown based on WGS 84 ellipsoid",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",50.72],PARAMETER["longitude_of_center",-3.51],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
#> 
#>  PROJCS["WGS 84 / UTM zone 30N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-3],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32630"]]
#> 
#>  PROJCS["OSGB36 / British National Grid",GEOGCS["OSGB36",DATUM["Ordnance_Survey_of_Great_Britain_1936",SPHEROID["Airy 1830",6377563.396,299.3249646,AUTHORITY["EPSG","7001"]],AUTHORITY["EPSG","6277"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4277"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",49],PARAMETER["central_meridian",-2],PARAMETER["scale_factor",0.9996012717],PARAMETER["false_easting",400000],PARAMETER["false_northing",-100000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","27700"]]
#> 
#> 
#> Bounding Box: NA
#> Pixel res: 19.9923198093722, 19.9923198093722
#> Start Date: NA
#> End Date: NA
#> Number of Items: 5
#> Assets: B02, B03, B04, SCL

# we can also combine multiple vrt collections
c(vrt_collect(s2files[1:2]), vrt_collect(s2files[3:4]))
#> → <VRT Collection>
#> 
#>  VRT SRS: 
#> PROJCS["unknown",GEOGCS["unknown",DATUM["Unknown based on WGS 84 ellipsoid",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",50.72],PARAMETER["longitude_of_center",-3.51],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH]]
#> 
#>  PROJCS["WGS 84 / UTM zone 30N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-3],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32630"]]
#> 
#> 
#> Bounding Box: NA
#> Pixel res: 19.9923198093722, 19.9923198093722
#> Start Date: NA
#> End Date: NA
#> Number of Items: 4
#> Assets: B02, B03, B04, SCL

if (FALSE) { # interactive()
s2q <- sentinel2_stac_query(
 bbox = c(-12.386, -37.214, -12.186, -37.014),
 start_date = "2023-01-01",
 end_date = "2023-01-31",
 max_cloud_cover = 10,
 assets = c("B02", "B03", "B04", "B08", "SCL")
)

vrt_collect(s2q)
}