Construct the base VRT object for composing VRT pipelines.

Usage

vrt_collect(x, band_descriptions, datetimes)

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

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

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

Arguments

x: An object to be used to create a vrt_x object see details.
band_descriptions: A character vector of band descriptions.
datetimes: A character vector of datetimes.
...: Additional arguments not used
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

For now 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 sentinel2_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 fro example and when summarisation is required we can use vrt_stack - however, in order to create a stack the collection must xontain images from a single spatial reference system (SRS). If there are mutliple SRS values, use vrt_warp() to unify the projection of the collection.