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63 lines
2.7 KiB
R
63 lines
2.7 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/cube.R
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\name{raster_cube}
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\alias{raster_cube}
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\title{Create a data cube from an image collection}
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\usage{
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raster_cube(
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image_collection,
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view,
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mask = NULL,
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chunking = .pkgenv$default_chunksize,
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incomplete_ok = TRUE
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)
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}
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\arguments{
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\item{image_collection}{Source image collection as from \code{image_collection} or \code{create_image_collection}}
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\item{view}{A data cube view defining the shape (spatiotemporal extent, resolution, and spatial reference), if missing, a default overview is used}
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\item{mask}{mask pixels of images based on band values, see \code{\link{image_mask}}}
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\item{chunking}{length-3 vector or a function returning a vector of length 3, defining the size of data cube chunks in the order time, y, x.}
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\item{incomplete_ok}{logical, if TRUE (the default), chunks will ignore IO failures and simply use as much images as possible, otherwise the result will contain empty chunks if IO errors or similar occur.}
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}
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\value{
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A proxy data cube object
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}
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\description{
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Create a proxy data cube, which loads data from a given image collection according to a data cube view
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}
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\details{
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The following steps will be performed when the data cube is requested to read data of a chunk:
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1. Find images from the input collection that intersect with the spatiotemporal extent of the chunk
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2. For all resulting images, apply gdalwarp to reproject, resize, and resample to an in-memory GDAL dataset
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3. Read the resulting data to the chunk buffer and optionally apply a mask on the result
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4. Update pixel-wise aggregator (as defined in the data cube view) to combine values of multiple images within the same data cube pixels
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If chunking is provided as a function, it must accept exactly three arguments for the total size of the cube in t, y, and x axes (in this order).
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}
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\note{
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This function returns a proxy object, i.e., it will not start any computations besides deriving the shape of the result.
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}
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\examples{
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# create image collection from example Landsat data only
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# if not already done in other examples
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if (!file.exists(file.path(tempdir(), "L8.db"))) {
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L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
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".TIF", recursive = TRUE, full.names = TRUE)
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create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE)
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}
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L8.col = image_collection(file.path(tempdir(), "L8.db"))
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v = cube_view(extent=list(left=388941.2, right=766552.4,
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bottom=4345299, top=4744931, t0="2018-01", t1="2018-12"),
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srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
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raster_cube(L8.col, v)
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# using a mask on the Landsat quality bit band to filter out clouds
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raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))
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}
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