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56 lines
2.5 KiB
R
56 lines
2.5 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/aggregate_space.R
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\name{aggregate_space}
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\alias{aggregate_space}
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\title{Spatial aggregation of data cubes}
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\usage{
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aggregate_space(cube, dx, dy, method = "mean", fact = NULL)
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}
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\arguments{
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\item{cube}{source data cube}
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\item{dx}{numeric value; new spatial resolution in x direction}
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\item{dy}{numeric value; new spatial resolution in y direction}
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\item{method}{aggregation method, one of "mean", "min", "max", "median", "count", "sum", "prod", "var", and "sd"}
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\item{fact}{simple integer factor defining how many cells (per axis) become aggregated to a single new cell, can be used instead of dx and dy}
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}
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\description{
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Create a proxy data cube, which applies an aggregation function to reduce the spatial resolution.
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}
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\details{
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This function reduces the spatial resolution of a data cube by applying an aggregation function to smaller blocks of pixels.
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The size of the cube may be expanded automatically in all directions if the original extent is not divisible by the new size of pixels.
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Notice that if boundaries of the target cube do not align with the boundaries of the input cube (for example, if aggregating from 10m to 15m spatial resolution), pixels of
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the input cube will contribute to the output pixel that contains its center coordinate. If the center coordinate is exactly on a boundary, the input pixel will contribute to
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the right / bottom pixel of the output cube.
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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", dx = 500, dy=500, dt="P3M", aggregation = "median")
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L8.cube = raster_cube(L8.col, v, mask=image_mask("BQA", bits=4, values=16))
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L8.rgb = select_bands(L8.cube, c("B02", "B03", "B04"))
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L8.5km = aggregate_space(L8.rgb, 5000,5000, "mean")
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L8.5km
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\donttest{
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plot(L8.5km, rgb=3:1, zlim=c(5000,12000))
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}
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}
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