R: Apply a user-defined R function over (multi-band) pixel time...

apply_time.cube {gdalcubes} | R Documentation

## Apply a user-defined R function over (multi-band) pixel time series

### Description

Create a proxy data cube, which applies a user-defined R function over all pixel time series of a data cube. In contrast to `reduce_time`, the time dimension is not reduced, i.e., resulting time series must have identical length as the input data cube but may contain a different number of bands / variables. Example uses of this function may include time series decompositions, cumulative sums / products, smoothing, sophisticated NA filling, or similar.

### Usage

    ## S3 method for class 'cube'
    apply_time(x, names = NULL, keep_bands = FALSE, FUN, ...)

### Arguments

`x` | source data cube
`names` | optional character vector to specify band names for the output cube
`keep_bands` | logical; keep bands of input data cube, defaults to FALSE, i.e., original bands will be dropped
`FUN` | user-defined R function that is applied on all pixel time series (see Details)
`...` | not used

### Details

FUN receives a single (multi-band) pixel time series as a matrix with rows corresponding to bands and columns corresponding to time. In general, the function must return a matrix with the same number of columns. If re result contains only a single band, it may alternatively return a vector with length identical to the length of the input time series (number of columns of the input).

### Value

a proxy data cube object

### 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"))
    }

    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="2018-06"),
                  srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
    L8.cube = raster_cube(L8.col, v)
    L8.cube = select_bands(L8.cube, c("B04", "B05"))
    L8.ndvi = apply_pixel(L8.cube, "(B05-B04)/(B05+B04)", "NDVI")

    # Apply a user defined R function
    L8.ndvi.resid = apply_time(L8.ndvi, names="NDVI_residuals",
       FUN=function(x) {
          y = x["NDVI",]
          if (sum(is.finite(y)) < 3) {
             return(rep(NA,ncol(x)))
          }
          t = 1:ncol(x)
          return(predict(lm(y ~ t)) -  x["NDVI",])
       })
    L8.ndvi.resid

    plot(L8.ndvi.resid)

[Package gdalcubes version 0.3.1 Index]