window_time

Apply a moving window operation over the time dimension of a data cube

Description

Create a proxy data cube, which applies one ore more moving window functions to selected bands over pixel time series of a data cube. The function can either apply a built-in aggregation function or apply a custom one-dimensional convolution kernel.

Usage

window_time(x, expr, ..., kernel, window)

Arguments

Argument	Description
x	source data cube
expr	either a single string, or a vector of strings defining which reducers wlil be applied over which bands of the input cube
…	optional additional expressions (if expr is not a vector)
kernel	numeric vector with elements of the kernel
window	integer vector with two elements defining the size of the window before and after a cell, the total size of the window is window[1] + 1 + window[2]

Details

The function either applies a kernel convolution (if the kernel argument is provided) or a general reducer function over moving temporal windows. In the former case, the kernel convolution will be applied over all bands of the input cube, i.e., the output cube will have the same number of bands as the input cubes. If a kernel is given and the window argument is missing, the window will be symmetric to the center pixel with the size of the provided kernel. For general reducer functions, the window argument must be provided and several expressions can be used to create multiple bands in the output cube.

Notice that expressions have a very simple format: the reducer is followed by the name of a band in parantheses. You cannot add more complex functions or arguments.

Possible reducers include “min”, “max”, “sum”, “prod”, “count”, “mean”, and “median”.

Value

proxy data cube object

Note

Implemented reducers will ignore any NAN values (as na.rm=TRUE does).

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"), quiet = TRUE) 
}

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="2018-07"),
                          srs="EPSG:32618", nx = 400, dt="P1M")
L8.cube = raster_cube(L8.col, v) 
L8.nir = select_bands(L8.cube, c("B05"))
L8.nir.min = window_time(L8.nir, window = c(2,2), "min(B05)")  
L8.nir.min

A data cube proxy object

Dimensions:
         low       high count       pixel_size chunk_size
t 2018-01-01 2018-07-31     7              P1M          1
y    4345299    4744931   423 944.756501182033        320
x   388941.2   766552.4   400          944.028        320

Bands:
     name offset scale nodata unit
1 B05_min      0     1    NaN     

L8.nir.kernel = window_time(L8.nir, kernel=c(-1,1), window=c(1,0))  
L8.nir.kernel

A data cube proxy object

Dimensions:
         low       high count       pixel_size chunk_size
t 2018-01-01 2018-07-31     7              P1M          1
y    4345299    4744931   423 944.756501182033        320
x   388941.2   766552.4   400          944.028        320

Bands:
  name offset scale nodata unit
1  B05      0     1    NaN