gdalcubes (version 0.7.0)

reduce_time.cube: Reduce a data cube over the time dimension

Description

Create a proxy data cube, which applies one or more reducer functions to selected bands over pixel time series of a data cube

Usage

# S3 method for cube
reduce_time(
  x,
  expr,
  ...,
  FUN,
  names = NULL,
  load_pkgs = FALSE,
  load_env = FALSE
)

Value

proxy data cube object

Arguments

x

source data cube

expr

either a single string, or a vector of strings defining which reducers will be applied over which bands of the input cube

...

optional additional expressions (if expr is not a vector)

FUN

a user-defined R function applied over pixel time series (see Details)

names

character vector; names of the output bands, if FUN is provided, the length of names is used as the expected number of output bands

load_pkgs

logical or character; if TRUE, all currently attached packages will be attached automatically before executing FUN in spawned R processes, specific packages can alternatively be provided as a character vector.

load_env

logical or environment; if TRUE, the current global environment will be restored automatically before executing FUN in spawned R processes, can be set to a custom environment.

Details

The function can either apply a built-in reducer if expr is given, or apply a custom R reducer function if FUN is provided.

In the former case, 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 currently are "min", "max", "sum", "prod", "count", "mean", "median", "var", "sd", "which_min", "which_max", "Q1" (1st quartile), and "Q3" (3rd quartile).

User-defined R reducer functions receive a two-dimensional array as input where rows correspond to the band and columns represent the time dimension. For example, one row is the time series of a specific band. FUN should always return a numeric vector with the same number of elements, which will be interpreted as bands in the result cube. Notice that it is recommended to specify the names of the output bands as a character vector. If names are missing, the number and names of output bands is tried to be derived automatically, which may fail in some cases.

For more details and examples on how to write user-defined functions, please refer to the gdalcubes website at https://gdalcubes.github.io/source/concepts/udfs.html.

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="2018-06"),
              srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
L8.cube = raster_cube(L8.col, v) 
L8.rgb = select_bands(L8.cube, c("B02", "B03", "B04"))
L8.rgb.median = reduce_time(L8.rgb, "median(B02)", "median(B03)", "median(B04)")  
L8.rgb.median

# \donttest{
plot(L8.rgb.median, rgb=3:1)
# }

# user defined reducer calculating interquartile ranges
L8.rgb.iqr = reduce_time(L8.rgb, names=c("iqr_R", "iqr_G","iqr_B"), FUN = function(x) {
    c(diff(quantile(x["B04",],c(0.25,0.75), na.rm=TRUE)),
      diff(quantile(x["B03",],c(0.25,0.75), na.rm=TRUE)),
      diff(quantile(x["B02",],c(0.25,0.75), na.rm=TRUE)))
})
L8.rgb.iqr
# \donttest{
plot(L8.rgb.iqr, key.pos=1)
# }