spatialEco (version 2.0-2)

smooth.time.series: Smooth Raster Time-series

Description

Smooths pixel-level data in raster time-series and can impute missing (NA) values.

Usage

smooth.time.series(x, f = 0.8, smooth.data = FALSE, ...)

Value

A terra SpatRaster containing imputed or smoothed data.

Arguments

x

A terra SpatRaster with > 8 layers

f

Smoothing parameter (see loess span argument)

smooth.data

(FALSE/TRUE) Smooth all of the data or just impute NA values

...

Additional arguments passed to terra::app (for writing results to disk)

Author

Jeffrey S. Evans <[e-mail]>

Details

This function uses a LOESS regression to smooth the time-series. If the data is smoothed, (using the smooth.data = TRUE argument) it will be entirely replaced by a loess estimate of the time-series (estimated distribution at the pixel-level). Alternately, with smooth.data = FALSE, the function can be used to impute missing pixel data (NA) in raster time-series (stacks/bricks). The results can dramatically be effected by the choice of the smoothing parameter (f) so caution is warranted and the effect of this parameter tested.

See Also

loess for details on the loess regression

app for details on additional (...) arguments

impute.loess for details on imputation model

Examples

# \donttest{
library(terra)
  random.raster <- function(rows=50, cols=50, l=20, min=0, max=1){ 
    do.call(c, replicate(l, rast(matrix(runif(rows * cols, min, max), 
	        rows , cols))))
  }
r <- random.raster()

#### Smooth time-series using raster stack/brick 
r.smooth <- smooth.time.series(r, f = 0.4, smooth.data = TRUE)  

# extract pixel 100 for plotting
y <- as.numeric(r[100])
ys <- as.numeric(r.smooth[100])

# plot results	
plot(y, type="l")
  lines(ys, col="red")
    legend("bottomright", legend=c("original","smoothed"),
         lty=c(1,1), col=c("black","red"))	
# }