Smooth Raster Time-series
Smooths pixel-level data in raster time-series and can impute missing (NA) values.
smooth.time.series(x, f = 0.8, smooth.data = FALSE, ...)
x |
A raster stack/brick or sp object with a @data slot |
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 raster calc (for writing results to disk) |
This function uses a LOESS regression to smooth the time-series (using the smooth.data = TRUE argument). If the data is smoothed, it will be replaced by a loess estimate of the time-series (estimated distribution at the pixel-level). 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. Alternately, with smooth.data = FALSE, the function can be used to impute missing pixel data (NA) in raster time-series (stacks/bricks).
A raster stack or brick pr data.frame object with imputed NA values or smoothed data.
Jeffrey S. Evans <jeffrey_evans@tnc.org>
loess for details on the loess regression
calc for details on additional (...) arguments
## Not run:
random.raster <- function(r=50, c=50, l=10, min=0, max=1){
do.call(stack, replicate(l, raster(matrix(runif(r*c, min, max),r,c))))
}
r <- random.raster()
#### Smooth time-series using raster stack/brick
r.smooth <- smooth.time.series(r, f = 0.6, smooth.data = TRUE)
#### sp SpatialPixelsDataFrame example
r <- as(r, "SpatialPixelsDataFrame")
# extract pixel 100 for plotting
y <- as.numeric(r@data[100,])
# Smooth data
r@data <- smooth.time.series(r, f = 0.6, smooth.data = TRUE)
# plot results
plot(y, type="l")
lines(as.numeric(r@data[100,]), col="red")
legend("bottomright", legend=c("original","smoothed"),
lty=c(1,1), col=c("black","red"))
# coerce back to raster stack object
r <- stack(r)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.