Applies a Gaussian smoothing kernel to smooth raster.
raster.gaussian.smooth(x, sigma = 2, n = 5, type = mean, ...)
Arguments
| x | raster object |
|---|---|
| sigma | standard deviation (sigma) of kernel (default is 2) |
| n | Size of the focal matrix, single value (default is 5 for 5x5 window) |
| type | The statistic to use in the smoothing operator (suggest mean or sd) |
| ... | Additional arguments passed to raster::focal |
Value
raster class object of the local distributional moment
Note
This is a simple wrapper for the focal function, returning local statistical moments
Author
Jeffrey S. Evans <jeffrey_evans@tnc.org>
Examples
library(raster) r <- raster(nrows=500, ncols=500, xmn=571823, xmx=616763, ymn=4423540, ymx=4453690) proj4string(r) <- crs("+proj=utm +zone=12 +datum=NAD83 +units=m +no_defs") r[] <- runif(ncell(r), 1000, 2500) r <- focal(r, focalWeight(r, 150, "Gauss") ) # Calculate Gaussian smoothing with sigma(s) = 1-4 g1 <- raster.gaussian.smooth(r, sigma=1, nc=11) g2 <- raster.gaussian.smooth(r, sigma=2, nc=11) g3 <- raster.gaussian.smooth(r, sigma=3, nc=11) g4 <- raster.gaussian.smooth(r, sigma=4, nc=11) opar <- par(no.readonly=TRUE) par(mfrow=c(2,2)) plot(g1, main="Gaussian smoothing sigma = 1") plot(g2, main="Gaussian smoothing sigma = 2") plot(g3, main="Gaussian smoothing sigma = 3") plot(g4, main="Gaussian smoothing sigma = 4")
