Im working with two rasters each with a different resolution. Im wondering if there is a more efficient way of matching the coarser raster resolution to the finer raster resolution. Right now I am using the mask function to save some time, clip to the correct extent and change the resolution:
library(raster)
#the raster template with the desired resolution
r <- raster(extent(-180, 180, -64, 84), res=0.04166667)
# set some pixels to values, others to NA
r <- setValues(r, sample(c(1:3, NA), ncell(r), replace=TRUE))
#load the raster
lc_r1 <- raster(r)
res(lc_r1) <- 0.5
values(lc_r1) <- 1:ncell(lc_r1)
lc_r1
##class : RasterLayer
##dimensions : 296, 720, 213120 (nrow, ncol, ncell)
##resolution : 0.5, 0.5 (x, y)
##extent : -180, 180, -64, 84 (xmin, xmax, ymin, ymax)
##coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
##data source : in memory
##names : layer
##values : 1, 213120 (min, max)
#create the new finer resolution raster.
lc_r2 <- mask (lc_r1, r2)
Error in compareRaster(x, mask) : different number or columns
Im also trying the disaggregate function in raster but I get this odd error!
lc_r2 <- disaggregate (lc_r1, nrows=3600 )
Error: !is.null(fact) is not TRUE
This seems to work for the time being but not sure if its correct:
lc_r2 <- disaggregate (lc_r1, fact=c(12,12 ), method='bilinear')
Why would this Error: !is.null(fact) is not TRUE be odd? If you look at ?disaggregate you will see that there is no argument nrows, but there is a required argument fact, which you did not supply.
You can do
lc_r2a <- disaggregate (lc_r1, fact=12)
Or
lc_r2b <- disaggregate(lc_r1, fact=12, method='bilinear')
which is equivalent to
lc_r2c <- resample(lc_r1, r)
Why are you not sure that this is correct?
However, given that you want to mask lc_r1, the logical approach would be to go the opposite direction and change the resolution of your mask, r,
ra <- aggregate(r, fact=12, na.rm=TRUE)
lcm <- mask(lc_r1, ra)