Arithmetic operations are faster for vectors in R

[kadyb]

One interesting observation is that arithmetic operations (e.g. computing NDVI) are faster for vector/matrices than for raster objects (stars or SpatRaster) in R. At this moment, this is especially true for the terra package, where it is probably better to convert the raster to a matrix, perform calculations, and then assign the results back to the raster object.

library(stars)
library(terra)

ndvi = function(red, nir) {(nir - red) / (nir + red)}

rasters = list.files("data/LC08_L1TP_190024_20200418_20200822_02_T1/",
                     pattern = "\\.TIF$", full.names = TRUE)


### calculate NDVI using 'stars' objects
ras = read_stars(rasters, along = 3, proxy = FALSE)

red = adrop(ras[,,,6])
nir = adrop(ras[,,,7])

system.time(ndvi(red, nir))
#>  user  system elapsed
#> 0.756   0.711   1.467


### calculate NDVI using 'SpatRaster' objects
ras = rast(rasters)
ras = ras * 1

red = ras[[6]]
nir = ras[[7]]

system.time(ndvi(red, nir))
#>  user  system elapsed
#> 4.141   3.320   7.462


### calculate NDVI using vectors
vred = values(red, mat = FALSE)
vnir = values(nir, mat = FALSE)

system.time(ndvi(vred, vnir))
#>  user  system elapsed
#> 0.558   0.209   0.767


### calculate NDVI using vectors
### and assign values to new 'SpatRaster'
new = rast(red)
system.time(new <- setValues(new, ndvi(vred, vnir)))
#>  user  system elapsed
#> 0.989   0.768   1.758