[GIS] R calculate raster cell size in map units

rraster

I have a RasterLayer and would like to know the cell size. I haven't find any function in the packages :-S . Does someone have an idea ?

info about the RasterLayer

class       : RasterLayer 
dimensions  : 40, 40, 1600  (nrow, ncol, ncell)
resolution  : 0.0001106628, 0.0001106628  (x, y)
extent      : 3.061558, 3.065984, 42.5269, 42.53132  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
data source : in memory
names       : layer 
values      : 0, 33  (min, max)

Best Answer

Use the ?area function.

library(raster)
r <- raster()  ## boilerplate longlat raster
ar <- area(r)  ## applies a correction for latitude, to km2

Load libraries and example data:

# Load libraries
library('raster')
library('rgdal')

# Load a SpatialPolygonsDataFrame example
# Load Brazil administrative level 2 shapefile
BRA_adm2 <- raster::getData(country = "BRA", level = 2)

# Convert NAMES level 2 to factor 
BRA_adm2$NAME_2 <- as.factor(BRA_adm2$NAME_2)

# Plot BRA_adm2
plot(BRA_adm2)
box()

# Define RasterLayer object
r.raster <- raster()

# Define raster extent
extent(r.raster) <- extent(BRA_adm2)

# Define pixel size
res(r.raster) <- 0.1

Figure 1: Brazil SpatialPolygonsDataFrame plot

Simple thread example

# Simple thread -----------------------------------------------------------

# Rasterize
system.time(BRA_adm2.r <- rasterize(BRA_adm2, r.raster, 'NAME_2'))

Time in my laptop:

# Output:
# user  system elapsed 
# 23.883    0.010   23.891

Multithread thread example

# Multithread -------------------------------------------------------------

# Load 'parallel' package for support Parallel computation in R
library('parallel')

# Calculate the number of cores
no_cores <- detectCores() - 1

# Number of polygons features in SPDF
features <- 1:nrow(BRA_adm2[,])

# Split features in n parts
n <- 50
parts <- split(features, cut(features, n))

# Initiate cluster (after loading all the necessary object to R environment: BRA_adm2, parts, r.raster, n)
cl <- makeCluster(no_cores, type = "FORK")
print(cl)

# Parallelize rasterize function
system.time(rParts <- parLapply(cl = cl, X = 1:n, fun = function(x) rasterize(BRA_adm2[parts[[x]],], r.raster, 'NAME_2')))

# Finish
stopCluster(cl)

# Merge all raster parts
rMerge <- do.call(merge, rParts)

# Plot raster
plot(rMerge)

Figure 2: Brazil Raster plot

Time in my laptop:

# Output:
# user  system elapsed 
# 0.203   0.033   8.688

More info about parallelization in R:

Best Answer

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Load libraries and example data:

Simple thread example

Multithread thread example

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