Calculating mean, median and standard deviation in stack raster for different time steps

Question

I have a raster brick/stack (using the raster package) in R for 45 years of annual rainfall data from 1970 to 2015. I want to calculate mean, median and standard deviation for a given year e.g. 2015 using the last 5 years, 10 years, 15 years, 20 years, 30 years. I want to do it from 2000 to 2015, where this processes repeated for every year using the stacked data and save the newly derived rasters for a given year. This the example code. Any help is greatly appreciated.

raster <- raster(ncol=10, nrow=10)
raster_brick <- brick( sapply(1:45, function(i) setValues(r, rnorm(ncell(r), i, 3))))
plot(raster_brick)
str(raster_brick)

Answer 1

To achieve this task, we can use the calc function from the raster package. We also need to know how to subset the RasterBrick object.

Data preparation

library(raster)
set.seed(123)
r <- raster(ncol=10, nrow=10)
r_brick <- brick(sapply(1:45, function(i) setValues(r, rnorm(ncell(r), i, 3))))

Example of calc function

calc can apply a function for all layers in a RasterBrick object. The end result is a raster layer.

# Calculate mean
r_mean <- calc(r_brick, mean)
# Calculate median
r_median <- calc(r_brick, median)
# Calculate sd
r_sd <- calc(r_brick, sd)

Notice that r_mean, r_median, and r_sd are all RasterLayer.

Example of subset a RasterBrick

We can use the index to subset the layer. For example,

r_sub <- r_brick[[1:3]]

r_sub is the first three layers of r_brick

A function to Conduct the Analysis

Knowing the technique of calc and subset, we can design a function to conduct the analysis.

The first thing to do is create a vector serving as a reference to year and index.

# Create the index
ind <- 1:45
names(ind) <- 1971:2015

Calling the year number to ind will return the index. For example,

# Get the index of 2015
ind[as.character(2015)]
#2015 
#  45

Now design the function, which has five arguments

end_year: The end year of analysis

n_year: The last n year in terms of the end year

FUN: A function, such as mean, median, and sd

index: The year index (ind)

ras_brick: RasterBrick to work with

# Define the function

raster_stat <- function(end_year, n_year, FUN, index, ras_brick){
  
  # Subset the raster
  index_temp <- index[as.character((end_year - n_year + 1):end_year)]
  ras_brick_temp <- ras_brick[[index_temp]]
  
  # Calculate the statistics
  ras_result <- calc(ras_brick_temp, FUN)
  
  # Set the name
  names(ras_result) <- paste("Y", end_year, n_year, substitute(FUN), sep = "_")
  
  return(ras_result)
}

Now we can test the function.

raster_stat(2015, 5, FUN = sd, index = ind, ras_brick = r_brick)
#class       : RasterLayer 
#dimensions  : 10, 10, 100  (nrow, ncol, ncell)
#resolution  : 36, 18  (x, y)
#extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 
#data source : in memory
#names       : Y_2015_5_sd 
#values      : 12.05333, 14.61298  (min, max)

Notice that the result of the raster_stat function has the name Y_2015_5_sd. This is helpful to identify which end_year, n_year, and FUN was applied.

Apply the function

We can use for loop to apply raster_stat through all end_year and n_year. Here is an example calculating the mean.

# Set the range of end_year and n_year
end_year_vec <- 2000:2015
n_year_vec <- c(5, 10 , 15, 20, 30)

# Create an empty list to store result
r_mean_list <- list()

for (i in end_year_vec){
  for(j in n_year_vec){
      result_temp <- raster_stat(end_year = i, n_year = j, FUN = mean, 
                                 index = ind, ras_brick = r_brick)
      # Add the raster layer to the result_list
      r_mean_list[[names(result_temp)]] <- result_temp
  }
} 

All the results are stored in r_mean_list with a unique name. We can use the same approach for median and sd.