How can I create an R loop with the code provided below?

Question

Please, I need help in creating a loop that would do the computation shown in the codes below on a hdflist containing 483 files in R. I have added a link that contains two .hdf files and the shapefiles for trial. The code seems to work just fine for a single .hdf file but I'm still struggling with looping. Thank you

download files from here https://beardatashare.bham.ac.uk/getlink/fi2gNzWbuv5H8Gp7Qg2aemdM/

# import .hdf file into R using get_subdatasets to access the subsets in the file`
sub <- get_subdatasets("MOD13Q1.A2020353.h18v08.006.2021003223721.hdf")

# convert red and NIR subsets and save them as raster`
gdalwarp(sub[4], 'red_c.tif')
gdalwarp(sub[5], 'NIR_c.tif')

# import red and NIR raster back into R`
# scale the rater while at it`

r_r=raster('red_c.tif') * 0.0001 
r_N=raster('NIR_c.tif') * 0.0001 

# calculate sigma using (0.5*(NIR+red))`
sigma <- (0.5*(r_N+r_r))

# calculate knr using exp((-(NIR-red)^2)/(2*sigma^2))`
knr <- exp((-(r_N-r_r)^2)/(2*sigma^2))

# calculate kndvi using (1 - knr) / (1 + knr)`
kndvi <- (1 - knr) / (1 + knr)

# import shapefile into R`
shp=readOGR(".", "National_Parks")
options(stringsAsFactors = FALSE)

#change crs of shapefile to crs of one of the rasters`
shp2 <- spTransform(shp, crs(kndvi))

# use extent to crop/clip raster`
## set extent`
e <- extent(910000,980000, 530000, 650000)

## clip using crop function`

crop_kndvi <- crop(kndvi, e)

# mask raster using the shapefile`
kndvi_mask <- mask(crop_kndvi, shp2)

And then save kndvi_mask as raster for 483 files

Answer 1

Here is how you can do that with terra. terra is the replacement for raster; it is much faster and more versatile. For example, with terra you can skip the gdalwarp step.

You can write one big for-loop, but I prefer to use functions and then call these in a loop or lapply.

Also, instead of your raster-algebra approach, it could be more efficient to wrap the kndvi computation into its own function and use it with lapp. I think that is a better approach as the code is clearer and it allows you to re-use the kndvi function.

library(terra)
parks <- vect("National_Parks.shp")
parks <- project(parks, "+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +R=6371007.181 +units=m")
e <- ext(910000,980000, 530000, 650000)

kndvi function to be used by lapp

kndvi <- function(red, NIR) {
    red <- red * 0.0001 
    NIR <- NIR * 0.0001 
    sigma <- (0.5 * (NIR + red))
    knr <- exp((-(NIR-red)^2)/(2*sigma^2))
    (1 - knr) / (1 + knr)
}

Main function. Note that I use crop before the other functions; that saves a lot of unnecessary processing.

fun <- function(f) {
    outf <- gsub(".hdf$", "_processed.tif", f) 
    # if file.exists(outf) return(rast(outf))
    r <- rast(f)[[4:5]]
    # or r <- sds(f)[4:5]
    r <- crop(r, e)
    kn <- lapp(r, kndvi)
    name <- substr(basename(f), 9, 16)
    mask(kn, parks, filename=outf, overwrite=TRUE, names=name)
}

Get the filenames and use the function with a loop or with lapply as shown by Elia.

ff <- list.files(pattern="hdf$", full=TRUE)

x <- list()
for (i in 1:length(ff)) {
    print(ff[i]); flush.console()
    x[[i]] <- fun(ff[i])
}
z <- rast(x)
z

#class       : SpatRaster 
#dimensions  : 518, 302, 2  (nrow, ncol, nlyr)
#resolution  : 231.6564, 231.6564  (x, y)
#extent      : 909946.2, 979906.4, 530029.7, 650027.7  (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=sinu +lon_0=0 +x_0=0 +y_0=0 +R=6371007.181 +units=m +no_defs 
#sources     : MOD13Q1.A2020337.h18v08.006.2020358165204_processed.tif  
#              MOD13Q1.A2020353.h18v08.006.2021003223721_processed.tif  
#names       :     A2020337,     A2020353 
#min values  : 0.0007564131, 0.0028829363 
#max values  :    0.7608207,    0.7303495

This takes about 1 second per file on my computer.

Or as a for-loop that you asked for:

ff <- list.files(pattern="hdf$", full=TRUE)
for (f in ff) {
    print(f); flush.console()
    outf <- gsub(".hdf$", "_processed.tif", f) 
    r <- rast(f)[[4:5]]
    r <- crop(r, e)
    kn <- lapp(r, kndvi)
    name <- substr(basename(f), 9, 16)
    mask(kn, parks, filename=outf, overwrite=TRUE, names=name)
}

outf <- list.files(pattern="_processed.tif$", full=TRUE)
x <- rast(outf)

Answer 2

You can wrap your code in a function and then lapply over the hdf path. In this way if your loop is too slow it will be easy to parallelize it. You could try this:

library(gdalUtils)
library(raster)
library(rgdal)
#set the directory where you have .hdf files. In my case I downloaded your data in "D:/download"
setwd("D:/download")
#function to save the masked index in your current working directory
#the final files name will depend on the name of the input hdf files
myfun <- function(path){
  name <- basename(tools::file_path_sans_ext(path))
  sub <- get_subdatasets(path)
  gdalwarp(sub[4], paste0(name,'_red_c.tif'))
  gdalwarp(sub[5], paste0(name,'NIR_c.tif'))
  r_r=raster(paste0(name,'_red_c.tif')) * 0.0001 
  r_N=raster(paste0(name,'NIR_c.tif')) * 0.0001 
  
  sigma <- (0.5*(r_N+r_r))
  knr <- exp((-(r_N-r_r)^2)/(2*sigma^2))
  kndvi <- (1 - knr) / (1 + knr)
  crop_kndvi <- crop(kndvi, e)
  kndvi_mask <- mask(crop_kndvi, 
  shp2,filename=paste0(name,"_kndvi_mask.tif"))
}



    #list the hdf file in your current working directory. Thanks to setwd("D:/download") there is no need to specify the path argument of list.files().
   b#however for the for peace of mind:
    hdf <- list.files(path=getwd(),pattern = "hdf",full.names = T)
    #since your shop is always the same you could keep this part out of the function
    shp=readOGR(".", "National_Parks")
    options(stringsAsFactors = FALSE)
    shp2 <- spTransform(shp,  "+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m
                        +no_defs ")
    e <- extent(910000,980000, 530000, 650000)
    #now run your function across the hdf files path
    lapply(hdf, myfun)

in your working directory now you find all the saved if

   list.files(pattern = "tif")
[1] "MOD13Q1.A2020337.h18v08.006.2020358165204_kndvi_mask.tif"
[2] "MOD13Q1.A2020337.h18v08.006.2020358165204_red_c.tif"     
[3] "MOD13Q1.A2020337.h18v08.006.2020358165204NIR_c.tif"      
[4] "MOD13Q1.A2020353.h18v08.006.2021003223721_kndvi_mask.tif"
[5] "MOD13Q1.A2020353.h18v08.006.2021003223721_red_c.tif"     
[6] "MOD13Q1.A2020353.h18v08.006.2021003223721NIR_c.tif"

With lapply on my PC the function run in 45 seconds. You could easily parallelize lapply by replacing it with sfLapply from the snowfall package, for example. For just 2 files it's not worth it, but if you have hundreds of files you can speed up the process a lot:

library(snowfall)

#open cluster with as many node as hdf file
sfInit(parallel=TRUE, cpus=length(hdf))
# Load the required packages inside the cluster
sfLibrary(raster)
sfLibrary(rgdal)
sfLibrary(gdalUtils)
sfExportAll()
system.time(sfLapply(hdf, myfun))
sfStop()

with sfLapply this function took 20 secs to run. It is a good improvement

Answer 3

hdf_files <- list.files("foldername", pattern = ".hdf")

for(f in files) { ... }

For the save code, you can use the string "f" to create a name for the file, so they don't save on top of each other.