Resample and matching raster stack using loop in R-CodePudding

I aim to combine biodiversity data with land cover information (rasters and vectors). However, I need to match the resolution, extent, CRS, and dimensions of each raster (predictor variables) with my biodiversity data (answer variables). I had succeed to do it individually but there are six rasters. Although, when I try a loop for the raster stack. I got some errors.

library(terra)
library(raster)
#Create a raster stack with land cover predictors:
CDI_stack<-raster::stack(list.files(path = dir_Proj1, pattern='.tif', full.names=T))
#Convert to cylindrical equal area projection
equalareaproj<-" proj=cea  lon_0=0  lat_ts=30  x_0=0  y_0=0  datum=WGS84  units=m  no_defs"
crs(CDI_stack, warn=FALSE)<-equalareaproj
#Raster with standard dimension, resolution, extention and CRS
standard<-terra::subset(study_area, 2) 
#Loop for the raster stack
for(i in 1:length(CDI_stack@layers)){
  #Creating a single raster with each layer to maintain values
  CDI_layer<-terra::rast(terra::subset(CDI_stack, i)) 
  #Matching a raster extention individually
  CDI_layer<-ext(standard) 
  #Cropping it with standard raster to reduce matching error
  raster::crop(CDI_layer[i],standard) 
  #Resample resolution 
  terra::resample(CDI_layer[i], standard, method= "near", threads= T) 
  #Write the raster:
  return(writeRaster(Resampled_layer, 
                     filename=paste0("~/Land use/Chronic_Anthropogenic_Disturbance_Surface/", CDI_layer[i]),
                     format="GTiff", overwrite=TRUE))
  }

I found these errors:

Error in h(simpleError(msg, call)) : 
 error evaluating argument 'x' in method selection for function 'crop': 'this S4 class is not subsettable
Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘crop’ for signature ‘"numeric"’

I would like to know if there's any complication to use raster stack or whether I am doing any code step wrongly. I expect to found the correction on the code or of the use of class object.

Please, I hope for your support. Thank you! G.

CodePudding user response：

It should be easy enough to find out what is going wrong when you run the code line by line; including inside the for-loop (set i to 1 or whatever the valye is when the error occurs).

You will see that this fails:

CDI_layer <- ext(standard) 
raster::crop(CDI_layer[i],standard)

Because CDI_layer[i] is a single number.

There are other things that are awkward. Especially, just use "terra", do not also use "raster" at the same time to avoid confusion.

CodePudding user response：

Thank you for your advices, Mr. Hijimanns. I found too many errors. I preferred to work with a list from the raster stack than the stack, it worked better in the loop. Also, I used a vector to crop the raster, it preserved raster values (avoiding return NA).

#Create a list from the stack with land cover predictors:
CDI_list<-terra::as.list(CDI_stack)  

rm(study_area,CDI_stack)

#Create a list to store the results
results <- list()

#Loop for each SpatRaster from the list
for(i in 1:length(CDI_list)) {
  r<-rast(CDI_list[[i]]) # create a raster for each layer
  ext(r) <-ext(standard)  # redefine extension for each layer
  #Crop rasters using the vector to avoid 'NA' values
  rc <- terra::crop(r, standard_vec)
  #Resample rasters following standard parameters
  rc <- terra::resample(rc, standard, method= "bilinear", threads= T)
  #Rewrite the list layers with the result     
  results[[i]] <- rc 
}

#Check the values
results[[4]]
#Rasterize the list to save it as a data frame
resampled<-rast(results)
df<-as.data.frame(resampled)
summary(df)
#Save the data frame in the project directory
data.table::fwrite(df, "~/Land use/DATASETS/resampled.csv")