问将多个栅格的像素值提取到R中的相同csv

EN

library(raster)
library(rgdal)

# List tif files
Files <- list.files(pattern = ".tif")

# Read shapefile with quadrats
uav <- readOGR("shapefile.shp")

# Name your output variable
# Raster Layer requires one value
# For RasterStack or Raster Brick, a value per layer
VAR = "median_ndvi"

# List collecting results of every run
DF <- list()

# Collecting information in a loop
for(i in Files) DF[[i]] <- {
        r <- raster(i)
        names(r) <- VAR # set a standard name for the layer
        imgext <- extract(x = r, y = uav, fun = median, df = TRUE)
        # Discard NA values
        imgext <- imgext[!is.na(imgext[ , VAR]),]
        imgext
}

# Since all data frames have the same columns
# you can call 'rbind()'
DF <- do.call(rbind, DF)

library(sf)
library(raster)
library(exactextractr)
library(data.table)
library(tidyr)

calc_median <- function(){
  #list raster files
  listImages <- lapply(list.files(dir, pattern = ".nc$", full.names = TRUE), raster)

  #create a mask (defined resolution, same extend and crs as shapefile)
  RasterMask <- raster() #create raster
  extent(RasterMask) <- extent(shp) #set extent to extent of shapefile
  res(RasterMask) <- c(100, 100) #define resolution
  crs(RasterMask) <- CRS("+init=epsg:28992") #set crs

  #create a list to bind results to
  result <- list() #create list
  
  #calculate median values using a for loop
  for(i in listImages){
    #read raster file
    rasterfile <- i
    
    #convert raster file to crs of rastermask
    rasterfile <- projectRaster(rasterfile, RasterMask, crs = crs(RasterMask))
    
    #extract median value for polygon
    poly_extract <- copy(shp) #copy shapefile
    poly_extract$Median <- exact_extract(rasterfile, poly_extract, 'median') #extract median value
    poly_extract$Raster <- names(rasterfile) #add name of raster file
    result[[names(i)]] <- poly_extract #add results to list
  }
  
  #rbind results, convert to data table in the process (for pivot_wider)
  result_bind <- setDT(do.call(rbind, result))
  
  #pivot_wider results (for overview)
  result_reordered <- pivot_wider(result_bind, names_from = Raster, values_from = Median)
  
  #return result
  return(result_reordered)
}

#run function. You can save it using fwrite if you want.
median_values <- calc_median()