Come modificare la risoluzione di un livello raster in R

Question

Ho diversi livelli raster ad alta risoluzione in R con cui sto lavorando. Il livello di dettaglio è eccessivo per alcune delle analisi che sto eseguendo, quindi vorrei accelerare le cose riducendo la risoluzione.

Il sistema di coordinate è UTM quindi le unità sono metri. La risoluzione dice che è 30, 30 (x, y). Quindi sembra che la risoluzione qui sia 30m.

Qualcuno potrebbe consigliarmi come cambiare la risoluzione a 120 m? Ho letto l'aiuto per le funzioni resample() e projectRaster() ma sembrano richiedere un template raster con la risoluzione desiderata, che non ho.

Ecco un esempio di uno dei miei raster:

alt.utm
classe: raster
dimensioni: 4572, 2495, 11.407.140 (nrow, ncol, NCell)
risoluzione: 30 30 (x, y)
estensione: 421661, 496511, 4402939, 4540099 (xmin, xmax, ymin, ymax)
coord. rif. : + Proj = utm + zona = 13 + = ellps GRS80 + towgs84 = 0,0,0,0,0,0,0 + unità = m + no_defs
origine dati: in memoria
nomi: strato
valori : 1485.127, 4275.202 (min, max)

Answer 1

È possibile utilizzare aggregate o disaggregate.

library(raster) 

#get some sample data 
data(meuse.grid) 
gridded(meuse.grid) <- ~x+y 
meuse.raster <- raster(meuse.grid) 
res(meuse.raster) 
#[1] 40 40 

#aggregate from 40x40 resolution to 120x120 (factor = 3) 
meuse.raster.aggregate <- aggregate(meuse.raster, fact=3) 
res(meuse.raster.aggregate) 
#[1] 120 120 

#disaggregate from 40x40 resolution to 10x10 (factor = 4) 
meuse.raster.disaggregate <- disaggregate(meuse.raster, fact=4) 
res(meuse.raster.disaggregate) 
#[1] 10 10 

Answer 2

Ecco un esempio di come procedere. (link to original)

######################################################################### 
# SubsampleImageRaster.r 
# 
# This function demonstrates resampling of raster images to a new 
# spatial resolution using the R raster package. 
# 
# Author: Rick Reeves 
# Date created: 6-October- 2010 
# Date modified:                
# NCEAS 
# 
######################################################################### 
# 
SubsampleImageRaster <- function() 
{ 
    library(raster) 

    sOutFile <- "" 
    resampleFactor <- 4 # For test, subsample incoming image by factor of 10 

# Read the mosaic components, stored in a subfolder, into a raster object list. 
# Within the same loop, obtain the (geographic) extent of each component. 
# Note: these images do not have same spatial extent, so they cant be stored 
# in a rasterStack. Instead, use a list of rasterLayers. 

    setwd("./ForUseCase") 
    inFiles <- list.files(pattern="*.tif") 
    nFiles <- length(inFiles) 
    inputRaster <- raster() 
    CoarseResampRaster <- raster() 
    FineResampRaster <- raster() 
    for (iCtr in 1 : nFiles) 
    { 
     message(sprintf("resampling file: %s",inFiles[iCtr])) 
     inputRaster <- raster(inFiles[iCtr]) 

# The aggregate()/disaggregate methods resample rasters to COARSER (bigger cells) 
# and FINER (smaller cells) resolutions, respectively 

     CoarseResampRaster <- aggregate(inputRaster,fact=resampleFactor,fun=mean) 
     sOutFile <- sprintf("CoarseSubsamp%s",inFiles[iCtr]) 
     writeRaster(CoarseResampRaster,filename=sOutFile,format="GTiff",datatype="INT1U",overwrite=TRUE)  
     FineResampRaster <- disaggregate(inputRaster,fact=resampleFactor,fun=mean) 
     sOutFile <- sprintf("FineSubsamp%s",inFiles[iCtr]) 
     writeRaster(FineResampRaster,filename=sOutFile,format="GTiff",datatype="INT1U",overwrite=TRUE)    
    } 

    message("resample demo") 
    browser() 

# second method: use the resample() method from raster package 

# Simple example: 
# This code produces a resampled raster, 's', 
# with correct resampled values. e.g.; 
# s[] prints a vector of resampled cell values. 

    r <- raster(nrow=3, ncol=3) 
    r[] <- 1:ncell(r) 
    s <- raster(nrow=10, ncol=10) 
    s <- resample(r, s, method='bilinear') 

# Useful example: 
# Resample a satellite image, stored in a GeoTiff file 
# into a NEW raster with 2x spatial resolution in 
# both dimensions (four times the number of cells) 
# Here is the technique: 
# 1) Create a new raster object with the correct 'resampled' number of cells. 
# 2) Set the extent (geographic 'bounding box') of the new raster 
#  to the extent of the original raster 
# 3) Generate the resampled raster. 

    resampleFactor <- .5 # reduce the cell size by 50% and double the number of rows and columns.  
    inputRaster <- raster("TmB50MosaicImg1.tif")  
    inCols <- ncol(inputRaster) 
    inRows <- nrow(inputRaster) 
    resampledRaster <- raster(ncol=(inCols/resampleFactor), nrow=(inRows/resampleFactor)) 
    extent(resampledRaster) <- extent(inputRaster) 

# The resample method will write the resampled raster image to a NEW disk file.. 

    resampledRaster <- resample(inputRaster,resampledRaster,datatype="INT1U",method='bilinear',filename="testOutResamp.tif",overwrite=TRUE) 

# Or, use writeRaster method to create the output file. 

    writeRaster(resampledRaster,filename="ResampleProduct.tif",format="GTiff",datatype="INT1U",overwrite=TRUE) 


# end 
} 

Answer 3

Recentemente ho avuto l'obbligo di ridurre la risoluzione di un oggetto ggmap. Ciò comporta l'estrazione e la trasformazione del raster ggmap (usando ggmap_rast()) di Robin Lovelace, aggregando il raster come discusso in questo thread, e quindi sostituendo il raster ad alta risoluzione ggmap con il raster di risoluzione inferiore qui sotto. Spero che questo sia utile:

original_map <- get_map("New York", scale = 1) #download a map at lowest resolution 
#ggmap_rast function courtesy of Robin Lovelace 
original_map.rast1 <- ggmap_rast(original_map) #extract RasterStack from ggmap object 

original_map.rast2 <- aggregate(original_map.rast, 2) #compress raster 

rast2_length <- sqrt(length(original_map.rast2)/3) ## find the number of cells in compressed raster 

map <- ggmap::ggmap(original_map) 
# from https://stackoverflow.com/questions/44225063/plot-ggmap-image-over-raster 
r <- map$layers[[2]]$geom_params$raster #pull hex raster pixel values from ggmap object 
#x <- r[,] #assign values to a variable (probably unnecessary) 

xx <- r[1:rast2_length,1:rast2_length] #filler values for raster to be created 

rgb_map <- original_map.rast2 

for(i in 1:rast2_length){ 
    for(j in 1:rast2_length){ 
    k=i*rast2_length+j 
    #many rgv values are non-integers; rgb2hex requires integer 
    red = as.integer(round(rgb_map$layer.1[k],0)) 
    green = as.integer(round(rgb_map$layer.2[k],0)) 
    blue = as.integer(round(rgb_map$layer.3[k],0)) 
    #rgb2hex from ggtern package 
    xx[i,j] <- rgb2hex(red,green,blue) 
    #rgb2hex is slow; export, calculate in excel, import is faster, sadly 
    #xx[i,j] <- as.character(rgb_from_excel$V1[k]) 
    } 
} 

map$layers[[2]]$geom_params$raster <- xx 
map