blocks|key|128066|text|这里有一个如何做到这一点的例子。(link+to+original)|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|128067|++#########################################################################
#+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:+%25s",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%25s",inFiles[iCtr])+
++++++writeRaster(CoarseResampRaster,filename=sOutFile,format="GTiff",datatype="INT1U",overwrite=TRUE)++++++
++++++FineResampRaster+<-+disaggregate(inputRaster,fact=resampleFactor,fun=mean)+
++++++sOutFile+<-+sprintf("FineSubsamp%25s",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%25+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
}|code-block|syntax|javascript|128068|entityMap|0|LINK|mutability|MUTABLE|url|https://www.nceas.ucsb.edu/scicomp/usecases/resamplerasterimages^0|G|I|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

Here is an example of how to do this. <a href="https://www.nceas.ucsb.edu/scicomp/usecases/resamplerasterimages" rel="noreferrer">(link to original)</a>

<pre><code> #########################################################################
# 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 &lt;- function()
{
 library(raster)

 sOutFile &lt;- ""
 resampleFactor &lt;- 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 &lt;- list.files(pattern="*.tif")
 nFiles &lt;- length(inFiles)
 inputRaster &lt;- raster()
 CoarseResampRaster &lt;- raster() 
 FineResampRaster &lt;- raster() 
 for (iCtr in 1 : nFiles)
 {
 message(sprintf("resampling file: %s",inFiles[iCtr]))
 inputRaster &lt;- raster(inFiles[iCtr])

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

 CoarseResampRaster &lt;- aggregate(inputRaster,fact=resampleFactor,fun=mean) 
 sOutFile &lt;- sprintf("CoarseSubsamp%s",inFiles[iCtr]) 
 writeRaster(CoarseResampRaster,filename=sOutFile,format="GTiff",datatype="INT1U",overwrite=TRUE) 
 FineResampRaster &lt;- disaggregate(inputRaster,fact=resampleFactor,fun=mean) 
 sOutFile &lt;- 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 &lt;- raster(nrow=3, ncol=3)
 r[] &lt;- 1:ncell(r)
 s &lt;- raster(nrow=10, ncol=10)
 s &lt;- 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 &lt;- .5 # reduce the cell size by 50% and double the number of rows and columns. 
 inputRaster &lt;- raster("TmB50MosaicImg1.tif") 
 inCols &lt;- ncol(inputRaster)
 inRows &lt;- nrow(inputRaster)
 resampledRaster &lt;- raster(ncol=(inCols / resampleFactor), nrow=(inRows / resampleFactor))
 extent(resampledRaster) &lt;- extent(inputRaster)

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

 resampledRaster &lt;- 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
}
</code></pre>

blocks|key|2901282|text|您可以使用aggregate或disaggregate。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2901283|library(raster)

#get+some+sample+data
data(meuse.grid)
gridded(meuse.grid)+<-+~x%2By
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|code-block|syntax|javascript|2901284|entityMap|0|LINK|mutability|MUTABLE|url|https://www.rdocumentation.org/packages/raster/versions/1.0.0-1/topics/aggregate|1|https://www.rdocumentation.org/packages/raster/versions/1.0.0-1/topics/disaggregate^0|5|9|0|F|C|1|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]|$A|W|B|X|1|Y]]|C|$]]|$1|D|3|E|5|F|7|Z|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|10|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]|Q|$5|L|M|N|C|$O|R]]]]

You can use <a href="https://www.rdocumentation.org/packages/raster/versions/1.0.0-1/topics/aggregate" rel="noreferrer">aggregate</a> or <a href="https://www.rdocumentation.org/packages/raster/versions/1.0.0-1/topics/disaggregate" rel="noreferrer">disaggregate</a>.

<pre><code>library(raster)

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

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

#disaggregate from 40x40 resolution to 10x10 (factor = 4)
meuse.raster.disaggregate &lt;- disaggregate(meuse.raster, fact=4)
res(meuse.raster.disaggregate)
#[1] 10 10
</code></pre>

blocks|key|130227|text|我最近要求降低ggmap对象的分辨率。这涉及到提取和转换ggmap栅格(使用Robin+Lovelace的ggmap_rast())，按照本文讨论的方式聚合栅格，然后用下面较低分辨率的栅格替换ggmap高分辨率栅格。希望这能对你有所帮助：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|130228|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%2Bj
++++#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|code-block|syntax|javascript|130229|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I recently had a requirement to reduce the resolution of a ggmap object. This involves extracting and transforming the ggmap raster (using Robin Lovelace's ggmap_rast()), aggregating the raster as discussed in this thread, and then replacing the ggmap high resolution raster with the lower resolution raster below. Hope this is useful:

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

original_map.rast2 &lt;- aggregate(original_map.rast, 2) #compress raster

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

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

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

rgb_map &lt;- 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$layer.3[k],0))
 #rgb2hex from ggtern package
 xx[i,j] &lt;- rgb2hex(red,green,blue)
 #rgb2hex is slow; export, calculate in excel, import is faster, sadly
 #xx[i,j] &lt;- as.character(rgb_from_excel$V1[k])
 }
}

map$layers[[2]]$geom_params$raster &lt;- xx
map
</code></pre>

blocks|key|127105|text|我已经尝试了3种不同的方法来放大DEM文件。首先，我像这样使用gdal_translate：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|127106|from+CMD:
gdal_translate+-tr+0.1+0.1+"C:\dem.tif"+"C:\dem_0.1.tif"|code-block|syntax|javascript|127107|在R中：|127108|res(raster('C:\dem_0.1.tif')
[1]+0.1+0.1|127109|然后，我尝试了R中raster包中的aggregate和resample命令：|127110|#
r+<-+raster("dem.tif")
>+res(r)
[1]+0.0002777778+0.0002777778+#+original+dem+resolution
#
r_up_0.1+<-+aggregate(r,+fact+=+0.1/res(r))+#+aggregate+output
>+res(r_up_0.1)
[1]+0.1+0.1
#
s+<-+raster(nrow+=+10,+ncol+=+10)
extent(s)+<-+extent(r)
s+<-+resample(r,+s,+method+=+'bilinear')+#+resample+output
>+res(s)
[1]+0.1000278+0.1000278|127111|以下是输出：|127112|📷|atomic|127113|这3个输出被提升为res+=+0.1x0.1，它们之间有一些差异，但我打算使用gdal输出。|127114|希望这能有所帮助。|127115|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/6e4f8e081a188daea24afe6b5708d723.png|imageAlt^0|V|E|0|0|0|0|9|6|I|9|S|8|0|0|0|0|1|0|0|9|D|0|0^^$0|@$1|2|3|4|5|6|7|1A|8|@$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1D|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1E|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|1F|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1G|8|@$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]|$9|1L|A|1M|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1N|8|@]|D|@]|E|$I|J]]|$1|S|3|T|5|6|7|1O|8|@]|D|@]|E|$]]|$1|U|3|V|5|W|7|1P|8|@]|D|@$9|1Q|A|1R|1|1S]]|E|$]]|$1|X|3|Y|5|6|7|1T|8|@$9|1U|A|1V|B|C]]|D|@]|E|$]]|$1|Z|3|10|5|6|7|1W|8|@]|D|@]|E|$]]|$1|11|3|-4|5|6|7|1X|8|@]|D|@]|E|$]]]|12|$13|$5|14|15|16|E|$17|18|19|-4]]]]

I've tried 3 different options for upscaling a DEM file. First I used <code>gdal_translate</code> like this:

<pre><code>from CMD:
gdal_translate -tr 0.1 0.1 "C:\dem.tif" "C:\dem_0.1.tif"
</code></pre>

In R:

<pre><code>res(raster('C:\dem_0.1.tif')
[1] 0.1 0.1
</code></pre>

Then I tried both <code>aggregate</code> and <code>resample</code> commands from <code>raster</code> package in R:

<pre><code>#
r &lt;- raster("dem.tif")
&gt; res(r)
[1] 0.0002777778 0.0002777778 # original dem resolution
#
r_up_0.1 &lt;- aggregate(r, fact = 0.1/res(r)) # aggregate output
&gt; res(r_up_0.1)
[1] 0.1 0.1
#
s &lt;- raster(nrow = 10, ncol = 10)
extent(s) &lt;- extent(r)
s &lt;- resample(r, s, method = 'bilinear') # resample output
&gt; res(s)
[1] 0.1000278 0.1000278
</code></pre>

These are the outputs:
<img src="https://i.imgur.com/rxIwQLo.png" alt="">
The 3 outputs were upscaled to a <code>res = 0.1x0.1</code> and there are some differences between them but I intend to use the gdal output. 

Hope this helps.

blocks|key|2688799|text|这是一个迟来的答案，但创建一个模板栅格和使用projectRaster是非常简单的。您可以将模板栅格分辨率设置为您想要的任何值，如果您想要矩形单元格而不是方形单元格，则包括两个值：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2688800|#+Create+the+template+raster,+with+120m+cells
TEMPLATE.RASTER+<-+raster(extent(ORIGINAL.RASTER),+resolution+=+120,+
++++++++++++++++++++++++++crs+=+st_crs(ORIGINAL.RASTER)$proj4string)

#+Project+the+original+raster+to+the+new+resolution
PROJECTED.RASTER+<-+projectRaster(from+=+ORIGINAL.RASTER,+to+=+TEMPLATE.RASTER)|code-block|syntax|javascript|2688801|我喜欢创建一个模板，因为它也会复制目标CRS。但是，您实际上可以跳过创建栅格模板，直接在projectRaster中设置分辨率和CRS。一定要检查帮助文件，因为有可能需要考虑的计算方法等选项。|2688802|entityMap^0|M|D|0|0|18|D|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|V|8|@]|D|@]|E|$]]]|N|$]]

This is a late answer, but creating a template raster and using <code>projectRaster</code> is very straightforward. You can set the template raster resolution to any value you would like, including two values if you want rectangular rather than square cells:
<pre><code># Create the template raster, with 120m cells
TEMPLATE.RASTER &lt;- raster(extent(ORIGINAL.RASTER), resolution = 120, 
 crs = st_crs(ORIGINAL.RASTER)$proj4string)

# Project the original raster to the new resolution
PROJECTED.RASTER &lt;- projectRaster(from = ORIGINAL.RASTER, to = TEMPLATE.RASTER)
</code></pre>
I like to create a template, as it copies over the target CRS as well. However, you can actually skip creating a template raster and set the resolution and CRS directly in <code>projectRaster</code>. Do check the help files, as there are options for calculation method etc which might be important to consider.

blocks|key|127146|text|现在，我们可以使用raster包的替代品terra|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|127147|示例数据|127148|library(terra)
f+<-+system.file("ex/elev.tif",+package="terra")
r+<-+rast(f)|code-block|syntax|javascript|127149|聚合栅格单元。从30米到120米是4倍|127150|a1+<-+aggregate(r,+4)|127151|您可以对行和列使用不同的因子，也可以使用不同的聚合函数(缺省值为"mean")|127152|a2+<-+aggregate(r,+c(2,3),+fun=sum)|127153|您也可以通过另一种方式进行解聚：|127154|d+<-+disagg(a2,+2)|127155|聚合只能合并整个单元格。但是，要合并来自不同源的光栅数据，可能需要匹配未对齐的光栅的几何图形。在这种情况下，您可以使用resample|127156|未对齐的SpatRaster示例|127157|x+<-+rast(r)
res(x)+<-+0.01|127158|解决方案|127159|x+<-+resample(r,+x)|127160|也可能是您想要将光栅数据转换为具有另一个坐标参考系的几何图形(“地图投影”)。你可以这样做：|127161|u1+<-+project(r,+"%2Bproj=utm+%2Bzone=32")|127162|但是，与矢量数据不同，栅格数据的转换定义不明确。因此，首选方法是提供您希望输出与之对齐的模板。这通常是您已经从另一个数据源获得的SpatRaster。但是在这里，我为这个例子创建了一个：|127163|temp+<-+rast(xmin=264000,+xmax=324000,+ymin=5479000,+ymax=5565000,+res=100,+crs="%2Bproj=utm+%2Bzone=32")|127164|现在使用它|127165|u2+<-+project(r,+temp)|127166|进一步的reading|127167|entityMap|0|LINK|mutability|MUTABLE|url|https://rspatial.org/terra/spatial/6-crs.html^0|9|6|K|5|0|0|0|0|0|0|0|0|0|1N|8|0|4|A|0|0|0|0|0|0|1S|A|0|0|0|0|4|7|0|0^^$0|@$1|2|3|4|5|6|7|1U|8|@$9|1V|A|1W|B|C]|$9|1X|A|1Y|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|1Z|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|20|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|21|8|@]|D|@]|E|$]]|$1|O|3|P|5|J|7|22|8|@]|D|@]|E|$K|L]]|$1|Q|3|R|5|6|7|23|8|@]|D|@]|E|$]]|$1|S|3|T|5|J|7|24|8|@]|D|@]|E|$K|L]]|$1|U|3|V|5|6|7|25|8|@]|D|@]|E|$]]|$1|W|3|X|5|J|7|26|8|@]|D|@]|E|$K|L]]|$1|Y|3|Z|5|6|7|27|8|@$9|28|A|29|B|C]]|D|@]|E|$]]|$1|10|3|11|5|6|7|2A|8|@$9|2B|A|2C|B|C]]|D|@]|E|$]]|$1|12|3|13|5|J|7|2D|8|@]|D|@]|E|$K|L]]|$1|14|3|15|5|6|7|2E|8|@]|D|@]|E|$]]|$1|16|3|17|5|J|7|2F|8|@]|D|@]|E|$K|L]]|$1|18|3|19|5|6|7|2G|8|@]|D|@]|E|$]]|$1|1A|3|1B|5|J|7|2H|8|@]|D|@]|E|$K|L]]|$1|1C|3|1D|5|6|7|2I|8|@$9|2J|A|2K|B|C]]|D|@]|E|$]]|$1|1E|3|1F|5|J|7|2L|8|@]|D|@]|E|$K|L]]|$1|1G|3|1H|5|6|7|2M|8|@]|D|@]|E|$]]|$1|1I|3|1J|5|J|7|2N|8|@]|D|@]|E|$K|L]]|$1|1K|3|1L|5|6|7|2O|8|@]|D|@$9|2P|A|2Q|1|2R]]|E|$]]|$1|1M|3|-4|5|6|7|2S|8|@]|D|@]|E|$]]]|1N|$1O|$5|1P|1Q|1R|E|$1S|1T]]]]

These days we can use <code>terra</code>, the replacement for the <code>raster</code> package
Example data
<pre><code>library(terra)
f &lt;- system.file(&quot;ex/elev.tif&quot;, package=&quot;terra&quot;)
r &lt;- rast(f)
</code></pre>
Aggregate raster cells. To go from 30 to 120m is a factor of 4
<pre><code>a1 &lt;- aggregate(r, 4)
</code></pre>
You can use a different factor for the rows and columns, and also different aggregation functions (the default is &quot;mean&quot;)
<pre><code>a2 &lt;- aggregate(r, c(2,3), fun=sum)
</code></pre>
You can also go the other way and disaggregate:
<pre><code>d &lt;- disagg(a2, 2)
</code></pre>
Aggregate can only combine entire cells. But to combine raster data from different sources, you may need to match the geometry of a raster that does not align. In that case you can use <code>resample</code>
Example of a non-aligned <code>SpatRaster</code>:
<pre><code>x &lt;- rast(r)
res(x) &lt;- 0.01
</code></pre>
Solution
<pre><code>x &lt;- resample(r, x)
</code></pre>
It could also be that you want to transform raster data to a geometry with another coordinate reference system (&quot;map projection&quot;). You can do that like this:
<pre><code>u1 &lt;- project(r, &quot;+proj=utm +zone=32&quot;)
</code></pre>
But, unlike for vector data, the transformation of raster data is not well-defined. Therefore, the preferred approach is to provide a template to which you want the output to align with. This would normally be a <code>SpatRaster</code> that you already have from another data source. But here I create one for the example:
<pre><code>temp &lt;- rast(xmin=264000, xmax=324000, ymin=5479000, ymax=5565000, res=100, crs=&quot;+proj=utm +zone=32&quot;)
</code></pre>
And now use it
<pre><code>u2 &lt;- project(r, temp)
</code></pre>
Further <a href="https://rspatial.org/terra/spatial/6-crs.html" rel="nofollow noreferrer">reading</a>

I have several high resolution raster layers in R that I am working with. The level of detail is excessive for some of the analyses I am running, so I would like to speed things up by reducing the resolution. 

The coordinate system is UTM so the units are meters. The resolution says it is 30, 30 (x, y). So it seems that the resolution here is 30m. 

Could someone please advise me on how to change the resolution to 120m instead? I have read the help for the resample() and projectRaster() functions but they seem to require a template raster with the desired resolution, which I don't have.

Here is an example of one of my raster layers:

<blockquote>
 alt.utm 
 class : RasterLayer 
 dimensions : 4572, 2495, 11407140 (nrow, ncol, ncell) 
 resolution : 30, 30 (x, y) 
 extent : 421661, 496511, 4402939, 4540099 (xmin, xmax, ymin, ymax) 
 coord. ref. : +proj=utm +zone=13 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs 
 data source : in memory 
 names : layer 
 values : 1485.127, 4275.202 (min, max) 
</blockquote>

How to change the resolution of a raster layer in R

数据转换

我在R中有几个高分辨率的栅格层，我正在处理。对于我正在运行的一些分析来说，细节级别过高，所以我想通过降低分辨率来加快速度。坐标系是UTM，所以单位是米。分辨率是30，30 (x，y)。所以看起来这里的分辨率是30m。有没有人可以建议我如何将分辨率改为120m？我已经阅读了resample()和projectRaster...

问如何在R中更改栅格图层的分辨率
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