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I am reading a raster file using both terra and raster package
library(raster)
library(terra)
fl_terra <- terra::rast('my_raster.tif')
fl_raster <- raster::raster('my_raster.tif')
fl_terra
class : SpatRaster
dimensions : 157450, 327979, 1 (nrow, ncol, nlyr)
resolution : 0.0002777778, 0.0002777778 (x, y)
extent : -142.4107, -51.30541, 41.12569, 84.86181 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326)
source : xxxx.tif
categories : DepthBand
name : DepthBand
min value : 0.0m <= x <= 0.3m
max value : x > 9.0m
fl_raster
class : RasterLayer
dimensions : 157450, 327979, 51640293550 (nrow, ncol, ncell)
resolution : 0.0002777778, 0.0002777778 (x, y)
extent : -142.4107, -51.30541, 41.12569, 84.86181 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : xxxx.tif
names : DepthBand
values : 1, 6 (min, max)
Why is reading the same file using the two packages showing different values?
If I open the same file in QGIS, the legend displays the values present in the fl_raster i.e. using the raster package.
The file has categorical values. fl_terra tells you that:
categories : DepthBand
And it also properly shows the (alphabetical) range of the categories (and it also uses them in the legend when using plot, and returns them when extracting cell values).
name : DepthBand
min value : 0.0m <= x <= 0.3m
max value : x > 9.0m
If you do not care about the categories, you can remove them with
levels(fl_terra) <- NULL
In contrast, fl_raster shows the numerical codes that are used to represent the categories (because the raster files can only store numeric cell values).
values : 1, 6 (min, max)
That makes it look like you have numeric values. But that is not the case.
The situation is similar to having a factor in R
f <- as.factor(c("red", "blue", "green"))
"terra" would show the category labels
f
#[1] red blue green
#Levels: blue green red
Whereas "raster" would show the equivalent of
as.integer(f)
#[1] 3 1 2
I have a SpatRaster in the following dimension
layer1
class : SpatRaster
dimensions : 3600, 3600, 1 (nrow, ncol, nlyr)
resolution : 0.0002777778, 0.0002777778 (x, y)
extent : -81.00014, -80.00014, 24.99986, 25.99986 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326)
source : nn.tif
name : nn_1
I have another SpatRaster which has data for revenue (in dollar) at a different resolution than layer1
layer2
class : SpatRaster
dimensions : 21600, 43200, 1 (nrow, ncol, nlyr)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84
source : XX.nc
varname : XX
name : XX
unit : US dollar
What I want to do is to take the average of layer1 using layer2 as weights. This is my approach:
Give them the same projections.
layer2 <- project(layer2, crs(layer1))
Clip layer2 to match the extent of layer1
r <- terra::crop(layer2, ext(layer1))
r <- terra::mask(r, layer1) # I get error in this step
[mask] number of rows and/or columns do not match
Suppose step 2 worked. Now I want to disaggregate clipped layer2 to match the resolution of layer1 i.e. 30 times finer.
This means revenue of each disaggregated cell should be 1/30 of the revenue of the parent cell.
Is the below the right way to do this:
r_disagg <- disaggregate(r, fact = 30)/30
Divide each cell value in r_disagg with total sum of r_disagg to get a weight raster r_disagg_wt
Multiply r_disagg_wt with layer1 and add the resulting raster to get the weighted average of layer1
I am stuck in step 2 and 3
When asking an R question, please include example data like this
Example data
library(terra)
r1 <- rast(nrow=360, ncol=360, ext=c(-81.00014, -80.00014, 24.99986, 25.99986))
r2 <- rast(nrow=2160, ncol=4320)
values(r1) <- 1:ncell(r1)
set.seed(0)
values(r2) <- runif(ncell(r2))
Solution
re2 <- resample(r2, r1)
global(r1, fun="mean", weights=re2)
# weighted_mean
#lyr.1 66190.15
In cases where the two input rasters align, it could be preferable to use disagg instead of resample (in this case, the extent of r1 is a bit odd, it seems to be shifted with half a cell).
There is no need to adjust the dollar values if they are only used as weights. In cases that the sum of the cell values should remain constant, when using resample or project, you could compute the density ($/km2) before resampling by dividing the values with the area of each cell (see cellSize) and multiply the values again with the area of the new cells after resampling.
I want to crop an elevation raster to add it to a raster stack. It's easy, I did this before smoothly, adding a ecoregions raster to the same stack. But with the elevation one, just doesn't work. Now, there are several questions here in overflow adressing this issue and I tryed a lot of things...
First of all, we need this:
library(rgdal)
library(raster)
My stack is predictors2:
#Downloading the stack
predictors2_full<-getData('worldclim', var='bio', res=10)
#Cropping it, I don' need the whole world
xmin=-120; xmax=-35; ymin=-60; ymax=35
limits <- c(xmin, xmax, ymin, ymax)
predictors2 <- crop(predictors2_full,limits)
Then I've downloaded the terr_ecorregions shapefile here: http://maps.tnc.org/files/shp/terr-ecoregions-TNC.zip
setwd("~/ORCHIDACEAE/Ecologicos/w2/layers/terr-ecoregions-TNC")
ecoreg = readOGR("tnc_terr_ecoregions.shp") # I've loaded...
ecoreg2 <- crop(ecoreg,extent(predictors2)) # cropped...
ecoreg2 <- rasterize(ecoreg2, predictors2) # made the shapefile a raster
predictors4<-addLayer(predictors2,elevation,ecoreg2) # and added the raster
# to my stack
With elevation, I just can't. The Digital elevation model is based in GMTED2010, which can be downloaded here: http://edcintl.cr.usgs.gov/downloads/sciweb1/shared/topo/downloads/GMTED/Grid_ZipFiles/mn30_grd.zip
elevation<-raster("w001001.adf") #I've loaded
elevation<-crop(elevation,predictors2) # and cropped
But elevation gets a slightly different extent instead of predictors2's extent:
> extent(elevation)
class : Extent
xmin : -120.0001
xmax : -35.00014
ymin : -60.00014
ymax : 34.99986
>
I tried to make then equal by all means I read about in questions here...
I tried to extend so elevation's ymax would meet predictors2's ymax
elevation<-extend(elevation,predictors2) #didn't work, extent remains the same
I tried the opposite... making predictors2 extent meet elevation's extent... nothing either.
But then I read that
You might not want to play with setExtent() or extent() <- extent(), as you could end with wrong geographic coordinates of your rasters - #ztl, Jun 29 '15
And I tried to get the minimal common extent of my rasters, following #zlt answer in another extent question, by doing this
# Summing your rasters will only work where they are not NA
r123 = r1+r2+r3 # r123 has the minimal common extent
r1 = crop(r1, r123) # crop to that minimal extent
r2 = crop(r2, r123)
r3 = crop(r3, r123)
For that, first I had to set the resolutions:
res(elevation)<-res(predictors2) #fixing the resolutions... This one worked.
But then, r123 = r1+r2+r didn't work:
> r123=elevation+ecoreg2+predictors2
Error in elevation + ecoreg2 : first Raster object has no values
Can anyone give me a hint on this? I really would like to add my elevation to the raster. Funny thing is, I have another stack named predictors1 with the exact same elevation's extent... And I was able to crop ecoreg and add ecoreg to both predictors1 and predictors2... Why can't I just do the same to elevation?
I'm quite new to this world and runned out of ideas... I appreciate any tips.
EDIT: Solution, Thanks to #Val
I got to this:
#Getting the factor to aggregate (rasters are multiples of each other)
res(ecoreg2)/res(elevation)
[1] 20 20 #The factor is 20
elevation2<-aggregate(elevation, fact=20)
elevation2 <- crop(elevation2,extent(predictors2))
#Finally adding the layer:
predictors2_eco<-addLayer(predictors2,elevation2,ecoreg)
New problem, thought...
I can't write stack to a geotiff
writeRaster(predictors2_eco, filename="cropped_predictors2_eco.tif", options="INTERLEAVE=BAND", overwrite=TRUE)
Error in .checkLevels(levs[[j]], value[[j]]) :
new raster attributes (factor values) should be in a data.frame (inside a list)
I think you're having issues because you're working with rasters of different spatial resolutions. So when you crop both rasters to the same extent, they'll have a slightly different actual extent because of that.
So if you want to stack rasters, you need to get them into the same resolution. Either you disaggregate the raster with the coarser resolution (i.e. increase the resolution by resampling or other methods) or you aggregate the raster with the higher resolution (i.e. decrease the resolution with for instance taking the mean over n pixel).
Please note that if you change the extent or resolution with setExtent(x), extent(x) <-, res(x) <- or similar will NOT work, since you're just changing slots in the raster object, not the actual underlying data.
So to bring the rasters into a common resolution, you need to change the data. You can use the functions (amongst others) aggregate, disaggregate and resample for that purpose. But since you're changing data, you need to be clear on what you're and the function you use is doing.
The most handy way for you should be resample, where you can resample a raster to another raster so they match in extent and resolution. This will be done using a defined method. Per default it's using nearest neighbor for computing the new values. If you're working with continuous data such as elevation, you might want to opt for bilinear which is bilinear interpolation. In this case you're actually creating "new measurements", something to be aware of.
If your two resolutions are multiples of each other, you could look into aggregate and disaggregate. In the case of disaggregate you would split a rastercell by a factor to get a higher resolution (e.g. if your first resolution is 10 degrees and your desired resolution is 0.05 degrees, you could disaggregate with a factor of 200 giving you 200 cells of 0.05 degree for every 10 degree cell). This method would avoid interpolation.
Here's a little working example:
library(raster)
library(rgeos)
shp <- getData(country='AUT',level=0)
# get centroid for downloading eco and dem data
centroid <- coordinates(gCentroid(shp))
# download 10 degree tmin
ecovar <- getData('worldclim', var='tmin', res=10, lon=centroid[,1], lat=centroid[,2])
ecovar_crop <- crop(ecovar,shp)
# output
> ecovar_crop
class : RasterBrick
dimensions : 16, 46, 736, 12 (nrow, ncol, ncell, nlayers)
resolution : 0.1666667, 0.1666667 (x, y)
extent : 9.5, 17.16667, 46.33333, 49 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9, tmin10, tmin11, tmin12
min values : -126, -125, -102, -77, -33, -2, 19, 20, 5, -30, -74, -107
max values : -31, -21, 9, 51, 94, 131, 144, 137, 106, 60, 18, -17
# download SRTM elevation - 90m resolution at eqt
elev <- getData('SRTM',lon=centroid[,1], lat=centroid[,2])
elev_crop <- crop(elev, shp)
# output
> elev_crop
class : RasterLayer
dimensions : 3171, 6001, 19029171 (nrow, ncol, ncell)
resolution : 0.0008333333, 0.0008333333 (x, y)
extent : 9.999584, 15.00042, 46.37458, 49.01708 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : srtm_39_03
values : 198, 3865 (min, max)
# won't work because of different resolutions (stack is equal to addLayer)
ecoelev <- stack(ecovar_crop,elev_crop)
# resample
elev_crop_RS <- resample(elev_crop,ecovar_crop,method = 'bilinear')
# works now
ecoelev <- stack(ecovar_crop,elev_crop_RS)
# output
> ecoelev
class : RasterStack
dimensions : 16, 46, 736, 13 (nrow, ncol, ncell, nlayers)
resolution : 0.1666667, 0.1666667 (x, y)
extent : 9.5, 17.16667, 46.33333, 49 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
names : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9, tmin10, tmin11, tmin12, srtm_39_03
min values : -126.0000, -125.0000, -102.0000, -77.0000, -33.0000, -2.0000, 19.0000, 20.0000, 5.0000, -30.0000, -74.0000, -107.0000, 311.7438
max values : -31.000, -21.000, 9.000, 51.000, 94.000, 131.000, 144.000, 137.000, 106.000, 60.000, 18.000, -17.000, 3006.011
I ran some species distribution models (SDM), using maxent of the 'dismo' package for several species, so I saved each one as a raster file.
After that I transformed this environmental suitability raster (SDM result) into a binary map based on a determined threshold value.
I used this function and these command lines to do the conversion:
bin <- function(x) {
ifelse(x <= 0.7, 0,
ifelse(x > 0.7, 1, NA)) }
sp_bin <- calc(spp, fun=bin) # "spp" is the object within my raster
writeRaster(sp_bin,filename='Results/RasterBin/Patagioenas_fasciata_bin.tif')
Now I have a raster of that binary map (i.e., with only two values, 0 and 1).
class : RasterLayer
dimensions : 1117, 1576, 1760392 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -68.39167, -55.25833, -0.7333333, 8.575 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : C:\Users\Ramiro\Desktop\SDM\Results\RasterBin\Patagioenas_fasciata_bin.tif
names : Patagioenas_fasciata_bin
values : 0, 1 (min, max)
I tried to use the following argument to bring the sum of the pixels with value 1, assuming that the others would be 0 and therefore the total sum would equal the area of all pixels with values 1:
cellStats(spp, stat='sum', na.rm=TRUE)
And I got a numerical result (12,471) that I thought was correct. However, I did this for other species, and therefore other rasters, and the result remains the same, although each species has a distinct binary map.
How can I calculate the area only for pixels with value "1" in a binary map raster in R?
Thank you all for the attention.
I am finding the average production days per year for maple syrup. My maple distribution data is in an ascii file. I have a raster (created from NetCDF files) called brick.Tmax. I want to match the specs of brick.Tmax to my maple distribution data.
## These are the specs I want to use for my maple distribution
brick.Tmax
class : RasterBrick
dimensions : 222, 462, 102564, 366 (nrow, ncol, ncell, nlayers)
resolution : 0.125, 0.125 (x, y)
extent : -124.75, -67, 25.125, 52.875 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : E:\all_files\gridded_obs.daily.Tmax.1980.nc
names : X1980.01.01, X1980.01.02, X1980.01.03, X1980.01.04, X1980.01.05, X1980.01.06, X1980.01.07, X1980.01.08, X1980.01.09, X1980.01.10, X1980.01.11, X1980.01.12, X1980.01.13, X1980.01.14, X1980.01.15, ...
Date : 1980-01-01, 1980-12-31 (min, max)
varname : Tmax
## reading in red maple data from ascii file into rasterLayer
red_raster <- raster("E:/all_files/Maple_Data/redmaple.asc")
red_raster
class : RasterLayer
dimensions : 140, 150, 21000 (nrow, ncol, ncell)
resolution : 20000, 20000 (x, y)
extent : -1793092, 1206908, -1650894, 1149106 (xmin, xmax, ymin, ymax)
coord. ref. : NA
data source : E:\all_files\Maple_Data\redmaple.asc
names : redmaple
values : -2147483648, 2147483647 (min, max)
How can I project all specs (dimension, crs, resoluion, and extent) from brick.Tmax onto red_raster, while still preserving the values of red_raster? It seems like the two are mutually exclusive.
NOTE: In an attempt to streamline my question, I edited my question quite a bit from my original post, so apologies if the comments below are confusing in the current context. (I removed the raster prodavg_rastwhich was acting like a middleman).
The two rasters clearly do not have the same extent. In fact are in different universes (coordinate reference systems). brick.Tmax has angular (longitude/latitude) coordinates: +proj=longlat +datum=WGS84
but red_raster clearly does not given extent : -1793092, 1206908, -1650894, 1149106. So to use these data together, one of the two needs to be transformed (projected into the the coordinate reference system of the other). The problem is that we do not know what the the crs of red_raster is (esri ascii files do not store that information!). So you need to find out what it is from your data source, or by guessing giving the area covered and conventions. After you find out, you could do something like:
library(raster)
tmax <- raster(nrow=222, ncol=462, xmn=-124.75, xmx=-67, ymn=25.125, ymx=52.875, crs="+proj=longlat +datum=WGS84")
red <- raster(nrow=140, ncol=150, xmn=-1793092, xmx=1206908, ymn=-1650894, ymx=1149106, crs=NA)
crs(red) <- " ?????? "
redLL <- projectRaster(red, tmax)
Projectiong rasters takes time. A good way to test whether you figured out the crs would be to transform some polygons that can show whether things align.
library(rgdal)
states <- shapefile('states.shp')
sr <- spTransform(states, crs(red)
plot(red)
plot(sr, add=TRUE)