I have a set of .adf files which contain spatially distributed data. The files look like this:
dblbnd.adf
hdr.adf
metadata.xml
prj.adf
sta.adf
w001001.adf
w001001x.adf
I can read in the large file (w001001.adf) into R as a raster. This is the raster characteristics:
class : RasterLayer
dimensions : 1692, 3611, 6109812 (nrow, ncol, ncell)
resolution : 6, 6 (x, y)
extent : -10833.68, 10832.32, -4713.677, 5438.323 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : C:/Users/ahsa361/Documents/CEDS_Data/gridding-preprocessing/input/Corbett/w001001.adf
names : w001001
values : 0, 5637.337 (min, max)
I would like to convert this to NetCDF with dimensions of 180 deg lat. x 360 deg lon. at 0.5 deg resolution. How do I go about doing this?
You have an extent that does not match your coordinate reference system
#extent : -10833.68, 10832.32, -4713.677, 5438.323 (xmin, xmax, ymin, ymax)
#crs : +proj=longlat +datum=WGS84 +no_defs
Without more information it not really possible to help you much. Most likely the crs is wrong and you need to replace it with the correct one. Did you set it to the wrong value? (that is the most common cause of this problem). If you know what it should be, you can set it like this
crs(x) <- "correct crs definition"
It is also possible that the extent is wrong, and you could also set that to the right value. But based on the what you say, you do not know, so you need to figure these things out before we can help with how to fix your problem.
To get a global extent, and a resolution close to 0.1 degrees, you can do
extent(x) <- c(-180,180,-90,90)
Given the number of rows and columns you have, that would get you:
library(raster)
raster(nrow=1692, ncol=3611)
#class : RasterLayer
#dimensions : 1692, 3611, 6109812 (nrow, ncol, ncell)
#resolution : 0.09969538, 0.106383 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#crs : +proj=longlat +datum=WGS84 +no_defs
Related
Hi my raster values for a Raster Layer are the following:
dimensions : 2225, 2286, 5086350 (nrow, ncol, ncell)
resolution : 0.03333146, 0.03333146 (x, y)
extent : -20.86612, 55.32961, -35.40306, 38.75945 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs
source : solar.tif
names : solar
values : 0, 2855 (min, max)
However whenever I try to do simple raster operations such as:
plot(solar)
It returns this error:
Error in setValues(outras, m) :
could not find symbol "values" in environment of the generic function
Thanks for any help
I would like to identify the correct coordinate reference system for the following ASCII raster file:
class : RasterLayer
dimensions : 2160, 4320, 9331200 (nrow, ncol, ncell)
resolution : 0.0833333, 0.0833333 (x, y)
extent : -180, 179.9999, -90, 89.99993 (xmin, xmax, ymin, ymax)
coord. ref. : NA
data source : C:/popc_0AD.asc
names : popc_0AD
I tried to guess the correct projection by setting the CRS to some of the common formats and plotting it, as suggested in related posts. But I am still not sure about the correct setting. As far as I am concerned raster and related packages do not entail any function able to estimate missing CRS information. Do you have any idea what this raster file's CRS could be or how to find out?
The extent suggests coordinates are not projected. This seems to be the extent of Earth in degrees.
Then, you may want to use EPSG 4326, which is also crs="+proj=longlat +datum=WGS84 +no_defs":
library(raster)
r <- raster("0AD_lu/cropland0AD.asc")
projection(r) <- "+proj=longlat +datum=WGS84 +no_defs"
However, it is much better to use dataset correctly built with the coordinates reference system. It is never recommended to guess it... But I know that having clean metadata is not always possible...
You have
r <- raster(nrow=2160, ncol=4320, xmn=-180, xmx=179.9999, ymn=-90, ymx=89.99993, crs=NA)
Sébastien Rochette already pointed out that this is surely lon/lat and that you can set the CRS to relfect that
crs(r) <- "+proj=longlat +datum=WGS84"
It seems to me that the extent is a bit suspect. It looks like it is supposed to be a global raster, but that there has been some loss of precision. If so, you may correct that like this:
extent(r) <- c(-180, 180, -90, 90)
To get
r
#class : RasterLayer
#dimensions : 2160, 4320, 9331200 (nrow, ncol, ncell)
#resolution : 0.08333333, 0.08333333 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
I am in trouble making raster stack which have slightly different extent. The answer (1st one) given here is useful but did not help in my case. For example, I want to make a raster stack using bio2 raster for Australia and this Australian raster. The second raster comes for Australia only and the first one is global. So I cropped the global bio2 raster to the same extent of Australian raster using crop() function, but the resultant raster extent (i.e., bio2.au) is slightly different (therefore, I cannot make raster using the cropped raster and the Australian raster, awc). Sample code is below:
library(raster)
awc <- raster("path to Australian raster")
bio2.g <- raster("path to Bio2 global raster")
# crop bio2.g to the same extent of awc
bio2.au <- crop(bio2.g, extent(awc))
# make a raster stack
st <- stack(awc, bio2.au)
Error in compareRaster(x) : different extent
I have also tried using quick=TRUE within the stack() function. But in this case the cell values in awc is lost. Note: the size of awc raster is 4gb.
# first make a list of rasters saved in the computer
li <- list.files("path to file", pattern = ".tif$", full.names = TRUE)
st <- stack(li, quick=TRUE)
st[[1]] # no cell values for awc
Your suggestions will be highly appreciated. My ultimate goal is to crop several bioclim rasters to the same extent of Australian raster awc and stack them together so that raster cell values are not lost.
Edit (after comment of #Cobin):
Below is the attribute of each raster
# global raster (bigger raster)
> r
class : RasterLayer
dimensions : 21600, 43200, 933120000 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : D:\Worldclim2_Bioclim\wc2.0_bio_30s_02.tif
names : wc2.0_bio_30s_02
values : 0, 37.06667 (min, max)
# Australian raster (smaller raster)
> r1
class : RasterLayer
dimensions : 43201, 49359, 2132358159 (nrow, ncol, ncell)
resolution : 0.0008333333, 0.0008333333 (x, y)
extent : 112.8921, 154.0246, -44.00042, -7.999583 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : D:\SoilAWC5cm.EV1.tif
names : SoilAWC5cm.EV1
values : 2.997789, 27.86114 (min, max)
# new raster, after crop() function is applied
> r2 <- crop(r,extent(r1))
> r2
class : RasterLayer
dimensions : 4320, 4936, 21323520 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : 112.8917, 154.025, -44, -8 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : C:\Users\Anwar\AppData\Local\Temp\Rtmpmg9fyF\raster\r_tmp_2018-11-23_164300_11308_65747.grd
names : wc2.0_bio_30s_02
values : 1.933333, 18.15833 (min, max)
# rebuild r2 to match r1
> r22 <- raster(vals=values(r2),ext=extent(r1), nrows=dim(r1)[1],ncols=dim(r1)[2])
Error in setValues(r, vals) :
length(values) is not equal to ncell(x), or to 1
I suppose that the extent of two raster are differet though the raster masked by crop function.You
should check the both of awc and bio.au extent base on same reolution, rows and columns. Because I couldn't download data from
hyperlink, I give an example of my own data.
r <- raster('/big_raster')
r1 <- raster('/small_raster')
r2 <- crop(r,extent(r1))
r1
class : RasterLayer
dimensions : 74, 157, 11618 (nrow, ncol, ncell)
resolution : 0.0833333, 0.0833333 (x, y)
extent : 89.2185, 102.3018, 30.96238, 37.12905 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : D:\D\temp\Rtest\modis8km.tif
names : modis8km
values : -32768, 32767 (min, max)
r2
class : RasterLayer
dimensions : 74, 157, 11618 (nrow, ncol, ncell)
resolution : 0.08333333, 0.08333333 (x, y)
extent : 89.25, 102.3333, 31, 37.16667 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : g201401a
values : -32768, 7789 (min, max)
Though r1 and r1 with same resolution and dimension, the extent have tiny offset. It cause stack error.
stack(r1,r2)
Error in compareRaster(x) : different extent
So, you should rebuid the r2 to match r1:
r22 <- raster(vals=values(r2),ext=extent(r1),crs=crs(r1),
nrows=dim(r1)[1],ncols=dim(r1)[2])
Now stack(r22,r1) will be successful.
I have a raster and I am using the raster package.
class : RasterLayer
dimensions : 103, 118, 12154 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -83.075, -82.09167, 34.95833, 35.81667 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : C:\fb.tif
names : fdr_fb
values : 1, 128 (min, max)
I know how to subset and all. But how can I find the cellnumber (preferred) or cellvalue by using the Lat-Long value?
For example, I can find cell value using lat/long:
extract(ras,SpatialPoints(cbind(-82.8,35.2)))
But I want to find the cell number (row,col) corresponding to (Say) Long= -82.1 and Lat= 35.0
Raster: https://www.dropbox.com/s/8nhfirxr2hm3l4v/fdr_fb.tif?dl=0
To get the cell number from a point, you can do:
cellFromXY(ras, cbind(-82.8, 35.2))
If you have an Extent object e you can do:
cellsFromExtent(ras, e)
I have data from various Global Circulation Models (GCM) that I need in at a finer resolution to perturb climate observations that are 0.5 degree pixel. I saw that I could use disaggregate because this function won't change pixels values, as 'resample' does using, e.g., the bilinear method. But still, the output doesn't match my fine-res-grids.
Here an example with the dimensions of the files I'm dealing with:
r = raster(ncols=720, nrows=360) #fine resolution grid
r[] = runif(1:100)
> r
class : RasterLayer
dimensions : 360, 720, 259200 (nrow, ncol, ncell)
resolution : 0.5, 0.5 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : layer
values : 0.0159161, 0.9876637 (min, max)
s = raster(ncols=192, nrows=145) #dimensions of one of the GCM
s[] = runif(1:10)
> s
class : RasterLayer
dimensions : 145, 192, 27840 (nrow, ncol, ncell)
resolution : 1.875, 1.241379 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : layer
values : 0.03861309, 0.9744665 (min, max)
d=disaggregate(s, fact=c(3.75,2.482759)) #fact equals r/s for cols and rows
> d
class : RasterLayer
dimensions : 290, 768, 222720 (nrow, ncol, ncell)
resolution : 0.46875, 0.6206897 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : layer
values : 0.03861309, 0.9744665 (min, max)
The dimensions of 'd' are not equal to the dimensions of 'r', so I can't do operations with the 2 grids. And I'm not meant to be interpolating the pixel values. So, what's the best method to achieve the disaggregation with GCM data?
Thanks in advance.
The code below should help- it uses aggregate to the closest integer scaling possible then resample to match the other raster's spatial characteristics exactly:
r = raster(ncols=720, nrows=360) #fine resolution grid
r[] = runif(1:100)
s = raster(ncols=192, nrows=145) #dimensions of one of the GCM
s[] = runif(1:10)
d=disaggregate(s, fact=c(round(dim(r)[1]/dim(s)[1]),round(dim(r)[2]/dim(s)[2])), method='') #fact equals r/s for cols and rows
e=resample(d, r, method="ngb")
But there a few caveats/ warnings: If you want to have the same values as the original raster, use disaggregate with method='' or else it will interpolate. But most important looking at the aspect ratio between your r and s rasters, they are not the same: dim(r)[1]/dim(s)[1] != dim(r)[2]/dim(s)[2]). I would double check the original data because if there is a difference in resolution, projection, or extent you will not get what you want from the steps above.