Unable to do raster operations in R - r

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

Related

How to compute r2 from two rasters in R?

I have two rasters, and I would like to see a corelation between the two, and obtain a r2.
TOTAL2
class : RasterLayer
dimensions : 2803, 5303, 14864309 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : 60.85, 105.0417, 15.95833, 39.31667 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : memory
names : layer
values : 0, 400 (min, max)
> lpjENLF$VegCX2X0.7
class : RasterLayer
dimensions : 2803, 5303, 14864309 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : 60.85, 105.0417, 15.95833, 39.31667 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : memory
names : VegCX2X0.7
values : 1.874989e-05, 350 (min, max)
How can I compute an r2 value between these two rasters? I have tried to convert both of the rasters into dataframes, but both of the dataframes return as NA. Then I apply, na.rm=T and try to find the r2 but the length of the dataframes for both rasters become different. The second solution I tried was stacking both of the rasters and applying this code:
layerStats(rasterstack,'pearson')
however I obtain :
$`pearson correlation coefficient`
VegCX2X0.7 layer
VegCX2X0.7 NA NA
layer NA NA
$mean
VegCX2X0.7 layer
NA NA
Option 1: You can use na.rm in layerStats
layerStats(rasterstack, 'pearson', na.rm=T)
Option 2: You can first extract the values from the raster objects and apply the build in function cor. With this function you should add the argument use="complete.obs" to get it working withNA` values.
cor(values(TOTAL2), values(lpjENLF$VegCX2X0.7), use="complete.obs", method = 'pearson')

Make raster stack with different extent

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.

Missing Values After Applying Mask Function

I have a raster called 'xx' and is the output from using the 'Distance' function within R Raster. As part of running the distance function, the ocean/sea surrounding my island has been set to a value of 0. In order to change this value from 0 back to NA, I have used the function 'Mask' like so:
asdf<-mask(xx,jamaica.raster, filename="distancesurface.asc", prj=TRUE, keepres=TRUE, overwrite=TRUE)
In the above example, jamaica.raster is being used to replace the 0 values with NA. I run the command and plot the result. As expected, it has left my distance surface in place and set the ocean to NA, exactly as I wanted.
After, when I attempt to writeRaster the object 'asdf' I get the error:
Error in .local(.Object, ...) :
Couldn't find data values in ASCII Grid file.
If I get a summary of the objects 'xx' and 'jamaica.raster' before applying 'mask' function, both have a 'values' section like so:
class : RasterLayer
dimensions : 135, 306, 41310 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -78.49359, -75.94359, 17.59729, 18.72229 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +no_defs
data source : D:\Dropbox\\...
names : jamaicapc1
values : -3.419589, 14.17305 (min, max)
If I do the same for my new object 'asdf' there are no values- They seem to have gone missing since using the Mask function. For example:
class : RasterLayer
dimensions : 135, 306, 41310 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -78.49359, -75.94359, 17.59729, 18.72229 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +ellps=WGS84 +no_defs
data source : C:\Users\Simon\Desktop\distancesurface.asc
names : distancesurface
I assume this is expected behaviour so my question is, how can I write my new raster (asdf) to disk? Is there a step I need to carry out before I can use the writeRaster function?
Thanks in advance for any help you may be able to provide.

How can I get the cell number of a raster using extent information?

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)

Using 'disaggregate' with GCM data

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.

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