I have a series of binary raster layers (ascii file) showing presence/absence of a species in Europe and Africa. The file is based on unprojected lat/long (WGS84) data. My aim is to calculate the area of presence using R (I don't have access to ArcGIS).
I know that the raster package has a function for calculating area, but I'm worried that this won't be accurate for unprojected data. I have also looked at the cellStats function in the raster package, and can use this to "sum" the number of cells occupied, but I feel this has the same problem.
jan<-raster("/filelocation/file.asc")
jan
class : RasterLayer
dimensions : 13800, 9600, 132480000 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -20, 60, -40, 75 (xmin, xmax, ymin, ymax)
coord. ref. : NA
data source : "/filelocation"
names : file.asc
values : -2147483648, 2147483647 (min, max)
area(jan)
class : RasterLayer
dimensions : 13800, 9600, 132480000 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -20, 60, -40, 75 (xmin, xmax, ymin, ymax)
coord. ref. : NA
names : layer
values : 6.944444e-05, 6.944444e-05 (min, max)
Warning messages:
1: In .local(x, ...) :
This function is only useful for Raster* objects with a longitude/latitude coordinates
2: In .rasterFromRasterFile(grdfile, band = band, objecttype, ...) :
size of values file does not match the number of cells (given the data type)
cellStats(jan,"sum")
[1] 3559779
Anybody know of a way to calculate the presence area accurately, accounting for the earth curvature?
Thanks!
I do not know what is going in with your file (why you get warning #2). But is here is a work around
r <- raster(nrow=13800, ncol=9600, xmn=-20, xmx=60, ymn=-40, ymx=75)
# equivalent to r <- raster(jan)
x = area(r)
x
class : RasterLayer
dimensions : 13800, 9600, 132480000 (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333 (x, y)
extent : -20, 60, -40, 75 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84
data source : c:\temp\R_raster_Robert\2015-01-26_213612_1208_85354.grd
names : layer
values : 0.2227891, 0.8605576 (min, max)
Now you have the area of each cell in km2. By multiplying these values with Raster objects with presence/absence values and then using cellStats( , 'sum') you can obtain the total area with presence.
Related
When I directly read into R the jp2 band files I get unusual high values compared to when I read the files in SNAP (version 9). To read the bands into R I use terra package (you can also use raster package) and the values range from 0 to 18000 more or less. I was wondering if SNAP is doing some conversion that I am not aware of to show values that range from 0 to 0.15 more or less.
> r_10
class : SpatRaster
dimensions : 10980, 10980, 4 (nrow, ncol, nlyr)
resolution : 10, 10 (x, y)
extent : 499980, 609780, 6690240, 6800040 (xmin, xmax, ymin, ymax)
coord. ref. : WGS 84 / UTM zone 32N (EPSG:32632)
sources : T32VNN_20181018T105031_B02_10m.jp2
T32VNN_20181018T105031_B03_10m.jp2
T32VNN_20181018T105031_B04_10m.jp2
... and 1 more source(s)
names : B02_10m_m10_2018, B03_10m_m10_2018, B04_10m_m10_2018, B08_10m_m10_2018
min values : 0, 0, 0, 0
max values : 18815, 17880, 17023, 15608
>
I have tried to export the bands from SNAP into TIF to see if it is a problem of format but it takes forever. I was hoping that there is a convesion factor to show the actual values that I need for my analysis.
SNAP applies a scaling factor of 1.0E4 so that values can be stored more efficiently as integers. You will need to either divide the values by this scaling factor in R, or else use the scaled units to benefit from more efficient integer arithmetic. See https://step.esa.int/docs/tutorials/Exporting%20data%20from%20SNAP.pdf for more details
Legacy formats ([Geo]TIFF, JPEG) have incomplete metadata support. You may lose missing value codes, offset and scale factors, processing history, etc. NetCDF4-CF has good metadata support for applications that actually use what is available. The R terra package can import NetCDF4-CF format, but is selective about what metadata are imported. For the files I have tested, missing data, scale, and offset values are used by the rast() function, but other metadata are lost.
With terra you can also set a scale/offset if you want:
library(terra)
#terra 1.6.21
f <- system.file("ex/elev.tif", package="terra")
r <- rast(f)
r
#class : SpatRaster
#dimensions : 90, 95, 1 (nrow, ncol, nlyr)
#resolution : 0.008333333, 0.008333333 (x, y)
#extent : 5.741667, 6.533333, 49.44167, 50.19167 (xmin, xmax, ymin, ymax)
#coord. ref. : lon/lat WGS 84 (EPSG:4326)
#source : elev.tif
#name : elevation
#min value : 141
#max value : 547
scoff(r) <- cbind(10, 0)
r
#class : SpatRaster
#dimensions : 90, 95, 1 (nrow, ncol, nlyr)
#resolution : 0.008333333, 0.008333333 (x, y)
#extent : 5.741667, 6.533333, 49.44167, 50.19167 (xmin, xmax, ymin, ymax)
#coord. ref. : lon/lat WGS 84 (EPSG:4326)
#source : elev.tif
#name : elevation
#min value : 1410
#max value : 5470
Essentially this is way to delay the evaluation of value * scale + offset, but now it may need to be done multiple times (each time that r is used), so it is not something I would generally recommend doing.
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 have three rasters. Raster1 is a landcover file for a land cover types. Raster2 and raster3 are rasters showing variable 'NPP'. As you can see each raster has different extent & resolution. I want to know how much NPP is in both raster 2 and 3 in accordance with the landcover for raster1. However what could be done in order to bring all rasters to same extent and resolution and find NPP in raster2 and raster3 accordance with the landcover class in raster1?
(How can I know which resolution should I choose for all the rasters?)
> raster1
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 :XXXXX
names : landusemaskedme
values : 1, 12 (min, max)
raster2
class : RasterLayer
dimensions : 2336, 4419, 10322784 (nrow, ncol, ncell)
resolution : 0.01, 0.01 (x, y)
extent : 60.85, 105.04, 15.96, 39.32 (xmin, xmax, ymin, ymax)
crs : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
source : memory
names : NPP
values : 0, 31.78096 (min, max)
> raster3
class : RasterLayer
dimensions : 47, 89, 4183 (nrow, ncol, ncell)
resolution : 0.5, 0.5 (x, y)
extent : 60.75, 105.25, 15.75, 39.25 (xmin, xmax, ymin, ymax)
crs : NA
source : memory
names : NPP
values : 0, 21.141 (min, max)
I can see that your rasters are having almost same extent and coordinate system except for raster3 which does not have any reference system (crs: NA). First, you need to have rasters of the same extent and coordinate reference system, then you can use resample function from raster package like
library(raster)
#To have the same projection for raster3 as that of your base landcover class in raster1
newproj <- projection(raster1)
praster3 <- projectRaster(raster3, crs=newproj)
#Conversion of rasters into same extent
raster2_resampled <- resample(raster2, raster1, method='bilinear')
raster3_resampled <- resample(praster3, raster1, method='bilinear')
It is always better to resample a finer resolution raster to coarser resolution, not the vice versa though it can be done as you have asked in your question. In your case, raster1 has the finer resolution (0.008333333 x 0.008333333) followed by raster2 (0.01 x 0.01). raster3 has the coarsest resolution (0.5 x 0.5). So, it would be better to convert all the rasters to the resolution and extent of raster3. Hope that helps you out.
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 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.