I downloaded temperature data from [NARR] (https://www.esrl.noaa.gov/psd/data/gridded/data.narr.monolevel.html) specifically "Air temperature at 2m" -monthly mean
I opened the file using the package "ncdf4". The data has 4 dimensions- time, x, y, nbnds. y corresponds to lat and x corresponds to lon. There is a variable (not dimension) called air which I do not know how to use, although this is the temperature information.
My end goal is to map the temperature data on a map of North America, using averaged temperature data for each month for each year (12 maps, one for each month).
I am having trouble identifying how to use the data as all of the dimensions are just really long lists of numbers that don't seem to have meaning (eg. the x coordinates look like this: 6232896 6265359 6297822 6330285 6362748 6395211 6427674 6460137 6492600 6525063 6557526 6589989, and so do the y values and time).
Here is the code I am using to view the dimensions:
temp2m <- nc_open("air.2m.mon.mean.nc")
time <- temp2m$dim$time$vals
lat <- temp2m$dim$x$vals
lon <- temp2m$dim$y$vals
nbnds <- temp2m$dim$nbnds$vals
If someone could help me view the data as well as map temperature data onto North America that would be great.
Thank you!
You can use the raster package to read these into a stack:
> library(raster)
> air = stack("./air.2m.mon.mean.nc")
(Note, you may need a raster package compiled with netcdf drivers...)
You can then plot them by slice or by time-name:
> plot(air[[23]])
> plot(air[["X1979.10.01.01.01.15"]])
The stack prints like this:
> air
class : RasterStack
dimensions : 277, 349, 96673, 450 (nrow, ncol, ncell, nlayers)
resolution : 32462.99, 32463 (x, y)
extent : -16231.49, 11313351, -16231.5, 8976020 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=lcc +x_0=5632642.22547 +y_0=4612545.65137 +lat_0=50 +lon_0=-107 +lat_1=50 +lat_2=50 +ellps=WGS84
names : X1979.01.01.00.01.15, X1979.02.01.00.01.15, X1979.03.01.00.01.15, X1979.04.01.01.01.15, X1979.05.01.01.01.15, X1979.06.01.01.01.15, X1979.07.01.01.01.15, X1979.08.01.01.01.15, X1979.09.01.01.01.15, X1979.10.01.01.01.15, X1979.11.01.00.01.15, X1979.12.01.00.01.15, X1980.01.01.00.01.15, X1980.02.01.00.01.15, X1980.03.01.00.01.15, ...
and those coordinates are not really lat-long, but are in a transformed coordinate system described by that "coord. ref." string. If you want to put it on a lat-long map you need to warp it:
> air_ll = projectRaster(air[[1]],crs="+init=epsg:4326")
> plot(air_ll)
It might be better for you to transform any other data to this system, and keep the grid unprojected. Just look up how to deal with spatial data in R for more info on projections and transformations.
Related
After some extensive googling, I wasn't able to find my answer (first time I couldn't surmount the issue by looking at others questions/answers). I am new to asking questions, so forgive any missteps.
I am attempting to perform what ArcGIS or QGIS performs with the slope tool, just within R. To do so I have been importing a raster that I exported from ArcGIS in GRID format with the following characteristics:
class : RasterLayer
dimensions : 821, 581, 477001 (nrow, ncol, ncell)
resolution : 4.996121, 4.996121 (x, y)
extent : 2832147, 2835049, 14234048, 14238150 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=34.75 +lon_0=-118.583333333333 +k=0.9999 +x_0=800000.000000001 +y_0=3999999.99999999 +datum=NAD83 +units=us-ft +no_defs
source : rr_2020_shell
names : rr_2020_shell
values : 5623.253, 6401.356 (min, max)
It is already projected in the correct coordinate system (EPSG: 3423) but when I go to find the slope using the following code:
RR_2020_Slope = terrain(RR_2020_St1_Raster,'slope', units = 'degrees', neighbors = 8, filename = 'RR_2020_Slope.grd', overwrite = T)
The result is a slope raster that ranges from 0 to 1.28°, which is very different from what I have calculated in ArcGIS using the slope tool. Using the same DEM raster in the same projection in ArcGIS I used the slope tool with an input of 'Degree' for the output measurement, 'Planar' for the method, and 1 for Z factor and my resulting slope raster ranges from 0.001 to 73.396°.
Overall I am wondering where my mistake in R originates from, is it an elevation resolution problem? Are there issues with my projection? Forgive me, I can't necessarily include the data as they are sensitive materials but perhaps there is a clear and obvious mistake in my approach or assumptions about the functions I have used?
The only red flag I see is that you say "it is already projected in the correct coordinate system". Projecting raster data degrades the quality. As cell values get smoothed, the slopes will get smaller. This may be particularly pronounced if the relief is at the scale of the cell size (e.g. sand dunes vs mountain chains). Have you compared with what you get with the original data?
Another source of error could be that the units of the values are different from the units of the coordinate reference system. But it would appear that in your case both are in feet.
Can you also try this with terra::terrain()?
I have a shapefile of 10 CA counties projected in NAD83(Hard)/CA Albers. I have a raster (a netCDF file of temperature) for the entire US projected in WGS84/WGS84. I want to use the shapefile to clip the raster. I know that I need to get them on the same datum/projection first. But I've tried re-projecting the raster using raster::projectRaster(). That failed (as in the data disappeared). So then I tried re-projecting the shapefile instead using sp::spTransform(). This also failed (as in the data don't overlap). I've searched through stackoverflow but didn't see anything that seemed to help. I'm not getting an error, but projectRaster is not working and re-projecting the shapefile using spTransform doesn't produce the desired outcome. I feel like there is something specific going on here, like the transformation from WGS84 to NAD83 or loading the raster in using raster() is the problem ... but then again, it could easily be something stupid that I'm missing! =)
my shapefile and raster are here: https://www.dropbox.com/sh/l3b2syzcioeqmyy/AAA5CstBZty4ofOcVFkAumNYa?dl=0
here is my code:
library(raster) #for creating rasters from .bil files
library(rgdal) #for reading .bil files and .gdb files
library(ncdf4) #for working with ncdf files
library(sp) #for working with spatial data files
load(my_counties.RData)
myraster <- raster(myraster.nc)
my.crs <- CRS("+init=EPSG:3311") #NAD83(HARN) / California Albers (HARN is high resolution)
newraster <- projectRaster(myraster, res = 6000, crs = my.crs) #raster resolution is 1/16th of a degree
#There is data in the raster.
plot(myraster)
#but none in newraster
plot(newraster)
#Now try re-projecting the shapefile
my.crs2 <- crs(myraster)
newshapefile <- spTransform(my_counties, my.crs2)
#but the data don't overlap
plot(newshapefile); plot(myraster, add = T)
You can do
library(raster)
library(rgdal)
load("my_counties.RData")
b <- brick("myraster.nc")
Now look at b
b
#class : RasterBrick
#dimensions : 490, 960, 470400, 365 (nrow, ncol, ncell, nlayers)
#resolution : 0.0625, 0.0625 (x, y)
#extent : 234, 294, 23.375, 54 (xmin, xmax, ymin, ymax)
#crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#source : myraster.nc
#names : X2005.01.01, X2005.01.02, X2005.01.03, X2005.01.04, X2005.01.05, X2005.01.06, X2005.01.07, X2005.01.08, X2005.01.09, X2005.01.10, X2005.01.11, X2005.01.12, X2005.01.13, X2005.01.14, X2005.01.15, ...
#Date : 2005-01-01, 2005-12-31 (min, max)
#varname : tasmax
The horizontal extent is between 234 and 294 degrees. That points to a system with longitudes that start in Greenwich at 0 and continues to 360 (again in Greenwich). Climatologist do that. To go to the more conventional -180 to 180 degrees system:
r <- shift(b, -360)
(if your data had a global extent, you would use raster::rotate instead)
Now, transform the counties to lonlat and show that they overlap.
counties <- spTransform(my_counties, crs(r))
plot(r, 1)
lines(counties)
It is generally best to transform vector data, not raster data if you can avoid it.
I'm trying to get RGB components from a geoTIFF file in R. The colours on the image correspond to different land classification types and I have a legend for each classification type in RGB components.
I'm using the raster library. My code so far is
library(raster)
my.map = raster("mygeoTIFFfile.tif")
Here is the information on the file once it has been read in:
> my.map[[1]]
class : RasterLayer
dimensions : 55800, 129600, 7231680000 (nrow, ncol, ncell)
resolution : 0.002777778, 0.002777778 (x, y)
extent : -180.0014, 179.9986, -64.99861, 90.00139 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : filepah/filename.tif
names : filename.tif
values : 11, 230 (min, max)
The specific geoTIFF file I'm working on can be found here:
http://due.esrin.esa.int/page_globcover.php
(just click on "Globcover2009_V2.3_Global_.zip")
Can someone please help me get the value from a single pixel location from this file please?
The rasterToPoints() function will convert your raster data to a matrix containing x, y, and value for each point. This will be very large, but may be what you're looking for if you want to do a broad analysis of the data.
library(raster)
map <- raster("GLOBCOVER_L4_200901_200912_V2.3.tif")
data <- rasterToPoints(map, progress="text")
head(data)
Another option is to use the extract() function to return a single point by passing a SpatialPoints object with latitude/longitude. If you only want a few individual data points, this will be a lot faster than loading the entire thing into a matrix.
library(raster)
map <- raster("GLOBCOVER_L4_200901_200912_V2.3.tif")
extract(map, SpatialPoints(cbind(-123.3680884, 48.4252848)))
It seems that you are asking the wrong question.
To get a value for a single pixel (grid cell), you can do use indexing. For example, for cell number 10,000 and 10,001 you can do r[10000:10001].
You could get all values by doing values(r). But that will fail for a very large raster like this (unless you have lots of RAM).
However, the question you need answered, it seems, is how to make a map by matching integer cell values with RGB colors.
Let's set up an example raster
library(raster)
r <- raster(nrow=4, ncol=4)
values(r) <- rep(c(11, 14, 20, 30), each=4)
And some matching RGB values
legend <- read.csv(text="Value,Label,Red,Green,Blue
11,Post-flooding or irrigated croplands (or aquatic),170,240,240
14,Rainfed croplands,255,255,100
20,Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%),220,240,100
30,Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%) ,205,205,102")
Compute the color code
legend$col <- rgb(legend$Red, legend$Green, legend$Blue, maxColorValue=255)
set up a "color table"
# start with white for all values (1 to 255)
ct <- rep(rgb(1,1,1), 255)
# fill in where necessary
ct[legend$Value+1] <- legend$col
colortable(r) <- ct
plot
plot(r)
You can also try:
tb <- legend[, c('Value', 'Label')]
colnames(tb)[1] = "ID"
tb$Label <- substr(tb$Label, 1,10)
levels(r) <- tb
library(rasterVis)
levelplot(r, col.regions=legend$col, at=0:length(legend$col))
For the following netcdf file with daily global sea surface temperatures for 2016, I'm trying to (i) subset temporally, (ii) subset geographically, (iii) then take long-term means for each pixel and create a basic plot.
Link to file: here
library(raster)
library(ncdf4)
open the netcdf after setting my working directory
nc_data <- nc_open('sst.day.mean.2016.v2.nc')
change the time variable so it's easy to interpret
time <- ncdf4::ncvar_get(nc_data, varid="time")
head(time)
change to dates that I can interpret
time_d <- as.Date(time, format="%j", origin=as.Date("1800-01-01"))
Now I'd like to subset only September 1 to October 15, but can't figure that out...
Following temporal subset, create raster brick (or stack) and geographical subset
b <- brick('sst.day.mean.2016.v2.nc') # I would change this name to my file with time subest
subset geographically
b <- crop(b, extent(144, 146, 14, 16))
Finally, I'd like to take the average for each pixel across all my days of data, assign this to a single raster, and make a simple plot...
Thanks for any help and guidance.
After b <- brick('sst.day.mean.2016.v2.nc'), we can type b to see information of the raster brick.
b
# class : RasterBrick
# dimensions : 720, 1440, 1036800, 366 (nrow, ncol, ncell, nlayers)
# resolution : 0.25, 0.25 (x, y)
# extent : 0, 360, -90, 90 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
# data source : C:\Users\basaw\Downloads\sst.day.mean.2016.v2.nc
# names : X2016.01.01, X2016.01.02, X2016.01.03, X2016.01.04, X2016.01.05, X2016.01.06, X2016.01.07, X2016.01.08, X2016.01.09, X2016.01.10, X2016.01.11, X2016.01.12, X2016.01.13, X2016.01.14, X2016.01.15, ...
# Date : 2016-01-01, 2016-12-31 (min, max)
# varname : sst
Notice that the Date slot has information from 2016-01-01 to 2016-12-31, which means the Z values already has date information and we can use that to subset the raster brick.
We can use the getZ function to access the values stored in the Z values. Type getZ(b) we can see a series of dates.
head(getZ(b))
# [1] "2016-01-01" "2016-01-02" "2016-01-03" "2016-01-04" "2016-01-05" "2016-01-06"
class(getZ(b))
# [1] "Date"
We can thus use the following code to subset the raster brick.
b2 <- b[[which(getZ(b) >= as.Date("2016-09-01") & getZ(b) <= as.Date("2016-10-15"))]]
We can then crop the image based on the code you provided.
b3 <- crop(b2, extent(144, 146, 14, 16))
To calculate the average, just use the mean function.
b4 <- mean(b3, na.rm = TRUE)
Finally, we can plot the average.
plot(b4)
The subsetting and averaging task is easy to do in CDO:
cdo timmean -sellonlatbox,lon1,lon2,lat1,lat2 -seldate,date1,date2 in.nc out.nc
where the lon1,lon2 etc define the lon-lat area to cut out and date1,date2 are the date bounds.
You can call this command directly from R using the climate operators package as per this question.
So for example, without the piping, on 3 lines would be in R:
cdo("seldate,date1,date2",in.fname,out1.fname,debug=TRUE)
cdo("sellonlatbox,lon1,lon2,lat1,lat", out1.fname,out2.fname,debug=TRUE)
cdo("timmean",out2.fname,out.fname,debug=TRUE)
I've downloaded precipitation data from the TRMM (rainfall across the tropics) satellite as a netCDF file and have been trying to plot the data in R as a rasterstack. However, R insists on plotting the latitude and longitude axes incorrectly, such that longitude is plotted on the x-axis (as it should be) but uses the latitude coordinates, while latitude is on the y-axis, but uses the longitude coordinates. I've tried using both the plot() and levelplot() functions but neither seems to work. Can anyone help me correct this?
These are the characteristic of the stack:
class : RasterStack
dimensions : 1440, 186, 267840, 12 (nrow, ncol, ncell, nlayers)
resolution : 0.25, 0.25 (x, y)
extent : -23.25, 23.25, -180, 180 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
names : X2016.01.16, X2016.02.15, X2016.03.16, X2016.04.15, X2016.05.16, X2016.06.15, X2016.07.16, X2016.08.16, X2016.09.15, X2016.10.16, X2016.11.15, X2016.12.16
Date : 2016-01-16, 2016-02-15, 2016-03-16, 2016-04-15, 2016-05-16, 2016-06-15, 2016-07-16, 2016-08-16, 2016-09-15, 2016-10-16, 2016-11-15, 2016-12-16
In the following image you can see the current output. It should show rainfall over the tropics from -23 to 23 degrees latitude, and -180 to 180 degrees longitude.
It's odd if the coordinates are switched prior to any processing. Maybe you want to asses the source you downloaded the data from and if there's maybe a better one.
Anyways, (in the meantime) the raster package can be of help for you .. specifically the transpose t() function. Here's an example:
# data before transpose
x <- getData('worldclim',var='tmean',res=10)
plot(x)
# data after transpose
y <- t(x)
plot(y)
There are also a couple of other functions in raster which could be of interest for you: flip and rotate
HTH
thanks for your response. It does seem strange that the coordinates are messed up right out of the box, and I did try downloading a fresh set of data and the same problem occurred. However, thanks to your input, I was able to rectify the problem through using the transpose() and flip() functions. I had to transpose the data, then flip it along both the x and y dimensions as the image was 'mirrored'. Here's the code I used in case anyone else encounters this problem with the TRMM data sets:
a.t = t(test.rasterstack)
a.flipy = flip(a.t, direction = 2)
a.t.flipxy = flip(a.t.flipy, direction = 1)
levelplot(a.t.flipxy)