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)
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))
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 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.