I am new to R and trying to extract a subset of values from a raster file. I am using the Ontario Land Cover Compilation (OLCC) v.2.0 and want to only extract impervious cover values within my buffer regions. According to the Data Specifications there are classification names for land use classes and associated codes. I only want to extract data from the Community/Infrastructure name (code 27). I have uploaded the entire raster into R. Is there a way to separate the raster by code name/class? If I get the separated raster subset I know how to extract within my buffer region from there.
I have tried the raster brick function to see if it would recognize the code names and separate them into different layers automatically but this didn't work. I saw another post where raster attributes were extracted by class, but I am not sure how the land use classes are being separated and defined in R here.
Here is some example data
library(terra)
#terra 1.5.6
set.seed(0)
x <- rast(nrows=10, ncols=10, names="cover")
values(x) <- sample(3, ncell(x), replace=TRUE) - 1
levels(x) <- c("forest", "water", "urban")
Inspect
x
#class : SpatRaster
#dimensions : 10, 10, 1 (nrow, ncol, nlyr)
#resolution : 36, 18 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#coord. ref. : lon/lat WGS 84
#source : memory
#name : cover
#min value : forest
#max value : water
levels(x)[[1]]
#[1] "forest" "water" "urban"
cats(x)[[1]]
# ID category
#1 0 forest
#2 1 water
#3 2 urban
So if you were interested in extracting the "urban" areas only, you can see that the ID (cell value) for that class is 2. And you can do
urban <- x == 2
plot(urban)
text(x)
urb <- x == "urban"
It should also have been possible to do
urb <- x == "urban"
But that may be offset by one class in some cases. To use this safely, you need terra 1.5-7 (currently the development version)
Also, if there are multiple categories, you may first need to activate the category you are interested in; like so:
activeCat(x) <- "cover"
Related
I am trying to plot only certain values from a categorical land cover raster I am working with. I have loaded it in to R using the terra package and it plots fine. However, since the original data did not come with a legend, I am trying to find out which raster value corresponds to what on the map.
Similar to the answer provided here: How to subset a raster based on grid cell values
I have tried using the following line:
> landcover
class : SpatRaster
dimensions : 20057, 63988, 1 (nrow, ncol, nlyr)
resolution : 0.0005253954, 0.0005253954 (x, y)
extent : -135.619, -102, 59.99989, 70.53775 (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326)
source : spat_n5WpgzBuVAV3Ijm.tif
name : CAN_LC_2015_CAL_wgs
min value : 1
max value : 18
> plot(landcover[landcover == 18])
Error: cannot allocate vector of size 9.6 Gb
However, this line takes a very long time to run and produces a vector memory error. The object is 1.3 kb in the global environment and the original tif is about 300 mb.
You can use cats to find out which values correspond to which categories.
library(terra)
set.seed(0)
r <- rast(nrows=10, ncols=10)
values(r) <- sample(3, ncell(r), replace=TRUE) - 1
cls <- c("forest", "water", "urban")
levels(r) <- cls
names(r) <- "land cover"
cats(r)[[1]]
# ID category
#1 0 forest
#2 1 water
#3 2 urban
To plot a logical (Boolean) layer for one category, you can do
plot(r == "water")
And from from the above you can see that in this case that is equivalent to
plot(r == 1)
I think I found the solution to write the conditional within the plot function as below:
plot(landcover == 18)
For those looking for a reproduceable example, just load the rlogo:
s <- rast(system.file("ex/logo.tif", package="terra"))
s <- s$red
plot(s == 255)
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 am trying to resample a forest cover raster with high resolution (25 meters) and categorical data (1 to 13) to a new RasterLayer with a lower resolution (~ 1 km). My idea is to combine the forest cover data with other lower-resolution raster data :
I tried raster::resample(), but since the data is categorical I lost a lot of information:
summary(as.factor(df$loss_year_mosaic_30m))
0 1 2 3 4 5 6 7 8 9 10 11 12 13
3777691 65 101 50 151 145 159 295 291 134 102 126 104 91
As you can see, the new raster has the desired resolution but have lots of zeros as well. I suppose that is normal since I used the ´ngb´ option in resample.
The second strategy was using raster::aggregate() but I find difficult to define a factor integer since the change of resolution is not straightforward (like the double of the resolution or alike).
My high-resolution raster has the following resolution, and I want it to aggregate it to a 0.008333333, 0.008333333 (x, y) resolution to the same extent.
loss_year
class : RasterLayer
dimensions : 70503, 59566, 4199581698 (nrow, ncol, ncell)
resolution : 0.00025, 0.00025 (x, y)
extent : -81.73875, -66.84725, -4.2285, 13.39725 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
data source : /Volumes/LaCie/Deforestacion/Hansen/loss_year_mosaic_30m.tif
names : loss_year_mosaic_30m
values : 0, 13 (min, max)
I have tried a factor of ~33.33 following the description of the aggregate help: "The number of cells is the number of cells of x divided by fact*fact (when fact is a single number)." Nonetheless, the resulting raster data do not seem to have the same number of rows and columns as my other low-resolution rasters.
I have never used this high-resolution data, and I am also computationally limited (some of this commands can be parallelized using clusterR, but sometimes they took the same time than the non-parallelized commands, especially since they do not work for nearest neighboor calculations).
I am short of ideas; maybe I can try layerize to obtain a count raster, but I have to ´aggregate´ and the factor problem arises. Since this processes are taking me days to process, I do want to know the most efficient way to create a lower resolution raster without losing much information
A reproducible example could be the following:
r_hr <- raster(nrow=70, ncol=70) #High resolution raster with categorical data
set.seed(0)
r_hr[] <- round(runif(1:ncell(r_hr), 1, 5))
r_lr <- raster(nrow=6, ncol=6) #Low resolution raster
First strategy: loss of information
r <- resample(r_hr, r_lr, method = "ngb") #The raster data is categorical
Second strategy: difficult to define an aggregate factor
r <- aggregate(r_hr, factor) #How to define a factor to get exactly the same number of cells of h_lr?
Another option: layerize
r_brick <- layerize(r_hr)
aggregate(r_brick, factor) #How to define factor to coincide with the r_lr dimensions?
Thanks for your help!
r_hr <- raster(nrow=70, ncol=70) #High resolution raster with categorical data
set.seed(0)
r_hr[] <- round(runif(1:ncell(r_hr), 1, 5))
r_lr <- raster(nrow=6, ncol=6)
r_hr
#class : RasterLayer
#dimensions : 70, 70, 4900 (nrow, ncol, ncell)
#resolution : 5.142857, 2.571429 (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 : 1, 5 (min, max)
r_lr
#class : RasterLayer
#dimensions : 6, 6, 36 (nrow, ncol, ncell)
#resolution : 60, 30 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
Direct aggregate is not possible, because 70/6 is not an integer.
dim(r_hr)[1:2] / dim(r_lr)[1:2]
#[1] 11.66667 11.66667
Nearest neighbor resampling is not a good idea either as the results would be arbitrary.
Here is a by layer approach that you suggested and dww also showed already.
b <- layerize(r_hr)
fact <- round(dim(r_hr)[1:2] / dim(r_lr)[1:2])
a <- aggregate(b, fact)
x <- resample(a, r_lr)
Now you have proportions. If you want a single class you could do
y <- which.max(x)
In that case, another approach would be to aggregate the classes
ag <- aggregate(r_hr, fact, modal)
agx <- resample(ag, r_lr, method='ngb')
Note that agx and y are the same. But they can both be problematic as you might have cells with 5 classes with each about 20%, making it rather unreasonable to pick one winner.
It is pretty standard practice to aggregate land cover maps into layers of %cover. I.e you should aim to produce 13 layers, each being something like %cover in that grid cell. Doing this allows you to reduce the resolution while retaining as much information as possible. N.B if you require a different summary statistic than %, should be easy to adapt the following method to whatever statistic you want, by changing the fun = function in aggregate.
The following method is pretty fast (it takes just a few seconds on my laptop to process raster with 100 million cells):
First, let's create some dummy rasters to use
Nhr <- 1e4 # resolution of high-res raster
Nlr <- 333 # resolution of low-res raster
r.hr <- raster(ncols=Nhr, nrows=Nhr)
r.lr <- raster(ncols=Nlr, nrows=Nlr)
r.hr[] <- sample(1:13, Nhr^2, replace=T)
Now, we begin by aggregating the high res raster to almost the same resolution as the low res one (to nearest integer number of cells). Each resulting layer contains the fraction of area within that cell in which value of original raster is N.
Nratio <- as.integer(Nhr/Nlr) # ratio of high to low resolutions, to nearest integer value for aggregation
layer1 <- aggregate(r.hr, Nratio, fun=function(x, na.rm=T) {mean(x==1, na.rm=na.rm)})
layer2 <- aggregate(r.hr, Nratio, fun=function(x, na.rm=T) {mean(x==2, na.rm=na.rm)})
And finally, resample low res raster to the desired resolution
layer1 <- resample(layer1, r.lr, method = "ngb")
layer2 <- resample(layer2, r.lr, method = "ngb")
repeat for each layer, and build your layers into a stack or a multi-band raster
I'm new to raster files, but they seem to be the best way to open up the large gov't files that have all the weather data, so I'm trying to figure out how to use them. For reference, I'm downloading the files located here (just some run of the mill weather stuff). When I use the raster package of R to import the file like this
> r <- raster("/path/to/file.grb")
Everything works fine. I can even get a little metadata when I type in
> r
class : RasterLayer
band : 1 (of 37 bands)
dimensions : 224, 464, 103936 (nrow, ncol, ncell)
resolution : 0.125, 0.125 (x, y)
extent : -125.0005, -67.0005, 25.0005, 53.0005 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +a=6371200 +b=6371200 +no_defs
data source : /path/to/file.grb
names : NLDAS_MOS0125_H.A20140629.0100.002
All I've managed to do at this point is index the raster in a very obvious way.
> r[100,100]
267.1
So, I guess I can "index" it, but I have no idea what the number 267.1 means. It's certainly not all there is in the cell. There should be a bunch of variables including, but not limited to, soil moisture, surface runoff, and evaporation.
How can I access this information in the same way using R?
# create two rasters
r1 <- raster(matrix(ncol = 10, nrow = 10, runif(100)))
r2 <- raster(matrix(ncol = 10, nrow = 10, runif(100)))
# creates a raster stack -- the stack (or brick function) allows you to
# to use multilayer band rasters
# http://www.inside-r.org/packages/cran/raster/docs/stack
st_r <- stack(r1, r2)
# extract values -- will create a matrix with 100 rows and two columns
vl <- getValues(st_r)
r <- raster("/path/to/file.grb")
values <- getValues(r)
You can read about the function here:
http://www.inside-r.org/packages/cran/raster/docs/values
I believe that the problem is that you are using raster and not stack. The raster function results in a single layer (matrix) whereas stack or brick read an array with all of the raster layers. Here is an example that demonstrates extracting values using an [i,j,z] index.
library(raster)
setwd("D:/TMP")
download.file("ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/NLDAS_MOS0125_H.002/2014/180/NLDAS_MOS0125_H.A20140629.0000.002.grb",
destfile="NLDAS_MOS0125_H.A20140629.0000.002.grb", mode="wb")
r <- stack("NLDAS_MOS0125_H.A20140629.0000.002.grb")
names(r) <- paste0("L", seq(1:nlayers(r)))
class(r)
# Values for [i,j]
i=100
j=100
r[i,j]
# Values for i,j and z at layer(s) 1, 5 and 10
z=c(1,5,10)
r[i,j][z]