I have a comma delimited text files with the header names
lat,long,z1,z2,z3,z4,z5
These columns contain the lat, long and multiple height values(z1, z2,z3, z4). The lat and long data is spaced at an equal interval of 5 meters.
What is the most efficient way to convert the data into individual rasters or an R raster stack each having the z values, given the projection information is UTM, zone 18.
The sample data can be accessed here.
You can do this:
x <- data.frame(lon=1:11, lat=5:15, z1=runif(11), z2=11:1)
library(raster)
b <- rasterFromXYZ(x)
#> b
#class : RasterBrick
#dimensions : 11, 11, 121, 2 (nrow, ncol, ncell, nlayers)
#resolution : 1, 1 (x, y)
#extent : 0.5, 11.5, 4.5, 15.5 (xmin, xmax, ymin, ymax)
#coord. ref. : NA
#data source : in memory
#names : z1, z2
#min values : 0.08495835, 1.00000000
Related
I have a SpatRaster with 100 layers, each corresponding to a result of a bootstrapping analysis (all slightly different variations but pretty much the same). I want to reorder each pixel so that the first layer is the smallest of each respective pixel, second layer is second smallest, etc.
It can be converted to a data.frame with 102 columns (x, y, result1, ...), but I don't know how to order each row of a data.frame by size and exclude columns at the same time.
You can use sort to order the values of each grid cell. Illustrated below
Example data
library(terra)
r <- rast(ncol=5, nrow=5)
values(r) <- 30
x <- rast(c(a=r, b=r/10, c=r/100))
x
#class : SpatRaster
#dimensions : 5, 5, 3 (nrow, ncol, nlyr)
#resolution : 72, 36 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#coord. ref. : lon/lat WGS 84
#sources : memory
# memory
# memory
#names : a, b, c
#min values : 30, 3, 0.3
#max values : 30, 3, 0.3
Solution
y <- sort(x)
y
#class : SpatRaster
#dimensions : 5, 5, 3 (nrow, ncol, nlyr)
#resolution : 72, 36 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#coord. ref. : lon/lat WGS 84
#source : memory
#names : a, b, c
#min values : 0.3, 3, 30
#max values : 0.3, 3, 30
I have a raster of 30m cell size, with either value 0 or 1. I try to aggregate this into a 1000m cell size. I can then see the sum of '1's, in a 1000m cell size raster.
But the Aggregate function only lets me use a round number as a factor, so the closest I get is with factor 33x (to cell size 990m).
I would then need to resample 990m to 1000m but then obviously the values will shift and be incorrect.
Is there a way to use a decimal factor such as 33,3333, or better yet, use a raster with resolution 1000m as a template for the aggregation?
FOR ILLUSTRATION:
Here's an example of one aggregated cell sourced from 30x30m cells with 0/1 values, now in an aggregate raster at 990x990m cell size:
The two original cells are correctly aggregated into a 990x990m cell, and the sum of all values that were of interest (value '1') is 2. You can see these same two cells in the resampled cell on the right, in the top of the cell. But due to the resampling, the raster has shifted and includes more of the 30x30m cells. Yet, the cell value for the now resampled aggregate raster is still 2, while this is an incorrect value and should be 5.
You can use resample.
Example data:
library(terra)
r <- rast(crs="+proj=utm +zone=1", resolution=30, xmin=0, ymin=0, xmax=1020, ymax=1020)
values(r) <- 1:ncell(r)
I first aggregate to a value close to what you want (in this example, I go from 30 to 100 m spatial resolution), and then use resample
a <- aggregate(r, 3, mean)
rr <- rast(crs="+proj=utm +zone=1", resolution=100, xmin=0, ymin=0, xmax=1000, ymax=1000)
b <- resample(r, rr)
r
#class : SpatRaster
#dimensions : 34, 34, 1 (nrow, ncol, nlyr)
#resolution : 30, 30 (x, y)
#extent : 0, 1020, 0, 1020 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=utm +zone=1 +datum=WGS84 +units=m +no_defs
#source : memory
#name : lyr.1
#min value : 1
#max value : 1156
b
#class : SpatRaster
#dimensions : 10, 10, 1 (nrow, ncol, nlyr)
#resolution : 100, 100 (x, y)
#extent : 0, 1000, 0, 1000 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=utm +zone=1 +datum=WGS84 +units=m +no_defs
#source : memory
#name : lyr.1
#min value : 68.89777
#max value : 1103.335
You could also first disaggregate and then aggregate. With the above example you could do
d <- disaggregate(r, 3)
da <- aggregate(d, 10)
da
#class : SpatRaster
#dimensions : 11, 11, 1 (nrow, ncol, nlyr)
#resolution : 100, 100 (x, y)
#extent : 0, 1100, -80, 1020 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=utm +zone=1 +datum=WGS84 +units=m +no_defs
#source : memory
#name : lyr.1
#min value : 43
#max value : 1093
The solution was pretty simple, and I highly suspect the phrasing of my question was just not very clear, apologies for that.
When using the aggregate function (whether it is by mean, sum,...) you cannot resample afterwards and expect the agreggated values to still be correct. The values will be preserved, but because cells shift a little or a lot due to resampling, they will not be correct.
The trick as such, is incredibly simple: resample before you aggregate, not afterwards.
I have a DEM raster file of a specific region
DEMRASTER
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 : newlowelevation1
values : 1, 6136.012 (min, max)
I have another raster file of variable "GPP":
GPPRASTER
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 : layer
values : -0.333333, 0 (min, max)
How can I find the values of the GPP raster according to elevation? For instance, if I want to find a mean value of GPP for elevation from 0-2000m or 5500 to 6136m? What will be the basic code for this?
I have tried removing pixels based on the elevation that I did not want from the DEM raster file, but it is a method that takes too long. I am sure that there is a code for this, but can't put my hand on it as I am a new beginner at R myself. Thanks in advance!
Here is a minimal self-contained, reproducible example:
library(raster)
elev <- raster(ncol=47, nrow=89, ext=extent(60.75, 105.25, 15.75, 39.25))
elev <- init(elev, "cell")
gdp <- flip(elev, "y")/100
Solution:
ezones <- cut(elev, c(0,1000,2000,Inf))
zonal(gdp, ezones)
# zone mean
#[1,] 1 36.83058
#[2,] 2 26.83396
#[3,] 3 10.92250
Or with terra:
library(terra)
#terra version 1.2.1
ev <- rast(ncol=47, nrow=89, ext=ext(60.75, 105.25, 15.75, 39.25))
ev <- init(ev, "cell")
gd <- flip(ev, "vertical")/100
names(gd) <- "gdp"
names(ev) <- "elevation"
Solution:
ez <- classify(ev, c(0,1000,2000,Inf))
zonal(gd, ez)
# elevation gdp
#1 0 - 1000 36.83580
#2 1000 - 2000 26.84370
#3 2000 - inf 10.92752
Suppose one runs the following R code
install.packages("raster")
library(raster)
r <- raster(ncol=18, nrow=18)
res(r)
The output of the res function is
[1] 20 10
How are these values defined? How does the raster function calculate them? In what units are they expressed?
As pointed out by Guillaume Devailly, the horizontal resolution is the horizontal extent divided by the number of columns. The vertical resolution is the vertical extent divided by the number of rows. The units are the units of your coordinate reference system. The default is degrees (for longitude/latitude). To add more to Guillaume's answer:
Create a raster with 10 rows and columns that goes from 0 to 10. The resolution is 1.
library(raster)
r <- raster(ncol=10, nrow=10, xmn=0, xmx=10, ymn=0, ymx=10)
r
#class : RasterLayer
#dimensions : 10, 10, 100 (nrow, ncol, ncell)
#resolution : 1, 1 (x, y)
#extent : 0, 10, 0, 10 (xmin, xmax, ymin, ymax)
#crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
Change the resolution to 0.5; the number of rows and columns double
res(r) <- 0.5
r
#class : RasterLayer
#dimensions : 20, 20, 400 (nrow, ncol, ncell)
#resolution : 0.5, 0.5 (x, y)
#extent : 0, 10, 0, 10 (xmin, xmax, ymin, ymax)
#crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
You can change the resolution indirectly by adjusting the extent
extent(r) <- c(0,5,0,5)
r
#class : RasterLayer
#dimensions : 20, 20, 400 (nrow, ncol, ncell)
#resolution : 0.25, 0.25 (x, y)
#extent : 0, 5, 0, 5 (xmin, xmax, ymin, ymax)
#crs : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
The x and y resolution can be set to a different value
res(r) <- c(1, 0.5)
When you change the resolution directly, via res any cell values associated with the Raster* object are lost; because the number of rows or columns has to change. If you change it indirectly, by changing the extent, the values stay.
From what I understand from the vignette
The default settings will create a global raster data structure with a longitude/latitude coordinate reference system and 1 by 1 degree cells.
r
# class : RasterLayer
# dimensions : 18, 18, 324 (nrow, ncol, ncell)
# resolution : 20, 10 (x, y)
# extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
# coord. ref. : +proj=longlat +datum=WGS84
r x extent goes to from -180 to +180 degrees by default (a total of 360 degrees), and 360 degrees / 18 points = a x resolution of 20 degrees.
r y extent goes form -90 to +90 degrees by default, and 180 degrees / 18 points results in a y resolution of 10 degrees.
I have a shapefile representing Thiessen polygons.
Each polygon is associated with many values of a table.
thiessen <- readOGR(dsn = getwd(), layer = poly)
OGR data source with driver: ESRI Shapefile
Source: ".../raingauges/shp", layer: "thiessen_pol"
with 10 features
It has 5 fields
head(thiessen)
est est_name p001 p002 p003
0 2 borges 1 8 2
1 0 e018 2 4 3
2 5 starosa 5 15 1
3 6 delfim 4 2 2
4 1 e087 1 1 3
5 3 e010 0 1 0
The columns 'est' and 'est_name' are related to the ID and name of the rain gauges. The following columns are important to me and represent precipitation values on day 1, 2, and so on (in the exemple I kept just three days, but actually, I have 8 years of daily precipitation data).
I need to convert the polygons to raster, but one raster for each field (column p001, p002, and so on) of the table.
There is a simple way to convert polygons to raster with the function rasterize in R.
r_p001 <- rasterize(thiessen, r, field = thiessen$p001)
plot(r_p001)
writeRaster(r_p001, filename=".../raingauges/shp/r_p001.tif")
The problem is that I need to set manually the field (column) of the table with the polygon values to be converted to raster. As I have about 2900 days (2900 columns with precipitation values for each rain gauge), it is impossible to do manually.
The documentation does not help to clarify how to automate this process and I did not find anything on the internet to help me.
Does anyone know how to automatically convert each field to raster and save as tif format?
Here is an approach:
Example data
library(raster)
r <- raster(ncols=36, nrows=18)
p1 <- rbind(c(-180,-20), c(-140,55), c(10, 0), c(-140,-60), c(-180,-20))
hole <- rbind(c(-150,-20), c(-100,-10), c(-110,20), c(-150,-20))
p1 <- list(p1, hole)
p2 <- rbind(c(-10,0), c(140,60), c(160,0), c(140,-55), c(-10,0))
p3 <- rbind(c(-125,0), c(0,60), c(40,5), c(15,-45), c(-125,0))
att <- data.frame(id=1:3, var1=10:12, var2=c(6,9,6))
pols <- spPolygons(p1, p2, p3, attr=att)
The important thing is to have a field with a unique If your data do not have it, add it like this
pols$id <- 1:nrow(pols)
Rasterize
r <- rasterize(pols, r, field='id')
Create a layer for all other variables
x <- subs(r, data.frame(pols), by='id', which=2:ncol(pols), filename="rstr.grd")
x
#class : RasterBrick
#dimensions : 18, 36, 648, 2 (nrow, ncol, ncell, nlayers)
#resolution : 10, 10 (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 : rstr.grd
#names : var1, var2
#min values : 10, 6
#max values : 12, 9
An alternative is to keep one layer with Raster Attribute Table, that is quicker, but depending on your purpose, perhaps a less useful method:
r <- rasterize(pols, r, field='id')
f <- as.factor(r)
v <- levels(f)[[1]]
v <- cbind(v, data.frame(pols)[,-1])
levels(f) <- v
f
#class : RasterLayer
#dimensions : 18, 36, 648 (nrow, ncol, ncell)
#resolution : 10, 10 (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, 3 (min, max)
#attributes :
# ID var1 var2
# 1 10 6
# 2 11 9
# 3 12 6
You can then do:
z <- deratify(f)
To get the same result as in the first example
z
#class : RasterBrick
#dimensions : 18, 36, 648, 2 (nrow, ncol, ncell, nlayers)
#resolution : 10, 10 (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 : var1, var2
#min values : 10, 6
#max values : 12, 9