Raster 01: Plot Raster Data in R
Last Updated: Apr 8, 2021
This tutorial reviews how to plot a raster in R using the
function. It also covers how to layer a raster on top of a hillshade to produce
an eloquent map.
After completing this tutorial, you will be able to:
- Know how to plot a single band raster in R.
- Know how to layer a raster dataset on top of a hillshade to create an elegant basemap.
Things You’ll Need To Complete This Tutorial
You will need the most current version of R and, preferably,
on your computer to complete this tutorial.
Install R Packages
More on Packages in R – Adapted from Software Carpentry.
NEON Teaching Data Subset: Airborne Remote Sensing Data
The LiDAR and imagery data used to create this raster teaching data subset were collected over the National Ecological Observatory Network's Harvard Forest and San Joaquin Experimental Range field sites and processed at NEON headquarters. The entire dataset can be accessed by request from the NEON Data Portal.
Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets.
An overview of setting the working directory in R can be found here.
R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.
Plot Raster Data in R
In this tutorial, we will plot the Digital Surface Model (DSM) raster
for the NEON Harvard Forest Field Site. We will use the
hist() function as a
tool to explore raster values. And render categorical plots, using the
breaks argument to get bins that are meaningful representations of our data.
We will use the
rgdal packages in this tutorial. If you do not
DSM_HARV object from the
Intro To Raster In R tutorial,
please create it now.
# if they are not already loaded library(rgdal) library(raster) # set working directory to ensure R can find the file we wish to import wd <- "~/Git/data/" # this will depend on your local environment environment # be sure that the downloaded file is in this directory setwd(wd) # import raster DSM_HARV <- raster(paste0(wd,"NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif"))
First, let's plot our Digital Surface Model object (
DSM_HARV) using the
plot() function. We add a title using the argument
# Plot raster object plot(DSM_HARV, main="Digital Surface Model\nNEON Harvard Forest Field Site")
Plotting Data Using Breaks
We can view our data "symbolized" or colored according to ranges of values
rather than using a continuous color ramp. This is comparable to a "classified"
map. However, to assign breaks, it is useful to first explore the distribution
of the data using a histogram. The
breaks argument in the
tells R to use fewer or more breaks or bins.
If we name the histogram, we can also view counts for each bin and assigned break values.
# Plot distribution of raster values DSMhist<-hist(DSM_HARV, breaks=3, main="Histogram Digital Surface Model\n NEON Harvard Forest Field Site", col="wheat3", # changes bin color xlab= "Elevation (m)") # label the x-axis ## Warning in .hist1(x, maxpixels = maxpixels, main = main, plot = ## plot, ...): 4% of the raster cells were used. 100000 values used.
# Where are breaks and how many pixels in each category? DSMhist$breaks ##  300 350 400 450 DSMhist$counts ##  32124 67392 484
Warning message!? Remember, the default for the histogram is to include only a subset of 100,000 values. We could force it to show all the pixel values or we can use the histogram as is and figure that the sample of 100,000 values represents our data well.
Looking at our histogram, R has binned out the data as follows:
- 300-350m, 350-400m, 400-450m
We can determine that most of the pixel values fall in the 350-400m range with a few pixels falling in the lower and higher range. We could specify different breaks, if we wished to have a different distribution of pixels in each bin.
We can use those bins to plot our raster data. We will use the
terrain.colors() function to create a palette of 3 colors to use in our plot.
breaks argument allows us to add breaks. To specify where the breaks
occur, we use the following syntax:
We can include as few or many breaks as we'd like.
# plot using breaks. plot(DSM_HARV, breaks = c(300, 350, 400, 450), col = terrain.colors(3), main="Digital Surface Model (DSM)\n NEON Harvard Forest Field Site")
If we need to create multiple plots using the same color palette, we can create
an R object (
myCol) for the set of colors that we want to use. We can then
quickly change the palette across all plots by simply modifying the
We can label the x- and y-axes of our plot too using
# Assign color to a object for repeat use/ ease of changing myCol = terrain.colors(3) # Add axis labels plot(DSM_HARV, breaks = c(300, 350, 400, 450), col = myCol, main="Digital Surface Model\nNEON Harvard Forest Field Site", xlab = "UTM Westing Coordinate (m)", ylab = "UTM Northing Coordinate (m)")
Or we can also turn off the axes altogether.
# or we can turn off the axis altogether plot(DSM_HARV, breaks = c(300, 350, 400, 450), col = myCol, main="Digital Surface Model\n NEON Harvard Forest Field Site", axes=FALSE)
Create a plot of the Harvard Forest Digital Surface Model (DSM) that has:
- Six classified ranges of values (break points) that are evenly divided among the range of pixel values.
- Axis labels
- A plot title
We can layer a raster on top of a hillshade raster for the same area, and use a transparency factor to created a 3-dimensional shaded effect. A hillshade is a raster that maps the shadows and texture that you would see from above when viewing terrain.
# import DSM hillshade DSM_hill_HARV <- raster(paste0(wd,"NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_DSMhill.tif")) # plot hillshade using a grayscale color ramp that looks like shadows. plot(DSM_hill_HARV, col=grey(1:100/100), # create a color ramp of grey colors legend=FALSE, main="Hillshade - DSM\n NEON Harvard Forest Field Site", axes=FALSE)
We can layer another raster on top of our hillshade using by using
DSM_HARV on top of the
# plot hillshade using a grayscale color ramp that looks like shadows. plot(DSM_hill_HARV, col=grey(1:100/100), #create a color ramp of grey colors legend=F, main="DSM with Hillshade \n NEON Harvard Forest Field Site", axes=FALSE) # add the DSM on top of the hillshade plot(DSM_HARV, col=rainbow(100), alpha=0.4, add=T, legend=F)
The alpha value determines how transparent the colors will be (0 being
transparent, 1 being opaque). Note that here we used the color palette
rainbow() instead of
- More information in the R color palettes documentation.
Make sure to:
- include hillshade in the maps,
- label axes on the DSM map and exclude them from the DTM map,
- a title for the maps,
- experiment with various alpha values and color palettes to represent the data.
Get Lesson Code
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