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R programming (50)
Hierarchical Data Formats (HDF5) (11)
Spatial Data & GIS (19)
LiDAR (5)
Remote Sensing (9)
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Hyperspectral Remote Sensing (7)
Time Series (15)
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dplyr (7)
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Overview

About

This tutorial reviews how to plot a raster in R using the plot() function. It also covers how to layer a raster on top of a hillshade to produce an eloquent map.

R Skill Level: Intermediate - you’ve got the basics of R down.

Goals / Objectives

After completing this activity, you will:

  • 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, RStudio loaded on your computer to complete this tutorial.

Install R Packages

Download Data

Download 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 Airborne Data Request Page on the NEON website.


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.


Additional Resources

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 raster and rgdal packages in this tutorial. If you do not have the 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
# setwd("working-dir-path-here")

# import raster
DSM_HARV <- raster("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 main="title".

# 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 hist() function 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

## [1] 300 350 400 450

DSMhist$counts

## [1] 32077 67470   453

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.

The breaks argument allows us to add breaks. To specify where the breaks occur, we use the following syntax: breaks=c(value1,value2,value3). 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")

Data Tip: Note that when we assign break values a set of 4 values will result in 3 bins of data.

Format Plot

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 myCol object.

We can label the x- and y-axes of our plot too using xlab and ylab.

# 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)

Challenge: Plot Using Custom Breaks

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

Layering Rasters

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("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)

Data Tip: Turn off, or hide, the legend on a plot using legend=FALSE.

We can layer another raster on top of our hillshade using by using add=TRUE. Let’s overlay DSM_HARV on top of the hill_HARV.

# 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 terrain.color().

Challenge: Create DTM & DSM for SJER

Use the files in the NEON_RemoteSensing/SJER/ directory to create a Digital Terrain Model map and Digital Surface Model map of the San Joaquin Experimental Range field site.

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.


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