# Plot Raster Data

Estimated time: 70 minutes

## Overview

### Questions

• How can I create categorized or customized maps of raster data?
• How can I customize the color scheme of a raster image?
• How can I layer raster data in a single image?

### Objectives

• Build customized plots for a single band raster using the ggplot2 package.
• Layer a raster dataset on top of a hillshade to create an elegant basemap.

### Things You’ll Need To Complete This Episode

See the lesson homepage for detailed information about the software, data, and other prerequisites you will need to work through the examples in this episode.

## Plot Raster Data in R

This episode covers how to plot a raster in R using the ggplot2 package with customized coloring schemes. It also covers how to layer a raster on top of a hillshade to produce an eloquent map. We will continue working with the Digital Surface Model (DSM) raster for the NEON Harvard Forest Field Site.

## Plotting Data Using Breaks

In the previous episode, we viewed our data using a continuous color ramp. For clarity and visibility of the plot, we may prefer to view the data “symbolized” or colored according to ranges of values. This is comparable to a “classified” map. To do this, we need to tell ggplot how many groups to break our data into, and where those breaks should be. To make these decisions, it is useful to first explore the distribution of the data using a bar plot. To begin with, we will use dplyr’s mutate() function combined with cut() to split the data into 3 bins.

### R

DSM_HARV_df <- DSM_HARV_df %>%
mutate(fct_elevation = cut(HARV_dsmCrop, breaks = 3))

ggplot() +
geom_bar(data = DSM_HARV_df, aes(fct_elevation))

If we want to know the cutoff values for the groups, we can ask for the unique values of fct_elevation:

### OUTPUT

[1] (400,450] (350,400] (300,350]
Levels: (300,350] (350,400] (400,450]

### Data Tips

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

The bin intervals are shown using ( to mean exclusive and ] to mean inclusive. For example: (305, 342] means “from 306 through 342”.

And now we can plot our bar plot again, using the new groups:

### R

ggplot() +
geom_bar(data = DSM_HARV_df, aes(fct_elevation_2))

And we can get the count of values in each group in the same way we did before:

### R

DSM_HARV_df %>%
count(fct_elevation_2)

### OUTPUT

  fct_elevation_2       n
1       (300,350]  741815
2       (350,400] 1567316
3       (400,450]   10668

We can use those groups to plot our raster data, with each group being a different color:

### R

ggplot() +
geom_raster(data = DSM_HARV_df , aes(x = x, y = y, fill = fct_elevation_2)) +
coord_quickmap()

The plot above uses the default colors inside ggplot for raster objects. We can specify our own colors to make the plot look a little nicer. R has a built in set of colors for plotting terrain, which are built in to the terrain.colors() function. Since we have three bins, we want to create a 3-color palette:

### R

terrain.colors(3)

### OUTPUT

[1] "#00A600" "#ECB176" "#F2F2F2"

The terrain.colors() function returns hex colors - each of these character strings represents a color. To use these in our map, we pass them across using the scale_fill_manual() function.

### R

ggplot() +
geom_raster(data = DSM_HARV_df , aes(x = x, y = y,
fill = fct_elevation_2)) +
scale_fill_manual(values = terrain.colors(3)) +
coord_quickmap()

### More Plot Formatting

If we need to create multiple plots using the same color palette, we can create an R object (my_col) for the set of colors that we want to use. We can then quickly change the palette across all plots by modifying the my_col object, rather than each individual plot.

We can label the x- and y-axes of our plot too using xlab and ylab. We can also give the legend a more meaningful title by passing a value to the name argument of the scale_fill_manual() function.

### R

my_col <- terrain.colors(3)

ggplot() +
geom_raster(data = DSM_HARV_df , aes(x = x, y = y,
fill = fct_elevation_2)) +
scale_fill_manual(values = my_col, name = "Elevation") +
coord_quickmap()

Or we can also turn off the labels of both axes by passing element_blank() to the relevant part of the theme() function.

### R

ggplot() +
geom_raster(data = DSM_HARV_df , aes(x = x, y = y,
fill = fct_elevation_2)) +
scale_fill_manual(values = my_col, name = "Elevation") +
theme(axis.title = element_blank()) +
coord_quickmap()

### Challenge: Plot Using Custom Breaks

Create a plot of the Harvard Forest Digital Surface Model (DSM) that has:

1. Six classified ranges of values (break points) that are evenly divided among the range of pixel values.
2. Axis labels.
3. A plot title.

### R

DSM_HARV_df <- DSM_HARV_df  %>%
mutate(fct_elevation_6 = cut(HARV_dsmCrop, breaks = 6))

my_col <- terrain.colors(6)

ggplot() +
geom_raster(data = DSM_HARV_df , aes(x = x, y = y,
fill = fct_elevation_6)) +
scale_fill_manual(values = my_col, name = "Elevation") +
ggtitle("Classified Elevation Map - NEON Harvard Forest Field Site") +
xlab("UTM Easting Coordinate (m)") +
ylab("UTM Northing Coordinate (m)") +
coord_quickmap()

## Layering Rasters

We can layer a raster on top of a hillshade raster for the same area, and use a transparency factor to create 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. We will add a custom color, making the plot grey.

First we need to read in our DSM hillshade data and view the structure:

### R

DSM_hill_HARV <-
rast("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_DSMhill.tif")

DSM_hill_HARV

### OUTPUT

class       : SpatRaster
dimensions  : 1367, 1697, 1  (nrow, ncol, nlyr)
resolution  : 1, 1  (x, y)
extent      : 731453, 733150, 4712471, 4713838  (xmin, xmax, ymin, ymax)
coord. ref. : WGS 84 / UTM zone 18N (EPSG:32618)
source      : HARV_DSMhill.tif
name        : HARV_DSMhill
min value   :   -0.7136298
max value   :    0.9999997 

Next we convert it to a dataframe, so that we can plot it using ggplot2:

### R

DSM_hill_HARV_df <- as.data.frame(DSM_hill_HARV, xy = TRUE)

str(DSM_hill_HARV_df)

### OUTPUT

'data.frame':	2313675 obs. of  3 variables:
$x : num 731454 731456 731456 731458 731458 ...$ y           : num  4713836 4713836 4713836 4713836 4713836 ...
\$ HARV_DSMhill: num  -0.15567 0.00743 0.86989 0.9791 0.96283 ...

Now we can plot the hillshade data:

### R

ggplot() +
geom_raster(data = DSM_hill_HARV_df,
aes(x = x, y = y, alpha = HARV_DSMhill)) +
scale_alpha(range =  c(0.15, 0.65), guide = "none") +
coord_quickmap()

### Data Tips

Turn off, or hide, the legend on a plot by adding guide = "none" to a scale_something() function or by setting theme(legend.position = "none").

The alpha value determines how transparent the colors will be (0 being transparent, 1 being opaque).

We can layer another raster on top of our hillshade by adding another call to the geom_raster() function. Let’s overlay DSM_HARV on top of the hill_HARV.

### R

ggplot() +
geom_raster(data = DSM_HARV_df ,
aes(x = x, y = y,
fill = HARV_dsmCrop)) +
geom_raster(data = DSM_hill_HARV_df,
aes(x = x, y = y,
alpha = HARV_DSMhill)) +
scale_fill_viridis_c() +
scale_alpha(range = c(0.15, 0.65), guide = "none") +
coord_quickmap()

### Challenge: Create DTM & DSM for SJER

Use the files in the data/NEON-DS-Airborne-Remote-Sensing/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,
• include a title for each map,
• experiment with various alpha values and color palettes to represent the data.

### R

# CREATE DSM MAPS

# import DSM data
DSM_SJER <-
rast("data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmCrop.tif")
# convert to a df for plotting
DSM_SJER_df <- as.data.frame(DSM_SJER, xy = TRUE)

DSM_hill_SJER <-
rast("data/NEON-DS-Airborne-Remote-Sensing/SJER/DSM/SJER_dsmHill.tif")
# convert to a df for plotting
DSM_hill_SJER_df <- as.data.frame(DSM_hill_SJER, xy = TRUE)

# Build Plot
ggplot() +
geom_raster(data = DSM_SJER_df ,
aes(x = x, y = y,
fill = SJER_dsmCrop,
alpha = 0.8)
) +
geom_raster(data = DSM_hill_SJER_df,
aes(x = x, y = y,
alpha = SJER_dsmHill)
) +
scale_fill_viridis_c() +
guides(fill = guide_colorbar()) +
scale_alpha(range = c(0.4, 0.7), guide = "none") +
# remove grey background and grid lines
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UTM Easting Coordinate (m)") +
ylab("UTM Northing Coordinate (m)") +
coord_quickmap()

### R

# CREATE DTM MAP
# import DTM
DTM_SJER <-
rast("data/NEON-DS-Airborne-Remote-Sensing/SJER/DTM/SJER_dtmCrop.tif")
DTM_SJER_df <- as.data.frame(DTM_SJER, xy = TRUE)

DTM_hill_SJER <-
rast("data/NEON-DS-Airborne-Remote-Sensing/SJER/DTM/SJER_dtmHill.tif")
DTM_hill_SJER_df <- as.data.frame(DTM_hill_SJER, xy = TRUE)

ggplot() +
geom_raster(data = DTM_SJER_df ,
aes(x = x, y = y,
fill = SJER_dtmCrop,
alpha = 2.0)
) +
geom_raster(data = DTM_hill_SJER_df,
aes(x = x, y = y,
alpha = SJER_dtmHill)
) +
scale_fill_viridis_c() +
guides(fill = guide_colorbar()) +
scale_alpha(range = c(0.4, 0.7), guide = "none") +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank()) +
coord_quickmap()
• Continuous data ranges can be grouped into categories using mutate() and cut().
• Use built-in terrain.colors() or set your preferred color scheme manually.
• Layer rasters on top of one another by using the alpha aesthetic.