Plot Raster Data
Last updated on 2024-11-19 | Edit this page
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
ggplot2package. - 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:
R
unique(DSM_HARV_df$fct_elevation)
OUTPUT
[1] (379,416] (342,379] (305,342]
Levels: (305,342] (342,379] (379,416]And we can get the count of values in each group using
dplyr’s count() function:
R
DSM_HARV_df %>%
count(fct_elevation)
OUTPUT
fct_elevation n
1 (305,342] 418891
2 (342,379] 1530073
3 (379,416] 370835We might prefer to customize the cutoff values for these groups. Lets
round the cutoff values so that we have groups for the ranges of 301–350
m, 351–400 m, and 401–450 m. To implement this we will give
mutate() a numeric vector of break points instead of the
number of breaks we want.
R
custom_bins <- c(300, 350, 400, 450)
DSM_HARV_df <- DSM_HARV_df %>%
mutate(fct_elevation_2 = cut(HARV_dsmCrop, breaks = custom_bins))
unique(DSM_HARV_df$fct_elevation_2)
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] 10668We 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:
- Six classified ranges of values (break points) that are evenly divided among the range of pixel values.
- Axis labels.
- 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") +
ggtitle("Elevation with hillshade") +
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)
# import DSM hillshade
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)") +
ggtitle("DSM with Hillshade") +
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 Hillshade
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()) +
ggtitle("DTM with Hillshade") +
coord_quickmap()
Key Points
- Continuous data ranges can be grouped into categories using
mutate()andcut(). - Use built-in
terrain.colors()or set your preferred color scheme manually. - Layer rasters on top of one another by using the
alphaaesthetic.