Intro to Raster Data
Last updated on 2024-12-03 | Edit this page
Estimated time: 32 minutes
Overview
Questions
- What is a raster dataset?
- How do I work with and plot raster data in R?
Objectives
After completing this episode, participants should be able to…
- Explore raster attributes and metadata using R.
- Import rasters into R using the
terrapackage. - Plot a raster file in R using the
ggplot2package. - Describe the difference between single- and multi-band rasters.
Things you’ll need to complete this episode
See the setup instructions for detailed information about the software, data, and other prerequisites you will need to work through the examples in this episode.
In this lesson, we will work with raster data. We will start with an introduction of the fundamental principles and metadata needed to work with raster data in R. We will discuss some of the core metadata elements needed to understand raster data in R, including CRS and resolution.
We continue to work with the tidyverse package and we
will use the terra package to work with raster data. Make
sure that you have those packages loaded.
R
library(tidyverse)
library(terra)
The data used in this lesson
In this and lesson, we will use:
- data extracted from the AHN digital elevation dataset of the Netherlands for the TU Delft campus area; and
- high-resolution RGB aerial photos of the TU Delft library obtained from Beeldmateriaal Nederland.
View Raster File Attributes
We will be working with a series of GeoTIFF files in this lesson. The
GeoTIFF format contains a set of embedded tags with metadata about the
raster data. We can use the function describe() from the
terra package to get information about our raster data
before we read that data into R. It is recommended to do this before
importing your data. We first examine the file
tud-dsm-5m.tif.
R
describe("data/tud-dsm-5m.tif")
OUTPUT
[1] "Driver: GTiff/GeoTIFF"
[2] "Files: data/tud-dsm-5m.tif"
[3] "Size is 722, 386"
[4] "Coordinate System is:"
[5] "PROJCRS[\"Amersfoort / RD New\","
[6] " BASEGEOGCRS[\"Amersfoort\","
[7] " DATUM[\"Amersfoort\","
[8] " ELLIPSOID[\"Bessel 1841\",6377397.155,299.1528128,"
[9] " LENGTHUNIT[\"metre\",1]]],"
[10] " PRIMEM[\"Greenwich\",0,"
[11] " ANGLEUNIT[\"degree\",0.0174532925199433]],"
[12] " ID[\"EPSG\",4289]],"
[13] " CONVERSION[\"RD New\","
[14] " METHOD[\"Oblique Stereographic\","
[15] " ID[\"EPSG\",9809]],"
[16] " PARAMETER[\"Latitude of natural origin\",52.1561605555556,"
[17] " ANGLEUNIT[\"degree\",0.0174532925199433],"
[18] " ID[\"EPSG\",8801]],"
[19] " PARAMETER[\"Longitude of natural origin\",5.38763888888889,"
[20] " ANGLEUNIT[\"degree\",0.0174532925199433],"
[21] " ID[\"EPSG\",8802]],"
[22] " PARAMETER[\"Scale factor at natural origin\",0.9999079,"
[23] " SCALEUNIT[\"unity\",1],"
[24] " ID[\"EPSG\",8805]],"
[25] " PARAMETER[\"False easting\",155000,"
[26] " LENGTHUNIT[\"metre\",1],"
[27] " ID[\"EPSG\",8806]],"
[28] " PARAMETER[\"False northing\",463000,"
[29] " LENGTHUNIT[\"metre\",1],"
[30] " ID[\"EPSG\",8807]]],"
[31] " CS[Cartesian,2],"
[32] " AXIS[\"easting (X)\",east,"
[33] " ORDER[1],"
[34] " LENGTHUNIT[\"metre\",1]],"
[35] " AXIS[\"northing (Y)\",north,"
[36] " ORDER[2],"
[37] " LENGTHUNIT[\"metre\",1]],"
[38] " USAGE["
[39] " SCOPE[\"Engineering survey, topographic mapping.\"],"
[40] " AREA[\"Netherlands - onshore, including Waddenzee, Dutch Wadden Islands and 12-mile offshore coastal zone.\"],"
[41] " BBOX[50.75,3.2,53.7,7.22]],"
[42] " ID[\"EPSG\",28992]]"
[43] "Data axis to CRS axis mapping: 1,2"
[44] "Origin = (83565.000000000000000,447180.000000000000000)"
[45] "Pixel Size = (5.000000000000000,-5.000000000000000)"
[46] "Metadata:"
[47] " AREA_OR_POINT=Area"
[48] "Image Structure Metadata:"
[49] " INTERLEAVE=BAND"
[50] "Corner Coordinates:"
[51] "Upper Left ( 83565.000, 447180.000) ( 4d20'49.32\"E, 52d 0'33.67\"N)"
[52] "Lower Left ( 83565.000, 445250.000) ( 4d20'50.77\"E, 51d59'31.22\"N)"
[53] "Upper Right ( 87175.000, 447180.000) ( 4d23'58.60\"E, 52d 0'35.30\"N)"
[54] "Lower Right ( 87175.000, 445250.000) ( 4d23'59.98\"E, 51d59'32.85\"N)"
[55] "Center ( 85370.000, 446215.000) ( 4d22'24.67\"E, 52d 0' 3.27\"N)"
[56] "Band 1 Block=722x2 Type=Float32, ColorInterp=Gray" We will be using this information throughout this episode. By the end of the episode, you will be able to explain and understand the output above.
Open a Raster in R
Now that we’ve previewed the metadata for our GeoTIFF, let’s import
this raster dataset into R and explore its metadata more closely. We can
use the rast() function to import a raster file in R.
Data tip - Object names
To improve code readability, use file and object names that make it
clear what is in the file. The raster data for this episode contain the
TU Delft campus and its surroundings so we’ll use a naming convention of
datatype_TUD. The first object is a Digital Surface Model
(DSM) in GeoTIFF format stored in a file tud-dsm-5m.tif
which we will load into an object named according to our naming
convention DSM_TUD.
First we will load our raster file into R and view the data structure.
R
DSM_TUD <- rast("data/tud-dsm-5m.tif")
DSM_TUD
OUTPUT
class : SpatRaster
dimensions : 386, 722, 1 (nrow, ncol, nlyr)
resolution : 5, 5 (x, y)
extent : 83565, 87175, 445250, 447180 (xmin, xmax, ymin, ymax)
coord. ref. : Amersfoort / RD New (EPSG:28992)
source : tud-dsm-5m.tif
name : tud-dsm-5m The information above includes a report on dimension, resolution,
extent and CRS, but no information about the values. Similar to other
data structures in R like vectors and data frame columns, descriptive
statistics for raster data can be retrieved with the
summary() function.
R
summary(DSM_TUD)
WARNING
Warning: [summary] used a sampleOUTPUT
tud.dsm.5m
Min. :-5.2235
1st Qu.:-0.7007
Median : 0.5462
Mean : 2.5850
3rd Qu.: 4.4596
Max. :89.7838 This output gives us information about the range of values in the
DSM. We can see, for instance, that the lowest elevation is
-5.2235, the highest is 89.7838. But note the
warning. Unless you force R to calculate these statistics using every
cell in the raster, it will take a random sample of 100,000 cells and
calculate from them instead. To force calculation all the values, you
can use the function values:
R
summary(values(DSM_TUD))
OUTPUT
tud-dsm-5m
Min. :-5.3907
1st Qu.:-0.7008
Median : 0.5573
Mean : 2.5886
3rd Qu.: 4.4648
Max. :92.0810 With a summary on all cells of the raster, the values range from a
smaller minimum of -5.3907 to a higher maximum of
92.0910.
To visualise the DSM in R using ggplot2, we need to
convert it to a data frame. We learned about data frames in an earlier lesson. The terra
package has the built-in function as.data.frame() for
conversion to a data frame.
R
DSM_TUD_df <- as.data.frame(DSM_TUD, xy = TRUE)
Now when we view the structure of our data, we will see a standard data frame format.
R
str(DSM_TUD_df)
OUTPUT
'data.frame': 278692 obs. of 3 variables:
$ x : num 83568 83572 83578 83582 83588 ...
$ y : num 447178 447178 447178 447178 447178 ...
$ tud-dsm-5m: num 10.34 8.64 1.25 1.12 2.13 ...We can use ggplot() to plot this data with a specific
geom_ function called geom_raster(). We will
make the colour scale in our plot colour-blindness friendly with
scale_fill_viridis_c, introduced in an earlier lesson. We will also
use the coord_quickmap() function to use an approximate
Mercator projection for our plots. This approximation is suitable for
small areas that are not too close to the poles. Other coordinate
systems are available in ggplot2 if needed, you can learn
about them at their help page ?coord_map.
R
ggplot() +
geom_raster(data = DSM_TUD_df , aes(x = x, y = y, fill = `tud-dsm-5m`)) +
scale_fill_viridis_c(option = "H") + # `option = "H"` provides a contrasting colour scale
coord_quickmap()
ggplot2 using the viridis color scale
Plotting tip
More information about the viridis palette used above at viridis package documentation.
Plotting tip
For faster previews, you can use the plot() function on
a terra object.
This map shows our Digital Surface Model, that is, the elevation of our study site including buildings and vegetation. From the legend we can confirm that the maximum elevation is around 90, but we cannot tell whether this is 90 feet or 90 meters because the legend doesn’t show us the units. We can look at the metadata of our object to see what the units are. Much of the metadata that we’re interested in is part of the CRS.
Now we will see how features of the CRS appear in our data file and what meanings they have.
View Raster Coordinate Reference System (CRS) in R
We can view the CRS string associated with our R object using the
crs() function.
R
crs(DSM_TUD, proj = TRUE)
OUTPUT
[1] "+proj=sterea +lat_0=52.1561605555556 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs"Challenge
What units are our data in?
+units=m in the output of the code above tells us that
our data is in meters (m).
Understanding CRS in PROJ.4 format
The CRS for our data is given to us by R in PROJ.4 format. Let’s
break down the pieces of a PROJ.4 string. The string contains all of the
individual CRS elements that R or another GIS might need. Each element
is specified with a + sign, similar to how a
.csv file is delimited or broken up by a ,.
After each + we see the CRS element such as projection
(proj=) being defined.
See more about CRS and PROJ.4 strings in this lesson.
Calculate Raster Min and Max values
It is useful to know the minimum and maximum values of a raster dataset. In this case, as we are working with elevation data, these values represent the min/max elevation range at our site.
Raster statistics are often calculated and embedded in a GeoTIFF for us. We can view these values:
R
minmax(DSM_TUD)
OUTPUT
tud-dsm-5m
min Inf
max -InfData tip - Set min and max values
If the min and max values are
Inf and -Inf respectively, it means that they
haven’t been calculated. We can calculate them using the
setMinMax() function.
R
DSM_TUD <- setMinMax(DSM_TUD)
R
min(values(DSM_TUD))
OUTPUT
[1] -5.39069R
max(values(DSM_TUD))
OUTPUT
[1] 92.08102We can see that the elevation at our site ranges from
-5.39069m to 92.08102m.
Raster bands
The Digital Surface Model object (DSM_TUD) that we’ve
been working with is a single band raster. This means that there is only
one dataset stored in the raster: surface elevation in meters for one
time period.
A raster dataset can contain one or more bands. We can view the
number of bands in a raster using the nlyr() function.
R
nlyr(DSM_TUD)
OUTPUT
[1] 1This dataset has only 1 band. However, raster data can also be multi-band, meaning that one raster file contains data for more than one variable or time period for each cell. We will discuss multi-band raster data in a later episode.
Creating a histogram of raster values
A histogram can be used to inspect the distribution of raster values visually. It can show if there are values above the max or below the min of the expected range.
We can inspect the distribution of values contained in a raster using
the ggplot2 function geom_histogram().
Histograms are often useful in identifying outliers and bad data values
in our raster data.
R
ggplot() +
geom_histogram(data = DSM_TUD_df, aes(`tud-dsm-5m`))
OUTPUT
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Notice that a message is displayed when R creates the histogram:
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.This message is caused by a default setting in
geom_histogram() enforcing that there are 30 bins for the
data. We can define the number of bins we want in the histogram by
giving another value to the bins argument. 60 bins, for
instance, will give a more detailed histogram of the same
distribution:
R
ggplot() +
geom_histogram(data = DSM_TUD_df, aes(`tud-dsm-5m`), bins = 60)
Note that the shape of this histogram looks similar to the previous one that was created using the default of 30 bins. The distribution of elevation values for our Digital Surface Model (DSM) looks reasonable. It is likely that there are no bad data values in this particular raster.
Challenge: Explore raster metadata
Use describe() to determine the following about the
tud-dsm-hill.tif file:
- Does this file have the same CRS as
DSM_TUD? - What is the resolution of the raster data?
- How large would a 5x5 pixel area be on the Earth’s surface?
- Is the file a multi- or single-band raster?
Note that this file is a hillshade raster. We will learn about hillshades in the Working with Multi-band Rasters in R episode.
R
describe("data/tud-dsm-5m-hill.tif")
OUTPUT
[1] "Driver: GTiff/GeoTIFF"
[2] "Files: data/tud-dsm-5m-hill.tif"
[3] "Size is 722, 386"
[4] "Coordinate System is:"
[5] "PROJCRS[\"Amersfoort / RD New\","
[6] " BASEGEOGCRS[\"Amersfoort\","
[7] " DATUM[\"Amersfoort\","
[8] " ELLIPSOID[\"Bessel 1841\",6377397.155,299.1528128,"
[9] " LENGTHUNIT[\"metre\",1]]],"
[10] " PRIMEM[\"Greenwich\",0,"
[11] " ANGLEUNIT[\"degree\",0.0174532925199433]],"
[12] " ID[\"EPSG\",4289]],"
[13] " CONVERSION[\"RD New\","
[14] " METHOD[\"Oblique Stereographic\","
[15] " ID[\"EPSG\",9809]],"
[16] " PARAMETER[\"Latitude of natural origin\",52.1561605555556,"
[17] " ANGLEUNIT[\"degree\",0.0174532925199433],"
[18] " ID[\"EPSG\",8801]],"
[19] " PARAMETER[\"Longitude of natural origin\",5.38763888888889,"
[20] " ANGLEUNIT[\"degree\",0.0174532925199433],"
[21] " ID[\"EPSG\",8802]],"
[22] " PARAMETER[\"Scale factor at natural origin\",0.9999079,"
[23] " SCALEUNIT[\"unity\",1],"
[24] " ID[\"EPSG\",8805]],"
[25] " PARAMETER[\"False easting\",155000,"
[26] " LENGTHUNIT[\"metre\",1],"
[27] " ID[\"EPSG\",8806]],"
[28] " PARAMETER[\"False northing\",463000,"
[29] " LENGTHUNIT[\"metre\",1],"
[30] " ID[\"EPSG\",8807]]],"
[31] " CS[Cartesian,2],"
[32] " AXIS[\"easting (X)\",east,"
[33] " ORDER[1],"
[34] " LENGTHUNIT[\"metre\",1]],"
[35] " AXIS[\"northing (Y)\",north,"
[36] " ORDER[2],"
[37] " LENGTHUNIT[\"metre\",1]],"
[38] " USAGE["
[39] " SCOPE[\"Engineering survey, topographic mapping.\"],"
[40] " AREA[\"Netherlands - onshore, including Waddenzee, Dutch Wadden Islands and 12-mile offshore coastal zone.\"],"
[41] " BBOX[50.75,3.2,53.7,7.22]],"
[42] " ID[\"EPSG\",28992]]"
[43] "Data axis to CRS axis mapping: 1,2"
[44] "Origin = (83565.000000000000000,447180.000000000000000)"
[45] "Pixel Size = (5.000000000000000,-5.000000000000000)"
[46] "Metadata:"
[47] " AREA_OR_POINT=Area"
[48] "Image Structure Metadata:"
[49] " INTERLEAVE=BAND"
[50] "Corner Coordinates:"
[51] "Upper Left ( 83565.000, 447180.000) ( 4d20'49.32\"E, 52d 0'33.67\"N)"
[52] "Lower Left ( 83565.000, 445250.000) ( 4d20'50.77\"E, 51d59'31.22\"N)"
[53] "Upper Right ( 87175.000, 447180.000) ( 4d23'58.60\"E, 52d 0'35.30\"N)"
[54] "Lower Right ( 87175.000, 445250.000) ( 4d23'59.98\"E, 51d59'32.85\"N)"
[55] "Center ( 85370.000, 446215.000) ( 4d22'24.67\"E, 52d 0' 3.27\"N)"
[56] "Band 1 Block=722x11 Type=Byte, ColorInterp=Gray"
[57] " NoData Value=0" More resources
- See the manual and tutorials of the
terrapackage on https://rspatial.org/.
Key Points
- The GeoTIFF file format includes metadata about the raster data.
- To plot raster data with the
ggplot2package, we need to convert them to data frames. - R stores CRS information in the PROJ.4 format.
- Histograms are useful to identify missing or bad data values.