Intro to Raster Data

Last updated on 2024-03-12 | Edit this page

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Overview

Questions

  • What is a raster dataset?
  • How do I work with and plot raster data in R?

Objectives

  • Describe the fundamental attributes of a raster dataset.
  • Explore raster attributes and metadata using R.
  • Import rasters into R using the terra package.
  • Plot a raster file in R using the ggplot2 package.

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.

In this episode, we will introduce the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R. We will discuss some of the core metadata elements that we need to understand to work with rasters in R, including CRS and resolution. We will also explore missing and bad data values as stored in a raster and how R handles these elements.

We will continue to work with the dplyr and ggplot2 packages that were introduced in the Introduction to R lesson. We will use two additional packages in this episode to work with raster data - the terra and sf packages. Make sure that you have these packages loaded.

R 

library(terra)
library(ggplot2)
library(dplyr)

Introduce the Data

If not already discussed, introduce the datasets that will be used in this lesson. A brief introduction to the datasets can be found on the Geospatial workshop homepage.

For more detailed information about the datasets, check out the Geospatial workshop data page.

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 open a raster in R.

Data Tip - Object names

To improve code readability, file and object names should be used that make it clear what is in the file. The data for this episode were collected from Harvard Forest so we’ll use a naming convention of datatype_HARV.

First we will load our raster file into R and view the data structure.

R 

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

DSM_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_dsmCrop.tif 
name        : HARV_dsmCrop 
min value   :       305.07 
max value   :       416.07

The information above includes a report of min and max values, but no other data range statistics. Similar to other R data structures like vectors and data frame columns, descriptive statistics for raster data can be retrieved like

R 

summary(DSM_HARV)

WARNING 

Warning: [summary] used a sample

OUTPUT 

  HARV_dsmCrop  
 Min.   :305.6  
 1st Qu.:345.6  
 Median :359.6  
 Mean   :359.8  
 3rd Qu.:374.3  
 Max.   :414.7

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 that instead. To force calculation all the values, you can use the function values:

R 

summary(values(DSM_HARV))

OUTPUT 

  HARV_dsmCrop  
 Min.   :305.1  
 1st Qu.:345.6  
 Median :359.7  
 Mean   :359.9  
 3rd Qu.:374.3  
 Max.   :416.1

To visualise this data in R using ggplot2, we need to convert it to a dataframe. We learned about dataframes in an earlier lesson. The terra package has an built-in function for conversion to a plotable dataframe.

R 

DSM_HARV_df <- as.data.frame(DSM_HARV, xy = TRUE)

Now when we view the structure of our data, we will see a standard dataframe format.

R 

str(DSM_HARV_df)

OUTPUT 

'data.frame':	2319799 obs. of  3 variables:
 $ x           : num  731454 731454 731456 731456 731458 ...
 $ y           : num  4713838 4713838 4713838 4713838 4713838 ...
 $ HARV_dsmCrop: num  409 408 407 407 409 ...

We can use ggplot() to plot this data. We will set the color scale to scale_fill_viridis_c which is a color-blindness friendly color scale. 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_HARV_df , aes(x = x, y = y, fill = HARV_dsmCrop)) +
    scale_fill_viridis_c() +
    coord_quickmap()
Raster plot with ggplot2 using the viridis color scale
Raster plot with ggplot2 using the viridis color scale

Plotting Tip

More information about the Viridis palette used above at R Viridis package documentation.

Plotting Tip

For faster, simpler plots, you can use the plot function from the terra package.

See ?plot for more arguments to customize the plot

R 

plot(DSM_HARV)

This map shows the elevation of our study site in Harvard Forest. From the legend, we can see that the maximum elevation is ~400, but we can’t tell whether this is 400 feet or 400 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. We introduced the concept of a CRS in an earlier lesson.

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 thecrs() function.

R 

crs(DSM_HARV, proj = TRUE)

OUTPUT 

[1] "+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs"

Challenge

What units are our data in?

+units=m tells us that our data is in meters.

Understanding CRS in Proj4 Format

The CRS for our data is given to us by R in proj4 format. Let’s break down the pieces of proj4 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 being defined. For example projection (proj=) and datum (datum=).

UTM Proj4 String

A projection string (like the one of DSM_HARV) specifies the UTM projection as follows:

+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0

  • proj=utm: the projection is UTM, UTM has several zones.
  • zone=18: the zone is 18
  • datum=WGS84: the datum is WGS84 (the datum refers to the 0,0 reference for the coordinate system used in the projection)
  • units=m: the units for the coordinates are in meters
  • ellps=WGS84: the ellipsoid (how the earth’s roundness is calculated) for the data is WGS84

Note that the zone is unique to the UTM projection. Not all CRSs will have a zone. Image source: Chrismurf at English Wikipedia, via Wikimedia Commons (CC-BY).

UTM zones in the USA.
The UTM zones across the continental United States. From: https://upload.wikimedia.org/wikipedia/commons/8/8d/Utm-zones-USA.svg

Calculate Raster Min and Max Values

It is useful to know the minimum or maximum values of a raster dataset. In this case, given 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_HARV)

OUTPUT 

    HARV_dsmCrop
min       305.07
max       416.07

R 

min(values(DSM_HARV))

OUTPUT 

[1] 305.07

R 

max(values(DSM_HARV))

OUTPUT 

[1] 416.07

Data Tip - Set min and max values

If the minimum and maximum values haven’t already been calculated, we can calculate them using the setMinMax() function.

R 

DSM_HARV <- setMinMax(DSM_HARV)

We can see that the elevation at our site ranges from 305.0700073m to 416.0699768m.

Key Points

  • The GeoTIFF file format includes metadata about the raster data.
  • To plot raster data with the ggplot2 package, we need to convert it to a dataframe.
  • R stores CRS information in the Proj4 format.