Intro to Raster Data
Overview
Teaching: 30 min
Exercises: 20 minQuestions
What is a raster dataset?
How do I work with and plot raster data in R?
How can I handle missing or bad data values for a raster?
Objectives
Describe the fundamental attributes of a raster dataset.
Explore raster attributes and metadata using R.
Import rasters into R using the
rasterpackage.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 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 for Geospatial Data lesson. We will use two additional packages in this episode to work with raster data - the
raster and rgdal packages. Make sure that you have these packages loaded.
library(raster)
library(rgdal)
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.
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 GDALinfo() to get information about our raster
data before we read that data into R. It is ideal to do this before importing
your data.
GDALinfo("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")
rows 1367
columns 1697
bands 1
lower left origin.x 731453
lower left origin.y 4712471
res.x 1
res.y 1
ysign -1
oblique.x 0
oblique.y 0
driver GTiff
projection +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
file data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif
apparent band summary:
GDType hasNoDataValue NoDataValue blockSize1 blockSize2
1 Float64 TRUE -9999 1 1697
apparent band statistics:
Bmin Bmax Bmean Bsd
1 305.07 416.07 359.8531 17.83169
Metadata:
AREA_OR_POINT=Area
If you wish to store this information in R, you can do the following:
HARV_dsmCrop_info <- capture.output(
GDALinfo("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")
)
Each line of text that was printed to the console is now stored as an element of
the character vector HARV_dsmCrop_info. We will be exploring this data throughout this
episode. By the end of this 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 raster()
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.
DSM_HARV <-
raster("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")
DSM_HARV
class : RasterLayer
dimensions : 1367, 1697, 2319799 (nrow, ncol, ncell)
resolution : 1, 1 (x, y)
extent : 731453, 733150, 4712471, 4713838 (xmin, xmax, ymin, ymax)
crs : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
source : HARV_dsmCrop.tif
names : HARV_dsmCrop
values : 305.07, 416.07 (min, max)
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
summary(DSM_HARV)
Warning in .local(object, ...): summary is an estimate based on a sample of 1e+05 cells (4.31% of all cells)
HARV_dsmCrop
Min. 305.5500
1st Qu. 345.6500
Median 359.6450
3rd Qu. 374.2825
Max. 413.9000
NA's 0.0000
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 on more, or even all values,
you can use the parameter maxsamp:
summary(DSM_HARV, maxsamp = ncell(DSM_HARV))
HARV_dsmCrop
Min. 305.07
1st Qu. 345.59
Median 359.67
3rd Qu. 374.28
Max. 416.07
NA's 0.00
You may not see major differences in summary stats as maxsamp increases,
except with very large rasters.
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 raster package has an built-in function for conversion to a plotable dataframe.
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.
str(DSM_HARV_df)
'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.
ggplot() +
geom_raster(data = DSM_HARV_df , aes(x = x, y = y, fill = HARV_dsmCrop)) +
scale_fill_viridis_c() +
coord_quickmap()
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
plotfunction from therasterpackage.Show plot
See
?plotfor more arguments to customize the plotplot(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.
crs(DSM_HARV)
CRS arguments:
+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs
Challenge
What units are our data in?
Answers
+units=mtells 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=).
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.
We already saw this in str(). This is an alternate method.
Raster statistics are often calculated and embedded in a GeoTIFF for us. We can view these values:
minValue(DSM_HARV)
[1] 305.07
maxValue(DSM_HARV)
[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.DSM_HARV <- setMinMax(DSM_HARV)
We can see that the elevation at our site ranges from 305.0700073m to 416.0699768m. For mountainous Massachusetts, that passes the smell test.
Raster Bands
The Digital Surface Model object (DSM_HARV) 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 use the raster()
function to import one single band from a single or multi-band raster. We can
view the number of bands in a raster using the nlayers() function.
nlayers(DSM_HARV)
[1] 1
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. By
default the raster() function only imports the first band in a raster
regardless of whether it has one or more bands. Jump to a later episode in
this series for information on working with multi-band rasters:
Work with Multi-band Rasters in R.
Dealing with Missing Data
Raster data often has a NoDataValue associated with it. This is a value
assigned to pixels where data is missing or no data were collected.
By default the shape of a raster is always rectangular. So if we have a dataset
that has a shape that isn’t rectangular, some pixels at the edge of the raster
will have NoDataValues. This often happens when the data were collected by an
airplane which only flew over some part of a defined region.
In the image below, the pixels that are black have NoDataValues. The camera
did not collect data in these areas.
In the next image, the black edges have been assigned NoDataValue. R doesn’t
render pixels that contain a specified NoDataValue. R assigns missing data
with the NoDataValue as NA.
The difference here shows up as ragged edges on the plot, rather than black spaces where there is no data.
If your raster already has NA values set correctly but you aren’t sure where they are, you can deliberately plot them in a particular colour. This can be useful when checking a dataset’s coverage. For instance, sometimes data can be missing where a sensor could not ‘see’ its target data, and you may wish to locate that missing data and fill it in.
To highlight NA values in ggplot, alter the scale_fill_*() layer to contain a colour instruction for NA values, like scale_fill_viridis_c(na.value = 'deeppink')
The value that is conventionally used to take note of missing data (the
NoDataValue value) varies by the raster data type. For floating-point rasters,
the figure -3.4e+38 is a common default, and for integers, -9999 is
common. Some disciplines have specific conventions that vary from these
common values.
In some cases, other NA values may be more appropriate. An NA value should
be a) outside the range of valid values, and b) a value that fits the data type
in use. For instance, if your data ranges continuously from -20 to 100, 0 is
not an acceptable NA value! Or, for categories that number 1-15, 0 might be
fine for NA, but using -.000003 will force you to save the GeoTIFF on disk
as a floating point raster, resulting in a bigger file.
If we are lucky, our GeoTIFF file has a tag that tells us what is the
NoDataValue. If we are less lucky, we can find that information in the
raster’s metadata. If a NoDataValue was stored in the GeoTIFF tag, when R
opens up the raster, it will assign each instance of the value to NA. Values
of NA will be ignored by R as demonstrated above.
Challenge
Use the output from the
GDALinfo()function to find out whatNoDataValueis used for ourDSM_HARVdataset.Answers
GDALinfo("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif")rows 1367 columns 1697 bands 1 lower left origin.x 731453 lower left origin.y 4712471 res.x 1 res.y 1 ysign -1 oblique.x 0 oblique.y 0 driver GTiff projection +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs file data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_dsmCrop.tif apparent band summary: GDType hasNoDataValue NoDataValue blockSize1 blockSize2 1 Float64 TRUE -9999 1 1697 apparent band statistics: Bmin Bmax Bmean Bsd 1 305.07 416.07 359.8531 17.83169 Metadata: AREA_OR_POINT=Area
NoDataValueare encoded as -9999.
Bad Data Values in Rasters
Bad data values are different from NoDataValues. Bad data values are values
that fall outside of the applicable range of a dataset.
Examples of Bad Data Values:
- The normalized difference vegetation index (NDVI), which is a measure of greenness, has a valid range of -1 to 1. Any value outside of that range would be considered a “bad” or miscalculated value.
- Reflectance data in an image will often range from 0-1 or 0-10,000 depending upon how the data are scaled. Thus a value greater than 1 or greater than 10,000 is likely caused by an error in either data collection or processing.
Find Bad Data Values
Sometimes a raster’s metadata will tell us the range of expected values for a raster. Values outside of this range are suspect and we need to consider that when we analyze the data. Sometimes, we need to use some common sense and scientific insight as we examine the data - just as we would for field data to identify questionable values.
Plotting data with appropriate highlighting can help reveal patterns in bad values and may suggest a solution. Below, reclassification is used to highlight elevation values over 400m with a contrasting colour.
Create A Histogram of Raster Values
We can explore the distribution of values contained within our raster using the
geom_histogram() function which produces a histogram. Histograms are often
useful in identifying outliers and bad data values in our raster data.
ggplot() +
geom_histogram(data = DSM_HARV_df, aes(HARV_dsmCrop))
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Notice that a warning message is thrown when R creates the histogram.
stat_bin() using bins = 30. Pick better value with binwidth.
This warning 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 using the bins value in the geom_histogram() function.
ggplot() +
geom_histogram(data = DSM_HARV_df, aes(HARV_dsmCrop), bins = 40)
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 there are
no bad data values in this particular raster.
Challenge: Explore Raster Metadata
Use
GDALinfo()to determine the following about theNEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_DSMhill.tiffile:
- Does this file have the same CRS as
DSM_HARV?- What is the
NoDataValue?- What is 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?
Notice: this file is a hillshade. We will learn about hillshades in the Working with Multi-band Rasters in R episode.
Answers
GDALinfo("data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_DSMhill.tif")rows 1367 columns 1697 bands 1 lower left origin.x 731453 lower left origin.y 4712471 res.x 1 res.y 1 ysign -1 oblique.x 0 oblique.y 0 driver GTiff projection +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs file data/NEON-DS-Airborne-Remote-Sensing/HARV/DSM/HARV_DSMhill.tif apparent band summary: GDType hasNoDataValue NoDataValue blockSize1 blockSize2 1 Float64 TRUE -9999 1 1697 apparent band statistics: Bmin Bmax Bmean Bsd 1 -0.7136298 0.9999997 0.3125525 0.4812939 Metadata: AREA_OR_POINT=Area
- If this file has the same CRS as DSM_HARV? Yes: UTM Zone 18, WGS84, meters.
- What format
NoDataValuestake? -9999- The resolution of the raster data? 1x1
- How large a 5x5 pixel area would be? 5mx5m How? We are given resolution of 1x1 and units in meters, therefore resolution of 5x5 means 5x5m.
- Is the file a multi- or single-band raster? Single.
More Resources
Key Points
The GeoTIFF file format includes metadata about the raster data.
To plot raster data with the
ggplot2package, we need to convert it to a dataframe.Be careful when dealing with missing or bad data values.