This tutorial will walk you through the fundamental principles of working with image raster data in R.

Raster Data

Raster or "gridded" data are data that are saved in pixels. In the spatial world, each pixel represents an area on the Earth's surface. An color image raster is a bit different from other rasters in that it has multiple bands. Each band represents reflectance values for a particular color or spectra of light. If the image is RGB, then the bands are in the red, green and blue portions of the electromagnetic spectrum. These colors together create what we know as a full color image.

Work with Multiple Rasters

In a previous tutorial, we loaded a single raster into R. We made sure we knew the CRS (coordinate reference system) and extent of the dataset among other key metadata attributes. This raster was a Digital Elevation Model so there was only a single raster that represented the ground elevation in each pixel. When we work with color images, there are multiple rasters to represent each band. Here we'll learn to work with multiple rasters together.

Raster Stacks

A raster stack is a collection of raster layers. Each raster layer in the raster stack needs to have the same

• projection (CRS),
• spatial extent and
• resolution.

You might use raster stacks for different reasons. For instance, you might want to group a time series of rasters representing precipitation or temperature into one R object. Or, you might want to create a color images from red, green and blue band derived rasters.

In this tutorial, we will stack three bands from a multi-band image together to create a composite RGB image.

First let's load the R packages that we need: sp and raster.

# load the raster, sp, and rgdal packages
library(raster)
library(sp)
library(rgdal)

# set the working directory to the data
#setwd("pathToDirHere")
wd <- ("~/Git/data/")
setwd(wd)

Next, let's create a raster stack with bands representing

• blue: band 19, 473.8nm
• green: band 34, 548.9nm
• red; band 58, 669.1nm

This can be done by individually assigning each file path as an object.

# import tiffs
band19 <- paste0(wd, "NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band19.tif")
band34 <- paste0(wd, "NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band34.tif")
band58 <- paste0(wd, "NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band58.tif")

# View their attributes to check that they loaded correctly:
band19

## [1] "~/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band19.tif"

band34

## [1] "~/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band34.tif"

band58

## [1] "~/Git/data/NEON-DS-Field-Site-Spatial-Data/SJER/RGB/band58.tif"

Note that if we wanted to create a stack from all the files in a directory (folder) you can easily do this with the list.files() function. We would use full.names=TRUE to ensure that R will store the directory path in our list of rasters.

# create list of files to make raster stack
rasterlist1 <- list.files(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB", full.names=TRUE))

rasterlist1

## character(0)

Or, if your directory consists of some .tif files and other file types you don't want in your stack, you can ask R to only list those files with a .tif extension.

rasterlist2 <-  list.files(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB", full.names=TRUE, pattern="tif"))

rasterlist2

## character(0)

Back to creating our raster stack with three bands. We only want three of the bands in the RGB directory and not the fourth band90, so will create the stack from the bands we loaded individually. We do this with the stack() function.

# create raster stack
rgbRaster <- stack(band19,band34,band58)

# example syntax for stack from a list
#rstack1 <- stack(rasterlist1)

This has now created a stack that is three rasters thick. Let's view them.

# check attributes
rgbRaster

## class      : RasterStack 
## dimensions : 502, 477, 239454, 3  (nrow, ncol, ncell, nlayers)
## resolution : 1, 1  (x, y)
## extent     : 256521, 256998, 4112069, 4112571  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## names      : band19, band34, band58 
## min values :     84,    116,    123 
## max values :  13805,  15677,  14343

# plot stack
plot(rgbRaster)

From the attributes we see the CRS, resolution, and extent of all three rasters. The we can see each raster plotted. Notice the different shading between the different bands. This is because the landscape relects in the red, green, and blue spectra differently.

Check out the scale bars. What do they represent?

This reflectance data are radiances corrected for atmospheric effects. The data are typically unitless and ranges from 0-1. NEON Airborne Observation Platform data, where these rasters come from, has a scale factor of 10,000.

Plot an RGB Image

You can plot a composite RGB image from a raster stack. You need to specify the order of the bands when you do this. In our raster stack, band 19, which is the blue band, is first in the stack, whereas band 58, which is the red band, is last. Thus the order for a RGB image is 3,2,1 to ensure the red band is rendered first as red.

Thinking ahead to next time: If you know you want to create composite RGB images, think about the order of your rasters when you make the stack so the RGB=1,2,3.

We will plot the raster with the rgbRaster() function and the need these following arguments:

• R object to plot
• which layer of the stack is which color
• stretch: allows for increased contrast. Options are "lin" & "hist".

Let's try it.

# plot an RGB version of the stack
plotRGB(rgbRaster,r=3,g=2,b=1, stretch = "lin")

Note: read the raster package documentation for other arguments that can be added (like scale) to improve or modify the image.

Explore Raster Values - Histograms

You can also explore the data. Histograms allow us to view the distrubiton of values in the pixels.

# view histogram of reflectance values for all rasters
hist(rgbRaster)

## Warning in .hist1(raster(x, y[i]), maxpixels = maxpixels, main =
## main[y[i]], : 42% of the raster cells were used. 100000 values used.

## Warning in .hist1(raster(x, y[i]), maxpixels = maxpixels, main =
## main[y[i]], : 42% of the raster cells were used. 100000 values used.

## Warning in .hist1(raster(x, y[i]), maxpixels = maxpixels, main =
## main[y[i]], : 42% of the raster cells were used. 100000 values used.

Note about the warning messages: R defaults to only showing the first 100,000 values in the histogram so if you have a large raster you may not be seeing all the values. This saves your from long waits, or crashing R, if you have large datasets.

Crop Rasters

You can crop all rasters within a raster stack the same way you'd do it with a single raster.

# determine the desired extent
rgbCrop <- c(256770.7,256959,4112140,4112284)

# crop to desired extent
rgbRaster_crop <- crop(rgbRaster, rgbCrop)

# view cropped stack
plot(rgbRaster_crop)

Raster Bricks in R

In our rgbRaster object we have a list of rasters in a stack. These rasters are all the same extent, CRS and resolution. By creating a raster brick we will create one raster object that contains all of the rasters so that we can use this object to quickly create RGB images. Raster bricks are more efficient objects to use when processing larger datasets. This is because the computer doesn't have to spend energy finding the data - it is contained within the object.

# create raster brick
rgbBrick <- brick(rgbRaster)

# check attributes
rgbBrick

## class      : RasterBrick 
## dimensions : 502, 477, 239454, 3  (nrow, ncol, ncell, nlayers)
## resolution : 1, 1  (x, y)
## extent     : 256521, 256998, 4112069, 4112571  (xmin, xmax, ymin, ymax)
## crs        : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs 
## source     : memory
## names      : band19, band34, band58 
## min values :     84,    116,    123 
## max values :  13805,  15677,  14343

While the brick might seem similar to the stack (see attributes above), we can see that it's very different when we look at the size of the object.

• the brick contains all of the data stored in one object
• the stack contains links or references to the files stored on your computer

Use object.size() to see the size of an R object.

# view object size
object.size(rgbBrick)

## 5762000 bytes

object.size(rgbRaster)

## 49984 bytes

# view raster brick
plotRGB(rgbBrick,r=3,g=2,b=1, stretch = "Lin")

Notice the faster plotting? For a smaller raster like this the difference is slight, but for larger raster it can be considerable.

Write to GeoTIFF

We can write out the raster in GeoTIFF format as well. When we do this it will copy the CRS, extent and resolution information so the data will read properly into a GIS program as well. Note that this writes the raster in the order they are in. In our case, the blue (band 19) is first but most programs expect the red band first (RGB).

One way around this is to generate a new raster stack with the rasters in the proper order - red, green and blue format. Or, just always create your stacks R->G->B to start!!!

# Make a new stack in the order we want the data in 
orderRGBstack <- stack(rgbRaster$band58,rgbRaster$band34,rgbRaster$band19)

# write the geotiff
# change overwrite=TRUE to FALSE if you want to make sure you don't overwrite your files!
writeRaster(orderRGBstack,paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB/rgbRaster.tif"),"GTiff", overwrite=TRUE)

Import A Multi-Band Image into R

You can import a multi-band image into R too. To do this, you import the file as a stack rather than a raster (which brings in just one band). Let's import the raster than we just created above.

# import multi-band raster as stack
multiRasterS <- stack(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB/rgbRaster.tif")) 

# import multi-band raster direct to brick
multiRasterB <- brick(paste0(wd,"NEON-DS-Field-Site-Spatial-Data/SJER/RGB/rgbRaster.tif")) 

# view raster
plot(multiRasterB)

plotRGB(multiRasterB,r=1,g=2,b=3, stretch="lin")

Mapping the Invisible

About Hyperspectral Remote Sensing Data

The electromagnetic spectrum is composed of thousands of bands representing different types of light energy. Imaging spectrometers (instruments that collect hyperspectral data) break the electromagnetic spectrum into groups of bands that support classification of objects by their spectral properties on the earth's surface. Hyperspectral data consists of many bands -- up to hundreds of bands -- that cover the electromagnetic spectrum.

The NEON imaging spectrometer collects data within the 380nm to 2510nm portions of the electromagnetic spectrum within bands that are approximately 5nm in width. This results in a hyperspectral data cube that contains approximately 426 bands - which means big, big data.

Key Metadata for Hyperspectral Data

Bands and Wavelengths

A band represents a group of wavelengths. For example, the wavelength values between 695nm and 700nm might be one band as captured by an imaging spectrometer. The imaging spectrometer collects reflected light energy in a pixel for light in that band. Often when you work with a multi or hyperspectral dataset, the band information is reported as the center wavelength value. This value represents the center point value of the wavelengths represented in that band. Thus in a band spanning 695-700 nm, the center would be 697.5).

Spectral Resolution

The spectral resolution of a dataset that has more than one band, refers to the width of each band in the dataset. In the example above, a band was defined as spanning 695-700nm. The width or spatial resolution of the band is thus 5 nanometers. To see an example of this, check out the band widths for the Landsat sensors.

Full Width Half Max (FWHM)

The full width half max (FWHM) will also often be reported in a multi or hyperspectral dataset. This value represents the spread of the band around that center point.

In the illustration above, the band that covers 695-700nm has a FWHM of 5 nm. While a general spectral resolution of the sensor is often provided, not all sensors create bands of uniform widths. For instance bands 1-9 of Landsat 8 are listed below (Courtesy of USGS)

In this tutorial, we will show how to read and extract NEON reflectance data stored within an HDF5 file using R.

About Hyperspectral Remote Sensing Data

The electromagnetic spectrum is composed of thousands of bands representing different types of light energy. Imaging spectrometers (instruments that collect hyperspectral data) break the electromagnetic spectrum into groups of bands that support classification of objects by their spectral properties on the Earth's surface. Hyperspectral data consists of many bands - up to hundreds of bands - that span a portion of the electromagnetic spectrum, from the visible to the Short Wave Infrared (SWIR) regions.

The NEON imaging spectrometer (NIS) collects data within the 380 nm to 2510 nm portions of the electromagnetic spectrum within bands that are approximately 5 nm in width. This results in a hyperspectral data cube that contains approximately 426 bands - which means BIG DATA.

The HDF5 data model natively compresses data stored within it (makes it smaller) and supports data slicing (extracting only the portions of the data that you need to work with rather than reading the entire dataset into memory). These features make it ideal for working with large data cubes such as those generated by imaging spectrometers, in addition to supporting spatial data and associated metadata.

In this tutorial we will demonstrate how to read and extract spatial raster data stored within an HDF5 file using R.

Read HDF5 data into R

We will use the terra and rhdf5 packages to read in the HDF5 file that contains hyperspectral data for the NEON San Joaquin (SJER) field site. Let's start by calling the needed packages and reading in our NEON HDF5 file.

Please be sure that you have at least version 2.10 of rhdf5 installed. Use: packageVersion("rhdf5") to check the package version.

# Load `terra` and `rhdf5` packages to read NIS data into R

library(terra)

library(rhdf5)

library(neonUtilities)

Set the working directory to ensure R can find the file we are importing, and we know where the file is being saved. You can move the file that is downloaded afterward, but be sure to re-set the path to the file.

wd <- "~/data/" #This will depend on your local environment

setwd(wd)

We can use the neonUtilities function byTileAOP to download a single reflectance tile. You can run help(byTileAOP) to see more details on what the various inputs are. For this exercise, we'll specify the UTM Easting and Northing to be (257500, 4112500), which will download the tile with the lower left corner (257000,4112000). By default, the function will check the size total size of the download and ask you whether you wish to proceed (y/n). This file is ~672.7 MB, so make sure you have enough space on your local drive. You can set check.size=FALSE if you want to download without a prompt.

byTileAOP(dpID='DP3.30006.001',

          site='SJER',

          year='2021',

          easting=257500,

          northing=4112500,

          check.size=TRUE, # set to FALSE if you don't want to enter y/n

          savepath = wd)

This file will be downloaded into a nested subdirectory under the ~/data folder, inside a folder named DP3.30006.001 (the Data Product ID). The file should show up in this location: ~/data/DP3.30006.001/neon-aop-products/2021/FullSite/D17/2021_SJER_5/L3/Spectrometer/Reflectance/NEON_D17_SJER_DP3_257000_4112000_reflectance.h5.

# Define the h5 file name to be opened

h5_file <- paste0(wd,"DP3.30006.001/neon-aop-products/2021/FullSite/D17/2021_SJER_5/L3/Spectrometer/Reflectance/NEON_D17_SJER_DP3_257000_4112000_reflectance.h5")

You can use h5ls and/or View(h5ls(...)) to look at the contents of the hdf5 file, as follows:

# look at the HDF5 file structure 

View(h5ls(h5_file,all=T))

When you look at the structure of the data, take note of the "map info" dataset, the Coordinate_System group, and the wavelength and Reflectance datasets. The Coordinate_System folder contains the spatial attributes of the data including its EPSG Code, which is easily converted to a Coordinate Reference System (CRS). The CRS documents how the data are physically located on the Earth. The wavelength dataset contains the wavelength values for each band in the data. The Reflectance dataset contains the image data that we will use for both data processing and visualization.

More Information on raster metadata:

• Raster Data in R - The Basics - this tutorial explains more about how rasters work in R and their associated metadata.

• About Hyperspectral Remote Sensing Data -this tutorial explains more about metadata and important concepts associated with multi-band (multi and hyperspectral) rasters.

We can use the h5readAttributes() function to read and extract metadata from the HDF5 file. Let's start by learning about the wavelengths described within this file.

# get information about the wavelengths of this dataset

wavelengthInfo <- h5readAttributes(h5_file,"/SJER/Reflectance/Metadata/Spectral_Data/Wavelength")

wavelengthInfo

## $Description
## [1] "Central wavelength of the reflectance bands."
## 
## $Units
## [1] "nanometers"

Next, we can use the h5read function to read the data contained within the HDF5 file. Let's read in the wavelengths of the band centers:

# read in the wavelength information from the HDF5 file

wavelengths <- h5read(h5_file,"/SJER/Reflectance/Metadata/Spectral_Data/Wavelength")

head(wavelengths)

## [1] 381.6035 386.6132 391.6229 396.6327 401.6424 406.6522

tail(wavelengths)

## [1] 2485.693 2490.703 2495.713 2500.722 2505.732 2510.742

Which wavelength is band 21 associated with?

(Hint: look at the wavelengths vector that we just imported and check out the data located at index 21 - wavelengths[21]).

Band 21 has a associated wavelength center of 481.7982 nanometers (nm) which is in the blue portion (~380-500 nm) of the visible electromagnetic spectrum (~380-700 nm).

Bands and Wavelengths

A band represents a group of wavelengths. For example, the wavelength values between 695 nm and 700 nm might be one band captured by an imaging spectrometer. The imaging spectrometer collects reflected light energy in a pixel for light in that band. Often when you work with a multi- or hyperspectral dataset, the band information is reported as the center wavelength value. This value represents the mean value of the wavelengths represented in that band. Thus in a band spanning 695-700 nm, the center would be 697.5 nm). The full width half max (FWHM) will also be reported. This value can be thought of as the spread of the band around that center point. So, a band that covers 800-805 nm might have a FWHM of 5 nm and a wavelength value of 802.5 nm.

The HDF5 dataset that we are working with in this activity may contain more information than we need to work with. For example, we don't necessarily need to process all 426 bands available in a full NEON hyperspectral reflectance file - if we are interested in creating a product like NDVI which only uses bands in the Near InfraRed (NIR) and Red portions of the spectrum. Or we might only be interested in a spatial subset of the data - perhaps an area where we have collected corresponding ground data in the field.

The HDF5 format allows us to slice (or subset) the data - quickly extracting the subset that we need to process. Let's extract one of the green bands - band 34.

By the way - what is the center wavelength value associated with band 34?

Hint: wavelengths[34].

How do we know this band is a green band in the visible portion of the spectrum?

In order to effectively subset our data, let's first read the reflectance metadata stored as attributes in the "Reflectance_Data" dataset.

# First, we need to extract the reflectance metadata:

reflInfo <- h5readAttributes(h5_file, "/SJER/Reflectance/Reflectance_Data")

reflInfo

## $Cloud_conditions
## [1] "For cloud conditions information see Weather Quality Index dataset."
## 
## $Cloud_type
## [1] "Cloud type may have been selected from multiple flight trajectories."
## 
## $Data_Ignore_Value
## [1] -9999
## 
## $Description
## [1] "Atmospherically corrected reflectance."
## 
## $Dimension_Labels
## [1] "Line, Sample, Wavelength"
## 
## $Dimensions
## [1] 1000 1000  426
## 
## $Interleave
## [1] "BSQ"
## 
## $Scale_Factor
## [1] 10000
## 
## $Spatial_Extent_meters
## [1]  257000  258000 4112000 4113000
## 
## $Spatial_Resolution_X_Y
## [1] 1 1
## 
## $Units
## [1] "Unitless."
## 
## $Units_Valid_range
## [1]     0 10000

# Next, we read the different dimensions



nRows <- reflInfo$Dimensions[1]

nCols <- reflInfo$Dimensions[2]

nBands <- reflInfo$Dimensions[3]



nRows

## [1] 1000

nCols

## [1] 1000

nBands

## [1] 426

The HDF5 read function reads data in the order: Bands, Cols, Rows. This is different from how R reads data. We'll adjust for this later.

# Extract or "slice" data for band 34 from the HDF5 file

b34 <- h5read(h5_file,"/SJER/Reflectance/Reflectance_Data",index=list(34,1:nCols,1:nRows)) 



# what type of object is b34?

class(b34)

## [1] "array"

A Note About Data Slicing in HDF5

Data slicing allows us to extract and work with subsets of the data rather than reading in the entire dataset into memory. In this example, we will extract and plot the green band without reading in all 426 bands. The ability to slice large datasets makes HDF5 ideal for working with big data.

Next, let's convert our data from an array (more than 2 dimensions) to a matrix (just 2 dimensions). We need to have our data in a matrix format to plot it.

# convert from array to matrix by selecting only the first band

b34 <- b34[1,,]



# display the class of this re-defined variable

class(b34)

## [1] "matrix" "array"

Arrays vs. Matrices

Arrays are matrices with more than 2 dimensions. When we say dimension, we are talking about the "z" associated with the data (imagine a series of tabs in a spreadsheet). Put the other way: matrices are arrays with only 2 dimensions. Arrays can have any number of dimensions one, two, ten or more.

Here is a matrix that is 4 x 3 in size (4 rows and 3 columns):

Dimensions in Arrays

An array contains 1 or more dimensions in the "z" direction. For example, let's say that we collected the same set of species data for every day in a 30 day month. We might then have a matrix like the one above for each day for a total of 30 days making a 4 x 3 x 30 array (this dataset has more than 2 dimensions). More on R object types here (links to external site, DataCamp).

Next, let's look at the metadata for the reflectance data. When we do this, take note of 1) the scale factor and 2) the data ignore value. Then we can plot the band 34 data. Plotting spatial data as a visual "data check" is a good idea to make sure processing is being performed correctly and all is well with the image.

# look at the metadata for the reflectance dataset

h5readAttributes(h5_file,"/SJER/Reflectance/Reflectance_Data")

## $Cloud_conditions
## [1] "For cloud conditions information see Weather Quality Index dataset."
## 
## $Cloud_type
## [1] "Cloud type may have been selected from multiple flight trajectories."
## 
## $Data_Ignore_Value
## [1] -9999
## 
## $Description
## [1] "Atmospherically corrected reflectance."
## 
## $Dimension_Labels
## [1] "Line, Sample, Wavelength"
## 
## $Dimensions
## [1] 1000 1000  426
## 
## $Interleave
## [1] "BSQ"
## 
## $Scale_Factor
## [1] 10000
## 
## $Spatial_Extent_meters
## [1]  257000  258000 4112000 4113000
## 
## $Spatial_Resolution_X_Y
## [1] 1 1
## 
## $Units
## [1] "Unitless."
## 
## $Units_Valid_range
## [1]     0 10000

# plot the image

image(b34)

What do you notice about the first image? It's washed out and lacking any detail. What could be causing this? It got better when plotting the log of the values, but still not great.

# this is a little hard to visually interpret - what happens if we plot a log of the data?

image(log(b34))

Let's look at the distribution of reflectance values in our data to figure out what is going on.

# Plot range of reflectance values as a histogram to view range

# and distribution of values.

hist(b34,breaks=50,col="darkmagenta")

# View values between 0 and 5000

hist(b34,breaks=100,col="darkmagenta",xlim = c(0, 5000))

# View higher values

hist(b34, breaks=100,col="darkmagenta",xlim = c(5000, 15000),ylim = c(0, 750))

As you're examining the histograms above, keep in mind that reflectance values range between 0-1. The data scale factor in the metadata tells us to divide all reflectance values by 10,000. Thus, a value of 5,000 equates to a reflectance value of 0.50. Storing data as integers (without decimal places) compared to floating points (with decimal places) creates a smaller file. This type of scaling is commin in remote sensing datasets.

Notice in the data that there are some larger reflectance values (>5,000) that represent a smaller number of pixels. These pixels are skewing how the image renders.

Data Ignore Value

Image data in raster format will often contain a data ignore value and a scale factor. The data ignore value represents pixels where there are no data. Among other causes, no data values may be attributed to the sensor not collecting data in that area of the image or to processing results which yield null values.

Remember that the metadata for the Reflectance dataset designated -9999 as data ignore value. Thus, let's set all pixels with a value == -9999 to NA (no value). If we do this, R won't render these pixels.

# there is a no data value in our raster - let's define it

noDataValue <- as.numeric(reflInfo$Data_Ignore_Value)

noDataValue

## [1] -9999

# set all values equal to the no data value (-9999) to NA

b34[b34 == noDataValue] <- NA



# plot the image now

image(b34)

Reflectance Values and Image Stretch

Our image still looks dark because R is trying to render all reflectance values between 0 and 14999 as if they were distributed equally in the histogram. However we know they are not distributed equally. There are many more values between 0-5000 than there are values >5000.

Images contain a distribution of reflectance values. A typical image viewing program will render the values by distributing the entire range of reflectance values across a range of "shades" that the monitor can render - between 0 and 255. However, often the distribution of reflectance values is not linear. For example, in the case of our data, most of the reflectance values fall between 0 and 0.5. Yet there are a few values >0.8 that are heavily impacting the way the image is drawn on our monitor. Imaging processing programs like ENVI, QGIS and ArcGIS (and even Adobe Photoshop) allow you to adjust the stretch of the image. This is similar to adjusting the contrast and brightness in Photoshop.

The proper way to adjust our data would be to apply what's called an image stretch. We will learn how to stretch our image data later. For now, let's plot the values as the log function on the pixel reflectance values to factor out those larger values.

image(log(b34))

The log applied to our image increases the contrast making it look more like an image. However, look at the images below. The top one is an RGB image as the image should look. The bottom one is our log-adjusted image. Notice a difference?

Transpose Image

Notice that there are three data dimensions for this file: Bands x Rows x Columns. However, when R reads in the dataset, it reads them as: Columns x Bands x Rows. The data are flipped. We can quickly transpose the data to correct for this using the t or transpose command in R.

The orientation is rotated in our log adjusted image. This is because R reads in matrices starting from the upper left hand corner. While most rasters read pixels starting from the lower left hand corner. In the next section, we will deal with this issue by creating a proper georeferenced (spatially located) raster in R. The raster format will read in pixels following the same methods as other GIS and imaging processing software like QGIS and ENVI do.

# We need to transpose x and y values in order for our 

# final image to plot properly

b34 <- t(b34)

image(log(b34), main="Transposed Image")

Create a Georeferenced Raster

Next, we will create a proper raster using the b34 matrix. The raster format will allow us to define and manage:

• Image stretch
• Coordinate reference system & spatial reference
• Resolution
• and other raster attributes...

It will also account for the orientation issue discussed above.

To create a raster in R, we need a few pieces of information, including:

• The coordinate reference system (CRS)
• The spatial extent of the image

Define Raster CRS

First, we need to define the Coordinate reference system (CRS) of the raster. To do that, we can first grab the EPSG code from the HDF5 attributes, and covert the EPSG to a CRS string. Then we can assign that CRS to the raster object.

# Extract the EPSG from the h5 dataset

h5EPSG <- h5read(h5_file, "/SJER/Reflectance/Metadata/Coordinate_System/EPSG Code")



# convert the EPSG code to a CRS string

h5CRS <- crs(paste0("+init=epsg:",h5EPSG))



# define final raster with projection info 

# note that capitalization will throw errors on a MAC.

# if UTM is all caps it might cause an error!

b34r <- rast(b34, 
        crs=h5CRS)



# view the raster attributes

b34r

## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 0, 1000, 0, 1000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :    32 
## max value   : 13129

# let's have a look at our properly oriented raster. Take note of the 

# coordinates on the x and y axis.



image(log(b34r), 
      xlab = "UTM Easting", 
      ylab = "UTM Northing",
      main = "Properly Oriented Raster")

Next we define the extents of our raster. The extents will be used to calculate the raster's resolution. Fortunately, the spatial extent is provided in the HDF5 file "Reflectance_Data" group attributes that we saved before as reflInfo.

# Grab the UTM coordinates of the spatial extent

xMin <- reflInfo$Spatial_Extent_meters[1]

xMax <- reflInfo$Spatial_Extent_meters[2]

yMin <- reflInfo$Spatial_Extent_meters[3]

yMax <- reflInfo$Spatial_Extent_meters[4]



# define the extent (left, right, top, bottom)

rasExt <- ext(xMin,xMax,yMin,yMax)

rasExt

## SpatExtent : 257000, 258000, 4112000, 4113000 (xmin, xmax, ymin, ymax)

# assign the spatial extent to the raster

ext(b34r) <- rasExt



# look at raster attributes

b34r

## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :    32 
## max value   : 13129

Learn more about working with Raster data in R in the Data Carpentry workshop: Introduction to Geospatial Raster and Vector Data with R.

We can adjust the colors of our raster as well, if desired.

# let's change the colors of our raster and adjust the zlim 

col <- terrain.colors(25)



image(b34r,  
      xlab = "UTM Easting", 
      ylab = "UTM Northing",
      main= "Spatially Referenced Raster",
      col=col, 
      zlim=c(0,3000))

We've now created a raster from band 34 reflectance data. We can export the data as a raster, using the writeRaster command. Note that it's good practice to close the H5 connection before moving on!

# write out the raster as a geotiff

writeRaster(b34r,

            file=paste0(wd,"band34.tif"),

            overwrite=TRUE)



# close the H5 file

H5close()

In this tutorial, we will learn how to create multi (3) band images from hyperspectral data. We will also learn how to perform some basic raster calculations (known as raster math in the GIS world).

About Hyperspectral Data

We often want to generate a 3 band image from multi or hyperspectral data. The most commonly recognized band combination is RGB which stands for Red, Green and Blue. RGB images are just like an image that your camera takes. But other band combinations can be useful too. For example, near infrared images highlight healthy vegetation, which makes it easy to classify or identify where vegetation is located on the ground.

Create a Raster Stack in R

In the previous lesson, we exported a single band of the NEON Reflectance data from a HDF5 file. In this activity, we will create a full color image using 3 (red, green and blue - RGB) bands. We will follow many of the steps we followed in the Intro to Working with Hyperspectral Remote Sensing Data in HDF5 Format in R tutorial. These steps included loading required packages, downloading the data (optionally, you don't need to do this if you downloaded the data from the previous lesson), and reading in our file and viewing the hdf5 file structure.

First, let's load the required R packages, terra and rhdf5.

library(terra)

library(rhdf5)

library(neonUtilities)

Next set the working directory to ensure R can find the file we wish to import. Be sure to move the download into your working directory!

# set working directory (this will depend on your local environment)

wd <- "~/data/"

setwd(wd)

We can use the neonUtilities function byTileAOP to download a single reflectance tile. You can run help(byTileAOP) to see more details on what the various inputs are. For this exercise, we'll specify the UTM Easting and Northing to be (257500, 4112500), which will download the tile with the lower left corner (257000, 4112000).

byTileAOP(dpID = 'DP3.30006.001',

          site = 'SJER',

          year = '2021',

          easting = 257500,

          northing = 4112500,

          savepath = wd)

This file will be downloaded into a nested subdirectory under the ~/data folder, inside a folder named DP3.30006.001 (the Data Product ID). The file should show up in this location: ~/data/DP3.30006.001/neon-aop-products/2021/FullSite/D17/2021_SJER_5/L3/Spectrometer/Reflectance/NEON_D17_SJER_DP3_257000_4112000_reflectance.h5.

Now we can read in the file. You can move this file to a different location, but make sure to change the path accordingly.

# Define the h5 file name to be opened

h5_file <- paste0(wd,"DP3.30006.001/neon-aop-products/2021/FullSite/D17/2021_SJER_5/L3/Spectrometer/Reflectance/NEON_D17_SJER_DP3_257000_4112000_reflectance.h5")

As in the last lesson, let's use View(h5ls) to take a look inside this hdf5 dataset:

View(h5ls(h5_file,all=T))

To spatially locate our raster data, we need a few key attributes:

• The coordinate reference system
• The spatial extent of the raster

We'll begin by grabbing these key attributes from the H5 file.

# define coordinate reference system from the EPSG code provided in the HDF5 file

h5EPSG <- h5read(h5_file,"/SJER/Reflectance/Metadata/Coordinate_System/EPSG Code" )

h5CRS <- crs(paste0("+init=epsg:",h5EPSG))



# get the Reflectance_Data attributes

reflInfo <- h5readAttributes(h5_file,"/SJER/Reflectance/Reflectance_Data" )



# Grab the UTM coordinates of the spatial extent

xMin <- reflInfo$Spatial_Extent_meters[1]

xMax <- reflInfo$Spatial_Extent_meters[2]

yMin <- reflInfo$Spatial_Extent_meters[3]

yMax <- reflInfo$Spatial_Extent_meters[4]



# define the extent (left, right, top, bottom)

rastExt <- ext(xMin,xMax,yMin,yMax)



# view the extent to make sure that it looks right

rastExt

## SpatExtent : 257000, 258000, 4112000, 4113000 (xmin, xmax, ymin, ymax)

# Finally, define the no data value for later

h5NoDataValue <- as.integer(reflInfo$Data_Ignore_Value)

cat('No Data Value:',h5NoDataValue)

## No Data Value: -9999

Next, we'll write a function that will perform the processing that we did step by step in the Intro to Working with Hyperspectral Remote Sensing Data in HDF5 Format in R. This will allow us to process multiple bands in bulk.

The function band2Rast slices a band of data from the HDF5 file, and extracts the reflectance array for that band. It then converts the data into a matrix, converts it to a raster, and finally returns a spatially corrected raster for the specified band.

The function requires the following variables:

• file: the hdf5 reflectance file
• band: the band number we wish to extract
• noDataValue: the noDataValue for the raster
• extent: a terra spatial extent (SpatExtent) object .
• crs: the Coordinate Reference System for the raster

The function output is a spatially referenced, R terra object.

# file: the hdf5 file

# band: the band you want to process

# returns: a matrix containing the reflectance data for the specific band



band2Raster <- function(file, band, noDataValue, extent, CRS){
    # first, read in the raster
    out <- h5read(file,"/SJER/Reflectance/Reflectance_Data",index=list(band,NULL,NULL))
	  # Convert from array to matrix
	  out <- (out[1,,])
	  # transpose data to fix flipped row and column order 
    # depending upon how your data are formatted you might not have to perform this
    # step.
	  out <- t(out)
    # assign data ignore values to NA
    # note, you might chose to assign values of 15000 to NA
    out[out == noDataValue] <- NA
	  
    # turn the out object into a raster
    outr <- rast(out,crs=CRS)
   
    # assign the extents to the raster
    ext(outr) <- extent
   
    # return the terra raster object
    return(outr)
}

Now that the function is created, we can create our list of rasters. The list specifies which bands (or dimensions in our hyperspectral dataset) we want to include in our raster stack. Let's start with a typical RGB (red, green, blue) combination. We will use bands 14, 9, and 4 (bands 58, 34, and 19 in a full NEON hyperspectral dataset).

# create a list of the bands (R,G,B) we want to include in our stack

rgb <- list(58,34,19)



# lapply tells R to apply the function to each element in the list

rgb_rast <- lapply(rgb,FUN=band2Raster, file = h5_file,
                   noDataValue=h5NoDataValue, 
                   ext=rastExt,
                   CRS=h5CRS)

Check out the properties or rgb_rast:

rgb_rast

## [[1]]
## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :     0 
## max value   : 14950 
## 
## [[2]]
## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :    32 
## max value   : 13129 
## 
## [[3]]
## class       : SpatRaster 
## dimensions  : 1000, 1000, 1  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## name        : lyr.1 
## min value   :     9 
## max value   : 11802

Note that it displays properties of 3 rasters. Finally, we can create a raster stack from our list of rasters as follows:

rgbStack <- rast(rgb_rast)

In the code chunk above, we used the lapply() function, which is a powerful, flexible way to apply a function (in this case, our band2Raster() function) multiple times. You can learn more about lapply() here.

NOTE: We are using the raster stack object in R to store several rasters that are of the same CRS and extent. This is a popular and convenient way to organize co-incident rasters.

Next, add the names of the bands to the raster so we can easily keep track of the bands in the list.

# Create a list of band names

bandNames <- paste("Band_",unlist(rgb),sep="")



# set the rasterStack's names equal to the list of bandNames created above

names(rgbStack) <- bandNames



# check properties of the raster list - note the band names

rgbStack

## class       : SpatRaster 
## dimensions  : 1000, 1000, 3  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## names       : Band_58, Band_34, Band_19 
## min values  :       0,      32,       9 
## max values  :   14950,   13129,   11802


# scale the data as specified in the reflInfo$Scale Factor

rgbStack <- rgbStack/as.integer(reflInfo$Scale_Factor)



# plot one raster in the stack to make sure things look OK.

plot(rgbStack$Band_58, main="Band 58")

We can play with the color ramps too if we want:

# change the colors of our raster 

colors1 <- terrain.colors(25)

image(rgbStack$Band_58, main="Band 58", col=colors1)

# adjust the zlims or the stretch of the image

image(rgbStack$Band_58, main="Band 58", col=colors1, zlim = c(0,.5))

# try a different color palette

colors2 <- topo.colors(15, alpha = 1)

image(rgbStack$Band_58, main="Band 58", col=colors2, zlim=c(0,.5))

The plotRGB function allows you to combine three bands to create an true-color image.

# create a 3 band RGB image

plotRGB(rgbStack,
        r=1,g=2,b=3,
        stretch = "lin")

A note about image stretching: Notice that we use the argument stretch="lin" in this plotting function, which automatically stretches the brightness values for us to produce a natural-looking image.

Once you've created your raster, you can export it as a GeoTIFF using writeRaster. You can bring this GeoTIFF into any GIS software, such as QGIS or ArcGIS.

# Write out final raster	

# Note: if you set overwrite to TRUE, then you will overwrite (and lose) any older version of the tif file! 

writeRaster(rgbStack, file=paste0(wd,"NEON_hyperspectral_tutorial_example_RGB_image.tif"), overwrite=TRUE)

Raster Math - Creating NDVI and other Vegetation Indices in R

In this last part, we will calculate some vegetation indices using raster math in R! We will start by creating NDVI or Normalized Difference Vegetation Index.

About NDVI

NDVI is a ratio between the near infrared (NIR) portion of the electromagnetic spectrum and the red portion of the spectrum.

$$ NDVI = \frac{NIR-RED}{NIR+RED} $$

Please keep in mind that there are different ways to aggregate bands when using hyperspectral data. This example is using individual bands to perform the NDVI calculation. Using individual bands is not necessarily the best way to calculate NDVI from hyperspectral data.

# Calculate NDVI

# select bands to use in calculation (red, NIR)

ndviBands <- c(58,90)



# create raster list and then a stack using those two bands

ndviRast <- lapply(ndviBands,FUN=band2Raster, file = h5_file,
                   noDataValue=h5NoDataValue, 
                   ext=rastExt, CRS=h5CRS)

ndviStack <- rast(ndviRast)



# make the names pretty

bandNDVINames <- paste("Band_",unlist(ndviBands),sep="")

names(ndviStack) <- bandNDVINames



# view the properties of the new raster stack

ndviStack

## class       : SpatRaster 
## dimensions  : 1000, 1000, 2  (nrow, ncol, nlyr)
## resolution  : 1, 1  (x, y)
## extent      : 257000, 258000, 4112000, 4113000  (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 11N 
## source(s)   : memory
## names       : Band_58, Band_90 
## min values  :       0,      11 
## max values  :   14950,   14887

#calculate NDVI

NDVI <- function(x) {
	  (x[,2]-x[,1])/(x[,2]+x[,1])
}

ndviCalc <- app(ndviStack,NDVI)

plot(ndviCalc, main="NDVI for the NEON SJER Field Site")

# Now, play with breaks and colors to create a meaningful map

# add a color map with 4 colors

myCol <- rev(terrain.colors(4)) # use the 'rev()' function to put green as the highest NDVI value

# add breaks to the colormap, including lowest and highest values (4 breaks = 3 segments)

brk <- c(0, .25, .5, .75, 1)



# plot the image using breaks

plot(ndviCalc, main="NDVI for the NEON SJER Field Site", col=myCol, breaks=brk)