Learning Objectives

After completing this tutorial, you will be able to:

• Define hyperspectral remote sensing.
• Explain the fundamental principles of hyperspectral remote sensing data.
• Describe the key attributes that are required to effectively work with hyperspectral remote sensing data in tools like R or Python.
• Describe what a "band" is.

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 explore reading and extracting spatial raster data stored within a 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 cover the electromagnetic spectrum.

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 428 bands - which means BIG DATA. Remember that the example dataset used here only has 1 out of every 4 bands included in a full NEON hyperspectral dataset (this substantially reduces size!). When we refer to bands in this tutorial, we will note the band numbers for this example dataset, which may be different from NEON production 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 in addition to the ability to support spatial data and associated metadata make it ideal for working with large data cubes such as those generated by imaging spectrometers.

In this tutorial we will explore reading and extracting spatial raster data stored within a HDF5 file using R.

Read HDF5 data into R

We will use the raster 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 `raster` and `rhdf5` packages and read NIS data into R
library(raster)
library(rhdf5)
library(rgdal)

# set working directory to ensure R can find the file we wish to import and where
# we want to save our files. Be sure to move the download into your working directory!
wd <- "~/Documents/data/" #This will depend on your local environment
setwd(wd)

# Define the file name to be opened
f <- paste0(wd,"NEON_hyperspectral_tutorial_example_subset.h5")

# look at the HDF5 file structure 
View(h5ls(f,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 middle 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(f,"/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(f,"/SJER/Reflectance/Metadata/Spectral_Data/Wavelength")
head(wavelengths)

## [1] 381.5437 401.5756 421.6075 441.6394 461.6713 481.7032

tail(wavelengths)

## [1] 2404.764 2424.796 2444.828 2464.860 2484.892 2504.924

Which wavelength is band 6 associated with?

(Hint: look at the wavelengths vector that we just imported and check out the data located at index 6 - wavelengths[6]).

Band 6 has a associate wavelength center of 481.7032 nanometers (nm) which is in the blue portion of the visible electromagnetic spectrum (~ 400-700 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 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 nm). The full width half max (FWHM) will also be reported. This value represents the spread of the band around that center point. So, a band that covers 800 nm-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 107 bands available in this example dataset (or all 426 bands available in a full NEON hyperspectral reflectance file, for that matter)

• if we are interested in creating a product like NDVI which only uses bands in the near infra-red and red portions of the spectrum. Or we might only be interested in a spatial subset of the data - perhaps a region where we have plots 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 in our dataset - band 9.

By the way - what is the center wavelength value associated with band 9?

Hint: wavelengths[9].

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 important reflectance metadata stored as attributes in the "Reflectance_Data" dataset.

# First, we need to extract the reflectance metadata:
reflInfo <- h5readAttributes(f, "/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] 500 500 107
## 
## $Interleave
## [1] "BSQ"
## 
## $Scale_Factor
## [1] 10000
## 
## $Spatial_Extent_meters
## [1]  257500  258000 4112500 4113000
## 
## $Spatial_Resolution_X_Y
## [1] 1 1
## 
## $Units
## [1] "Unitless."
## 
## $Units_Valid_range
## [1]     0 10000
## 
## $dim
## [1] 107 500 500

# Next, we read the different dimensions

nRows <- reflInfo$Dimensions[1]
nCols <- reflInfo$Dimensions[2]
nBands <- reflInfo$Dimensions[3]

nRows

## [1] 500

nCols

## [1] 500

nBands

## [1] 107

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 9 from the HDF5 file
b9 <- h5read(f,"/SJER/Reflectance/Reflectance_Data",index=list(9,1:nCols,1:nRows)) 

# what type of object is b9?
class(b9)

## [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. Thus, in this case, we can extract and plot the green band without reading in all 107 bands of information. 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
b9 <- b9[1,,]

# check it
class(b9)

## [1] "matrix"

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 9 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(f,"/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] 500 500 107
## 
## $Interleave
## [1] "BSQ"
## 
## $Scale_Factor
## [1] 10000
## 
## $Spatial_Extent_meters
## [1]  257500  258000 4112500 4113000
## 
## $Spatial_Resolution_X_Y
## [1] 1 1
## 
## $Units
## [1] "Unitless."
## 
## $Units_Valid_range
## [1]     0 10000
## 
## $dim
## [1] 107 500 500

# plot the image
image(b9)

# oh, that is hard to visually interpret.
# what happens if we plot a log of the data?
image(log(b9))

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.

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(b9,breaks=40,col="darkmagenta")

# View values between 0 and 5000
hist(b9,breaks=40,col="darkmagenta",xlim = c(0, 5000))

# View higher values
hist(b9, breaks=40,col="darkmagenta",xlim = c(5000, 15000),ylim=c(0,100))

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. You will see this done often when working with remote sensing data.

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 try to render these pixels.

# there is a no data value in our raster - let's define it
myNoDataValue <- as.numeric(reflInfo$Data_Ignore_Value)
myNoDataValue

## [1] -9999

# set all values equal to -9999 to NA
b9[b9 == myNoDataValue] <- NA

# plot the image now
image(b9)

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 then there are values >5000.

Images have 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 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(b9))

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 what our log adjusted image looks like when plotted. The bottom on is an RGB version of the same 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. Whereas, 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
b9 <- t(b9)
image(log(b9), main="Transposed Image")

Create a Georeferenced Raster

Next, we will create a proper raster using the b9 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
myEPSG <- h5read(f, "/SJER/Reflectance/Metadata/Coordinate_System/EPSG Code")

# convert the EPSG code to a CRS string
myCRS <- crs(paste0("+init=epsg:",myEPSG))

# 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!
b9r <- raster(b9, 
        crs=myCRS)

# view the raster attributes
b9r

## class      : RasterLayer 
## dimensions : 500, 500, 250000  (nrow, ncol, ncell)
## resolution : 0.002, 0.002  (x, y)
## extent     : 0, 1, 0, 1  (xmin, xmax, ymin, ymax)
## crs        : +init=epsg:32611 +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## source     : memory
## names      : layer 
## values     : 0, 9210  (min, max)

# let's have a look at our properly oriented raster. Take note of the 
# coordinates on the x and y axis.

image(log(b9r), 
      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 <- extent(xMin,xMax,yMin,yMax)
rasExt

## class      : Extent 
## xmin       : 257500 
## xmax       : 258000 
## ymin       : 4112500 
## ymax       : 4113000

# assign the spatial extent to the raster
extent(b9r) <- rasExt

# look at raster attributes
b9r

## class      : RasterLayer 
## dimensions : 500, 500, 250000  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 257500, 258000, 4112500, 4113000  (xmin, xmax, ymin, ymax)
## crs        : +init=epsg:32611 +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## source     : memory
## names      : layer 
## values     : 0, 9210  (min, max)

Learn more about raster attributes including extent, and coordinate reference systems here.

We can adjust the colors of our raster too if we want.

# let's change the colors of our raster and adjust the zlims 
col <- terrain.colors(25)

image(b9r,  
      xlab = "UTM Easting", 
      ylab = "UTM Northing",
      main= "Raster w Custom Colors",
      col=col, 
      zlim=c(0,3000))

We've now created a raster from band 9 reflectance data. We can export the data as a raster, using the writeRaster command.

# write out the raster as a geotiff
writeRaster(b9r,
            file=paste0(wd,"band9.tif"),
            format="GTiff",
            overwrite=TRUE)

# It's always good practice to close the H5 connection before moving on!
# 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 the images that your camera takes. But there are other band combinations that are useful too. For example, near infrared images emphasize vegetation and help us classify or identify where vegetation is located on the ground.

Create a Raster Stack in R

In the previous activity, 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, reading in our file and viewing the file structure.

# Load required packages
library(raster)
library(rhdf5)

# set working directory to ensure R can find the file we wish to import and where
# we want to save our files. Be sure to move the download into your working directory!
wd <- "~/Documents/data/" # This will depend on your local environment
setwd(wd)

# create path to file name
f <- paste0(wd,"NEON_hyperspectral_tutorial_example_subset.h5")


# View HDF5 file structure 
View(h5ls(f,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
myEPSG <- h5read(f,"/SJER/Reflectance/Metadata/Coordinate_System/EPSG Code" )
myCRS <- crs(paste0("+init=epsg:",myEPSG))

# get the Reflectance_Data attributes
reflInfo <- h5readAttributes(f,"/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)
rasExt <- extent(xMin,xMax,yMin,yMax)

# view the extent to make sure that it looks right
rasExt

## class      : Extent 
## xmin       : 257500 
## xmax       : 258000 
## ymin       : 4112500 
## ymax       : 4113000

# Finally, define the no data value for later
myNoDataValue <- as.integer(reflInfo$Data_Ignore_Value)
myNoDataValue

## [1] -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. It them converts the data to a matrix, converts it to a raster and returns a spatially corrected raster for the specified band.

The function requires the following variables:

• file: the file
• band: the band number we wish to extract
• noDataValue: the noDataValue for the raster
• extent: a raster Extent object .
• crs: the Coordinate Reference System for the raster

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

# file: the hdf 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 == myNoDataValue] <- NA
	  
    # turn the out object into a raster
    outr <- raster(out,crs=CRS)
   
    # assign the extents to the raster
    extent(outr) <- extent
   
    # return the 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 we want in our stack
rgb <- list(14,9,4) #list(58,34,19) when using full NEON hyperspectral dataset

# lapply tells R to apply the function to each element in the list
rgb_rast <- lapply(rgb,FUN=band2Raster, file = f,
                   noDataValue=myNoDataValue, 
                   extent=rasExt,
                   CRS=myCRS)

# check out the properties or rgb_rast
# note that it displays properties of 3 rasters.
rgb_rast

## [[1]]
## class      : RasterLayer 
## dimensions : 500, 500, 250000  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 257500, 258000, 4112500, 4113000  (xmin, xmax, ymin, ymax)
## crs        : +init=epsg:32611 +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## source     : memory
## names      : layer 
## values     : 0, 9418  (min, max)
## 
## 
## [[2]]
## class      : RasterLayer 
## dimensions : 500, 500, 250000  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 257500, 258000, 4112500, 4113000  (xmin, xmax, ymin, ymax)
## crs        : +init=epsg:32611 +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## source     : memory
## names      : layer 
## values     : 0, 9210  (min, max)
## 
## 
## [[3]]
## class      : RasterLayer 
## dimensions : 500, 500, 250000  (nrow, ncol, ncell)
## resolution : 1, 1  (x, y)
## extent     : 257500, 258000, 4112500, 4113000  (xmin, xmax, ymin, ymax)
## crs        : +init=epsg:32611 +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## source     : memory
## names      : layer 
## values     : 0, 9704  (min, max)

# finally, create a raster stack from our list of rasters
rgbStack <- stack(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() fucntion) 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      : RasterStack 
## dimensions : 500, 500, 250000, 3  (nrow, ncol, ncell, nlayers)
## resolution : 1, 1  (x, y)
## extent     : 257500, 258000, 4112500, 4113000  (xmin, xmax, ymin, ymax)
## crs        : +init=epsg:32611 +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## names      : Band_14, Band_9, Band_4 
## min values :       0,      0,      0 
## max values :    9418,   9210,   9704

# 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_14, main="Band 14")

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

# change the colors of our raster 
myCol <- terrain.colors(25)
image(rgbStack$Band_14, main="Band 14", col=myCol)

# adjust the zlims or the stretch of the image
myCol <- terrain.colors(25)
image(rgbStack$Band_14, main="Band 14", col=myCol, zlim = c(0,.5))

# try a different color palette
myCol <- topo.colors(15, alpha = 1)
image(rgbStack$Band_14, main="Band 14", col=myCol, zlim=c(0,.5))

The plotRGB function allows you to combine three bands to create an 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. You can bring this GeoTIFF into any GIS program!

# write out final raster	
# note: if you set overwrite to TRUE, then you will overwite or lose the older
# version of the tif file! Keep this in mind.
writeRaster(rgbStack, file=paste0(wd,"NEON_hyperspectral_tutorial_example_RGB_stack_image.tif"), format="GTiff", 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. 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)
ndvi_bands <- c(16,24) #bands c(58,90) in full NEON hyperspectral dataset

# create raster list and then a stack using those two bands
ndvi_rast <- lapply(ndvi_bands,FUN=band2Raster, file = f,
                   noDataValue=myNoDataValue, 
                   extent=rasExt, CRS=myCRS)
ndvi_stack <- stack(ndvi_rast)

# make the names pretty
bandNDVINames <- paste("Band_",unlist(ndvi_bands),sep="")
names(ndvi_stack) <- bandNDVINames

# view the properties of the new raster stack
ndvi_stack

## class      : RasterStack 
## dimensions : 500, 500, 250000, 2  (nrow, ncol, ncell, nlayers)
## resolution : 1, 1  (x, y)
## extent     : 257500, 258000, 4112500, 4113000  (xmin, xmax, ymin, ymax)
## crs        : +init=epsg:32611 +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## names      : Band_16, Band_24 
## min values :       0,       0 
## max values :    9386,    9424

#calculate NDVI
NDVI <- function(x) {
	  (x[,2]-x[,1])/(x[,2]+x[,1])
}
ndvi_calc <- calc(ndvi_stack,NDVI)
plot(ndvi_calc, 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(ndvi_calc, main="NDVI for the NEON SJER Field Site", col=myCol, breaks=brk)

Everything on our planet reflects electromagnetic radiation from the Sun, and different types of land cover often have dramatically different refelectance properties across the spectrum. One of the most powerful aspects of the NEON Imaging Spectrometer (a.k.a. NEON's hyperspectral imager) is that it can accurately measure these reflectance properties at a very high spectral resolution. When you plot the reflectance values across the observed spectrum, you will see that different land cover types (vegetation, pavement, bare soils, etc.) have distinct patterns in their reflectance values, a feature that we call the 'spectral signature' of a particular land cover class.

In this tutorial, we will extract a single pixel's worth of reflectance values to plot a spectral signature for that pixel. In order to plot the spectral signature for a given pixel in this hyperspectral dataset, we will need to extract the reflectance values for that pixel, and pair those with the wavelengths that are represented in those measurements. We will also need to adjust the reflectance values by the scaling factor that is saved as an 'attribute' in the HDF5 file. First, let's start by defining the working directory and reading in the example dataset.

# Call required packages
library(rhdf5)
library(plyr)
library(ggplot2)

# set working directory to ensure R can find the file we wish to import and where
# we want to save our files. Be sure to move the download into your working directory!
wd <- "~/Documents/data/" #This will depend on your local environment
setwd(wd)

Now, we need to access the H5 file.

# Define the file name to be opened
f <- paste0(wd,"NEON_hyperspectral_tutorial_example_subset.h5")
# look at the HDF5 file structure 
h5ls(f,all=T) 

##                                           group                     name         ltype
## 0                                             /                     SJER H5L_TYPE_HARD
## 1                                         /SJER              Reflectance H5L_TYPE_HARD
## 2                             /SJER/Reflectance                 Metadata H5L_TYPE_HARD
## 3                    /SJER/Reflectance/Metadata        Coordinate_System H5L_TYPE_HARD
## 4  /SJER/Reflectance/Metadata/Coordinate_System Coordinate_System_String H5L_TYPE_HARD
## 5  /SJER/Reflectance/Metadata/Coordinate_System                EPSG Code H5L_TYPE_HARD
## 6  /SJER/Reflectance/Metadata/Coordinate_System                 Map_Info H5L_TYPE_HARD
## 7  /SJER/Reflectance/Metadata/Coordinate_System                    Proj4 H5L_TYPE_HARD
## 8                    /SJER/Reflectance/Metadata            Spectral_Data H5L_TYPE_HARD
## 9      /SJER/Reflectance/Metadata/Spectral_Data               Wavelength H5L_TYPE_HARD
## 10                            /SJER/Reflectance         Reflectance_Data H5L_TYPE_HARD
##    corder_valid corder cset       otype num_attrs  dclass          dtype  stype rank
## 0         FALSE      0    0   H5I_GROUP         0                                  0
## 1         FALSE      0    0   H5I_GROUP         5                                  0
## 2         FALSE      0    0   H5I_GROUP         0                                  0
## 3         FALSE      0    0   H5I_GROUP         0                                  0
## 4         FALSE      0    0 H5I_DATASET         0  STRING     H5T_STRING SIMPLE    1
## 5         FALSE      0    0 H5I_DATASET         0  STRING     H5T_STRING SIMPLE    1
## 6         FALSE      0    0 H5I_DATASET         1  STRING     H5T_STRING SIMPLE    1
## 7         FALSE      0    0 H5I_DATASET         0  STRING     H5T_STRING SIMPLE    1
## 8         FALSE      0    0   H5I_GROUP         0                                  0
## 9         FALSE      0    0 H5I_DATASET         3   FLOAT H5T_IEEE_F64LE SIMPLE    1
## 10        FALSE      0    0 H5I_DATASET        13 INTEGER  H5T_STD_I32LE SIMPLE    3
##                dim          maxdim
## 0                                 
## 1                                 
## 2                                 
## 3                                 
## 4                1               1
## 5                1               1
## 6                1               1
## 7                1               1
## 8                                 
## 9              107             107
## 10 107 x 500 x 500 107 x 500 x 500

Read Wavelength Values

Next, let's read in the wavelength center associated with each band in the HDF5 file. We will later match these with the reflectance values and show both in our final spectral signature plot.

# read in the wavelength information from the HDF5 file
wavelengths <- h5read(f,"/SJER/Reflectance/Metadata/Spectral_Data/Wavelength")

Extract Z-dimension data slice

Next, we will extract all reflectance values for one pixel. This makes up the spectral signature or profile of the pixel. To do that, we'll use the h5read() function. Here we pick an arbitrary pixel at (100,35), and use the NULL value to select all bands from that location.

# extract all bands from a single pixel
aPixel <- h5read(f,"/SJER/Reflectance/Reflectance_Data",index=list(NULL,100,35))

# The line above generates a vector of reflectance values.
# Next, we reshape the data and turn them into a dataframe
b <- adply(aPixel,c(1))

# create clean data frame
aPixeldf <- b[2]

# add wavelength data to matrix
aPixeldf$Wavelength <- wavelengths

head(aPixeldf)

##    V1 Wavelength
## 1 720   381.5437
## 2 337   401.5756
## 3 336   421.6075
## 4 399   441.6394
## 5 406   461.6713
## 6 426   481.7032

Scale Factor

Then, we can pull the spatial attributes that we'll need to adjust the reflectance values. Often, large raster data contain floating point (values with decimals) information. However, floating point data consume more space (yield a larger file size) compared to integer values. Thus, to keep the file sizes smaller, the data will be scaled by a factor of 10, 100, 10000, etc. This scale factor will be noted in the data attributes.

# grab scale factor from the Reflectance attributes
reflectanceAttr <- h5readAttributes(f,"/SJER/Reflectance/Reflectance_Data" )

scaleFact <- reflectanceAttr$Scale_Factor

# add scaled data column to DF
aPixeldf$scaled <- (aPixeldf$V1/as.vector(scaleFact))

# make nice column names
names(aPixeldf) <- c('Reflectance','Wavelength','ScaledReflectance')
head(aPixeldf)

##   Reflectance Wavelength ScaledReflectance
## 1         720   381.5437            0.0720
## 2         337   401.5756            0.0337
## 3         336   421.6075            0.0336
## 4         399   441.6394            0.0399
## 5         406   461.6713            0.0406
## 6         426   481.7032            0.0426

Plot Spectral Signature

Now we're ready to plot our spectral signature!

ggplot(data=aPixeldf)+
   geom_line(aes(x=Wavelength, y=ScaledReflectance))+
   xlab("Wavelength (nm)")+
   ylab("Reflectance")

In this tutorial, we will learn how to plot spectral signatures of several different land cover types using an interactive clicking feature of the raster package.

Getting Started

First, we need to load our required packages, and import the hyperspectral data (in HDF5 format). We will also collect a few other important pieces of information (band wavelengths and scaling factor) while we're at it.

# Load required packages
library(rhdf5)
library(reshape2)
library(raster)
library(plyr)
library(ggplot2)

# set working directory to ensure R can find the file we wish to import and where
# we want to save our files. Be sure to move the download into your working directory!
wd <- "~/Documents/data/" #This will depend on your local environment
setwd(wd)

# define filepath to the hyperspectral dataset
fhs <- paste0(wd,"NEON_hyperspectral_tutorial_example_subset.h5")

# read in the wavelength information from the HDF5 file
wavelengths <- h5read(fhs,"/SJER/Reflectance/Metadata/Spectral_Data/Wavelength")

# grab scale factor from the Reflectance attributes
reflInfo <- h5readAttributes(fhs,"/SJER/Reflectance/Reflectance_Data" )

scaleFact <- reflInfo$Scale_Factor

Now, we read in the RGB image that we created in an earlier tutorial and plot it. If you didn't make this image before, you can download it from the link at the top of this page.

# Read in RGB image as a 'stack' rather than a plain 'raster'
rgbStack <- stack(paste0(wd,"NEON_hyperspectral_tutorial_example_RGB_stack_image.tif"))

# Plot as RGB image
plotRGB(rgbStack,
        r=1,g=2,b=3, scale=300, 
        stretch = "lin")

Interactive click Function from raster Package

Next, we use an interactive clicking function to identify the pixels that we want to extract spectral signatures for. To follow along best with this tutorial, we suggest the following five cover types (exact location shown below).

1. Irrigated grass
2. Tree canopy (avoid the shaded northwestern side of the tree)
3. Roof
4. Bare soil (baseball diamond infield)
5. Open water

As shown here:

# change plotting parameters to better see the points and numbers generated from clicking
par(col="red", cex=3)

# use the 'click' function
c <- click(rgbStack, id=T, xy=T, cell=T, type="p", pch=16, col="magenta", col.lab="red")

Once you have clicked your five points, press the ESC key to save your clicked points and close the function before moving on to the next step. If you make a mistake in the step, run the plotRGB() function again to start over.

The click() function identifies the cell number that you clicked, but in order to extract spectral signatures, we need to convert that cell number into a row and column, as shown here:

# convert raster cell number into row and column (used to extract spectral signature below)
c$row <- c$cell%/%nrow(rgbStack)+1 # add 1 because R is 1-indexed
c$col <- c$cell%%ncol(rgbStack)

Extract Spectral Signatures from HDF5 file

Next, we loop through each of the cells that we selected to use the h5read() function to etract the reflectance values of all bands from the given row and column.

# create a new dataframe from the band wavelengths so that we can add
# the reflectance values for each cover type
Pixel_df <- as.data.frame(wavelengths)

# loop through each of the cells that we selected
for(i in 1:length(c$cell)){
# extract Spectra from a single pixel
aPixel <- h5read(fhs,"/SJER/Reflectance/Reflectance_Data",
                 index=list(NULL,c$col[i],c$row[i]))

# scale reflectance values from 0-1
aPixel <- aPixel/as.vector(scaleFact)

# reshape the data and turn into dataframe
b <- adply(aPixel,c(1))

# rename the column that we just created
names(b)[2] <- paste0("Point_",i)

# add reflectance values for this pixel to our combined data.frame called Pixel_df
Pixel_df <- cbind(Pixel_df,b[2])
}

Plot Spectral signatures using ggplot2

Finally, we have everything that we need to plot the spectral signatures for each of the pixels that we clicked. In order to color our lines by the different land cover types, we will first reshape our data using the melt() function, then plot the spectral signatures.

# Use the melt() function to reshape the dataframe into a format that ggplot prefers
Pixel.melt <- reshape2::melt(Pixel_df, id.vars = "wavelengths", value.name = "Reflectance")

# Now, let's plot some spectral signatures!
ggplot()+
  geom_line(data = Pixel.melt, mapping = aes(x=wavelengths, y=Reflectance, color=variable), lwd=1.5)+
  scale_colour_manual(values = c("green2", "green4", "grey50","tan4","blue3"),
                      labels = c("Field", "Tree", "Roof","Soil","Water"))+
  labs(color = "Cover Type")+
  ggtitle("Land cover spectral signatures")+
  theme(plot.title = element_text(hjust = 0.5, size=20))+
  xlab("Wavelength")

Nice! However, there seems to be something weird going on in the wavelengths near 1400nm and 1850 nm...

Atmospheric Absorbtion Bands

Those irregularities around 1400nm and 1850 nm are two major atmospheric absorbtion bands - regions where gasses in the atmosphere (primarily carbon dioxide and water vapor) absorb radiation, and therefore, obscure the reflected radiation that the imaging spectrometer measures. Fortunately, the lower and upper bound of each of those atmopheric absopbtion bands is specified in the HDF5 file. Let's read those bands and plot rectangles where the reflectance measurements are obscured by atmospheric absorbtion.

# grab Reflectance metadata (which contains absorption band limits)
reflMetadata <- h5readAttributes(fhs,"/SJER/Reflectance" )

ab1 <- reflMetadata$Band_Window_1_Nanometers
ab2 <- reflMetadata$Band_Window_2_Nanometers

# Plot spectral signatures again with rectangles showing the absorption bands
ggplot()+
  geom_line(data = Pixel.melt, mapping = aes(x=wavelengths, y=Reflectance, color=variable), lwd=1.5)+
  geom_rect(mapping=aes(ymin=min(Pixel.melt$Reflectance),ymax=max(Pixel.melt$Reflectance), xmin=ab1[1], xmax=ab1[2]), color="black", fill="grey40", alpha=0.8)+
  geom_rect(mapping=aes(ymin=min(Pixel.melt$Reflectance),ymax=max(Pixel.melt$Reflectance), xmin=ab2[1], xmax=ab2[2]), color="black", fill="grey40", alpha=0.8)+
  scale_colour_manual(values = c("green2", "green4", "grey50","tan4","blue3"),
                      labels = c("Field", "Tree", "Roof","Soil","Water"))+
  labs(color = "Cover Type")+
  ggtitle("Land cover spectral signatures")+
  theme(plot.title = element_text(hjust = 0.5, size=20))+
  xlab("Wavelength")

Now we can clearly see that the noisy sections of each spectral signature are within the atmospheric absorbtion bands. For our final step, let's take all reflectance values from within each absorbtion band and set them to NA to remove the noisy sections from the plot.

# Duplicate the spectral signatures into a new data.frame
Pixel.melt.masked <- Pixel.melt

# Mask out all values within each of the two atmospheric absorbtion bands
Pixel.melt.masked[Pixel.melt.masked$wavelengths>ab1[1]&Pixel.melt.masked$wavelengths<ab1[2],]$Reflectance <- NA
Pixel.melt.masked[Pixel.melt.masked$wavelengths>ab2[1]&Pixel.melt.masked$wavelengths<ab2[2],]$Reflectance <- NA

# Plot the masked spectral signatures
ggplot()+
  geom_line(data = Pixel.melt.masked, mapping = aes(x=wavelengths, y=Reflectance, color=variable), lwd=1.5)+
  scale_colour_manual(values = c("green2", "green4", "grey50","tan4","blue3"),
                      labels = c("Field", "Tree", "Roof", "Soil", "Water"))+
  labs(color = "Cover Type")+
  ggtitle("Land cover spectral signatures")+
  theme(plot.title = element_text(hjust = 0.5, size=20))+
  xlab("Wavelength")

There you have it, spectral signatures for five different land cover types, with the readings from the atmospheric absorbtion bands removed.

These capstone challenges utilize the skills that you learned in the previous tutorials in the:

• Primer on Raster Data in R series,
• Introduction to Hyperspectral Remote Sensing Data - in R series, or
• Introduction to the Hierarchical Data Format (HDF5) - Using HDFView & R series.

Capstone One: Calculate NDVI for the SJER field sites

The Normalized Difference Vegetation Index (NDVI) is calculated using the equation:

(NIR - Red) / (NIR + Red)

where NIR is the near infrared band in an image and Red is the red band in an image.

Use the Red (Band 58 in the GeoTIFF files) and the NIR (band 90 in the GeoTIFF files) GeoTIFF files to

1. Calculate NDVI in R.
2. Plot NDVI. Make sure your plot has a title and a legend.
3. Assign a colormap to the plot and specify the breaks for the colors to represent NDVI values that make sense to you. For instance, you might chose to color the data into quartiles using breaks at .25,.5, .75 and 1.
4. Expore your final NDVI dataset as a GeoTIFF. Make sure the CRS is correct.
5. To test your work, bring it into QGIS. Does it line up with the other GeoTIFFs (for example the band 19 tiff). Did it import properly?

Capstone Two: Create an HDF5 file

If you have some of your own data that you'd like to explore for this activity, feel free to do so. Otherwise, use the vegetation structure data that we've provided in the data downloads for this workshop.

1. Create a new HDF5 file using the vegetation structure data in D17_2013_vegStr.csv and D17_2013_SOAP_vegStr.csv. (Note that previously the working directory was set to SJER. You'll have to change this to easily access the SOAP vegetation data).
2. Create two groups within a California group:
   • one for the San Joaquin (SJER) field site
   • one for the Soaproot Saddle (SOAP) field site.
3. Attribute each of the above groups with information about the field sites. HINT: you can explore the NEON field sites page for more information about each site.
4. Extract the vegetation structure data for San Joaquin and add it as a dataset to the San Joaquin group. Do the same for the Soaproot Saddle dataset.
5. Add the plot centroids data to the SJER group. Include relevant attributes for this dataset including the CRS string and any other metadata with the dataset.
6. Open the metadata file for the vegetation structure data. Attribute the structure dataset as you see fit to make it usable. As you do this, think about the following:
   • Is there a better way to provide or store these metadata?
   • Is there a way to automate adding the metadata to the H5 file?