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19 changes: 9 additions & 10 deletions tutorials/R/AOP/Hyperspectral/Plot-Hyperspectral-Spectra/Plot-Hyperspectral-Spectra.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ description: Extract a single pixel's worth of spectra from a hyperspectral data
estimatedTime: 1.0 - 1.5 Hours
languagesTool: R
dataProduct: DP3.30006.001
packagesLibraries: rhdf5, raster, rgdal, plyr
packagesLibraries: rhdf5, terra, plyr
authors: Leah A. Wasser, Donal O'Leary
topics: hyperspectral, HDF5, remote-sensing
tutorialSeries: null
Expand Down Expand Up @@ -93,18 +93,17 @@ before moving on to this tutorial.
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
Imaging Spectrometer (NIS, or hyperspectral sensor) 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
distinct patterns in their reflectance values, a property 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
In this tutorial, we will extract the reflectance values for all bands of a
single pixel to plot a spectral signature for that pixel. In order to do this,
we need to pair the reflectance values for that pixel with the wavelength values
of the bands 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.
Expand All @@ -118,12 +117,12 @@ 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
wd <- "~/data/" #This will depend on your local environment
setwd(wd)
```

Now, we need to access the H5 file.
Now, we can look at the contents of the H5 file as follows:

```{r open-H5-file }
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768 changes: 516 additions & 252 deletions tutorials/R/AOP/Hyperspectral/Plot-Hyperspectral-Spectra/Plot-Hyperspectral-Spectra.html
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186 changes: 74 additions & 112 deletions ...ls/R/AOP/Hyperspectral/Plot-Hyperspectral-Spectra/Plot-Hyperspectral-Spectra.md
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Expand Up @@ -2,13 +2,13 @@
syncID: 79f902f6c0264f16a9be13f50560860a
title: "Plot Spectral Signatures Derived from Hyperspectral Remote Sensing Data in HDF5 Format in R"
code1: https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/tutorials/R/Hyperspectral/Intro-hyperspectral/Plot-Hyperspectral-Spectra/Plot-Hyperspectral-Spectra.R
contributors: null
contributors: Felipe Sanchez
dateCreated: 2015-08-08 20:49:52
description: Extract a single pixel's worth of spectra from a hyperspectral dataset stored in HDF5 format in R. Visualize the spectral signature.
estimatedTime: 1.0 - 1.5 Hours
languagesTool: R
dataProduct: null
packagesLibraries: rhdf5, raster, rgdal, plyr
dataProduct: DP3.30006.001
packagesLibraries: rhdf5, terra, plyr
authors: Leah A. Wasser, Donal O'Leary
topics: hyperspectral, HDF5, remote-sensing
tutorialSeries: null
Expand Down Expand Up @@ -93,77 +93,45 @@ before moving on to this tutorial.
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
Imaging Spectrometer (NIS, or hyperspectral sensor) 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
distinct patterns in their reflectance values, a property 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
In this tutorial, we will extract the reflectance values for all bands of a
single pixel to plot a spectral signature for that pixel. In order to do this,
we need to pair the reflectance values for that pixel with the wavelength values
of the bands 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.

```{r call-libraries, results="hide" }
# 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
# 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 <- "~/data/" #This will depend on your local environment
setwd(wd)
```

Now, we can look at the contents of the H5 file as follows:

```{r open-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)
```


## Read Wavelength Values
Expand All @@ -172,9 +140,12 @@ 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.

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


## Extract Z-dimension data slice
Expand All @@ -184,29 +155,24 @@ 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.

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

## Scale Factor

Expand All @@ -216,36 +182,32 @@ However, floating point data consume more space (yield a larger file size) compa
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.

```{r pull-scale-factor }
# 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)
# 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!

```{r plot-spectra, fig.width=9, fig.height=6, fig.cap="Spectral signature plot with wavelength in nanometers on the x-axis and reflectance on the y-axis."}
ggplot(data=aPixeldf)+
geom_line(aes(x=Wavelength, y=ScaledReflectance))+
xlab("Wavelength (nm)")+
ylab("Reflectance")
ggplot(data=aPixeldf)+
geom_line(aes(x=Wavelength, y=ScaledReflectance))+
xlab("Wavelength (nm)")+
ylab("Reflectance")
![ ](https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/tutorials/R/Hyperspectral/Intro-hyperspectral/Plot-Hyperspectral-Spectra/rfigs/plot-spectra-1.png)
```

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