NEON Brownbag: Working With Hyperspectral Imagery in HDF5 Format in R

This workshop will providing hands on experience with working with hyperspectral imagery in hierarchical data formats (HDF5) in R. It will also cover basic raster data analysis in R.

Objectives

After completing this workshop, you will be able to:

• Explain what hyperspectral remote sensing data are.
• Create and read from HDF5 files containing spatial data in R.
• Describe the key attributes of raster data that you need to spatially locate raster data in R.

Things to do before the workshop

To participant in this workshop, you will need a laptop with the most current version of R and, preferably, RStudio loaded on your computer. For details on setting up R & RStudio in Mac, PC, or Linux operating systems please see Additional Set up Resources below

R Libraries to Install

Please have these packages installed and updated prior to the start of the workshop.

• rhdf5: source("http://bioconductor.org/biocLite.R"); biocLite("rhdf5")
• raster: install.packages("raster")
• rgdal: install.packages("rgdal")

Updating R Packages

In RStudio, you can go to Tools --> Check for package updates to update previously installed packages on your computer. Or you can use update.packages() to update all packages that are installed in R automatically. More on Packages in R

Data to Download

Download NEON Teaching Data Subset: Imaging Spectrometer Data - HDF5

These hyperspectral remote sensing data provide information on the National Ecological Observatory Network's San Joaquin Experimental Range field site. The data were collected over the San Joaquin field site located in California (Domain 17) and processed at NEON headquarters. The entire dataset can be accessed by request from the NEON Data Portal.

DOWNLOAD DATASET

Background Materials

• The Relationship Between Raster Resolution, Spatial Extent & Number of Pixels - in R
• Optional: rhdf5 package documentation
• Optional: rgdal package documentation

Schedule

The workshop is held in the classroom that the NEON headquarters in Boulder, CO.

Additional Set Up Instructions

R & RStudio

Prior to the workshop you should have R and, preferably, RStudio installed on your computer.

Setting Up R & RStudio

Windows R/RStudio Setup

• Download R for Windows here
• Run the .exe file that was just downloaded
• Go to the RStudio Download page
• Under Installers select RStudio X.XX.XXX - Windows Vista/7/8/10
• Double click the file to install it

Once R and RStudio are installed, click to open RStudio. If you don't get any error messages you are set. If there is an error message, you will need to re-install the program.

Mac R/RStudio Setup

• Go to CRAN and click on Download R for (Mac) OS X
• Select the .pkg file for the version of OS X that you have and the file will download.
• Double click on the file that was downloaded and R will install
• Go to the RStudio Download page
• Under Installers select RStudio 0.98.1103 - Mac OS X XX.X (64-bit) to download it.
• Once it's downloaded, double click the file to install it

Once R and RStudio are installed, click to open RStudio. If you don't get any error messages you are set. If there is an error message, you will need to re-install the program.

Linux R/RStudio Setup

• R is available through most Linux package managers. You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R).
• To install RStudio, go to the RStudio Download page
• Under Installers select the version for your distribution.
• Once it's downloaded, double click the file to install it

Once R and RStudio are installed, click to open RStudio. If you don't get any error messages you are set. If there is an error message, you will need to re-install the program.

Set Working Directory to Downloaded Data

1) Download Data

After clicking on the Download Data button, the data will automatically download to the computer.

2) Locate .zip file

Second, find the downloaded .zip file. Many browsers save downloaded files to your computer’s Downloads directory. If you have previously specified a specific directory (folder) for downloaded files, the .zip file will download there.

3) Move to **data** directory

Third, move the downloaded file to a directory called data within the Documents directory on your computer. You can choose to place the data in other locations, however, you will need to remember to set your R Working Directory to that location and not as we demonstrate in the workshop.

4) Unzip/uncompress

Fourth, we need to unzip/uncompress the file so that the data files can be accessed. Use your favorite tool that can unpackage/open .zip files (e.g., winzip, Archive Utility, etc). The files will now be accessible in three directories:

These directories contain all of the subdirectories and files that we will use in this workshop.

5) Set working directory

Fifth, we need to set the working directory in R to this data directory that is parent to the directories containing the data we want. For complete directions, on how to do that check out the Set A Working Directory in R tutorial.

Install HDFView

The free HDFView application allows you to explore the contents of an HDF5 file.

To install HDFView:

1. Click to go to the download page.
2. From the section titled HDF-Java 2.1x Pre-Built Binary Distributions select the HDFView download option that matches the operating system and computer setup (32 bit vs 64 bit) that you have. The download will start automatically.
3. Open the downloaded file.
   • Mac - You may want to add the HDFView application to your Applications directory.
   • Windows - Unzip the file, open the folder, run the .exe file, and follow directions to complete installation.
4. Open HDFView to ensure that the program installed correctly.

 Data Tip: The HDFView application requires Java to be up to date. If you are having issues opening HDFView, try to update Java first!

QGIS (Optional)

QGIS is a cross-platform Open Source Geographic Information system.

Online LiDAR Data/las Viewer (Optional)

Plas.io is a open source LiDAR data viewer developed by Martin Isenberg of Las Tools and several of his colleagues.