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A NEON Data Carpentry Workshop

Date: Spring 2015

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

Goals / Objectives

After completing this workshop, you will:
  1. Know what the hyperspectral remote sensing data are
  2. Know how to create and read from HDF5 files containing spatial data in R.
  3. Know they key attributes of raster data that you need to spatially locate raster data in R.

Things to do, before the workshop:

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 from the NEON website. </p>

R Libraries to Install

  • rhdf5: source("") ; biocLite("rhdf5")
  • raster: install.packages('raster')
  • rgdal: install.packages('rgdal')

Background Materials to Read

Click here for the Etherpad

Please also watch this video prior to the workshop


Time Topic Instructor
12:00 Raster Data in R - the skinny  
12:30 About Hyperspectral Remote Sensing Data  
1:00 Working with Hyperspectral Remote Sensing Data in R - P1  
2:30 Raster Stacks in R - Working with Hyperspectral Remote Sensing Data  


To participate in the workshop, you will need working copies of the software described below. Please make sure to install everything (or at least to download the installers) before the start of the workshop.


Hierarchical Data Format 5 (HDF5) is a file format used to store, package, and simultaneously organize large quantities of related data. Although we will use R to analyze data stored in this format, HDFView is free-ware that allows for quick and easy viewing and editing of these files.


R is a programming language that specializes in statistical computing. It is a powerful tool for exploratory data analysis. To interact with R, we recommend, but do not require, RStudio, an interactive development environment (IDE).

R Packages to Install

We will use several packages, including


  1. The package installation script here,
  2. The gdal libraries, and
  3. The hdf5 libraries.

Optional resources


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

Online LiDAR Data Viewer (las viewer) is a Open Source LiDAR data viewer developed by Martin Isenberg of Las Tools and several of his colleagues.