Introduction to Hyperspectral Remote Sensing Data feature image Source: National Ecological Observatory Network (NEON)
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Series: Introduction to Hyperspectral Remote Sensing Data

About

In this series, we cover the basics of working with NEON hyperspectral remote sensing data. We cover the principles of hyperspectral data, how to open hyperspectral data stored in HDF5 format in R and how to extract bands and create rasters in GeoTiff format. Finally we explore extracting a hyperspectral

  • spectral signature from a single pixel using R.

Data used in this series are from the National Ecological Observatory Network (NEON) and are in HDF5 format.

Series Goals / Objectives

After completing the series you will:

  • Understand the collection of hyperspectral remote sensing data and how they can be used
  • Understand how HDF5 data can be used to store spatial data and the associated benefits of this format when working with large spatial data cubes
  • Know how to extract metadata from HDF5 files
  • Know how to plot a matrix as an image and a raster
  • Understand how to extract and plot spectra from an HDF5 file
  • Know how to work with groups and datasets within an HDF5 file
  • Know how to export a spatially projected GeoTIFF
  • Create a rasterstack in R which can then be used to create RGB images from bands in a hyperspectral data cube
  • Plot data spatially on a map
  • Create basic vegetation indices, like NDVI, using raster-based calculations in R

Things You’ll Need To Complete This Series

Setup RStudio

To complete some of the tutorials in this series, you will need an updated version of R and, preferably, RStudio installed on your computer.

R is a programming language that specializes in statistical computing. It is a powerful tool for exploratory data analysis. To interact with R, we strongly recommend RStudio, an interactive development environment (IDE).

Download Data

Data is available for download in each tutorial that it is needed in.


Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets. An overview of setting the working directory in R can be found here.

R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.

Tutorials in Workshop Series