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.
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:
Data to Download
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.
R Libraries to Install
Please have these packages installed and updated prior to the start of the workshop.
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.
Background Materials to Read
An Introduction to Remote Sensing
Please watch this video on remote sensing prior to the workshop
|12:00||Raster Data in R - the skinny|
|12:30||About Hyperspectral Remote Sensing Data|
|1:00||Intro to Working with Hyperspectral Remote Sensing Data in HDF5 Format in R|
|2:30||Create a Raster Stack from Hyperspectral Imagery in HDF5 Format in R|
To participate in the workshop, you will need working copies of the software described below. Please make sure to install everything 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).
QGIS is a cross-platform Open Source Geographic Information system.
Online LiDAR Data Viewer (las viewer)
http://plas.io is a Open Source LiDAR data viewer developed by Martin Isenberg of Las Tools and several of his colleagues.