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 / ObjectivesAfter completing this workshop, you will:
- Know what the hyperspectral remote sensing data are
- Know how to create and read from HDF5 files containing spatial data in R.
- 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 DownloadNational 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
source("http://bioconductor.org/biocLite.R") ; biocLite("rhdf5")
Background Materials to Read
- Working with Rasters in Non Gui Tools.
- The basics of raster data in R.
- The basics of hyperspectral remote sensing data.
Please also watch this video prior to the workshop
|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
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.