Data Tutorials

Looking to improve your data skills using tools like R or Python? Want to learn more about working with a specific NEON data product? NEON develops online tutorials to help you improve your research. These self-paced tutorials are designed for you to used as standalone help on a single topic or as a series to learn new techniques.

Code for all script based tutorials can be downloaded at the end of the tutorial. Original files can also be found on GitHub.

Filter

Hyperspectral Variation Uncertainty Analysis in Python

Learn to analyze the difference between rasters taken a few days apart to assess the uncertainty between days.

Assessing Spectrometer Accuracy using Validation Tarps with Python

Learn to analyze the difference between rasters taken a few days apart to assess the uncertainty between days.

Classify a Raster Using Threshold Values in Python

Learn how to read NEON lidar raster GeoTIFFs (e.g., CHM, slope, aspect) into Python numpy arrays with gdal and create a classified raster object.

Classification of Hyperspectral Data with Ordinary Least Squares in Python

Learn to classify spectral data using the Ordinary Least Squares method.

Classification of Hyperspectral Data with Principal Components Analysis (PCA) in Python

Learn to classify spectral data using the Principal Components Analysis (PCA) method.

Pages

Dialog content.