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.


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.

Create a Hillshade from a Terrain Raster in Python

Learn how to create a hillshade from a terrain raster in Python.

Mask a Raster Using Threshold Values in Python

In this tutorial, we will learn how to remove parts of a raster based on pixel values using a mask we create.

Classification of Hyperspectral Data with Ordinary Least Squares in Python

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

Hyperspectral Variation Uncertainty Analysis in Python

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


Dialog content.