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.

All material are freely available for you to use and reuse. We suggest the following citation:

[AUTHOR(S)]. Data Tutorial:[TUTORIAL NAME]. Accessed:[DATE OF ACCESS]. National Ecological Observatory Network, Battelle, Boulder, CO, USA. [URL]

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

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

Exploring Uncertainty in LiDAR Data using Python

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

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.

Calculate Vegetation Biomass from LiDAR Data in Python

Learn to calculate the biomass of standing vegetation using a canopy height model data product.

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