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.

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Unsupervised Spectral Classification in Python: KMeans & PCA

Learn to classify spectral data using KMeans and Principal Components Analysis (PCA).

Unsupervised Spectral Classification in Python: Endmember Extraction

Learn to classify spectral data using Endmember Extraction, Spectral Information Divergence, and Spectral Angle Mapping.

Merging GeoTIFF Files to Create a Mosaic

Learn to merge multiple GeoTIFF files to great a larger area of interest.

Calculate NDVI & Extract Spectra Using Masks in Python - Tiled Data

Learn to calculate Normalized Difference Vegetation Index (NDVI) and extract spectral using masks with Python and NEON tiled hyperspectral data products.

Classify a Raster Using Threshold Values in Python - 2018

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.

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