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.


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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