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 materials are freely available for you to use and reuse. We suggest the following citation for tutorials: [AUTHOR(S), NEON (National Ecological Observatory Network)]. Data Tutorial: [TUTORIAL NAME]. [URL] (accessed [DATE OF ACCESS]). See Citation Guidelines for examples, and for guidance in citing data and code.
Tutorials
About Hyperspectral Remote Sensing Data0.25 - 0.5 Hours Learn about the fundamental principles of hyperspectral remote sensing data. |
Access and Work with NEON Geolocation Data30 minutes Use files available on the NEON data portal, NEON API, and neonUtilities R package to access the locations of NEON sampling events and infrastructure. Calculate more precise locations for certain sampling types and reference ground sampling to airborne data. |
Access NEON Data for Metagenomics0.4 hours Using NEON tools to access metadata for metagenomic samples. |
Assessing Spectrometer Accuracy using Validation Tarps with Python30 minutes Comparison of reflectance curves collected over spectral validation tarps with ASD and NIS sensors. |
Assignment: Reproducible Workflows with Jupyter Notebooks1 hour This page details how to complete the assignment for pre-Institute week 3 on documenting your code with Jupyter Notebooks. |
Assignment: Version Control with GitHub1 hour Data Institute Assignment: The page lists the requirements for the week 2 assignment on version control and GitHub. |
Basic R SkillsThis series provides tutorials and references on key skills needed to complete more complex tasks in R. It is not intended as an guide for the introduction to or initial learning of how to use R. Series
4 part series
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Build & Work With Functions in R30 minutes This tutorial teaches the basics of creating a function in R. |
Calculate NDVI & Extract Spectra Using Masks in Python0.5 hours Learn to calculate Normalized Difference Vegetation Index (NDVI) and extract spectral using masks with Python. |
Calculate Vegetation Biomass from LiDAR Data in Python1 hour Learn to calculate the biomass of standing vegetation using a canopy height model data product. |
Classify a Lidar Raster in Python30 minutes Read NEON lidar raster GeoTIFFs (e.g., CHM, slope, aspect) and create a classified raster object. |
Cleaning and Gap-Filling NEON Aquatic Instrument Data2 hours Tutorial for Cleaning NEON Level 1 AIS data |
Compare tree height measured from the ground to a Lidar-based Canopy Height Model1 hour Investigate the relationship between two methods for measuring canopy height |
Convert to Julian Day20 minutes This tutorial explains why Julian days are useful and teaches how to create a Julian day variable from a Date or Data/Time class variable. |
Create a Canopy Height Model from Lidar-derived rasters in R0.5 Hours In this tutorial, you will bring lidar-derived raster data (DSM and DTM) into R and difference them to create a canopy height model (CHM). |
Create a Hillshade from a Terrain Raster in Python0.5 hour Learn how to create a hillshade from a terrain raster in Python. |
Create A Square Buffer Around a Plot Centroid in R1.0 - 1.5 Hours This tutorial walks you through creating square polygons from a plot centroid (x,y format) in R. |
Create HDF5 Files in R Using Loops1.0 - 1.5 Hours Create a HDF5 in R from scratch! Add groups and datasets. View the files with HDFView. |
Creating a Raster Stack from Hyperspectral Imagery in HDF5 Format in R1.0 - 1.5 Hours Open up and explore hyperspectral imagery in HDF format R. Combine multiple bands to create a raster stack. Use these steps to create various band combinations such as RGB, Color-Infrared and False color images. |
Data Activity: Visualize Palmer Drought Severity Index Data in R to Better Understand the 2013 Colorado Floods1 hour This tutorial walks through how to download and visualize Palmer Drought |
Data Activity: Visualize Precipitation Data in R to Better Understand the 2013 Colorado Floods1 hour This lesson walks through the steps need to download and visualize precipitation |
Data Activity: Visualize Stream Discharge Data in R to Better Understand the 2013 Colorado Floods1 hour This lesson walks through the steps needed to download and visualize USGS |
Data Institute Activity: Calculate Index of Interest30 mins This page details the remote sensing hyperspectral imaging indices activity used during Data Institutes. |
Data Institute: Install Required R Packages1.0 - 1.5 Hours This tutorial covers the R packages that you will need to have installed for the Institute. |
Detecting changes in vegetation structure following fires using discrete-return LiDAR1 hour Use discrete lidar point cloud and raster data to understand post-wildfire vegetation changes. |
Detecting Foggy Images using the hazer Package0.5 hrs Learn how to estimate image haziness in a image as an indication of fog, cloud or other natural or artificial factors using the hazer R package. |
Document & Publish Your Workflow: Jupyter Notebooks0.5 hour This tutorial introduces the importance of tools supporting documenting & publishing a workflow using the Python kernel of Jupyter Notebooks. |
Document & Publish Your Workflow: R Markdown & knitr20 minutes This tutorial introduces the importance of tools supporting documenting & publishing a workflow. |
Document Code with R Markdown1 hour This tutorial cover how to use R Markdown files to document code. |