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 most script based tutorials can be downloaded at the end of the tutorial. Source files can also be found on GitHub. If you are interested in contributing a tutorial to this collection, please reach out using the Contact Us form, and we can guide you through the process of submitting resources to the GitHub repository.

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 Data

0.25 - 0.5 Hours

Learn about the fundamental principles of hyperspectral remote sensing data.

Access and Work with NEON Geolocation Data

30 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 Metagenomics

0.4 hours

Using NEON tools to access metadata for metagenomic samples.

Assessing Spectrometer Accuracy using Validation Tarps with Python

30 minutes

Comparison of reflectance curves collected over spectral validation tarps with ASD and NIS sensors.

Assignment: Reproducible Workflows with Jupyter Notebooks

1 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 GitHub

1 hour

Data Institute Assignment: The page lists the requirements for the week 2 assignment on version control and GitHub.

Basic R Skills

This 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

Build & Work With Functions in R

30 minutes

This tutorial teaches the basics of creating a function in R.

Calculate NDVI & Extract Spectra Using Masks in Python

0.5 hours

Learn to calculate Normalized Difference Vegetation Index (NDVI) and extract spectral using masks with Python.

Calculate Vegetation Biomass from LiDAR Data in Python

1 hour

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

Classify a Lidar Raster in Python

30 minutes

Read NEON lidar raster GeoTIFFs (e.g., CHM, slope, aspect) and create a classified raster object.

Cleaning and Gap-Filling NEON Aquatic Instrument Data

2 hours

Tutorial for Cleaning NEON Level 1 AIS data

Compare tree height measured from the ground to a Lidar-based Canopy Height Model

1 hour

Investigate the relationship between two methods for measuring canopy height

Convert to Julian Day

20 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 R

0.5 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 Python

0.5 hour

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

Create A Square Buffer Around a Plot Centroid in R

1.0 - 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 Loops

1.0 - 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 R

1.0 - 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 Precipitation Data in R to Better Understand the 2013 Colorado Floods

1 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 Floods

1 hour

This lesson walks through the steps needed to download and visualize USGS

Data Institute Activity: Calculate Index of Interest

30 mins

This page details the remote sensing hyperspectral imaging indices activity used during Data Institutes.

Data Institute: Install Required R Packages

1.0 - 1.5 Hours

This tutorial covers the R packages that you will need to have installed for the Institute.

Data Management using National Ecological Observatory Network’s (NEON) Small Mammal Data with Accompanying Lesson on Mark Recapture Analysis

Detecting changes in vegetation structure following fires using discrete-return LiDAR

1 hour

Use discrete lidar point cloud and raster data to understand post-wildfire vegetation changes.

Detecting Foggy Images using the hazer Package

0.5 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 Notebooks

0.5 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 & knitr

20 minutes

This tutorial introduces the importance of tools supporting documenting & publishing a workflow.

Document Code with R Markdown

1 hour

This tutorial cover how to use R Markdown files to document code.

Document Your Code with Jupyter Notebooks

This series teaches you to use Jupyter Notebooks formats to document code and efficiently publish code results & outputs.

Series

2 part series