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

Hyperspectral Variation Uncertainty Analysis in Python

0.5 hour

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

Image Raster Data in R - An Intro

30 minutes

This tutorial explains the fundamental principles, functions and metadata that you need to work with raster data, in image format, in R. Topics include raster stacks, raster bricks, plotting RGB images and exporting an RGB image to a GeoTIFF.

Install & Set Up Docker For Use With eddy4R

1 hour

This tutorial provides the basic steps for setting up and using Docker to work with the eddy4R R package in a Docker container.

Install Git, Bash Shell, Python

1 hour

This page outlines the tools and resources that you will need to

Install Git, Bash Shell, R & RStudio

1.0 - 1.5 Hours

This page outlines the tools and resources that you will need to complete the Data Institute activities.

Install QGIS & HDF5View

1.0 - 1.5 Hours

Installing & Updating Packages in R

30 minutes

This tutorial provides the basics of installing and working with packages in R.

Interacting with the PhenoCam Server using phenocamapi R Package

0.5 hrs

Learn the basics of how to extract PhenoCam data and metadata through the Phenocam API

Interactive Data Vizualization with R and Plotly

30 minutes

Learn the basics of how to use the plotly package to create interactive plots and use the Plotly API in R to share these plots.

Intro to Vector Data in R

The data tutorials in this series cover how to open, work with and plot with vector-format spatial data (points, lines and polygons) in R. Additional, topics include working with spatial metadata (extent and coordinate reference system), working with spatial attributes and plotting data by attributes.

Series

6 part series

Intro to Working with Hyperspectral Remote Sensing Data in HDF5 Format in R

1.0 - 1.5 Hours

Open up and explore a hyperspectral dataset stored in HDF5 format in

Introduction to AOP Data in Google Earth Engine (GEE)

20 minutes

Introductory tutorial on exploring AOP datasets in GEE.

Introduction to AOP Hyperspectral Data in GEE

30 minutes

Read in and visualize Surface Directional Reflectance data at NEON site SRER

Introduction to HDF5 Files in R

1.0 - 1.5 Hours

Learn how to build a HDF5 file in R from scratch! Add groups, datasets and attributes. Read data out from the file.

Introduction to Hierarchical Data Format (HDF5) - Using HDFView and R

In this series we cover what a HDF5 format is, and how to open, read, create HDF5 files in R. We also cover extracting and plotting data from HDF5 files.

Series

7 part series

Introduction to Hyperspectral Remote Sensing Data

In this series, we cover the basics of working with NEON hyperspectral remote sensing data. We cover the principles of hyperspectral data, how to open hyperspectral data stored in HDF5 format in R and how to extract bands and create rasters in GeoTiff format.
Finally we explore extracting a hyperspectral - spectral signature from one pixel using R.

Series

6 part series

Introduction to Light Detection and Ranging (LiDAR) – Explore Point Clouds and Work with LiDAR Raster Data in R

In this series we cover the basics of lidar data including 3 key lidar data products - the Canopy Height Model, Digital Surface Model (DSM) and the Digital Terrain Model (DTM). We explore lidar point clouds using the free, online 3d point cloud viewer. Finally, we cover working with LiDAR derived rasters in R.

Series

6 part series

Introduction to NEON API in Python

1 hour

Use the NEON API in Python, via requests package and json package.

Introduction to NEON Remote Sensing Data in Google Earth Engine

This series walks new GEE users through working with AOP datasets in Earth Engine, including JavaScript workflows for visualizing and analyzing AOP data.

Series

5 part series

Introduction to the National Ecological Observatory Network (NEON)

1.0 Hour

This page provides an overview of NEON and the data provided by NEON for use with NEON workshops and Data Institutes.

Introduction to using Jupyter Notebooks

1 hour

This tutorial cover how to use Jupyter Notebooks to document code.

Introduction to working with NEON eddy flux data

1 hour

Download and navigate NEON eddy flux data, including basic transformations and merges

Introduction to working with PhenoCam Images

This series provides instruction on how to work with phenocam images, including those from NEON sites. Many of the techniques can be applied to any repeat RGB photography.

Series

4 part series

Introduction to Working with Raster Data in R

A series of data tutorials that teach you how to open, plot and perform basic calculations on raster data in R. It also covers key spatial attributes associated with raster data include extent, projection and resolution. Finally we cover dealing with missing and bad data when working with remote sensing imagery.

Series

8 part series

Introduction to Working With Time Series Data in Text Formats in R

The tutorials in this series cover how to open, work with and plot with phenology-related micrometeorological data in R. Additional topics include working with time and date classes (e.g., POSIXct, POSIXlt, and Date), subsetting time series data by date and time and created facetted or tiles sets of plots.

Series

8 part series

Mask a Raster Using Threshold Values in Python

In this tutorial, we will learn how to remove parts of a raster based on pixel values using a mask we create.

Merging AOP L3 Tiles in R into Full-Site Rasters

30 - 45 Minutes

Download, mosaic, and write out AOP L3 raster data to full-site geotiffs and cloud-optimized geotiffs (COG).

Merging GeoTIFF Files to Create a Mosaic

30 minutes

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

Modeling phenology with the R package phenor

40 min

This tutorial explains how download and format data the model the phenology.

NEON AOP Hyperspectral Data in HDF5 format with Python - Flightlines

1 hour

Learn how to read NEON AOP hyperspectral flightline data using Python and develop skills to manipulate and visualize spectral data.