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

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 AOP Data in Google Earth Engine Tutorial Series

GEE 2023 introductory tutorial series for 2023+ AOP GEE format

Series

6 part series

Intro to AOP Datasets in Google Earth Engine (GEE) using Python

30 minutes

Explore AOP reflectance, camera, and lidar datasets in GEE

Intro to AOP Hyperspectral Data in Google Earth Engine (GEE) using Python geemap

30 minutes

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

0.5 - 1 Hours

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

Introduction to AOP Public Datasets in Google Earth Engine (GEE)

20 minutes

Introductory tutorial on exploring AOP Image Collections in Earth Engine.

Introduction to Bidirectional Hyperspectral Reflectance Data in Python

1 hour

Learn how to read NEON AOP L3 bidirectional (BRDF-corrected) reflectance h5 data in Python and visualize spectral data.

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 Python

In this series, we go over the basics of working with the NEON Level 3 NEON Spectrometer orthorectified surface directional reflectance - mosaic data (https://data.neonscience.org/data-products/DP3.30006.001) in Python.

This series covers how to:
- Open hyperspectral reflectance data stored in HDF5 format in Python
- Read in and explore the data and metadata contents
- Extract and plot a single reflectance band and RGB and false-color band combinations
- Read in and plot the spectral signature of a single pixel
- Interactively plot the spectral signature of various pixels within a reflectance tile

Data used in this series are from the National Ecological Observatory Network (NEON) and are in HDF5 format.

Series

4 part series

Introduction to Hyperspectral Remote Sensing Data in R

In this series, we cover the basics of working with NEON hyperspectral remote sensing data in R. 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 (.tif) format.
Finally we demonstrate how to extract a spectral signature from a single pixel using R, and show how to interactively compare spectra from different land cover types.

Series

5 part series

Introduction to Light Detection and Ranging (LiDAR) in R

In this series we cover the basics of lidar data including 3 key lidar data products - the Canopy Height Model (CHM), Digital Surface Model (DSM) and the Digital Terrain Model (DTM). We explore lidar point clouds using plas.io - a free, online 3D lidar point cloud viewer . Finally, we demonstrate how to work with lidar-derived rasters in R.

Series

5 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 Discrete Lidar Data in Python

45 minutes - 1 hour

Programmatically download lidar data and metadata and explore discrete lidar point clouds and rasters in Python

Introduction to NEON soil sensor data

1 hour

Create a time series plot of soil temperature, moisture, and CO<sub>2</sub> concentrations

Introduction to Small Mammal Data

1.5 hrs

This tutorial will provide an introduction to discovering, accessing and preparing NEON small mammal collection data using R

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 Rasters Using Thresholds in Python

45 minutes

Mask Lidar Aspect and Spectrometer NDVI rasters by threshold values in Python.

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).