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

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.

NEON AOP Hyperspectral Data in HDF5 format with Python - Tiled Data

1 hour

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

NEON Data Access via BigQuery: A Pilot Project

45 minutes

Explore NEON stream discharge and water chemistry data in the pilot project for BigQuery datasets.

NEON Domain and Site Shapefiles and Maps

20 minutes

Use files available on the NEON data portal and NEON API to create maps of NEON domains and sites.

Open HDF5 files with Python Sample Code

0.5 hours

This tutorial provides basic code for opening an HDF5 file in Python using the h5py, numpy, and matplotlib libraries.

Plas.io: Free Online Data Viz to Explore LiDAR Data

0.5 Hours

Learn about LiDAR point cloud file formats .las and .laz. Explore LiDAR point cloud data using the free, online Plas.io viewer .

Plot a Spectral Signature in Python - Tiled Data

0.5 hours

Learn how to extract and plot a spectral profile from a single pixel of a reflectance band using NEON tiled hyperspectral data.

Plot Continuous & Discrete Data Together

30 minutes

This tutorial discusses ways to plot plant phenology (discrete time series) and single-aspirated temperature ((near-)continuous time series) together.

Plot Spectral Signatures Derived from Hyperspectral Remote Sensing Data in HDF5 Format in R

1.0 - 1.5 Hours

Extract a single pixel's worth of spectra from a hyperspectral dataset stored in HDF5 format in R. Visualize the spectral signature.

Plotting a NEON RGB Camera Image (GeoTIFF) in Python

0.5 hour

This lesson is a brief introduction to RGB camera images and the GeoTIFF raster format in Python.

Plotting and Clustering Megapit Soils Data

45 minutes

Learn to download, analyze, and visualize NEON soils data.

Primer on Raster Data in R

This series provides a brief primer on raster spatial data in R.

Series

5 part series

Publish Code - From R Markdown to HTML with knitr

30 minutes

This tutorial introduces how to use the R knitr package to publish from R Markdown files to HTML (or other) file format.

Quantifying The Drivers and Impacts of Natural Disturbance Events – The 2013

4 hours

This teaching module demonstrates ways that scientists identify and use

Random Forest Species Classification using AOP and TOS data in GEE

1 hour

Classifying species using AOP and observational field data at CLBJ

Raster 00: Intro to Raster Data in R

1 hour

This tutorial reviews the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R. It covers the three core metadata elements that we need to understand to work with rasters in R: CRS, Extent and Resolution. It also explores missing and bad data values as stored in a raster and how R handles these elements. Finally, it introduces the GeoTiff file format.

Raster 01: Plot Raster Data in R

30 minutes

This tutorial explains how to plot a raster in R using R's base plot function. It also covers how to layer a raster on top of a hillshade to produce an eloquent map.

Raster 02: When Rasters Don't Line Up - Reproject Raster Data in R

30 minutes

This tutorial explores issues associated with working with rasters in different Coordinate Reference Systems (CRS) & projections. When two rasters are in different CRS, they will not plot nicely together on a map. We will learn how to reproject a raster in R using the projectRaster function in the raster package.

Raster 03: Raster Calculations in R - Subtract One Raster from Another and Extract Pixel Values For Defined Locations

30 minutes

This tutorial covers how to subtract one raster from another using efficient methods - the overlay function compared to basic subtraction. We also cover how to extract pixel values from a set of locations - for example a buffer region around plot locations at a field site. Finally, it explains the basic principles of writing functions in R.

Raster 04: Work With Multi-Band Rasters - Image Data in R

1 hour

This tutorial explores how to import and plot a multi-band raster in R. It also covers how to plot a three-band color image using the plotRGB function in R.

Raster 05: Raster Time Series Data in R

30 minutes

This tutorial covers how to work with and plot a raster time series, using an R RasterStack object. It also covers the basics of practical data quality assessment of remote sensing imagery.

Raster 06: Plot Raster Time Series Data in R Using RasterVis and Levelplot

30 minutes

This tutorial covers how to efficiently and effectively plot a stack of rasters using rasterVis package in R. Specifically it covers using levelplot and adding meaningful, custom names to band labels in a RasterStack.

Raster 07: Extract NDVI Summary Values from a Raster Time Series

30 minutes

This tutorial covers how to extract and plot NDVI pixel values from a raster time series stack in R. We will use ggplot2 to plot our data.

Raster Data in R - The Basics

1 hour

This tutorial explains the fundamental principles, functions and metadata that you need to work with raster data in R.

Reflectance pre-processing: masking out bad weather data in GEE

30 minutes

Learn how find and use weather quality information from the Reflectance QA band in GEE

Relating Reflectance Indices to Flux Data

120 minutes

Explore the relationship between NDVI in the flux footprint and NEE. This tutorial is designed as a data exercise for Flux Course.