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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 . 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

NDVI Time Series using AOP Reflectance and Landsat 8 Data in GEE

45 minutes

NEON AOP Hyperspectral Data in HDF5 format with Python

1 hour

Learn how to read NEON AOP L3 reflectance h5 data in Python 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.

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 from Reflectance Data in Python

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 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 NEON RGB Camera Imagery in Python

20 minutes

Introduction to RGB camera images and reading in multi-band images in Python with rasterio.

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.

Plot spectral signatures of AOP Reflectance data in GEE

45 minutes

Interactively plot the spectral signature of an AOP reflectance data pixel in GEE

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

Principal Component Analysis of AOP Hyperspectral Data in GEE

1 hour

Apply Principal Component Analysis (PCA) to NEON AOP hyperspectral reflectance data to reduce data dimensionality

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 Colorado Floods

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 shows how to create a hillshade and layer a DSM raster on top of a hillshade to produce an eloquent map.

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

1 hour

Import and plot each band and all bands of a three-band raster in R.

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.

Read in and visualize hyperspectral data in Python

1 hour

Learn to efficiently work with tiled NEON AOP hyperspectral data in Python using functions.

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.

Resources for Learning R

20 minutes

A brief overview of available resource to get started learning R.

Select pixels and compare spectral signatures in R

0.5 - 1 Hours

Plot and comapre the spectral signatures of multiple different land cover types using an interactive click-to-extract interface to select pixels.

Set up GitHub Working Directory - Quick Intro to Bash

1.0 - 1.5 Hours

This page reviews how to check that github is installed on your computer. It also provides a quick overview of Bash shell. Finally, we will setup a working GitHub directory.

Soil temperature and moisture controls on soil microbial biomass

1 hour

Explore the relationships between microbial biomass and soil temperature and moisture

Subsetting NEON HDF5 hyperspectral files to reduce file size

1.0 Hour

Take a large NEON hyperspectral HDF5 file and extract only the information that you need, then save as a new HDF5 file. For an example, we will take an existing hyperspectral dataset (~600Mb) and reduce it in size for subsequent tutorials.

The Basics of LiDAR - Light Detection and Ranging - Remote Sensing

0.25 Hours

Explore the basics of how a LiDAR system works and what a LiDAR system measures.