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 material are freely available for you to use and reuse. We suggest the following citation: [AUTHOR(S)]. Data Tutorial:[TUTORIAL NAME]. Accessed:[DATE OF ACCESS]. National Ecological Observatory Network, Battelle, Boulder, CO, USA. [URL]

Tutorials

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

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.

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 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.

Spatial Data Tutorial Series Capstone Challenges

0.5 - 1.0 Hours

This page contains capstone activities that complement several spatial data tutorial series.

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.

The Importance of Reproducible Science

1.0 Hour

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

The Relationship Between Raster Resolution, Spatial Extent & Number of Pixels

1 hour

Learn about the key attributes needed to work with raster data in non-GUI programs. Examples in R.

Time Series 00: Intro to Time Series Data in R - Managing Date/Time Formats

30 minutes

This tutorial will demonstrate how to import a time series dataset stored

Time Series 01: Why Metadata Are Important: How to Work with Metadata in Text & EML Format

30 minutes

This tutorial covers what metadata are, and why we need to work with metadata. It covers the 3 most common metadata formats: text file format, web page format and Ecological Metadata Language (EML).

Time Series 02: Dealing With Dates & Times in R - as.Date, POSIXct, POSIXlt

30 minutes

This tutorial explores working with date and time classes in R. We will

Time Series 03: Cleaning & Subsetting Time Series Data in R - NoData Values & Subset by Date

30 minutes

This tutorial explores how to deal with NoData values encountered in a time series dataset, in R. It also covers how to subset large data files by date and export the results to a csv (text format) file.

Time Series 04: Subset and Manipulate Time Series Data with dplyr

30 minutes

In this tutorial, we will use the group_by, summarize and mutate functions in the `dplyr` package to efficiently manipulate atmospheric data collected at the NEON Harvard Forest Field Site. We will use pipes to efficiently perform multiple tasks within a single chunk of code.

Time Series 05: Plot Time Series with ggplot2 in R

30 minutes

This tutorial uses ggplot2 to create customized plots of time series data. We will learn how to adjust x- and y-axis ticks using the scales package, how to add trend lines to a scatter plot and how to customize plot labels, colors and overall theme.

Time Series 06: Create Plots with Multiple Panels, Grouped by Time Using ggplot Facets

30 minutes

This tutorial covers how to plot subsetted time series data (e.g., plot by season) using facets() and ggplot2. It also covers how to plot multiple metrics in one display panel.

Time Series Culmination Activity: Plot using Facets & Plot NDVI with Time Series Data

30 minutes

This tutorial is a data integration wrap-up culmination activity for the spatio-temporal time series tutorials.