Tutorials | NSF NEON | Open Data to Understand our Ecosystems

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About Hyperspectral Remote Sensing Data

0.25 - 0.5 Hours

Learn about the fundamental principles of hyperspectral remote sensing data.

Series

6 part series

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Access and Work with NEON Geolocation Data

40 minutes

Use files available on the NEON data portal, NEON API, and neonUtilities R package to access the locations of NEON sampling events and infrastructure. Calculate more precise locations for certain sampling types and reference ground sampling to airborne data.

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Assessing Spectrometer Accuracy using Validation Tarps with Python

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

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Assignment: Reproducible Workflows with Jupyter Notebooks

This page details how to complete the assignment for pre-Institute week 3 on documenting your code with Jupyter Notebooks.

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Assignment: Reproducible Workflows with R Markdown

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Assignment: Version Control with GitHub

Data Institute Assignment: The page lists the requirements for the week 2 assignment on version control and GitHub.

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Band Stacking, RGB & False Color Images, and Interactive Widgets in Python - Flightline Data

Learn to efficiently work with flightline NEON AOP spectral data using functions.

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Band Stacking, RGB & False Color Images, and Interactive Widgets in Python - Tiled Data

Learn to efficiently work with tiled NEON AOP spectral data using functions.

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Basic R Skills

This series provides tutorials and references on key skills needed to complete more complex tasks in R. It is not intended as an guide for the introduction to or initial learning of how to use R.

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Build & Work With Functions in R

This tutorial teaches the basics of creating a function in R.

Series

4 part series

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Calculate NDVI & Extract Spectra Using Masks in Python - Tiled Data

Learn to calculate Normalized Difference Vegetation Index (NDVI) and extract spectral using masks with Python and NEON tiled hyperspectral data products.

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Calculate Vegetation Biomass from LiDAR Data in Python

Learn to calculate the biomass of standing vegetation using a canopy height model data product.

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Calculating Forest Structural Diversity Metrics from NEON LiDAR Data

0.5 hour

Read in a NEON LiDAR file (.laz) and calculate several forest structural diversity

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Classification of Hyperspectral Data with Ordinary Least Squares in Python

Learn to classify spectral data using the Ordinary Least Squares method.

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Classification of Hyperspectral Data with Principal Components Analysis (PCA) in Python

Learn to classify spectral data using the Principal Components Analysis (PCA) method.

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Classification of Hyperspectral Data with Support Vector Machine (SVM) Using SciKit in Python

Learn to classify spectral data using the Support Vector Machine (SVM) method.

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Classify a Raster Using Threshold Values in Python - 2017

Learn how to read NEON lidar raster GeoTIFFs (e.g., CHM, slope, aspect) into Python numpy arrays with gdal and create a classified raster object.

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Classify a Raster Using Threshold Values in Python - 2018

Learn how to read NEON lidar raster GeoTIFFs (e.g., CHM, slope, aspect) into Python numpy arrays with gdal and create a classified raster object.

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Compare tree height measured from the ground to a Lidar-based Canopy Height Model

60 minutes

Investigate the relationship between two methods for measuring canopy height

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Convert to Julian Day

This tutorial explains why Julian days are useful and teaches how to create a Julian day variable from a Date or Data/Time class variable.

Series

8 part series

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Create a Canopy Height Model from lidar-derived Rasters in R

0.5 Hours

In this tutorial, you will bring lidar-derived raster data (DSM and DTM) into R to create a canopy height model (CHM).

Series

6 part series

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Create a Hillshade from a Terrain Raster in Python

Learn how to create a hillshade from a terrain raster in Python.

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Create A Square Buffer Around a Plot Centroid in R

1.0 - 1.5 Hours

This tutorial walks you through creating square polygons from a plot centroid (x,y format) in R.

Series

5 part series

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Create HDF5 Files in R Using Loops

1.0 - 1.5 Hours

Create a HDF5 in R from scratch! Add groups and datasets. View the files with HDFView.

Series

7 part series

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Creating a Raster Stack from Hyperspectral Imagery in HDF5 Format in R

1.0 - 1.5 Hours

Open up and explore hyperspectral imagery in HDF format R. Combine multiple bands to create a raster stack. Use these steps to create various band combinations such as RGB, Color-Infrared and False color images.

Series

6 part series

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