Data to Study Continental-scale Ecological Change: Access and Analyze NEON Remote Sensing Data in Python | AGU 2019

The Airborne Observation Platform (AOP) that is part of the standard data collection by the National Ecological Observatory Network (NEON) provides high resolution RGB camera imagery, discrete and waveform lidar, and hyperspectral remote sensing data products from 81 terrestrial and aquatic sites from across the US. The coincident collection of AOP data with over 150 additional NEON data products provides a rich resource for carrying ecological research at multiple scales. This workshop focuses on remote sensing of vegetation and landforms using open source tools and reproducible science workflows -- the primary programming language will be Python. Through data intensive live-coding, use of existing scripts and Python modules, and short presentations we will cover topics including: fundamental concepts required to download, visualize, process, and analyze NEON hyperspectral and LiDAR data, scientific spatio-temporal applications of remote sensing data using open-source tools, namely Python and Jupyter Notebooks. The workshop will culminate with an exercise that brings together lidar, hyperspectral, and camera imagery to carry out a post-fire burn detection. Participants will leave with a suite of open-source Python tools at their disposal which can be leveraged to further their own research interests, as well as knowledge of how to access NEON data and resources to investigate ecological questions. 

Registration 

The workshop is limited to 40 participants – make sure to secure your spot by signing up by Dec 2, 2019! If spaces are available, registration may be extended beyond this date. 

This workshop is not affiliated with the AGU Annual Meeting and participants in this workshop do not have to be registered for the AGU meeting. 

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Workshop Learning Objectives

The participants of the workshop will learn about the available datasets and tools to study ecological processes using NEON remote sensing data. By the end of the workshop, participants will understand how they can use the suite of NEON data products to address their research questions. After attending the workshop, the attendees will be able to:

• Programmatically access NEON data from the NEON data portal and know where to access more information about the data project.
• Know how to access other code resources for working with NEON data.
• Open and work with raster data stored in HDF5 format in Python; including using the key components of the HDF5 data structure (groups, datasets and attributes) and working with attribute data (metadata) from an HDF5 file.
• Open and work with NEON lidar remote sensing geoTIFF data.