Colleagues -
Are you interested in working with remote sensing data to address science 
questions?  Do you work in Python, yet haven’t worked with tools including 
GitHub or Jupyter Notebooks?  If so, applying for the 2018 NEON Data Institute 
on Remote Sensing with Reproducible Workflows in Python! The Institute will 
take place July 9-14, 2017 at NEON Headquarters in Boulder, CO.

More information about the event and the application process is on the NEON 
website: .  Applications are due March 20, 

This Data Institute is designed to teach skills and foundational knowledge for 
graduate students and early career scientists working with heterogeneous 
spatio-temporal data to address ecological questions. Through data intensive 
live-coding, short presentations, and small group work, we will cover:
* Background theoretical concepts related to LiDAR and hyperspectral remote 
* Fundamental concepts required to ingest, visualize, process, and analyze NEON 
hyperspectral and LiDAR data.
* Best practices on reproducible research workflows: the importance of 
documentation, organization, version control, and automation.
* Scientific spatio-temporal applications of remote sensing data using 
open-source tools, namely Python and Jupyter Notebooks.
* Machine learning for prediction of biophysical variables such as above-ground 
biomass using NEON LiDAR and ground measurements.
* Classification of hyperspectral data using deep-learning approaches.
* Using remote sensing data products with in situ data to quantify uncertainty 
associated with remote sensing observations.

The cost of the course is $650 which includes all instruction and lunches 
during the course. Limited tuition scholarships are available for graduate 
students and post-doctoral researchers.  Applications are due March 20, 2018.

Thank you,


Megan A. Jones, PhD
Staff Scientist & Science Educator
National Ecological Observatory Network (NEON)

Battelle/NEON HQ
1685 38th Street
Suite 1000
Boulder, Colorado 80301


For tutorials and resources on working with ecological data, visit .


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