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R Bloggers
Date(s): Jun 19 - Jun 24, 2017

NEON’s Data Institutes provide critical skills and foundational knowledge for graduate students and early career scientists working with heterogeneous spatio-temporal data to address ecological questions.

June 2017: Remote Sensing with Reproducible Workflows

Our 2017 Institute focused on remote sensing of vegetation using open source tools and reproducible science workflows – the primary programming language will be Python. This Institute will be held at NEON headquarters in June 2017.

In addition to the 6 day institute there are three weeks of pre-institute materials is to ensure that everyone comes to the Institute ready to work in a collaborative research environment. Pre-institute materials are online & individually paced, expect to spend 1-5 hrs/week depending on your familiarity with the topic.

Time Day Description
  Computer Setup Materials
25 May - 1 June Intro to NEON & Reproducible Science
2-8 June Version Control & Collaborative Science with Git & GitHub
9-15 June Documentation of Your Workflow with iPython/Jupyter Notebooks
19-24 June Data Institute
7:50am - 6:30 pm Monday Intro to NEON, Intro to HDF5 & Hyperspectral Remote Sensing
8:00am - 6:30pm Tuesday Reproducible & Automated Workflows, Intro to LiDAR data
8:00am - 6:30pm Wednesday Remote Sensing Uncertainty
8:00am - 6:30pm Thursday Hyperspectral Data & Vegetation
8:00am - 6:30pm Friday Individual/Group Applications
9:00am - 1:00pm Saturday Group Application Presentations

Online Resources

The teaching materials from the 2017 Data Institute will be available as self-paced online tutorials after the Institute. In the meantime, you can access the 2016 Data Institute materials here. These 2016 materials were designed to be used in the context of the workshop with an instructor. In 2016, the primary programming language was R.


Dr. Tristan Goulden, Associate Scientist-Airborne Platform, Battelle-NEON: Tristan is a remote sensing scientist with NEON specializing in LiDAR. He also co-lead NEON’s Remote Sensing IPT (integrated product team) which focusses on developing algorithms and associated documentation for all of NEON’s remote sensing data products. His past research focus has been on characterizing uncertainty in LiDAR observations/processing and propagating the uncertainty into downstream data products. During his PhD, he focused on developing uncertainty models for topographic attributes (elevation, slope, aspect), hydrological products such as watershed boundaries, stream networks, as well as stream flow and erosion at the watershed scale. His past experience in LiDAR has included all aspects of the LIDAR workflow including; mission planning, airborne operations, processing of raw data, and development of higher level data products. During his graduate research he applied these skills on LiDAR flights over several case study watersheds of study as well as some amazing LiDAR flights over the Canadian Rockies for monitoring change of alpine glaciers. His software experience for LiDAR processing includes Applanix’s POSPac MMS, Optech’s LMS software, Riegl’s LMS software, LAStools, Pulsetools, TerraScan, QT Modeler, ArcGIS, QGIS, Surfer, and self-written scripts in Matlab for point-cloud, raster, and waveform processing.

Dr. Naupaka Zimmerman, Assistant Professor of Biology, University of San Francisco: Naupaka’s research focuses on the microbial ecology of plant-fungal interactions. Naupaka brings to the course experience and enthusiasm for reproducible workflows developed after discovering how challenging it is to keep track of complex analyses in his own dissertation and postdoctoral work. As a co-founder of the International Network of Next-Generation Ecologists and an instructor and lesson maintainer for Software Carpentry and Data Carpentry, Naupaka is very interested in providing and improving training experiences in open science and reproducible research methods.

Bridget Hass, Remote Sensing Data Processing Technician, Battelle-NEON: Bridget’s daily work includes processing LiDAR and hyperspectral data collected by NEON’s Aerial Observation Platform (AOP). Prior to joining NEON, Bridget worked in marine geophysics as a shipboard technician and research assistant. She is excited to be a part of producing NEON’s AOP data and to share techniques for working with this data during the 2017 Data Institute.

Dr. Paul Gader, Professor, University of Florida: Paul is a Professor of Computer & Information Science & Engineering (CISE) at the Engineering School of Sustainable Infrastructure and the Environment (ESSIE) at the University of Florida(UF). Paul received his Ph.D. in Mathematics for parallel image processing and applied mathematics research in 1986 from UF, spent 5 years in industry, and has been teaching at various universities since 1991. His first research in image processing was in 1984 focused on algorithms for detection of bridges in Forward Looking Infra-Red (FLIR) imagery. He has investigated algorithms for land mine research since 1996, leading a team that produced new algorithms and real-time software for a sensor system currently operational in Afghanistan. His landmine detection projects involve algorithm development for data generated from hand-held, vehicle-based, and airborne sensors, including ground penetrating radar, acoustic/seismic, broadband IR (emissive and reflective bands), emissive and reflective hyperspectral imagery, and wide-band electro-magnetic sensors. In the past few years, he focused on
algorithms for imaging spectroscopy. He is currently researching nonlinear unmixing for object and material detection, classification and segmentation, and estimating plant traits. He has given tutorials on nonlinear unmixing at International Conferences. He is a Fellow of the Institute of Electrical and Electronic Engineers, an Endowed Professor at the University of Florida, was selected for a 3-year term as a UF Research Foundation Professor, and has over 100 refereed journal articles and over 300 conference articles.

Key 2017 Dates

  • Applications Open: 17 January 2017
  • Application Deadline: 10 March 2017
  • Notification of Acceptance: late March 2017
  • Tuition payment due by: mid April 2017
  • Pre-institute online activities: June 1-17, 2017
  • Institute Dates: June 19-24, 2017

More information about the NEON Data Institutes can be found on the NEON website.