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Data Institute Details: 

Our 2017 Institute focuses on remote sensing of vegetation using open source tools and reproducible science workflows â€� the primary programming language will be Python. The Institute will be held at NEON headquarters in June 2017.  For more information on the institute, view the 2017 NEON Data Institute page. 

Pre-institute activities: Participants complete a series of online activities for three weeks prior to the Institute that provided the fundamental knowledge for everyone to succeed in the in-person portion. Topics include how NEON collects data as well as reproducible workflow tools and techniques.

In-person course: The in-person portion of the Institute includes guest speakers on specific topics and hands-on data-intensive activities, as well as several individual/group activities and projects. The following topics will be covered:

• Day 1 - Using HDF5 & Intro to Using Hyperspectral Remote Sensing Data
• Day 2 - Automating Workflows & Intro to Using LiDAR Data 
• Day 3 - Uncertainty in Remote Sensing Data
• Day 4 - Hyperspectral Remote Sensing of Vegetation 
  • Classification of Spectra 
  • Tree crown mapping 
  • Vegetation biomass calculations
• Day 5 - Individual & Small Group Applications w/ Instruction 
• Day 6 - Presentations of Individual Applications 

Who should attend?

Are you interested in heterogeneous ecological, biological and remote sensing data?  The Institute is geared towards graduate students and early career scientists with some programming experience who want to develop critical skills and foundational knowledge for working with heterogeneous spatio-temporal data to address ecological questions. Qualified applicants are required to have some prior basic experience in the Python programming environment (or experience in another programming environment and willing to learn Python). All participants must bring their own laptop to participate in the hands-on data activities.

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: 1-17 June 2017
• Institute Dates: 19-24 June 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. Learn more about NEON Data Institutes.

View all materials for the 2017 Data Institute here 

Registration Information

Applications for the 2017 Remote Sensing with Reproducible Workflows Data Institute have closed. 

Tuition for the course is $750. Tuition includes all instruction as well as lunches, snacks, and coffee/tea each day of the course.  Read the logistics page for more information. 

The application primarily consists of answering multiple choice questions pertaining to your background using different data and tools in addition to a short statement of why you want to participate in the Data Institute.

 If you have any questions, please contact us.

Schedule

Instructors

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.

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.

 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.

 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.