Course

Flux Course

clunch1606160961 — Mon, 23 May 2022 23:40:36 +0000

Flux Course

Jul 25-Aug 5, 2022

clunch1606160961 Mon, 05/23/2022 - 17:40

Nederland, CO
United States

The Fluxcourse is a two week early-career workshop focused on the foundations of land-atmosphere flux measurement, modeling, and synthesis.

NEON staff will be teaching components of Fluxcourse dedicated to working with NEON data, network data synthesis, and using NEON data to inform land-surface models.

Applications for Fluxcourse 2022 have closed; if you are interested in attending Fluxcourse 2023, watch http://www.fluxcourse.org/ for applications to open in early 2023.

Language English

Mountain Research Station

http://www.fluxcourse.org/

Learn More

Course

Data Institute 2018: Remote Sensing with Reproducible Workflows in Python

lgoldman — Fri, 09 Feb 2018 19:53:12 +0000

Data Institute 2018: Remote Sensing with Reproducible Workflows in Python

Jul 9-14, 2018

lgoldman Fri, 02/09/2018 - 12:53

United States

A deep dive into NEON’s remote sensing data products

During the course, participants will work with hyperspectral, lidar and RGB camera data collected by NEON’s Airborne Observation Platform (AOP). Students will have direct access to NEON’s science staff responsible for the collection, algorithm development and production of NEON AOP data. Workshop lessons will focus on how to efficiently utilize NEON AOP data for scientific applications and how common remote sensing processing methods will impact data quality. Additionally, participants will tour the NEON calibration facilities to gain a deeper understanding of the importance of calibration to reduce data uncertainty during collection and processing.

Through data intensive, hands-on activities, this course covers:

Background theoretical concepts related to LiDAR and hyperspectral remote sensing
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.

This Institute will be held at the NEON project headquarters 9-14 July 2018. In addition to the six days of in-person training, 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.

View all materials for the 2018 Data Institute here.

Interested in NEON Data Institutes on other topics? Want to work with staff on the NEON project to provide training in other areas? Learn more about other topics or ways to collaborate here.

Registration Information

Applications for the 2018 Remote Sensing with Reproducible Workflows Data Institute are due 20 March 2017. Read more about the application process.

Tuition for the course is $650. Tuition includes all instruction as well as lunches, snacks, and coffee/tea each day of the course.

If you have any questions, please [email protected]?subject=Data%20Institute%20Inquiry">contact us.

Schedule

Please note that slight changes may be made to the schedule of the Data Institute.

Time	Day	Description
--	1 - 7 June	Computer Setup Materials
--	8 -14 June	Intro to NEON & Reproducible Science
--	15 - 21 June	Version Control & Collaborative Science with Git & GitHub
--	22 June - 5 July	Documentation of Your Workflow with Jupyter Notebooks �� due to 4 July holiday this will be a 2-week interval
--	9 - 14 July	Data Institute
7:50am - 6:30 pm	Monday	Working with HDF5 & Hyperspectral Remote Sensing
8:00am - 6:30pm	Tuesday	Reproducible & Automated Workflows, Working with LiDAR data
8:00am - 6:30pm	Wednesday	Remote Sensing Uncertainty
8:00am - 6:30pm	Thursday	Measuring Vegetation & Working at Scale with CyVerse
8:00am - 6:30pm	Friday	Hyperspectral Classification/Waveform Lidar & Applications in Remote Sensing
9:00am - 5:00pm	Saturday	Applications cont. & Presentations

Instructors

Tristan Goulden, Associate Scientist-Airborne Platform, Battelle NEON Project
Bridget Hass, Remote Sensing Data Processing Technician, Battelle NEON Project
Dr. Naupaka Zimmerman, Assistant Professor of Biology, University of San Francisco
Dr. Tyson Swetnam, Science Informatician with CyVerse and Research Associate with Bio5 Institute at the University of Arizona

Materials

All participants will need to bring their own laptop with them to the event. More details about the necessary software that will be required will be available prior to the Institute. All required software will be free and available for download.

Logistics

LOCATION

All aspects of the course are held at the Battelle-NEON headquarters in Boulder, Colorado.

1685 38th St., Suite 100

Boulder, CO 80301

HOUSING

Participants are responsible for finding their own housing. Boulder offers a wide variety of housing options.

TRANSPORTATION

Transportation to/from Denver International Airport

The RTD AB route runs directly between DIA and Boulder. Transit time is roughly 1 hr and the cost is $9 (see below for more information on bus passes, some passes include airport transit). Please note that if you pay on the bus, you must pay cash and the drivers can’t make change.

There are also several shuttle services: Greenride Boulder, SuperShuttle, and other car services.

Transportation to/from NEON & around Boulder

There are several transportation options to get you to/from NEON (located at 1685 38th Street, Suite 100, Boulder, CO 80301) for attendance at the NEON Data Institute.

Bus passes can be purchased for Boulder’s/Colorado’s Regional Transit District (RTD). �� Bus route maps and route planners can be found online on RTD’s website.

��Bike trails and routes connect many parts of Boulder.

Many local bike shops in Boulder offer bike rentals.
Boulder also offers a bike-share system, Boulder B-Cycle. The B-Cycle station at 38th & Arapahoe (13 parking docks) within a block of NEON. Helmets are not available with B-Cycle rentals, you may wish to bring your own.

Taxi/Uber/Lyft

Numerous taxi companies, as well as Uber and Lyft, operate in Boulder.

Vehicle Rental & Parking

Vehicles can be rented from Hertz, Avis, Budget, etc.
Visitor parking will be available at NEON for participants.

FOOD

Lunches, snacks and coffee/tea will be provided each day. Participants are responsible for all other meals during the program. Dietary restrictions and preferences can be accommodated if requested prior to the event.

CANCELLATION POLICY

Cancellations must be received in writing to the [email protected]">NEON Data Skills team. In the case of cancellation after the payment of tuition,

Full tuition, less processing fees, will be refunded if cancellation is more than 6 weeks prior to the start of the event.

This cancellation policy applies to all registrants. If extenuating circumstances arise causing the cancellation, please contact the NEON Data Skills team to make arrangements.

Language Not specified

National Ecological Observatory Network

Learn More

Course

2017 Data Institute: Remote Sensing with Reproducible Workflows in Python

mjones01 — Wed, 30 Nov 2016 22:12:42 +0000

2017 Data Institute: Remote Sensing with Reproducible Workflows in Python

Jun 19-24, 2017

mjones01 Wed, 11/30/2016 - 15:12

Boulder, CO 80301
United States

Data Institute Details:

Our 2017 Institute focuses on remote sensing of vegetation using open source tools and reproducible science workflows �� 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 [email protected]?subject=Data%20Institute%202017%20Inquiry">contact us.

Schedule

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

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.

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.

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.

Language Not specified

NEON

Learn More

Course

2016 Data Institute: Remote sensing with reproducible workflows in R

mjones01 — Wed, 27 Apr 2016 18:56:33 +0000

2016 Data Institute: Remote sensing with reproducible workflows in R

Jun 20-25, 2016

mjones01 Wed, 04/27/2016 - 12:56

Boulder, CO
United States

Data Institute Overview

Our 2016 Institute focused on remote sensing of vegetation using open source tools and reproducible science workflows. The programming language of instruction in 2016 was R. This Institute was held at NEON headquarters in June 2016.

In addition to the six day institute there were 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 familiarity with the topic.

Schedule

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 R Markdown
--	19-24 June	Data Institute
7:50am - 10:00 pm	Monday	Intro to NEON, Intro to HDF5 & Hyperspectral Remote Sensing
8:00am - 6:30pm	Tuesday	Intro to LiDAR data, Automated Workflows
8:00am - 6:30pm	Wednesday	Remote Sensing Uncertainty
8:00am - 6:30pm	Thursday	LiDAR & Hyperspectral Data Fusion
9:00am - 6:30pm	Friday	Individual/Group Applications
9:00am - 2:00pm	Saturday	Group Application Presentations

Key Dates

Application Deadline: March 28, 2016
Notification of Acceptance: April 4, 2016
Tuition payment due by: April 18, 2016
Pre-institute online activities: June 1-17, 2016
Institute Dates: June 20-25, 2016

Instructors

Dr. Leah Wasser, Supervising Scientist, NEON: As part of her work at NEON, Leah is passionate about helping the scientific community harness the power of remote sensing and other large spatio-temporal data using efficient, quantitative, reproducible approaches and open science workflows to better understand ecological change over time. Leah has a Ph.D. in ecology with a focus on using remote sensing techniques to measure landscape level ecological change.

Dr. Kyla Dahlin, Assistant Professor, Michigan State University: Kyla's research aims to better understand and quantify ecosystem processes and disturbance responses through the application of emerging technologies, including air- and space-borne remote sensing, spatial statistics, and process-based modeling. She is currently interested in semi-arid forest/grassland transition zones, where vegetation patterns are readily observable but poorly understood. Kyla approaches questions by integrating observational data, modeling, and focused field experiments to both refine our understanding of ecosystem function and to improve our ability to predict how ecosystems and the climate will change in the future.

Online Resources

The teaching materials from the 2016 Data Institute are provided free on this site for use outside the Data Institute. They can be found in the Workshop Materials section of this page. These materials were designed to be used in the context of the workshop with an instructor, however, they may also be suitable for self-paced online instruction.

You too can watch several of the presentations that were given at the 2016 Data Institute!

2016 Data Institute Recap

In addition to the three core faculty listed above the Data Institute participants were instructed by and interacted with guest instructors and NEON project scientists:

Lindsay Powers, H5 Group �� HDF5 data structure
Chris Crosby, UNAVCO/Open Topography �� LiDAR remote sensing
David Schimel, NASA Jet Propulsion Lab �� remote sensing, open science, ecology
David Hulslander, NEON �� Remote sensing data processing
Tristan Goulden, NEON �� Remote sensing theory & Hyperspectral remote sensing
Nathan Leisso, NEON �� Introduction to NEON AOP data collection and processing
Courtney Meier, NEON �� NEON in situ field measurements.
Keith Krause, NEON �� NEON full waveform LiDAR

Participants

Participants came from institutions in the USA, Canada and the Netherlands. While 70% of the participants were graduate students, the Data Institute also attracted an undergraduate student, post-docs, and university research staff and faculty.

Participant were interested in using remote sensing data to answer a wide range of questions from wanting to be able to characterize forest structure and composition to using time series to detect vegetation disturbance patterns to from remote sensing data.

According to NEON science educator, Megan Jones, “Participants really appreciated the opportunities to work with data in small-group settings and the emphasis of using reproducible science methods. The science theme for 2016 was use of remote sensing data, but this was taught along with reproducible science methods including the importance of well documented code, version control and collaborative tools like GitHub, and quick sharing of results using RMarkdown and knitr.��

Institute outcomes

At the end of the Institute, participants presented group projects illustrating the use of reproducible workflows with remote sensing data. The skills learned are applicable to remote sensing data from any source, however, all participants were allowed to use NEON remote sensing data as well as their own data sets. According to Robert Paul from the University of Illinois at Urbana-Champaign, “The course offered a comprehensive overview of best practices for managing and analyzing remote sensing data, and how to make data analysis workflows well-documented, collaborative, and reproducible.��

Sarah Graves, from the University of Florida, said, “The NEON Data Institute gave us the tools to work with novel ecological data. With our own knowledge of the domain combined with NEON data and tools, we are in a position to ask novel ecological questions that will advance the field of ecology beyond what has been traditionally possible.�� Jeff Atkins of Virginia Commonwealth University added, “Ecology increasingly depends on "big data" and remote sensing and scientists need the skills necessary to work with this data and to inform their hypotheses. NEON does an amazing job at helping scientists learn how to work with and use a suite of data and data products.��

Group projects

Exploring the relationship between functional traits and spectral reflectance for Ordway Swisher Biological Station, FL
Sarah Graves, Jeff Atkins, Kunxuan Wang, and Catherine Hulshof de la Pena

We calculated plot-level foliar nitrogen content and functional diversity from in situ data. These metrics were related to mean plot reflectance and a spectral diversity metric from a PCA transformation.

Describing landscape-level phenology with MODIS vegetation index time series
Robert Paul, Jeff Stephens

This workflow detects the length of time for NDVI and EVI to go from baseline to peak over the course of the year. Each pixel is classified with a value reflecting the length of time in the year for NDVI and EVI to reach peak greenness.

Characterizing the forest using trees: how do forest characteristics vary with respect to disturbance history at Soaproot Saddle
We attempted species-level classification using Random Forest on LiDAR and imaging spectroscopy.
Megan Cattau, Stella Cousins, Kristin Braziunas, Allie Weill

Towards individual tree crown segmentation with spectral indices
Enrique Montano & Dave McCaffrey

We attempted to implement an individual tree crown extraction algorithm, optimized with vegetation structure data from in situ plots. The ability to identify individual tree canopy with confidence will allow for comparison of spectral indices among individuals and across species.

Plant structure and function in complex terrain: Landscape controls and microclimatic consequences
Holly Andrews, Nate Looker, Amy Hudson

We examined climate, topography, and vegetation interactions. Specifically, we assessed spectral and LiDAR-based properties of vegetation across topographic gradients of water availability and compared land surface temperature to NDVI.

Upscaling Structure for Soaproot Field Site, California
Cassondra Walker, Jon Weiner, Richard Remigio

We attempted to link vegetation indices to plot-level tree characteristics, and then upscale those indices to the landscape scale to predict structure that was derived from LiDAR.

Using HyperSpectral Imaging techniques to predict foliar nutrient concentrations
Michiel Veldhuis

Language Not specified

NEON

Learn More

Course