2016 Data Institute: Remote sensing with reproducible workflows in R

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

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. 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. 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!

• Big Data, Open Data and Biodiversity with David Schimel
• An Introduction to Hyperspectral Remote Sensing
• An Introduction to Full Waveform LiDAR
• NEON Remote Sensing Vegetation Indices, Data Products & Uncertainty Measurements
• NEON Terrestrial Observation Vegetation Sampling

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