OOS 81
Hyperspectral Remote Sensing Data Supports 21st Century Ecological Research

Thursday, August 13, 2015: 1:30 PM-5:00 PM
341, Baltimore Convention Center
Organizer:
Shawn P. Serbin
Co-organizers:
Kyla M. Dahlin , Keely L. Roth and Leah A. Wasser
Moderator:
Shawn P. Serbin
Spectroscopic, often called hyperspectral, remote sensing methods are advancing terrestrial ecological research by enabling the rapid, non-destructive, and ‘wall to wall’ mapping of key plant biochemical properties, physiological traits, metabolic function, and biodiversity. These types of data products directly support efforts to map and monitor ecosystem health, air quality, animal habitat, wildfire impacts, and the urban-wildland interface at very high spatial and spectral resolutions and through time. At scales ranging from individual leaves to entire regions, hyperspectral data provide unique insights into ecosystem structure and function, including plant community composition, the origins of biodiversity, point-source pollution, ecosystem health, and human impacts. Historically, the analysis of hyperspectral data has been limited to a small number of research groups with access to specific instrumentation. Data were thus only available for a few geographic locations, and over short time frames. In recent years, however, a significant increase in publicly available datasets has occured. These include the opening up of the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) archive held by NASA’s Jet Propulsion Laboratory, The G-LiGHT platform at NASA Goddard, free access to the Earth Observing 1 satellite (EO-1) which contains the Hyperion hyperspectral sensor, and, soon, the National Ecological Observatory Network’s (NEON) Airborne Observation Platform (AOP). Moreover, future satellite missions including the German Environmental Mapping and Analysis Program (EnMAP) and NASA’s next generation Hyperspectral Infrared Imager (HyspIRI) will further increase data availability. At the same time, instrumentation costs, size, and weights have steadily dropped in recent years enabling relatively low cost deployments of hyperspectral sensors from towers, balloons, and unmanned aerial systems (UASs). This session will present specific ways in which hyperspectral remote sensing data, collected using near-surface to space-borne platforms, have been used to support ecological research. It will highlight a breadth of applications that explore important and challenging ecological research questions across a range of spatial and temporal scales. Finally, it will facilitate a dynamic and engaging conversations between field ecologists, remote sensing scientists, and environmental practitioners about both current hyperspectral remote sensing applications and the future of these data to support ecology. With this session, we hope to broaden interest in new hyperspectral remote sensing applications as well as illustrate how far the field has come since its first use in ecology over three decades ago.
1:30 PM
Relationships among leaf traits and leaf spectra: Prediction, clustering and functional types
Keely L. Roth, University of California Davis; Angeles Casas, University of California Davis; Margarita Huesca, University of California Davis; Michael L. Whiting, University of California Davis; Susan Ustin, University of California Davis
1:50 PM
From point to pixel: Using in situ measurements to validate and derive higher level NEON hyperspectral data products
Leah A. Wasser, NEON, Inc.; Shawn P. Serbin, Brookhaven National Laboratory; Kyla Dahlin, Michigan State University; Keely L. Roth, University of California Davis; Nathan Leisso, National Ecological Observatory Network (NEON, Inc.); Shelley Petroy, NEON, Inc.
2:30 PM
Hyperspectral remote sensing for mapping species and community composition in a diverse tropical forest
Claire A. Baldeck, Carnegie Institution for Science; Gregory P. Asner, Carnegie Institution for Science; S. Joseph Wright, Smithsonian Tropical Research Institute
2:50 PM
An innovative way to monitor leaf age demographics in a tropical evergreen forest
Jin Wu, University of Arizona; Neill Prohaska, University of Arizona; Shawn P. Serbin, Brookhaven National Laboratory; Cecilia Chavana-Bryant, Oxford University; Loren P. Albert, University of Arizona; Giordnae Martins, Brazil’s National Institute for Amazon Research (INPA); Anthony john Junqueira Garnello, University of Arizona; Xi Yang, Brown University; Alejandro Macias, University of Arizona; Scott R. Saleska, University of Arizona
3:10 PM
3:20 PM
Can plant traits be used to classify functionality in remote sensing data
Susan Ustin, Center for Spatial Technologies and Remote Sensing; Keely L. Roth, University of California Davis; Margarita Huesca, University of California Davis; Alexander Koltunov, Center for Spatial Technologies and Remote Sensing; Carlos Ramirez, USDA Forest Service
3:40 PM
Relative influences of landscape-level drivers on insect-mediated forest processes
John J. Couture, University of Wisconsin-Madison; Aditya Singh, University of Wisconsin-Madison; Alexander Brito, University of Wisconsin-Madison; Clayton C. Kingdon, University of Wisconsin - Madison; Shawn P. Serbin, Brookhaven National Laboratory; Philip Townsend, University of Wisconsin-Madison
4:00 PM
Remote sensing of belowground variation in trembling aspen forests
Michael D. Madritch, Appalachian State University; Karen E. Mock, Utah State University; Aditya Singh, University of Wisconsin-Madison; Richard L. Lindroth, University of Wisconsin - Madison; Philip Townsend, University of Wisconsin-Madison
4:20 PM
Combined hyperspectral VSWIR and broadband thermal infrared analysis of vegetation-substrate mixtures in a mixed natural and anthropogenic landscape
Dar A. Roberts, University of California Santa Barbara; Philip E. Dennison, University of Utah; Keely L. Roth, University of California Davis; Glynn Hulley, Jet Propulsion Laboratory; Kenneth Dudley, University of Utah; Erin Wetherley, University of California Santa Barbara
4:40 PM
A single hyperspectral image can detect growth rate variation within and among tropical tree species
Stephanie Bohlman, University of Florida; T. Trevor Caughlin, University of Florida; Sarah J. Graves, University of Florida; Jefferson Hall, Smithsonian Tropical Research Institute; Roberta Martin, Carmegie Institution; Gregory P. Asner, Carnegie Institution for Science