B52A:
Advancing Understanding of Ecosystem Structure and Function through Remote Sensing III

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Session ID#: 15601

Session Description:
Remote sensing technologies - applied at different spatial and temporal scales, and using a variety of sensors – are transforming the biogeosciences by producing previously unattainable ecosystem structural and functional information. For example, full-waveform lidar and hyperspectral data products provide new capabilities to link, in novel ways, ecosystem structure and foliar chemistry with biogeochemical processes at scales from leaves to biomes. Moving forward, a variety of emerging remote sensing technologies are poised to dramatically improve understanding of ecosystem structure-function, helping to resolve long-standing fundamental questions within the biogeosciences. We seek submissions from researchers using remote sensing to advance understanding of ecosystem structure and function. This interdisciplinary session will be broad in scope, with submissions highlighting an array of experimental, observational, and modeling studies using remote sensing to address questions in the biogeosciences.
Primary Convener:  Brady S Hardiman, Purdue University, Forestry & Natural Resources and Division of Environmental & Ecological Engineering, West Lafayette, United States
Conveners:  Christopher Michael Gough, Virginia Commonwealth University, Biology, Richmond, VA, United States, Domingo Alcaraz-Segura, Universidad de Granada, Granada, Spain and Javier Cabello, Universidad de Almeria, Almeria, Spain
Chairs:  Brady S Hardiman, Purdue University, Forestry & Natural Resources and Division of Environmental & Ecological Engineering, West Lafayette, United States, Christopher Michael Gough, Virginia Commonwealth University, Biology, Richmond, VA, United States, Domingo Alcaraz-Segura, Universidad de Granada, Granada, Spain and Javier Cabello, Universidad de Almeria, Almeria, Spain
OSPA Liaison:  Christopher Michael Gough, Virginia Commonwealth University, Biology, Richmond, VA, United States
Index Terms:

0439 Ecosystems, structure and dynamics [BIOGEOSCIENCES]
0466 Modeling [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]

Abstracts Submitted to this Session:

The challenge of establishing decomposition functional types to estimate heterotrophic respiration at large scales (Invited) (141124)
Ben P Bond-Lamberty, Pacific Northwest National Laboratory, Joint Global Change Research Institute, Richland, WA, United States, Daniel Epron, University of Lorraine, AgroParisTech, INRAE, UMR SILVA, Nancy, France, Jennifer W W Harden, Stanford University, Earth System Science, Stanford, United States, Mark Harmon, Oregon State University, Corvallis, OR, United States, Dr. Forrest M Hoffman, PhD, Oak Ridge National Laboratory, Oak Ridge, TN, United States, Jitendra Kumar, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, United States, Anthony David McGuire, U.S. Geological Survey, Fairbanks, AK, United States and Prof. Rodrigo Vargas, Arizona State University, School of Life Sciences, Tempe, United States
Which factors limit constraining ecosystem models with remote sensing observations? (165953)
Toni Viskari1, Dr. Shawn Serbin, BA, MS, PhD2, Alexey N Shiklomanov3 and Michael Dietze3, (1)Brookhaven National Laboratory, Upton, NY, United States, (2)Brookhaven National Laboratory, Biological, Environmental & Climate Sciences, Upton, United States, (3)Boston University, Boston, MA, United States
Towards a global characterization of ecosystem functional heterogeneity based on essential biodiversity variables (184396)
Néstor Fernández1, Miguel Fernández2, Domingo Alcaraz-Segura3, Pedro J Leitao4 and Carlos A Guerra2, (1)Estación Biológica de Doñana, Spanish National Research Council CSIC, Sevilla, Spain, (2)German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany, (3)University of Granada, Botany, Granada, Spain, (4)Humboldt University of Berlin, Geography, Berlin, Germany
Performance of One-Class Classifiers for Invasive Species Mapping using Hyperspectral Remote Sensing (187279)
Sandra Skowronek1,2, Gregory Paul Asner2 and Hannes Feilhauer1, (1)University of Erlangen-Nuremberg, Erlangen, Germany, (2)Carnegie Institution for Science Washington, Washington, DC, United States
Remotely-sensed predictive models of forest composition: community-unit classification versus continuous gradient modeling (190428)
Christopher R Hakkenberg, University of California Los Angeles, Los Angeles, CA, United States, Conghe Song, University of North Carolina at Chapel Hill, Department of Geography and Environment, Chapel Hill, NC, United States and Robert K Peet, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
Integrating lidar-derived forest structure metrics into two source energy balance models (199463)
Cameron Houser, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States

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