[ESIP-all] AGU 2015 Session 8815 - Remote Sensing Scaling - Scaling From Points to Pixels: Remote Sensing Estimates of Ecosystem Characteristics Across Space and Time” (ID 8815
lwasser at neoninc.org
Thu Jul 23 12:04:11 EDT 2015
Hi ESIP Colleagues!
Please consider submitting an abstract to our AGU 2015 session. The session will be convened by Scott Ollinger, Keely Roth (Susan Ustin's lab) and Shawn Serbin and myself! The title:
"Scaling From Points to Pixels: Remote Sensing Estimates of Ecosystem Characteristics Across Space and Time” (ID 8815 listed under biogeosciences).
If you are interested in submitting a poster / presentation - please do so. OR if you know of colleagues who's work might fit well in the session, please spread the word!
This session focuses on the challenges and opportunities associated with using long-term in situ and remote sensing data, collected across multiple geographic regions and at varying scales, to characterize ecosystem structure and function (e.g., biochemistry, physiology, LAI, biomass). This session will explore scaling methods to quantify ecosystem characteristics and derive remote sensing products and associated uncertainties. Topics may include use of passive or active sensor data to estimate ecosystem characteristics across multiple scales, scaling between multi-resolution RS pixels, or scaling in situ point measurements over broad areas.
Visit the link below to see the full session details: https://agu.confex.com/agu/fm15/preliminaryview.cgi/Session8815
Confirmed invited speakers include Liane Guild, Phil Townsend and Scott Stark!
Abstracts can be submitted online until August 5th, 2015.
For any questions on the session, please contact one the session organizers:
Leah Wasser, lwasser at neoninc.org
Keely Roth, klroth at ucdavis.edu
Scott Ollinger, scott.ollinger at unh.edu
Shawn Serbin, sserbin at bnl.gov
Session ID#: 8815
A - Atmospheric Sciences
EP - Earth and Planetary Surface Processes
GC - Global Environmental Change
H - Hydrology
Leah A. Wasser, Ph.D.
National Ecological Observatory Network (NEON)
For free tutorials and resources on working with big data:
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