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24-hr Latency End to End Data Processing Using Open Source Technologies for the Airborne Snow Observatory

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Titel 24-hr Latency End to End Data Processing Using Open Source Technologies for the Airborne Snow Observatory
Serientitel FOSS4G 2014 Portland
Autor Ramirez, Paul
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - keine Bearbeitung 3.0 Unported:
Sie dürfen das Werk bzw. den Inhalt in unveränderter Form zu jedem legalen und nicht-kommerziellen Zweck nutzen, vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/31707
Herausgeber FOSS4G
Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2014
Sprache Englisch
Produzent Foss4G
Open Source Geospatial Foundation (OSGeo)
Produktionsjahr 2014
Produktionsort Portland, Oregon, United States of America

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Fachgebiet Informatik
Abstract JPL's Airborne Snow Observatory is an integrated imaging spectrometer and scanning LIDAR for measuring mountain snow albedo, snow depth/snow water equivalent, and ice height (once exposed), led by PI Dr. Tom Painter. The team recently wrapped our second "Snow On" campaign where over a course of 3 months, we flew the Tuolumne River Basin, Sierra Nevada, California above the O'Shaughnessy Dam of the Hetch Hetchy reservoir; focusing initial on the Tuolumne, and then moving to weekly flights over the Uncompahgre Basin, Colorado.To meet the needs of its customers including Water Resource managers who are keenly interested in Snow melt, the ASO team had to develop and end to end 24 hour latency capability for processing spectrometer and LIDAR data from Level 0 to Level 4 products. Fondly referring to these processing campaigns as "rodeos" the team rapidly constructed a Big Data open source data processing system at minimal cost and risk that not only met our processing demands, but taught the entire team many lessons about remote sensing of snow and dust properties, algorithm integration, the relationship between computer scientists, and snow hydrologist; flight and engineering teams, geographers, and most importantly lessons about camaraderie that will engender highly innovative and rapid data systems development, and quality science products for years to come.Chris Mattmann, Paul Ramirez, and Cameron Goodale for the ASO project will present this talk and will detail the story of the Compute processing capability on behalf of the larger team, highlighting contributions of its key members along the way. We will cover the blending of open source technologies and proprietary software packages that have helped us attain our goals and discuss areas that we are actively investigating to expand our use of open source.
Schlagwörter snow
science
remote sensing
lidar
point clouds
open source
decision support

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