The recent increase in SAR data, the result of an unprecedented number of satellites, including Sentinel-1A/B and the upcoming NISAR satellite, and the increasing variety of data they produce, has resulted in a golden age of SAR and InSAR results. This wealth of data has resulted in enormous scientific opportunities coupled with significant technical and logistical challenges. Here we solicit contributions detailing methods and strategies that address these opportunities and challenges, highlighting recent SAR and InSAR developments in the following areas: (1) Data processing techniques that take full advantage of the densely sampled time-series information and development of new data products; (2) Advances in big data analytics, deep learning and cloud-based processing techniques applied to SAR and InSAR, and development and distribution of value-added products. (3) Submissions that employ new analysis strategies and applications using the wide variety of SAR and InSAR data available today to analyze complex geoscience problems.
Primary Convener: Kristy French Tiampo, University of Colorado Boulder, Cooperative Institute for Research in Environmental Sciences and Dept. of Geological Sciences, Boulder, United States
Conveners: Jingyi Chen, University of Texas at Austin, Aerospace Engineering & Engineering Mechanics, Austin, TX, United States and Franz Josef Meyer, University of Alaska Fairbanks Geophysical Institute, Fairbanks, AK, United States
Primary Liaison: Kristy French Tiampo, University of Western Ontario, Department of Earth Sciences, London, ON, Canada
Chairs: Jingyi Chen, University of Texas at Austin, Aerospace Engineering & Engineering Mechanics, Austin, TX, United States, Kristy French Tiampo, University of Western Ontario, Department of Earth Sciences, London, ON, Canada and Franz Josef Meyer, University of Alaska Fairbanks Geophysical Institute, Fairbanks, AK, United States
OSPA Liaison: Jingyi Chen, University of Texas at Austin, Aerospace Engineering & Engineering Mechanics, Austin, TX, United States
Rapid characterization of 2019 Mw7.1 Ridgecrest Earthquake using 3D Finite Element Modeling and geodetic data (591717)
Magali Barba-Sevilla1,2, Margaret T Glasscoe3, Michael J Willis4,5, Jay W Parker3, Gregory A Lyzenga3, Wanpeng Feng6, Sergey V Samsonov7 and Kristy French Tiampo8,9, (1)University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences and Dept. of Geological Sciences, Boulder, CO, United States, (2)Cooperative Institute for Research in Environmental Sciences, Department of Geological Sciences, Boulder, CO, United States, (3)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (4)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (5)University of Colorado at Boulder, Geological Sciences, Boulder, CO, United States, (6)Natural Resources Canada, Ottawa, ON, Canada, (7)Canada Centre for Remote Sensi, Ottawa, ON, Canada, (8)University of Colorado Boulder, Department of Geological Sciences, Boulder, United States, (9)Cooperative Institute for Research in Environmental Sciences, Boulder, United States
Deep Learning Application on Volcanic Deformation Detection and Blind Source Separation in InSAR Data (606341)
Jian Sun1, Christelle Wauthier, Kirsten Stephens3, Melissa Gervais4, Guido Cervone1, Peter C La Femina5 and Machel Higgins6, (1)Pennsylvania State University Main Campus, University Park, PA, United States, (2)University of Witwatersrand, Johannesburg, South Africa, (3)Pennsylvania State University Main Campus, Meteorology and Atmospheric Science, University Park, PA, United States, (4)Pennsylvania State University Main Campus, Department of Geosciences, University Park, PA, United States, (5)The Pennsylvania State University, Geological Sciences, University Park, PA, United States
The Norwegian National Ground Motion Service (InSAR.no): Service Evolution (586197)
Marie Bredal1, John Fitzgerald Dehls1, Yngvar Larsen2, Petar Marinkovic3, Tom Rune Lauknes2, Daniel Stødle2 and Dag Anders Moldestad4, (1)Geological Survey of Norway, Trondheim, Norway, (2)Norwegian Research Centre (NORCE), Tromsø, Norway, (3)PPO Labs, The Hague, Netherlands, (4)Norwegian Space Agence, Oslo, Norway
Deep Learning-based Damage Mapping with InSAR Coherence Time Series (616202)
Oliver Stephenson1, Tobias Koehne2, Eric Zhan2, Brent Cahill2, Zachary E. Ross1, Mark Simons3 and Sang-Ho Yun4, (1)California Institute of Technology, Pasadena, CA, United States, (2)California Institute of Technology, Pasadena, United States, (3)CALTECH, Pasadena, CA, United States, (4)Jet Propulsion Laboratory, Pasadena, CA, United States
Mapping Surface Roughness over the Greater Houston Area using Polarimetric and Interferometric Synthetic Aperture Radar Data (506092)
Ke Wang, University of Texas at Austin, Department of Aerospace Engineering and Engineering Mechanics, Austin, TX, United States, Jingyi Chen, University of Texas at Austin, Aerospace Engineering & Engineering Mechanics, Austin, TX, United States, Amin Kiaghadi, University of Houston, Civil and Environmental Engineering, Houston, TX, United States and Clint Dawson, University of Texas at Austin, Institute for Computational Engineering and Sciences, Department of Aerospace Engineering and Engineering Mechanics, Austin, TX, United States
From Level-1 to Level-3: Integrating Time Series Analysis Into the Sentinel-1-based SARVIEWS Volcano Monitoring Service (619699)
David B McAlpin1, Franz Josef Meyer1,2, Kirk Hogenson2,3, Rowan Biessel4,5 and Kim D Fairbanks4,5, (1)University of Alaska Fairbanks Geophysical Institute, Fairbanks, AK, United States, (2)Alaska Satellite Facility, Fairbanks, AK, United States, (3)University of Alaska Fairbanks, Fairbanks, AK, United States, (4)Alaska Satellite Facility, Fairbanks, United States, (5)University of Alaska Fairbanks, Fairbanks, United States
From mapping to monitoring - InSAR monitoring of the wider Seattle area (538532)
John Fitzgerald Dehls1, Susan Chang2, Steve Hou2, Mark Joselyn3, Juan Carlos Ramirez4, Trevor Contreras5, Abigail Gleason6, Gregory Wessel7, Cathleen Jones8, Yngvar Larsen9 and Petar Marinkovic10, (1)Geological Survey of Norway, Trondheim, Norway, (2)City of Seattle, Geotechnical Engineering, Seattle, WA, United States, (3)City of Seattle, Seattl, WA, United States, (4)City of Seattle, Seattle, WA, United States, (5)Washington State. Department of Natural Resources, Forest Practices Division, Olympia, WA, United States, (6)Washington State Department of Natural Resources, Port Angeles, WA, United States, (7)King County, Permitting Division, Seattle, WA, United States, (8)NASA Jet Propulsion Laboratory, Pasadena, United States, (9)Norwegian Research Centre (NORCE), Tromsø, Norway, (10)PPO Labs, The Hague, Netherlands
Automated Processing, Streaming, and Integration of InSAR Time Series and GNSS Data; as part of the Collaborative GeoSciFramework Research Project (551663)
Brie D Corsa, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States; University of Colorado Boulder, Department of Geologic Sciences, Boulder, CO, United States, Kristy French Tiampo, Cooperative Institute for Research in Environmental Sciences, Boulder, United States; University of Colorado Boulder, Department of Geological Sciences, Boulder, United States, Krisztina Kelevitz, University of Colorado at Boulder, CIRES, Boulder, United States, Scott Baker, UNAVCO, Inc., Boulder, CO, United States and Charles Meertens, UNAVCO, Inc. Boulder, Boulder, CO, United States
On scaling InSAR to a planetary scale - the final frontier (585577)
Yngvar Larsen1, John Fitzgerald Dehls2, Kirk Hogenson3, Tom Rune Lauknes1, Petar Marinkovic4, Franz Josef Meyer5 and Daniel Stødle1, (1)Norwegian Research Centre (NORCE), Tromsø, Norway, (2)Geological Survey of Norway, Trondheim, Norway, (3)University of Alaska Fairbanks, Fairbanks, AK, United States, (4)PPO Labs, The Hague, Netherlands, (5)University of Alaska Fairbanks Geophysical Institute, Fairbanks, AK, United States
<< Previous Session Proposal
|
Next Session Proposal >>