Jeff Falgout
Jeff Falgout is a Development Computer Scientist focusing on supercomputing, high end cyberinfrastructure, and advanced computing capabilities in the Advanced Research Computing (ARC) program, Science Analytics and Synthesis, Core Science Systems.
Jeff’s work is dedicated to architectural design, technical development, and integration of large scale, high performance computing and storage platforms for the U.S. Geological Survey and Department of the Interior.
Science and Products
Mapping burned areas using dense time-series of Landsat data Mapping burned areas using dense time-series of Landsat data
Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information...
Authors
Todd Hawbaker, Melanie K. Vanderhoof, Yen-Ju G. Beal, Joshua Takacs, Gail L. Schmidt, Jeff T. Falgout, Brad Williams, Nicole M. Brunner, Megan K. Caldwell, Joshua J. Picotte, Stephen M. Howard, Susan Stitt, John L. Dwyer
Community for Data Integration 2016 annual report Community for Data Integration 2016 annual report
The Community for Data Integration (CDI) represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the U.S. Geological Survey and the CDI community. This annual report describes the various presentations, activities, and outcomes of the CDI monthly forums, working groups, virtual training series, and other...
Authors
Madison L. Langseth, Leslie Hsu, Jon Amberg, Norman Bliss, Andrew R. Bock, Rachel T. Bolus, R. Sky Bristol, Katherine J. Chase, Theresa M. Crimmins, Paul S. Earle, Richard Erickson, A. Lance Everette, Jeff T. Falgout, John Faundeen, Michael N. Fienen, Rusty Griffin, Michelle R. Guy, Kevin D. Henry, Nancy J. Hoebelheinrich, Randall J. Hunt, Vivian B. Hutchison, Drew A. Ignizio, Dana M. Infante, Catherine Jarnevich, Jeanne M. Jones, Tim Kern, Scott Leibowitz, Francis L. Lightsom, R. Lee Marsh, S. Grace McCalla, Marcia McNiff, Jeffrey T. Morisette, John C. Nelson, Tamar Norkin, Todd M. Preston, Alyssa Rosemartin, Roy Sando, Jason T. Sherba, Richard P. Signell, Benjamin M. Sleeter, Eric T. Sundquist, Colin B. Talbert, Roland J. Viger, Jake F. Weltzin, Sharon Waltman, Marc Weber, Daniel J. Wieferich, Brad Williams, Lisamarie Windham-Myers
Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing
Hydrographic networks form an important data foundation for cartographic base mapping and for hydrologic analysis. Drainage density patterns for these networks can be derived to characterize local landscape, bedrock and climate conditions, and further inform hydrologic and geomorphological analysis by indicating areas where too few headwater channels have been extracted. But natural...
Authors
Larry V. Stanislawski, Jeff T. Falgout, Barbara P. Buttenfield
Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry
Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of...
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Paulo Raposo, Madeline Cameron, Jeff T. Falgout
Science and Products
Mapping burned areas using dense time-series of Landsat data Mapping burned areas using dense time-series of Landsat data
Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information...
Authors
Todd Hawbaker, Melanie K. Vanderhoof, Yen-Ju G. Beal, Joshua Takacs, Gail L. Schmidt, Jeff T. Falgout, Brad Williams, Nicole M. Brunner, Megan K. Caldwell, Joshua J. Picotte, Stephen M. Howard, Susan Stitt, John L. Dwyer
Community for Data Integration 2016 annual report Community for Data Integration 2016 annual report
The Community for Data Integration (CDI) represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the U.S. Geological Survey and the CDI community. This annual report describes the various presentations, activities, and outcomes of the CDI monthly forums, working groups, virtual training series, and other...
Authors
Madison L. Langseth, Leslie Hsu, Jon Amberg, Norman Bliss, Andrew R. Bock, Rachel T. Bolus, R. Sky Bristol, Katherine J. Chase, Theresa M. Crimmins, Paul S. Earle, Richard Erickson, A. Lance Everette, Jeff T. Falgout, John Faundeen, Michael N. Fienen, Rusty Griffin, Michelle R. Guy, Kevin D. Henry, Nancy J. Hoebelheinrich, Randall J. Hunt, Vivian B. Hutchison, Drew A. Ignizio, Dana M. Infante, Catherine Jarnevich, Jeanne M. Jones, Tim Kern, Scott Leibowitz, Francis L. Lightsom, R. Lee Marsh, S. Grace McCalla, Marcia McNiff, Jeffrey T. Morisette, John C. Nelson, Tamar Norkin, Todd M. Preston, Alyssa Rosemartin, Roy Sando, Jason T. Sherba, Richard P. Signell, Benjamin M. Sleeter, Eric T. Sundquist, Colin B. Talbert, Roland J. Viger, Jake F. Weltzin, Sharon Waltman, Marc Weber, Daniel J. Wieferich, Brad Williams, Lisamarie Windham-Myers
Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing
Hydrographic networks form an important data foundation for cartographic base mapping and for hydrologic analysis. Drainage density patterns for these networks can be derived to characterize local landscape, bedrock and climate conditions, and further inform hydrologic and geomorphological analysis by indicating areas where too few headwater channels have been extracted. But natural...
Authors
Larry V. Stanislawski, Jeff T. Falgout, Barbara P. Buttenfield
Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry
Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of...
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Paulo Raposo, Madeline Cameron, Jeff T. Falgout