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
Enabling AI for citizen science in fish ecology
Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection.This proje
Subsidence Susceptibility Map for the Conterminous U.S.
Sinkholes present hazards to humans due to subsidence and by focusing contaminated surface water runoff into groundwater. Sinkholes create instability in the foundations of buildings, roads and other infrastructure, resulting in damage and in some cases loss of life, but may also play an important role as vernal pools in some ecosystems. This project created a prototype nationwide subsidence susc
National Stream Summarization: Standardizing Stream-Landscape Summaries
As research and management of natural resources shift from local to regional and national scales, the need for information about aquatic systems to be summarized to multiple scales is becoming more apparent. Recently, four federally funded national stream assessment efforts (USGS Aquatic GAP, USGS National Water-Quality Assessment Program, U.S. Environmental Protection Agency [EPA] StreamCat, and
Understanding the Impacts of Glaciers on Streamflow in Alaska and Washington
Glaciers are a central component to the hydrology of many areas in Alaska and the Pacific Northwest. Glacier melt plays a crucial role in the movement of nutrients through a landscape and into the ocean, and the flow of water into streams that sustain many species. As air temperatures rise, increased rates of glacier melt may have significant impacts to the hydrology and ecology in these areas. T
The Landsat Burned Area products for the conterminous United States (ver. 3.0, March 2022)
The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally-dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm makes use of predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions.
Closed depression density in karst regions of the conterminous United States: features and grid data
Most methods for the assessment of sinkhole hazard susceptibility are predicated upon knowledge of pre-existing closed depressions in karst areas. In the United States (U.S.), inventories of existing karst depressions are piecemeal, and are often obtained through inconsistent methodologies applied at the state or county level and at various scales. Here, we present a first attempt at defining a ka
Landsat Burned Area Essential Climate Variable products for the conterminous United States (1984 -2015)
The U.S. Geological Survey (USGS) has developed and implemented an automated algorithm that identifies burned areas in temporally-dense time series of Landsat image stacks to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm makes use of predictors derived from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and
New operational national satellite burned area product
Introduction
Lack of consistent spatial and temporal fire information with relevant spatial resolution hinders land management and broad-scale assessments of fire activity, especially in the eastern United States and the Great Plains where fi re is important ecologically and culturally. Remote sensing can be used to monitor fi re activity, augment existing fi re data, and fill information gaps. In
Authors
Todd Hawbaker, Melanie K. Vanderhoof, Gail L. Schmidt, Yen-Ju G. Beal, Joshua J. Picotte, Joshua Takacs, Jeff T. Falgout, John L. Dwyer
Progress toward a preliminary karst depression density map for the conterminous United States
Most methods for the assessment of sinkhole hazard susceptibility are predicated upon knowledge of pre-existing closed depressions in karst areas. In the United States (U.S.), inventories of existing karst depressions are piecemeal, and are often obtained through inconsistent methodologies applied at the state or county level and at various scales. Here, we present a first attempt at defining a ka
Authors
Daniel H. Doctor, Jeanne M. Jones, Nathan J. Wood, Jeff T. Falgout, Natalya Igorevna Rapstine
The Landsat Burned Area algorithm and products for the conterminous United States
Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. In this study, we developed the Landsat Burned Area (BA) algorithm, an update from the Landsat Burned Area Essential Climate Variable (BAECV) algorithm. Here, we present the BA algorithm and products, changes re
Authors
Todd Hawbaker, Melanie K. Vanderhoof, Gail L. Schmidt, Yen-Ju G. Beal, Joshua J. Picotte, Joshua Takacs, Jeff T. Falgout, John L. Dwyer
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 has been re
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
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 CDI-sponso
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
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 drainage densi
Authors
Larry V. Stanislawski, Jeff T. Falgout, Barbara P. Buttenfield
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 the features
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Paulo Raposo, Madeline Cameron, Jeff T. Falgout
xstrm
Python package to assist with stream network summarization. This package is intended to support efforts for any stream network having general topology (i.e. to/from nodes). Specifically this package was built to support fisheries based analyses using multiple versions of the National Hydrography Database Plus (NHDPlus) representing streams within the United States along with HydroBasins which repr
Science and Products
Enabling AI for citizen science in fish ecology
Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection.This proje
Subsidence Susceptibility Map for the Conterminous U.S.
Sinkholes present hazards to humans due to subsidence and by focusing contaminated surface water runoff into groundwater. Sinkholes create instability in the foundations of buildings, roads and other infrastructure, resulting in damage and in some cases loss of life, but may also play an important role as vernal pools in some ecosystems. This project created a prototype nationwide subsidence susc
National Stream Summarization: Standardizing Stream-Landscape Summaries
As research and management of natural resources shift from local to regional and national scales, the need for information about aquatic systems to be summarized to multiple scales is becoming more apparent. Recently, four federally funded national stream assessment efforts (USGS Aquatic GAP, USGS National Water-Quality Assessment Program, U.S. Environmental Protection Agency [EPA] StreamCat, and
Understanding the Impacts of Glaciers on Streamflow in Alaska and Washington
Glaciers are a central component to the hydrology of many areas in Alaska and the Pacific Northwest. Glacier melt plays a crucial role in the movement of nutrients through a landscape and into the ocean, and the flow of water into streams that sustain many species. As air temperatures rise, increased rates of glacier melt may have significant impacts to the hydrology and ecology in these areas. T
The Landsat Burned Area products for the conterminous United States (ver. 3.0, March 2022)
The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally-dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm makes use of predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions.
Closed depression density in karst regions of the conterminous United States: features and grid data
Most methods for the assessment of sinkhole hazard susceptibility are predicated upon knowledge of pre-existing closed depressions in karst areas. In the United States (U.S.), inventories of existing karst depressions are piecemeal, and are often obtained through inconsistent methodologies applied at the state or county level and at various scales. Here, we present a first attempt at defining a ka
Landsat Burned Area Essential Climate Variable products for the conterminous United States (1984 -2015)
The U.S. Geological Survey (USGS) has developed and implemented an automated algorithm that identifies burned areas in temporally-dense time series of Landsat image stacks to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm makes use of predictors derived from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and
New operational national satellite burned area product
Introduction
Lack of consistent spatial and temporal fire information with relevant spatial resolution hinders land management and broad-scale assessments of fire activity, especially in the eastern United States and the Great Plains where fi re is important ecologically and culturally. Remote sensing can be used to monitor fi re activity, augment existing fi re data, and fill information gaps. In
Authors
Todd Hawbaker, Melanie K. Vanderhoof, Gail L. Schmidt, Yen-Ju G. Beal, Joshua J. Picotte, Joshua Takacs, Jeff T. Falgout, John L. Dwyer
Progress toward a preliminary karst depression density map for the conterminous United States
Most methods for the assessment of sinkhole hazard susceptibility are predicated upon knowledge of pre-existing closed depressions in karst areas. In the United States (U.S.), inventories of existing karst depressions are piecemeal, and are often obtained through inconsistent methodologies applied at the state or county level and at various scales. Here, we present a first attempt at defining a ka
Authors
Daniel H. Doctor, Jeanne M. Jones, Nathan J. Wood, Jeff T. Falgout, Natalya Igorevna Rapstine
The Landsat Burned Area algorithm and products for the conterminous United States
Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. In this study, we developed the Landsat Burned Area (BA) algorithm, an update from the Landsat Burned Area Essential Climate Variable (BAECV) algorithm. Here, we present the BA algorithm and products, changes re
Authors
Todd Hawbaker, Melanie K. Vanderhoof, Gail L. Schmidt, Yen-Ju G. Beal, Joshua J. Picotte, Joshua Takacs, Jeff T. Falgout, John L. Dwyer
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 has been re
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
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 CDI-sponso
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
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 drainage densi
Authors
Larry V. Stanislawski, Jeff T. Falgout, Barbara P. Buttenfield
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 the features
Authors
Larry V. Stanislawski, Barbara P. Buttenfield, Paulo Raposo, Madeline Cameron, Jeff T. Falgout
xstrm
Python package to assist with stream network summarization. This package is intended to support efforts for any stream network having general topology (i.e. to/from nodes). Specifically this package was built to support fisheries based analyses using multiple versions of the National Hydrography Database Plus (NHDPlus) representing streams within the United States along with HydroBasins which repr