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USGS2010

EAARL Coastal Topography-Fire Island National Seashore, New York, Post-Nor'Ida, 2009

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Fire Island National Seashore in New York, acquired post-Nor'Ida (November 2009 nor'easter) on December 4, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2010

EAARL coastal topography and imagery-Fire Island National Seashore, New York, 2009

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Fire Island National Seashore in New York, acquired on July 9 and August 3, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral CIR camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2010

EAARL Coastal Topography-Sandy Hook Unit, Gateway National Recreation Area, New Jersey, Post-Nor'Ida, 2009

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Sandy Hook Unit of Gateway National Recreation Area in New Jersey, acquired post-Nor'Ida (November 2009 nor'easter) on December 4, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2013

EAARL coastal topography and imagery–Western Louisiana, post-Hurricane Rita, 2005: First surface

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived first-surface (FS) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, Virginia. This project provides highly detailed and accurate datasets of a portion of the Louisiana coastline beachface, acquired post-Hurricane Rita on September 27-28 and October 2, 2005. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the National Aeronautics and Space Administration (NASA) Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Website.

Louisiana↗
USGS2010

EAARL coastal topography-Cape Hatteras National Seashore, North Carolina, post-Nor'Ida, 2009: first surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the National Park Service Southeast Coast Network's Cape Hatteras National Seashore in North Carolina, acquired post-Nor'Ida (November 2009 nor'easter) on November 27 and 29 and December 1, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2010

EAARL Coastal Topography-Mississippi and Alabama Barrier Islands, Post-Hurricane Gustav, 2008

These remotely sensed, geographically referenced elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL; the National Park Service (NPS), Gulf Coast Network, Lafayette, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the Mississippi and Alabama barrier islands, acquired post-Hurricane Gustav (September 2008 hurricane) on September 8, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2010

EAARL Coastal Topography and Imagery-Assateague Island National Seashore, Maryland and Virginia, Post-Nor'Ida, 2009

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived bare-earth (BE) and first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Park Service (NPS), Northeast Coastal and Barrier Network, Kingston, RI. This project provides highly detailed and accurate datasets of a portion of the Assateague Island National Seashore in Maryland and Virginia, acquired post-Nor'Ida (November 2009 nor'easter) on November 28 and 30, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar(EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2010

EAARL Coastal Topography and Imagery-Naval Live Oaks Area, Gulf Islands National Seashore, Florida, 2007

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived bare-earth (BE) topography, first-surface (FS) topography, and canopy-height (CH) datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Science Center, St. Petersburg, FL; the National Park Service (NPS), Gulf Coast Network, Lafayette, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Naval Live Oaks Area in Florida's Gulf Islands National Seashore, acquired June 30, 2007. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral CIR camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2010

Vegetation classification and distribution mapping report: Canyon de Chelly National Monument

Executive Summary: The classification and distribution mapping of the vegetation of Canyon de Chelly National Monument (CACH) and surrounding environment was accomplished through a multi-agency effort between 2003 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center and Fort Collins Science Center, Navajo Natural Heritage Program, Northern Arizona University, and NatureServe. The project team described 48 plant communities for CACH—35 of which were described from quantitative classification based on field-relevé data collected in 2004. Five additional plant communities were based on field relevés collected in a previous study. The team derived four additional plant communities from field observations during the photointerpretation phase of the project, and field documented them during accuracy assessment. The National Vegetation Classification Standard served as a conceptual framework for assigning these plant communities to the alliance and association level. Ten of the 48 plant communities were designated “park specials”, that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing CACH, with three different map-class schemas: base, group, and management map classes. The base map classes represented the finest level of spatial detail. Photointerpreters delineated initial polygons through manual interpretation of 2003/2004 1:12,000-scale true color aerial photography supplemented by occasional computer screen digitizing on a mosaic of digitized aerial photos. These polygons were labeled with base map classes during photointerpretation. Field visits verified interpretation concepts. The vegetation map database includes • ? 53 base map classes, which consist of associations and park specials classified with the quantitative analysis • ? additional associations noted during photointerpretation • ? non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 4,718 polygons in the project area. A field-based accuracy assessment of the base map classes showed the overall accuracy to be 50.8% The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, Version 2 (Federal Geographic Data Committee 2008). Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as a first approximation of the group level. The project team identified 16 group map classes in this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 79.9%. In consultation with park staff, the team developed management map classes that consisted of park-defined groupings of base map classes and were intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 28 management map classes have an overall accuracy of 77.1%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary geographic information system and digital database products were also produced that can be used independently, or to augment the main products. These products include shapefiles of the location of field-collected data and relational databases of field-collected data.

Report↗
USGS2023

Latency and geofence testing of wireless emergency alerts intended for the ShakeAlert® earthquake early warning system for the West Coast of the United States of America

ShakeAlert, the earthquake early warning (EEW) system for the West Coast of the United States, attempts to provides crucial warnings before strong shaking occurs. However, because the alerts are triggered only when an earthquake is already in progress, and the alert latencies and delivery times are platform dependent, the time between these warnings and the arrival of shaking is variable. The ShakeAlert system uses, among other public alerting platforms like a mobile phone operating system, smartphone apps, and the Federal Emergency Management Agency Integrated Public Alert & Warning System (IPAWS). IPAWS sends Wireless Emergency Alerts (WEAs) informing people via their smartphones and other mobile devices about various events, such as natural hazards, child abductions, or public health information about COVID-19. However, little is known about the IPAWS delivery latencies. Given that people may have only a few seconds of notice after they receive an alert to take a protective action before they feel earthquake shaking, quantifying latencies is critical to understanding whether the IPAWS system is useful for EEW. In this study, we developed new methods to test the IPAWS distribution system's performance, both with devices in a controlled environment and as well as with a 2019 community-based feedback form, in Oakland and San Diego County, California, respectively. The controlled environment test used mobile phones (including smart and non-smart phones) and associated devices to determine alert receipt times; the community research form had participants self-report their receipt times. By triangulating the data between the controlled test environment and the community research, we determined the latency statistics as well as whether the geofence (the geographic area where the alert was intended to be sent) held broadly. We found that the latencies were similar between the two tests despite the large differences in population sizes. WEA messages were received within a median time frame of 6–12 s, and the geofence held with only a few exceptions. We use this latency to assess how the system would have performed in two large earthquakes, the 1989 M6.9 Loma Prieta and 2019 M7.1 Ridgecrest earthquakes, which both occurred near our WEA test locations. Our analysis revealed that had IPAWS been available during those earthquakes, particularly Loma Prieta, it would have provided crucial seconds of notice that damaging shaking was imminent in some locations relatively far from the epicenter. Further, we find affordable non-smart phones can receive WEAs as fast as smartphones. Finally, our new method can be used for latency and geospatial testing going forward for IPAWS and other similar alerting systems.

California↗
USGS2009

Vegetation classification and distribution mapping report: Mesa Verde National Park

The classification and distribution mapping of the vegetation of Mesa Verde National Park (MEVE) and surrounding environment was achieved through a multi-agency effort between 2004 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center, Fort Collins Research Center, and Rocky Mountain Geographic Science Center; Northern Arizona University; Prescott College; and NatureServe. The project team described 47 plant communities for MEVE, 34 of which were described from quantitative classification based on f eld-relevé data collected in 1993 and 2004. The team derived 13 additional plant communities from field observations during the photointerpretation phase of the project. The National Vegetation Classification Standard served as a framework for classifying these plant communities to the alliance and association level. Eleven of the 47 plant communities were classified as “park specials;” that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing MEVE, with three different map-class schemas: base, group, and management map classes. The base map classes represent the fi nest level of spatial detail. Initial polygons were developed using Definiens Professional (at the time of our use, this software was called eCognition), assisted by interpretation of 1:12,000 true-color digital orthophoto quarter quadrangles (DOQQs). These polygons (base map classes) were labeled using manual photo interpretation of the DOQQs and 1:12,000 true-color aerial photography. Field visits verified interpretation concepts. The vegetation map database includes 46 base map classes, which consist of associations, alliances, and park specials classified with quantitative analysis, additional associations and park specials noted during photointerpretation, and non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 5,007 polygons in the project area. A field-based accuracy assessment of the base map classes showed overall accuracy to be 43.5%. Seven map classes comprise 89.1% of the park vegetated land cover. The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, version 2 (Federal Geographic Data Committee 2007), and reflecting physiognomy and floristics. Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as the fi rst approximation of the group level. The project team identified 14 group map classes for this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 80.3%. In consultation with park staff , the team developed management map classes, consisting of park-defined groupings of base map classes intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 23 management map classes had an overall accuracy of 73.3%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary digital geographic information system and database products were also produced that can be used independently or to augment the main products. These products include shapefiles of the locations of field-collected data and relational databases of field-collected data.

Mesa Verde National Park↗
USGS2021

Detrital zircon record of magmatism and sediment dispersal across the North American Cordilleran arc system (28-48°N)

As zircon U-Pb geochronology has become a leading method in sediment provenance studies and basin analysis over the past 20 years, the volume of detrital zircon data made available in published literature has enabled researchers to go beyond source-to-sink provenance studies to explore increasingly complex geologic problems. In this review, we utilize the growing body of detrital zircon data acquired from Jurassic-Paleocene forearc and foreland basin strata of the North American Cordillera to investigate the Mesozoic to earliest Cenozoic evolution of the arc and its associated basins between 28°N and 48°N. Our compilation includes 830 detrital zircon samples (101,898 individual ages) from 70 studies published between 2000 and 2020. For comparative purposes, we also compile 1307 igneous zircon U-Pb ages that characterize the magmatic history of the arc. We place primary emphasis on detrital zircon ages between 251 and 56 Ma that we infer to be uniquely derived from magmatic sources in the arc. Informed by existing knowledge of magmatic, structural, and sedimentological processes that acted on the orogen, we investigate spatial and temporal trends in these “arc-derived zircon” to establish a detrital record of arc magmatism, investigate source-to-sink relationships between the arc and adjacent basins, and discuss controls on sediment dispersal across the orogen. Our review shows that compilations of detrital zircon data from the Cordilleran forearc and foreland basin systems are excellent proxies for arc magmatism because the basins are enriched in arc-derived zircon and compilations provide space- and time-integrated records of crystallization ages. The compiled detrital zircon data support a history of continuous arc magmatism throughout Mesozoic and earliest Cenozoic time, characterized by low-volume magmatism from Triassic-Early Jurassic time (~251–174 Ma) and episodic higher-volume magmatism from Middle Jurassic-Late Cretaceous time (~174–66 Ma). These trends elucidate the initiation and timing of magmatic events at the orogen-scale and corroborate our understanding of cyclic arc behavior. Detrital zircon distributions are spatially and temporally variable both within and across basins, which we discuss relative to topographic development of the orogen and attendant responses of sediment dispersal systems. Detrital zircon distributions in the forearc signal rapid transfer of sediment from the arc to basins dominantly via fluvial processes. In contrast, detrital zircon distributions across the foreland reflect the presence of topographic barriers in the hinterland region of the arc that effectively isolated parts of the foreland. The presence of hinterland topography in turn highlights the important role of ash-fall events in delivering arc-derived zircon to the foreland, underscoring the need to consider ash-fall processes in paleodrainage reconstructions. These broad regional trends, and in general the close linkage between orogenic process and sediment dispersal, emerge from our compilation because it averages out much of the local variability observed in studies of more limited geographic or temporal extent.

North American Cordilleran arc system↗
USGS2012

User’s guide and metada for the PICES Nonindigenous Species Information System

Introduction Welcome to the PICES Nonindigenous Species Information System, a Microsoft Access database that displays the biogeographic distributions, invasion status, vectors, and key life history attributes of the approximately 740 reported nonindigenous species (NIS) in the estuarine and near-coastal habitats of the North Pacific and Hawaii. This database was developed by the U.S. Environmental Protection Agency and the U.S. Geological Survey under the auspices of Working Group 21 (Invasive Species) of the North Pacific Marine Science Organization (PCIES). The PICES database contains the data used to generate the “Atlas of Nonindigenous Marine and Estuarine Species in the North Pacific” (Lee and Reusser, 2012; herein referred to as the “Atlas”). The User’s Guide provides instructions on how to use the PICES database as well as metadata for the database and the Atlas. We note that for most users, the Atlas provides a simpler approach to accessing key information on NIS in the PICES countries than the database, though the database does provide additional information on species and sources as well as allowing users to extract information on specific taxa and/or locations (see Section 4). The PICES database also includes species reported from the PICES Rapid Assessment Surveys (RAS). PICES sponsored four rapid assessment surveys with the objective of quickly characterizing the native, non-native, and cryptogenic species present in different locations. Surveys were sponsored in Dalian, China in 2008, Jeju, Korea in 2009, Newport, Oregon, USA in 2010, and Peter the Great Bay, near Vladivostok and Nakhodka, Russia in 2011 ( http://www.pices.int/publications/pices_press/volume19/v19_n1/pp_30-31_Kobe-WS_f.pdf , http://www.pices.int/publications/pices_press/volume20/v20_n1/pp_26-29_RAS-2011.pdf ). The PICES database contains the RAS species that were made available in time for inclusion. Thus, the database does not capture all the species found in these surveys. In addition, much of the information on the RAS species was provided by the experts conducting the survey, and their distributions, environmental requirements, and life history attributes were not evaluated to the same level of detail by the PICES authors as the North Pacific NIS. In lieu of the more extensive review as conduced with the Atlas species, the information on the RAS species needs to be considered preliminary. Additionally, it is important to use the “Map All Distributions” option (see Section 3.6.6) when mapping their distribution or conducting a query. The general reference for the RAS surveys in the PICES database is “PICES Working Group 21, YEAR SURVEY”. The overall goal of both the database and Atlas was to simplify and standardize the dissemination of distributional, habitat, and life history characteristics of near-coastal and estuarine nonindigenous species. This database provides a means of querying these data and displaying the information in a consistent format. The specific classes of information the database captures include: Regional and global ranges of native and nonindigenous near-coastal and estuarine species at different hierarchical spatial scales. Habitat and physiological requirements of near-coastal and estuarine species. Life history characteristics of near-coastal and estuarine species. Invasion history and vectors for nonindigenous species. This standardized and synthesized data in the database and the Atlas provide the basic information needed to address a number of managerial and scientific needs. Thus, users will be able to: Create a baseline on the extent of invasion by region in order to assess new invasions. Use existing geographical patterns of invasion to gain some insights into potential new invaders. Use existing geographical patters of invasion to gain some insights into mechanisms affecting relative invasibility of different areas. Use life history attributes and environmental requirements of the reported nonindigenous species to evaluate traits of invaders. Understand the potential spread of invaders based on their habitat and environmental requirements. Understand importance of different vectors of introduction of nonindigenous species by region. The data in the Atlas of Nonindigenous Marine and Estuarine Species in the North Pacific (Lee and Reusser, 2012) are up-to-date as of June 2012. Updates to the PICES database were made in September 2012.

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USGS2013

A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region

High-latitude terrestrial ecosystems are key components in the global carbon cycle. The Northern Circumpolar Soil Carbon Database (NCSCD) was developed to quantify stocks of soil organic carbon (SOC) in the northern circumpolar permafrost region (a total area of 18.7 × 10 6 km 2 ). The NCSCD is a geographical information system (GIS) data set that has been constructed using harmonized regional soil classification maps together with pedon data from the northern permafrost region. Previously, the NCSCD has been used to calculate SOC storage to the reference depths 0–30 cm and 0–100 cm (based on 1778 pedons). It has been shown that soils of the northern circumpolar permafrost region also contain significant quantities of SOC in the 100–300 cm depth range, but there has been no circumpolar compilation of pedon data to quantify this deeper SOC pool and there are no spatially distributed estimates of SOC storage below 100 cm depth in this region. Here we describe the synthesis of an updated pedon data set for SOC storage (kg C m -2 ) in deep soils of the northern circumpolar permafrost regions, with separate data sets for the 100–200 cm (524 pedons) and 200–300 cm (356 pedons) depth ranges. These pedons have been grouped into the North American and Eurasian sectors and the mean SOC storage for different soil taxa (subdivided into Gelisols including the sub-orders Histels, Turbels, Orthels, permafrost-free Histosols, and permafrost-free mineral soil orders) has been added to the updated NCSCDv2. The updated version of the data set is freely available online in different file formats and spatial resolutions that enable spatially explicit applications in GIS mapping and terrestrial ecosystem models. While this newly compiled data set adds to our knowledge of SOC in the 100–300 cm depth range, it also reveals that large uncertainties remain. Identified data gaps include spatial coverage of deep (> 100 cm) pedons in many regions as well as the spatial extent of areas with thin soils overlying bedrock and the quantity and distribution of massive ground ice. An open access data-portal for the pedon data set and the GIS-data sets is available online at http://bolin.su.se/data/ncscd/ .

Earth System Science Data↗
USGS2020

Understanding the relationship between stream metabolism and biological assemblages

Biological assemblages are commonly used for assessing stream health, but there is increased interest among the freshwater research community in incorporating measures of stream function, such as metabolism, to strengthen stream-health assessments. Presently, there is limited information about the relationships between stream metabolism and biological assemblages, along with the measurement period required to relate metabolism with stream biota. Our study assessed which environmental factors explained stream metabolism and to what degree stream metabolism and minimum dissolved oxygen (DOmin) were related to invertebrate and fish metrics in streams distributed across several regions of the United States. Furthermore, we evaluated the number of metabolism monitoring days required for maximizing the ability to detect relationships between stream metabolism and biological assemblage metrics. We sampled 17 sites distributed among reference, agricultural, and urban areas for stream metabolism, nutrients, habitat, and biological assemblages (invertebrates and fishes). Overall, sites were heterotrophic with gross primary production (GPP) and ecosystem respiration (ER) related primarily to days since last high flow, canopy cover, maximum water temperature, and total phosphorus. DOmin was related to days since last high flow, canopy cover, and maximum water temperature. We were unable to determine a clear statistical relationship between invertebrate metrics (invertebrate richness; Ephemeroptera, Plecoptera, and Trichoptera richness; and scraper-taxa richness) and GPP, ER, or DOmin. In contrast, we found that 2 fish-assemblage metrics were associated with stream metabolism and DOmin. A fish multimetric index (FMMI) was negatively correlated with GPP ( r = −0.5, p = 0.048) and positively correlated with DOmin ( r = 0.47, p = 0.06). Percentage of omnivorous fish taxa was positively correlated with GPP ( r = 0.72, p = 0.001) and ER ( r = 0.55, p = 0.02) and negatively correlated with DOmin ( r = −0.67, p = 0.003). The lack of detected relationships for most of the biological-assemblage metrics with stream metabolism may be partially due to 1 or more factors, including high variability, low sample size, limited range in metabolism values, assemblage metrics used, and geographic distribution of sites. Comparing stream-metabolism measurement periods (in days) to biological-assemblage metrics indicated that optimum correlations occurred at 2 d for DOmin, 3 d for GPP, and 14 d for ER. Although our study found limited relationships of stream metabolism and DOmin with biological assemblages, future studies should consider a larger sample size (≥30), 14-d or longer metabolism measurement period, and assessment of other taxa-specific or assemblage metrics.

Freshwater Science↗
USGS2014

The digital global geologic map of Mars: Chronostratigraphic ages, topographic and crater morphologic characteristics, and updated resurfacing history

A new global geologic map of Mars has been completed in a digital, geographic information system (GIS) format using geospatially controlled altimetry and image data sets. The map reconstructs the geologic history of Mars, which includes many new findings collated in the quarter century since the previous, Viking-based global maps were published, as well as other discoveries that were made during the course of the mapping using new data sets. The technical approach enabled consistent and regulated mapping that is appropriate not only for the map's 1:20,000,000 scale but also for its widespread use by diverse audiences. Each geologic unit outcrop includes basic attributes regarding identity, location, area, crater densities, and chronostratigraphic age. In turn, units are grouped by geographic and lithologic types, which provide synoptic global views of material ages and resurfacing character for the Noachian, Hesperian, and Amazonian periods. As a consequence of more precise and better quality topographic and morphologic data and more complete crater-density dating, our statistical comparisons identify significant refinements for how Martian geologic terrains are characterized. Unit groups show trends in mean elevation and slope that relate to geographic occurrence and geologic origin. In comparison with the previous global geologic map series based on Viking data, the new mapping consists of half the number of units due to simpler, more conservative and globally based approaches to discriminating units. In particular, Noachian highland surfaces overall have high percentages of their areas now dated as an epoch older than in the Viking mapping. Minimally eroded (i.e., pristine) impact craters ≥3 km in diameter occur in greater proportion on Hesperian surfaces. This observation contrasts with a deficit of similarly sized craters on heavily cratered and otherwise degraded Noachian terrain as well as on young Amazonian surfaces. We interpret these as reflecting the relatively stronger, lava-rich, yet less-impacted materials making up much of the younger units. Reconstructions of resurfacing of Mars by its eight geologic epochs using the Hartmann and Neukum chronology models indicate high rates of highland resurfacing during the Noachian (peaking at 0.3 km 2 /yr during the Middle Noachian), modest rates of volcanism and transition zone and lowland resurfacing during the Hesperian (∼0.1 km 2 /yr), and low rates of mainly volcanic and polar resurfacing (∼0.01 km 2 /yr) for most of the Amazonian. Apparent resurfacing increased in the Late Amazonian (∼0.03 km 2 /yr), perhaps due to better preservation of this latest record.

Planetary and Space Science↗
USGS2006

Scoping of flood hazard mapping needs for Cumberland County, Maine

This report was prepared by the U.S. Geological Survey (USGS) Maine Water Science Center as the deliverable for scoping of flood hazard mapping needs for Cumberland County, Maine, under Federal Emergency Management Agency (FEMA) Inter-Agency Agreement Number HSFE01-05-X-0018. This section of the report explains the objective of the task and the purpose of the report. The Federal Emergency Management Agency (FEMA) developed a plan in 1997 to modernize the FEMA flood mapping program. FEMA flood maps delineate flood hazard areas in support of the National Flood Insurance Program (NFIP). FEMA's plan outlined the steps necessary to update FEMA's flood maps for the nation to a seamless digital format and streamline FEMA's operations in raising public awareness of the importance of the maps and responding to requests to revise them. The modernization of flood maps involves conversion of existing information to digital format and integration of improved flood hazard data as needed. To determine flood mapping modernization needs, FEMA has established specific scoping activities to be done on a county-by-county basis for identifying and prioritizing requisite flood-mapping activities for map modernization. The U.S. Geological Survey (USGS), in cooperation with FEMA and the Maine State Planning Office Floodplain Management Program, began scoping work in 2005 for Cumberland County. Scoping activities included assembling existing data and map needs information for communities in Cumberland County, documentation of data, contacts, community meetings, and prioritized mapping needs in a final scoping report (this document), and updating the Mapping Needs Update Support System (MNUSS) Database or its successor with information gathered during the scoping process. The average age of the FEMA floodplain maps in Cumberland County, Maine is 21 years. Most of these studies were in the early to mid 1980s. However, in the ensuing 20-25 years, development has occurred in many of the watersheds, and the characteristics of the watersheds have changed with time. Therefore, many of the older studies may not depict current conditions nor accurately estimate risk in terms of flood heights. The following is the scope of work as defined in the FEMA/USGS Statement of Work: Task 1: Collect data from a variety of sources including community surveys, other Federal and State Agencies, National Flood Insurance Program (NFIP) State Coordinators, Community Assistance Visits (CAVs) and FEMA archives. Lists of mapping needs will be obtained from the MNUSS database, community surveys, and CAVs, if available. FEMA archives will be inventoried for effective FIRM panels, FIS reports, and other flood-hazard data or existing study data. Best available base map information, topographic data, flood-hazard data, and hydrologic and hydraulic data will be identified. Data from the Maine Floodplain Management Program database also will be utilized. Task 2: Contact communities in Cumberland County to notify them that FEMA and the State have selected them for a map update, and that a project scope will be developed with their input. Topics to be reviewed with the communities include (1) Purpose of the Flood Map Project (for example, the update needs that have prompted the map update); (2) The community's mapping needs; (3) The community's available mapping, hydrologic, hydraulic, and flooding information; (4) target schedule for completing the project; and (5) The community's engineering, planning, and geographic information system (GIS) capabilities. On the basis of the collected information from Task 1 and community contacts/meetings in Task 2, the USGS will develop a Draft Project Scope for the identified mapping needs of the communities in Cumberland County. The following items will be addressed in the Draft Project Scope: review of available information, determine if and how effective FIS data can be used in new project, and identify other data needed to

Open-File Report↗
USGS2022

Status and trends of North American bats: Summer occupancy analysis 2010-2019

• We developed an analytical pipeline supported by web-based infrastructure for integrating continental scale bat monitoring data (stationary acoustic, mobile acoustic, and capture records) to estimate summer (May 1–Aug 31) occupancy probabilities and changes in occupancy over time for 12 North American bat species. This serves as one of multiple lines of evidence that inform the status and trends of bat populations. • We analyzed data from a total of 12 bat species (Table 1), 11 of which have tested positive for Pseudogymnoascus destructans (Pd), a fungal pathogen that causes white-nose syndrome (WNS)—a disease that has led to significant rates of mortality for subterranean hibernating bat species in North America. A twelfth species was also selected because of high rates of mortality at wind energy facilities. Additional species were considered but not selected due to data limitations. • We estimated occupancy probabilities for 2010 through 2019 for three species ( Myotis lucifugus , MYLU; Myotis septentrionalis , MYSE; and P erimyotis subflavus , PESU). For an additional nine species, we estimated occupancy probabilities for 2016 through 2019 ( Myotis evotis , MYEV; Myotis grisescens , MYGR; Myotis leibii , MYLE; Myotis thysanodes , MYTH; Myotis volans , MYVO; Myotis yumanensis , MYYU; Eptesicus fuscus , EPFU; Lasionycteris noctivagans , LANO; and Lasiurus cinereus , LACI). • For each species, we provide range-wide occupancy probability predictions (e.g., predicted summer occupancy distribution maps) each year at a spatial resolution of 100 km2 and provide regional estimates of mean occupancy probability aggregated at larger spatial scales (state/province/territory, range-wide). • For each species, we also provide trends over time (average annual change rate and total change rate) in mean occupancy probabilities at multiple spatial scales (state/province/territory, range-wide) and when possible, over multiple timescales (short, medium, long). • Results suggest that over the short-term (2016-2019), two ( Myotis lucifugus and Perimyotis subflavus ) of 12 species have experienced declines in range-wide average occupancy probability with at least 95% certainty. Seven species showed either minor increases or decreases in range-wide average occupancy probability but with less than 95% certainty in both trend indicators. Results over the longer term (eight years and 10 years of sampling) suggest that three hibernating species known to be highly affected by white-nose syndrome ( Myotis lucifugus , Myotis septentrionalis , and Perimyotis subflavus ) have experienced marked declines in range-wide average occupancy probabilities, with severity varying by species and region. Finally, the results for three species ( Eptesicus fuscus , Lasiurus cinereus , Lasionycteris noctivagans ) were inconclusive due to 1) borderline convergence issues in the model fitting procedure which suggests potentially unreliable estimates, 2) failure to reliably distinguish between false positives and true positive detections for ambiguous detections, and 3) largely uninformative covariates for occupancy and detection. • For Myotis lucifugus , Myotis septentrionalis , and Perimyotis subflavus we found meaningful associations in space and time between declining winter populations (likely a result of WNS) and summer occupancy distributions. • The representativeness of sampling data for each species’ status and trend estimates (e.g., state/province/territory) were also evaluated based on the percent of grid cells sampled each year with a goal of understanding the reliability of regional estimates and improving future monitoring efforts. • This work represents the most comprehensive effort to date to model North American bat distributions across their continental ranges. Despite current limitations highlighted in the discussion, the analytical methods and resulting status and trends estimates provide the best available science on summer bat populations across North America and will continue to improve over time as monitoring data sets and analytical methods improve. • Moving forward, our occupancy analyses will continue to improve with submission of more 1) data from currently underrepresented areas (i.e., improved geographic representation), 2) manually-vetted acoustic recordings, 3) capture records, and 4) roost location and count data (summer and winter).

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