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At least 163 records · Page 9Linked to original sources
USGS2010

A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios

he Energy Independence and Security Act of 2007 (EISA), Section 712, mandates the U.S. Department of the Interior to develop a methodology and conduct an assessment of the Nation’s ecosystems, focusing on carbon stocks, carbon sequestration, and emissions of three greenhouse gases (GHGs): carbon dioxide, methane, and nitrous oxide. The major requirements include (1) an assessment of all ecosystems (terrestrial systems, such as forests, croplands, wetlands, grasslands/shrublands; and aquatic ecosystems, such as rivers, lakes, and estuaries); (2) an estimate of the annual potential capacities of ecosystems to increase carbon sequestration and reduce net GHG emissions in the context of mitigation strategies (including management and restoration activities); and (3) an evaluation of the effects of controlling processes, such as climate change, land-use and land-cover change, and disturbances such as wildfires. The concepts of ecosystems, carbon pools, and GHG fluxes follow conventional definitions in use by major national and international assessment or inventory efforts. In order to estimate current ecosystem carbon stocks and GHG fluxes and to understand the potential capacity and effects of mitigation strategies, the method will use two time periods for the assessment: 2001 through 2010, which establishes a current ecosystem carbon and GHG baseline and will be used to validate the models; and 2011 through 2050, which will be used to assess potential capacities based on a set of scenarios. The scenario framework will be constructed using storylines of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES), along with both reference and enhanced land-use and land-cover (LULC) and land-management parameters. Additional LULC and land-management mitigation scenarios will be constructed for each storyline to increase carbon sequestration and reduce GHG fluxes in ecosystems. Input from regional experts and stakeholders will be solicited to construct these scenarios. The methods for mapping the current LULC and ecosystem disturbances will require the extensive use of both remote-sensing data and field-survey data (for example, forest inventories) to capture and characterize landscape-changing events. For potential LULC changes and ecosystem disturbances, key drivers such as socioeconomic and climate changes will be used in addition to the biophysical data. The result of these analyses will be a series of maps for each future year for each scenario. These annual maps will form the basis for estimating carbon storage and GHG emissions. For terrestrial ecosystems, carbon storage, carbon-sequestration capacities, and GHG emissions under the present conditions and future scenarios will be assessed using the LULC-change and ecosystem-disturbance estimates in map format with a spatially explicit biogeochemical ensemble modeling system that incorporates properties of management activities (such as tillage or harvesting) and properties of individual ecosystems (such as energy exchange, vegetation characteristics, hydrological cycling, and soil attributes). For aquatic ecosystems, carbon burial in sediments and fluxes of GHG are functions of the present and future potential stream flow and sediment transport and will be assessed using empirical hydrological modeling methods. Validation and uncertainty analysis methods described in the methodology will follow established guidelines to assess the quality of the assessment results. The U.S. Environmental Protection Agency’s Level II ecoregions map will be the practical instrument for developing and delivering assessment results. Consequently, the ecoregion (there are 22 modified ecoregions) will be the reporting unit of the assessment because the scenarios, assessment results, validation, and uncertainty analysis will be produced at that scale. The implementation of these methods will require collaborations among various Federal agencies, State agencies, nongovernmental organizations, and the science community. Using the method described in this document, the assessment can be completed in approximately 3 to 4 years. The primary deliverables will be assessment reports containing tables, charts, and maps that will present the estimated GHG parameters annually for 2001 through 2050 by ecosystem, pool, and scenario. The results will permit the evaluation of a range of policies, mitigation options, and research topics, such as the demographic, LULC-change, or climate-change effects on carbon stocks, carbon sequestration, and GHG fluxes in ecosystems.

Scientific Investigations Report↗
USGS2003

Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision‐making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

Journal of Geophysical Research E: Planets↗
USGS2015

Archive of bathymetry data collected at Cape Canaveral, Florida, 2014

Remotely sensed, geographically referenced elevation measurements of the sea floor, acquired by boat- and aircraft-based survey systems, were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida, for the area at Cape Canaveral. The work was conducted as part of a study to describe an updated bathymetric dataset collected in 2014 and compare it to previous data sets. The updated data focus on the bathymetric features and sediment transport pathways that connect the offshore regions to the shoreline and, therefore, are related to the protection of other portions of the coastal environment, such as dunes, that support infrastructure and ecosystems. Cape Canaveral Coastal System (CCCS) is a prominent feature along the Southeast U.S. coastline and is the only large cape south of Cape Fear, North Carolina. Most of the CCCS lies within the Merritt Island National Wildlife Refuge and included within its boundaries are the Cape Canaveral Air Force Station (CCAFS), NASA’s Kennedy Space Center (KSC), and a large portion of Canaveral National Seashore. The actual promontory of the modern cape falls within the jurisdictional boundaries of the CCAFS. Hydrographic survey data were collected August 18-20, 2014 ( USGS Field Activity Number 2014-324-FA ). The study covered a 20 kilometer (km) section of shoreline extending from Port Canaveral, Fla., to the northern end of the KSC property, and from the shoreline to about 2.5 km offshore. Data were acquired using both sound navigation and ranging (sonar) and light detection and ranging (lidar) systems. Two jet skis and a 17-foot (ft) outboard motor boat equipped with the USGS SANDS (System for Accurate Nearshore Depth Surveying) hydrographic system collected precision sonar data. The USGS airborne EAARL-B mapping system flown in a twin engine airplane was used to collect lidar data. The missions were synchronized so that there was temporal and spatial overlap between the sonar and lidar operations. Additional data were collected to evaluate water clarity to verify the ability of lidar to receive bathymetric returns. Both systems used differential Global Positioning System GPS and utilized the National Oceanic and Atmospheric Administration/National Geodetic Survey (NOAA/NGS) Continuously Operating Reference Station (CORS) station located at CCAFS was used as the reference station. This data series serves as an archive of processed single-beam sonar and lidar bathymetry data. Graphical Information System (GIS) data products include XYZ point bathymetry data files, a color coded bathymetry map, and interpolated bathymetry grid surface. Additional information includes an error analysis and formal Federal Geographic Data Committee (FGDC) metadata. For more information about similar projects, please visit the Barrier Island Evolution Web site.

Florida↗
USGS2009

Remote sensing of snow and ice

Monitoring of snow and ice on the Earth's surface will require increasing use of satellite remote sensing techniques. These techniques are evolving rapidly. Active and passive sensors operating in the visible, near infrared, thermal infrared, and microwave wavelengths are described in regard to general applications and in regard to specific USA or USSR satellites. Meteorological satellites (frequent images of relatively crude resolution) and Earth resources satellites such as Landsat (less frequent images of higher resolution) have been used to monitor the areal extent of seasonal snow, but problems exist with cloud cover or dense forest canopies. Snow mass (water equivalent) can be measured from a low-flying aircraft using natural radioactivity, but cannot yet be measured from satellite altitudes. A combination of active and passive microwave sensors may permit this kind of measurement, but not until more is known about radiation scattering in snow. Satellite observations are very useful in glacier inventories, correcting maps of glacier extent, estimating certain mass balance parameters, and monitoring calving or surging glaciers. Ground ice is virtually impossible to monitor from satellites; ice on rivers and lakes can be monitored only with very high-resolution sensors. Microwave sensors, due to their all-weather capability (the ability to see through clouds) provide exciting data on sea ice distribution. Analysis of digital tapes of satellite data requires the archiving and scanning of huge amounts of data. Simple methods for extracting quantitative data from satellite images are described.

Hydrological Sciences Bulletin↗
USGS2024

Remote sensing large-wood storage downstream of reservoirs during and after dam removal: Elwha River, Washington, USA

Large wood is an integral part of many rivers, often defining river-corridor morphology and habitat, but its occurrence, magnitude, and evolution in a river system are much less well understood than the sedimentary and hydraulic components, and due to methodological limitations, have seldom previously been mapped in substantial detail. We present a new method for this, representing a substantial advance in automated deep-learning-based image segmentation. From these maps, we measured large wood and sediment deposits from high-resolution orthoimages to explore the dynamics of large wood in two reaches of the Elwha River, Washington, USA, between 2012 and 2017 as it adjusted to upstream dam removals. The data set consists of a time series of orthoimages (12.5-cm resolution) constructed using Structure-from-Motion photogrammetry on imagery from 14 aerial surveys. Model training was optimized to yield maximum accuracy for estimated wood areas, compared to manually digitized wood, therefore model development and intended application were coupled. These fully reproducible methods and model resulted in a maximum of 15% error between observed and estimated total wood areas and wood deposit size-distributions over the full spatio-temporal extent of the data. Areal extent of wood in the channel margin approximately doubled in the years following dam removal, with greatest increases in large wood in wider, lower-gradient sections. Large-wood deposition increased between the start of dam removal (2011) and winter 2013, then plateaued. Sediment bars continued to grow up until 2016/17, assisted by a partially static wood framework deposited predominantly during the period up to winter 2013.

Washington↗
USGS2009

ATM Coastal Topography-Alabama 2001

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Alabama coastline, acquired October 3-4, 2001. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative scanning Lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning Lidar system that measures high-resolution topography of the land surface, and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM 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 pre-survey 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 routinely used to create maps that represent submerged or first surface topography.

Alabama↗
USGS2009

ATM Coastal Topography-Florida 2001: Eastern Panhandle

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the eastern Florida panhandle coastline, acquired October 2, 2001. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative scanning Lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning Lidar system that measures high-resolution topography of the land surface and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM 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 routinely used to create maps that represent submerged or first surface topography.

Florida↗
USGS2009

ATM coastal topography-Florida 2001: Western Panhandle

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the western Florida panhandle coastline, acquired October 2-4 and 7-10, 2001. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative scanning Lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning Lidar system that measures high-resolution topography of the land surface and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM 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 routinely used to create maps that represent submerged or first surface topography.

Florida↗
USGS2009

ATM Coastal Topography-Mississippi, 2001

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Mississippi coastline, from Lakeshore to Petit Bois Island, acquired September 9-10, 2001. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative scanning lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning lidar system that measures high-resolution topography of the land surface and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM 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 first-surface topography.

Mississippi↗
USGS2009

ATM Coastal Topography-Texas, 2001: UTM Zone 14

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, 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 Texas coastline within UTM zone 14, acquired October 12-13, 2001. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative scanning lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning lidar system that measures high-resolution topography of the land surface and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM 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 first-surface topography.

Texas↗
USGS2009

ATM Coastal Topography-Texas, 2001: UTM Zone 15

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, 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 Texas coastline within UTM zone 15, from Matagorda Peninsula to Galveston Island, acquired October 12-13, 2001. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative scanning lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning lidar system that measures high-resolution topography of the land surface and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM 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 first-surface topography.

Texas↗
USGS2010

ATM Coastal Topography-Louisiana, 2001: UTM Zone 15 (Part 1 of 2)

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, 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 Louisiana coastline beach face within UTM Zone 15, from Isles Dernieres to Grand Isle, acquired September 7 and 10, 2001. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative scanning lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning lidar system that measures high-resolution topography of the land surface and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM 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 first-surface topography.

Louisiana↗
USGS2009

ATM Coastal Topography - Louisiana, 2001: UTM Zone 16 (Part 2 of 2)

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, 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 Louisiana coastline beach face within UTM Zone 16, from Grand Isle to the Chandeleur Islands, acquired September 7 and 9, 2001. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative scanning lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning lidar system that measures high-resolution topography of the land surface and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM 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 first-surface topography.

Louisiana↗
USGS2025

Effect of land cover type on 3D deformation recovery from synthetically deformed high-resolution satellite optical imagery

The limits of detection for earthquake surface deformation in the spatial domain have improved with advances in remote sensing imagery data availability, resolution, and analysis. Sub-pixel correlation and digital elevation model (DEM) differencing from sub-meter, earthquake-spanning satellite optical imagery has enhanced surface rupture mapping and deformation measurements. However, knowledge of measurement accuracy and uncertainty is limited. To address this, we construct orthophotos and digital elevation models (DEMs) from repeat high resolution (∼0.5 m) satellite optical imagery along two sections of the Garlock fault, California with clear fault geomorphology and differing land cover. We deform later sets of DEMs and images with synthetic earthquakes containing both diffuse and discrete horizontal and vertical displacements. Sub-pixel image correlation and DEM differencing demonstrate how vegetation degrades recovered displacement accuracy. In barren land cover, horizontal displacements are detectable to an expected ∼1/10th-pixel size. With shrubs, trees, and grass, detectable displacements increase to >1/2-pixel size, and filtering results by correlation score and using elevation values as input rather than image values improves accuracy. Vertical displacement detection thresholds remain lower in vegetation, at >1-pixel size. Higher slope angles degrade displacement recovery, worsened by vegetation. Diminishing seasonal separation improves accuracy over vegetated regions, though not to the level achieved in barren environments. These results will inform research and operational efforts on the utility of high resolution satellite optical imagery for detecting deformation in varied land cover. Furthermore, they reveal where alternative measurements, such as from LiDAR or radar interferometry, are required to mitigate the effects of vegetation and capture fine-scale crustal deformation.

California↗
USGS2019

Giving ecological meaning to satellite-derived fire severity metrics across North American forests

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R 2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.

Remote Sensing↗
USGS2022

A geospatial knowledge graph prototype for national topographic mapping

Knowledge graphs are a form of database representation and handling that show the potential to better meet the challenges of data interoperability, semi-automated information reasoning, and information retrieval. Geospatial knowledge graphs (GKG) have at their core specialized forms of applied ontology that provide coherent spatial context to a domain of information including non-spatial attributes. This paper discusses research toward the development of a prototype GKG based on national topographic databases of geospatial feature instances, attributes, properties, metadata, and annotations. The challenges are to capture and represent geographic semantics inherent in the source data, to align such graph models with standards where possible, to test logical computations, and to visualize the data using a cartographic user interface. Data integration from outside sources was tested through SPARQL and GeoSPARQL queries. Called the MapKB, the approaches applied in this prototype use a number of software components to build a system architecture aligned with those objectives and are composed entirely of free and open-source software. The system and ontology design were validated through reasoning and competency questions. Technical aspects of the prototype software succeeded, but customization was found to be needed for user-based design.

International Archives of the Photogrammetry, Remo↗
USGS2025

Global methane budget 2000-2020

Understanding and quantifying the global methane (CH 4 ) budget is important for assessing realistic pathways to mitigate climate change. CH 4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO 2 ), and both emissions and atmospheric concentrations of CH 4 have continued to increase since 2007 after a temporary pause. The relative importance of CH 4 emissions compared to those of CO 2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH 4 sources and from the uncertain magnitude and temporal change in the destruction of CH 4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH 4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH 4 budget, integrating results of top-down CH 4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH 4 yr −1 ). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH 4 yr −1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH 4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH 4 yr −1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH 4 yr −1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH 4 yr −1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH 4 yr −1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH 4 yr −1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH 4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH 4 yr −1 ) larger global emissions (669 Tg CH 4 yr −1 , range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH 4 yr −1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH 4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH 4 yr −1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty ( ∼ 25 %). The total sink of CH 4 is estimated at 633 [507–796] Tg CH 4 yr −1 by the bottom-up approaches and at 554 [550–567] Tg CH 4 yr −1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH 4 yr −1 in the top-down budget and 211 [195–231] Tg CH 4 yr −1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH 4 yr −1 in the top-down budget and 120 [117–125] Tg CH 4 yr −1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH 4 yr −1 in the top-down budget and 28 [21–39] Tg CH 4 yr −1 in the bottom-up budget. We identify five major priorities for improving the CH 4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH 4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH 4 observations at local (e.g. FLUXNET-CH 4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.

Earth System Science Data↗
USGS2022

Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product

The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985-2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~ 25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5%. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.

Earth System Science Data↗