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USGS1996

EDC DAAC: Serving the land processes science community

The primary objective of the Earth Resources Observation System (EROS) Data Center (EDC) Distributed Active Archive Center (DAAC) for land processes data is to promote the interdisciplinary study and understanding of the integrated Earth system by providing remotely sensed and related ancillary data for the study, characterization, and monitoring of natural and anthropogenic conditions and processes existing and operating at or near the land surface. Generating, distributing, and preserving data and products from the National Aeronautics and Space Administration's (NASA) earth observing system (EOS) are primary functions of the DAAC. To that end, the EDC DAAC is using existing data sets in developing capabilities to efficiently and effectively ingest, process, manage, distribute, and archive for future generations land-related data collected by EOS sensors. Capabilities also are being developed to help users search for and acquire data and products, as well as to support their scientific application of those data and products. EDC DAAC programs and capability development activities address DAAC-defined science support requirements that relate to a broad spectrum of services and capabilities needed by science users.

Conference Paper↗
USGS2023

John Wesley Powell Center for Analysis and Synthesis Newsletter, volume 7, issue 1

The John Wesley Powell Center for Synthesis & Analysis is a USGS initiative that aims to foster innovative thinking in Earth system science through collaborative analysis and synthesis of existing data and information. The Powell Center supports working groups that address some of the most pressing and complex questions facing society, such as climate change, biodiversity loss, water scarcity, natural hazards, and human-environment interactions. In this newsletter, we highlight some of the recent activities of the Powell Center and its working groups.

John Wesley Powell Center for Analysis and Synthes↗
USGS1988

Knowledge-based geographic information systems (KBGIS): New analytic and data management tools

In its simplest form, a geographic information system (GIS) may be viewed as a data base management system in which most of the data are spatially indexed, and upon which sets of procedures operate to answer queries about spatial entities represented in the data base. Utilization of artificial intelligence (AI) techniques can enhance greatly the capabilities of a GIS, particularly in handling very large, diverse data bases involved in the earth sciences. A KBGIS has been developed by the U.S. Geological Survey which incorporates AI techniques such as learning, expert systems, new data representation, and more. The system, which will be developed further and applied, is a prototype of the next generation of GIS's, an intelligent GIS, as well as an example of a general-purpose intelligent data handling system. The paper provides a description of KBGIS and its application, as well as the AI techniques involved. ?? 1988 International Association for Mathematical Geology.

Mathematical Geology↗
USGS2007

Geoinformatics 2007: data to knowledge

Geoinformatics is the term used to describe a variety of efforts to promote collaboration between the computer sciences and the geosciences to solve complex scientific questions. It refers to the distributed, integrated digital information system and working environment that provides innovative means for the study of the Earth systems, as well as other planets, through use of advanced information technologies. Geoinformatics activities range from major research and development efforts creating new technologies to provide high-quality, sustained production-level services for data discovery, integration and analysis, to small, discipline-specific efforts that develop earth science data collections and data analysis tools serving the needs of individual communities. The ultimate vision of Geoinformatics is a highly interconnected data system populated with high quality, freely available data, as well as, a robust set of software for analysis, visualization, and modeling.

Scientific Investigations Report↗
USGS2008

Geoinformatics 2008 - Data to Knowledge

Geoinformatics is the term used to describe a variety of efforts to promote collaboration between the computer sciences and the geosciences to solve complex scientific questions. It refers to the distributed, integrated digital information system and working environment that provides innovative means for the study of the Earth systems, as well as other planets, through use of advanced information technologies. Geoinformatics activities range from major research and development efforts creating new technologies to provide high-quality, sustained production-level services for data discovery, integration and analysis, to small, discipline-specific efforts that develop earth science data collections and data analysis tools serving the needs of individual communities. The ultimate vision of Geoinformatics is a highly interconnected data system populated with high quality, freely available data, as well as, a robust set of software for analysis, visualization, and modeling. This volume is a collection of extended abstracts for oral papers presented at the Geoinformatics 2008 conference, June 11 and 13, 2008, in Potsdam, Germany.

Scientific Investigations Report↗
USGS1998

Digital mine claim density map for Federal lands in Montana, 1996

This report describes a digital map and data files generated by the U.S. Geological Survey (USGS) to provide digital spatial mining claim information for Federal lands in Montana as of March, 1997. Statewide, 159,704 claims had been recorded with the Bureau of Land Management since 1975. Of those claims, 21,055 (13%) are still actively held while 138,649 (87%) are closed and are no longer held. Montana contains 147,704 sections (usually 1 section equals 1 square mile) in the Public Land Survey System, with 8,569 sections (6%) containing claim data. Of the sections with claim data, 2,192 (26%) contain actively held claims. Only 1.5% of Montana’s sections contains actively held mining claims. The four types of mining claim are lode, placer, mill, and tunnel. A mill claim may be as much as 5 acres or 1/128th (0.78125%) of a square mile. A lode claim, about 20 acres, would cover 1/32nd (3.125%) of a square mile. Mining claim data is earth science information deemed to be relevant to the assessment of historic, current, and future ecological, economic, and social systems. The digital map and data files that are available in this report are suitable for geographic information system (GIS)-based regional assessments at a scale of 1:100,000 or smaller. Campbell (1996) summarized the methodology and GIS techniques that were used to produce the mining claim density map of the Pacific Northwest. Campbell and Hyndman (1997) displayed mining claim information for the Pacific Northwest that used data acquired in 1994. Appendix A of this report lists the attribute data for the digital data files. Appendix B contains the GIS metadata.

Montana↗
USGS2016

The value of earth observations: methods and findings on the value of Landsat imagery

Data from Earth observation systems are used extensively in managing and monitoring natural resources, natural hazards, and the impacts of climate change, but the value of such data can be difficult to estimate, particularly when it is available at no cost. Assessing the socioeconomic and scientific value of these data provides a better understanding of the existing and emerging research, science, and applications related to this information and contributes to the decision making process regarding current and future Earth observation systems. Recent USGS research on Landsat data has advanced the literature in this area by using a variety of methods to estimate value. The results of a 2012 survey of Landsat users, a 2013 requirements assessment, and 2013 case studies of applications of Landsat imagery are discussed.

Book chapter↗
USGS2021

System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)

Executive Summary This report addresses system characterization of the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present the methodology and procedures for characterization and the technical and operational information about the specific sensing system being evaluated. These reports also provide a description of data measurements, data retention practices, and data analysis results and provide system characterization conclusions. In partnership with Teledyne Brown Engineering, DLR built the DESIS hyperspectral instrument, which Teledyne Brown Engineering then integrated onto its International Space Station-based imaging platform, the Multi-User System for Earth Sensing. DLR developed the processing software and, together with Innovative Imaging and Research, completes the validation and calibration of the data products. DESIS was launched in 2018, and the data are used for scientific research in atmospheric physics and Earth sciences. The DESIS sensor contributes to the scientific and commercial utilization of the International Space Station and helps to further hyperspectral remote sensing technologies for future satellites. More information on DLR satellites and sensors is included within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.dlr.de/DE/Home/home_node.html . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that DESIS has an interior geometric performance of less than a 3.30-meter (less than 0.11 pixel) root mean square error in band-to-band registration, an exterior geometric performance in the range of a 2.40- (0.08 pixel) to 17.40-meter (0.58 pixel) offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.013 to 1.011 (offset and slope), and a spatial performance for band 130 of 1.5 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency of 0.167.

Open-File Report↗
USGS2008

Development of landsat-5 thematic mapper internal calibrator gain and offset table

The National Landsat Archive Production System (NLAPS) has been the primary processing system for Landsat data since U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) started archiving Landsat data. NLAPS converts raw satellite data into radiometrically and geometrically calibrated products. NLAPS has historically used the Internal Calibrator (IC) to calibrate the reflective bands of the Landsat-5 Thematic Mapper (TM), even though the lamps in the IC were less stable than the TM detectors, as evidenced by vicarious calibration results. In 2003, a major effort was made to model the actual TM gain change and to update NLAPS to use this model rather than the unstable IC data for radiometric calibration. The model coefficients were revised in 2007 to reflect greater understanding of the changes in the TM responsivity. While the calibration updates are important to users with recently processed data, the processing system no longer calculates the original IC gain or offset. For specific applications, it is useful to have a record of the gain and offset actually applied to the older data. Thus, the NLAPS calibration database was used to generate estimated daily values for the radiometric gain and offset that might have been applied to TM data. This paper discusses the need for and generation of the NLAPSIC gain and offset tables. A companion paper covers the application of and errors associated with using these tables.

Conference Paper↗
USGS2008

Development of Landsat-5 thematic mapper internal calibrator gain and offset table

The National Landsat Archive Production System (NLAPS) has been the primary processing system for Landsat data since U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) started archiving Landsat data. NLAPS converts raw satellite data into radiometrically and geometrically calibrated products. NLAPS has historically used the Internal Calibrator (IC) to calibrate the reflective bands of the Landsat-5 Thematic Mapper (TM), even though the lamps in the IC were less stable than the TM detectors, as evidenced by vicarious calibration results. In 2003, a major effort was made to model the actual TM gain change and to update NLAPS to use this model rather than the unstable IC data for radiometric calibration. The model coefficients were revised in 2007 to reflect greater understanding of the changes in the TM responsivity. While the calibration updates are important to users with recently processed data, the processing system no longer calculates the original IC gain or offset. For specific applications, it is useful to have a record of the gain and offset actually applied to the older data. Thus, the NLAPS calibration database was used to generate estimated daily values for the radiometric gain and offset that might have been applied to TM data. This paper discusses the need for and generation of the NLAPS IC gain and offset tables. A companion paper covers the application of and errors associated with using these tables.

Conference Paper↗
USGS2007

Shuttle Radar Topography Mission - New Products in 2005

In February 2000, the Shuttle Radar Topography Mission (SRTM) successfully collected Interferometric C-Band Synthetic Aperture Radar data over 80 percent of the Earth's land surface, for most of the area between 60?N and 56?S latitude. NASA and the National Geospatial-Intelligence Agency (NGA), formerly known as the National Imagery and Mapping Agency (NIMA), co-sponsored the mission. NASA's Jet Propulsion Laboratory (JPL) performed preliminary processing of SRTM data and forwarded partially finished data directly to NGA for finishing by NGA contractors and subsequent monthly deliveries to the NGA Digital Products Data Warehouse (DPDW). All data products delivered by the contractors conform to NGA SRTM Data Products and NGA Digital Terrain Elevation Data? (DTED?) specifications. The DPDW ingests the SRTM data products, checks them for formatting errors, loads the public SRTM DTED? into the NGA data distribution system, and ships them to the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS). In addition to NGA's SRTM DTED? format, USGS EROS has reformatted the data into a non-proprietary, generic raster binary SRTM format that is readable by most remote sensing software packages. The SRTM format is also publicly available from USGS EROS.

Fact Sheet↗
USGS2021

System characterization report on the WorldView-3 Imager

Executive Summary This report addresses system characterization of the Maxar WorldView-3 satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2020. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. WorldView-3 is a high-resolution multispectral satellite launched in 2014 by Maxar Technologies on an Atlas V launch vehicle from Vandenberg Air Force Base in California for Earth resources monitoring. WorldView-3 provides substantial technical improvements to previous WorldView satellites, including spectral bands, ground sample distance, and swath. The WorldView-3 satellite was designed and built by Lockheed Martin for Maxar Technologies using the BCP–5000 bus with the WorldView-3 Imager and the Clouds, Aerosols, Vapors, Ice, and Snow sensor. The high-resolution WorldView-3 Imager is the main instrument, and the Clouds, Aerosols, Vapors, Ice, and Snow sensor provides additional data on obscurants and other atmospheric effects used in data production. More information on Maxar WorldView satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.maxar.com/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that WorldView-3 has a range of interior geometric performance of −0.09 (−0.07 pixel) to 0.24 meter (0.19 pixel) in band-to-band registration; an exterior geometric performance in the range of a −21.10- (−2.11 pixels) to 28.23-meter (2.82 pixels) offset in comparison to Sentinel-2; a radiometric performance in the range of −0.121 to 1.420 (offset and slope); and a spatial performance in the range of 1.2 to 1.7 pixels at full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.093 to 0.185.

Open-File Report↗
USGS2021

Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality

This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.

Technical Report↗
USGS2022

System characterization report on Planet SkySat

This report addresses system characterization of Planet’s SkySat and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. SkySat is a constellation of submeter resolution Earth observation satellites providing analytics services, high-definition video, and imagery. The goal for the constellation is to capture multiple daily repeats of high-resolution imagery over any spot on the Earth. As of September 2020, 21 SkySat satellites have been launched, and the first launch occurred in November 2013. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that SkySat has an interior geometric performance in the range of a 0.38- (0.47 pixel) to 0.75-meter (m; 0.93 pixel) root mean square error in easting and a 0.27- (0.33 pixel) to 0.55-m (0.68 pixel) root mean square error in northing, in band-to-band registration; an exterior geometric performance in the range of 0.26 (0.32 pixel) to 1.04 m (1.28 pixels) offset in comparison to ground control points; a radiometric performance in the range of 0.033 to 0.797 (linear regression); and a spatial performance in the range of 3.7 to 4.3 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.004 to 0.009.

Open-File Report↗
USGS2010

A portfolio approach to evaluating natural hazard mitigation policies: An application to lateral-spread ground failure in coastal California

In the past, efforts to prevent catastrophic losses from natural hazards have largely been undertaken by individual property owners based on site—specific evaluations of risks to particular buildings. Public efforts to assess community vulnerability and encourage mitigation have focused on either aggregating site—specific estimates or adopting standards based upon broad assumptions about regional risks. This paper develops an alternative, intermediate—scale approach to regional risk assessment and the evaluation of community mitigation policies. Properties are grouped into types with similar land uses and levels of hazard, and hypothetical community mitigation strategies for protecting these properties are modeled like investment portfolios. The portfolios consist of investments in mitigation against the risk to a community posed by a specific natural hazard. and are defined by a community's mitigation budget and the proportion of the budget invested in locations of each type. The usefulness of this approach is demonstrated through an integrated assessment of earthquake—induced lateral—spread ground failure risk in the Watsonville, California area. Data from the magnitude 6.9 Loma Prieta earthquake of 1989 are used to model lateral—spread ground failure susceptibility. Earth science and economic data are combined and analyzed in a Geographic Information System (CIS). The portfolio model is then used to evaluate the benefits of mitigating the risk in different locations. Two mitigation policies, one that prioritizes mitigation by land use type and the other by hazard zone, are compared with a status quo policy of doing no further mitigation beyond that which already exists. The portfolio representing the hazard zone rule yields a higher expected return than the land use portfolio does; however, the hazard zone portfolio experiences a higher standard deviation. Therefore, neither portfolio is clearly preferred. The two mitigation policies both reduce expected losses and increase overall expected community wealth compared to the status quo policy.

California↗
USGS2024

System characterization report on the Gaofen-6

Executive Summary Gaofen-6 represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellites in this series is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium.” The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances of Gaofen-6. Results of these analyses indicate that Gaofen-6 has an interior geometric performance root mean square error ranging from 2.84 meters (m; 0.18 pixel) to 7.42 m (0.46 pixel) in easting and from 2.84 m (0.18 pixel) to 11.57 m (0.72 pixel) in northing in band-to-band registration, an exterior geometric performance root mean square error ranging from 154.50 m (8.80 pixels) in easting to 14.65 m (0.80 pixel) in northing in comparison to a corresponding Sentinel-2 scene, a radiometric performance ranging from 0.018 to 0.055 (in offset) and from 0.620 to 0.858 (in slope), and a spatial performance ranging from 2.10 to 2.30 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency ranging from 0.040 to 0.055.

Open-File Report↗