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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 613 records · Page 34Linked to original sources
USGS2008

Data management and digital delivery of analog data

The U.S. Geological Survey's (USGS) data archive at the Earth Resources Observation and Science (EROS) Center is a comprehensive and impartial record of the Earth's changing land surface. USGS/EROS has been archiving and preserving land remote sensing data for over 35 years. This remote sensing archive continues to grow as aircraft and satellites acquire more imagery. As a world leader in preserving data, USGS/EROS has a reputation as a technological innovator in solving challenges and ensuring that access to these collections is available. Other agencies also call on the USGS to consider their collections for long-term archive support. To improve access to the USGS film archive, each frame on every roll of film is being digitized by automated high performance digital camera systems. The system robotically captures a digital image from each film frame for the creation of browse and medium resolution image files. Single frame metadata records are also created to improve access that otherwise involves interpreting flight indexes. USGS/EROS is responsible for over 8.6 million frames of aerial photographs and 27.7 million satellite images.

Conference Paper↗
USGS2009

USGS field activity 08FSH01 on the west Florida shelf, Gulf of Mexico, in August 2008

From August 11 to 15, 2008, a cruise led by the U.S. Geological Survey (USGS) collected air and sea surface partial pressure of carbon dioxide (pCO2), pH, dissolved inorganic carbon (DIC), and total alkalinity (TA) data on the west Florida shelf. Approximately 1,600 data points were collected underway over a 650-kilometer (km) trackline using the Multiparameter Inorganic Carbon Analyzer (MICA). The collection of data extended from Crystal River southward to Marco Island, Florida (~400 km), and westward up to 160 km off the Florida coast. Discrete water samples from approximately 40 locations were also taken at specific localities to corroborate underway data measurements. The USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, 08FSH01 tells us the data were collected in 2008 for the Response of Florida Shelf (FSH) Ecosystems to Climate Change project, and the data were collected during the first field activity for that study in that calendar year.

Florida↗
USGS2020

Land change monitoring, assessment, and projection

There is a pressing need to monitor and understand the rapid land change happening around the world. The U.S. Geological Survey is developing a new capability, called Land Change Monitoring, Assessment, and Projection (LCMAP), to innovate the understanding of land change. This capability is the Earth Resources Observation and Science Center's foundation for an integrated U.S. Geological Survey-wide land change science framework. LCMAP supports the development of consistent data and land cover products spanning large geographic extents, over extended periods, and at a higher frequency than in the past. LCMAP provides solutions to the science and management communities’ growing need for an improved understanding of the fundamental drivers of land change, the consequences of change in human and natural systems, and feedbacks associated with land change processes.

Fact Sheet↗
USGS2013

Streamflow, groundwater, and water-quality monitoring by USGS Nevada Water Science Center

The U.S. Geological Survey (USGS) has monitored and assessed the quantity and quality of our Nation's streams and aquifers since its inception in 1879. Today, the USGS provides hydrologic information to aid in the evaluation of the availability and suitability of water for public and domestic supply, agriculture, aquatic ecosystems, mining, and energy development. Although the USGS has no responsibility for the regulation of water resources, the USGS hydrologic data complement much of the data collected by state, county, and municipal agencies, tribal nations, U.S. District Court Water Masters, and other federal agencies such as the Environmental Protection Agency, which focuses on monitoring for regulatory compliance. The USGS continues its mission to provide timely and relevant water-resources data and information that are available to water-resource managers, non-profit organizations, industry, academia, and the public. Data collected by the USGS provide the science needed for informed decision-making related to resource management and restoration, assessment of flood and drought hazards, ecosystem health, and effects on water resources from land-use changes.

Nevada↗
USGS2008

Wyoming Landscape Conservation Initiative Science Workshop Proceedings, May 15-17, 2007

The U.S. Geological Survey (USGS) hosted a Wyoming Landscape Conservation Initiative (WLCI) Science Workshop at the University of Wyoming on May 15, 16, and 17, 2007. The goal of the workshop was to gather information from stakeholders about research needs and existing data resources to help develop the USGS WLCI science plan. The workshop focused on six research and management needs identified by WLCI partners prior to the workshop: *evaluate the cumulative effects of development activities; *identify key drivers of change; *identify condition and distribution of key wildlife species, habitat, and species habitat requirements; *evaluate wildlife and livestock responses to development; *develop an integrated inventory and monitoring strategy; and *develop a data clearinghouse and an information-management framework. These topics correlated to six plenary panels and discussions and six breakout sessions. Several collective needs were identified: *create a long-term, accessible information database; *identify key habitats, indicator species; *collect and research missing critical baseline data; *begin on-the-ground projects as soon as possible; and *implement a monitoring program to assist with adaptive management techniques. Several concerns were expressed repeatedly: *secure adequate and long-term funding; *meeting the WLCI workload with agencies that are already understaffed; *assess cumulative effects as an analysis approach; *perform offsite mitigation in a way that is valuable and effective; *focus all research on providing practical applications; and *involve the public in WLCI proceedings.

Scientific Investigations Report↗
USGS2022

Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data

The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random. In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, Desmognathus quadramaculatus (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters. We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.

Alabama, Georgia, North Carolina, Tennessee, Virgi↗
USGS2008

Information science and technology developments within the National Biological Information Infrastructure

Whether your vantage point is that of an office window or a national park, your view undoubtedly encompasses a rich diversity of life forms, all carefully studied or managed by some scientist, resource manager, or planner. A few simple calculations-the number of species, their interrelationships, and the many researchers studying them-and you can easily see the tremendous challenges that the resulting biological data presents to the information and computer science communities. Biological information varies in format and content: it may pertain to a particular species or an entire ecosystem; it can contain land use characteristics, and geospatially referenced information. The complexity and uniqueness of each individual species or ecosystem do not easily lend themselves to today's computer science tools and applications. To address the challenges that the biological enterprise presents, the National Biological Information Infrastructure (NBII) ( http://www.nbii.gov ) was established in 1993 on the recommendation of the National Research Council (National Research Council 1993). The NBII is designed to address these issues on a national scale, and through international partnerships. This paper discusses current information and computer science efforts within the National Biological Information Infrastructure Program, and future computer science research endeavors that are needed to address the ever-growing issues related to our nation's biological concerns.

Science and Technology Libraries↗
USGS2009

USGS field activity 09FSH01 on the west Florida shelf, Gulf of Mexico, in February 2009

From February 24 to 28, 2009, a cruise led by the U.S. Geological Survey (USGS) collected air and sea surface partial pressure of carbon dioxide (pCO2), pH, dissolved inorganic carbon (DIC), and total alkalinity (TA) data on the west Florida shelf. Approximately 1,800 data points were collected underway over a 1,300-kilometer (km) trackline using the Multiparameter Inorganic Carbon Analyzer (MICA). The collection of data extended from Crystal River to Marco Island, Florida (~400 km), and westward up to 160 km off the Florida coast. Discrete water samples were also taken at specific localities to corroborate underway data measurements. The USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, 09FSH01 tells us that the data were collected in 2009 for the Response of Florida Shelf (FSH) Ecosystems to Climate Change project, and the data were collected during the first field activity for that study in that calendar year.

Florida↗
USGS2009

USGS field activity 09FSH02 on the west Florida shelf, Gulf of Mexico, in August 2009

From August 17 to 21, 2009, a cruise led by the U.S. Geological Survey (USGS) collected air and sea surface partial pressure of carbon dioxide (pCO2), pH, dissolved inorganic carbon (DIC), and total alkalinity (TA) data on the west Florida shelf. Approximately 2,000 data points were collected underway over a 1,320-kilometer (km) track line using the Multiparameter Inorganic Carbon Analyzer (MICA). The collection of data extended from Crystal River to Marco Island, Florida (~400 km), and westward up to 160 km off the Florida coast. Discrete water samples were also taken at specific localities to corroborate underway data measurements. The USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, 09FSH02 tells us that the data were collected in 2009 for the Response of Florida Shelf (FSH) Ecosystems to Climate Change project, and the data were collected during the second field activity for that study in that calendar year.

Florida↗
USGS2006

Enabling scientific workflows in virtual reality

To advance research and improve the scientific return on data collection and interpretation efforts in the geosciences, we have developed methods of interactive visualization, with a special focus on immersive virtual reality (VR) environments. Earth sciences employ a strongly visual approach to the measurement and analysis of geologic data due to the spatial and temporal scales over which such data ranges, As observations and simulations increase in size and complexity, the Earth sciences are challenged to manage and interpret increasing amounts of data. Reaping the full intellectual benefits of immersive VR requires us to tailor exploratory approaches to scientific problems. These applications build on the visualization method's strengths, using both 3D perception and interaction with data and models, to take advantage of the skills and training of the geological scientists exploring their data in the VR environment. This interactive approach has enabled us to develop a suite of tools that are adaptable to a range of problems in the geosciences and beyond. Copyright ?? 2008 by the Association for Computing Machinery, Inc.

Conference Paper↗
USGS2009

Comparison of Surface Flow Features from Lidar-Derived Digital Elevation Models with Historical Elevation and Hydrography Data for Minnehaha County, South Dakota

The U.S. Geological Survey (USGS) has taken the lead in the creation of a valuable remote sensing product by incorporating digital elevation models (DEMs) derived from Light Detection and Ranging (lidar) into the National Elevation Dataset (NED), the elevation layer of 'The National Map'. High-resolution lidar-derived DEMs provide the accuracy needed to systematically quantify and fully integrate surface flow including flow direction, flow accumulation, sinks, slope, and a dense drainage network. In 2008, 1-meter resolution lidar data were acquired in Minnehaha County, South Dakota. The acquisition was a collaborative effort between Minnehaha County, the city of Sioux Falls, and the USGS Earth Resources Observation and Science (EROS) Center. With the newly acquired lidar data, USGS scientists generated high-resolution DEMs and surface flow features. This report compares lidar-derived surface flow features in Minnehaha County to 30- and 10-meter elevation data previously incorporated in the NED and ancillary hydrography datasets. Surface flow features generated from lidar-derived DEMs are consistently integrated with elevation and are important in understanding surface-water movement to better detect surface-water runoff, flood inundation, and erosion. Many topographic and hydrologic applications will benefit from the increased availability of accurate, high-quality, and high-resolution surface-water data. The remotely sensed data provide topographic information and data integration capabilities needed for meeting current and future human and environmental needs.

Scientific Investigations Report↗
USGS2010

The ASTER data system: An overview of the data products in Japan and in the United States

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data system is a cooperative system, which is operated jointly by Japan’s Ministry of Economy, Trade, and Industry (METI) through its Earth Remote Sensing Data Analysis Center (ERSDAC), and by the National Aeronautics and Space Administration (NASA) primarily through its Goddard Space Flight Center (GSFC) and Land Processes (LP) Distributed Active Archive Center (DAAC). ASTER is a moderate-resolution land remote sensing system onboard the Earth Observing System (EOS) Terra spacecraft. ASTER-acquired data are received at the White Sands, New Mexico, ground receiving station, and then transmitted via land network to the EOS Data and Operations System (EDOS) within the Goddard DAAC, located at the GSFC. EDOS pre-processes raw ASTER data to Level-0 (L0) data, and sends them via the high-speed Asia-Pacific Advanced Network (APAN) to the ASTER Ground Data System (GDS) in Japan. ASTER GDS processes the L0 data to level-1 (L1) datasets; they distribute these data to users, and also use them to generate higher-level products for their user community. ASTER GDS sends a copy of all L1A data they produce to NASA’s LP DAAC, located at the U.S. Geological Survey’s Center for Earth Resources Observation and Science (EROS) near Sioux Falls, South Dakota. All L1 data received from Japan are ingested, archived, and available for users at LP DAAC. The LP DAAC also generates and distributes higher-level products from L1 data based on requests from users. To meet time-critical needs related to sensor health and performance, natural disasters, national emergencies, and certain field campaigns, the ASTER Expedited Data System (EDS) was developed, and is operated jointly by U.S. and Japanese partners.

Book chapter↗
USGS2002

Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data

Accurate, current, and cost-effective fire fuel data are required by management and fire science communities for use in reducing wildland fire hazards over large areas. In this paper we present results of applying decision-tree techniques to mapping vegetation parameters (such as vegetation types and canopy structure classification) required for fire fuel characterization. Specifically, we present preliminary results of mapping forest types and average basal area by different forest types at 30-meter resolution. Input data into the decision tree model included Landsat-7 ETM+ spring, summer and fall greenness, brightness and wetness of the tasseled cap transformation, topographic data layers such as slope and elevation, and forest variables measured on inventory plots in the Mid-Atlantic region. Using decision-tree models, eight forest types were successfully identified in training cases and mapped for the entire mapping area. Forest basal area per unit area (conifer and deciduous) was estimated as well using regression tree models. Cross-validation conducted for both forest types and basal area showed that discrete forest type estimation error was 35% and continuous basal area relative errors were between 58 and 72%. Accuracy was higher in homogeneous forested lands and lower in areas with fragmented forest cover. The study demonstrated that decision tree and regression tree methods are efficient for large-area vegetation mapping if sufficient large-amount of reference data are available.

Conference Paper↗
USGS2025

System characterization report on the Environmental Mapping and Analysis Program (EnMAP)

This report addresses system characterization of the Environmental Mapping and Analysis Program hyperspectral sensor by the DLR (German Aerospace Center, ground segment project management), GFZ (Deutsches Geoforschungszentrum, science lead) 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 EnMAP hyperspectral sensor; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), and radiometric performances of the EnMAP hyperspectral sensor. Results of these analyses indicate that the Environmental Mapping and Analysis Program has a band-to-band geometric performance in the range of −0.135 to 0.15 pixel, geometric performance relative to the Operational Land Imager in the range of −27.716 meters (−0.92 pixel) to 32.892 meters (1.09 pixels) offset in comparison to Landsat 8 Operational Land Imager, offset of a radiometric comparison in the range of −0.012 to 0.020, slope of a radiometric comparison in the range of 0.947 to 1.031.

Open-File Report↗
USGS2019

Bayesian statistics for beginners: A step-by-step approach

Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources. Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields.

Book↗
USGS2016

Identifying and preserving high-water mark data

High-water marks provide valuable data for understanding recent and historical flood events. The proper collection and recording of high-water mark data from perishable and preserved evidence informs flood assessments, research, and water resource management. Given the high cost of flooding in developed areas, experienced hydrographers, using the best available techniques, can contribute high-quality data toward efforts such as public education of flood risk, flood inundation mapping, flood frequency computations, indirect streamflow measurement, and hazard assessments. This manual presents guidance for skilled high-water mark identification, including marks left behind in natural and man-made environments by tranquil and rapid flowing water. This manual also presents pitfalls and challenges associated with various types of flood evidence that help hydrographers identify the best high-water marks and assess the uncertainty associated with a given mark. Proficient high-water mark data collection contributes to better understanding of the flooding process and reduces risk through greater ability to estimate flood probability. The U.S. Geological Survey, operating the Nation’s premier water data collection network, encourages readers of this manual to familiarize themselves with the art and science of high-water mark collection. The U.S. Geological survey maintains a national database at http://water.usgs.gov/floods/FEV/ that includes high-water mark information for many flood events, and local U.S. Geological Survey Water Science Centers can provide information to interested readers about participation in data collection and flood documentation efforts as volunteers or observers.

Techniques and Methods↗
USGS2022

System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA)

Executive Summary This report addresses system characterization of the Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) 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. The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (band to band and image to image), radiometric, and spatial performances. Results of these analyses indicate that PRISMA has a band-to-band geometric performance in the range of −0.046 to 0.040 pixel; an image-to-image geometric performance (relative to the Landsat 8 Operational Land Imager) in the range of −60.791 meters (m; −2.03 pixels) to 299.541 m (9.98 pixels); a radiometric performance in the range of −0.037 to −0.001 in offset and 1.026 to 1.274 in slope; and a spatial performance with a relative edge response in the range of 0.56 to 0.63, full width at half maximum in the range of 1.84 to 1.97 pixels, and a modulation transfer function at a Nyquist frequency in the range of 0.054 to 0.096. Regarding fairly large geometric accuracy, the following explanation is provided to help the reader. The geometric accuracy required for PRISMA is a 200-m circular error at 90 percent (CE90) without ground control points (GCPs), a 15-m CE90 using GCPs is documented in the PRISMA mission overview (Agenzia Spaziale Italiana, 2021). The PRISMA images used for the current system characterization were georeferenced without using any GCPs; thus, the 200-m geometric accuracy requirement is applied. Beginning in 2022, a worldwide GCP database will be used in the PRISMA product processing chain, which will improve georeferencing accuracy to meet the 15-m CE90 requirement.

Open-File Report↗
USGS2024

System characterization report on the Earth Surface Mineral Dust Source Investigation (EMIT) sensor

Executive Summary This report addresses system characterization of the Earth Surface Mineral Dust Source Investigation (EMIT) sensor, an imaging spectrometer developed by the National Aeronautics and Space Administration. This report 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. The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior) and radiometric performances. Results of these analyses indicate that the EMIT sensor has a band-to-band geometric performance in the range of −0.355 to 0.210 pixel with a few exceptions of shortwave infrared channels. Geometric offset relative to the Landsat 8 Operational Land Imager ranged from −15.966 meters (−0.266 pixel) to 43.844 meters (0.731 pixel). Offset of a radiometric comparison ranged from −0.016 to 0.025, and slope of a radiometric comparison ranged from 0.837 to 0.985. EMIT agreed with Radiometric Calibration Network measurements within 5 percent across most of the spectral channels.

Open-File Report↗