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At least 973 records · Page 54Linked to original sources
USGS2000

U.S. Geological Survey, remote sensing, and geoscience data: Using standards to serve us all

The U.S. Geological Survey (USGS) advocates the use of standards with geosciences and remotely sensed data and metadata for its own purposes and those of its customers. In activities that range from archiving data to making a product, the incorporation of standards makes these functions repeatable and understandable. More important, when accepted standards are followed, data discovery and sharing can be more efficient and the overall value to society can be expanded. The USGS archives many terabytes of digital geoscience and remotely sensed data. Several million photographs are also available to the research community. To manage these vast holdings and ensure that strict preservation and high usability criteria are observed, the USGS uses standards within the archival, data management, public access and ordering, and data distribution areas. The USGS uses Federal and international standards in performing its role as the U.S. National Satellite Land Remote Sensing Data Archive and in its mission as the long-term archive and production center for aerial photographs and cartographic data covering the United States.

Conference Paper↗
USGS2023

Reanalysis ignores pertinent data, includes inappropriate observations, and disregards realities of applied ecology: Response to Huso and Dalthorp (2023)

1) We recently demonstrated efficacy of automated curtailment of wind turbines in reducing fatalities of eagles at a study site in Wyoming, USA. Huso and Dalthorp criticize our work, asserting that there are several ‘major errors’ that render our previous work as providing ‘no meaningful support’ for automated curtailment. As we show here, our data do indeed provide support for the efficacy of automated curtailment. 2) The purported major errors they identify include: having only 1 year of post-treatment data, having only one control and one treatment site, using the control site for inference, choosing an arbitrary demarcation date between the ‘before’ and ‘after’ periods, analysing the number, rather than the rate, of fatalities, and not including data they consider pertinent. These claims are unsupported because all result from misreading our study, ignoring realities of causal inference in field ecology, or are matters of preference. 3) Most importantly, we did not fail to include pertinent data and we provided sufficient criteria in the manuscript to explain inclusion of carcasses in our study. As stated in our previous study, the eagle carcasses we did not include were found either outside of search areas or incidentally, and thus did not meet criteria for inclusion. Further, Huso and Dalthorp present a standard for spatial and temporal replication that is inconsistent with their own recent work and many other studies in this field. Finally, their reanalysis of our data ignores the control site while including inappropriate data and thus is not suitable for inference. 4) We appreciate the unusually thorough critique of our work provided by Huso and Dalthorp. Despite the major errors in their argument, this discussion provides a platform for further evaluation of our original work. 5) Synthesis and applications . We show that incorporating control site data and using our a priori demarcation of before and after periods into Huso and Dalthorp's reanalysis returns meaningful support for automated curtailment. We also direct the reader to an updated analysis that uses components of the approach Huso and Dalthorp suggest, along with additional data and a number of other analytical improvements, to validate and strengthen our original conclusions.

Journal of Applied Ecology↗
USGS2025

Simulated soundscapes and transfer learning boost the performance of acoustic classifiers under data scarcity

1. The biodiversity crisis necessitates spatially extensive methods to monitor multiple taxonomic groups for evidence of change in response to evolving environmental conditions. Programs that combine passive acoustic monitoring and machine learning are increasingly used to meet this need. These methods require large, annotated datasets, which are time-consuming and expensive to produce, creating potential barriers to adoption in data- and funding-poor regions. Recently released pre-trained avian acoustic classification models provide opportunities to reduce the need for manual labelling and accelerate the development of new acoustic classification algorithms through transfer learning. Transfer learning is a strategy for developing algorithms under data scarcity that uses pre-trained models from related tasks to adapt to new tasks. 2. Our primary objective was to develop a transfer learning strategy using the feature embeddings of a pre-trained avian classification model to train custom acoustic classification models in data-scarce contexts. We used three annotated avian acoustic datasets to test whether transfer learning and soundscape simulation-based data augmentation could substantially reduce the annotated training data necessary to develop performant custom acoustic classifiers. We also conducted a sensitivity analysis for hyperparameter choice and model architecture. We then assessed the generalizability of our strategy to increasingly novel non-avian classification tasks. 3. With as few as two training examples per class, our soundscape simulation data augmentation approach consistently yielded new classifiers with improved performance relative to the pre-trained classification model and transfer learning classifiers trained with other augmentation approaches. Performance increases were evident for three avian test datasets, including single-class and multi-label contexts. We observed that the relative performance among our data augmentation approaches varied for the avian datasets and nearly converged for one dataset when we included more training examples. 4. We demonstrate an efficient approach to developing new acoustic classifiers leveraging open-source sound repositories and pre-trained networks to reduce manual labelling. With very few examples, our soundscape simulation approach to data augmentation yielded classifiers with performance equivalent to those trained with many more examples, showing it is possible to reduce manual label-ling while still achieving high-performance classifiers and, in turn, expanding the potential for passive acoustic monitoring to address rising biodiversity monitoring needs.

Methods in Ecology and Evolution↗
USGS2025

Integrated species distribution model using historical data shows decline in a common semi-aquatic mammal

Effective conservation requires an understanding of drivers of a species' distribution as well as long-term changes in their distribution. In recent decades, advances in data collection and analysis have allowed researchers to integrate a wide range of information to model species distributions, particularly by allowing presence-only data and detection-nondetection data to be formally combined in integrated species distribution models (ISDMs). However, these models are rarely used to investigate long-term trends, which are important in evaluating a species' status. Here, we use historical presence-only data of river otters ( Lontra canadensis ; 366 latrine locations from 1999 to 2007 and 105 locations of road-killed individuals recorded from 1999 to 2020) and 919 detection-nondetection surveys from 230 sites between 2021 and 2023 to understand the current distribution of river otters in Rhode Island, USA, as well as the changes in river otter distribution over the past two decades. We found that river otters were strongly associated with key habitat features such as streams and water, positively associated with urban areas, and tolerant of some contaminants, such as lead. Furthermore, despite uncertainties in historical river otter occurrence, we found clear supporting evidence that river otter intensity of use had declined from 1999 to 2023. This decline occurred despite being protected from harvest and in contrast to range expansions in other parts of the northeastern USA throughout the second half of the 20th century. Our results suggest the utility of this approach to detect declines in species for which historical data are available and a need for better understanding the cause of river otter declines. Where monitoring consists of opportunistically collected data, species conservation could benefit by continuing to collect these data as well as introducing designed surveys, as this would allow better integration of data types, improving trend estimation and reducing the amount of (typically more expensive) designed surveys needed.

Rhode Island↗
USGS2024

Trends in plant cover derived from vegetation plot data using ordinal zero-augmented beta regression

Questions Plant cover values in vegetation plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non-equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is whether we can detect species trends in vegetation plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners. Location The Netherlands, Western Europe. Methods We used vegetation plot data collected from 10,000 fixed plots which were surveyed once every four years during 1999–2022. We used the ordinal zero-augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four-year period. Thereafter we assessed trends in cover and in occurrence across all periods. Results We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence. Conclusions Our model enables researchers to detect trends in cover using longitudinal vegetation plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring data sets.

Journal of Vegetation Science↗
USGS2015

Assessing accuracy and precision for field and laboratory data: a perspective in ecosystem restoration

Unlike most laboratory studies, rigorous quality assurance/quality control (QA/QC) procedures may be lacking in ecosystem restoration (“ecorestoration”) projects, despite legislative mandates in the United States. This is due, in part, to ecorestoration specialists making the false assumption that some types of data (e.g. discrete variables such as species identification and abundance classes) are not subject to evaluations of data quality. Moreover, emergent behavior manifested by complex, adapting, and nonlinear organizations responsible for monitoring the success of ecorestoration projects tend to unconsciously minimize disorder, QA/QC being an activity perceived as creating disorder. We discuss similarities and differences in assessing precision and accuracy for field and laboratory data. Although the concepts for assessing precision and accuracy of ecorestoration field data are conceptually the same as laboratory data, the manner in which these data quality attributes are assessed is different. From a sample analysis perspective, a field crew is comparable to a laboratory instrument that requires regular “recalibration,” with results obtained by experts at the same plot treated as laboratory calibration standards. Unlike laboratory standards and reference materials, the “true” value for many field variables is commonly unknown. In the laboratory, specific QA/QC samples assess error for each aspect of the measurement process, whereas field revisits assess precision and accuracy of the entire data collection process following initial calibration. Rigorous QA/QC data in an ecorestoration project are essential for evaluating the success of a project, and they provide the only objective “legacy” of the dataset for potential legal challenges and future uses.

Restoration Ecology↗
USGS2002

Geographic information systems compilation of geophysical, geologic, and tectonic data for the Bering Shelf, Chukchi Sea, Arctic margin, and adjacent landmasses

The accompanying CD-ROM contains a compilation of geophysical, geologic, and tectonic data for the Bering Shelf, the Chukchi Sea, the Arctic margin, and adjacent landmasses. These data sets focus on Alaska, the Russian Far East, and the continental shelves that link these two landmasses. For compatibility with other available geographic information system (GIS) products, our GIS compilation extends from 120°E to 115°W, and from 40°N to 80°N. This area encompasses the region from the modern Paciµc plate boundary of the Japan, Kurile, and Aleutian subduction zones, the Queen Charlotte transform fault, and the Cascadia subduction zone (in the south) to the continent-ocean transition from the Eurasian and North American continents to the Arctic Ocean (in the north); and from the diffuse Eurasian–North American plate boundary, including the probable Okhotsk plate (in the west) to the Alaskan-Canadian Cordilleran fold belt (in the east). The CD-ROM comprises thematic layers of spatial data sets for topography, gravity µeld, magnetic µeld, earthquakes, volcanoes, geology, tectonostratigraphic terranes, and cultural reference features, and also includes metadata (data about the data) for all these data sets. The spatial data sets can be viewed, analyzed, and plotted with commercial GIS software (ArcView and ARC/Info) or through a freeware program (ArcExplorer) that is included on this CD-ROM. This GIS compilation provides data for studies of the Mesozoic and Cenozoic collisional and accretionary tectonics that assembled this continental crust and of the neotectonics of active and passive plate margins in this region, and for constructing and interpreting geophysical, geologic, and tectonic models of the region.

Alaska↗
USGS1981

The National Coal Resources Data System: A status report

The National Coal Resources Data System (NCRDS) of the U.S. Geological Survey is an interactive computerized storage, retrieval, and display system to assess the quantity and quality of the nation's coal resources. It has been developed to provide geological coal-resource data currently available, to update that data, and to expand to new types of data. To this end the U.S. Geological Survey has initiated a 5- to 10-yr program to acquire point-source data for all coal-bearing regions through its own programs and cooperative projects with state agencies. Presently, files containing summary areal coal-tonnage estimates and proximate/ultimate chemical analyses, as well as point-located major-, minor-, and trace-element analyses, and stratigraphic data are available. New resource assessments can be made with point-source data, including drill-hole records, field measurements, and so on. Data may be displayed in literal form or graphically — for example, tables, isoline maps, and cross sections. The system software can calculate coal-resource estimates, generate overburden or interburden distribution, and delineate areas of coal with selected parameters (for example, < .3% sulfur, > 28 in.) within specified boundaries (for example, quadrangle, county).

Geological Society of America Bulletin↗
USGS2022

A novel approach for directly incorporating disease into fish stock assessment: A case study with seroprevalence data

When estimating mortality from disease with fish population models, common disease surveillance data such as infection prevalence are not always informative, especially for fast-acting diseases that may go unobserved in infrequently sampled populations. In these cases, seroprevalence — the proportion of fish with measurable antibody levels in their blood — may be more informative. In cases of life-long immunity, seroprevalence data require less frequent sampling intervals than infection prevalence data and can reflect the cumulative exposure history of fish. We simulation tested the usefulness of seroprevalence data in an age-structured fish stock assessment model using viral hemorrhagic septicemia virus (VHSV) in Pacific herring ( Clupea pallasii ) as a case study. We developed a novel epidemiological model to simulate population dynamics and seroprevalence data and fitted to these data in an integrated catch-at-age model with equations that estimate age- and time-varying mortality from disease. We found that simulated seroprevalence data can provide accurate estimates of infection history and disease-associated mortality. Importantly, even models that misspecified nonstationary processes in background or disease-associated mortality, but included seroprevalence data, accurately estimated annual infection and population abundance.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2016

TopoLens: Building a cyberGIS community data service for enhancing the usability of high-resolution National Topographic datasets

Geospatial data, often embedded with geographic references, are important to many application and science domains, and represent a major type of big data. The increased volume and diversity of geospatial data have caused serious usability issues for researchers in various scientific domains, which call for innovative cyberGIS solutions. To address these issues, this paper describes a cyberGIS community data service framework to facilitate geospatial big data access, processing, and sharing based on a hybrid supercomputer architecture. Through the collaboration between the CyberGIS Center at the University of Illinois at Urbana-Champaign (UIUC) and the U.S. Geological Survey (USGS), a community data service for accessing, customizing, and sharing digital elevation model (DEM) and its derived datasets from the 10-meter national elevation dataset, namely TopoLens, is created to demonstrate the workflow integration of geospatial big data sources, computation, analysis needed for customizing the original dataset for end user needs, and a friendly online user environment. TopoLens provides online access to precomputed and on-demand computed high-resolution elevation data by exploiting the ROGER supercomputer. The usability of this prototype service has been acknowledged in community evaluation.

Conference Paper↗
USGS2025

Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024

Microbiome research is revolutionizing human and environmental health, but the value and reuse of microbiome data are significantly hampered by the limited development and adoption of data standards. While several ongoing efforts are aimed at improving microbiome data management, significant gaps still remain in terms of defining and promoting adoption of consensus standards for these datasets. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines for human microbiome research have been endorsed and successfully utilized by many research organizations, publishers, and funding agencies, and have been recognized as a consensus community standard. No equivalent effort has occurred for environmental, synthetic, and non-human host-associated microbiomes. To address this growing need within the microbiome research community, we convened the Microbiome Data Management in Action Workshop (June 12–13, 2024, in Atlanta, GA, USA), to bring together key decision makers in microbiome science including researchers, publishers, funders, and data repositories. The 50 attendees, representing the diverse and interdisciplinary nature of microbiome research, discussed recent progress and challenges, and brainstormed actionable recommendations and paths forward for coordinated environmental microbiome data management and the modifications necessary for the STORMS guidelines to be applied to environmental, non-human host, and synthetic microbiomes. The outcomes of this workshop will form the basis of a formalized data management roadmap to be implemented across the field. These best practices will drive scientific innovation now and in years to come as these data continue to be used not only in targeted reanalyses but in large-scale models and machine learning efforts.

Environmental Microbiome↗
USGS2000

Fire frequency in the Interior Columbia River Basin: Building regional models from fire history data

Fire frequency affects vegetation composition and successional pathways; thus it is essential to understand fire regimes in order to manage natural resources at broad spatial scales. Fire history data are lacking for many regions for which fire management decisions are being made, so models are needed to estimate past fire frequency where local data are not yet available. We developed multiple regression models and tree-based (classification and regression tree, or CART) models to predict fire return intervals across the interior Columbia River basin at 1-km resolution, using georeferenced fire history, potential vegetation, cover type, and precipitation databases. The models combined semiqualitative methods and rigorous statistics. The fire history data are of uneven quality; some estimates are based on only one tree, and many are not cross-dated. Therefore, we weighted the models based on data quality and performed a sensitivity analysis of the effects on the models of estimation errors that are due to lack of cross-dating. The regression models predict fire return intervals from 1 to 375 yr for forested areas, whereas the tree-based models predict a range of 8 to 150 yr. Both types of models predict latitudinal and elevational gradients of increasing fire return intervals. Examination of regional-scale output suggests that, although the tree-based models explain more of the variation in the original data, the regression models are less likely to produce extrapolation errors. Thus, the models serve complementary purposes in elucidating the relationships among fire frequency, the predictor variables, and spatial scale. The models can provide local managers with quantitative information and provide data to initialize coarse-scale fire-effects models, although predictions for individual sites should be treated with caution because of the varying quality and uneven spatial coverage of the fire history database. The models also demonstrate the integration of qualitative and quantitative methods when requisite data for fully quantitative models are unavailable. They can be tested by comparing new, independent fire history reconstructions against their predictions and can be continually updated, as better fire history data become available.

California, Idaho, Montana, Nevada, Oregon, Utah, ↗
USGS2012

Estimating survival rates with time series of standing age‐structure data

It has long been recognized that age‐structure data contain useful information for assessing the status and dynamics of wildlife populations. For example, age‐specific survival rates can be estimated with just a single sample from the age distribution of a stable, stationary population. For a population that is not stable, age‐specific survival rates can be estimated using techniques such as inverse methods that combine time series of age‐structure data with other demographic data. However, estimation of survival rates using these methods typically requires numerical optimization, a relatively long time series of data, and smoothing or other constraints to provide useful estimates. We developed general models for possibly unstable populations that combine time series of age‐structure data with other demographic data to provide explicit maximum likelihood estimators of age‐specific survival rates with as few as two years of data. As an example, we applied these methods to estimate survival rates for female bison ( Bison bison ) in Yellowstone National Park, USA. This approach provides a simple tool for monitoring survival rates based on age‐structure data.

Ecology↗
USGS2015

Groundwater data network interoperability

Water data networks are increasingly being integrated to answer complex scientific questions that often span large geographical areas and cross political borders. Data heterogeneity is a major obstacle that impedes interoperability within and between such networks. It is resolved here for groundwater data at five levels of interoperability, within a Spatial Data Infrastructure architecture. The result is a pair of distinct national groundwater data networks for the United States and Canada, and a combined data network in which they are interoperable. This combined data network enables, for the first time, transparent public access to harmonized groundwater data from both sides of the shared international border.

Journal of Hydroinformatics↗
USGS1995

Historical Landsat data comparisons: illustrations of the Earth's changing surface

The U.S. Geological Survey's (USGS) EROS Data Center (EDC) has managed the Landsat data archive for more than two decades. This archive provides a rich collection of information about the Earth's land surface. Major changes to the surface of the planet can be detected, measured, and analyzed using Landsat data. The effects of desertification, deforestation, pollution, cataclysmic volcanic activity, and other natural and anthropogenic events can be examined using data acquired from the Landsat series of Earth-observing satellites. The information obtainable from the historical and current Landsat data play a key role in studying surface changes through time. This document provides an overview of the Landsat program and illustrates the application of the data to monitor changes occurring on the surface of the Earth. To reveal changes that have taken place within the past 20 years, pairs and triplicates of images were constructed from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Landsat MSS data provide a historical record of the Earth's land surface from the early 1970's to the early 1990's. Landsat TM data provide land surface information from the early 1980's to the present.

Report↗
USGS1990

Historical Landsat data comparisons: illustrations of land surface change

The U.S. Geological Survey's (USGS) EROS Data Center (EDC) has archived two decades of Landsat data, providing a rich collection of information about the dynamics of the Earth's land surface. Major changes to the surface features of the planet can be detected, measured, and studied using Landsat data. The effects of desertification, deforestation, pollution, cataclysmic volcanic activity, and other natural and anthropogenic events can be examined by resource scientists using data acquired from the Landsat series of Earth-observing satellites. The availability of a nearly uninterrupted flow of information from the Landsats, in a consistent data format, gives researchers an important tool for studying surface changes over time. This booklet provides an overview of the Landsat program and shows the application of the data to monitor changes occurring on the surface of the Earth. To show changes that have taken place within the last 20 years or less, image pairs were constructed from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Landsat MSS data provide a historical global record of the land surface from the early 1970's to present. Landsat TM data provide land surface information from the early 1980's to present.

Report↗
USGS1997

Landsat Data

In the mid-1960's, the National Aeronautics and Space Administration (NASA) embarked on an initiative to develop and launch the first Earth monitoring satellite to meet the needs of resource managers and earth scientists. The U.S. Geological Survey (USGS) entered into a partnership with NASA in the early 1970?s to assume responsibility for archiving data and distributing data products. On July 23, 1972, NASA launched the first in a series of satellites designed to provide repetitive global coverage of the Earth?s land masses. Designated initially as the "Earth Resources Technology Satellite-A" ("ERTS-A"), it used a Nimbus-type platform that was modified to carry sensor systems and data relay equipment. When operational orbit was achieved, it was designated "ERTS-1." The satellite continued to function beyond its designed life expectancy of 1 year and finally ceased to operate on January 6, 1978, more than 5 years after its launch date. The second in this series of Earth resources satellites (designated ?ERTS-B?) was launched January 22, 1975. It was renamed "Landsat 2" by NASA, which also renamed "ERTS-1" as "Landsat 1." Three additional Landsats were launched in 1978, 1982, and 1984 (Landsats 3, 4, and 5 ). (See table 1). NASA was responsible for operating the program through the early 1980?s. In January 1983, operation of the Landsat system was transferred to the National Oceanic and Atmospheric Administration (NOAA). In October 1985, the Landsat system was commercialized and the Earth Observation Satellite Company, now Space Imaging EOSAT, assumed responsibility for its operation under contract to NOAA. Throughout these changes, the USGS EROS Data Center (EDC) retained primary responsibility as the Government archive of Landsat data. The Land Remote Sensing Policy Act of 1992 (Public Law 102-5555) officially authorized the National Satellite Land Remote Sensing Data Archive and assigned responsibility to the Department of the Interior. In addition to its Landsat data management responsibility, the EDC investigates new methods of characterizing and studying changes on the land surface with Landsat data.

Fact Sheet↗
USGS2006

Availability of Ground-Water Data for California, Water Year 2005

The U.S. Geological Survey, Water Resources, in cooperation with Federal, State, and local agencies, obtains a large amount of data pertaining to the ground-water resources of California each water year (October 1-September 30). These data constitute a valuable database for developing an improved understanding of the water resources of the State. This Fact Sheet serves as an index to ground-water data for water year 2005. The 2-page report contains a map of California showing the number of wells (by county) with available water-level and water-quality data for water year 2005 (fig. 2) and instructions for obtaining this and other ground-water information contained in the databases of the U.S. Geological Survey, California Water Science Center. From 1985 to 1993, data were published in the annual report 'Water Resources Data for California, Volume 5. Ground-Water Data'; prior to 1985, the data were published in U.S. Geological Survey Water-Supply Papers.

Fact Sheet↗