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At least 991 records · Page 55Linked to original sources

USGS2025

A scientific vision and roadmap for earthquake rupture forecast developments, a USGS perspective

We articulate a scientific vision and roadmap for the development of improved Earthquake Rupture Forecast models, which are one of the two main modeling components used in modern seismic hazard and risk analysis. One primary future objective is to provide fully time-dependent models that include both elastic rebound and spatiotemporal clustering nationwide, which is particularly important for shorter-term hazard and risk considerations (e.g., earthquake insurance products). We also discuss the importance and perennial challenges associated with quantifying epistemic uncertainties, including those associated with deformation-model slip rates, un-quantified sampling errors with respect to off-fault seismicity, and any spatial covariances. The need for more physics-based approaches is also emphasized, as is the benefit of adding model valuation (quantifying usefulness) to our verification and validation protocols. Given the multidisciplinary and system-level nature of this activity, modular design is critical. Future updates will also draw from best-available science by both the United States Geological Survey and the external community. The primary goal of this paper is to highlight plans that guide research and facilitate community engagement with model development, especially with respect to lowering the entry barrier for early career scientists and engineers. The paper is written so readers can focus on the sections that interest them most (see table of contents), with the Introduction and Discussion providing a stand-alone overview and summary.

Bulletin of the Seismological Society of America↗

USGS2004

Use of qualitative and quantitative information in neural networks for assessing agricultural chemical contamination of domestic wells

A neural network analysis of agrichemical occurrence in groundwater was conducted using data from a pilot study of 192 small-diameter drilled and driven wells and 115 dug and bored wells in Illinois, a regional reconnaissance network of 303 wells across 12 Midwestern states, and a study of 687 domestic wells across Iowa. Potential factors contributing to well contamination (e.g., depth to aquifer material, well depth, and distance to cropland) were investigated. These contributing factors were available in either numeric (actual or categorical) or descriptive (yes or no) format. A method was devised to use the numeric and descriptive values simultaneously. Training of the network was conducted using a standard backpropagation algorithm. Approximately 15% of the data was used for testing. Analysis indicated that training error was quite low for most data. Testing results indicated that it was possible to predict the contamination potential of a well with pesticides. However, predicting the actual level of contamination was more difficult. For pesticide occurrence in drilled and driven wells, the network predictions were good. The performance of the network was poorer for predicting nitrate occurrence in dug and bored wells. Although the data set for Iowa was large, the prediction ability of the trained network was poor, due to descriptive or categorical input parameters, compared with smaller data sets such as that for Illinois, which contained more numeric information.

Journal of Hydrologic Engineering↗

USGS1989

Spectral analysis and filtering techniques in digital spatial data processing

A filter toolbox has been developed at the EROS Data Center, US Geological Survey, for retrieving or removing specified frequency information from two-dimensional digital spatial data. This filter toolbox provides capabilities to compute the power spectrum of a given data and to design various filters in the frequency domain. Three types of filters are available in the toolbox: point filter, line filter, and area filter. Both the point and line filters employ Gaussian-type notch filters, and the area filter includes the capabilities to perform high-pass, band-pass, low-pass, and wedge filtering techniques. These filters are applied for analyzing satellite multispectral scanner data, airborne visible and infrared imaging spectrometer (AVIRIS) data, gravity data, and the digital elevation models (DEM) data. -from Author

Photogrammetric Engineering and Remote Sensing↗

USGS2014

In vivo retention of ingested Au NPs by Daphnia magna: No evidence for trans-epithelial alimentary uptake

In vivo studies with Daphnia magna remain inconclusive as to whether engineered nanoparticles (NPs) are internalized into tissues after ingestion. Here we used a three-pronged approach to study the in vivo retention and efflux kinetics of 20 nm citrate stabilized Au NPs ingested by this key aquatic species. Daphnids were exposed to suspended particles (600 μg L −1 ) for 5 h after which they were depurated for 24 h in clean water containing algae. Light microscopy was used to follow the passage of Au NPs through the gastrointestinal tract, Au body burdens were determined by ICP-MS (inductively coupled plasma mass spectrometry), and transmission electron microscopy (TEM) was used to examine the presence and distribution of Au NPs in tissues. Results revealed that the elimination of Au NPs was bi-phasic. The fast elimination phase lasted <1 h and the rate constant at which Au (of Au NPs) was eliminated was 1.12 ± 0.34 h −1 (±SE) which accounted for ∼75% of the ingested Au. The remaining ∼25% of the ingested Au NPs was eliminated at a 100-fold slower rate. TEM analysis revealed that Au NPs in the midgut were in close proximity to the peritrophic membrane after 1 and 24 h of depuration. There were no observations of Au NP uptake at the microvilli. Thus, although Au NPs were retained in the gut lumen, there was no observable internalization into the gut epithelial cells. Similar to carbon nanotubes and CuO NPs, our findings indicate that in daphnids the in vivo retention of Au NPs does not necessarily result in their internalization.

Chemosphere↗

USGS2008

Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Photogrammetric Engineering and Remote Sensing↗

USGS2019

Characterizing 12 years of wildland fire science at the U.S. Geological Survey: Wildland Fire Science Publications, 2006–17

Wildland fire characteristics, such as area burned, number of large fires, burn intensity, and fire season duration, have increased steadily over the past 30 years, resulting in substantial increases in the costs of suppressing fires and managing damages from wildland fire events (National Academies of Sciences, Engineering, and Medicine, 2017). Wildland fire management could benefit from sound decision making based on reliable scientific information. Fire scientists produce data, tools, and information to support fire and land management decision making. With ever-changing land use scenarios, environmental conditions, and emerging technological capabilities, new assessments and studies are continually needed. Established by Congress in 1879, the U.S. Geological Survey (USGS) is the primary science branch of the Department of the Interior (DOI), which manages more than 400 million acres of public lands in the United States. The USGS has more than 100 scientists across seven Mission Areas that help address the wildland fire science needs of DOI bureaus and their stakeholders. The diverse expertise of these scientists allows them to address complex interdisciplinary challenges. In this report, we identify and characterize scientific literature produced by USGS scientists during 2006–17 that addresses topics associated with wildland fire science. Our goals were to (1) make the most complete list possible of product citations readily available in an organized format, and (2) use bibliometric analysis approaches to highlight the productivity of USGS scientists and the impact of contributions that the Bureau has provided to the scientific, land management, and fire management communities.

Open-File Report↗

USGS1987

DISCRIMINATION OF ALTERED BASALTIC ROCKS IN THE SOUTHWESTERN UNITED STATES BY ANALYSIS OF LANDSAT THEMATIC MAPPER DATA.

Landsat Thematic Mapper image data were analyzed to determine their ability to discriminate red cone basalts from gray flow basalts and sedimentary country rocks for three volcanic fields in the southwestern United States. Analyses of all of the possible three-band combinations of the six nonthermal bands indicate that the combination of bands 1, 4, and 5 best discriminates among these materials. The color-composite image of these three bands unambiguously discriminates 89 percent of the mapped red volcanic cones in the three volcanic fields. Mineralogic and chemical analyses of collected samples indicate that discrimination is facilitated by the presence of hematite as a major mineral phase in the red cone basalts (hematite is only a minor mineral phase in the gray flow basalts and red sedimentary rocks).

Photogrammetric Engineering and Remote Sensing↗

USGS2005

Assessment of seismic risk in Tashkent, Uzbekistan and Bishkek, Kyrgyz Republic

The impact of earthquakes in urban centers prone to disastrous earthquakes necessitates the analysis of associated risk for rational formulation of contingency plans and mitigation strategies. In urban centers the seismic risk is best quantified and portrayed through the preparation of 'Earthquake damage and Loss Scenarios'. The components of such scenarios are the assessment of the hazard, inventories and the vulnerabilities of elements at risk. For the development of earthquake risk scenario in Tashkent-Uzbekistan and Bishkek-Kyrgyzstan an approach based on spectral displacements is utilized. This paper will present the important features of a comprehensive study, highlight the methodology, discuss the results and provide insights to the future developments. ?? 2005 Elsevier Ltd. All rights reserved.

Soil Dynamics and Earthquake Engineering↗

USGS2000

Evaluation of seismic slope-performance models using a regional case study

This paper compares four permanent displacement models based on Newmark's sliding-block analogy for assessing regional seismic slope-performance. The models vary primarily by the ground motion descriptor used to correlate with Newmark displacement. The first uses peak ground-acceleration (PGA). The second uses PGA but normalizes displacements by predominant period and equivalent cycles. The third uses Arias intensity. The fourth calculates cumulative displacements from double-integrating simulated earthquake accelerograms. The models are implemented in a GIS to characterize seismic slope-performance for the Oakland East quadrangle near San Francisco, California. The resulting slope-performance maps are compared visually and through statistical analysis to expose potential differences and assess the effects of using a particular approach within a decision-making context. These maps were created for the purpose of comparison and are not suitable for use as critical decision-making tools. The models forecast notably different levels of slope-performance, with the PGA-based models predicting the greatest Newmark displacement on average. Thus, considering the variety of slope-performance models, it is suggested that practitioners avoid reliance on a single model. Instead, multiple models can be implemented in a GIS framework to gain a better perspective of the potential hazard and make a more informed decision.

California↗

USGS2012

Status and trends of land change in the United States--1973 to 2000

Summary U.S. Geological Survey (USGS) Professional Paper 1794 is a four-volume series on the status and trends of the Nation’s land use and land cover, providing an assessment of the rates and causes of land-use and land-cover change in the United States between 1973 and 2000. Volumes A, B, C, and D provide analyses for the Western United States, the Great Plains, the Midwest–South Central United States, and the Eastern United States, respectively. The assessments of land-use and land-cover trends are conducted on an ecoregion-by-ecoregion basis, and each ecoregion assessment is guided by a nationally consistent study design that includes mapping, statistical methods, field studies, and analysis. Individual assessments provide a picture of the characteristics of land change occurring in a given ecoregion; in combination, they provide a framework for understanding the complex national mosaic of change and also the causes and consequences of change. Thus, each volume in this series provides a regional assessment of how (and how fast) land use and land cover are changing, and why. The four volumes together form the first comprehensive picture of land change across the Nation. This report is only one of the products produced by USGS on land-use and land-cover change in the United States. Other reports and land-cover statistics are available online at http://landcovertrends.usgs.gov .

Professional Paper↗

USGS2018

Full-range, solar-reflected hyperspectral microscopy to support earth remote sensing research

Over the past 20 years, hyperspectral microscopy has grown into a robust field of analysis for a number of applications. The visible to near-infrared (VNIR; 400 to 1000 nm) region of the spectrum has demonstrated utility for the characterization of healthy and diseased tissue and of biomolecular indicators at the cellular level. Here, we describe the development of a hyperspectral imaging (HSI) microscope that is aimed at material characterization to complement traditional stand-off, earth remote sensing with hyperspectral sensors. We combine commercial off the shelf technology to build an HSI microscope to collect spectral data with illumination provided by a tunable laser. Hyperspectral imaging microscopy (HIM) facilitates detailed examination of target materials at the subcentimeter spatial scale. The custom-built, laser illumination HSI microscope covers the NIR to shortwave infrared (NIR/SWIR; 900 to 2500 nm) solar-reflected spectral range. It is combined with a separate VNIR sensor (400 to 900 nm) that utilizes quartz–tungsten–halogen lamps for illumination. The combined sensors provide a means to collect <10,000 s of spectra in the full VNIR/SWIR spectral range from both pure substances and precisely engineered linear and nonlinear mixtures. The large abundance of spectra allows for a more detailed understanding of the variability and multivariate probability distributions of spectral signatures. This additional information aids in understanding the variability observed in ground truth spectra collected from portable spectrometers, and it greatly enhances sample description and metadata content. In addition, HIM data cubes can serve as proxies, as “microscenes,” for systems engineering applications such as trade studies for HSI acquired by air- and space-borne sensors.

Journal of Applied Remote Sensing↗

USGS2012

Significance of rotating ground motions on nonlinear behavior of symmetric and asymmetric buildings in near fault sites

Building codes in the U.S. require at least two horizontal ground motion components for three-dimensional (3D) response history analysis (RHA) of structures. For sites within 5 km of an active fault, these records should be rotated to fault-normal/fault-parallel (FN/FP) directions, and two RHA analyses should be performed separately (when FN and then FP are aligned with transverse direction of the structural axes). It is assumed that this approach will lead to two sets of responses that envelope the range of possible responses over all non-redundant rotation angles. This assumption is examined here using 3D computer models of a single-story structure having symmetric (that is, torsionally-stiff) and asymmetric (that is, torsionally flexible) layouts subjected to an ensemble of bi-directional near-fault strong ground motions with and without apparent velocity pulses. In this parametric study, the elastic vibration period of the structures is varied from 0.2 to 5 seconds, and yield strength reduction factors R is varied from a value that leads to linear-elastic design to 3 and 5. The influence that the rotation angle of the ground motion has on several engineering demand parameters (EDPs) is examined in linear-elastic and nonlinear-inelastic domains to form a benchmark for evaluating the use of the FN/FP directions as well as the maximum-direction (MD) ground motion, a new definition of horizontal ground motions for use in the seismic design of structures according to the 2009 NEHRP Provisions and Commentary.

Book↗

USGS2009

Multi-temporal RADARSAT-1 and ERS backscattering signatures of coastal wetlands in southeastern Louisiana

Using multi-temporal European Remote-sensing Satellites (ERS-1/-2) and Canadian Radar Satellite (RADARSAT-1) synthetic aperture radar (SAR) data over the Louisiana coastal zone, we characterize seasonal variations of radar backscat-tering according to vegetation type. Our main findings are as follows. First, ERS-1/-2 and RADARSAT-1 require careful radiometric calibration to perform multi-temporal backscattering analysis for wetland mapping. We use SAR backscattering signals from cities for the relative calibration. Second, using seasonally averaged backscattering coefficients from ERS-1/-2 and RADARSAT-1, we can differentiate most forests (bottomland and swamp forests) and marshes (freshwater, intermediate, brackish, and saline marshes) in coastal wetlands. The student t-test results support the usefulness of season-averaged backscatter data for classification. Third, combining SAR backscattering coefficients and an optical-sensor-based normalized difference vegetation index can provide further insight into vegetation type and enhance the separation between forests and marshes. Our study demonstrates that SAR can provide necessary information to characterize coastal wetlands and monitor their changes.

Photogrammetric Engineering and Remote Sensing↗

USGS2012

Variance of discharge estimates sampled using acoustic Doppler current profilers from moving boats

This paper presents a model for quantifying the random errors (i.e., variance) of acoustic Doppler current profiler (ADCP) discharge measurements from moving boats for different sampling times. The model focuses on the random processes in the sampled flow field and has been developed using statistical methods currently available for uncertainty analysis of velocity time series. Analysis of field data collected using ADCP from moving boats from three natural rivers of varying sizes and flow conditions shows that, even though the estimate of the integral time scale of the actual turbulent flow field is larger than the sampling interval, the integral time scale of the sampled flow field is on the order of the sampling interval. Thus, an equation for computing the variance error in discharge measurements associated with different sampling times, assuming uncorrelated flow fields is appropriate. The approach is used to help define optimal sampling strategies by choosing the exposure time required for ADCPs to accurately measure flow discharge.

Journal of Hydraulic Engineering↗

USGS2008

Designing a multi-objective, multi-support accuracy assessment of the 2001 National Land Cover Data (NLCD 2001) of the conterminous United States

The database design and diverse application of NLCD 2001 pose significant challenges for accuracy assessment because numerous objectives are of interest, including accuracy of land-cover, percent urban imperviousness, percent tree canopy, land-cover composition, and net change. A multi-support approach is needed because these objectives require spatial units of different sizes for reference data collection and analysis. Determining a sampling design that meets the full suite of desirable objectives for the NLCD 2001 accuracy assessment requires reconciling potentially conflicting design features that arise from targeting the different objectives. Multi-stage cluster sampling provides the general structure to achieve a multi-support assessment, and the flexibility to target different objectives at different stages of the design. We describe the implementation of two-stage cluster sampling for the initial phase of the NLCD 2001 assessment, and identify gaps in existing knowledge where research is needed to allow full implementation of a multi-objective, multi-support assessment. ?? 2008 American Society for Photogrammetry and Remote Sensing.

Photogrammetric Engineering and Remote Sensing↗

USGS2020

The 3D Elevation Program and energy for the Nation

High-resolution light detection and ranging (lidar) data are used in energy infrastructure siting, design, permitting, construction, and monitoring to promote public safety through the reduction of risks. For example, lidar data are used to identify safe locations for energy infrastructure by analyzing terrain parameters and identifying and evaluating geologic hazards (for example, landslide and fault locations) and their potential public safety effects on the location or design of infrastructure. Increasingly, engineering companies and regulatory agencies are using lidar and other remote sensing techniques as an efficient method to collect accurate, comprehensive data while reducing risks to field personnel. The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) is collecting lidar data nationwide (interferometric synthetic aperture radar [IfSAR] data in Alaska) to support a wide range of applications, including projects related to energy infrastructure construction and safety. Renewable energy resources, resource mining, and oil and gas resources were identified by the National Enhanced Elevation Assessment as business uses requiring three-dimensional (3D) elevation data. Elevation data are critical in assessing potential sites for energy infrastructure, such as pipelines, refineries and other facilities, to mitigate risks from natural hazards. For example, the Federal Energy Regulatory Commission (FERC), an independent agency that regulates the interstate transmission of electricity, natural gas, and oil, uses enhanced elevation data to conduct National Environmental Policy Act (NEPA) compliance assessments. The acquisition of high-resolution lidar data by the USGS 3DEP initiative helps the FERC and NEPA permit applicants by providing accurate and consistent data for hazards analysis. The use of these data accelerates the application and review process and avoids the much higher costs of acquiring elevation data along proposed energy facility locations and pipeline corridors.

Fact Sheet↗

USGS2021

Cloud-native repositories for big scientific data

Scientific data have traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow toward the petabyte scale. A “cloud-native data repository,” as defined in this article, offers several advantages over traditional data repositories—performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access and inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing’s full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.

Computing in Science and Engineering↗

USGS2006

Canopy reflectance related to marsh dieback onset and progression in Coastal Louisiana

In this study, we extended previous work linking leaf spectral changes, dieback onset, and progression of Spartina alterniflora marshes to changes in site-specific canopy reflectance spectra. First, we obtained canopy reflectance spectra (approximately 20 m ground resolution) from the marsh sites occupied during the leaf spectral analyses and from additional sites exhibiting visual signs of dieback. Subsequently, the canopy spectra were analyzed at two spectral scales: the first scale corresponded to whole-spectra sensors, such as the NASA Earth Observing-1 (EO-1) Hyperion, and the second scale corresponded to broadband spectral sensors, such as the EO-1 Advanced Land Imager and the Landsat Enhanced Thematic Mapper. In the whole-spectra analysis, spectral indicators were generated from the whole canopy spectra (about 400 nm to 1,000 nm) by extracting typical dead and healthy marsh spectra, and subsequently using them to determine the percent composition of all canopy reflectance spectra. Percent compositions were then used to classify canopy spectra at each field site into groups exhibiting similar levels of dieback progression ranging from relatively healthy to completely dead. In the broadband reflectance analysis, blue, green, red, red-edge, and near infrared (NIR) spectral bands and NIR/green and NIR/red transforms were extracted from the canopy spectra. Spectral band and band transform indicators of marsh dieback and progression were generated by relating them to marsh status indicators derived from classifications of the 35 mm slides collected at the same time as the canopy reflectance recordings. The whole spectra and broadband spectral indicators were both able to distinguish (a) healthy marsh, (b) live marsh impacted by dieback, and (c) dead marsh, and they both provided some discrimination of dieback progression. Whole-spectra resolution sensors like the EO-1 Hyperion, however, offered an enhanced ability to categorize dieback progression.

Louisiana↗