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At least 1,423 records · Page 79Linked to original sources
USGS1997

Pesticides in surface and ground water of the San Joaquin-Tulare basins, California: Analysis of available data, 1966 Through 1992

Available pesticide data (1966-92) for surface and ground water were analyzed for the San Joaquin-Tulare Basins, California, one of 60 large hydrologic systems being studied as part of the National Water-Quality Assessment Program of the U.S. Geological Survey. Most of the pesticide data were for the San Joaquin Valley, one of the most intensively farmed and irrigated areas of the United States. Data were obtained from the Storage and Retrieval data base of the U.S. Environmental Protection Agency, the water-quality data base of the U.S. Geological Survey, and from data files of State agencies. Pesticides detected in surface water include organochlorine pesticides, organophosphate pesticides, carbamate pesticides, and triazine herbicides. Pesticides detected in ground water include triazine and other organonitrogen herbicides and soil fumi gants. Surface-water data indicate seasonal patterns for the detection of organophosphate and carbamate pesticides, which are attributed to their use on almond orchards and alfafa fields. Organochlorine pesticides were detected primarily in river-bed sediments. Concentrations detected in bed sediments of the San Joaquin River near Vernalis are among the highest of any major river system in the United States. Patterns and timing of pesticide use indicate that pesticides might be present in surface-water systems during most months of a year. The most commonly detected pesticide in ground water is the soil fumigant, dibromochloropropane. Dibromochloropropane, used primarily on vineyards and orchards, was detected in ground water near the city of Fresno. Triazine and other organonitrogen herbicides were detected near vineyards and orchards in the same general locations as the detections of dibromochloropropane. Pesticides were detected in ground water of the east side of the valley floor, where the soils are sandy or coarsegrained, and water-soluble pesticides with long environmental half-lives were used. In contrast, fewer pesticides were detected in ground water of the west side of the valley, where soils generally are finer grained.

California↗
USGS2008

Strong-motion data from the two Pingtung, Taiwan, earthquakes of 26 December 2006

1016 strong-motion records at 527 free-field stations and 131 records at 42 strong-motion arrays at buildings and bridges were obtained for the Pingtung earthquake doublet from the Taiwan Central Weather Bureau's dense, digital strong-motion network. We carried out standard processing of these strong-motion records at free-field stations. A data set, including the originally recorded files, processed data files, and supporting software and information, is archived online http:// tecdc.earth.sinica.edu.tw/data/EQ2006Pingtung/. We have not yet completed the processing of the strong-motion array data at buildings and bridges. However, some preliminary results and the strong-motion array data recorded at the second nearest instrumented building to the Pingtung earthquake doublet are shown. This paper is intended to document our data processing procedures and the online archived data files, so that researchers can efficiently use the data. We also include two preliminary analyses: (1) a comparison of ground motions recorded by multiple accelerographs at a common site, the TAP 117 station in Taipei, and (2) attenuation of the horizontal ground motions (peak acceleration and response spectra at periods of 0.2, 1.0, and 3.0 s) with respect to distance. Our comparison study of multiple recordings at TAP 117 indicates that waveform coherence among 20- and 24-bit accelerograph records is much higher as compared to records from 16-bit or 12-bit accelerographs, suggesting that the former are of better quality. For the 20- and 24-bit accelerographs, waveform coherence is nearly 1 over the frequency range 1 to 8 Hz for all components, and is greater than about 0.9 from 8 to 20 Hz for the horizontal component, but only from 8 to 12 Hz for the vertical component. Plots of pseudo-acceleration response spectra (PSA) as a function of distance, however, show no clear indication for a difference related to the performance level of the accelerographs. The ground-motions of the first event (Mw = 7.0) are comparable, or even somewhat lower, than those from the smaller second event (Mw = 6.9), consistent with the relative difference of the local magnitudes (ML = 6.96 and 6.99 for the first and second events, respectively). The ground motions from the first event are generally lower than those predicted from equations based on other in-slab subduction earthquakes, whereas the ground motions from the second event are closer to the predictions. Ground-motions for soil sites are generally larger than those from rock sites.

Terrestrial, Atmospheric and Oceanic Sciences↗
USGS2020

Good practices for species distribution modeling of deep-sea corals and sponges for resource management: Data collection, analysis, validation, and communication

Resource managers in the United States and worldwide are tasked with identifying and mitigating trade-offs between human activities in the deep sea (e.g., fishing, energy development, and mining) and their impacts on habitat-forming invertebrates, including deep-sea corals and sponges (DSCS). Related management decisions require information about where DSCS occur and in what densities. Species distribution modeling (SDM) provides a cost-effective means of identifying potential DSCS habitat over large areas to inform these management decisions and data collection. Here we describe good practices for DSCS SDM, especially in the context of data collection and management applications. Managers typically need information regarding DSCS encounter probabilities, densities, and sizes, defined at sub-regional to basin-wide scales and validated using subsequent, targeted data collections. To realistically achieve these goals, we suggest analysts: 1) integrate available data sources in SDMs including fine-scale visual sampling and broad-scale resource surveys (e.g., fisheries trawl surveys); and 2) include environmental predictor variables representing multiple spatial scales, model residual spatial autocorrelation, and quantify prediction uncertainty. When possible, models fitted to presence-absence and density data are preferred over models fitted only to presence data, which are difficult to validate and can confound estimated probability of occurrence or density with sampling effort. Ensembles of models can provide robust predictions, while multi-species models leverage information across taxa and facilitate community inference. To facilitate the use of models by managers, predictions should be expressed in units that are widely understood and validated at an appropriate spatial scale using a sampling design that provides strong statistical inference. We present three case studies for the Pacific Ocean that illustrate good practices with respect to data collection, modeling, and validation; these case studies demonstrate it is possible to implement our good practices in real-world settings.

Frontiers in Marine Science↗
USGS2020

Training data selection for annual land cover classification for the LCMAP initiative

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative characterizes changes in land cover, use, and condition with the goal of producing land change information that improves understanding of the earth system and provides insight into the impacts of land change on society. For LCMAP, all available high-quality data from the Landsat archive is used in a time series approach to detect land surface change. Annual thematic land cover maps are produced by classifying time series models. In this paper, we describe optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). Results were evaluated and validated based on independent data from the training dataset. We found that refining auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability (i.e., SLC-off). The classification accuracy and stability were improved considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class. Finally, the NLCD 2001 training data delivered the best classification accuracy. Comparing to the original LCMAP classification strategy (Trends training data, 20,000 samples), the optimized classification strategy considerably improved the annual land cover map accuracy.

Remote Sensing↗
USGS2022

Giant planet observations in NASA's Planetary Data System

While there have been far fewer missions to the outer Solar System than to the inner Solar System, spacecraft destined for the giant planets have conducted a wide range of fundamental investigations, returning data that continues to reshape our understanding of these complex systems, sometimes decades after the data were acquired. These data are preserved and accessible from national and international planetary science archives. For all NASA planetary missions and instruments the data are available from the science discipline nodes of the NASA Planetary Data System (PDS). Looking ahead, the PDS will be the primary repository for giant planets data from several upcoming missions and derived datasets, as well as supporting research conducted to aid in the interpretation of the remotely sensed giant planets data already archived in the PDS.

Remote Sensing↗
USGS2024

Mitigating disparate elevation differences between adjacent topobathymetric data models using binary code

Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data.

Remote Sensing↗
USGS2013

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Minnesota↗
USGS2023

Recharge estimation approach in a data-scarce semi-arid region, Northern Ethiopian Rift Valley

Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.

Kobo Valley, Riya Valley↗
USGS2021

Beyond streamflow: Call for a national data repository of streamflow presence for streams and rivers in the United States

Observations of the presence or absence of surface water in streams are useful for characterizing streamflow permanence, which includes the frequency, duration, and spatial extent of surface flow in streams and rivers. Such data are particularly valuable for headwater streams, which comprise the vast majority of channel length in stream networks, are often non-perennial, and are frequently the most data deficient. Datasets of surface water presence exist across multiple data collection groups in the United States but are not well aligned for easy integration. Given the value of these data, a unified approach for organizing information on surface water presence and absence collected by diverse surveys would facilitate more effective and broad application of these data and address the gap in streamflow data in headwaters. In this paper, we highlight the numerous existing datasets on surface water presence in headwater streams, including recently developed crowdsourcing approaches. We identify the challenges of integrating multiple surface water presence/absence datasets that include differences in the definitions and categories of streamflow status, data collection method, spatial and temporal resolution, and accuracy of geographic location. Finally, we provide a list of critical and useful components that could be used to integrate different streamflow permanence datasets.

Water↗
USGS2014

Detecting emergence, growth, and senescence of wetland vegetation with polarimetric synthetic aperture radar (SAR) data

Wetlands provide ecosystem goods and services vitally important to humans. Land managers and policymakers working to conserve wetlands require regularly updated information on the statuses of wetlands across the landscape. However, wetlands are challenging to map remotely with high accuracy and consistency. We investigated the use of multitemporal polarimetric synthetic aperture radar (SAR) data acquired with Canada’s Radarsat-2 system to track within-season changes in wetland vegetation and surface water. We speculated, a priori , how temporal and morphological traits of different types of wetland vegetation should respond over a growing season with respect to four energy-scattering mechanisms. We used ground-based monitoring data and other ancillary information to assess the limits and consistency of the SAR data for tracking seasonal changes in wetlands. We found the traits of different types of vertical emergent wetland vegetation were detected well with the SAR data and corresponded with our anticipated backscatter responses. We also found using data from Landsat’s optical/infrared sensors in conjunction with SAR data helped remove confusion of wetland features with upland grasslands. These results suggest SAR data can provide useful monitoring information on the statuses of wetlands over time.

Water↗
USGS2014

Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

Journal of Fish and Wildlife Management↗
USGS2010

Ocean and coastal data management

We introduce data management concepts, including what we mean by "data" and its "management," sources of data, interoperability, and data geometry. We then discuss various components of a data management system. Finally, we summarize some existing ocean and coastal data management efforts. We make specific recommendations throughout the paper. We are generally optimistic that ocean and coastal data management is an interesting and solvable challenge that will provide great benefit to society.

Conference Paper↗
USGS2017

Capturing change: the duality of time-lapse imagery to acquire data and depict ecological dynamics

We investigate the scientific and communicative value of time-lapse imagery by exploring applications for data collection and visualization. Time-lapse imagery has a myriad of possible applications to study and depict ecosystems and can operate at unique temporal and spatial scales to bridge the gap between large-scale satellite imagery projects and observational field research. Time-lapse data sequences, linking time-lapse imagery with data visualization, have the ability to make data come alive for a wider audience by connecting abstract numbers to images that root data in time and place. Utilizing imagery from the Platte Basin Timelapse Project, water inundation and vegetation phenology metrics are quantified via image analysis and then paired with passive monitoring data, including streamflow and water chemistry. Dynamic and interactive time-lapse data sequences elucidate the visible and invisible ecological dynamics of a significantly altered yet internationally important river system in central Nebraska.

Nebraska↗
USGS2001

Access to bird population data

Access to bird population data is critical for effective conservation planning and implementation. Although a tremendous volume of baseline data exists, it is often diffusely distributed and inaccessible to the resource manager and decision maker. A mechanism that facilitates assembly, documentation and delivery of avian data in a user-friendly manner is needed in order to integrate bird-related information resources across agencies and organizations. To address this fundamental need, the National Biological Information Infrastructure (NBII), in partnership with the U.S. Geological Survey's Patuxent Wildlife Research Center and the U.S. Fish and Wildlife Service, is developing a web-based interactive system that will focus on access to bird population and habitat data used in bird management and conservation. This system, known as the NBII Bird Conservation Node, will support planning and evaluation of bird conservation activities within the context of the North American Bird Conservation Initiative (NABCI), a framework for collaboration among organizations interested in bird conservation across North America. Initial development of the NBII Bird Conservation Node will focus on creating a prototype mapping application that will provide interactive access to data from the North American Breeding Bird Survey, the Colonial Waterbird Survey, the Breeding Waterfowl Population and Habitat Survey, and the Atlantic Flyway Mid-winter Waterfowl Survey. This prototype mapping application, to be available on-line at http://www.nbii.gov by Sep 2001, will lay the foundation for establishment of a Migratory Bird Data Center at Patuxent Wildlife Research Center, and will provide an opportunity for linking to and establishing partnerships with other sources of bird population and habitat data available over the Internet.

Book chapter↗
USGS2002

Using Christmas Bird Count data in analysis of population change

The scientific credibility of Christmas Bird Count (CBC) results depend on the development and implementation of appropriate methods of statistical analysis. The key to any successful analysis of CBC data is to begin with a careful review of how the limitations of the data are likely to influence the results of the analysis, then to choose methods of analysis that accommodate as much as possible the limitations of the survey. For our analyses of CBC data, we develop a flexible model for effort adjustment and use information from the data to guide the selection of the best model. We include geographic structuring to accommodate the regional variation in number of samples, use a model that allows for overdispersed poisson data appropriate for counts, and employ empirical Bayes procedures to accommodate differences in quality of information in regional summaries. This generalized linear model approach is very flexible, and can be applied to a variety of studies focused on factors influencing wintering bird populations. In particular, the model can be easily modified to contain covariates, allowing for assessment of associations between CBC counts and winter weather, disturbance, and a variety of other environmental factors. These new survey analysis methods have added value in that they provide insights into changes in survey design that can enhance the value of the information. The CBC has been extremely successful as a tool for increasing public interest in birding and bird conservation. Use of the information for bird conservation creates new demands on quality of information, and it is important to maintain a dialogue between users of the information, information needs for the analyses, and survey coordinators and participants. Our work as survey analysts emphasizes the value and limitations of existing data, and provides some indications of what features of the survey could be modified to make the survey a more reliable source of bird population data. Surveys only remain useful if they adapt to current needs, while still maintaining consistency with historical goals.

American Birds↗
USGS1984

JOINT DEVELOPMENT OF A NATIONAL 1:100,000-SCALE DIGITAL CARTOGRAPHIC DATA BASE.

The U. S. Geological Survey is beginning a major new program to create a nationwide digital cartographic data base from 1:100,000-scale maps by the end of the decade. This data base will supplement the currently available 1:2,000,000-scale national data base and the selected coverage at 1:24,000-scale. It is anticipated that the data will be useful for both the production of custom graphics and as basic input to geographic information systems. The 1:100,000-scale USGS map series was designed to facilitate automated data capture. The methodologies and procedures used in the data capture process have developed enough so that the two agencies can take advantage of the design features of the 1:100,000-scale maps and work together on complementary tasks to build a digital cartographic data base.

Conference Paper↗
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↗
USGS1991

Use of electronic microprocessor-based instrumentation by the U.S. geological survey for hydrologic data collection

The U.S. Geological Survey is acquiring a new generation of field computers and communications software to support hydrologic data-collection at field locations. The new computer hardware and software mark the beginning of the Survey's transition from the use of electromechanical devices and paper tapes to electronic microprocessor-based instrumentation. Software is being developed for these microprocessors to facilitate the collection, conversion, and entry of data into the Survey's National Water Information System. The new automated data-collection process features several microprocessor-controlled sensors connected to a serial digital multidrop line operated by an electronic data recorder. Data are acquired from the sensors in response to instructions programmed into the data recorder by the user through small portable lap-top or hand-held computers. The portable computers, called personal field computers, also are used to extract data from the electronic recorders for transport by courier to the office computers. The Survey's alternative to manual or courier retrieval is the use of microprocessor-based remote telemetry stations. Plans have been developed to enhance the Survey's use of the Geostationary Operational Environmental Satellite telemetry by replacing the present network of direct-readout ground stations with less expensive units. Plans also provide for computer software that will support other forms of telemetry such as telephone or land-based radio.

Conference Paper↗