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At least 1,063 records · Page 59Linked to original sources
USGS2011

Evaluation of LiDAR-acquired bathymetric and topographic data accuracy in various hydrogeomorphic settings in the Deadwood and South Fork Boise Rivers, West-Central Idaho, 2007

High-quality elevation data in riverine environments are important for fisheries management applications and the accuracy of such data needs to be determined for its proper application. The Experimental Advanced Airborne Research LiDAR (Light Detection and Ranging)-or EAARL-system was used to obtain topographic and bathymetric data along the Deadwood and South Fork Boise Rivers in west-central Idaho. The EAARL data were post-processed into bare earth and bathymetric raster and point datasets. Concurrently with the EAARL surveys, real-time kinematic global positioning system surveys were made in three areas along each of the rivers to assess the accuracy of the EAARL elevation data in different hydrogeomorphic settings. The accuracies of the EAARL-derived raster elevation values, determined in open, flat terrain, to provide an optimal vertical comparison surface, had root mean square errors ranging from 0.134 to 0.347 m. Accuracies in the elevation values for the stream hydrogeomorphic settings had root mean square errors ranging from 0.251 to 0.782 m. The greater root mean square errors for the latter data are the result of complex hydrogeomorphic environments within the streams, such as submerged aquatic macrophytes and air bubble entrainment; and those along the banks, such as boulders, woody debris, and steep slopes. These complex environments reduce the accuracy of EAARL bathymetric and topographic measurements. Steep banks emphasize the horizontal location discrepancies between the EAARL and ground-survey data and may not be good representations of vertical accuracy. The EAARL point to ground-survey comparisons produced results with slightly higher but similar root mean square errors than those for the EAARL raster to ground-survey comparisons, emphasizing the minimized horizontal offset by using interpolated values from the raster dataset at the exact location of the ground-survey point as opposed to an actual EAARL point within a 1-meter distance. The average error for the wetted stream channel surface areas was -0.5 percent, while the average error for the wetted stream channel volume was -8.3 percent. The volume of the wetted river channel was underestimated by an average of 31 percent in half of the survey areas, and overestimated by an average of 14 percent in the remainder of the survey areas. The EAARL system is an efficient way to obtain topographic and bathymetric data in large areas of remote terrain. The elevation accuracy of the EAARL system varies throughout the area depending upon the hydrogeomorphic setting, preventing the use of a single accuracy value to describe the EAARL system. The elevation accuracy variations should be kept in mind when using the data, such as for hydraulic modeling or aquatic habitat assessments.

Idaho↗
USGS2013

Summary and interpretation of discrete and continuous water-quality monitoring data, Mattawoman Creek, Charles County, Maryland, 2000-11

Discrete samples and continuous (15-minute interval) water-quality data were collected at Mattawoman Creek (U.S. Geological Survey station number 01658000) from October 2000 through January 2011, in cooperation with the Charles County (Maryland) Department of Planning and Growth Management, the Maryland Department of the Environment, and the Maryland Geological Survey. Mattawoman Creek is a fourth-order Maryland tributary to the tidal freshwater Potomac River; the creek’s watershed is experiencing development pressure due to its proximity to Washington, D.C. Data were analyzed for the purpose of describing ambient water quality, identifying potential contaminant sources, and quantifying nutrient and sediment loads to the tidal freshwater Mattawoman estuary. Continuous data, collected at 15-minute intervals, included discharge, derived from stage measurements made using a pressure transducer, as well as water temperature, pH, specific conductance, dissolved oxygen, and turbidity, all measured using a water-quality sonde. In addition to the continuous data, a total of 360 discrete water-quality samples, representative of monthly low-flow and targeted storm conditions, were analyzed for suspended sediment and nutrients. Continuous observations gathered by a second water-quality sonde, which was temporarily deployed in 2011 for quality-control purposes, indicated substantial lateral water-quality gradients due to inflow from a nearby tributary, representing about 10 percent of the total gaged area upstream of the sampling location. These lateral gradients introduced a time-varying bias into both the continuous and discrete data, resulting in observations that were at some times representative of water-quality conditions in the main channel and at other times biased towards conditions in the tributary. Despite this limitation, both the continuous and discrete data provided insight into the watershed-scale factors that influence water quality in Mattawoman Creek. Annual precipitation over the study period was representative of the long-term record for southern Maryland. The median value of continuously measured discharge was 25 cubic feet per second (ft 3 /s), and the maximum observed value was 3,210 ft 3 /s; there were 498 days, or about 15 percent of the study period, when flow was zero or too low to measure. Continuously measured water temperature followed a seasonal trend characteristic of the geographic setting; the trend in dissolved oxygen was inverted relative to temperature, and reflected nearly saturated conditions year round. Relations between discharge and both pH and specific conductance indicate that stream water can be conceptualized as a mixture of acidic, dilute precipitation with pH-neutral groundwater of higher conductance. Specific conductance data showed a pronounced winter peak in both median and extreme measurements, indicating the influence of road salt. However, this influence is minor relative to that observed in the Northeast Branch Anacostia River (U.S. Geological Survey station number 01649500), a nearby, more heavily urbanized comparison basin. The median suspended-sediment concentration in discrete samples was 24 milligrams per liter (mg/L), with minimum and maximum concentrations of 1 mg/L and 2,890 mg/L, respectively. Total nitrogen ranged from 0.21 mg/L to 4.09 mg/L, with a median of 0.69 mg/L; total phosphorus ranged from less than 0.01 mg/L to 0.98 mg/L, with a median of 0.07 mg/L. Total nitrogen was dominated by the dissolved organic fraction (49 percent based on median species concentrations); total phosphorus was predominantly particulate (70 percent). Seasonal trends in suspended-sediment concentration indicate a supply subsidy in late winter and spring; this could be linked to flood-plain interaction, mobilization of sediment from the channel or banks, or anthropogenic input. Seasonal trends for both total phosphorus and total nitrogen generally corresponded to seasonal trends for suspended sediment, indicating a common underlying physical control, likely acting in synchrony with seasonal biological controls on total nutrient concentrations. Speciation of phosphorus, including proportional concentration of the biologically available dissolved inorganic fraction, did not vary seasonally. The speciation of nitrogen reflected demand for inorganic nitrogen and associated transformation into organic nitrogen during the growing season. Stepwise regression models were developed, using continuous data corresponding to collection times for discrete samples as candidate surrogates for suspended sediment, total phosphorus, and total nitrogen. Turbidity and discharge were both included in the model for suspended sediment (R 2 = 0.76, n = 185); only turbidity was selected as a robust predictor of total phosphorus and nitrogen (R 2 = 0.68 and 0.61, respectively, n = 186 for both). Loads of sediment and nutrients to the downstream Mattawoman estuary were computed using the U.S. Geological Survey computer program LOADEST. Load estimation included comparison of a routinely applied seven-parameter regression model based on time, season, and discharge, with an eight-parameter model that also includes turbidity. Adding turbidity decreased total load estimates, based on hourly data for a fixed 2-month period, by 21, 8, and 3 percent for suspended sediment, total phosphorus, and total nitrogen, respectively, in addition to decreasing the standard error of prediction for all three constituents. The seasonal pattern in specific conductance, reflecting road salt application, is the strongest evidence of the effect of upstream development on water quality at Mattawoman Creek. Accordingly, ongoing continuous monitoring for trends in specific conductance would be the most reliable means of detecting further degradation associated with increased development.

Charles County, Maryl↗
USGS2014

Discovery and analysis of time delay sources in the USGS personal computer data collection platform (PCDCP) system

Intermagnet is an international oversight group which exists to establish a global network for geomagnetic observatories. This group establishes data standards and standard operating procedures for members and prospective members. Intermagnet has proposed a new One-Second Data Standard, for that emerging geomagnetic product. The standard specifies that all data collected must have a time stamp accuracy of ±10 milliseconds of the top-of-the-second Coordinated Universal Time. Therefore, the U.S. Geological Survey Geomagnetism Program has designed and executed several tests on its current data collection system, the Personal Computer Data Collection Platform. Tests are designed to measure the time shifts introduced by individual components within the data collection system, as well as to measure the time shift introduced by the entire Personal Computer Data Collection Platform. Additional testing designed for Intermagnet will be used to validate further such measurements. Current results of the measurements showed a 5.0–19.9 millisecond lag for the vertical channel (Z) of the Personal Computer Data Collection Platform and a 13.0–25.8 millisecond lag for horizontal channels (H and D) of the collection system. These measurements represent a dynamically changing delay introduced within the U.S. Geological Survey Personal Computer Data Collection Platform.

Scientific Investigations Report↗
USGS2024

Novel data in recreation monitoring—Summary proceedings from interagency workshops in 2019 and 2023

Two interagency workshops were held in 2019 and 2023 in Fort Collins, Colorado, to discuss the use of novel data in recreation monitoring. During the workshops, the phrase “novel data in recreation monitoring” was primarily used to refer to data from social media, mobile device applications, and other online secondary sources. The goals of these workshops were to share information across agencies and researchers on the state of the science and applications for using novel data and to collectively discuss best practices for using novel data for understanding recreation on public lands and waters. Presentations during the workshops focused on use-cases, current applications, and the current state of research (as of the time of the workshops) for using novel data in recreation monitoring. Group discussions during the workshops focused on the strengths and limitations of novel data sources, potential approaches for integrating new and emerging data sources and methods with traditional approaches, and research and management needs. This report provides the proceedings of the 2019 and 2023 interagency workshops on novel data in recreation monitoring.

Scientific Investigations Report↗
USGS2002

Standards for the Analysis and Processing of Surface-Water Data and Information Using Electronic Methods

Surface-water computation methods and procedures are described in this report to provide standards from which a completely automated electronic processing system can be developed. To the greatest extent possible, the traditional U. S. Geological Survey (USGS) methodology and standards for streamflow data collection and analysis have been incorporated into these standards. Although USGS methodology and standards are the basis for this report, the report is applicable to other organizations doing similar work. The proposed electronic processing system allows field measurement data, including data stored on automatic field recording devices and data recorded by the field hydrographer (a person who collects streamflow and other surface-water data) in electronic field notebooks, to be input easily and automatically. A user of the electronic processing system easily can monitor the incoming data and verify and edit the data, if necessary. Input of the computational procedures, rating curves, shift requirements, and other special methods are interactive processes between the user and the electronic processing system, with much of this processing being automatic. Special computation procedures are provided for complex stations such as velocity-index, slope, control structures, and unsteady-flow models, such as the Branch-Network Dynamic Flow Model (BRANCH). Navigation paths are designed to lead the user through the computational steps for each type of gaging station (stage-only, stagedischarge, velocity-index, slope, rate-of-change in stage, reservoir, tide, structure, and hydraulic model stations). The proposed electronic processing system emphasizes the use of interactive graphics to provide good visual tools for unit values editing, rating curve and shift analysis, hydrograph comparisons, data-estimation procedures, data review, and other needs. Documentation, review, finalization, and publication of records are provided for with the electronic processing system, as well as archiving, quality assurance, and quality control.

Water-Resources Investigations Report↗
USGS1995

Synthesis of nutrient and sediment data for watersheds within the Chesapeake Bay drainage basin

Nutrient and sediment data collected by Federal and state agencies from 1972 through 1992 at 1,058 surface-water sites in nontidal parts of the Chesapeake Bay Basin were compiled into a large database. Adequate nutrient, sediment, and streamflow data were not available to compute annual loads for all sites because water-quality monitoring at many of the sites was either short term or noncontinuous or because stream-flow was not measured. Annual nutrient and sediment loads were calculated at a total of 127 sites. Annual loads of dissolved nitrate were calculated for 108 sites, but total nitrogen loads could be calculated for only 48 of these sites because ammonia plus organic nitrogen data were not available for many of these 108 sites. Annual loads of total phosphorus were calculated for 99 sites, and annual loads of suspended sediment were calculated for 33 sites. Loads could be calculated for only a very few sites in the Juniata River Basin (a tributary to the Susquehanna River), the York River Basin, the middle and lower reaches of the James River, and the nontidal parts of the eastern shore of the Bay. Geographic Information System (GIS) spatial data sets of land use, physiographic province, rock type, and watershed delineation were compiled for the entire Chesapeake Bay Basin (approximately 64,000 square miles). The nutrient- and sediment-yield were evaluated with respect to land use, physiographic province, rock type, and hydrologic characteristics. During years that the mean streamflow was about equal to the long-term mean streamflow, the Susquehanna River contributed about 50 percent of the freshwater, 66 percent of the total nitrogen, and 40 percent of the total phosphorus transported by tributaries to the Bay. Nutrient and sediment data were available for less than 18 percent of the predominantly agricultural areas underlain by siliciclastic rock and for less than 35 percent of the predominantly agricultural areas underlain by either carbonate rock or unconsolidated rock. Nutrient and sediment data were available for about 91 percent of the predominantly forested areas underlain by siliciclastic rock. Spatial and temporal gaps in the water-quality data and GIs data sets limited some data analysis. Correlations of annual yields or nutrients and sediment with respect to land use, physiographic province, and rock type indicated (1) basins with larger percentages of agricultural land had larger nutrient and sediment yields, (2) basins with larger percentages of forest land had smaller nutrient and sediment loads, (3) the largest total nitrogen yields were from agricultural basins underlain by carbonate rock, (4) yields or nutrients from urban basins were substantially less than yields from agricultural basins, and (5) basins with small amounts of agricultural and urban land had relatively small nutrient and sediment yields.

Chesapeake Bay drainage basin↗
USGS1960

Double-mass curves, with a section fitting curves to cyclic data

The double.-mass curve is used to check the consistency of many kinds of hydrologic data by comparing data for a single station with that of a pattern composed of the data from several other stations in the area The double-mass curve can be used to adjust inconsistent precipitation data. The graph of the cumulative data of one variable versus the cumulative data of a related variable is a straight line so long as the relation between the variables is a fixed ratio. Breaks in the double-mass curve of such variables are caused by changes in the relation between the variables. These changes may be due to changes in the method of data collection or to physical changes that affect the relation. Applications of the double-mass curve to precipitation, streamflow, and sediment data, and to precipitation-runoff relations are described. A statistical test for significance of an apparent break in the slope of the double-mass curve is described by an example. Poor correlation between the variables can prevent detection of inconsistencies in a record, but an increase in the length of record tends to offset the effect of poor correlation. The residual-mass curve, which is a modification of the double-mass curve, magnifies imperceptible breaks in the double-mass curve for detailed study. Of the several methods of fitting a smooth curve to cyclic or periodic data, the moving-arc method and the double-integration method deserve greater use in hydrology. Both methods are described in this manual. The moving-arc method has general applicability, and the double integration method is useful in fitting a curve to cycles of sinusoidal form.

Water Supply Paper↗
USGS2019

From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows

Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.

Frontiers in Marine Science↗
USGS2015

Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition

Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An “expedited” processing system called “eMODIS” operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

Remote Sensing↗
USGS2016

Technical Note: Harmonizing met-ocean model data via standard web services within small research groups

Work over the last decade has resulted in standardised web services and tools that can significantly improve the efficiency and effectiveness of working with meteorological and ocean model data. While many operational modelling centres have enabled query and access to data via common web services, most small research groups have not. The penetration of this approach into the research community, where IT resources are limited, can be dramatically improved by (1) making it simple for providers to enable web service access to existing output files; (2) using free technologies that are easy to deploy and configure; and (3) providing standardised, service-based tools that work in existing research environments. We present a simple, local brokering approach that lets modellers continue to use their existing files and tools, while serving virtual data sets that can be used with standardised tools. The goal of this paper is to convince modellers that a standardised framework is not only useful but can be implemented with modest effort using free software components. We use NetCDF Markup language for data aggregation and standardisation, the THREDDS Data Server for data delivery, pycsw for data search, NCTOOLBOX (MATLAB®) and Iris (Python) for data access, and Open Geospatial Consortium Web Map Service for data preview. We illustrate the effectiveness of this approach with two use cases involving small research modelling groups at NATO and USGS.

Ocean science and engineering↗
USGS2018

Data quality from a community-based, water-quality monitoring project in the Yukon River basin

This paper examines the quality of data collected by the Indigenous Observation Network, a community-based water-quality project in the Yukon River Basin of Alaska and Canada. The Indigenous Observation Network relies on community technicians to collect surface-water samples from as many as fifty locations to achieve their goals of monitoring the quality of the Yukon River and major tributaries in the basin and maintaining a long-term record of baseline data against which future changes can be measured. This paper addresses concerns about the accuracy, precision, and reliability of data collected by non-professionals. The Indigenous Observation Network data are examined in the context of a standard data life cycle: plan, collect, assure, and describe; as compared to professional scientific activities. Field and laboratory protocols and procedures of the Indigenous Observation Network are compared to those utilized by professional scientists. The data of the Indigenous Observation Network are statistically compared to those collected by professional scientists through a retrospective analysis of a set of water-quality parameters reported by all three projects over a number of years. No statistical differences were found among the three projects for pH, Calcium, Magnesium, or Alkalinity, although statistically significant differences were found for Sodium, Chloride, Sulfate, and Potassium concentrations. The statistical differences found were small and likely not significant in terms of interpreting the data for a variety of uses. Our results suggest that Indigenous Observation Network data are of high quality, and with consistent protocols and participant training, community based monitoring projects can collect data that are accurate, precise, and reliable.

Alaska, British Columbia, Yukon Territory↗
USGS2014

Land cover mapping of Greater Mesoamerica using MODIS data

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: ( i ) qualitative assessment provided good insight in identifying and correcting gross errors; ( ii ) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest ( r 2  = 0.88), shrubland ( r 2  = 0.75), and cropland ( r 2  = 0.97) but weak positive association for grassland ( r 2  = 0.26); and ( iii ) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Canadian Journal of Remote Sensing↗
USGS1991

Techniques and strategies for data integration in mineral resource assessment

The Geologic and the National Mapping divisions of the U.S. Geological Survey have been involved formally in cooperative research and development of computer-based geographic information systems (GISs) applied to mineral-resource assessment objectives since 1982. Experience in the Conterminous United States Mineral Assessment Program (CUSMAP) projects including the Rolla, Missouri; Dillon, Montana; Butte, Montana; and Tonopah, Nevada 1?? ?? 2?? quadrangles, has resulted in the definition of processing requirements for geographically and mineral-resource data that are common to these studies. The diverse formats of data sets collected and compiled for regional mineral-resource assessments necessitate capabilities for digitally encoding and entering data into appropriate tabular, vector, and raster subsystems of the GIS. Although many of the required data sets are either available or can be provided in a digital format suitable for direct entry, their utility is largely dependent on the original intent and consequent preprocessing of the data. In this respect, special care must be taken to ensure the digital data type, encoding, and format will meet assessment objectives. Data processing within the GIS is directed primarily toward the development and application of models that can be used to describe spatially geological, geophysical, and geochemical environments either known or inferred to be associated with specific types of mineral deposits. Consequently, capabilities to analyze spatially, aggregate, and display relations between data sets are principal processing requirements. To facilitate the development of these models within the GIS, interfaces must be developed among vector-, raster-, and tabular-based processing subsystems to reformat resident data sets for comparative analyses and multivariate display of relations.

Conference Paper↗
USGS2011

Building transparent data access for ocean observatories: Coordination of U.S. IOOS DMAC with NSF's OOI Cyberinfrastructure

The NOAA-led U.S. Integrated Ocean Observing System (IOOS) and the National Science Foundation's Ocean Observatories Initiative (OOI) have been collaborating since 2007 on advanced tools and technologies that ensure open access to ocean observations and models. Initial collaboration focused on serving ocean data via cloud computing-a key component of the OOI cyberinfrastructure (CI) architecture. As the OOI transitioned from planning to execution in the Fall of 2009, an OOI/IOOS team developed a customer-based "use case" to align more closely with the emerging objectives of OOI-CI team's first software release scheduled for Summer 2011 and provide a quantitative capacity for stress-testing these tools and protocols. A requirements process was initiated with coastal modelers, focusing on improved workflows to deliver ocean observation data. Accomplishments to date include the documentation and assessment of scientific workflows for two "early adopter" modeling teams from IOOS Regional partners (Rutgers-the State University of New Jersey and University of Hawaii's School of Ocean and Earth Science and Technology) to enable full understanding of data sources and needs; generation of all-inclusive lists of the data sets required and those obtainable through IOOS; a more complete understanding of areas where IOOS can expand data access capabilities to better serve the needs of the modeling community; and development of "data set agents" (software) to facilitate data acquisition from numerous data providers and conversions of the data format to the OOI-CI canonical form. ?? 2011 MTS.

Conference Paper↗
USGS2013

Priority data on marine and estuarine resources within northeastern National Parks: Inventory and acquisition needs

The purpose of this project was to guide development of a strategy for the inventory and mapping of submerged natural resources associated within 10 coastal parks of the National Park Service (NPS) Northeast Region (NER; see Table 1). Priority data needs were identified by the NER Ocean Stewardship Task Force. The majority of the NER priority data needs involve the biotic, chemical, and geological characterization of the seabed. Taken collectively, this demands a consistent and unified approach to habitat classification. The Coastal and Marine Ecological Classification Standard (CMECS) is endorsed by the Federal Geographic Data Committee (FGDC-STD-018) for classifying ecological units in coastal and marine environments, and is recommended as a framework for acquiring and organizing NER data. We prepared an inventory of existing data on priority marine and estuarine natural resources within the ten NER coastal parks. This report describes the data and information sources relevant to each park and identifies gaps in available data. Overwhelmingly and uniformly across all parks, the most pressing needs are consistent, high-resolution bathymetry and seafloor characterization data. Approaches for acquiring these data using an integrated, multi-resolution sampling framework are recommended.

Natural Resource Report↗
USGS1993

Identification of requirements and sources for global digital topographic data

Many of the physical processes being studied by global change researchers are affects by land surface topography and consequently topographic data are an important requirement for these investigations. Remotely sensed data, especially those that will be collected by the instruments of the Earth Observing System, require significant correction to remove topographic effects. Although some requirements are met by existing topographic data, there are serious data shortages that will affect global change science. The interdisciplinary and multi temporal natural of global change research requires that remotely sensed data be processed using a consistent, highly accurate global topographic database so that information extracted from these data for different areas and times can be compared quantitively. Cartographic and remote sensing sources for the generation of new topographic data exist or are planned and will be helpful for fulfilling these requirements. More consistent use of accuracy statement terminology by data users and producers is necessary to better compare the requirements with existing or future data sets.

Pecora 12 Symposium↗
USGS1993

Raster profile development for the spatial data transfer standard

The Spatial Data Transfer Standard (SDTS), recently approved as Federal Information Processing Standard (FIPS) Publication 173, is designed to transfer various types of spatial data. Implementing all of the standard's options at one time is impractical. Profiles, or limited subsets of the SDTS, are the mechanisms by which the standards will be implemented. The development of a raster profile is being coordinated by the U.S. Geological Survey's (USGS) SDTS Task Force. This raster profile is intended to accommodate digital georeferenced image data and regularly spaces, georeferenced gridded data. The USGS's digital elevation models (DEMs) and digital orthophoto quadrangles (DOQs), National Oceanic and Atmospheric Administration's (NOAA) advanced very huh resolution radiometer (AVHRR) and Landsat data, and National Aeronautics and Space Administration's (NASA) Earth observing system (EOS) data are among the candidate data sets for this profile. Other raster profiles, designed to support nongeoreferenced and other types of "raw" sensor data will be consider in the future. As with the Topological Vector Profile (TVP) for the SDTS, development of the raster profile includes designing a prototype profile, testing the prototype profile using sample data sets, and finally, requesting and receiving FIPS approval.

Pecora 12 Symposium↗
USGS2007

Integration of seafloor point data in usSEABED

Sediments of the beach, nearshore, and continental shelves record a complex interplay of processes including wave energy and direction , currents, beach erosion or accretion, bluff or cliff retreat, fluvial input, sediment longshore and cross-shelf transport processes, contaminant content and transport, sediment sources and sinks, and others. In turn, sediments and rocks modify wave patterns, affect recreation and tourism, and provide habitat for fish, epifauna, and infauna. Character of the surficial seafloor also influences navigation, commercial and recreational fishing and gathering of other food sources, communication, piplines, national defense, and provides geologic resources including sand and gravel aggregates, minerals, and real or potential energy sources. The beaches, nearshore, and continental margins fall under overlapping levels of managerial responsibility between Federal, State, regional, and local government agencies and consortia. In addition, universities and other academic institutions investigate these places for pure or applied scientific reasons. Mapping is usually the first step in understanding any issue and is often comprised of remotely gathered geophysical data such as bathymetry and backscatter imagery, and groundtruthing; that is, the collection of physical and virtual samples to tie the remotely gathered data to reality. The physical samples are described and (or) carefully analyzed for grain-size information -- which records both the site's physical conditions and geologic past -- and commonly, for constituent components such as mineral and rock types (to determine onland sources and in situ chemical processes), carbonate and organic content and microfossils (for biological and oceanographic influences), and structure such as layering and bioturbation (for physical influences). The samples may also be subjected to physical tests such as comp[action analyses, liquefaction or plasticity limits, ans other parameters important when considering construction of offshore structures. In recent years, virtual sampling of the seafloor has become popular, through the use of towed video or photographic equipment and the addition of camera to oceanographic equipment such as corers and tripods. Before about ten years ago, most maps were made by hand. Recently, with the advent of desktop GIS packages, map making and resource analysis can be done nearly "on-the-fly" if geographically located data exist. While the problems of projection, scale, and resolution of digitized paper maps are commonly known amongst GIS-users, access to the original underlying point data allows for maps to be regenerated for digital use using statistically proven methods, provides increasing data density by including multiple studies, as well as allows the point data to be used in other ways than just mapping. These point data may be available in raw or refined or in worded descriptions. Raw data such as granulometric analyses can be manipulated through the use of known equations or empirical relationships to provide information about other parameters of the sediment, such as mean grainsize, sorting, erodability, or rugosity. If refined data are presented such as gravel, sand, and mud percentages, the parameter noted earlier may be estimated. In the case of worded descriptions, values for geologic terms can be assigned, for example, "fine sand" equate to 0.2 mm sized particles, to provide numeric terms for GIS or modeling purposes.

Conference Paper↗