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At least 127 records · Page 7Linked to original sources
USGS1993

Water Resources Data, New Jersey, Water Year 1992, Volume 1. Surface-Water Data

Water resources data for the 1992 water year for New Jersey consists of records of stage, discharge, and water quality of streams; stage, contents, and water quality of lakes and reservoirs; and water levels and water quality of ground water. This volume of the report contains discharge records for 99 gaging stations; tide summaries for 2 stations; stage and contents for 37 lakes and reservoirs; water quality for 95 surface-water sites. Also included are data for 65 crest-stage partial-record stations, 13 tidal crest-stage gages, and 94 low-flow partial-record stations. Locations of these sites are shown on Figures 11 and 12. Additional water data were collected at various sites not involved in the systematic data-collection program. Miscellaneous data were collected at 42 measuring sites and 9 water-quality sampling sites. These data represent that part of the National Water Data System operated by U.S. Geological Survey and cooperating State and Federal agencies in New Jersey.

New Jersey↗
USGS2015

A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data

The methodology for selection, creation, and application of a global remote sensing validation dataset using high resolution commercial satellite data is presented. High resolution data are obtained for a stratified random sample of 500 primary sampling units (5 km × 5 km sample blocks), where the stratification based on Köppen climate classes is used to distribute the sample globally among biomes. The high resolution data are classified to categorical land cover maps using an analyst mediated classification workflow. Our initial application of these data is to evaluate a global 30 m Landsat-derived, continuous field tree cover product. For this application, the categorical reference classification produced at 2 m resolution is converted to percent tree cover per 30 m pixel (secondary sampling unit)for comparison to Landsat-derived estimates of tree cover. We provide example results (based on a subsample of 25 sample blocks in South America) illustrating basic analyses of agreement that can be produced from these reference data. Commercial high resolution data availability and data quality are shown to provide a viable means of validating continuous field tree cover. When completed, the reference classifications for the full sample of 500 blocks will be released for public use.

Remote Sensing of Environment↗
USGS2022

New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification

Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luftund Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in California's Central Valley during the 2020 growing season. The DESIS sensor onboard the International Space Station collects data at 235 hyperspectral narrowbands (HNB) each with 2.55 nm bandwidth from 400–1000 nm and 30 m spatial resolution. In contrast, PlanetScope Dove-R data have four multispectral broadbands (MBB) with 3–4 m spatial resolution. We obtained best classification accuracies using 14 DESIS HNB from the August 2020 image, with an overall accuracy of 85% and producer's and user's accuracies of 72–100% and 75–100%, respectively, for the eight crops. The best classification accuracies using PlanetScope data were obtained using an image mosaic pair from June and August 2020; this resulted in an overall accuracy of 79% and producer's and user's accuracies of 56–100% and 61–100%, respectively. Combining the best 14 DESIS HNB from August 2020 with the 4 PlanetScope MBB from August 2020 yielded an overall accuracy of 82% and producer's and user's accuracies of 65–100% and 60–94%, respectively. On one-to-one single date comparisons of DESIS versus PlanetScope data, the hyperspectral data always outperformed high spatial resolution data in crop type classification. Nevertheless, high spatial resolution data will remain invaluable in assessing within-field variability and crop biophysical/biochemical modeling in precision agriculture.

IEEE Journal of Selected Topics in Applied Earth O↗
USGS2008

Understanding the relationship between audiomagnetotelluric data and models, and borehole data in a hydrological environment

Audiomagnetotelluric (AMT) data and resulting models are analyzed with respect to geophysical and geological borehole logs in order to clarify the relationship between the two methodologies of investigation of a hydrological environment. Several profiles of AMT data collected in basins in southwestern United States are being used for groundwater exploration and hydrogeological framework studies. In a systematic manner, the AMT data and models are compared to borehole data by computing the equivalent one-dimensional AMT model and comparing with the two-dimensional (2-D) inverse AMT model. The spatial length is used to determine if the well is near enough to the AMT profile to quantify the relationship between the two datasets, and determine the required resolution of the AMT data and models. The significance of the quality of the borehole data when compared to the AMT data is also examined.

Conference Paper↗
USGS2022

Living with wildfire in Log Hill Mesa, Ouray County, Colorado: 2017 data report and a comparison to 2011 and 2012 data

Over the last decade, a team of researchers and practitioners, referred to as the Wildfire Research Team (WiRē1 Team), has worked with wildfire practitioners seeking to create communities that are adapted to wildfire through an evidenced-based approach. The West Region Wildfire Council (WRWC) has been an integral partner amongst the WiRē Team throughout this time. Together, the WiRē Team has developed a systematic data collection and integration approach (the WiRē approach) that informs local wildfire risk education efforts and allows for monitoring of community adaptation over time. Through this approach, we collect and analyze locally relevant wildfire risk and social science data to enhance the effectiveness of local wildfire risk mitigation efforts. A unique aspect of this report is that the data collection effort was replicated. The results of the 2011/2012 data collection effort are summarized in Meldrum et. al. (2013). In this report, the results of the 2017 data collection effort are summarized first, followed by a brief comparison of the 2017 results to the 2011/2012 data. This comparison is offered to provide context to the 2017 data; in-depth investigation of change over time is beyond the scope of this report.

Colorado↗
USGS2012

U.S. Geological Survey community for data integration: data upload, registry, and access tool

As a leading science and information agency and in fulfillment of its mission to provide reliable scientific information to describe and understand the Earth, the U.S. Geological Survey (USGS) ensures that all scientific data are effectively hosted, adequately described, and appropriately accessible to scientists, collaborators, and the general public. To succeed in this task, the USGS established the Community for Data Integration (CDI) to address data and information management issues affecting the proficiency of earth science research. Through the CDI, the USGS is providing data and metadata management tools, cyber infrastructure, collaboration tools, and training in support of scientists and technology specialists throughout the project life cycle. One of the significant tools recently created to contribute to this mission is the Uploader tool. This tool allows scientists with limited data management resources to address many of the key aspects of the data life cycle: the ability to protect, preserve, publish and share data. By implementing this application inside ScienceBase, scientists also can take advantage of other collaboration capabilities provided by the ScienceBase platform.

Fact Sheet↗
USGS2013

The U.S. Geological Survey Climate Geo Data Portal: an integrated broker for climate and geospatial data

The increasing availability of downscaled climate projections and other data products that summarize or predict climate conditions, is making climate data use more common in research and management. Scientists and decisionmakers often need to construct ensembles and compare climate hindcasts and future projections for particular spatial areas. These tasks generally require an investigator to procure all datasets of interest en masse, integrate the various data formats and representations into commonly accessible and comparable formats, and then extract the subsets of the datasets that are actually of interest. This process can be challenging and time intensive due to data-transfer, -storage, and(or) -processing limits, or unfamiliarity with methods of accessing climate data. Data management for modeling and assessing the impacts of future climate conditions is also becoming increasingly expensive due to the size of the datasets. The Climate Geo Data Portal (http://cida.usgs.gov/climate/gdp/) addresses these limitations, making access to numerous climate datasets for particular areas of interest a simple and efficient task.

Fact Sheet↗
USGS2007

Advances through collaboration: sharing seismic reflection data via the Antarctic Seismic Data Library System for Cooperative Research (SDLS)

The Antarctic Seismic Data Library System for Cooperative Research (SDLS) has served for the past 16 years under the auspices of the Antarctic Treaty (ATCM Recommendation XVI-12) as a role model for collaboration and equitable sharing of Antarctic multichannel seismic reflection (MCS) data for geoscience studies. During this period, collaboration in MCS studies has advanced deciphering the seismic stratigraphy and structure of Antarctica’s continental margin more rapidly than previously. MCS data compilations provided the geologic framework for scientific drilling at several Antarctic locations and for high-resolution seismic and sampling studies to decipher Cenozoic depositional paleoenvironments. The SDLS successes come from cooperation of National Antarctic Programs and individual investigators in “on-time” submissions of their MCS data. Most do, but some do not. The SDLS community has an International Polar Year (IPY) goal of all overdue MCS data being sent to the SDLS by end of IPY. The community science objective is to compile all Antarctic MCS data to derive a unified seismic stratigraphy for the continental margin – a stratigraphy to be used with drilling data to derive Cenozoic circum-Antarctic paleobathymetry maps and local-to-regional scale paleoenvironmental histories.

Open-File Report↗
USGS2014

Geologic and geophysical data for wells drilled at Raft River Valley, Cassia County, Idaho, in 1977-1978 and data for wells drilled previously

In order to better define the size of the thermal anomaly in the Raft River Valley, Idaho, the U.S. Geological Survey drilled a series of intermediate-depth (nominal 500-ft depth) wells in 1977 and 1978. This report presents geologic, geophysical, and temperature data for these drill holes, along with data for five wells drilled by the Idaho National Engineering Laboratory with U.S. Department of Energy Funding. Data previously reported for other drill holes are also included in order to make them available as digital files. For purposes of defining the thermal anomaly for the geothermal system, temperature gradients are calculated over long depth intervals on the basis of the appearance of reasonable linear segments on a temperature versus plot depth. Temperature versus depth data for some drill holes can be represented by a single gradient, whereas others require multiple gradients to match the data. Data for some drill holes clearly reflect vertical flows of water in the formation surrounding the drill holes, and water velocities are calculated for these drill holes. Within The Narrows area, temperature versus depth data show reversals at different depth in different drill holes. In the main thermal area, temperatures in intermediate-depth drill holes vary approximately linearly but with very high values of temperature gradient. Temperature gradients on a map of the area can be reasonable divided into a large area of regional gradients and smaller areas defining the thermal anomalies.

Idaho↗
USGS2020

Quality assurance/quality control procedure for New Jersey’s water-use data for the New Jersey Water Transfer Data System (NJWaTr)

This report is an instructional reference document that describes methods developed and used by the U.S. Geological Survey (USGS) New Jersey Water Science Center (NJWSC) to assure the quality and completeness of water-use data as provided by the New Jersey Department of Environmental Protection (NJDEP) Bureau of Water Allocation. These data are owned wholly by the State of New Jersey. The role of the USGS NJWSC is to assure the quality of these data by compiling, reviewing, and checking the datasets before uploading them into the New Jersey Water Transfer Data System (NJWaTr) database on an annual basis. The complete uploaded version of the NJWaTr database serves as the repository for New Jersey’s approved and published water-use data. The State of New Jersey maintains a public-facing version of the NJWaTr database (available online at https://www.nj.gov/dep/njgs/geodata/dgs10-3.htm ) that contains monthly water-use data at the municipality and 14-digit Hydrologic Unit Code subwatershed level. The protected version of the NJWaTr database that contains monthly site-specific water-use data is available from the NJDEP upon request.

New Jersey↗
USGS1970

Digital computer terrain mapping from multispectral data, and evaluation of proposed Earth Resources Technology Satellite (ERTS) data channels, Yellowstone National Park preliminary report

Digital computer processing of 12 wavelength bands of visible and reflective infrared scanner data has resulted in successful automatic computer mapping of eight terrain units in a Yellowstone National Park test site. Target areas in the scene were selected for training the computer. Statistical parameters of radiance such as mean, standard deviation, divergence, and covariance were computed for each category of material. These data were used in the computer program to determine which channels are most useful for recognition of all object categories studied, and to actually classify all the unknown data points into the known categories. The following terrain types have been mapped with greater than 80 percent accuracy in a 12-square-mile area with 1,800 feet of relief: bedrock exposures, talus, vegetated rock rubble, glacial kame terrace, glacial till, forest, bog, and water, and shadows. In addition, shadows of clouds and cliffs are depicted. In addition, studies were made of the effectiveness of the proposed Earth Resources Technology Satellite (ERTS) data channels as compared to the computer-selected best four channels in the automatic recognition and mapping of the same terrain types based on simulations, using the same set of data. These simulations resulted in maps whose accuracies were only a few percent less than that using the best set of four channels; they indicate that the ERTS data channels are likely to be successful for terrain analysis of a wide variety of categories encompassing a broad range of spectral reflectance. These studies also indicate that, for a broad range of terrain categories, many' combinations of 3 or 11 channels of data would be satisfactory. We need worry about careful selection of specific wavelength bands only if there is a specific category being sought.

Idaho, Montana, Wyoming↗
USGS1972

Analysis of potential errors in real-time streamflow data and methods of data verification by digital computer

The magnitude, frequency, and types of errors inherent in real-time streamflow data are presented in part I. It was found that real-time data are generally less accurate than are historical data, primarily because real-time data are often used before errors can be detected and corrections applied. Various methods of verifying real-time streamflow data are outlined in part II. Relatively large errors (those greater than 20-30 percent) can be detected readily by use of well-designed verification programs for a digital computer, and smaller errors can be detected only by discharge measurements and field observations. The capability to substitute a simulated discharge value for missing or erroneous data is incorporated in some of the verification routines described. The routines represent concepts ranging from basic statistical comparisons to complex watershed modeling and provide a selection from which real-time data users can choose a suitable level of verification.

Open-File Report↗
USGS1986

Vector data structure conversion at the EROS Data Center

With the increasing prevalence of GIS systems and the processing of spatial data, conversion of data from one system to another has become a more serious problem. This report describes the approach taken to arrive at a solution at the EROS Data Center. The report consists of a main section and a number of appendices. The methodology is described in the main section, while the appendices have system specific descriptions. The overall approach is based on a central conversion hub consisting of a relational database manager and associated tools, with a standard data structure for the transfer of spatial data. This approach is the best compromise between the two goals of reducing the overall interfacing effort and producing efficient system interfaces, while the tools can be used to arrive at a progression of interface sophistication ranging from toolbench to smooth flow. The appendices provide detailed information on a number of spatial data handling systems and data structures and existing interfaces as well as interfaces developed with the described methodology.

Open-File Report↗
USGS1987

Data-collection methods and data summary for the assessment of water quality in Cedar Creek, west-central Illinois

A water-quality, data-collection network on a 26.2-mile reach of Cedar Creek in west-central Illinois was operated from May through December 1985 and from March through October 1986. The study reach drains a 60.8-square-mile area of predominantly agricultural land. However, the city of Galesburg contributes combined- and storm-sewer discharge to the creek that can affect the water quality. This report presents the streamflow and water-quality data collected and describes the methods and instrumentation used. Continuous discharge data were determined at eight gaging stations--five on the main stem and three on tributaries of Cedar Creek. Flows ranged from 0 to 1,200 cubic feet per second. A total of 8 storm sewers and 51 combined sewers were monitored or inspected as part of the study. Continuous precipitation data were collected at five sites. The maximum hourly precipitation intensity during the study period was 1.97 inches per hour. The maximum total precipitation for a storm during the study period was 4.16 inches. Sediment-oxygen-demand rates were measured at 45 locations in the creek. The measured sediment-oxygen-demand rates ranged from 0.4 to 9.1 grams of oxygen per square meter per day. Ninety-seven bottom-material samples were collected and analyzed for chemical oxygen demand, percentage of volatile organic constituents, and concentrations of total organic plus ammonia nitrogen, arsenic, cadmium, chromium, copper, iron, lead, manganese, mercury, and zinc. Traveltime and reaeration rates were determined for 15 subreaches of Cedar Creek during various flow conditions. The reaeration rates ranged from 3.10 to 20.8 per day. Three intensive diel (24-hour) data collections were conducted during low-flow conditions. Data included dissolved oxygen concentration, biochemical oxygen demand, nutrient concentrations, and metal concentrations. During storm-related high-flow conditions, water-quality samples were collected at 5 main-stem sites, 3 tributary sites, the Galesburg wastewater treatment-facility outfall, and 15 sewer outfalls. These samples were analyzed for nutrients and metals, suspended solids, chemical oxygen demands, and biochemical oxygen demands. This report describes methods for measuring stage, discharge, precipitation, sediment oxygen demand, traveltime, and reaeration rate. Collection methods for bottom-material samples, diel water-quality data and samples, combined-sewer overflow frequency and duration, and storm- related sampling of combined sewers, storm sewers, and streams are described. A brief discussion of streamflow computation methods also is presented.

Open-File Report↗
USGS1991

Physical, chemical, and biological data for detailed study of irrigation drainage in the Middle Green River basin, Utah 1988-89, with selected data for 1982-87

Physical, chemical, and biological data were collected in the middle Green River basin, eastern Utah, between 1988 and 1989, as part of a detailed study of the effects of irrigation drainage on wetland areas. Data-collection efforts were concentrated in the Stewart Lake Waterfowl Management Area near Jensen, and Ouray National Wildlife Refuge near Ouray. Data also were collected from Ashley Creek near Vernal, Pelican Lake near Ouray, and in Pariette Wetlands near Myton. A limited quantity of data collected during earlier studies (1982-87), funded by the U.S. Fish and Wildlife Service, also is included. This report contains data needed to assess the effects of selenium and other potentially toxic contaminants on streams and wetlands. Data consist of concentrations of trace elements and common elements in samples of water, sediment, plants, waterfowl, birds, fish, and invertebrates. Other data presented in the report are ground-water levels, surface-water discharges, radiochemical constituents in water, analyses of organochlorine compounds in biota, and morphonetric measurements of biota.

Utah↗
USGS1995

Land-cover sampling designs, data-collection procedures, and land-cover data for the Central Nebraska Basins, 1993-94

Within the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program, land-cover data are used in characterizing drainage areas upstream from surface-water sampling sites and areas selected for spatially distributed ground-water sampling. During the period of time when the initial 20 NAWQA study study-unit investigations were evaluating existing land-cover data, a Prototype 190 Conterminous U.S. land Cover Characteristics Data Set was produced by the Survey's EROS Data Center in Sioux Falls, South Dakota. As part of the Central Nebraska Basins (CNB) study-unit investigation, a method was developed to estimate the areal extent of the principal land-cover types within selected seasonally distinct land-cover (SDLC) regions defined in the 1990 prototype data set. This report describes the sampling designs and methods used to collect land-cover data in the CNB study unit. Data collected at 309 sampling sites during the summers of 1993 and 1994 are presented and statistically summarized. Eleven land-cover categories were quantified, including major field crops and broad noncropland cover types.

Open-File Report↗
USGS1993

Hydro-Climatic Data Network (HCDN) Streamflow Data Set, 1874-1988

The potential consequences of climate change to continental water resources are of great concern in the management of those resources. Critically important to society is what effect fluctuations in the prevailing climate may have on hydrologic conditions, such as the occurrence and magnitude of floods or droughts and the seasonal distribution of water supplies within a region. Records of streamflow that are unaffected by artificial diversions, storage, or other works of man in or on the natural stream channels or in the watershed can provide an account of hydrologic responses to fluctuations in climate. By examining such records given known past meteorologic conditions, we can better understand hydrologic responses to those conditions and anticipate the effects of postulated changes in current climate regimes. Furthermore, patterns in streamflow records can indicate when a change in the prevailing climate regime may have occurred in the past, even in the absence of concurrent meteorologic records. A streamflow data set, which is specifically suitable for the study of surface-water conditions throughout the United States under fluctuations in the prevailing climatic conditions, has been developed. This data set, called the Hydro-Climatic Data Network, or HCDN, consists of streamflow records for 1,659 sites throughout United States and its Territories. Records cumulatively span the period 1874 through 1988, inclusive, and represent a total of 73,231 water years of information. Development of the HCDN Data Set: Records for the HCDN were obtained through a comprehensive search of the extensive surface- water data holdings of the U.S. Geological Survey (USGS), which are contained in the USGS National Water Storage and Retrieval System (WATSTORE). All streamflow discharge records in WATSTORE through September 30, 1988, were examined for inclusion in the HCDN in accordance with strictly defined criteria of measurement accuracy and natural conditions. No reconstructed records of 'natural flow' were permitted, nor was any record extended or had missing values 'filled in' using computational algorithms. If the streamflow at a station was judged to be free of controls for only a part of the entire period of record that is available for the station, then only that part was included in the HCDN, but only if it was of sufficient length (generally 20 years) to warrant inclusion. In addition to the daily mean discharge values, complete station identification information and basin characteristics were retrieved from WATSTORE for inclusion in the HCDN. Statistical characteristics, including the monthly mean discharge, as well as the annual mean, minimum and maximum discharge values, were derived for the records in the HCDN data set. For a full description of the development and content of the Hydro-Climatic Data Network, please take a look at the HCDN Report.

Water-Resources Investigations Report↗
USGS2016

An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data

Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MAD training = 2.5 and MAD testing = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.

Remote Sensing↗