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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 1,495 records · Page 83Linked to original sources
USGS2000

Development of a contour map showing generalized skew coefficients of annual peak discharges of rural, unregulated streams in New York, excluding Long Island

Flood-frequency relations that are developed by fitting the logarithms of annual peak discharges to a Pearson Type-III distribution are sensitive to skew coefficients. Estimates of population skew for a site are improved when computed from the weighted average of (1) the sample (station) skew, and (2) an unbiased, generalized skew estimate. A weighting technique based on the number of years of record at each of 226 sites was used to develop a contour map of unbiased, generalized skew coefficients for New York. An attempt was made to group (regionalize) the station skew coefficients into five hydrologically similar areas of New York, but the statewide version proved to be as accurate as the regionalized version and therefore was adopted as the final generalized skew-coefficient map for New York. An error analysis showed the statewide contour map to have lower MSE?s (mean square errors) than those computed from (1) the five regional skewcoefficient contour maps, (2) a previously used (1982) nationwide skew coefficient map, and (3) the weighted mean of skew coefficients for sites within each of five hydrologically uniform, but distinct areas of New York.

New York↗
USGS2003

Development and testing of method for assessing and mapping agricultural areas susceptible to atrazine leaching in the state of Washington

In a joint effort by the Washington State Department of Agriculture, the Washington Department of Ecology, and the U.S. Geological Survey, the Environmental Protection Agency's Pesticide Root Zone Model and a Geographic Information System were used to develop and test a method for screening and mapping the susceptibility of ground water in agricultural areas to pesticide contamination. The objective was to produce a map that would be used by the Washington State Department of Agriculture to allocate resources for monitoring pesticide levels in ground water. The method was tested by producing a map showing susceptibility to leaching of the pesticide atrazine for the Columbia Basin Irrigation Project, which encompasses an area of intensive agriculture in eastern Washington. The reliability of the atrazine map was assessed by using statistical procedures to determine whether the median of the percentage of atrazine simulated to leach below the root zone in wells where atrazine was detected was statistically greater than the median percentage at wells where atrazine was not detected (at or above 0.001 microgram per liter) in 134 wells sampled by the U.S. Geological Survey. A statistical difference in medians was not found when all 134 wells were compared. However, a statistical difference was found in medians for two subsets of the 134 wells that were used in land-use studies (studies examining the quality of ground water beneath specific crops). The statistical results from wells from the land-use studies indicate that the model potentially can be used to map the relative susceptibility of agricultural areas to atrazine leaching. However, the distinction between areas of high and low susceptibility may not yet be sufficient to use the method for allocating resources to monitor water quality. Several options are offered for improving the reliability of future simulations.

Water-Resources Investigations Report↗
USGS1982

Hydrologic maps of the High Plains aquifer, January 1981, southwestern Kansas

The U.S. Geological Survey furnishes maps depicting saturated thickness of the High Plains aquifer to the Southwest Kansas Groundwater Management District No. 3 through a continuing cooperative agreement. The Groundwater Management District uses the maps to calculated a planned depletion rate of 40 percent of the saturated thickness of the aquifer in 25 years for a 13-county area. Because the information contained on the maps is used in management decisions, the Groundwater Management District is concerned with the accuracy, reliability, and consistency of the hydrologic information that the maps show. A mathematical technique, called kriging, has been used in the form of a computer program to interpolate hydrologic data based on a network of measured values (Karlinger and Skrivan, 1980). The technique has been applied to geohydrologic data in west-central Kansas with good results (Dunlap and Spinazola, 1981). The program calculates the values at the center of each 1-mile section in a predefined region and facilitates contouring of selected values. The kriging technique was used to produce the hydrologic maps in this report.

Kansas↗
USGS1984

Map showing outcrops of granitic rocks, Basin and Range Province, Nevada

This map report is one of a series of geologic and hydrologic maps covering all or parts of States within the Basin and Range province of the western United States. The map reports contain information on subjects that characterize the geohydrology of the province, including the ground-water hydrology, ground-water quality, surface distribution of selected rock types, tectonic conditions, areal geophysics, Pleistocene lakes and marshes, and mineral and energy resources. This work is a part of the U.S. Geological Survey's program for geologic and hydrologic evaluation of the Basin and Range province to identify potentially suitable regions for further study relative to isolation of high-level nuclear waste (Bedinger, Sargent, and Reed, 1984). Data for this report were taken largely from Spengler and others (1979), supplemented by data from individual geologic maps and reports, following the project guidelines as defined in Sargent and Bedinger (1984). As used in this report, granitic rocks include granodiorite, quartz monzonite, and granite, and locally associated mafic rocks such as diorite and gabbro. In the Snake Range and Ruby Mountains, Stewart (1980) has recognized large areas as metamorphic core complexes, and in these complexes some plutonic masses may not extend to great depth because of lateral displacement along low-angle faults subsequent to intrusion. The Description of Map Units includes the sources of data, the geologic, and if available, the radiometric age, lithologic character, type of intrusive body and relation to rock units, where known, for the granitic rocks within outlined and numbered areas in each county of the study area. The radiometric ages of the rock units are only those which are available and do not necessarily represent the entire age range of the geologic units.

Nevada↗
USGS1986

Potentiometric-surface map of the Wyodak-Anderson coal bed, Powder River structural basin, Wyoming, 1973-84

Previous water level maps of shallow aquifers in the Powder River structural basin in Wyoming were based on water levels from wells completed in different stratigraphic intervals within thick sequences of sedimentary rocks. A potentiometric surface using water levels from a single aquifer had never been mapped throughout the basin. The sandstone aquifers in the Fort Union Formation of Paleocene age and the Wasatch Formation of Eocene age are discontinuous and lenticular, and do not extend even short distances. Coal aquifers are more continuous and the Wyodak-Anderson coal bed, in the Fort Union Formation, has been mapped in much of the Powder River structural basin in Wyoming. Water level altitudes in the Wyodak-Anderson coal bed and other stratigraphically equivalent coal beds were mapped to determine if they represent a continuous potentiometric surface in the Powder River structural basin. The potentiometric surface, except in the vicinity of the Wyodak mine east of Gillette, represents a premining condition as it was based on water level measurements made during 1973-84 that were not significantly affected by mining. The map was prepared in cooperation with the U.S. Bureau of Land Management. (Lantz-PTT)

Wyoming↗
USGS2020

Semantically enabling map projections knowledge

Map projections are an area of cartography with a firm mathematical foundation for their creation and display providing a basis for a knowledge representation. Using only variations on a single equation set, an infinite number of projections can be created, but less than 100 are in active use. Because each projection preserves specific characteristics, such as area, angles, global look, or a compromise of properties, classifications of map projections have been developed to aid in knowledge representation. These classifications are used for decision-making. They help select the correct projection for the map use. They assist users with determining the correct orientation, standard parallels and meridians. The classifications also inform the user how to adjust the selection based on size, extent, and latitude. Semantics can be used to automate map projections knowledge into a knowledge base that can be accessed by humans and machines. This work details a semantic representation of map projections knowledge and provides a simple example of a use case that exploits the knowledge base.

Cartography and Geoinformation↗
USGS2021

Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: A case study of the 2018 Anchorage, Alaska Earthquake

Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (M w ) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.

Alaska↗
USGS2022

Submarine landslide susceptibility mapping in recently deglaciated terrain, Glacier Bay, Alaska

Submarine mass wasting events have damaged underwater structures and propagated waves that have inundated towns and affected human populations in nearby coastal areas. Susceptibility to submarine landslides can be pronounced in degrading cryospheric environments, where existing glaciers can provide high volumes of sediment, while cycles of glaciation and ice-loss can damage and destabilize slopes. Despite their contribution to potential tsunami hazard, submarine landslides can be difficult to study because of limited access and data collection in underwater environments. Here we present a method to quantify and map the submarine landslide susceptibility of sediment-covered slopes in Glacier Bay, Glacier Bay National Park and Preserve, Alaska, using multibeam-sonar bathymetric digital elevation models (DEMs) and historical maps of glacial extents over the last ∼250 years. After mapping an inventory of >7,000 landslide scarps in submarine sediments, we filtered the inventory by size to account for limitations in DEM resolution and spatial scales relevant to tsunami hazards. We then assessed landslide concentration, accounting for the age of the initial exposure of submarine slopes by deglaciation. We found a positive correlation between landslide concentration and deglaciation age, which we interpreted as a mean landslide accumulation rate over the period of record. Local deviations from this rate indicated differences in susceptibility. Additionally, we accounted for some of the effect of material and morphometric properties by estimating the submarine bedrock-sediment distribution using a morphometric model and assessing the relationship between slope angle and landslide incidence. Finally, we supplemented our susceptibility assessment with a geomorphic component based on the propensity of active submarine fans and deltas to produce landslides. Thus, our map of submarine landslide susceptibility incorporates three components: age-adjusted landslide concentration, slope angle, and geomorphology. We find that areas of mapped high susceptibility correlate broadly with areas of high sediment input and availability, locations of fans and deltas, and steep sediment-covered glacially carved fjords and troughs. Areas of high submarine landslide susceptibility in Glacier Bay moderately correspond with locations of known high-hazard subaerial slopes, but more research on submarine and subaerial landslides in degrading cryospheric environments would be beneficial to better understand landslide and tsunami hazards.

Alaska↗
USGS2024

Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape

Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km 2 ) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m 3 /m 3 and a bias of 0.02 m 3 /m 3 . Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides.

California↗
USGS2023

Mapping abandoned uranium mine features using Worldview-3 imagery in portions of Karnes, Atascosa and Live Oak Counties, Texas

Worldview-3 (WV3) 16-band multispectral data were used to map exposed bedrock and mine waste piles associated with legacy open-pit mining of sandstone-hosted roll-front uranium deposits along the South Texas Coastal Plain. We used the “spectral hourglass” approach to extract spectral endmembers representative of these features from the image. This approach first requires calibrating the imagery to reflectance, then masking for vegetation, followed by spatial and spectral data reduction using a principal component analysis-based procedure that reduces noise and identifies homogeneous targets which are “pure” enough to be considered spectral endmembers. In this case, we used a single WV3 image which covered an ~11.5 km by ~19.5 km area of Karnes, Atascosa and Live Oak Counties, underlain by mined rocks from the Jackson Group and Catahoula Formation. Up to 58 spectral endmembers were identified using a further multi-dimensional class segregation method and were used as inputs for spectral angle mapper (SAM) classification. SAM classification resulted in the identification of at least 117 mine- and mine waste-related features, most of which were previously unknown. Class similarity was further evaluated, and the dominant minerals in each class were identified by comparison to spectral libraries and measured samples of actual Jackson Group uranium host rocks. Redundant classes were eliminated, and SAM was run a second time using a reduced set of 23 endmembers, which were found to map these same features as effectively as using the full 58 set of endmembers, but with significantly reduced noise and spectral outliers. Our classification results were validated by evaluating detailed scale mapping of three known mine sites (Esse-Spoonamore, Wright-McCrady and Garbysch-Thane) with published ground truth information about the vegetation cover, extent of erosion and exposure of waste pile materials and/or geologic information about host lithology and mineralization. Despite successful demonstration of the utility of WV3 data for inventorying mine features, additional landscape features such as bare agricultural fields and oil and gas drill pads were also identified. The elimination of such features will require combining the spectral classification maps presented in this study with high-quality topographic data. Also, the spectral endmembers identified during the course of this study could be useful for larger-scale mapping efforts using additional well-calibrated WV3 images beyond the coverage of our initial study area.

Texas↗
USGS2014

Mapping forest height in Alaska using GLAS, Landsat composites, and airborne LiDAR

Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data.

Alaska↗
USGS2026

Thermal detectability of subsurface water ice on Mars: A comparative analysis for the Subsurface Water Ice Mapping (SWIM) Project

We have developed a new global map of shallow ground ice distribution, SWIM23, based on Mars Global Surveyor Thermal Emission Spectrometer data and made systematic comparisons between this new map and two similar, previously developed data products. We have explored the origin of differences between the three ice maps by detailing technical and procedural differences in their development, by making global pixel-by-pixel comparisons, and by carrying out a series of one-dimensional thermal simulations to explore fundamental physical limitations of thermal ice-detection techniques. These efforts and the production of a composite thermal ice-consistency map supported integration of multiple geophysical data products relevant to ice detection in the upper meter of the Martian regolith into the larger Mars Subsurface Water Ice Mapping project. Our work also highlights fundamental physical limitations to thermal ice detection as a technique, particularly the rapid fall-off in ice detection sensitivity at depths >30 cm, which produces maximum uncertainty in the presence and depth of ice within regions preferred for potential human landing sites. A future Mars orbiter mission designed to detect ice and support crewed landing site selection in the midlatitude region should give payload priority to an instrument capable of probing the 1–5 m depth range (i.e., a high-frequency radar), over a next-generation thermal spectrometer, which is unlikely to offer clarity on ice table depths or lateral continuity of the ice table in the locations of highest interest.

Planetary Science Journal↗
USGS2007

The need for sustained and integrated high-resolution mapping of dynamic coastal environments

The coastal zone of the United States is a dynamic environment evolving in response to both natural processes and human activities. In order to protect coastal populations and resources, a detailed understanding of the physical setting and of the processes responsible for change is required. A sustained program of mapping coastal areas provides a means to establish baseline conditions, document change, and, in conjunction with models of physical processes, predict future behavior. Recent advances in mapping technology, including airborne lidars and hyperspectral imagers, allow for the rapid collection of high-resolution elevation data and land use information on a national scale. These rich data sets are critical to evaluating risk associated with coastal hazards, such as flooding during extreme storms. For example, coastal elevation data is a fundamental parameter in storm surge models that predict where flooding will occur, and land use maps serve as the foundation of assessments that identify the resources and populations that are most vulnerable. A comprehensive, national coastal mapping plan that is designed to collect, manage, and distribute these data, as well as to take advantage of recent progress in mapping technology, will provide a wealth of information for studying the processes of physical change, for determining areas vulnerable to coastal hazards, and for protecting and managing our coastal communities and resources.

Marine Technology Society Journal↗
USGS2014

Utilizing multi-sensor fire detections to map fires in the United States

In 2006, the Monitoring Trends in Burn Severity (MTBS) project began a cooperative effort between the US Forest Service (USFS) and the U.S.Geological Survey (USGS) to map and assess burn severity all large fires that have occurred in the United States since 1984. Using Landsat imagery, MTBS is mandated to map wildfire and prescribed fire that meet specific size criteria: greater than 1000 acres in the west and 500 acres in the east, regardless of ownership. Relying mostly on federal and state fire occurrence records, over 15,300 individual fires have been mapped. While mapping recorded fires, an additional 2,700 “unknown” or undocumented fires were discovered and assessed. It has become apparent that there are perhaps thousands of undocumented fires in the US that are yet to be mapped. Fire occurrence records alone are inadequate if MTBS is to provide a comprehensive accounting of fire across the US. Additionally, the sheer number of fires to assess has overwhelmed current manual procedures. To address these problems, the National Aeronautics and Space Administration (NASA) Applied Sciences Program is helping to fund the efforts of the USGS and its MTBS partners (USFS, National Park Service) to develop, and implement a system to automatically identify fires using satellite data. In near real time, USGS will combine active fire satellite detections from MODIS, AVHRR and GOES satellites with Landsat acquisitions. Newly acquired Landsat imagery will be routinely scanned to identify freshly burned area pixels, derive an initial perimeter and tag the burned area with the satellite date and time of detection. Landsat imagery from the early archive will be scanned to identify undocumented fires. Additional automated fire assessment processes will be developed. The USGS will develop these processes using open source software packages in order to provide freely available tools to local land managers providing them with the capability to assess fires at the local level.

Conference Paper↗
USGS1995

Mapping of bird distributions from point count surveys

Maps generated from bird survey data are used for a variety of scientific purposes, but little is known about their bias and precision. We review methods for preparing maps from point count data and appropriate sampling methods for maps based on point counts. Maps based on point counts can be affected by bias associated with incomplete counts, primarily due to changes in proportion counted as a function of observer or habitat differences. Large-scale surveys also generally suffer from regional and temporal variation in sampling intensity. A simulated surface is used to demonstrate sampling principles for maps.

Book chapter↗
USGS1995

Accuracy of migrant landbird habitat maps produced from LANDSAT TM data: Two case studies in southern Belize

The study investigated the utility of Landsat TM data applied to produce geo-referenced habitat maps for two study areas (Toledo and Stann Creek). Locational and non-site-specific map accuracy was evaluated by stratified random sampling and statistical analysis of satellite classification (SCR) versus air photo interpretation results (PIR) for the overall classification and individual classes. The effect of classification scheme specificity on map accuracy was also assessed. A decision criteria was developed for the minimum acceptable level of map performance (i.e., classification accuracy and scheme specificity). A satellite map was deemed acceptable if it has a useful degree of classification specificity, plus either an adequate overall locational agreement (< 70%) and/or non-site specific agreement (Chi Square goodness of fit test results indicating insufficient evidence to reject the null hypothesis that the overall classification distribution for the SCR and PIR are equal). For the most detailed revised classification, overall locational accuracy ranges from 52% (5 classes) for the Toledo to 63% (9 classes) for the Stann Creek. For the least detailed revised classification, overall locational accuracy ranges from 91% (2 classes) for Toledo to 86% (5 classes) for Stann Creek. Considering both location and non-site-specific accuracy results, the most detailed yet insufficient accurate classification for both sites includes low/medium/tall broadleaf forest, broadleaf forest scrub and herb-dominated openings. For these classifications, the overall locational accuracy is 72% for Toledo (4 classes) and 75% for Stann Creek (7 classes). This level of classification detail is suitable for aiding many analyses of migrant landbird habitat use.

Book chapter↗
USGS2007

Local search for optimal global map generation using mid-decadal landsat images

NASA and the US Geological Survey (USGS) are seeking to generate a map of the entire globe using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor data from the "mid-decadal" period of 2004 through 2006. The global map is comprised of thousands of scene locations and, for each location, tens of different images of varying quality to chose from. Furthermore, it is desirable for images of adjacent scenes be close together in time of acquisition, to avoid obvious discontinuities due to seasonal changes. These characteristics make it desirable to formulate an automated solution to the problem of generating the complete map. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. Preliminary results of running the algorithm on image data sets are summarized. The results suggest a significant improvement in map quality using constraint-based solutions. Copyright ?? 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Conference Paper↗
USGS1983

EFFICIENT TRANSFER OF DATA BETWEEN MAPS OF DIFFERENT PROJECTIONS.

There are several ways to transfer data from an existing map to another map by computer. If the projection and parameters of both maps are known, analytic formulas may be used to convert rectangular coordinates of the first map to latitude and longitude and then to rectangular coordinates of the second map. This general solution, contained in a USGS computer program, involves inverse formulas for the first projection and forward formulas for the second projection.

Conference Paper↗