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At least 937 records · Page 52Linked to original sources

USGS2025

An integrated sensor network and data driven approach to satellite remote sensing of dissolved organic matter

Traditional remote sensing retrieval models for water quality have historically relied on limited, localized data sets due to the prohibitive costs of extensive field campaigns and logistical challenges of collecting match-up data with satellite overpasses. As a result, these models often lack generalizability across seasons, tides, and sites. Furthermore, small field data sets limit the utility of modern machine learning techniques to advance remote sensing retrieval models. In situ optical sensors deployed in a sensor network to continuously monitor larger water bodies can drastically increase the number of measurements, providing the opportunity to develop new approaches for building robust remote sensing retrieval models by leveraging both remote sensing data and in situ networks as an integrated monitoring system. This study leverages a large “ground-to-space” sensor network that combines an in situ optical sensor network with satellite-based remote sensing to overcome these limitations. Utilizing a large-scale data set from the U.S. Geological Survey's Sacramento—San Joaquin River Delta monitoring network, of dissolved organic matter fluorescence measurements, and remote sensing data from the European Space Agency's Sentinel-2A and -2B satellites, this study implemented a data driven approach for dissolved organic matter models. The data set, consisting of 982 samples collected between 2018 and 2021 was used to train and validate a random forest model ( R 2 = 0.76, RMSE = 6.1 Quinine Sulfate Equivalents), with demonstrated applicability across diverse site conditions, tidal stages, and seasons. This work provides a scalable solution to address critical challenges in water quality monitoring and offers a replicable framework for global water quality management.

Calfornia↗

USGS2017

Examining the occupancy–density relationship for a low-density carnivore

The challenges associated with monitoring low-density carnivores across large landscapes have limited the ability to implement and evaluate conservation and management strategies for such species. Non-invasive sampling techniques and advanced statistical approaches have alleviated some of these challenges and can even allow for spatially explicit estimates of density, one of the most valuable wildlife monitoring tools. For some species, individual identification comes at no cost when unique attributes (e.g. pelage patterns) can be discerned with remote cameras, while other species require viable genetic material and expensive laboratory processing for individual assignment. Prohibitive costs may still force monitoring efforts to use species distribution or occupancy as a surrogate for density, which may not be appropriate under many conditions. Here, we used a large-scale monitoring study of fisher Pekania pennanti to evaluate the effectiveness of occupancy as an approximation to density, particularly for informing harvest management decisions. We combined remote cameras with baited hair snares during 2013–2015 to sample across a 70 096-km 2 region of western New York, USA. We fit occupancy and Royle–Nichols models to species detection–non-detection data collected by cameras, and spatial capture–recapture (SCR) models to individual encounter data obtained by genotyped hair samples. Variation in the state variables within 15-km 2 grid cells was modelled as a function of landscape attributes known to influence fisher distribution. We found a close relationship between grid cell estimates of fisher state variables from the models using detection–non-detection data and those from the SCR model, likely due to informative spatial covariates across a large landscape extent and a grid cell resolution that worked well with the movement ecology of the species. Fisher occupancy and density were both positively associated with the proportion of coniferous-mixed forest and negatively associated with road density. As a result, spatially explicit management recommendations for fisher were similar across models, though relative variation was dampened for the detection–non-detection data. Synthesis and applications . Our work provides empirical evidence that models using detection–non-detection data can make similar inferences regarding relative spatial variation of the focal population to models using more expensive individual encounters when the selected spatial grain approximates or is marginally smaller than home range size. When occupancy alone is chosen as a cost-effective state variable for monitoring, simulation and sensitivity analyses should be used to understand how inferences from detection–non-detection data will be affected by aspects of study design and species ecology.

Journal of Applied Ecology↗

USGS1981

Heavy metals and manganese oxides in the genesee watershed, New York state: Effects of geology and land use

Manganese oxide coatings on gravels from 255 sites on tributary streams in the Genesee River Watershed were analyzed for Mn, Fe, Zn, Cd, Co, Ni, Pb, and Cu. The results were compared with data on bedrock geology, surficial geology and land use, using factor analysis and stepwise multiple regression. All metals except Pb show strong positive correlation with Mn. This association results from the well-known tendency of Mn oxide precipitates to adsorb and incorporate dissolved trace metals. Pb may be present in a separate phase on the gravel surfaces; alternatively Pb abundance may be so strongly influenced by environmental factors that the effect of varying abundance of the carrier phase becomes relatively unimportant. When the effects of varying Mn abundance are allowed for, Pb and to a lesser extent Zn and Cu abundances are seen to be related to commercial, industrial and residential land use. In addition to this pollution effect, all the trace metals, Cd and Ni most strongly, tend to be more abundant in oxide coatings from streams in the forested uplands in the southern part of the area. This probably reflects increased geochemical mobility of the metals in the more acid soils and groundwater of the southern region. A strong Zn anomaly is present in streams draining areas underlain by the Lockport Formation. Oxide coatings in these streams contain up to 5% Zn, originating from disseminated sphalerite in the Lockport and secondary Zn concentrations in the overlying muck soils. The same group of metals, plus calcium and loss on ignition, were determined in the silt and clay (minus 230 mesh) fraction of stream sediments from 129 of the same sites, using a hot nitric acid leach. The amounts of manganese in the sediments are low (average 1020 ppm) and manganese oxides are, at most, of relatively minor significance in the trace-metal geochemistry of these sediments. The bulk of the trace metals in sediment appears to be associated with iron oxides, clays and organic matter.

New York↗

USGS2003

Fire and aquatic ecosystems of the western USA: Current knowledge and key questions

Understanding of the effects of wildland fire and fire management on aquatic and riparian ecosystems is an evolving field, with many questions still to be resolved. Limitations of current knowledge, and the certainty that fire management will continue, underscore the need to summarize available information. Integrating fire and fuels management with aquatic ecosystem conservation begins with recognizing that terrestrial and aquatic ecosystems are linked and dynamic, and that fire can play a critical role in maintaining aquatic ecological diversity. To protect aquatic ecosystems we argue that it will be important to: (1) accommodate fire-related and other ecological processes that maintain aquatic habitats and biodiversity, and not simply control fires or fuels; (2) prioritize projects according to risks and opportunities for fire control and the protection of aquatic ecosystems; and (3) develop new consistency in the management and regulatory process. Ultimately, all natural resource management is uncertain; the role of science is to apply experimental design and hypothesis testing to management applications that affect fire and aquatic ecosystems. Policy-makers and the public will benefit from an expanded appreciation of fire ecology that enables them to implement watershed management projects as experiments with hypothesized outcomes, adequate controls, and replication.

Forest Ecology and Management↗

USGS2013

The 3D Elevation Program: summary for Wisconsin

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Wisconsin, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, water supply and quality, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Wisconsin↗

USGS2011

Mapping the Natchez Trace Parkway

Based on a National Park Service (NPS) landcover classification, a landcover map of the 715-km (444-mile) NPS Natchez Trace Parkway (hereafter referred to as the "Parkway") was created. The NPS landcover classification followed National Vegetation Classification (NVC) protocols. The landcover map, which extended the initial landcover classification to the entire Parkway, was based on color-infrared photography converted to 1-m raster-based digital orthophoto quarter quadrangles, according to U.S. Geological Survey mapping standards. Our goal was to include as many alliance classes as possible in the Parkway landcover map. To reach this goal while maintaining a consistent and quantifiable map product throughout the Parkway extent, a mapping strategy was implemented based on the migration of class-based spectral textural signatures and the congruent progressive refinement of those class signatures along the Parkway. Progressive refinement provided consistent mapping by evaluating the spectral textural distinctiveness of the alliance-association classes, and where necessary, introducing new map classes along the Parkway. By following this mapping strategy, the use of raster-based image processing and geographic information system analyses for the map production provided a quantitative and reproducible product. Although field-site classification data were severely limited, the combination of spectral migration of class membership along the Parkway and the progressive classification strategy produced an organization of alliances that was internally highly consistent. The organization resulted from the natural patterns or alignments of spectral variance and the determination of those spectral patterns that were compositionally similar in the dominant species as NVC alliances. Overall, the mapped landcovers represented the existent spectral textural patterns that defined and encompassed the complex variety of compositional alliances and associations of the Parkway. Based on that mapped representation, forests dominate the Parkway landscape. Grass is the second largest Parkway land cover, followed by scrub-shrub and shrubland classes and pine plantations. The map provides a good representation of the landcover patterns and their changes over the extent of the Parkway, south to north.

Alabama;Mississippi;Tennessee↗

USGS2002

Guidance on the use of passive-vapor-diffusion samplers to detect volatile organic compounds in ground-water-discharge areas, and example applications in New England

Polyethylene-membrane passive-vapor-diffusion samplers, or PVD samplers, have been shown to be an effective and economical reconnaissance tool for detecting and identifying volatile organic compounds (VOCs) in bottom sediments of surface-water bodies in areas of ground-water discharge. The PVD samplers consist of an empty glass vial enclosed in two layers of polyethylene membrane tubing. When samplers are placed in contaminated sediments, the air in the vial equilibrates with VOCs in pore water. Analysis of the vapor indicates the presence or absence of VOCs and the likely magnitude of concentrations in pore water.

Maine, Vermont, New Hampshire, Massachusetts, Penn↗

USGS2013

The 3D Elevation Program: summary for Rhode Island

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Rhode Island, elevation data are critical for flood risk management, natural resources conservation, coastal zone management, sea level rise and subsidence, agriculture and precision farming, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative (Snyder, 2012a,b), managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Rhode Island↗

USGS2022

West Virginia and Landsat

Many may recall “Take Me Home, Country Roads,” made famous by John Denver, leads with the lyric “almost heaven, West Virginia, Blue Ridge Mountains, Shenandoah River.” The descriptors are apt. Nicknamed the “Mountain State,” West Virginia inspires thoughts of coal mining or logging in the Appalachian Mountains and valleys, or the leaping trout in the winding waters of New River Gorge National Park & Reserve . West Virginia is second only to Wyoming in coal production nationwide. Its mines produced more than 67 million tons of coal in 2020. Logging pumped about $3.4 billion into the State’s economy in 2019; tourist spending added another $4.6 billion. Those industries are key for West Virginia, but agriculture and fisheries also play a role in the State's economic fortunes. Peaches and apples are major drivers of food production, as are beef and poultry. Trout, meanwhile, are caught and sold commercially, and are stocked throughout the State's rivers for local and visiting anglers. Scientists, land managers, and others use imagery from the U.S. Geological Survey Landsat satellite program's deep historical archive to better understand and manage West Virginia’s storied forests, fields, mountains, and foothills.

West Virginia↗

USGS2013

The 3D Elevation Program: summary for Virginia

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the Commonwealth of Virginia, elevation data are critical for urban and regional planning, natural resources conservation, flood risk management, agriculture and precision farming, resource mining, infrastructure and construction management, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Virginia↗

USGS2013

The 3D Elevation Program: summary for Minnesota

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Minnesota, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, infrastructure and construction management, water supply and quality, coastal zone management, and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Minnesota↗

USGS2013

The 3D Elevation Program: summary for Texas

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Texas, elevation data are critical for natural resources conservation; wildfire management, planning, and response; flood risk management; agriculture and precision farming; infrastructure and construction management; water supply and quality; and other business uses. Today, high-quality light detection and ranging (lidar) data are the source for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

Texas↗

USGS2016

Estimating 40 years of nitrogen deposition in global biomes using the SCIAMACHY NO 2 column

Owing to human activity, global nitrogen (N) cycles have been altered. In the past 100 years, global N deposition has increased. Currently, the monitoring and estimating of N deposition and the evaluation of its effects on global carbon budgets are the focus of many researchers. NO 2 columns retrieved by space-borne sensors provide us with a new way of exploring global N cycles and these have the ability to estimate N deposition. However, the time range limitation of NO 2 columns makes the estimation of long timescale N deposition difficult. In this study we used ground-based NO x emission data to expand the density of NO 2 columns, and 40 years of N deposition (1970–2009) was inverted using the multivariate linear model with expanded NO 2 columns. The dynamic of N deposition was examined in both global and biome scales. The results show that the average N deposition was 0.34 g N m –2 year –1 in the 2000s, which was an increase of 38.4% compared with the 1970s’. The total N deposition in different biomes is unbalanced. N deposition is only 38.0% of the global total in forest biomes; this is made up of 25.9%, 11.3, and 0.7% in tropical, temperate, and boreal forests, respectively. As N-limited biomes, there was little increase of N deposition in boreal forests. However, N deposition has increased by a total of 59.6% in tropical forests and croplands, which are N-rich biomes. Such characteristics may influence the effects on global carbon budgets.

International Journal of Remote Sensing↗

USGS1986

Bibliography on ground water in glacial-aquifer systems in the Northeastern United States

The U.S. Geological Survey established the Regional Aquifer-System Analysis (RASA) program to evaluate major interconnected aquifers or groups of aquifers that share similar characteristics within a region. One of the objectives of the Northeastern Glacial RASA is to provide information on the occurrence and quality of ground water in glacial deposits in ten States: Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, Ohio, Pennsylvania, and New Jersey. To help meet the objectives of the RASA program, an automated bibliographic data base was developed. The data base contains references to ground-water resources of glacial-aquifer systems in the ten States listed above. This bibliography contains more than 700 ground-water related references that date from 1839 through 1984. The bibliography lists books, journal articles, conference proceedings, government and other technical reports, theses, and maps. Unpublished manuscripts, publications in press, newspaper articles, and book reviews are omitted from the bibliography.

Circular↗

USGS1985

Geochemical map of the Guadalupe Escarpment Wilderness Study Area, Eddy County, New Mexico

During 1982, the U.S. Geological Survey and the U.S. Bureau of Mines conducted field investigations to evaluate the mineral resource potential of the Guadalupe Escarpment Wilderness Study Area. Field studies included geologic mapping, geochemical sampling, and a survey of known mines, prospects, and mineralized zones. This map presents the results of a geochemical survey of the area by the Geological Survey and complements the mineral resource assessment of the area by Hayes and others (1983). The Guadalupe Escarpment Wilderness Study Area encompasses approximately 21,300 acres along Guadalupe Ridge in the southern end of the Guadalupe Mountains about 35 miles southwest of Carlsbad, N. Mex. (fig. 1). The area trends northeasterly, is bounded on the south by the Texas State line and the northern boundary of Guadalupe Mountains National Park. The study area is bounded on the northeast by Carlsbad Caverns National Park. The area comprises several narrow, gently sloping mesas bounded by deeply incised canyons. Elevations range from 7,413 feet on Camp Wilderness Ridge to approximately 4,875 feet at Franks Spring. A rough jeep road along the northwest boundary of the study area can be reached by U.S. Forest Service roads from the northwest. The southeastern part of the study area can be approached via unimproved ranch roads leading off U.S. Highway 62-180.

New Mexico↗

USGS2019

Probability of streamflow permanence model (PROSPER): A spatially continuous model of annual streamflow permanence throughout the Pacific Northwest

The U.S. Geological Survey (USGS) has developed the PRObability of Streamflow PERmanence (PROSPER) model, a GIS raster-based empirical model that provides streamflow permanence probabilities (probabilistic predictions) of a stream channel having year-round flow for any unregulated and minimally-impaired stream channel in the Pacific Northwest region, U.S. The model provides annual predictions for 2004-2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions and static physiographic variables associated with the upstream basin. Predictions correspond to any pixel on the channel network consistent with the medium resolution National Hydrography Dataset channel network stream grid. Total annual precipitation and percent forest cover were consistently the most important predictor variables among global and most subregional models, which had error rates between 17 and 22%. Probabilities were converted to wet and dry streamflow permanence classes with an associated confidence. Wet and dry classifications were used to derive descriptors that characterize the statistical and spatial distribution of streamflow permanence in three focal basins. Predicted dry channel segments account for 52 to 92% of the stream network across the three focal basins; streamflow permanence decreased during climatically drier years. Predictions are publicly available through the USGS StreamStats platform. Results demonstrate the utility of the PROSPER model as a tool for identifying areas that may be resilient or sensitive to drought conditions, allowing for management efforts that target protecting critical reaches. Importantly, PROSPER’s successful predictive performance can be improved with new datasets of streamflow permanence underscoring the importance of field observations.

Idaho, Montana, Nevada, Oregon, Utah, Washington↗

USGS2013

The 3D Elevation Program: summary for California

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of California, elevation data are critical for infrastructure and construction management; natural resources conservation; flood risk management; wildfire management, planning, and response; agriculture and precision farming; geologic resource assessment and hazard mitigation; and other business uses. Today, high-quality light detection and ranging (lidar) data are the sources for creating elevation models and other elevation datasets. Federal, State, and local agencies work in partnership to (1) replace data, on a national basis, that are (on average) 30 years old and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The new 3D Elevation Program (3DEP) initiative, managed by the U.S. Geological Survey (USGS), responds to the growing need for high-quality topographic data and a wide range of other three-dimensional representations of the Nation’s natural and constructed features.

California↗

USGS2006

Long-term population monitoring: Lessons learned from an endangered passerine in Hawai‘i

Obtaining reliable population estimates is crucial to monitoring endangered species and developing recovery strategies. The palila ( Loxioides bailleui ) is an endangered seed-eating Hawaiian honeycreeper restricted to the subalpine forests of Mauna Kea, a volcano on the island of Hawai‘i, USA. The species is vulnerable to extinction primarily because >90% of the population is concentrated in <30 km 2 of habitat on the western slope of this high, dormant volcano. Annual surveys of the palila population have been conducted for ecological, legal, and other purposes since 1980. Because refinements to sampling protocols and analytical methods have evolved, we examined means of adapting the monitoring program to produce comparable estimates of abundance over the past 25-year period and into the future. We conducted variable circular plot surveys during the nonbreeding season (Jan–Mar) and this used data to obtain estimates of effective detection radius and annual density with Distance 4.0, Release 2. For comparability over the time-series, we excluded from analysis the data from new transects. We partitioned the 25-year data set (1980–1996 and 1997–2004) into 2 separate analyses because, beginning in 1997, observers received more training to reduce their tendency to estimate distances to 5-m intervals. We used geographic strata in the analysis of recent surveys because changes in habitat may have invalidated the density-based strata used previously. By adding observer and year and observer and time of day as co-variables, we improved the model fit to the 2 data sets, respectively. Annual estimates were confounded by changes in sampling methodology and analytical procedures over time. However, the addition of new transects, increased training for observers, and use of exact distance estimates instead of rounding also improved model fit. Habitat characteristics and behavior of palila that potentially influenced detection probability, sampling, analysis, and interpretation were regeneration of trees in response to reduced numbers of introduced browsing mammals, seasonally variable rates of vocalization, non-territoriality, and resource-tracking along an elevation gradient. We believe our adaptive approach to analysis and interpretation of 25 years of annual variable circular plot data could help guide similar long-term monitoring efforts.

Hawai'i↗