Hydrous ferric-oxide (HFO) coatings on streambed sediments may attenuate dissolved phosphate (PO4) concentrations at acidic to neutral pH conditions, limiting phosphorus (P) transport and availability in aquatic ecosystems. Mesh-covered tiles on which “natural” HFO from abandoned mine drainage (AMD) had precipitated were exposed to treated municipal wastewater (MWW) effluent or a mixture of stream water and effluent. Between 42 and 99% of the dissolved P in effluent was removed from the water to a thin coating (~2 μm) of HFO on the mesh. Geochemical equilibrium model results predicted the removal of 76 to 99% of PO4 from the water by adsorption to the HFO, depending on the HFO quantity, initial PO4 concentration, and pH. The measurements and model results indicated the capacity for P removal decreased as the concentration of P associated with the HFO increased. Continuing accumulation of HFO from upstream AMD sources replenish the in-stream capacity for P attenuation below the MWW discharge. This indicates AMD pollution may conceal P inputs and limit the amount of dissolved P transported to downstream ecosystems. However, HFO-rich sediments also represent a potential source of “legacy” P that could confound management practices intended to decrease nutrient and metal loadings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.152672","usgsCitation":"Smyntek, P.M., Lamagna, N., Cravotta, C., and Strosnider, W., 2022, Mine drainage precipitates attenuate and conceal wastewater-derived phosphate pollution in stream water: Science of the Total Environment, v. 815, 152672, 8 p., https://doi.org/10.1016/j.scitotenv.2021.152672.","productDescription":"152672, 8 p.","ipdsId":"IP-132530","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":436002,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D8VQDV","text":"USGS data release","linkHelpText":"Interactive PHREEQ-N-Titration-PO4-Adsorption water-quality modeling tools to evaluate potential attenuation of phosphate and associated dissolved constituents by aqueous-solid equilibrium processes (software download)"},{"id":400574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Four Mile Run","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.44746017456055,\n 40.28279959861071\n ],\n [\n -79.39699172973633,\n 40.28279959861071\n ],\n [\n -79.39699172973633,\n 40.32050383546901\n ],\n [\n -79.44746017456055,\n 40.32050383546901\n ],\n [\n -79.44746017456055,\n 40.28279959861071\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"815","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smyntek, Peter M.","contributorId":291642,"corporation":false,"usgs":false,"family":"Smyntek","given":"Peter","email":"","middleInitial":"M.","affiliations":[{"id":62738,"text":"Saint Vincent College","active":true,"usgs":false}],"preferred":false,"id":842810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamagna, Natalie","contributorId":291643,"corporation":false,"usgs":false,"family":"Lamagna","given":"Natalie","email":"","affiliations":[{"id":62738,"text":"Saint Vincent College","active":true,"usgs":false}],"preferred":false,"id":842811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":842812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strosnider, William H. J.","contributorId":291644,"corporation":false,"usgs":false,"family":"Strosnider","given":"William H. J.","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":842813,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229655,"text":"70229655 - 2022 - Leveraging community science data for population assessments during a pandemic","interactions":[],"lastModifiedDate":"2022-04-12T13:42:34.235584","indexId":"70229655","displayToPublicDate":"2022-01-11T06:38:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging community science data for population assessments during a pandemic","docAbstract":"The COVID-19 pandemic has disrupted field research programs, making conservation and management decision-making more challenging. However, it may be possible to conduct population assessments using integrated models that combine community science data with existing data from structured surveys. We developed a space-time integrated model to characterize spatial and temporal variability in population distribution. We fit our integrated model to 10 years of eBird (2010-2020) and 9 years of aerial survey (2010-2019) mottled duck count data to forecast 2020 population size along the western Gulf Coast of Texas and Louisiana. Estimates of mottled duck abundance were similar in magnitude to estimates calculated using previous methods, but were more precise and showed evidence of a declining population. The spatial distribution for mottled ducks each year was characterized by several concentrations of relatively high abundance, although the location of these abundance ‘hotspots’ varied over time. Expected abundance was higher for areas with a higher proportion of area covered by marsh habitat. By leveraging large-scale community science data, we were able to conduct a population assessment despite the disruption in structured surveys caused by the pandemic. As participation in community science platforms continues to increase, we anticipate modeling frameworks, like the integrated model we developed here, will become increasingly useful for informing conservation and management decision-making.
Deep continental crustal structures are enigmatic due to lack of direct exposures and limited tools to investigate them remotely. Seismic waves can sample these rocks, but most seismic methods focus on coarse crustal structures while laboratory measurements concentrate on crystal-scale rock properties, and little work has been conducted to bridge this interpretation gap. In some places, geologic maps of crystalline basement provide samples of the intermediate-scale fabrics and structures that may represent in situ deep crust. However, previous research has not considered natural geometric variations from map data, nor is this heterogeneity typically included in map-scale seismic property calculations. Here, we test how map-scale fabrics influence crustal seismic anisotropy in Colorado by analyzing structural data from geologic maps, combining those data with bulk rock elastic tensors to calculate map-scale seismic properties, and evaluating the resulting comparisons with observed receiver function A1 (360° periodic) arrivals. Crystalline fabrics, predicted seismic properties, and tectonic structures positively correlate with shallow and deep crustal A1 arrivals. Additionally, widespread correlations occur between mapped fault traces and regional foliations, implying that preexisting mechanical heterogeneity may have strongly influenced subsequent reactivation. We interpret that various mapped geologic contact types (e.g., lithologic and structural) generate A1 arrivals and that multiple parallel features (e.g., faults, foliations, and intrusions) contribute to a seismically visible tectonic grain. Therefore, Colorado's exhumed basement, as expressed in outcrops and maps, offers insight into modern deep crustal geological and geophysical structure.
Predicting baseflow dynamics, protecting aquatic habitat, and managing legacy contaminants requires explicit characterization and prediction of groundwater discharge patterns throughout river networks. Using handheld thermal infrared (TIR) cameras, we surveyed 47 km of stream length across the Farmington River watershed (1,570 km2; CT and MA, USA), mapping locations of bank and waterline groundwater discharges based on their thermal signature. Using the observed groundwater discharge locations and predicted groundwater discharge rates from 6 variations of a numerical groundwater-flow model (MODFLOW-NWT), we compared 1) predicted groundwater-discharge rates in areas with and without observed groundwater discharge, 2) spatial patterns of observed and predicted groundwater discharge locations, and 3) density of observed groundwater discharge locations with predicted discharge rates. Five of six models reasonably predicted the spatial patterns of discharge locations along the 5th order mainstem, but fewer models predicted groundwater discharge patterns in smaller streams. Our results highlight 1) the feasibility of using TIR observations to evaluate groundwater models, 2) model parameters that influence discharge prediction accuracy (riverbed sediment and bedrock hydraulic conductivity and river-aquifer connections), and 3) current strengths and future opportunities for improved modeling of groundwater-discharge patterns.
Connectivity is vital to the resiliency of populations to environmental change and stochastic events, especially for cold-adapted species as Arctic and alpine tundra habitats retract as the climate warms. We examined the influence of past and current landscapes on genomic connectivity in cold-adapted galliformes as a critical first step to assess the vulnerability of Alaska ptarmigan and grouse to environmental change. We hypothesize that the mosaic of physical features and habitat within Alaska promoted the formation of genetic structure across species.
Alaska, United States of America.
Ptarmigan and Grouse (Galliformes: Tetraoninae).
We collected double digest restriction-site-associated DNA sequence data from six ptarmigan and grouse species (N = 13–145/species) sampled across multiple ecosystems up to ~10 degrees of latitude. Spatial genomic structure was analysed using methods that reflect different temporal scales: (1) principal components analysis to identify major trends in the distribution of genomic variation; (2) maximum likelihood clustering analyses to test for the presence of multiple genomic groupings; (3) shared co-ancestry analyses to assess contemporary relationships and (4) effective migration surfaces to identify regions that deviate from a null model of isolation by distance.
Levels of genomic structure varied across species (ΦST =0.009–0.042). Three general patterns of structure emerged: (1) east-west partition located near the Yukon-Tanana uplands; (2) north-south split coinciding with the Alaska Range and (3) northern group near the Brooks Range. Species-specific patterns were observed; not all landscape features were barriers to gene flow for all ptarmigan and grouse and temporal contrasts were detected at the Brooks Range.
Within Alaska galliformes, patterns of genomic structure coincide with physiographic features and highlight the importance of physical and ecological barriers in shaping how genomic diversity is arrayed across the landscape. Lack of concordance in spatial patterns indicates that species behaviour and habitat affinities play key roles in driving the contrasting patterns of genomic structure.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.14294","usgsCitation":"Sonsthagen, S.A., Wilson, R.E., and Talbot, S.L., 2022, Species-specific responses to landscape features shaped genomic structure within Alaska galliformes: Journal of Biogeography, v. 49, no. 2, p. 261-273, https://doi.org/10.1111/jbi.14294.","productDescription":"13 p.","startPage":"261","endPage":"273","ipdsId":"IP-119634","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":449226,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.14294","text":"Publisher Index Page"},{"id":436007,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DDB01R","text":"USGS data release","linkHelpText":"Genomic Data from Ptarmigan and Grouse, Alaska"},{"id":394242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":830649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":830650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":830651,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227296,"text":"ofr20211030E - 2022 - System characterization report on Planet SkySat","interactions":[{"subject":{"id":70227296,"text":"ofr20211030E - 2022 - System characterization report on Planet SkySat","indexId":"ofr20211030E","publicationYear":"2022","noYear":false,"chapter":"E","displayTitle":"System Characterization Report on Planet SkySat","title":"System 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These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
SkySat is a constellation of submeter resolution Earth observation satellites providing analytics services, high-definition video, and imagery. The goal for the constellation is to capture multiple daily repeats of high-resolution imagery over any spot on the Earth. As of September 2020, 21 SkySat satellites have been launched, and the first launch occurred in November 2013. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that SkySat has an interior geometric performance in the range of a 0.38- (0.47 pixel) to 0.75-meter (m; 0.93 pixel) root mean square error in easting and a 0.27- (0.33 pixel) to 0.55-m (0.68 pixel) root mean square error in northing, in band-to-band registration; an exterior geometric performance in the range of 0.26 (0.32 pixel) to 1.04 m (1.28 pixels) offset in comparison to ground control points; a radiometric performance in the range of 0.033 to 0.797 (linear regression); and a spatial performance in the range of 3.7 to 4.3 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.004 to 0.009.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030E","usgsCitation":"Kim, M., Park, S., Sampath, A., Anderson, C., and Stensaas, G.L., 2022, System characterization report on Planet SkySat, chap. E of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 17 p., https://doi.org/10.3133/ofr20211030E.","productDescription":"iv, 17 p.","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126680","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":394021,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/e/coverthb.jpg"},{"id":394022,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/e/ofr20211030e.pdf","text":"Report","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-E"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
gTOOLS is an open-source software for the processing of relative gravity data. gTOOLS is available in MATLAB and as a compiled executable to be run under the free MATLAB Runtime Compiler. The software has been designed for time-lapse (temporal) gravity monitoring. Although programmed to read the Scintrex CG-5 and CG-6 gravimeters output data files, it can be easily modified to read data files from other gravimeters. The software binds together single-task processing modules within a very simple user interface that is based on one text file. Gravity processing involves three modules: (a) gravimeter calibration; (b) automatic processing of gravity data to find adjusted gravity differences; and (c) post processing of results. Each module is optional and runs independently from the others. Data processing includes (a) averaging out the measurements noise, and correction for solid Earth tides, and ocean loading, and residual instrumental drift, and (b) calculate the residual instrumental drift and gravity differences between the base station and monitoring sites, and their uncertainties, by a weighted least square analysis of the gravity data. The software allows the automatic processing of a gravity campaign spanning multiple days in a single run. The software is tested on gravity data from 2015 eruption at Cotopaxi volcano, Ecuador.
California’s Central Valley provides critical habitat for migratory waterbirds, yet only 10% of naturally occurring wetlands remain. Competition for limited water supplies and climate change will impact the long-term viability of these intensively managed habitats.
Forecast the distribution, abundance, and connectivity of surface water and managed wetland habitats, using 5 spatially explicit (270 m2) climate/land use/water prioritization scenarios. Mapping potential future dynamic flooded habitat used by waterbirds and other wetland-dependent wildlife to inform management decisions.
We integrated a climate-driven hydrologic water use model with a spatially explicit land change model, to examine stakeholder-driven scenarios of future land change, climate, and water use and their impacts on future habitat availability.
Declining water availability is the dominant driver of habitat loss across scenarios. The hot/dry scenarios showed the greatest declines in January flooded area by 2101—an important month for overwintering waterbirds. In contrast, higher water supplies in wet climates drive perennial cropland conversion and loss of potential habitat. Potential flooded cropland declined (25 and 33%) under warmer/wetter climate conditions due to this conversion to perennial crops, exposing habitat vulnerability.
Climate-driven loss of water availability had a greater impact on flooded habitat availability than land-use change. When combined, climate change and the conversion of potentially flooded cropland to perennial cropland will threaten future waterbird habitat particularly in January, the peak of the migratory bird season, even when habitat restoration goals are met. Stakeholder-informed scenario analysis can identify target areas for potential habitat change, vulnerability, and conservation.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01398-1","usgsCitation":"Wilson, T., Matchett, E., Byrd, K.B., Conlisk, E., Reiter, M.E., Wallace, C., Flint, L.E., Flint, A.L., and Moritsch, M.M., 2022, Climate and land change impacts on future managed wetland habitat: A case study from California’s Central Valley: Landscape Ecology, v. 37, p. 861-881, https://doi.org/10.1007/s10980-021-01398-1.","productDescription":"21 p.","startPage":"861","endPage":"881","ipdsId":"IP-127595","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":394093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":830455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":830456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":830457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conlisk, Erin","contributorId":270185,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":830458,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reiter, Matthew E. 0000-0002-0587-786X","orcid":"https://orcid.org/0000-0002-0587-786X","contributorId":271031,"corporation":false,"usgs":false,"family":"Reiter","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":56258,"text":"Point Blue","active":true,"usgs":false}],"preferred":false,"id":830459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wallace, Cynthia 0000-0003-0001-8828 cwallace@usgs.gov","orcid":"https://orcid.org/0000-0003-0001-8828","contributorId":149179,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia","email":"cwallace@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":830460,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science 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it accommodates modern shortening remain debated. Here, we addressed this problem using geologic mapping along two transects across the southern half of the western Greater Caucasus to reveal a suite of regionally coherent stratigraphic packages that are juxtaposed across a series of thrust faults, which we call the North Georgia fault system. From south to north within this system, stratigraphically repeated ~5–10-km-thick thrust sheets show systematically increasing bedding dip angles (<30° in the south to subvertical in the core of the range). Likewise, exhumation depth increases toward the core of the range, based on low-temperature thermochronologic data and metamorphic grade of exposed rocks. In contrast, active shortening in the modern system is accommodated, at least in part, by thrust faults along the southern margin of the orogen. Facilitated by the North Georgia fault system, the western Greater Caucasus Mountains broadly behave as an in-sequence, southward-propagating imbricate thrust fan, with older faults within the range progressively abandoned and new structures forming to accommodate shortening as the thrust propagates southward. We suggest that the single-fault-centric “Main Caucasus thrust” paradigm is no longer appropriate, as it is a system of faults, the North Georgia fault system, that dominates the architecture of the western Greater Caucasus Mountains.
Predictive modeling of submerged archaeological sites requires accurate sea-level predictions in order to reconstruct coastal paleogeography and associated geographic features that may have influenced the locations of occupation sites such as rivers and embayments. Earlier reconstructions of the paleogeography of parts of the western U.S. coast used an assumption of eustatic sea level, but this neglects the large spatial variations in relative sea level (RSL) associated with glacial isostatic adjustment (GIA) and tectonics. Subsequent work using a one-dimensional (1-D) solid Earth model showed that reconstructions that accounted for GIA result in significant differences from those based on eustatic sea level. However, these analyses neglected the complex three-dimensional (3-D) solid Earth structure associated with the Cascadia subduction zone that has also strongly influenced RSL along the Oregon-Washington (OR-WA) coast, requiring that the paleogeographic reconstructions must also account for this effect. Here we use RSL predictions from a 3-D solid Earth model that have been validated by RSL data to update previous paleogeographic reconstructions of the OR-WA coast for the last 12 kyr based on a 1-D solid Earth model. The large differences in the spatial variations in RSL on the OR-WA continental shelves predicted by the 3-D model relative to eustatic and 1-D models demonstrate that accurate reconstructions of coastal paleogeography for predictive modeling of submerged archaeological sites need to account for 3-D viscoelastic Earth structure in areas of complex tectonics.
The U.S. Geological Survey, in cooperation with DeKalb County Department of Watershed Management, established a long-term water-quantity and water-quality monitoring program in 2012 to monitor and analyze the hydrologic and water-quality conditions of 15 watersheds in DeKalb County, Georgia—an urban and suburban area located in north-central Georgia that includes the easternmost part of the City of Atlanta. This report synthesizes the watershed characteristics and monitoring data collected for the first 5 years of the program, 2012 through 2016. The study area was predominantly medium-density residential (43.9 percent), commercial/industrial/institutional (21.4 percent), forest/park/agriculture (13.6 percent), and high-density residential (11.5 percent) land uses. Land-surface slope averaged 8.7 percent, imperviousness averaged 25.3 percent, and population density averaged 2,936 people per square mile. Watershed imperviousness ranged from 8.7 to 36.6 percent.
In the study area for 2014 to 2016 (when streamflow data were available for all watersheds), runoff represented 40.9 percent of precipitation. Hydrograph separations indicated that 43 percent of runoff occurred as base flow, whereas the remainder occurred as stormflow. Higher watershed imperviousness was significantly related to higher amounts of runoff (Pearson product-moment correlation coefficient [r] = 0.517), higher runoff ratios (r = 0.646), and lower amounts (r = −0.637) and proportions (r = −0.898) of base-flow runoff. Stormwater best management practices have been implemented in the study watersheds; however, these practices do not appear to fully mitigate the effects of urban development and land use on stream hydrology.
Total copper, lead, and zinc concentrations in base-flow and stormflow samples exceeded the national recommended aquatic life criteria for chronic and acute conditions, respectively, to varying degrees. Escherichia coli density predictive regression models indicated that the U.S. Environmental Protection Agency’s Beach Action Value was exceeded at individual watersheds between 44.6 and 100 percent of the time. Exceedance of the Beach Action Value indicates possible unsafe conditions for primary contact recreation and could be used for timely notification of the potential health risks. Annual loads and yields were estimated for 15 constituents. Loads were typically higher for years with higher runoff while variations among watershed yields appear associated with watershed and land use characteristics. The lowest yields for almost all constituents occurred in the Stone Mountain Creek watershed—likely the result of the retention of sediment and reduction of nutrients in Stone Mountain Lake and two smaller downstream reservoirs within the watershed. The Little Stone Mountain Creek watershed also had some of the lowest yields for most constituents, likely due to the lack of many pollutant sources associated with its predominantly medium-density residential land use (95.5 percent), but had the highest total nitrate plus nitrite yields. The Intrenchment Creek watershed consistently had some of the highest yields across all constituents except for total nitrate plus nitrite. The high yields may be related to its high percentage of impervious area (36.0 percent) and high amount of heavily developed land use (high-density residential, 29.9 percent and commercial/industrial/institutional, 26.0 percent). Mean watershed constituent yields in this study were significantly higher than those from a similar analysis of 13 suburban to urban watersheds in adjacent Gwinnett County for 6 of the 10 constituents compared.
This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions of the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.
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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
The Des Moines River alluvial aquifer is an important source of water for Des Moines Water Works, the municipal water utility that provides residential and commercial water resources to the residents of Des Moines, Iowa, and surrounding municipalities. As an initial step in developing a better understanding of the groundwater resources of the Des Moines River alluvial aquifer, the U.S. Geological Survey constructed a steady-state numerical groundwater flow model in cooperation with Des Moines Water Works to simulate water-table elevations in the Des Moines River alluvial aquifer near Prospect Park in Des Moines under winter low-flow conditions.
A simple conceptual model consisting of a hydrogeologic framework, water budget, and inferred water-table elevation map was developed for the model area. The inferred water-table elevation map was constructed based on general knowledge of hydrogeology within the model area and was used to set calibration targets for numerical model calibration. A steady-state numerical model was constructed based on the conceptual model using MODFLOW-NWT to simulate an area of about 15 square kilometers near Prospect Park in Des Moines. Parameter ESTimation software was used for model calibration to assess and optimize performance of the horizontal hydraulic conductivity and recharge parameters. The numerical groundwater flow model and supporting data are available in the USGS data release associated with this report, which contains the model archive.
Performance of the calibrated steady-state model was assessed by comparing observed and simulated water-table elevations, as well as estimated and simulated contributions to streamflow within the model area. The difference between observed water-table elevations and simulated water-table elevations was −0.1 meter at the majority of calibration targets, with the negative value indicating an overestimation of the simulated water-table elevation value compared to the observed water-table elevation value, and the root mean square error was 0.13 meter, which represents about 20 percent of the difference in observed water-table elevations. The simulated value of contributions to streamflow within the model area was considered similar to the estimated value, increasing confidence in the ability of the model to accurately represent the groundwater flow system in the Des Moines River alluvial aquifer in the model area during winter low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211110","collaboration":"Prepared in cooperation with Des Moines Water Works","usgsCitation":"FitzGerald, K.M., Ha, W.S., Haj, A.E., Gruhn, L.R., Bristow, E.L., and Weber, J.R., 2022, A steady-state groundwater flow model for the Des Moines River alluvial aquifer near Prospect Park, Des Moines, Iowa: U.S. Geological Survey Open-File Report 2021–1110, 20 p., https://doi.org/10.3133/ofr20211110.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130288","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501532,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112070.htm","linkFileType":{"id":5,"text":"html"}},{"id":393916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1110/ofr20211110.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1110"},{"id":393915,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1110/coverthb.jpg"},{"id":393917,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":393918,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Prospect Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.65175247192383,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.611463744813506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The lesser prairie-chicken (Tympanuchus pallidicinctus) is a species of conservation concern on the Southern High Plains of Texas and New Mexico, USA. Because fragmentation and isolation have increased since pre-settlement, dispersal through this heterogeneous landscape may be constrained, with serious implications for conservation and management of this species. Our objectives were to quantify landscape connectivity for lesser prairie-chickens within a patch network of potentially isolated leks (breeding display grounds), and examine effects of land use change on modeled lesser prairie-chicken movements through the landscape. We used graph theory to quantify structural landscape connectivity and circuit theory to quantify functional landscape connectivity for lesser prairie-chickens in the Sand Shinnery Oak Prairie Ecoregion of the Southern High Plains. There was a high degree of clustering among leks (n = 1,023 leks), with a 41.9-km coalescence distance of the network. We identified 3 leks as cutpoints within the network, meaning if the habitat patches containing these leks were fragmented, the remaining leks would become isolated from each other. We also identified several leks that were important for maintaining overall population connectivity for lesser prairie-chickens on the Southern High Plains. Conservation Reserve Program land was important for maintaining connectivity among leks to the north and west of the main lek core area located in New Mexico, but wind energy development constrained pathways to the north and south of this main lek core area. Our results suggest that landscape connectivity was reduced by row-crop agriculture and energy production and facilitated by the Conservation Reserve Program within the Sand Shinnery Oak Prairie Ecoregion.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22146","usgsCitation":"Schilder, L., Heintzman, L., Mcintyre, N., Harryman, S., Hagen, C., Martin, R.E., Boal, C.W., and Grisham, B., 2022, Structural and functional landscape connectivity for lesser prairie-chickens in the Sand Shinnery Oak Prairie Ecoregion: Journal of Wildlife Management, v. 86, no. 1, e22146, 17 p., https://doi.org/10.1002/jwmg.22146.","productDescription":"e22146, 17 p.","ipdsId":"IP-123187","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Sand Shinnery Oak Prairie ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.08205634648147,\n 35.20735590338778\n ],\n [\n -105.08205634648147,\n 32.04246611821024\n ],\n [\n -101.86163388662169,\n 32.04246611821024\n ],\n [\n -101.86163388662169,\n 35.20735590338778\n ],\n [\n -105.08205634648147,\n 35.20735590338778\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Schilder, L.J.","contributorId":341785,"corporation":false,"usgs":false,"family":"Schilder","given":"L.J.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heintzman, L.J.","contributorId":341786,"corporation":false,"usgs":false,"family":"Heintzman","given":"L.J.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mcintyre, N.E.","contributorId":215186,"corporation":false,"usgs":false,"family":"Mcintyre","given":"N.E.","email":"","affiliations":[{"id":39194,"text":"Department of Biological Sciences, Texas Tech University, Lubbock, TX 79409-3131 USA","active":true,"usgs":false}],"preferred":false,"id":908895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harryman, S.","contributorId":341788,"corporation":false,"usgs":false,"family":"Harryman","given":"S.","email":"","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":908896,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hagen, C.A.","contributorId":276129,"corporation":false,"usgs":false,"family":"Hagen","given":"C.A.","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":908897,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martin, R. E.","contributorId":138911,"corporation":false,"usgs":false,"family":"Martin","given":"R.","email":"","middleInitial":"E.","affiliations":[{"id":12575,"text":"Ecological Associates, Inc, Jensen Beach, Florida","active":true,"usgs":false}],"preferred":false,"id":908898,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908899,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grisham, Blake A.","contributorId":341793,"corporation":false,"usgs":false,"family":"Grisham","given":"Blake A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908900,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227175,"text":"sir20215125 - 2022 - Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20","interactions":[],"lastModifiedDate":"2026-04-02T20:01:25.048208","indexId":"sir20215125","displayToPublicDate":"2022-01-05T10:55:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5125","displayTitle":"Continuous Monitoring of Nutrient and Sediment Loads from the Des Plaines River at Route 53 at Joliet, Illinois, Water Years 2018–20","title":"Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20","docAbstract":"The Des Plaines River in southern Wisconsin and northern Illinois is the principal conduit for the discharge of wastewater effluent and stormwater runoff from the greater Chicago metropolitan area. In November 2017, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, installed a continuous monitoring station to measure water quality and streamflow in the Des Plaines River at Joliet, Illinois. Surrogate models encompassing continuous data and discrete water-quality samples were used to estimate loads of nitrate, total phosphorus, and suspended sediment. Comparisons to other major rivers in Illinois show that the Des Plaines River is a substantial contributor to statewide loading estimates for nitrate and total phosphorus but only a minor contributor to suspended sediment. Future loading estimates of total phosphorus could include more research into the effects of combined sewage overflows because these effects likely increased model uncertainty. The results in this report document current loadings and provide a baseline from which to assess future water-quality management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215125","collaboration":"Prepared in cooperation with Metropolitan Water Reclamation District of Greater Chicago","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"Peake, C.S., and Hodson, T.O., 2022, Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20 (ver. 1.1, February 2022): U.S. Geological Survey Scientific Investigations Report 2021–5125, 15 p., https://doi.org/10.3133/sir20215125.","productDescription":"Report: vii, 15 p.; Data Release; Database","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-129874","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":393780,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M4BH1C","text":"USGS data release","linkHelpText":"Modeled nutrient and sediment concentrations from the Des Plaines River at Route 53 at Joliet, Illinois, based on continuous monitoring from October 1, 2017, through September 30, 2020"},{"id":396575,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5125/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":393783,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5125/images/"},{"id":393782,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5125/sir20215125.XML"},{"id":393781,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5125/sir20215125.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5125"},{"id":393778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5125/coverthb2.jpg"},{"id":502126,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112067.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois","city":"Joliet","otherGeospatial":"Des Plaines River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.25,\n 41.5\n ],\n [\n -87.5,\n 41.5\n ],\n [\n -87.5,\n 42.25\n ],\n [\n -88.25,\n 42.25\n ],\n [\n -88.25,\n 41.5\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 5, 2022; Version 1.1: February 28, 2022","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
We use particle tracking to determine contributing areas (CAs) to wells for transient flow models that simulate cyclic domestic pumping and extreme recharge events in a small synthetic watershed underlain by dipping sedimentary rocks. The CAs consist of strike-oriented bands at locations where the water table intersects high-hydraulic conductivity beds, and from which groundwater flows to the pumping well. Factors that affect the size and location of the CAs include topographic flow directions, rock dip direction, cross-bed fracture density, and position of the well relative to streams. For an effective fracture porosity (ne) of 10-4, the fastest advective travel times from CAs to wells are only a few hours. These results indicate that wells in this type of geologic setting can be highly vulnerable to contaminants or pathogens flushed into the subsurface during extreme recharge events. Increasing ne to 10-3 results in modestly smaller CAs and delayed well vulnerability due to slower travel times. CAs determined for steady-state models of the same setting, but with long-term average recharge and pumping rates, are smaller than CAs in the models with extreme recharge. Also, the earliest-arriving particles arrive at the wells later in the steady-state models than in the extreme-recharge models. The results highlight the importance of characterizing geologic structure, simulating plausible effective porosities, and simulating pumping and recharge transience when determining CAs in fractured rock aquifers to assess well vulnerability under extreme precipitation events.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13169","usgsCitation":"Tiedeman, C.R., and Shapiro, A.M., 2022, Contributing areas to domestic wells in dipping sedimentary rocks under extreme recharge events: Groundwater, v. 60, no. 4, p. 460-476, https://doi.org/10.1111/gwat.13169.","productDescription":"17 p.","startPage":"460","endPage":"476","ipdsId":"IP-128281","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":449254,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13169","text":"Publisher Index Page"},{"id":436015,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93DZ84P","text":"USGS data release","linkHelpText":"MODFLOW-NWT and MODPATH6 models used to simulate contributing areas in hypothetical sedimentary rock aquifers under extreme recharge events"},{"id":394014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Tiedeman, Claire R. 0000-0002-0128-3685 tiedeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0128-3685","contributorId":196777,"corporation":false,"usgs":true,"family":"Tiedeman","given":"Claire","email":"tiedeman@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":830286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":830287,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228312,"text":"70228312 - 2022 - Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels","interactions":[],"lastModifiedDate":"2022-02-08T13:07:56.282931","indexId":"70228312","displayToPublicDate":"2022-01-05T07:05:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10095,"text":"ACM Transactions on Intelligent Systems and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels","docAbstract":"In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.
Yasur volcano, Vanuatu is a continuously active open-vent basaltic-andesite stratocone with persistent and long-lived eruptive activity. We present results from a seismo-acoustic field experiment at Yasur, providing locally dense broad-band seismic and infrasonic network coverage from 2016 July 27 to August 3. We corroborate our seismo-acoustic observations with coincident video data from cameras deployed at the crater and on an unoccupied aircraft system (UAS). The waveforms contain a profusion of signals reflecting Yasur’s rapidly occurring and persistent explosive activity. The typical infrasonic signature of Yasur explosions is a classic short-duration and often asymmetric explosion waveform characterized by a sharp compressive onset and wideband frequency content. The dominant seismic signals are numerous repetitive very-long-period (VLP) signals with periods of ∼2–10 s. The VLP seismic events are ‘high-rate’, reoccurring near-continuously throughout the data set with short interevent times (∼20–60 s). We observe variability in the synchronization of seismic VLP and acoustic sources. Explosion events clearly delineated by infrasonic waveforms are underlain by seismic VLPs. However, strong seismic VLPs also occur with only a weak infrasonic expression. Multiplet analysis of the seismic VLPs reveals a systematic progression in the seismo-acoustic source decoupling. The same dominant seismic VLP multiplet occurs with and without surficial explosions and infrasound, and these transitions occur over a timescale of a few days during our field campaign. We subsequently employ template matching, stacking, and full-waveform inversion to image the source mechanism of the dominant VLP multiplet. Inversion of the dominant VLP multiplet stack points to a composite source consisting of either a dual-crack (plus forces) or pipe-crack (plus forces) mechanism. The derived mechanisms correspond to a point-source directly beneath the summit vents with centroid depths in the range ∼900–1000 m below topography. All mechanisms suggest a northeast trending crack dipping relatively shallowly to the northwest and indicate a VLP source centroid and mechanism controlled by a stable structural geologic feature beneath Yasur. We interpret the results in the framework of gas slug ascent through the conduit responsible for Yasur explosions. The VLP mechanism and timing with infrasound (when present) are explained by a shallow-buffered top-down model in which slug ascent is relatively aseismic until reaching the base of a shallow section. Slug disruption in this shallow zone triggers a pressure disturbance that propagates downward and couples at the conduit base (VLP centroid). If the shallow section is open, an explosion propagates to the surface, producing infrasound. In the case of (the same multiplet) VLPs occurring without surficial explosions and weak or no infrasound, the decoupling of the dominant VLPs at ∼900–1000 m depth from surficial explosions and infrasound strongly indicates buffering of the terminal slug ascent. This buffering could be achieved by a variety of conditions at or directly beneath the vents, such as a high-viscosity layer of crystal-rich magma, a debris cap from backfill, a foam layer, or a combination of these. The dominant VLP at Yasur captured by our experiment has a source depth and mechanism separated from surface processes and is stable over time.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab533","usgsCitation":"Matoza, R.S., Chouet, B.A., Jolly, A., Dawson, P.B., Fitzgerald, R.H., Kennedy, B.M., Fee, D., Iezzi, A., Kilgour, G.N., Garaebiti, E., and Cevuard, S., 2022, High-rate very-long-period seismicity at Yasur volcano, Vanuatu: source mechanism and decoupling from surficial explosions and infrasound: Geophysical Journal International, v. 230, no. 1, p. 392-426, https://doi.org/10.1093/gji/ggab533.","productDescription":"35 p.","startPage":"392","endPage":"426","ipdsId":"IP-135455","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449266,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggab533","text":"Publisher Index Page"},{"id":413341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Yasur volcano, Vanuatu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 169.33766678462047,\n -19.539368583023972\n ],\n [\n 169.33766678462047,\n -19.662219083395883\n ],\n [\n 169.49415550272943,\n -19.662219083395883\n ],\n [\n 169.49415550272943,\n -19.539368583023972\n ],\n [\n 169.33766678462047,\n -19.539368583023972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"230","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Matoza, Robin S","contributorId":215528,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":864922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chouet, Bernard A","contributorId":302631,"corporation":false,"usgs":false,"family":"Chouet","given":"Bernard","email":"","middleInitial":"A","affiliations":[{"id":65522,"text":"Arzier-Le Muids","active":true,"usgs":false}],"preferred":false,"id":864923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jolly, A.D. 0000-0003-1020-9062","orcid":"https://orcid.org/0000-0003-1020-9062","contributorId":296487,"corporation":false,"usgs":true,"family":"Jolly","given":"A.D.","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":864924,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dawson, Phillip B. 0000-0003-4065-0588 dawson@usgs.gov","orcid":"https://orcid.org/0000-0003-4065-0588","contributorId":206751,"corporation":false,"usgs":true,"family":"Dawson","given":"Phillip","email":"dawson@usgs.gov","middleInitial":"B.","affiliations":[{"id":615,"text":"Volcano Hazards 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0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":864928,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":864929,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kilgour, Geoff N","contributorId":302633,"corporation":false,"usgs":false,"family":"Kilgour","given":"Geoff","email":"","middleInitial":"N","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":864930,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Garaebiti, E.","contributorId":302634,"corporation":false,"usgs":false,"family":"Garaebiti","given":"E.","email":"","affiliations":[{"id":65523,"text":"Vanuatu Meteorology and Geohazards Department","active":true,"usgs":false}],"preferred":false,"id":864931,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cevuard, Sandrine","contributorId":296494,"corporation":false,"usgs":false,"family":"Cevuard","given":"Sandrine","email":"","affiliations":[{"id":64079,"text":"Vanuatu Meteorology and Geohazards Department, Port Vila, Vanuatu","active":true,"usgs":false}],"preferred":false,"id":864932,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70254806,"text":"70254806 - 2022 - Climatic drivers and ecological impacts of a rapid range expansion by non-native smallmouth bass","interactions":[],"lastModifiedDate":"2024-06-10T16:36:37.181839","indexId":"70254806","displayToPublicDate":"2022-01-04T11:28:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Climatic drivers and ecological impacts of a rapid range expansion by non-native smallmouth bass","docAbstract":"Smallmouth bass (Micropterus dolomieu) are a globally introduced fish species that have experienced widespread range expansions in recent decades and which can have deleterious effects on native fish communities. Rapidly assessing their expansions will aid conservation and management actions geared towards controlling their spread and mitigating their impacts. Smallmouth bass have recently experienced a rapid upstream expansion in a Great Plains river (Laramie River, Wyoming, USA), which provided an opportunity to evaluate the drivers and impacts of this expansion by using a modified before-after, control-impact (BACI) design. Our objectives were to test whether climatic drivers (temperature, precipitation, flow) were related to this range expansion and subsequent effects of the expansion on native fish communities. Smallmouth bass population size in Grayrocks Reservoir increased following a climatically extreme wet year, with statistically extreme amounts of spring-time and June precipitation creating high discharge events that coincided with the upstream expansion. Unlike previous studies highlighting the invasive nature of smallmouth bass, the modified BACI analysis revealed no declines in species richness induced by the expansion. However, there was evidence that native small-bodied minnow species (family Leuciscidae) declined in relative abundance and that community-level and species-level trophic niches were compressed for invaded sites. Our findings provide important insight into how climatic extremes can prompt biological invasions that can alter community composition and food web structure even if local extirpations do not occur.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10530-021-02724-z","usgsCitation":"Kirk, M.A., Maitland, B., Hickerson, B.T., Walters, A.W., and Rahel, F., 2022, Climatic drivers and ecological impacts of a rapid range expansion by non-native smallmouth bass: Biological Invasions, v. 24, p. 1311-1326, https://doi.org/10.1007/s10530-021-02724-z.","productDescription":"16 p.","startPage":"1311","endPage":"1326","ipdsId":"IP-130981","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467207,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":429777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Laramie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.4233646467736,\n 42.345457333880034\n ],\n [\n -105.45001084859337,\n 42.345457333880034\n ],\n [\n -105.45001084859337,\n 41.907720974986404\n ],\n [\n -104.4233646467736,\n 41.907720974986404\n ],\n [\n -104.4233646467736,\n 42.345457333880034\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2022-01-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirk, Mark A.","contributorId":337682,"corporation":false,"usgs":false,"family":"Kirk","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":902615,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maitland, Bryan M.","contributorId":337683,"corporation":false,"usgs":false,"family":"Maitland","given":"Bryan M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":902616,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hickerson, Brian T.","contributorId":337684,"corporation":false,"usgs":false,"family":"Hickerson","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":902617,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rahel, Frank J.","contributorId":337685,"corporation":false,"usgs":false,"family":"Rahel","given":"Frank J.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":902618,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227180,"text":"70227180 - 2022 - Landscape and stocking effects on population genetics of Tennessee Brook Trout","interactions":[],"lastModifiedDate":"2022-03-28T16:36:37.268059","indexId":"70227180","displayToPublicDate":"2022-01-04T10:26:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Landscape and stocking effects on population genetics of Tennessee Brook Trout","docAbstract":"Throughout their range, Brook Trout (Salvelinus fontinalis) occupy thousands of disjunct drainages with varying levels of disturbance, which presents substantial challenges for conservation. Within the southern Appalachian Mountains, fragmentation and genetic drift have been identified as key threats to the genetic diversity of the Brook Trout populations. In addition, extensive historic stocking of domestic lineages of Brook Trout to augment fisheries may have eroded endemic diversity and impacted locally adapted populations. We used 12 microsatellite loci to describe patterns of genetic diversity within 108 populations of wild Brook Trout from Tennessee and used linear models to explore the impacts of land use, drainage area, and hatchery stockings on metrics of genetic diversity, effective population size, and hatchery introgression. We found levels of within-population diversity varied widely, although many populations showed very limited diversity. The extent of hatchery introgression also varied across the landscape, with some populations showing high affinity to hatchery lineages and others appearing to retain their endemic character. However, we found relatively weak relationships between genetic metrics and landscape characteristics, suggesting that contemporary landscape variables are not strongly related to observed patterns of genetic diversity. We consider this result to reflect both the complex history of these populations and the challenges associated with accurately defining drainages for each population. Our study highlights the importance of genetic data to guide management decisions, as complex processes interact to shape the genetic structure of populations and make it difficult to infer the status of unsampled populations.
The seismic potential of the Lesser Antilles megathrust remains poorly known, despite the potential hazard it poses to numerous island populations and its proximity to the Americas. As it has not produced any large earthquakes in the instrumental era, the megathrust is often assumed to be aseismic. However, historical records of great earthquakes in the 19th century and earlier, which were most likely megathrust ruptures, demonstrate that the subduction is not entirely aseismic. Recent occurrences of giant earthquakes in areas where such events were previously thought to be improbable have illustrated the importance of critically evaluating the seismic potential of other “low-hazard” subduction zones, such as the Lesser Antilles.
Using the method of coral microatoll paleogeodesy developed in Sumatra, we examine 20th-century vertical deformation on the forearc islands of the Lesser Antilles and model the underlying strain accumulation on the megathrust. Our data indicate that the eastern coasts of the forearc islands have been subsiding by up to ∼8 mm/yr relative to sites closer to the arc, suggesting that on the time scale of the 20th century, a portion of the megathrust just east of the forearc islands has been locked. Our findings are in contrast to recent models based on satellite geodesy that suggest little or no strain accumulation anywhere along the Lesser Antilles megathrust. This discrepancy is potentially explained by the different time scales of measurement, as recent studies elsewhere have indicated that interseismic coupling patterns may vary on decadal time scales and that century-scale or longer records are required to fully assess seismic potential. The accumulated strain we have detected will likely be released in future megathrust earthquakes, uplifting previously subsiding areas and potentially causing widespread damage from strong ground motion and tsunami waves.
Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2–7 metrics were compared for three measures of performance. After ranking the best-performing MMIs, we selected the final MMIs by evaluating stress-response relations in independent regional datasets of diatom samples paired with measures of several water-quality stressors, including herbicides and streamflow flashiness. Each regional MMI performed well at calibration sites and represented diverse aspects of the structure and function of diatom communities. Most metrics included in the best MMIs were modeled to account for natural variation including climate, topography, soil characteristics, lithology, and groundwater influence on streamflow. MMI performance improved with higher numbers of component metrics, but this effect diminished beyond six metrics. Component metrics of MMIs were associated with a broad suite of measured stressors in every region, including salinity, nutrients, herbicides, and streamflow flashiness. We provide a web-based software application that allows users in the conterminous United States to apply our MMIs to their own datasets and compare MMI scores from their sites to a broader regional context.
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S","contributorId":245621,"corporation":false,"usgs":false,"family":"Lee","given":"Sylvia","email":"","middleInitial":"S","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":854305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mitchell, Richard M.","contributorId":215406,"corporation":false,"usgs":false,"family":"Mitchell","given":"Richard M.","affiliations":[{"id":39239,"text":"USEPA, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":854306,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pollard, Amina A.","contributorId":297613,"corporation":false,"usgs":false,"family":"Pollard","given":"Amina","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":854307,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230414,"text":"70230414 - 2022 - Inter-nesting movements, migratory pathways, and resident foraging areas of green sea turtles (Chelonia mydas) satellite-tagged in Southwest Florida","interactions":[],"lastModifiedDate":"2022-04-12T11:56:49.96919","indexId":"70230414","displayToPublicDate":"2022-01-04T06:51:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Inter-nesting movements, migratory pathways, and resident foraging areas of green sea turtles (Chelonia mydas) satellite-tagged in Southwest Florida","docAbstract":"Globally, sea turtle research and conservation efforts are underway to identify important high-use areas where these imperiled individuals may be resident for weeks to months to years. In the southeastern Gulf of Mexico, recent telemetry studies highlighted post-nesting foraging sites for federally endangered green turtles (Chelonia mydas) around the Florida Keys. In order to delineate additional areas that may serve as inter-nesting, migratory, and foraging hotspots for reproductively active females nesting in peninsular southwest Florida, we satellite-tagged 14 green turtles that nested at two sites along the southeast Gulf of Mexico coastline between 2017 and 2019: Sanibel and Keewaydin Islands. Prior to this study, green turtles nesting in southwest Florida had not previously been tracked and their movements were unknown. We used switching state space modeling to show that an area off Cape Sable (Everglades), Florida Bay, and the Marquesas Keys are important foraging areas that support individuals that nest on southwest Florida mainland beaches. Turtles were tracked for 39–383 days, migrated for a mean of 4 days, and arrived at their respective foraging grounds in the months of July through September. Turtles remained resident in their respective foraging sites until tags failed, typically after several months, where they established mean home ranges (50% kernel density estimate) of 296 km2. Centroid locations for turtles at common foraging sites were 1.2–36.5 km apart. The area off southwest Florida Everglades appears to be a hotspot for these turtles during both inter-nesting and foraging; this location was also used by turtles that were previously satellite tagged in the Dry Tortugas after nesting. Further evaluation of this important habitat is warranted. Understanding where and when imperiled yet recovering green turtles forage and remain resident is key information for designing surveys of foraging resources and developing additional protection strategies intended to enhance population recovery trajectories.
This report addresses system characterization of Satellogic’s NewSat satellite (also known as ÑuSat) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2016, Satellogic has launched 17 NewSat satellites. All NewSat satellites have four-band imagers with a 1-meter (m) ground sample distance, and values in pixels are identical to values in meters. All NewSats have been launched into Sun-synchronous orbits of about 475 kilometers, with inclinations of about 97.5 degrees. The satellites have expected lifetimes of about 3 years. More information on the Satellogic satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://satellogic.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the NewSat satellites have an interior geometric performance in the range of −0.119 (−0.119 pixel) to 0.020 m (0.020 pixel) in easting and −0.148 (−0.148 pixel) to 0.014 m (0.014 pixel) in northing in band-to-band registration, an exterior geometric performance of −9.04 (−9.04 pixels) to −5.84 m (−5.84 pixels) in easting and 1.25 (1.25 pixels) to 3.11 m (3.11 pixels) in northing offset in comparison to Sentinel-2, an exterior geometric performance using ground control points of a 6.5-m circular error (95 percent), a radiometric performance in the range of 0.034 to 0.081 in offset and 0.652 to 0.808 in slope, and a spatial performance in the range of 1.61 to 1.76 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.081 to 0.138.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030L","usgsCitation":"Vrabel, J.C., Bresnahan, P., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., and Park, S., 2022, System characterization report on the Satellogic NewSat multispectral sensor (ver. 1.1, April 2022), chap. L of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030L.","productDescription":"v, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-135435","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":393738,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/l/ofr20211030l.pdf","text":"Report","size":"7.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-L"},{"id":393737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/l/coverthb2.jpg"},{"id":399739,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/l/versionHist.txt","size":"1 kB"}],"edition":"Version 1.0: January 3, 2022; Version 1.1: April 28, 2022","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
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