A geometric accuracy assessment of lidar data collected in eastern Iowa in 2019 as part of the 3D Elevation Program (3DEP) was conducted. The assessment involved evaluating interswath accuracy, same surface precision, point density, absolute accuracy, and consistency with adjacent 3DEP datasets. The results demonstrate that the data meet or exceed the quality level 2 specifications outlined in the Lidar Base Specifications (LBS). Interswath and same surface precision values were within specified tolerances, with a root mean square difference of 0.03 meters for interswath vertical accuracy and 0.03 meters for same surface precision. Vertical accuracy in flat areas was excellent, with root mean square error values consistently below 0.10 meters. Horizontal accuracy assessments also showed good agreement between lidar and reference data. Point density generally exceeded the minimum requirement of 2 points per square meter, and the inter-project consistency assessment indicated good agreement between the Iowa lidar data and adjacent datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251017","usgsCitation":"Sampath, A., Irwin, J., and Kropuenske, T., 2025, Validation of the geometric accuracy of airborne light detection and ranging data for eastern Iowa, 2019: U.S. Geological Survey Open-File Report 2025–1017, 16 p., https://doi.org/10.3133/ofr20251017.","productDescription":"Report: v, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167726","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":487536,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1017/ofr20251017.XML","text":"XML","size":"90.4 KB","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1017 XML"},{"id":487523,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1017/images"},{"id":487518,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1017/ofr20251017.pdf","text":"Report","size":"3.15","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1017"},{"id":487578,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DI0G64","text":"USGS data release","linkHelpText":"2019 Eastern Iowa topographic lidar validation – USGS field survey data"},{"id":487546,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251017/full"},{"id":487511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1017/coverthb.jpg"},{"id":494125,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118628.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Iowa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.29294140522802,\n 43.01135367569728\n ],\n [\n -93.29294140522802,\n 40.35261453917326\n ],\n [\n -89.92207321354842,\n 40.35261453917326\n ],\n [\n -89.92207321354842,\n 43.01135367569728\n ],\n [\n -93.29294140522802,\n 43.01135367569728\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
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No abstract available.
","language":"English","publisher":"Massachusetts Geological Survey","doi":"10.7275/ds6v-gy64","usgsCitation":"Stone, J.R., DiGiacomo-Cohen, M.L., Mabee, S.B., Brigham-Grette, J., Stone, B.D., Graham, B.L., and Yellen, B., 2025, Ice-lobe retreat, glacial lake levels and the North American varve chronology in the Deerfield basin of western Massachusetts: Open-File Report 25-01, 52 p., https://doi.org/10.7275/ds6v-gy64.","productDescription":"52 p.","ipdsId":"IP-179170","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":498338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Deerfield basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.50160195053968,\n 42.62009992849204\n ],\n [\n -72.76339223621905,\n 42.62009992849204\n ],\n [\n -72.76339223621905,\n 42.24421898621074\n ],\n [\n -72.50160195053968,\n 42.24421898621074\n ],\n [\n -72.50160195053968,\n 42.62009992849204\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stone, Janet R. 0000-0003-0095-8238 jrstone@usgs.gov","orcid":"https://orcid.org/0000-0003-0095-8238","contributorId":299644,"corporation":false,"usgs":true,"family":"Stone","given":"Janet","email":"jrstone@usgs.gov","middleInitial":"R.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":953323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiGiacomo-Cohen, Mary L. 0000-0003-2384-8912 mdicohen@usgs.gov","orcid":"https://orcid.org/0000-0003-2384-8912","contributorId":2527,"corporation":false,"usgs":true,"family":"DiGiacomo-Cohen","given":"Mary","email":"mdicohen@usgs.gov","middleInitial":"L.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":953324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mabee, Steven B.","contributorId":364850,"corporation":false,"usgs":false,"family":"Mabee","given":"Steven","middleInitial":"B.","affiliations":[{"id":35248,"text":"Massachusetts Geological Survey","active":true,"usgs":false}],"preferred":false,"id":953325,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brigham-Grette, Julie","contributorId":364853,"corporation":false,"usgs":false,"family":"Brigham-Grette","given":"Julie","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":953326,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stone, Byron D. 0000-0001-6092-0798 bdstone@usgs.gov","orcid":"https://orcid.org/0000-0001-6092-0798","contributorId":1702,"corporation":false,"usgs":true,"family":"Stone","given":"Byron","email":"bdstone@usgs.gov","middleInitial":"D.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":953327,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graham, Brandon L. 0000-0002-7197-0413","orcid":"https://orcid.org/0000-0002-7197-0413","contributorId":340458,"corporation":false,"usgs":true,"family":"Graham","given":"Brandon","middleInitial":"L.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":953328,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yellen, Brian","contributorId":198491,"corporation":false,"usgs":false,"family":"Yellen","given":"Brian","email":"","affiliations":[{"id":33278,"text":"Department of Geosciences, University of Massachusetts, Amherst, MA","active":true,"usgs":false}],"preferred":false,"id":953329,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70267734,"text":"ofr20251029 - 2025 - Restoration of Gavia immer (common loon) in Minnesota—2024 annual report","interactions":[],"lastModifiedDate":"2026-04-24T19:28:35.624822","indexId":"ofr20251029","displayToPublicDate":"2025-05-29T12:45:42","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1029","displayTitle":"Restoration of Gavia immer (Common Loon) in Minnesota—2024 Annual Report","title":"Restoration of Gavia immer (common loon) in Minnesota—2024 annual report","docAbstract":"In cooperation with the Minnesota Department of Natural Resources, the U.S. Geological Survey monitored 98 common loon (Gavia immer) focal territories and an additional 37 nonfocal territories in 2024 across 53 study lakes in Minnesota. Focal territories were those territories from which study inferences will be made, whereas nonfocal territories were observed to monitor common loon dynamics in territories adjacent to focal territories. In collaboration with lake associations and private citizens, we deployed 44 artificial nesting platforms within 44 treatment territories, and the remaining 54 focal territories were controls. We completed territorial surveys from April 29 to August 9, 2024, to evaluate occupancy, nest success, and chick survival. We attempted to visit each territory once a week. At least one nest attempt was observed in 41 of 54 control territories. Precisely one nest attempt was observed in 30 control territories, two nest attempts (first attempts failed) were observed in 9 control territories, and three nest attempts (first and second attempts failed) were observed in 2 control territories. At least one nest attempt was observed in 33 of 44 treatment territories. Precisely one nest attempt was observed in 25 treatment territories, two nest attempts (first attempts failed) were observed in 7 treatment territories, and three nest attempts (first and second attempts failed) were observed in 1 treatment territory. In treatment territories, 8 nests were on an artificial nesting platform; the remaining nest locations were natural. Chicks or other evidence of hatching were observed in 26 of 54 (48.1 percent) control territories and 21 of 44 (47.7 percent) treatment territories, with 7 of those successful treatment nests on an artificial nesting platform. This report includes no formal analysis, but we plan to analyze data after collection of all field data in subsequent years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251029","collaboration":"Prepared in cooperation with the Minnesota Department of Natural Resources and Minnesota Pollution Control Agency","usgsCitation":"Beatty, W.S., Amoth, K., Fara, L.J., Gray, B.R., Hall, K., Houdek, S.C., Jech, J., Kenow, K.P., Wellik, M., and Yang, S., 2025, Restoration of Gavia immer (common loon) in Minnesota—2024 annual report: U.S. Geological Survey Open-File Report 2025–1029, 7 p., https://doi.org/10.3133/ofr20251029.","productDescription":"Report: v, 7 p.; Data Release","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-174903","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":486737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1029/coverthb.jpg"},{"id":486746,"rank":5,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603
Director, National Civil Applications Center
National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 562
Reston, VA 20192
Email: cac@usgs.gov
","tableOfContents":"The U.S. Geological Survey and the U.S. Fish and Wildlife Service have developed a three-tiered strategy for monitoring eelgrass (Zostera marina) beds at Izembek Lagoon, Alaska, that targets different spatial and temporal scales. The broadest-scale monitoring (tier-1) uses satellite imagery about every 5 years to delineate the spatial extent of eelgrass beds throughout the lagoon. This report describes the most recent (mid-2020s) tier-1 eelgrass monitoring at Izembek Lagoon. The monitoring effort began by canvasing all satellite imagery collected during summer, under clear daytime skies and at low-tide, since the last tier-1 effort in 2006. Two eelgrass maps of Izembek Lagoon were generated by first creating maps of spectrally unique classes from two Sentinel-2 satellite images collected on July 1, 2016, and August 14, 2020, then attributing those spectral classes with information about eelgrass conditions based on field data. Specifically, maps depicting various eelgrass metrics, such as percentage of cover and modeled biomass, were generated using summaries of the ground data that spatially intersected each spectral class. Comparisons of the 2016 and 2020 Sentinel-2 maps showing eelgrass distributional extent, as well as a 2006 Landsat map, indicated that areas where eelgrass presence may have declined during 2006–20 were most prevalent in the central part of Izembek Lagoon. More recently, during 2016-20, areas of possible biomass decline were more prevalent in the southern part of the lagoon. Monitoring eelgrass conditions at Izembek Lagoon with satellite imagery and concurrent ground data allows conditions to be compared over time, but the influences of tide levels, growing season phenology, and spatiotemporal co-registration accuracy should be considered when designing and interpreting change detection analyses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251007","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","programNote":"Land Management Research Program","usgsCitation":"Douglas, D.C., Fleming, M.D., Patil, V.P., and Ward, D.H., 2025, Mapping eelgrass (Zostera marina) cover and biomass at Izembek Lagoon, Alaska, using in-situ field data and Sentinel-2 satellite imagery: U.S. Geological Survey Open-File Report 2025–1007, 30 p., https://doi.org/10.3133/ofr20251007. [Supersedes preprint https://doi.org/10.1101/2024.08.07.607047.]","productDescription":"Report: vii, 30 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-169599","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":485960,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1007/coverthb2.jpg"},{"id":485963,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HLTAHD","text":"USGS data release","description":"USGS data release","linkHelpText":"Eelgrass (Zostera marina) maps from 2016 and 2020, at Izembek Lagoon, Alaska"},{"id":485961,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1007/ofr20251007.pdf","text":"Report","size":"10.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1007"},{"id":485962,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251007/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1007"},{"id":485964,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1007/images"},{"id":485965,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1007/ofr20251007.XML"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163.098854980302,\n 55.171004418891414\n ],\n [\n -162.87580882559715,\n 55.150219377165826\n ],\n [\n -162.79513255687405,\n 55.2819754305454\n ],\n [\n -162.63219813180595,\n 55.3494888427272\n ],\n [\n -162.52937543637472,\n 55.342292888511395\n ],\n [\n -162.47875503247008,\n 55.40162041992025\n ],\n [\n -162.50248334680037,\n 55.47879257840398\n ],\n [\n -162.74767592913727,\n 55.39443394016618\n ],\n [\n -162.92959300566955,\n 55.31259575418295\n ],\n [\n -163.098854980302,\n 55.171004418891414\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The Trinity River is managed in two sections: (1) from the upper 64-kilometer “restoration reach” downstream from Lewiston Dam to the confluence with the North Fork Trinity River, and (2) the 120-kilometer lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously applied to these reaches and the Klamath River. To estimate fish growth, past S3 calibration efforts in the Trinity and Klamath Rivers used maximum likelihood methods that considered only the abundance of juvenile Chinook salmon (Oncorhynchus tshawytscha) passing a fish trap to estimate survival and movement parameters, but not fish consumption. Previous calibrations did not estimate the average proportion of maximum consumption (Cy) when estimating survival (Sy) and movement (M0y) parameters across years (y) of data, but because no other information was available in the literature a fixed value of Cy=0.66 was assumed. Therefore, the goal of this report is to present an alternative approach that calibrates the S3 model to multivariate data (that is, abundance and size), enabling the estimation of the average proportion of maximum consumption, in conjunction with survival and movement parameters for a particular migration year. We fit the S3 model to individual years of weekly trap abundance estimates and mean fish sizes (fork length) at the Pear Tree Gulch (hereafter referred to as Pear Tree) fish trap representing the restoration reach. We used the Earth Mover’s Distance (EMD) as the objective value to be minimized in parameter optimization. This approach estimated survival, movement, and consumption parameters for each migration year. Because we had information on the abundance of natural and hatchery produced juvenile salmon at the fish traps, we estimated survival and movement for natural and hatchery fish.
S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile Chinook Salmon. A key theme of the model is that river discharge affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive survival and growth within each habitat unit and movement of fish among habitat units.
The physical template of the restoration reach of the Trinity River was classified into 356 meso-habitat units comprised of runs, riffles, and pools. For each habitat unit, we developed a timeseries of daily discharge, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity time series were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg maturation and survival, and in turn, juvenile movement, growth, and survival.
We estimated movement, survival, and consumption parameters by calibrating the model to 12 years of weekly juvenile abundance estimates and fish sizes at the Pear Tree fish trap near the downstream end of the restoration reach. We estimated parameters for 12 years that included a wide range of female spawner abundances (1,414–11,494) and water year types (critically dry–extremely wet). We contrast the estimated parameters to the corresponding number of female spawners and the total annual volume of water discharged for the Trinity River (Trinity River Restoration Program; https://www.trrp.net/restoration/flows/summary/).
The calibration consisted of replicating historical conditions as closely as possible (for example, discharge; temperature; spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location from each brood year of adults and subsequent migration year of their juvenile progeny. Because density-dependent movement was favored in past evaluations, we estimated S3 parameters based on density-independent survival and density-dependent movement. Likewise, each year’s estimated survival parameter for natural (SNy) and hatchery (SHy) fish may be interpreted as the mean daily survival probability from emergence or hatchery release to the Pear Tree fish trap. Under density dependence, the estimated movement parameter for natural (M0Ny) and hatchery (M0Hy) fish represents the intercept of the Beverton-Holt model; the probability of remaining in a habitat at near-zero abundance.
We estimated Cy by using EMD and incorporating abundance and fish size into model calibration. Average daily proportions of maximum consumption, , across the years were generally high (=0.640; standard deviation (SD) SD=0.176), suggesting that fish were feeding at about two-thirds of expected maximum consumption rates. This average proportion of maximum consumption,is very similar to what has been assumed (=0.66) in previous Trinity and Klamath River S3 calibration and simulation efforts. In 2017, we estimated the lowest Cy, suggesting lower average consumption for juvenile salmon in high-discharge water years. When this high discharge year was excluded, there was no apparent trend in Cy with annual water volume. Estimates of survival showed little trend over the range in spawner abundances, but a trend towards higher natural and hatchery fish survival with higher annual volumes of water was apparent. Over the 12 years, the average survival of hatchery fish was =0.888 (SD=0.079) and the average survival natural fish was=0.969 (SD=0.01).
With respect to fish movement, we estimated higher M0Ny and M0Hy with higher annual volumes of water in the Trinity River. Higher MN0y or MH0y suggest greater probability of remaining in a habitat at low fish densities, with potential for density-dependent processes in movement to occur. The highest M0Ny = 0.676 was estimated during brood year 2012, and the overall average for natural fish was =0.276 (SD=0.188) and for hatchery fish was=0.467 (SD=0.235). Under the Beverton-Holt model, as M0Ny or M0Hy approach zero, there is less capacity for change in fish movement as fish density increases.
The S3 model was initialized with only the spatiotemporal distribution of spawners, so it performed well at capturing the essential outmigration features that are ultimately governed by rates of growth, movement, and mortality. We used a new optimization method that could accommodate multivariate data on abundance and fish size collected at the Pear Tree fish trap, enabling the calibration of S3 to estimate five parameters for 12 separate years of data. Incorporating weekly fish size data for each year in our parameter optimization process made the estimation of Cy possible and represents a step forward in the fitting of the S3 model to fish trap data for the purposes of parameter calibration and the estimation of growth parameters with respect to annual conditions. We identified lack of fit and adding important effects into the S3 model may improve the S3 estimation and simulation of water scenarios.
The Trinity River Restoration Program (TRRP) Science Advisory Board recommended that the TRRP focus on developing core elements of a decision support system (DSS; Buffington and others, 2014). Toward that end, the habitat and S3 models described in this report are both core elements of the DSS. The structure of S3 makes it a particularly useful fish production model for the DSS because population dynamics are sensitive to (1) water temperature, (2) daily discharge management, and (3) habitat quality and quantity. Each of these variables are key management parameters under consideration in the TRRP. As such, the S3 model may provide valuable insights into the potentially variable effects of different management decisions on the Trinity River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241070","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., and De Juilio, K., 2025, Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18: U.S. Geological Survey Open-File Report 2024–1070, 21 p., https://doi.org/10.3133/ofr20241070.","productDescription":"vii, 22 p.","onlineOnly":"Y","ipdsId":"IP-156648","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":485969,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1070/images"},{"id":485968,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241070/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1070"},{"id":485967,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1070/ofr20241070.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1070"},{"id":485966,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1070/coverthb.jpg"},{"id":485970,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1070/ofr20241070.XML"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.1406578585185,\n 40.785753827016435\n ],\n [\n -123.1406578585185,\n 40.69151845163566\n ],\n [\n -122.79146166523228,\n 40.69151845163566\n ],\n [\n -122.79146166523228,\n 40.785753827016435\n ],\n [\n -123.1406578585185,\n 40.785753827016435\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) at the Mojave River Dam study area near Hesperia, California, in 2024. Four vireo surveys were completed between April 17 and July 2, 2024, and three flycatcher surveys were completed between May 23 and July 2, 2024.
We detected three territorial male vireos, all of which were paired. No juveniles were observed during surveys. Vireo territories were reported in two habitat types: riparian scrub and willow-cottonwood. Red or arroyo willow (Salix laevigata or lasiolepis) was the dominant plant species in most vireo territories. No territorial or transient flycatchers were observed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251025","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2025, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, California—2024 data summary: U.S. Geological Survey Open-File Report 2025–1025, 8 p., https://doi.org/10.3133/ofr20251025.","productDescription":"vi, 8 p.","onlineOnly":"Y","ipdsId":"IP-172384","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":485895,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1025/ofr20251025.XML"},{"id":485894,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1025/images"},{"id":485893,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251025/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1025"},{"id":485892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1025/ofr20251025.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1025"},{"id":485891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1025/coverthb.jpg"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Mojave River Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.2768615020681,\n 34.370295162773715\n ],\n [\n -117.2768615020681,\n 34.31872800675019\n ],\n [\n -117.21059336313478,\n 34.31872800675019\n ],\n [\n -117.21059336313478,\n 34.370295162773715\n ],\n [\n -117.2768615020681,\n 34.370295162773715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Gravity data require submeter elevation accuracy for data processing, and differential global navigation satellite system (dGNSS) equipment is commonly used to acquire three-dimensional positional data to achieve such accuracy. However, lidar (light detection and ranging) data are commonly used to develop digital elevation models (DEMs) of Earth’s surface. Therefore, using elevations from lidar-derived DEMs for gravity-data acquisition and reduction may improve field efficiency and reduce cost. This study examines the feasibility of using DEMs for gravity-data reduction by comparing dGNSS elevation data from 435 gravity stations in Michigan, Wyoming, and Colorado with their respective DEM elevations. The results show that the average difference between DEM and dGNSS elevations is 13 centimeters (cm) and that 93 percent of those differences are less than 50 cm, even in areas with steep terrain. Because an elevation discrepancy of 50 cm corresponds to an error of roughly 0.1 milligals (mGal) in the simple Bouguer gravity anomaly, the results suggest that lidar-derived DEMs are a viable source for acquiring the elevation data needed to process gravity data, thus improving both the cost and efficiency of data collection for regional surveys where an accuracy of less than 1.0 mGal is desired.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251019","programNote":"Mineral Resources Program","usgsCitation":"Murchek, J.T., Drenth, B.J., Reitman, J.J., Anderson, E.D., Magnin, B.P., and DeGraff, J.M., 2025, The feasibility of using lidar-derived digital elevation models for gravity data reduction (ver. 1.1, July 2025): U.S. Geological Survey Open-File Report 2025–1019, 33 p., https://doi.org/10.3133/ofr20251019.","productDescription":"vii, 33 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-163043","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":491565,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1019/coverthb2.jpg"},{"id":491633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118572.htm"},{"id":491634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1019 PDF"},{"id":491636,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1019 XML"},{"id":491635,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251019/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1019 HTML"},{"id":491638,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2025/1019/versionHist.txt","size":"654 B","linkFileType":{"id":2,"text":"txt"}},{"id":491637,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1019/images/"}],"edition":"Version 1.0: May 12, 2025; Version 1.1: July 1, 2025","contact":"Director, Energy and Minerals Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192-0002
The Clear Lake Hitch (Lavinia exilicauda chi) is a minnow endemic to Clear Lake, Lake County, California. This species is listed as a threatened species under the California Endangered Species Act and has been petitioned for listing under the United States Endangered Species Act. In 2017, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, initiated a Clear Lake Hitch monitoring program to generate information annually on relative abundance and size structure. The monitoring program was organized around a conceptual life cycle diagram, focused on life stages approximately ≥1 year of age, and incorporated a probabilistic study design involving approximately 10 days of short-duration (approximately 40 minutes) gillnet sampling undertaken during daytime. This report documents monitoring program activities from 2017 to 2023 and presents the results of an evaluation of the monitoring program. The evaluation was done after the 2023 sampling event, following 6 years of implementation, which is the approximate generation cycle of Clear Lake Hitch. The results of the evaluation indicated the following: (1) gillnets used in the monitoring program were effective at capturing Clear Lake Hitch aged 1 year or more; (2) the study design was effective at generating the information needed to characterize Clear Lake Hitch relative abundance and size structure, and meaningful operational efficiencies can be obtained by implementing simple changes; and (3) future sampling can be scaled to approximately 4–7 days of effort and maintain at least 80-percent confidence in detecting at least a 25-percent change in abundance, assuming past work productivity is maintained and future data are typical of previous data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251018","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","programNote":"Water Resources Mission Area","usgsCitation":"Feyrer, F., Young, M.J., Huntsman, B., Violette, V., Clause, J.K., Buxton, J., Palm, D., Wulff, M., Gronemyer, J., and Santana, L., 2025, Gillnet sampling methods for monitoring status and trends of Clear Lake Hitch in Clear Lake, Lake County, California: U.S. Geological Survey Open-File Report 2025–1018, 26 p., https://doi.org/10.3133/ofr20251018.","productDescription":"Report: viii, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-168545","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":484873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1018/coverthb.jpg"},{"id":484874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1018/ofr20251018.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1018"},{"id":484875,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251018/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1018"},{"id":484876,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KXAMKP","text":"USGS data release","description":"USGS data release","linkHelpText":"Abundance and distribution of fishes in Clear Lake, Lake County, California"},{"id":484877,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1018/images"},{"id":484878,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1018/ofr20251018.XML"}],"country":"United States","state":"California","county":"Lake County","otherGeospatial":"Clear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.65917771122702,\n 39.01735284231793\n ],\n [\n -122.72999697064151,\n 39.029142588016896\n ],\n [\n -122.751242748466,\n 39.044466317884144\n ],\n [\n -122.76945341517263,\n 39.038965874124074\n ],\n [\n -122.78108800779074,\n 39.076281906791024\n ],\n [\n -122.83015563752795,\n 39.12142755641764\n ],\n [\n -122.8807408228241,\n 39.123782188115115\n ],\n [\n -122.90046904508966,\n 39.101017451759986\n ],\n [\n -122.91311534141363,\n 39.076281906791024\n ],\n [\n -122.91665630438428,\n 39.031107354476376\n ],\n [\n -122.892881267295,\n 39.02285496831968\n ],\n [\n -122.83673171161647,\n 39.024033939613446\n ],\n [\n -122.80688645229174,\n 39.014994657245325\n ],\n [\n -122.79727526708535,\n 38.99887828791458\n ],\n [\n -122.77653534111394,\n 39.007133472381724\n ],\n [\n -122.7628773410841,\n 39.00123701034022\n ],\n [\n -122.74820763734814,\n 38.99298113784059\n ],\n [\n -122.73960815584789,\n 38.98275824692357\n ],\n [\n -122.74466667437748,\n 38.972533879655856\n ],\n [\n -122.72999697064151,\n 38.95955467222447\n ],\n [\n -122.72240919284712,\n 38.96152137166055\n ],\n [\n -122.71633897061167,\n 38.95916132578665\n ],\n [\n -122.71077460022903,\n 38.96584791826234\n ],\n [\n -122.70217511872879,\n 38.95444099821404\n ],\n [\n -122.69357563722826,\n 38.94499939981313\n ],\n [\n -122.66980060013927,\n 38.941065029307254\n ],\n [\n -122.65361334084434,\n 38.94735991731659\n ],\n [\n -122.65563674825637,\n 38.93949121997264\n ],\n [\n -122.6404611926676,\n 38.93437609838975\n ],\n [\n -122.63236756302015,\n 38.942638803709116\n ],\n [\n -122.64754311860891,\n 38.9627013651191\n ],\n [\n -122.65361334084434,\n 38.96899433164984\n ],\n [\n -122.66271867419766,\n 38.972533879655856\n ],\n [\n -122.69104637796346,\n 38.98236502932696\n ],\n [\n -122.69812830390507,\n 38.98039890858698\n ],\n [\n -122.69205808166961,\n 38.985903909070544\n ],\n [\n -122.7133038594938,\n 39.0043818512562\n ],\n [\n -122.68194104461014,\n 38.99455375924509\n ],\n [\n -122.66929474828619,\n 38.99848515986562\n ],\n [\n -122.66929474828619,\n 39.009098850512345\n ],\n [\n -122.65766015566808,\n 39.011457232184966\n ],\n [\n -122.65917771122702,\n 39.01735284231793\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
This report documents the system characterization of the Indian Space Research Organisation Resourcesat-2A Advanced Wide Field Sensor (AWiFS) and is part of a series of system characterization reports produced by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports describe the methodology and procedures used 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.
Resourcesat-2A was launched in 2016 on the Polar Satellite Launch Vehicle-C36; it is identical to Resourcesat-2, and together, they decrease imaging revisit time from 5 days to 2–3 days, providing data continuity and improved temporal resolution. Resourcesat-2 and -2A carry the AWiFS, Linear Imaging Self Scanning-3, and Linear Imaging Self Scanning-4 medium-resolution imaging sensors, continuing the legacy of the Indian Space Research Organisation’s Indian Remote Sensing-1C/1D/P3 satellite programs. More information about Indian Space Research Organisation satellites and sensors is available through the Joint Agency Commercial Imagery Evaluation Earth Observing Satellites Online Compendium and from the Indian Space Research Organisation at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team assessed the geometric, radiometric, and spatial performance of the Resourcesat-2A AWiFS sensor. Geometric performance is divided into the interior geometric performance of band-to-band registration and the exterior geometric performance of geolocation accuracy. The interior geometric performance had offsets in the range of −1.10 meters (m; −0.020 pixel) to 3.67 m (0.066 pixel) in easting and −5.68 m (−0.101 pixel) to 10.38 m (0.185 pixel) in northing with root mean square error values from 5.60 m (0.100 pixel) to 11.31 m (0.202 pixel) in easting and from 3.00 m (0.054 pixel) to 13.52 m (0.241 pixel) in northing.
The exterior geometric performance had mean offsets of −25.29 m in easting and 16.22 m northing with root mean square error values of 26.07 m in easting and 17.60 m in northing compared to the Landsat 8 Operational Land Imager sensor. The radiometric performance had offsets from −0.002 to 0.029 and slopes from 0.733 to 1.012. Spatial performance was in the range of 1.354 to 1.639 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.108 to 0.174.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030V","usgsCitation":"Shrestha, M., Kim, M., Sampath, A., and Clausen, J., 2025, System characterization report on Resourcesat-2A Advanced Wide Field Sensor, chap. V of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 18 p., https://doi.org/10.3133/ofr20211030V.","productDescription":"v, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170096","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":485174,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030V/full"},{"id":485170,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/v/coverthb.jpg"},{"id":485171,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/v/ofr20211030v.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-V"},{"id":485172,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/v/ofr20211030v.XML"},{"id":485173,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/v/images/"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Captive breeding and release programs aimed at recovery of rare species can be informed by genetic data to help select high-diversity source populations, make pairing decisions to minimize inbreeding, and manage release strategies. We developed a set of 54 microsatellite loci to assess genetic structure and diversity across the United States range of the Light-footed Ridgway’s Rail (Rallus obsoletus levipes), a federally endangered marsh bird for which populations have been augmented by a captive breeding program annually since 2001. We identified three regional genetic clusters, with the highest genetic diversity reported in the central cluster, which included all sampled wetlands in north San Diego County. Recent (2019–24) captive-breeding adults all clustered within the northernmost cluster (Orange and Ventura Counties), which was expected given that this cluster included the source wetland for the captive breeding program. Gene flow rates, which approximate the proportions of individuals in a population originating from other populations, were relatively high among clusters (4–24 percent) and may have been enhanced through the release of captive-bred rails. Based on the genetic data analyzed in a genetic rescue decision framework, sourcing new breeding birds from the north San Diego County cluster could provide the greatest genetic diversity benefits. The northernmost cluster, which included Mugu Lagoon and all sampled Orange County wetlands, was considered the most in need of genetic rescue. Recent breeding pairs in the captive breeding program have comparatively low diversity and high interrelatedness. Sourcing birds from wetlands with high genetic diversity and population sizes, assessing genetic relatedness before pairing, and focusing releases in areas that have low estimates of genetic diversity could improve the distribution of genetic diversity across wild populations in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251011","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, Carlsbad Fish and Wildlife Office","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Vandergast, A.G., Smith, J.G., Mitelberg, A., Wood, D.A., Sawyer, K.A., and Conway, C.J., 2025, Genetic structure and diversity in wild populations of the Light-footed Ridgway’s Rail reflect 20 years of augmentation through captive breeding and release: U.S. Geological Survey Open-File Report 2025–1011, 24 p., https://doi.org/10.3133/ofr20251011.","productDescription":"Report: viii, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-169671","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":485012,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1011/coverthb.jpg"},{"id":485013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1011/ofr20251011.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1011"},{"id":485015,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14CYDJC","text":"USGS data release","description":"USGS data release","linkHelpText":"Microsatellite genotypes for light-footed Ridgway's rail (Rallus obsoletus levipes) sampled in southern California"},{"id":485017,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1011/ofr20251011.XML"},{"id":485014,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251011/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1011"},{"id":485016,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1011/images"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.10331263380175,\n 34.10687099022617\n ],\n [\n -118.03807861620402,\n 33.46101273577156\n ],\n [\n -117.15926055168606,\n 32.54485080859107\n ],\n [\n -116.83812382579282,\n 32.56519890177364\n ],\n [\n -117.45219990652568,\n 33.65951802680661\n ],\n [\n -118.60202603728568,\n 34.09630540323134\n ],\n [\n -119.10331263380175,\n 34.10687099022617\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-3 sensor 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 since 2021. 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.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 for continuity of data and improved temporal resolution. The Resourcesat-2 platform (which includes Resourcesat-2A) is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Linear Imaging Self Scanning-3 and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
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.
To summarize the results, we have determined that this sensor provides an interior geometric performance with mean offsets in the range of 1.75 meters (m; 0.06 pixel) to 6.83 m (0.23 pixel) in easting and −1.83 m (−0.06 pixel) to 1.81 m (0.06 pixel) in northing in band-to-band registration and a root mean square error in the range of 3.81 m (0.13 pixel) to 8.19 m (0.27 pixel) in easting and 2.21 m (0.09 pixel) to 4.72 m (0.16 pixel) in northing.
We have measured an exterior geometric error offset in the range of −21.29 to 6.88 m in easting and −7.35 to −2.63 m in northing, and the root mean square error is in the range of 7.19 to 21.43 m in easting and 3.64 to 8.19 m in northing in comparison to the Landsat 8 Operational Land Imager.
The measured radiometric performance was in the range of −0.002 to 0.031 in offset and 0.701 to 0.940 in slope, and the spatial performance was in the range of 1.204 to 1.265 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.251 to 0.277.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030T","usgsCitation":"Park, S., Shrestha, M., Kim, M., Sampath, A., and Clauson, J., 2025, System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor, chap. T 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/ofr20211030T.","productDescription":"v, 17 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170097","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":484900,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/t/images/"},{"id":484899,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/t/ofr20211030t.XML"},{"id":484898,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/t/ofr20211030t.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-T"},{"id":484897,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/t/coverthb.jpg"},{"id":484901,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030T/full"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The hydrogeologic framework at closed-loop geothermal sites in the Erie-Ontario Lowlands and Allegheny Plateau of central and western New York is the result of the complex interaction of bedrock geology, glacial geology, and groundwater hydrology, and the occurrence of petroleum and gas. Considerations for closed-loop geothermal bore installation include the thickness and character of glacial deposits, bedrock solubility and depth to competent rock, karst development, the distribution of highly permeable zones and their hydraulic heads, and the presence of saline water, gas, and oil. The hydrogeology of the Erie-Ontario Lowlands and Allegheny Plateau poses challenges to closed-loop geothermal bore drilling and casing; managing drill cuttings, discharge water, and gas; and grouting. The potential to encounter severe challenges typically increases with bore depth. This report highlights hydrogeologic considerations for closed-loop geothermal bore installation in New York’s Erie-Ontario Lowlands and Allegheny Plateau to help guide the efficient and safe development of geothermal resources in the regions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251013","usgsCitation":"Williams, J.H., Kappel, W.M., and Woda, J.C., 2025, Hydrogeologic framework and considerations for drilling and grouting of closed-loop geothermal bores in the Erie-Ontario Lowlands and Allegheny Plateau of New York State: U.S. Geological Survey Open-File Report 2025–1013, 11 p., https://doi.org/10.3133/ofr20251013.","productDescription":"v, 11 p.","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-160254","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":484773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1013/coverthb.jpg"},{"id":484774,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1013/ofr20251013.pdf","text":"Report","size":"3.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1013 PDF"},{"id":484776,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1013/ofr20251013.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1013 XML"},{"id":484775,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251013/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1013 HTML"},{"id":484777,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1013/images/"},{"id":493765,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118544.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Allegheny Plateau, Erie-Ontario Lowlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.81166805859044,\n 42.45817957611891\n ],\n [\n -79.70949328319377,\n 41.9887101346182\n ],\n [\n -75.25480564049155,\n 41.98783382644345\n ],\n [\n -74.87533808065217,\n 41.42844676638893\n ],\n [\n -74.68982238759088,\n 44.66640491894796\n ],\n [\n -75.55631580076837,\n 44.66459292990035\n ],\n [\n -76.3423531557645,\n 44.2288156067778\n ],\n [\n -76.41053068058827,\n 43.979744421465796\n ],\n [\n -76.30005176487545,\n 43.60034279040653\n ],\n [\n -76.88563948226266,\n 43.34164590639\n ],\n [\n -77.46843046198688,\n 43.34036285729903\n ],\n [\n -78.42846779801299,\n 43.474518439670874\n ],\n [\n -79.08970961322291,\n 43.31146453979213\n ],\n [\n -78.97892018115594,\n 42.958836576630915\n ],\n [\n -78.81395126277387,\n 42.855237313375795\n ],\n [\n -79.81166805859044,\n 42.45817957611891\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
During prolonged periods of above-average precipitation, rising groundwater levels have the potential to cause damage to and interfere with underground infrastructure and building foundations. To understand the relations between precipitation and groundwater in the vicinity of Lake Nokomis, the U.S. Geological Survey, in collaboration with the University of Minnesota, quantified five components of the groundwater budget: groundwater recharge, change in surficial aquifer storage, surficial aquifer groundwater discharge to Lake Nokomis, groundwater evapotranspiration, and groundwater discharge to underlying bedrock aquifers. Field data, geologic records, and empirical calculation methods were used to quantify groundwater budget components for April 2023 through April 2024. Lake water budget data indicate that Lake Nokomis is a flowthrough system during periods with no outflow through the weir, with groundwater inputs equal to outputs. Roughly 40 percent of precipitation that fell in the study area was added to the surficial aquifer as recharge. Uncertainty in the vertical hydraulic conductivity resulted in wide-ranging estimates (spanning three orders of magnitude) of water discharging from the surficial aquifer to the underlying bedrock aquifer. Drought conditions persisted for the duration of this study and were not representative of the conditions that motivated this study. This study is a start towards understanding relations between precipitation, Lake Nokomis levels, and groundwater levels that could affect local underground infrastructure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251021","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources and in collaboration with the University of Minnesota","usgsCitation":"Livdahl, C.T., 2025, Groundwater budget for the surficial aquifer surrounding Lake Nokomis, Minneapolis, Minnesota: U.S. Geological Survey Open-File Report 2025–1021, 15 p., https://doi.org/10.3133/ofr20251021.","productDescription":"Report: vi, 15 p.; Dataset","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-166630","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":493763,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118543.htm","linkFileType":{"id":5,"text":"html"}},{"id":484783,"rank":6,"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"},{"id":484781,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1021/images/"},{"id":484780,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1021/ofr20251021.XML"},{"id":484782,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251021/full"},{"id":484779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1021/ofr20251021.pdf","text":"Report","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1021"},{"id":484778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1021/coverthb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Lake Nokomis","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.23325811481614,\n 44.91617052326589\n ],\n [\n -93.2517175215867,\n 44.91617052326589\n ],\n [\n -93.2517175215867,\n 44.901128494318755\n ],\n [\n -93.23325811481614,\n 44.901128494318755\n ],\n [\n -93.23325811481614,\n 44.91617052326589\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Each year, U.S. Geological Survey (USGS) personnel collect approximately 52,000 water-quality samples from rivers and streams across the United States. Several samplers are used by the USGS for water-quality sample collection in riverine environments. These samplers are coated with Plasti Dip to protect the exterior of the sampler; however, Plasti Dip is susceptible to fraying and wear, requiring maintenance. Alternative coatings were tested to determine if a different coating is better suited for the samplers. The alternative coatings included Raptor, powder coating, and DuraCoat; a fifth option was bare metal. Samplers with different coatings were evaluated based on initial coating application, equipment blank samples, a controlled wear test, blank sample collection with worn samplers, maintenance and re-coating of samplers, and field-use and wear tracking. The powder-coated sampler proved to be the top performer overall in the study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251016","usgsCitation":"Thornton, A.M., 2025, Evaluation of alternative coatings for U.S. Geological Survey water-quality samplers: U.S. Geological Survey Open-File Report 2025–1016, 15 p., https://doi.org/10.3133/ofr20251016.","productDescription":"Report: iv, 15 p.; Data Release","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-173533","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":484591,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P144VS6G","text":"USGS data release","linkHelpText":"Data to support the evaluation of alternative Coatings for USGS Water-Quality Samplers"},{"id":484590,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1016/images/"},{"id":484589,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1016/ofr20251016.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1016 XML"},{"id":484588,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251016/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1016 HTML"},{"id":484587,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1016/ofr20251016.pdf","text":"Report","size":"3.46 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1016 PDF"},{"id":484586,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1016/coverthb.jpg"}],"contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
The U.S. Geological Survey Astrogeology Science Center houses the Meteor Crater sample collection, an assemblage of over 2,500 meters of cuttings from 161 drill holes into Meteor Crater’s rim, flanks, and ejecta blanket. We have utilized this unique collection to study the composition and spatial distribution of impact-generated materials from within the ejecta blanket. Meteor Crater has historically been known to have generated only a relatively small amount of impact melt compared to other terrestrial craters of similar size. A detailed compositional and textural dataset of impact-derived melts from this impact can therefore be a useful asset in improving our understanding of crater formation, and in particular impact melt formation.
We have characterized 42 impact-melt particles from Meteor Crater using a scanning electron microscope and an electron microprobe for textural and compositional analysis. We analyzed samples from six drill holes in the ejecta blanket, situated to the northwest, southeast, south, and southwest of the crater (ejecta northeast of the crater is devoid of impact melts). Impact melts were collected from drill cuttings at various depths within the ejecta blanket, ranging from a few centimeters below the surface down to ~6.5 meters.
Backscattered electron (BSE) images were acquired for each analyzed impact-melt particle. To characterize the various textures and phases present in each impact melt, we also took many detailed BSE images. Our geochemical analyses include full spectral profiles using energy dispersive X-ray spectrometry and well-calibrated wavelength dispersive spectrometry for a number of phases, including minerals (olivine, pyroxene, and so on), pristine glass, and metallic inclusions. The full dataset is available in ScienceBase as a data release (Gullikson and others, 2024), accessible at https://doi.org/10.5066/P9OGAJ8P.
Our goal for this Open-File Report is to provide a summary of this immense dataset, details on data collection, descriptions of the different phases observed within impact-melt particles (both geochemically and texturally), and observable trends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241062","usgsCitation":"Gullikson, A.L., Gaither, T.A., and Hagerty, J.J., 2024, Characterizing Meteor Crater impact melts through geochemistry and textural analysis: U.S. Geological Survey Open-File Report 2024–1062, 23 p., https://doi.org/10.3133/ofr20241062.","productDescription":"Report: vii, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-162108","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":493758,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118538.htm","linkFileType":{"id":5,"text":"html"}},{"id":484534,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241062/full"},{"id":484524,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1062/images"},{"id":484387,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OGAJ8P","text":"USGS Data Release","description":"Gullikson, A.L., Gaither, T.A., and Hagerty, J.J., 2024, Geochemistry and high-resolution backscattered electron imaging of Meteor Crater impact melts: U.S. Geological Survey data release, https://doi.org/10.5066/P9OGAJ8P.","linkHelpText":"Geochemistry and high-resolution backscattered electron imaging of Meteor Crater impact melts"},{"id":484384,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1062/covrthb.jpg"},{"id":484386,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1062/ofr20241062.XML","size":"200 KB","linkFileType":{"id":8,"text":"xml"}},{"id":484385,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1062/ofr20241062.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","otherGeospatial":"Meteor Crater","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.03682408526112,\n 35.03726468620707\n ],\n [\n -111.03682408526112,\n 35.01842427092913\n ],\n [\n -111.01097319209285,\n 35.01842427092913\n ],\n [\n -111.01097319209285,\n 35.03726468620707\n ],\n [\n -111.03682408526112,\n 35.03726468620707\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
The geologic history and anthropogenic modifications of Minnesota’s Wild Rice River have caused major morphological adjustments, which induce erosion and excess fluvial sediment transport. The excess sediment deposits in the lower Wild Rice River, exacerbating flooding. To help mitigate these problems, the Wild Rice Watershed District has future plans to implement a river restoration on the lower Wild Rice River. The Wild Rice Watershed District collaborated with the U.S. Geological Survey to measure and analyze sediment transport along the Wild Rice River’s Main and South Branches to assess any potential changes in sediment transport among sites and time periods. Time differencing results indicated that all suspended-sediment constituents showed a significant difference between the two sampling periods at one South Branch site but not at the Main Branch site. Piecewise regression analysis better matched the suspended-sediment constituents transport process at most sites by differentiating no relation between suspended-sediment constituents at lower streamflows and a positive relation at higher streamflows at most Wild Rice River sites. Five of the sites showed elevated sediment transport with increasing streamflow. In contrast, the site farthest downstream showed a negative relation with increasing streamflow, indicating that that the lower Wild Rice River is supply limited and deposition is likely occurring upstream and (or) near the site. Overall, the uncertainty in results indicates the complexity of sediment transport in a river when using streamflow as the sole explanatory variable and suggests a need for multisite, multiyear, and multifaceted data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251008","collaboration":"Prepared in cooperation with the Wild Rice Watershed District","usgsCitation":"Groten, J.T., Levin, S.B., Storey, G.G., Coenen, E.N., Blount, J.D., Lund, J.W., and Brannon, D.J., 2025, Suspended sediment and bedload transport along the Main and South Branches, Wild Rice River, northwestern Minnesota, 1979 through 2023: U.S. Geological Survey Open-File Report 2025–1008, 38 p., https://doi.org/10.3133/ofr20251008.","productDescription":"Report: vii, 38 p.; Dataset","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154644","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":493754,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118525.htm","linkFileType":{"id":5,"text":"html"}},{"id":483763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1008/coverthb.jpg"},{"id":483764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1008/ofr20251008.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1008"},{"id":483765,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1008/ofr20251008.XML"},{"id":483766,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1008/images/"},{"id":483767,"rank":5,"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"},{"id":483768,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251008/full"}],"country":"United States","state":"Minnesota","otherGeospatial":"Wild Rice River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97,\n 47.5833\n ],\n [\n -97,\n 47\n ],\n [\n -95.25,\n 47\n ],\n [\n -95.25,\n 47.5833\n ],\n [\n -97,\n 47.5833\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Climate change presents new and compounding challenges to natural resource management. With changing climate patterns, managers are confronted with difficult decisions on how to minimize climate effects on habitats, infrastructure, and wildlife populations. To support climate adaptation decision making, we first conceptualized an approach that integrates the principles of the resist–accept–direct framework, climate scenario planning, and decision analysis into a general framework to support adaptation planning. This framework was implemented and refined by working with three National Wildlife Refuge System refuges within the Midwest Region. The objectives of this report are to describe the climate adaptation decision framework and provide guidance for how to apply the framework to support transparent, consistent, and decision-focused adaptation planning. We include a workbook to support the application of each step of the framework as well as lessons learned from our experiences developing the framework. The climate adaptation decision framework has wide applicability to aid adaptation planning within natural resource management and underscores the important role of engaging interest groups in climate adaptation decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251005","issn":"2331-1258","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Bouska, K.L., Booker, J., Clark, S., Delaney, J., Eash, J., Post van der Burg, M., and Roop, H., 2025, Managing for tomorrow—A climate adaptation decision framework: U.S. Geological Survey Open-File Report 2025–1005, 53 p., https://doi.org/10.3133/ofr20251005.","productDescription":"vi; 53 p.","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-165050","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":493752,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118522.htm","text":"Chautauqua NWR, Emiquon NWR, and Meredosia 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Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118523.htm","text":"Agassiz NWR","linkFileType":{"id":5,"text":"html"}},{"id":484372,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251005/full","description":"OFR 2025-1005 HTML"}],"country":"USA","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, 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\"}}]}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603
The fringing coral reef off Olowalu, Maui, Hawaii, has been identified as a local conservation priority site. In 2007, the National Oceanic and Atmospheric Administration (NOAA) produced a benthic habitat map of the Hawaiian Islands that was used as a foundation for this study. To support place-based management of the reef in the future, the U.S. Geological Survey (USGS) mapped the geologic zone, major and dominant geomorphological structure, biological cover type, and percent of biological cover for 11 square kilometers (km2) of Olowalu Reef at a minimum mapping unit (MMU) of 100 square meters (m2) to create a benthic habitat map. Heads-up digitization was employed on 0.50-meter (m) natural color satellite orthoimagery with ancillary 1-m acoustic backscatter imagery from single-scan sonar (sound navigation and ranging). A 1-m, 4-m, and 8-m digital bathymetric model (DBM) was interpolated from bathymetric lidar (light detection and ranging), and various geomorphometric layers derived from the DBMs were used for habitat interpretation. Still-frame imagery of the seafloor extracted from vessel-towed underwater video transects on Olowalu Reef served as ground validation points (n=870) during active mapping and accuracy assessment points (n=216) for thematic accuracy assessment. Thematic accuracy was cross-validated by the Hawai‘i Department of Land and Natural Resources Division of Aquatic Resources. Final thematic accuracy was 88.8 percent for major structure, 85.6 percent for dominant structure, 86.0 percent for major biological cover, and 78.6 percent for type and percent of major biological cover. Reef and hardbottom constituted 52 percent of the total mapped habitat, comprising mostly aggregate reef (31 percent) and pavement (11 percent), with large swaths of spur-and-groove (9 percent). Of this hardbottom, 17 percent was covered with moderate (10 to <50 percent) coral and 27 percent with high coral cover (50 to <90 percent). High (50 to <90 percent) macroalgae cover dominated the continuous sand sheets in offshore bank/shelf zones.
The map created in this study supplements the NOAA 2007 map and expands on the observations made by USGS sampling of the reef. The NOAA 2007 map and our map differed in total areal extent by a negligible 6 m2 and were in general thematic agreement. Our map is intended to serve as a baseline for public access, general research, local-level management, and reef change for future studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251010","usgsCitation":"Heberer, L.N., Alkins, K.A., Storlazzi, C.D., Cochran, S.A., Gibbs, A.E., Sparks, R., Stone, K., Silva, I., Martinez, T., Peralto, C., Levine, A.S., Stow, D., and Maloney, J., 2025, Benthic Habitat Map of Olowalu Reef, Maui, Hawaii—Geomorphological Structure, Biological Cover, and Geologic Zonation Determined with Spectral, Lidar, and Acoustic Data: U.S. Geological Survey Open-File Report 2025–1010, 32 p., https://doi.org/10.3133/ofr20251010.","productDescription":"Report: vi, 32 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-152179","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":493749,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118524.htm","linkFileType":{"id":5,"text":"html"}},{"id":484394,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ICJ7CF","text":"USGS Data Release","description":"Heberer, L.N., Alkins, K.A., Storlazzi, C.D., Cochran, S.A., Gibbs, A.E., Sparks, R., Silva, I., Stone, K., Martinez, T., and Peralto, C., 2025, Benthic habitat map of the geomorphological structure, biological cover, and geologic zonation of Olowalu reef, Maui: U.S. Geological Survey data release, https://doi.org/10.5066/P9ICJ7CF.","linkHelpText":"Benthic habitat map of the geomorphological structure, biological cover, and geologic zonation of Olowalu reef, Maui"},{"id":484407,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1010/covrthb.jpg"},{"id":484408,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1010/ofr20251010.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui, Olowalu Reef","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.66330945257621,\n 20.84439182546096\n ],\n [\n -156.66330945257621,\n 20.763341421657273\n ],\n [\n -156.5212712939503,\n 20.763341421657273\n ],\n [\n -156.5212712939503,\n 20.84439182546096\n ],\n [\n -156.66330945257621,\n 20.84439182546096\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
This report documents the system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-4 (LISS–4) sensor. It is part of a series of system characterization reports produced by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports describe the methodology and procedures used 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.
Resourcesat-2A was launched in 2016 on the Polar Satellite Launch Vehicle-C36; it is identical to Resourcesat-2, and together, they decrease imaging revisit time from 5 days to 2–3 days, providing data continuity and improved temporal resolution. Resouresat-2 and 2A carry the Advanced Wide Field Sensor, Linear Imaging Self Scanning-3, and LISS–4 medium-resolution imaging sensors, continuing the legacy of the Indian Space Research Organisation’s Indian Remote Sensing-1C/1D/P3 satellite programs. More information about the Indian Space Research Organisation’s satellites and sensors is available through the Joint Agency Commercial Imagery Evaluation Earth Observing Satellites Online Compendium at https://calval.cr.usgs.gov/apps/compendium/ and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team assessed the geometric, radiometric, and spatial performances of the Resourcesat-2A LISS–4 sensor. Geometric performance is divided into the interior geometric performance of band-to-band registration and the exterior geometric performance of geolocation accuracy. The interior geometric performance had mean offsets in the range of −0.118 to 0.024 pixel in easting and −0.053 to 0.022 pixel in northing with root mean square error values from 0.067 to 0.230 pixel in easting and from 0.087 to 0.2 pixel in northing. The exterior geometric performance had offsets in the range of 2.55 to 7.85 meters (m) in easting and −6.15 to 11.15 m in northing with root mean square error values in the range of 2.6 to 8.2 m in easting and 6.35 to 11.8 m in northing compared to the U.S. Department of Agriculture National Agriculture Imagery Program and WorldView-3 orthoimages. The measured radiometric performance had offsets from 0.003 to 0.024 and slopes from 0.736 to 0.952, and spatial performance was in the range of 1.633 to 1.903 pixels for the full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.0529 to 0.0952.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030U","usgsCitation":"Shrestha, M., Sampath, A., Kim, M., Park, S., and Clauson, J., 2025, System characterization report on Resourcesat-2A Linear Imaging Self Scanning-4 sensor, chap. U of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 16 p., https://doi.org/10.3133/ofr20211030U.","productDescription":"iv, 16 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170098","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":483933,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/u/coverthb.jpg"},{"id":483934,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/u/ofr20211030u.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-U"},{"id":483935,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/u/ofr20211030u.XML"},{"id":483936,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/u/images/"},{"id":483938,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030U/full"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Southeastern United States has many lakes, streams, and reservoirs that serve as important drinking water sources with recreational, agricultural, and ecological uses. However, harmful algal blooms (HABs) are becoming more common in these waters, causing health issues for humans and animals. HABs have been listed as a contaminant of emerging concern, and the magnitude, frequency, and duration of HABs appear to be increasing at the global scale. While it is well known that nutrients stimulate algae growth, it is not clear how climate change and other parameters stimulate the development of toxin production by HABs. The scientific literature describes parameters, such as storm occurrence, temperature, dissolved metals, erosion of soils, increasing length of growing season, discharge, and hydroperiod, that may affect algae growth and toxin production. Climate and hydrologic models address many of the physical and environmental parameters that influence HABs, but no climate models directly address HABs. This report compiles information from the existing literature pertaining to HABs and the modeling and forecasting of HABS. This compilation is done through the incorporation of climate change models. HAB research that involves climate change will require multiple disciplines that bring together ecologists, hydrologists, climatologists, engineers, economists, and new technology. Resource managers could use geographic data about the occurrence and distribution of HABs to develop models that identify waterbodies more vulnerable to HAB events. Development of such models will require teams capable of integrating biological, chemical, and physical factors. Model development will require additional research that can resolve anthropogenic and climate-related environmental factors to identify trends in freshwater HABs. The complexity and interconnectedness of the parameters that influence HAB occurrences will make model development challenging and require rigorous regional calibration.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Air Force (USAF) issued funds to the U.S. Geological Survey (USGS) to update the biosecurity plan, create a current (2019) flora and fauna species identification index, and do container evaluations for the presence of potential invasives. The current (2019) biosecurity protocols used for prevention were evaluated, and new biodiversity surveys were completed for terrestrial vegetation and arthropods and included the first formal reptile surveys. Results from field efforts add to existing knowledge and may identify new species arrivals to Wake.
One goal of this project was to update and compile established species information for the atoll and create species identification guides for the three taxonomic groups surveyed. We made these flora and fauna species identification guides by compiling results of the recent (2019) and historical surveys. The guides can be used as resident desktop references, as a baseline for assessing future natural resource surveys, and to assist with guiding management actions. We refer herein to biosecurity and integrated pest management plan materials, which we created simultaneously to inform current (2019) biosecurity and to identify some of the top invasive species at Wake. This study was done in cooperation with the USAF, and surveys were performed for the 611th Civil Engineer Squadron Natural Resources Program, ACES PROJECT #YGFZ170002 under agreement number F2MUAA7116GW02 between the USAF and the USGS Western Ecological Research Center.
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Laterallus jamaicensis jamaicensis (eastern black rails; Gmelin, 1789) are facing increasing risk from flooding in coastal breeding habitats because of rising sea levels combined with standard high tide flooding. In this report, we examine regional differences in relative rates of sea level rise, days in the breeding season above historical high tide flooding thresholds, future inundation of current (2021) emergent wetlands, and potential marsh resiliency for the breeding distribution of the eastern black rail across the Atlantic and U.S. Gulf coasts. By midcentury (2050), two sea level rise scenarios (intermediate low and intermediate) indicate that areas analyzed in Texas and the Mid-Atlantic will experience at least minor flood levels for more than half of the breeding season. By the end of the century (2100), all tidal gages in the Atlantic and U.S. Gulf coasts are projected to experience at least moderate flood levels for most of the current (April–September) eastern black rail breeding season. In some areas like New Jersey, this translates to inundation for most of the emergent wetlands in the representative parishes and counties analyzed in this report. In other parts of the coastal distribution, estimates of increases in inundation are lower or more variable, stemming from differences in the elevation of existing emergent marsh, especially at the herbaceous wetland/woody wetland transition zone. Sea level rise and tidal flooding are not projected to pose an equal risk across the coastal distribution of the eastern black rail, leading to variation in risk of nest loss because of flooding. The degree to which these wetlands and birds will adapt to changing sea level and salinity depends on a range of factors including future expansion of developed areas and the ability of marsh areas to move inland. Restoration and active management of coastal wetland areas may be necessary to maintain appropriate breeding habitat.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211104F","usgsCitation":"Nikiel, C.A., and Lyons, M.P., 2025, Potential effects of sea level rise and high tide flooding on Laterallus jamaicensis jamaicensis (eastern black rail) coastal breeding areas: U.S. Geological Survey Open-File Report 2021–1104–F, 40 p., https://doi.org/10.3133/ofr20211104F.","productDescription":"vii, 40 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-172341","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":483246,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211104F/full"},{"id":483244,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1104/f/ofr20211104f.XML"},{"id":483243,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1104/f/ofr20211104f.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1104-F"},{"id":483242,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1104/f/coverthb.jpg"},{"id":483245,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1104/f/images/"},{"id":492780,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118482.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Atlantic Coast, Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.73369411465748,\n 44.87861692762931\n ],\n [\n -67.9395715369188,\n 45.91302389167819\n ],\n [\n -68.52891205677287,\n 46.14233382856975\n ],\n [\n -69.66609739851674,\n 44.904840367756236\n ],\n [\n -71.8455845133013,\n 43.01632555370077\n ],\n [\n -75.76042364090121,\n 40.73258218965182\n ],\n [\n -77.41422284991526,\n 39.32302567933269\n ],\n [\n -77.11953264135055,\n 36.58955309284522\n ],\n [\n -79.0153316043617,\n 34.430624116007294\n ],\n [\n -82.3380440342791,\n 31.342877305678\n ],\n [\n -87.14213042595998,\n 31.473505392669267\n ],\n [\n -87.74548189802837,\n 32.39195075385649\n ],\n [\n -89.00813665013129,\n 32.354083649747\n ],\n [\n -89.30755056658234,\n 31.271920212222028\n ],\n [\n -92.00443786768639,\n 31.222304379727817\n ],\n [\n -95.98918412828937,\n 30.505582509882984\n ],\n [\n -98.19210772787893,\n 28.060885389466677\n ],\n [\n -98.10578570219339,\n 26.094479941263742\n ],\n [\n -97.05626247225148,\n 25.880720528127156\n ],\n [\n -97.21103649173618,\n 27.232424454623654\n ],\n [\n -95.67794712232511,\n 28.598718054492764\n ],\n [\n -93.37262411904577,\n 29.53246193068931\n ],\n [\n -90.09201823404798,\n 28.868540112379208\n ],\n [\n -88.84955647397418,\n 28.787680093703898\n ],\n [\n -88.58294319870708,\n 29.755309328858473\n ],\n [\n -86.26626920949336,\n 30.023219777079703\n ],\n [\n -84.98796116952035,\n 29.29748111241001\n ],\n [\n -83.97817016398578,\n 29.677155423717906\n ],\n [\n -82.92684471060598,\n 28.711279338050616\n ],\n [\n -83.33453111961292,\n 28.007674975959418\n ],\n [\n -81.38398690858428,\n 24.942184191892167\n ],\n [\n -81.48743115456318,\n 24.146104924998653\n ],\n [\n -79.34128084180263,\n 25.993764942081356\n ],\n [\n -81.14405051637162,\n 30.851402909345737\n ],\n [\n -74.95193417969506,\n 35.431852837245344\n ],\n [\n -75.10789103469754,\n 37.38614632113877\n ],\n [\n -73.3440560833726,\n 40.10806605773732\n ],\n [\n -69.15888557248354,\n 41.691566830140545\n ],\n [\n -70.25945555475435,\n 43.01474979449651\n ],\n [\n -66.73369411465748,\n 44.87861692762931\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Midwest Climate Adaptation Science Center
U.S. Geological Survey
1954 Buford Avenue
St. Paul, MN 55108
This report addresses system characterization of the Environmental Mapping and Analysis Program hyperspectral sensor by the DLR (German Aerospace Center, ground segment project management), GFZ (Deutsches Geoforschungszentrum, science lead) 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 EnMAP hyperspectral sensor; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), and radiometric performances of the EnMAP hyperspectral sensor. Results of these analyses indicate that the Environmental Mapping and Analysis Program has a band-to-band geometric performance in the range of −0.135 to 0.15 pixel, geometric performance relative to the Operational Land Imager in the range of −27.716 meters (−0.92 pixel) to 32.892 meters (1.09 pixels) offset in comparison to Landsat 8 Operational Land Imager, offset of a radiometric comparison in the range of −0.012 to 0.020, slope of a radiometric comparison in the range of 0.947 to 1.031.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030S","usgsCitation":"Kim, M., Park, S., and Anderson, C., 2025, System characterization report on the Environmental Mapping and Analysis Program (EnMAP), chap. S 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/ofr20211030S.","productDescription":"vi, 28 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167720","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":483138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/s/coverthb.jpg"},{"id":483141,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/s/images/"},{"id":483142,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030S/full"},{"id":483139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/s/ofr20211030s.pdf","text":"Report","size":"14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–S"},{"id":483140,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/s/ofr20211030s.XML"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198