Constructed water quality wetlands, designed to accept tile drainage and surface runoff, are a promising solution for reducing surface water nutrient loading from agricultural systems. In addition to their water quality benefits, these systems may also offset losses of migratory waterbird stopover sites resulting from historical and future agricultural drainage modernization. To assess this possibility, we developed spatially explicit habitat models informed with expert opinion to explore the: 1) potential of water quality wetlands to provide suitable stopover resources for waterbirds during spring migration; and 2) the extent these wetlands can offset likely losses of stopover resources due to drainage modernization. We focused our modeling on the Iowa portion of the Prairie Pothole Region of North America as it was a historically important area within this vital region for waterbirds, but it has experienced widespread subsurface drainage. Model results indicate that unmitigated drainage modernization is likely to have a large negative effect on spring migratory resources for dabbling ducks and shorebirds and minimal effect on diving ducks. Water quality wetland installations are likely to provide habitat for dabbling and diving ducks, but wetland installation is unlikely to completely offset habitat losses for dabbling ducks and shorebirds. Drainage modernization aside, our results indicate that water quality wetlands can address several environmental issues associated with agricultural expansion and intensification by improving water quality and providing wetland resources for waterbirds and other organisms. Field-scale research is needed to validate these results.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-025-01930-y","usgsCitation":"Mitchell, M., Anteau, M.J., Pearse, A.T., Newcomer-Johnson, T., Christensen, J.R., Crumpton, W.R., Dyson, B., Canfield, T.J., Helmers, M., Green, D., and Forshay, K.J., 2025, Modeling wetland resources for spring migratory waterbirds under different agricultural management scenarios in the Iowa portion of the Prairie Pothole Region, USA: Wetlands, v. 45, 48, 19 p., https://doi.org/10.1007/s13157-025-01930-y.","productDescription":"48, 19 p.","ipdsId":"IP-167627","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":499637,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-025-01930-y","text":"Publisher Index Page"},{"id":499372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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R.","contributorId":238115,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","middleInitial":"R.","affiliations":[],"preferred":false,"id":954811,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crumpton, William R.","contributorId":167788,"corporation":false,"usgs":false,"family":"Crumpton","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":954812,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dyson, Brian","contributorId":365794,"corporation":false,"usgs":false,"family":"Dyson","given":"Brian","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":954813,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Canfield, Timothy J.","contributorId":217452,"corporation":false,"usgs":false,"family":"Canfield","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":39634,"text":"U.S. Environmental Protection Agency, Ada, OK USA","active":true,"usgs":false}],"preferred":false,"id":954814,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Helmers, Matthew","contributorId":189905,"corporation":false,"usgs":false,"family":"Helmers","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":954815,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Green, David","contributorId":167789,"corporation":false,"usgs":false,"family":"Green","given":"David","affiliations":[],"preferred":false,"id":954816,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Forshay, Kenneth J.","contributorId":221060,"corporation":false,"usgs":false,"family":"Forshay","given":"Kenneth","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":954817,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70268896,"text":"70268896 - 2025 - Experimental evaluation of Eastern box turtle (Terrapene carolina carolina) detectability in visual search surveys","interactions":[],"lastModifiedDate":"2025-07-10T14:28:35.360554","indexId":"70268896","displayToPublicDate":"2025-04-30T09:09:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"title":"Experimental evaluation of Eastern box turtle (Terrapene carolina carolina) detectability in visual search surveys","docAbstract":"Understanding how detection probability varies over time, space, or in response to measurable covariates is important to inform the monitoring and assessment of many species. A standard model to understand detectability, the availability/perception model, admits that detection probability is the composite of two components: availability and ability of surveyors to detect the target. Availability is largely affected by environmental and behavioral factors, whereas perception is primarily affected by attributes of individual observers and survey protocols, and thus can potentially be partially controlled by survey design. We designed and implemented a field study to understand the perception component of detection for Eastern Box Turtles (Terrapene carolina carolina) using visual encounter surveys. We obtained and deployed museum specimens of Eastern Box Turtle shells and subjected them to visual search surveys by observers in realistic field situations. Overall, about 50% of the box turtle shells were detected by observers, including 41.5% in what we categorized as partially visible and 63.0% as fully visible. There were significant differences among observers, which may be due to observer-specific variation in search technique; the observers varied in how well they achieved the protocol guidance. Therefore, in visual search surveys, care in study design and analysis should be taken to account for variation in perception to determine detectability, as our study suggests 37% of perceptible targets are missed by surveyors.
","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Heinle, W., Beswick, N., Wapman, E., and Royle, A., 2025, Experimental evaluation of Eastern box turtle (Terrapene carolina carolina) detectability in visual search surveys: Herpetological Conservation and Biology, v. 20, no. 1, p. 82-93.","productDescription":"12 p.","startPage":"82","endPage":"93","ipdsId":"IP-160033","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":492011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":491993,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol20_issue1.html"}],"country":"United States","state":"Maryland","otherGeospatial":"Patuxent Research Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.809722,\n 39.050556\n ],\n [\n -76.809722,\n 39.047222\n ],\n [\n -76.805556,\n 39.047222\n ],\n [\n -76.805556,\n 39.050556\n ],\n [\n -76.809722,\n 39.050556\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Heinle, William","contributorId":357763,"corporation":false,"usgs":false,"family":"Heinle","given":"William","affiliations":[{"id":85554,"text":"Dept. of Biology, Univ. Richmond","active":true,"usgs":false}],"preferred":false,"id":942537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beswick, Noelle","contributorId":357764,"corporation":false,"usgs":false,"family":"Beswick","given":"Noelle","affiliations":[{"id":85555,"text":"Dept. of Biology, University of Richmond","active":true,"usgs":false}],"preferred":false,"id":942538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wapman, Emily","contributorId":357765,"corporation":false,"usgs":false,"family":"Wapman","given":"Emily","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":942539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":942540,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267841,"text":"70267841 - 2025 - Coastal fine-grained sediment plumes from beach nourishment near Santa Barbara, California","interactions":[],"lastModifiedDate":"2025-08-18T15:08:09.593383","indexId":"70267841","displayToPublicDate":"2025-04-30T09:07:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21808,"text":"Coastal Engineering Journal","active":true,"publicationSubtype":{"id":10}},"title":"Coastal fine-grained sediment plumes from beach nourishment near Santa Barbara, California","docAbstract":"Terrestrial sediments captured by flood control facilities such as dams, debris basins, and engineered stream channels can reduce sediment fluxes to littoral cells. The beneficial use of these sediments for beach nourishment may induce negative environmental effects from turbidity or sedimentation caused by the source material. Here, we examine the size and extent of turbid coastal plumes produced by beach nourishment with sediment containing significant fine-grained (silt and clay) fractions near Santa Barbara, California, to evaluate the potential effects of beneficial use of fluvial sediment captured in upstream facilities. Using remotely sensed imagery and hydrodynamic and sediment transport model simulations, we find that wave height, wind speed and direction, and sediment settling velocity have strong controls on the direction and extent of the turbid plume produced by beach nourishment. These results are consistent with monitored nourishment projects, suggesting generalizable patterns and processes across the studies. Additionally, we find that sediment placement method (hauling versus dredging) can influence the magnitude and duration of fine-sediment concentrations in the adjacent coastal waters. Combined, these results will inform sediment management strategies that intend to minimize the environmental effects of beach nourishment within the study area and across similar coastal sites.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/21664250.2025.2497705","usgsCitation":"Warrick, J.A., Stevens, A.W., and Tehranirad, B., 2025, Coastal fine-grained sediment plumes from beach nourishment near Santa Barbara, California: Coastal Engineering Journal, v. 67, no. 3, p. 558-582, https://doi.org/10.1080/21664250.2025.2497705.","productDescription":"25 p.","startPage":"558","endPage":"582","ipdsId":"IP-172366","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":489545,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":490617,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/21664250.2025.2497705","text":"Publisher Index Page"}],"country":"United States","state":"California","city":"Santa Barbara","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.84691794723162,\n 34.42243236962841\n ],\n [\n -119.84691794723162,\n 34.399358663431116\n ],\n [\n -119.7692183271959,\n 34.399358663431116\n ],\n [\n -119.7692183271959,\n 34.42243236962841\n ],\n [\n -119.84691794723162,\n 34.42243236962841\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"67","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":939109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Andrew W. 0000-0003-2334-129X astevens@usgs.gov","orcid":"https://orcid.org/0000-0003-2334-129X","contributorId":139313,"corporation":false,"usgs":true,"family":"Stevens","given":"Andrew","email":"astevens@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":939110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tehranirad, Babak 0000-0002-1634-9165","orcid":"https://orcid.org/0000-0002-1634-9165","contributorId":299107,"corporation":false,"usgs":false,"family":"Tehranirad","given":"Babak","affiliations":[{"id":64774,"text":"contracted to USGS PCMSC","active":true,"usgs":false}],"preferred":false,"id":939111,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70270593,"text":"70270593 - 2025 - Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse","interactions":[],"lastModifiedDate":"2025-08-21T14:57:31.436416","indexId":"70270593","displayToPublicDate":"2025-04-30T07:46:39","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1457,"text":"Ecological Informatics","active":true,"publicationSubtype":{"id":10}},"title":"Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse","docAbstract":"Autonomous recording units are increasingly being used to monitor wildlife on large geographic and temporal scales, paired with machine learning (ML) to automate detection of wildlife. However, false positive detections from ML classifiers can result in erroneous ecological models that can lead to misguided management and conservation actions. We used a two-stage general approach to understand and reduce false positive detections, a technique in which outputs of the primary classification model are passed to a secondary classification model to yield the probability that a detection from the primary model is a true positive detection. This approach is demonstrated on two open-source models that detect Ruffed Grouse (Bonasa umbellus). We analyzed over 9500 h of acoustic data collected in 2022–2023 from the Green Mountain National Forest in Vermont, USA, and found the two models detected different types of acoustic signals associated with differing life history traits. The first model yielded 4106 detections (71.5 % true positives) while the second model yielded 524 detections (17.0 % true positives). Secondary logistic regression models separated true positives and false positives with high accuracy (84.5 % and 89.8 % respectively). Our findings go beyond improving Ruffed Grouse monitoring and conservation efforts to, more broadly, illustrate how two-stage ML approaches can improve the use of model-derived detections in wildlife research.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2025.103166","usgsCitation":"Clarfeld, L.A., Gieder, K.D., Abrams, R., Bernier, C., Cahill, J., Staats, S., Wixsom, S., and Donovan, T.M., 2025, Two-stage models improve machine learning classifiers in wildlife research: A case study in identifying false positive detections of Ruffed Grouse: Ecological Informatics, v. 89, 103166, 14 p., https://doi.org/10.1016/j.ecoinf.2025.103166.","productDescription":"103166, 14 p.","ipdsId":"IP-172423","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494459,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoinf.2025.103166","text":"Publisher Index Page"},{"id":494382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","otherGeospatial":"Green Mountain National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.16516605268014,\n 43.209373047171994\n ],\n [\n -73.16516605268014,\n 42.88542467752458\n ],\n [\n -72.86267666610819,\n 42.88542467752458\n ],\n [\n -72.86267666610819,\n 43.209373047171994\n ],\n [\n -73.16516605268014,\n 43.209373047171994\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Clarfeld, Laurence A.","contributorId":359990,"corporation":false,"usgs":false,"family":"Clarfeld","given":"Laurence","middleInitial":"A.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":946634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gieder, Katherina D.","contributorId":359991,"corporation":false,"usgs":false,"family":"Gieder","given":"Katherina","middleInitial":"D.","affiliations":[{"id":39587,"text":"Vermont Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":946635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abrams, Robert","contributorId":359992,"corporation":false,"usgs":false,"family":"Abrams","given":"Robert","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":946636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bernier, Christopher","contributorId":359993,"corporation":false,"usgs":false,"family":"Bernier","given":"Christopher","affiliations":[{"id":39587,"text":"Vermont Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":946637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cahill, Joseph","contributorId":359994,"corporation":false,"usgs":false,"family":"Cahill","given":"Joseph","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":946638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staats, Susan","contributorId":359995,"corporation":false,"usgs":false,"family":"Staats","given":"Susan","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":946639,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wixsom, Scott","contributorId":359996,"corporation":false,"usgs":false,"family":"Wixsom","given":"Scott","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":946640,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":946641,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266480,"text":"70266480 - 2025 - Exposure to ultraviolet radiation induces escape hatching of Cisco (Coregonus artedi) embryos","interactions":[],"lastModifiedDate":"2025-05-08T14:59:37.345953","indexId":"70266480","displayToPublicDate":"2025-04-29T09:42:18","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Exposure to ultraviolet radiation induces escape hatching of Cisco (Coregonus artedi) embryos","title":"Exposure to ultraviolet radiation induces escape hatching of Cisco (Coregonus artedi) embryos","docAbstract":"Context
The relationship between slope and terrestrial animal locomotion is key to landscape ecology but underexplored across species. This is partly due to a lack of scalable methodology that applies to a diversity of wildlife.
Objectives
This study investigates the slope-speed relationship for two species, Texas tortoise (Gopherus berlandieri) and pronghorn (Antilocapra americana), through the combined application of remote sensing, GPS tracking, behavior models, and parametric distribution. While using readily available Digital Elevation Models (DEM) for pronghorn, we explore the use of very high-resolution lidar Digital Terrain Models (DTM) from Unoccupied Aerial Systems (UAS) to characterize tortoise movements at micro-scales.
Methods
After classifying animal behavior with GPS tracking data and Hidden Markov Models (HMMs), we analyzed the relationship between the speed of the animals and the slope of the terrain using a 30-m DEM for pronghorn, and a fine-scale UAS DTM for Texas tortoise, and three nonlinear models: Laplace, Gauss, and Lorentz.
Results
High-resolution DTM, coupled with GPS tracking, accurately models the relationship of speed and slope at a micro-scale, while a DEM is suitable for a larger scale. Laplace models best predicted the speed of both the Texas tortoise and pronghorn. Models showed tortoises, which are not known for rapid and agile movement like the pronghorn, have a broader tolerance for varying slopes at a fine scale.
Conclusions
These findings enhance understanding of species-specific movement offering valuable insights for habitat management and conservation tailored to species’ behaviors and capabilities.
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A.","contributorId":354033,"corporation":false,"usgs":false,"family":"Guerra","given":"Daniel A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":934986,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":934981,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266188,"text":"ofr20211030V - 2025 - System characterization report on Resourcesat-2A Advanced Wide Field Sensor","interactions":[{"subject":{"id":70266188,"text":"ofr20211030V - 2025 - System characterization report on Resourcesat-2A Advanced Wide Field Sensor","indexId":"ofr20211030V","publicationYear":"2025","noYear":false,"chapter":"V","displayTitle":"System Characterization Report on Resourcesat-2A Advanced Wide Field Sensor","title":"System characterization report on Resourcesat-2A Advanced Wide Field Sensor"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation 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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.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Dryland ecosystems, covering 45% of the Earth's land and supporting over one-third of the global population, face significant threats from land degradation and ecological state change. Managing these ecosystems is complex, and science-based frameworks like Ecological Site Descriptions and state-and-transition models are essential tools for guiding decisions to support ecological health while maintaining stakeholder values such as grazing, wildlife, and recreation. However, alignment of these frameworks with smaller scale soil survey maps limits their applicability to broader ecological processes. Here, we extend these frameworks to larger landscapes with a machine learning approach that integrates large-scale, high-resolution vegetation data with identified ecological states from a data-driven state-and-transition model developed for a landscape-scale Ecological Site Group. A “global” model, which used combined inputs from multiple remotely sensed datasets, outperformed individual dataset models based on evaluation with independent data. Ecological state maps generated through this approach broaden the utility of state-and-transition models across Ecological Site Groups, providing a more spatially robust tool for land management at watershed and larger landscape scales. These methods, and the associated ecological state maps, can help meet critical needs for improved land condition assessments that support development of resource management plans and help identify priority areas for restoration and conservation.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70243","usgsCitation":"Kleist, N.J., Domschke, C.T., Knight, A.C., Nauman, T.W., Duniway, M.C., and Carter, S.K., 2025, Mapping predicted ecological states at landscape scales using remote sensing data and machine learning: Ecosphere, v. 16, no. 4, e70243, 16 p., https://doi.org/10.1002/ecs2.70243.","productDescription":"e70243, 16 p.","ipdsId":"IP-157413","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":496158,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70243","text":"Publisher Index Page"},{"id":496018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.0509637142687,\n 42.64630716588371\n ],\n [\n -111.8266792759012,\n 39.96910670756075\n ],\n [\n -114.76249387460862,\n 35.930067724930424\n ],\n [\n -108.04708953433448,\n 35.58278337785393\n ],\n [\n -107.09257565522607,\n 37.663677272255455\n ],\n [\n -107.38346712125988,\n 40.56490356337224\n ],\n [\n -108.74500520215983,\n 41.22226787777939\n ],\n [\n -111.0509637142687,\n 42.64630716588371\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Kleist, Nathan J. 0000-0002-2468-4318","orcid":"https://orcid.org/0000-0002-2468-4318","contributorId":260598,"corporation":false,"usgs":true,"family":"Kleist","given":"Nathan","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":949335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Domschke, Christopher T.","contributorId":361734,"corporation":false,"usgs":false,"family":"Domschke","given":"Christopher","middleInitial":"T.","affiliations":[{"id":86338,"text":"Bureau of Land Management, Colorado State Office, 2850 Youngfield St., Lakewood, CO 80215","active":true,"usgs":false}],"preferred":false,"id":949336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W.","contributorId":360619,"corporation":false,"usgs":false,"family":"Nauman","given":"Travis","middleInitial":"W.","affiliations":[{"id":86060,"text":"USDA Natural Resources Conservation Service, Soil and Plant Science Division, Moab, UT, USA","active":true,"usgs":false}],"preferred":false,"id":949338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":949339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":949340,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266300,"text":"70266300 - 2025 - Slow rupture, long rise times, and multi-fault geometry: The 2020 M6.4 southwestern Puerto Rico mainshock","interactions":[],"lastModifiedDate":"2025-05-02T15:13:47.284869","indexId":"70266300","displayToPublicDate":"2025-04-28T08:08:25","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Slow rupture, long rise times, and multi-fault geometry: The 2020 M6.4 southwestern Puerto Rico mainshock","docAbstract":"The M6.4 mainshock of the southwestern Puerto Rico seismic sequence on 7 January 2020, was one of the most impactful modern earthquakes in the northeastern Caribbean. Due to its offshore location and complex aftershock distribution, its source kinematics remain poorly constrained. This active sequence illuminated a complex set of previously unrecognized structures that indicate multiple causative faults may have slipped during its rupture. Here, we utilize seismic and geodetic observations to enhance model resolution, estimate the finite slip of the mainshock, and test a multi-segment, geologically realistic fault geometry. Our refined model finds a lower rupture velocity and longer rise times than typical for an event of this magnitude. This indicates a slow-evolving rupture process that resembles characteristics of a tsunami earthquake. Although this normal/strike-slip faulting event was not tsunamigenic, these qualities, if pervasive for this region, have important implications for future seismic monitoring and hazards in southwestern Puerto Rico.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL109740","usgsCitation":"Solares-Colón, M., Goldberg, D.E., Melgar, D., Vanacore, E.A., Sahakian, V., Yeck, W.L., Hernández, F., and Lopez-Venegas, A., 2025, Slow rupture, long rise times, and multi-fault geometry: The 2020 M6.4 southwestern Puerto Rico mainshock: Geophysical Research Letters, v. 52, no. 8, e2024GL109740, 12 p., https://doi.org/10.1029/2024GL109740.","productDescription":"e2024GL109740, 12 p.","ipdsId":"IP-170544","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":487926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl109740","text":"Publisher Index Page"},{"id":485330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.39442494689831,\n 18.578883979264447\n ],\n [\n -67.39442494689831,\n 17.849447857579676\n ],\n [\n -65.47820852132409,\n 17.849447857579676\n ],\n [\n -65.47820852132409,\n 18.578883979264447\n ],\n [\n -67.39442494689831,\n 18.578883979264447\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Solares-Colón, Margarita M. 0000-0002-9387-7551","orcid":"https://orcid.org/0000-0002-9387-7551","contributorId":353919,"corporation":false,"usgs":false,"family":"Solares-Colón","given":"Margarita M.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":935442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldberg, Dara Elyse 0000-0002-0923-3180","orcid":"https://orcid.org/0000-0002-0923-3180","contributorId":289891,"corporation":false,"usgs":true,"family":"Goldberg","given":"Dara","email":"","middleInitial":"Elyse","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":935443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Melgar, Diego","contributorId":341315,"corporation":false,"usgs":false,"family":"Melgar","given":"Diego","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":935444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanacore, Elizabeth A.","contributorId":316311,"corporation":false,"usgs":false,"family":"Vanacore","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[{"id":62735,"text":"University of Puerto Rico Mayagüez","active":true,"usgs":false}],"preferred":false,"id":935445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sahakian, Valerie J.","contributorId":208097,"corporation":false,"usgs":false,"family":"Sahakian","given":"Valerie J.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":935446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":935447,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hernández, Francisco","contributorId":353921,"corporation":false,"usgs":false,"family":"Hernández","given":"Francisco","affiliations":[{"id":62735,"text":"University of Puerto Rico Mayagüez","active":true,"usgs":false}],"preferred":false,"id":935448,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lopez-Venegas, Alberto 0000-0002-3413-0546","orcid":"https://orcid.org/0000-0002-3413-0546","contributorId":350939,"corporation":false,"usgs":false,"family":"Lopez-Venegas","given":"Alberto","affiliations":[{"id":34129,"text":"University of Puerto Rico Mayaguez","active":true,"usgs":false}],"preferred":false,"id":935449,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70269031,"text":"70269031 - 2025 - Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements","interactions":[],"lastModifiedDate":"2025-07-14T14:48:30.146948","indexId":"70269031","displayToPublicDate":"2025-04-28T07:43:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements","docAbstract":"Evapotranspiration (ET) describes the sum of water transfer from the ground surface through soil evaporation and water loss from leaf stomata into the atmosphere − critical factors linking the global water and carbon cycles. Myriad ET models based on remote sensing data provide spatially continuous estimates of ET; however, leaf photosynthetic information is critical to ensure accurate ET estimates, which are difficult to measure from space. Remotely sensed sun-induced chlorophyll fluorescence (SIF) provides a proxy of stomatal conductance activity with high performance in predicting plant transpiration, which can account for a large proportion of terrestrial and riverine ET. This study aims to improve estimates of tree water use in semi-arid to arid environments. In this study, a fixed stomatal conductance model and three SIF-driven canopy conductance (gsc) models were applied to model potential ET (PET). The models estimated PET using the Penman-Monteith equation with: (1) a constant leaf stomatal conductance; (2) a transpiration-driven gsc model; (3) a gsc model based on electron-transfer rate and vapor pressure deficit, and a (4) Ball-Berry stomatal conductance model. A machine learning model was then applied to scale PET to actual ET (AET) using remote sensing and climate data. Accordingly, four AET models were cross-validated with in-situ measured AET at 52 sites, including 21 eddy covariance flux tower sites, and 31 sap-flow measurement sites (semi-arid and plantation area), for various plant functional types in Australia. This study demonstrated that SIF effectively captured seasonal variations of gsc, finding that AET models with SIF-driven gsc models correlated well with in-situ measured AET (R2 = 0.64). Modelled AET with dynamic variations of gsc generated lower prediction error (0.85 mm day−1), while the AET model with fixed stomatal conductance tended to overestimate AET in floodplains and underestimate it in evergreen broadleaf forests, indicating using fixed stomatal conductance results in unstable performance when modelling AET. This study demonstrated that SIF-driven AET models improved broadscale estimation of ET. Our findings provide vital broadscale hydrological data to assist catchment and regional water management, particularly over unmonitored areas at risk of future climate-driven reductions in rainfall.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2025.133404","usgsCitation":"Gao, S., Nagler, P.L., Woodgate, W., Huete, A., and Doody, T.M., 2025, Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements: Journal of Hydrology, v. 660, no. Part B, 133404, 16 p., https://doi.org/10.1016/j.jhydrol.2025.133404.","productDescription":"133404, 16 p.","ipdsId":"IP-172126","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2025.133404","text":"Publisher Index Page"},{"id":492203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 123.28690979572599,\n -14.507783026538078\n ],\n [\n 113.28039887008524,\n -21.66065633263682\n ],\n [\n 112.17079996753354,\n -24.994521175001637\n ],\n [\n 115.10354300417447,\n -36.486501164702105\n ],\n [\n 122.24168422144697,\n -35.62034073401294\n ],\n [\n 131.93124828895753,\n -32.58906983701101\n ],\n [\n 141.10544225154464,\n -40.23302031896691\n ],\n [\n 152.81278701443853,\n -40.33448456027517\n ],\n [\n 155.92719705356842,\n -24.451431761649573\n ],\n [\n 144.11359477726128,\n -8.385497835754798\n ],\n [\n 130.88500806969773,\n -10.415819254694402\n ],\n [\n 123.28690979572599,\n -14.507783026538078\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"660","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Sicong","contributorId":303040,"corporation":false,"usgs":false,"family":"Gao","given":"Sicong","email":"","affiliations":[{"id":65623,"text":"CSIRO, Land and Water, Waite Campus, Adelaide, South Australia, Australia; University of Canberra, Canberra, Australian Capital Territory, Australia","active":true,"usgs":false}],"preferred":false,"id":942957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":942958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodgate, William","contributorId":357983,"corporation":false,"usgs":false,"family":"Woodgate","given":"William","affiliations":[{"id":85572,"text":"Earth Observation Research Centre, School of the Environment, The University of Queensland, Brisbane, QLD 4072, Australia; CSIRO, Space and Astronomy, Kensington, 6151, WA, Australia","active":true,"usgs":false}],"preferred":false,"id":942959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huete, Alfredo 0000-0003-2809-2376","orcid":"https://orcid.org/0000-0003-2809-2376","contributorId":208294,"corporation":false,"usgs":false,"family":"Huete","given":"Alfredo","email":"","affiliations":[],"preferred":false,"id":942960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doody, Tanya M.","contributorId":138691,"corporation":false,"usgs":false,"family":"Doody","given":"Tanya","email":"","middleInitial":"M.","affiliations":[{"id":12494,"text":"CSIRO Land and Water, Australia","active":true,"usgs":false}],"preferred":false,"id":942961,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266138,"text":"70266138 - 2025 - Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs","interactions":[],"lastModifiedDate":"2025-04-29T15:23:06.190421","indexId":"70266138","displayToPublicDate":"2025-04-27T08:15:25","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16456,"text":"Frontiers in Enviornmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs","docAbstract":"Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO2) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO2 storage resource in these reservoirs to formulate a comprehensive strategy for CO2 mitigation. Even reconnaissance-type estimates of CO2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO2 storage resources in reservoirs capable of hosting CO2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.
","language":"English","publisher":"frontiers","doi":"10.3389/fenvs.2025.1562087","usgsCitation":"Attanasi, E., Freeman, P., and Coburn, T.C., 2025, Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs: Frontiers in Enviornmental Science, v. 13, 1562087, 14 p., https://doi.org/10.3389/fenvs.2025.1562087.","productDescription":"1562087, 14 p.","ipdsId":"IP-166626","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":487848,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2025.1562087","text":"Publisher Index Page"},{"id":485138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"western Europe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -13.428924366035233,\n 54.504852491541925\n ],\n [\n -13.428924366035233,\n 41.74320075493446\n ],\n [\n 28.707525813502826,\n 41.74320075493446\n ],\n [\n 28.707525813502826,\n 54.504852491541925\n ],\n [\n -13.428924366035233,\n 54.504852491541925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2025-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":1809,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":934732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":934733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coburn, Timothy C.","contributorId":26011,"corporation":false,"usgs":true,"family":"Coburn","given":"Timothy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":934734,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266320,"text":"70266320 - 2025 - Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole","interactions":[],"lastModifiedDate":"2025-05-02T15:25:36.189617","indexId":"70266320","displayToPublicDate":"2025-04-25T10:22:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17061,"text":"Planetary Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole","docAbstract":"We use a high-resolution digital elevation model and a numerical thermal model to produce a variety of inputs for a water-ice prospectivity model for the Volatiles Investigating Polar Exploration Rover (VIPER) landing site. These input data are maps of topography, surface slope, surface aspect, surface curvature, maximum temperature, depth to ice stability, permanently shadowed regions (PSRs), distance to PSRs, and PSR density. This model predicts where water ice is most likely within the top meter of regolith, assuming plausible relationships between ice concentration and the various inputs. The model is designed to be adjusted in near-real time as data are collected during the VIPER mission. As such, it is a tool for both analyzing data from the mission as well as planning operations. Since the current model, at this point, relies only on orbital remote sensing, the final version will also be a tool to extrapolate the VIPER mission results across the lunar poles.
","language":"English","publisher":"American Astronomical Society","doi":"10.3847/PSJ/adbc6c","usgsCitation":"Coyan, J.A., Siegler, M., Martinez-Comacho, J., Beyer, R.A., and Shirley, M., 2025, Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole: Planetary Science Journal, v. 6, no. 5, 105, 9 p., https://doi.org/10.3847/PSJ/adbc6c.","productDescription":"105, 9 p.","ipdsId":"IP-168617","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":487929,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3847/psj/adbc6c","text":"Publisher Index Page"},{"id":485333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mons Mouton, Moon","volume":"6","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Coyan, Joshua Aaron 0000-0002-8450-7364","orcid":"https://orcid.org/0000-0002-8450-7364","contributorId":247291,"corporation":false,"usgs":true,"family":"Coyan","given":"Joshua","email":"","middleInitial":"Aaron","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":935581,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siegler, Matthew","contributorId":237898,"corporation":false,"usgs":false,"family":"Siegler","given":"Matthew","email":"","affiliations":[{"id":24584,"text":"PSI","active":true,"usgs":false}],"preferred":false,"id":935582,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martinez-Comacho, José 0000-0003-0542-7866","orcid":"https://orcid.org/0000-0003-0542-7866","contributorId":354404,"corporation":false,"usgs":false,"family":"Martinez-Comacho","given":"José","affiliations":[{"id":84624,"text":"University of Hawai’i at Manoa, Hawaii Institute for Geophysics and Planetology, 1680 East-West Road, POST Building, Honolulu, HI 96822","active":true,"usgs":false}],"preferred":false,"id":935583,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beyer, Ross A.","contributorId":204235,"corporation":false,"usgs":false,"family":"Beyer","given":"Ross","email":"","middleInitial":"A.","affiliations":[{"id":36890,"text":"Sagan Center at the SETI Institute and NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":935584,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shirley, Mark 0000-0001-8767-1760","orcid":"https://orcid.org/0000-0001-8767-1760","contributorId":354405,"corporation":false,"usgs":false,"family":"Shirley","given":"Mark","affiliations":[{"id":84625,"text":"SETI Institute/NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":935585,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270067,"text":"70270067 - 2025 - Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake","interactions":[],"lastModifiedDate":"2025-08-08T14:30:32.119094","indexId":"70270067","displayToPublicDate":"2025-04-25T09:26:17","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake","docAbstract":"Stochastic ground motion simulation models are often less accurate at lower frequencies than at higher frequencies when fitting recorded data unless supplemented by a deterministic forward directivity velocity pulse model. Moreover, time-modulated stochastic models, which adjust ground motion amplitudes over time, typically use functions that fail to capture multiple strong-motion phases. The February 2023 Turkey (Türkiye) earthquake exhibited diverse recordings, including near-fault and far-field motions with pulse-like and non-pulse-like characteristics, along with single and multiple strong-motion phases. To better represent such a diverse set of recordings, this study enhances a fully non-stationary site-based stochastic model without combining it with a deterministic model. Improvements include a new band-pass filter with upper- and lower-frequency limits, which refines the representation of the low-frequency content. Moreover, a time-modulating function that can represent energy arrival in multiple strong phases is introduced. The reference model’s parameters are identified by fitting to the energy content, zero-level crossings, and cumulative counts of positive-minima and negative-maxima of a target accelerogram. This fitting procedure is modified to address the increased number of parameters. These improvements broaden the reference model’s applicability while preserving its simplicity, a key aspect appealing to engineering practitioners. The improved model’s applicability is demonstrated by simulating a dataset from the February 2023 Türkiye earthquake, and the accuracy is tested using a pulse-like Next Generation Attenuation Relationships for Western United States dataset. Validations are performed based on total energy, zero-level crossings, Fourier amplitude spectrum, elastic response spectra, and peak ground motion parameters. Validations are performed schematically in the time and frequency domains and quantitatively using goodness-of-fit scores, various validation-metrics errors, and inter-period correlations. Overall, the improved stochastic model can effectively simulate a set of diverse ground motion recordings, including near-fault pulse-like records, records with multiple strong phases, and far-field motions across a broad frequency range.
","language":"English","publisher":"Sage Publications","doi":"10.1177/87552930251331981","usgsCitation":"Hussaini, S.M., Karimzadeh, S., Rezaeian, S., and Lourenco, P., 2025, Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake: Earthquake Spectra, v. 41, no. 3, p. 2399-2435, https://doi.org/10.1177/87552930251331981.","productDescription":"37 p.","startPage":"2399","endPage":"2435","ipdsId":"IP-174088","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":494180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930251331981","text":"Publisher Index Page"},{"id":493834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","city":"Kahramanmaraş","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hussaini, S. M. Sajad","contributorId":359418,"corporation":false,"usgs":false,"family":"Hussaini","given":"S.","middleInitial":"M. Sajad","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karimzadeh, Shaghayegh","contributorId":359419,"corporation":false,"usgs":false,"family":"Karimzadeh","given":"Shaghayegh","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lourenco, Paulo B.","contributorId":359420,"corporation":false,"usgs":false,"family":"Lourenco","given":"Paulo B.","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945291,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273016,"text":"70273016 - 2025 - Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","interactions":[],"lastModifiedDate":"2025-12-12T15:31:33.490472","indexId":"70273016","displayToPublicDate":"2025-04-25T09:25:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","title":"Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","docAbstract":"The Zone-tailed Hawk (Buteo albonotatus) is one of the least studied raptors in North America and lacks contemporary literature allowing informed management decisions for this species. Zone-tailed Hawks occupy rugged areas in the southwestern region of the United States and are listed as state threatened in Texas. Our objectives were to assess habitat, productivity, and daily survival rate (DSR) of Zone-tailed Hawk nests in riparian zones of the Chihuahuan Desert Ecoregion of Texas. We surveyed for Zone-tailed Hawk nests along ∼30 km of 12 riparian corridors in Brewster, Jeff Davis, and Presidio Counties, Texas. We monitored 11 and 15 Zone-tailed Hawk nests in 2018 and 2019, respectively, and conducted vegetation surveys at the nest tree, nest site (11.3-m radius), and paired random locations. We used nest survival modeling to evaluate the effects of eight habitat variables (nest tree diameter at breast height [DBH], nest tree height, nest height, nest distance to main stem, nest to tree height ratio, mean stand height, number of trees within nest site, and mean nest site DBH) on nest DSR. DSR was positively correlated with nest to tree height ratio and nest tree DBH. Zone-tailed Hawk nests had an estimated 0.991 (standard error [SE] = 0.004, 95% CI = 0.980–0.996) constant DSR and ultimately a 51.4% chance of nest success (SE = 0.0943) across the nesting season. Our results suggest that by selecting larger trees for nesting as well as placing nests higher within the tree, Zone-tailed Hawks may increase their chances of successfully fledging young.
","language":"English","publisher":"Raptor Research Foundation","doi":"10.3356/jrr2436","usgsCitation":"Skidmore, C., Boal, C.W., Skipper, B.R., and Martin, R., 2025, Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas: Journal of Raptor Research, v. 59, no. 2, p. 1-9, https://doi.org/10.3356/jrr2436.","productDescription":"9","startPage":"1","endPage":"9","ipdsId":"IP-165271","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":497700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr2436","text":"Publisher Index Page"},{"id":497468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Chihuahuan Desert ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.16016896102632,\n 29.02969764012022\n ],\n [\n -102.6187673940317,\n 29.82558478770983\n ],\n [\n -102.34533119746091,\n 29.825904876682998\n ],\n [\n -103.31770853819764,\n 31.40373063096237\n ],\n [\n -105.27499788066719,\n 30.839874155801937\n ],\n [\n -104.70567543260988,\n 30.257905616135616\n ],\n [\n -104.50920724722273,\n 29.627352150897323\n ],\n [\n -103.81983396318309,\n 29.241412262608875\n ],\n [\n -103.16016896102632,\n 29.02969764012022\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skidmore, Caroline","contributorId":363911,"corporation":false,"usgs":false,"family":"Skidmore","given":"Caroline","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":952099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":952100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skipper, Ben R.","contributorId":198462,"corporation":false,"usgs":false,"family":"Skipper","given":"Ben","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":952101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Russell","contributorId":267876,"corporation":false,"usgs":false,"family":"Martin","given":"Russell","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":952102,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269917,"text":"70269917 - 2025 - Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community","interactions":[],"lastModifiedDate":"2025-08-07T14:34:46.482133","indexId":"70269917","displayToPublicDate":"2025-04-25T09:23:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community","docAbstract":"Batrachochytrium dendrobatidis (Bd) has been decimating amphibian populations globally; previous work indicates that infection risk increases with moisture and thermal mismatch from a host’s optimum. We hypothesized that, in addition to these abiotic influences, mismatch of hosts from their reproductive habitat heightens infection risk via exposure and/or susceptibility mechanisms. We evaluated this “reproductive habitat mismatch hypothesis” by quantifying the interplay of host breeding mode, habitat, and rainfall on Bd infection dynamics using two years of frog survey data—including swab data for 3427 captures representing 44 species—from Brazil’s Atlantic Forest. We modeled infection prevalence, infection intensity, and the number of frogs captured as a function of rainfall, reproductive mode (aquatic or terrestrial), and habitat (aquatic or terrestrial) using hierarchical models. High rainfall was associated with increases in infection prevalence and infection intensity; however, these increases were particularly apparent for species in habitats that were mismatched from the species’ reproductive habitat. Tropical regions experiencing increases in precipitation will likely see higher Bd risk, and our results indicate that such increases in rainfall will be particularly problematic for species that are forced to move from their reproductive habitats by factors such as habitat loss or thermal stress.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2025.e03599","usgsCitation":"Gilbert, N.A., Bell, R.C., Catenazzi, A., Martins, R.A., Buttimer, S., Neely, W.J., Lambertini, C., Saenz Calderon, V., Haddad, C.F., Becker, C.G., and DiRenzo, G.V., 2025, Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community: Global Ecology and Conservation, v. 60, e03599, 12 p., https://doi.org/10.1016/j.gecco.2025.e03599.","productDescription":"e03599, 12 p.","ipdsId":"IP-171930","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494048,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13TRTVG","text":"USGS data release","linkHelpText":"Code for reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community"},{"id":493796,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2025.e03599","text":"Publisher Index Page"},{"id":493706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Parque Estadual da Serra do Mar–Núcleo Santa Virgínia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -45.079106081456246,\n -23.236053619691134\n ],\n [\n -45.236468001362624,\n -23.236053619691134\n ],\n [\n -45.236468001362624,\n -23.443275890477395\n ],\n [\n -45.079106081456246,\n -23.443275890477395\n ],\n [\n -45.079106081456246,\n -23.236053619691134\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, Neil A.","contributorId":359068,"corporation":false,"usgs":false,"family":"Gilbert","given":"Neil","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":944942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bell, Rayna C.","contributorId":359069,"corporation":false,"usgs":false,"family":"Bell","given":"Rayna","middleInitial":"C.","affiliations":[{"id":12937,"text":"California Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":944943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catenazzi, Alessandro","contributorId":359070,"corporation":false,"usgs":false,"family":"Catenazzi","given":"Alessandro","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":944944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martins, Renato A.","contributorId":359071,"corporation":false,"usgs":false,"family":"Martins","given":"Renato","middleInitial":"A.","affiliations":[{"id":85744,"text":"Universidade Federal de São Carlo","active":true,"usgs":false}],"preferred":false,"id":944945,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buttimer, Shannon","contributorId":359073,"corporation":false,"usgs":false,"family":"Buttimer","given":"Shannon","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944946,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neely, Wesley J.","contributorId":359075,"corporation":false,"usgs":false,"family":"Neely","given":"Wesley","middleInitial":"J.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":944947,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lambertini, Carolina","contributorId":359077,"corporation":false,"usgs":false,"family":"Lambertini","given":"Carolina","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944948,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Saenz Calderon, Veronica","contributorId":359079,"corporation":false,"usgs":false,"family":"Saenz Calderon","given":"Veronica","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944949,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haddad, Célio F.B.","contributorId":359081,"corporation":false,"usgs":false,"family":"Haddad","given":"Célio","middleInitial":"F.B.","affiliations":[{"id":48854,"text":"Universidade Estadual Paulista","active":true,"usgs":false}],"preferred":false,"id":944950,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Becker, C. Guilherme","contributorId":359083,"corporation":false,"usgs":false,"family":"Becker","given":"C.","middleInitial":"Guilherme","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944951,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":944952,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70269918,"text":"70269918 - 2025 - Satellite imagery can predict bird species occupancy and inform multispecies management in pine savannas","interactions":[],"lastModifiedDate":"2025-08-07T15:09:06.947409","indexId":"70269918","displayToPublicDate":"2025-04-25T07:59:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Satellite imagery can predict bird species occupancy and inform multispecies management in pine savannas","docAbstract":"Multispecies management can contribute to meeting growing challenges of preserving biodiversity, yet current game and threatened species management often focuses on individual species. Satellite imagery available at high spatial and temporal resolution provides a potential tool to overcome the challenge posed by multispecies management of linking patterns of habitat use among species. We sought to determine whether satellite imagery could be used to describe patterns of species occupancy and inform multispecies management in pine savannas in Georgia, USA. We conducted point-count surveys at 7 sites in 2022 for 3 bird species: Colinus virginianus (Northern Bobwhite), Dryobates borealis (Red-cockaded Woodpecker), and Peucaea aestivalis (Bachman’s Sparrow). We built single-season occupancy models comparing a set of models using covariates collected from field vegetation surveys and another set using covariates extracted from Sentinel-2 satellite imagery. We then used a multi-objective optimization algorithm to identify quasi-optimal management solutions (i.e., sets of covariate values from top satellite imagery metric models). We found that models created using satellite imagery performed well at predicting occupancy of all 3 species as measured by the area under the receiver operating characteristic curve (AUC > 0.8) and had higher AUC scores than field-derived habitat covariate-based models. We found combinations of metrics that could result in high rates of predicted probability of occupancy for all species (within 86% of highest possible occupancy probability), but these combinations did not exist at any of the sites. Our results demonstrate that (1) satellite imagery can allow users to build reliable occupancy models without intensive field-based vegetation surveys; and (2) C. virginianus, D. borealis, and P. aestivalis in pine savanna ecosystems could be simultaneously managed through more frequent burning, changes in canopy cover or by producing suitable heterogeneity of habitats after identifying an appropriate scale of management.
","language":"English","publisher":"American Ornithological Society","doi":"10.1093/ornithapp/duaf029","usgsCitation":"Allred, C.R., Schneider, T.M., and Hunter, E.A., 2025, Satellite imagery can predict bird species occupancy and inform multispecies management in pine savannas: Ornithological Applications, https://doi.org/10.1093/ornithapp/duaf029.","ipdsId":"IP-172250","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493711,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","edition":"Online First","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Allred, Cory R.","contributorId":350504,"corporation":false,"usgs":false,"family":"Allred","given":"Cory","middleInitial":"R.","affiliations":[{"id":34764,"text":"Virginia Tech, Department of Fish and Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":944953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schneider, Todd M.","contributorId":359085,"corporation":false,"usgs":false,"family":"Schneider","given":"Todd","middleInitial":"M.","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":944954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunter, Elizabeth Ann 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distributions and applications for mineral resource assessments","docAbstract":"Atmospheric rivers (ARs) drive most riverine floods on the United States (U.S.) West Coast. However, estimating flood risk based solely on AR intensity and duration is challenging because precipitation phase, antecedent conditions, and physical watershed characteristics (e.g., slope and soil depth) can influence the magnitude of floods. Here, we analyze how antecedent soil moisture (ASM) conditions contribute to variability in streamflow during AR events and how that changes across climatic regimes and physiography in 122 U.S. West Coast watersheds. We identify a robust non-linear relationship between streamflow and ASM during ARs in 89% of watersheds. The inflection point in this relationship represents a watershed-specific critical ASM threshold, above which event maximum streamflow is, on average, two to four and a half times larger. Wet ASM conditions amplify the hydrologic impacts of more frequent but weaker, lower moisture transport AR events, while dry ASM conditions attenuate the hydrologic impacts that stronger, higher moisture transport AR events could otherwise cause. Our research shows that watersheds prone to ASM-amplified streamflows have higher evaporation ratios, lower cold-season precipitation, lower snow-to-rain ratios, and shallower, clay-rich soils. Higher evaporation and lower precipitation lead to greater ASM variability during the cold season, increasing streamflow during wet periods and buffering streamflow during dry periods. Lower snow fraction and shallower soils limit the antecedent water storage capacity of a watershed, contributing to greater sensitivity of streamflow peaks to ASM variability. Incorporating ASM thresholds into hydrologic models in these regions prone to AR-amplified streamflow could improve forecasts and decrease uncertainty.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-24-0078.1","collaboration":"Desert Research Institute, Reno, NV","usgsCitation":"Webb, M., Albano, C., Harpold, A., Wagner, D.M., and Wilson, A.M., 2025, Wet antecedent soil moisture increases atmospheric river streamflow magnitudes non-linearly: Journal of Hydrometeorology, v. 26, no. 6, p. 741-758, https://doi.org/10.1175/JHM-D-24-0078.1.","productDescription":"18 p.","startPage":"741","endPage":"758","ipdsId":"IP-166108","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":485998,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.92121191786663,\n 33.407810452030205\n ],\n [\n -116.523784768193,\n 36.41502072713284\n ],\n [\n -119.64994516515054,\n 39.88994270690637\n ],\n [\n -120.51138519071847,\n 42.20041323206641\n ],\n [\n -119.3720758190301,\n 48.777534854733574\n ],\n [\n -122.9287960859929,\n 48.977595241145025\n ],\n [\n -123.36076938971661,\n 48.210450784064506\n ],\n [\n -125.09530509112804,\n 48.5114622057049\n ],\n [\n -124.0899142321606,\n 45.77238715755897\n ],\n [\n -124.63673786495912,\n 42.844084204189784\n ],\n [\n -124.31096484013645,\n 41.342509106103535\n ],\n [\n -124.81776995325123,\n 40.42865225014583\n ],\n [\n -123.57485679616491,\n 38.65999426544576\n ],\n [\n -122.45718830306802,\n 37.1653556881036\n ],\n [\n -121.77165200602349,\n 35.90307547409293\n ],\n [\n -120.64970139084915,\n 34.560810038256434\n ],\n [\n -117.92121191786663,\n 33.407810452030205\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Webb, Mariana J. 0000-0003-0331-2635","orcid":"https://orcid.org/0000-0003-0331-2635","contributorId":353576,"corporation":false,"usgs":false,"family":"Webb","given":"Mariana J.","affiliations":[{"id":84438,"text":"Division of Hydrological Sciences, Desert Research Institute, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":933999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albano, Christine M.","contributorId":17681,"corporation":false,"usgs":true,"family":"Albano","given":"Christine M.","affiliations":[],"preferred":false,"id":934000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harpold, Adrian A. 0000-0002-2566-9574","orcid":"https://orcid.org/0000-0002-2566-9574","contributorId":353577,"corporation":false,"usgs":false,"family":"Harpold","given":"Adrian A.","affiliations":[{"id":84439,"text":"Dept. of Natural Resources and Environmental Science, Univ. of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":934001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Daniel M. 0000-0002-0432-450X dwagner@usgs.gov","orcid":"https://orcid.org/0000-0002-0432-450X","contributorId":4531,"corporation":false,"usgs":true,"family":"Wagner","given":"Daniel","email":"dwagner@usgs.gov","middleInitial":"M.","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":934002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Anna M.","contributorId":211536,"corporation":false,"usgs":false,"family":"Wilson","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":934003,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266282,"text":"70266282 - 2025 - Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning","interactions":[],"lastModifiedDate":"2025-05-02T14:54:35.492386","indexId":"70266282","displayToPublicDate":"2025-04-24T09:53:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning","docAbstract":"Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/tc-19-1675-2025","usgsCitation":"Aberle, R., Enderlin, E., O'Neel, S., Florentine, C., Sass, L., Dickson, A., Marshall, H., and Flores, A., 2025, Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning: The Cryosphere, v. 19, p. 1675-1693, https://doi.org/10.5194/tc-19-1675-2025.","productDescription":"19 p.","startPage":"1675","endPage":"1693","ipdsId":"IP-161789","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":487924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-19-1675-2025","text":"Publisher Index Page"},{"id":485326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Unite States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.76360091465392,\n 47.06180837633883\n ],\n [\n -121.3176201884703,\n 48.805343460206615\n ],\n [\n -120.0015482436147,\n 50.335300241584264\n ],\n [\n -130.45623413084917,\n 62.431155673423405\n ],\n [\n -144.1767604606605,\n 64.77803371822662\n ],\n [\n -156.74065293077143,\n 62.94965967944643\n ],\n [\n -153.58716512013862,\n 58.34813914012679\n ],\n [\n -151.25294354192766,\n 56.670611414741046\n ],\n [\n -147.2303378681512,\n 59.785680900702204\n ],\n [\n -139.72127967128716,\n 59.315677918279675\n ],\n [\n -134.82069406634898,\n 55.48786621236388\n ],\n [\n -131.59648302534626,\n 51.752911462025\n ],\n [\n -123.76360091465392,\n 47.06180837633883\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Aberle, Rainey","contributorId":354302,"corporation":false,"usgs":false,"family":"Aberle","given":"Rainey","affiliations":[],"preferred":false,"id":935376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":935377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":935378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":935381,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dickson, Adam","contributorId":354305,"corporation":false,"usgs":false,"family":"Dickson","given":"Adam","affiliations":[],"preferred":false,"id":935383,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marshall, Hans-Peter","contributorId":330964,"corporation":false,"usgs":false,"family":"Marshall","given":"Hans-Peter","email":"","affiliations":[{"id":33038,"text":"Department of Geosciences, Boise State University","active":true,"usgs":false}],"preferred":false,"id":935379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":935382,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268481,"text":"70268481 - 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This work reviews and synthesizes existing post-wildfire applications of water-quality models in the context of geographic and ecohydrological distribution, hydrologic and water-quality response process representation, model parameterization, model and input data scales, model calibration data availability, as well as calibration and performance evaluation approaches. Emphasis is placed on models that simulate water-quality output, rather than sediment and erosional response as the primary focus. Here, identified gaps and opportunities to advance the post-wildfire application of water-quality models include: 1. applying models in under-represented geographic and ecohydrologic regions, 2. simulating multiple streamflow generation mechanisms, including groundwater, with an emphasis on shifting dominant flow pathways as the landscape recovers following wildfire, 3. adding studies that include the simulation of metals, 4. incorporating more biogeochemical and in-stream processes to model applications, 5. applying finer spatial and temporal resolution of precipitation data input as well as finer spatial resolution hydrologic response units, 6. implementing fully distributed grid or element models or finer resolution response units to capture burn severity heterogeneity, 7. collecting enhanced water-quality data for model calibration and validation, 8. conducting model-intercomparison studies, and 9. developing model parameter value guidance in post-wildfire applications. These identified gaps and opportunities may assist users in deciding on key processes and approaches to consider in modeling post-wildfire water-quality conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.179435","usgsCitation":"Shephard, Z.M., Partridge, T.F., Murphy, S.F., Walvoord, M.A., and Ebel, B., 2025, A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities: Science of the Total Environment, v. 979, 179435, 15 p., https://doi.org/10.1016/j.scitotenv.2025.179435.","productDescription":"179435, 15 p.","ipdsId":"IP-165381","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":491530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"979","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shephard, Zachary M. 0000-0003-2994-3355","orcid":"https://orcid.org/0000-0003-2994-3355","contributorId":222581,"corporation":false,"usgs":true,"family":"Shephard","given":"Zachary","email":"","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":941497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941499,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266130,"text":"70266130 - 2025 - HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa","interactions":[],"lastModifiedDate":"2025-04-30T14:55:18.955047","indexId":"70266130","displayToPublicDate":"2025-04-24T07:42:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12552,"text":"Scientific Data - Nature","active":true,"publicationSubtype":{"id":10}},"title":"HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa","docAbstract":"Sub-Saharan Africa (SSA) faces severe agricultural data scarcity amidst high food insecurity and a large agricultural yield gap, making crop production data crucial for understanding and enhancing food systems. To address this gap, HarvestStat Africa presents the largest compilation of open-access subnational crop statistics and time-series across SSA. Based on agricultural statistics collated by USAID’s Famine Early Warning Systems Network, the subnational crop statistics are standardized and calibrated across changing administrative units to produce consistent and continuous time-series. The dataset includes 546,605 records, primarily spanning from 1980 to 2022, detailing crop production, harvested areas, and yields for 33 countries and 90 crop types, including key cereals in SSA such as wheat, maize, rice, sorghum, barley, millet, and fonio. This new dataset enhances our understanding of how climate variability and change influence agricultural production, supports subnational food system analysis, and aids in operational yield forecasting. As an open-source resource, it sets an important precedent for sharing subnational crop statistics to inform decision-making and modeling efforts.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-025-05001-z","usgsCitation":"Lee, D., Anderson, W., Chen, X., Davenport, F., Shukla, S., Sahajpal, R., Budde, M., Rowland, J., Verdin, J., You, L., Ahouangbenon, M., Frankel Davis, K., Kebede, E., Ehrmann, S., Justice, C., and Meyer, C., 2025, HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa: Scientific Data - Nature, v. 12, 690, 13 p., https://doi.org/10.1038/s41597-025-05001-z.","productDescription":"690, 13 p.","ipdsId":"IP-171185","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":487839,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-025-05001-z","text":"Publisher Index 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sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2025-04-24T14:11:03.291909","indexId":"ofr20211030T","displayToPublicDate":"2025-04-23T12:23:04","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":"2021-1030","chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","docAbstract":"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.
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U.S. Geological Survey
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Many coastal urban areas are prone to extreme pluvial flooding due to limitations in stormwater system capacity, with the additional potential for flooding compounded by storm surge, tides, and waves. Understanding and simulating these processes can improve prediction and flood risk management. Here, we adapt the Coupled Ocean–Atmosphere–Wave–Sediment Transport modeling framework (COAWST) to simulate pluvial flooding from post-tropical Cyclone Ida (2021) in the Jamaica Bay watershed of New York City (NYC). We modify the model to capture the volumetric effects of rainfall and parameterize soil infiltration and a stormwater conveyance system as the drainage rate. We generate a spatially continuous flood map of Ida with a root-mean-square error (RMSE) of 20 cm when compared to high-water marks, useful for understanding Ida's impacts and subsequent mitigation planning. Results show that over 23 km2 and 4621 buildings were flooded deeper than 0.3 m during Ida. Sensitivity analyses are used to study the broader risk from events like Ida (pluvial flooding) as well as potential compound (pluvial–coastal) flooding. Spatial shifting of the storm track within a typical 12 h forecast uncertainty reveals a worst-case scenario that increases this flooded area to 62 km2 (5907 buildings). Shifting Ida's rainfall to coincide with high tide increases this flooded area by 1 km2, a relatively small change due to the lack of significant storm surge. The application of COAWST to this storm event addresses a broader goal of developing the capability to model compound pluvial–coastal flooding by simultaneously representing coastal storm processes such as rain, tide, waves, erosion, and atmosphere–wave–ocean interactions. The sensitivity analysis results underscore the need for detailed flood risk assessments, showing that Ida, already NYC's worst rain event, could have been even more devastating with slight shifts in the storm track.
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