River connectivity is defined as the water-mediated exchange of matter, energy, and biota between different elements of the riverine landscape. Connectivity is an especially important concept in large-river corridors (channel plus floodplain ) because large rivers integrate fluxes of water, sediment, nutrients, contaminants, and other transported constituents emanating from large contributing drainage basins, and thereby contribute to the complexity of large-river ecosystems. Large rivers are also highly valued for socioeconomic goods and services, which has led to historical fragmentation, lack of connectivity, and contentiousness about best policies for managing large-river corridors. The classification is intended to serve as a template for understanding geographic variation in large rivers within the Midwest, to aid in designing scientific studies of large river ecological processes, and to match specific river-management and restoration objectives to specific river reaches. The focus of the classification is on measuring river connectivity from available hydrological and geomorphic data.
We provide a multiscale assessment and classification for segments of 15 rivers that meet various criteria for largeness. All rivers are tributaries to the Mississippi River system. The 11,600 kilometers (km) that qualified as large were classified by major alterations (unimpounded, navigation pools, storage reservoir) and additionally assessed for their network continuity as a function of numbers and heights of dams. Among the 15 rivers, 55 percent of segment length was unimpounded, 30 percent was in navigation pools, and 15 percent was under storage reservoirs. Assessment of network longitudinal connectivity among river segments documented the contrast between river segments with low-head navigation dams (Upper Mississippi, Illinois, Ohio, Green, and Cumberland Rivers) and those segments with high-head dams (mostly in the Upper Missouri River). The longest unimpounded river pathways exist in the Lower Missouri River and connected tributaries where nearly 1,300 km of the Missouri River connect to an additional 1,800 km of the Middle and Lower Mississippi Rivers.
At our finest scale, we present a statistically based, component classification based on 10-km segments. Cluster analysis of hydrologic variables from 66 streamflow-gaging stations yielded 5 clusters calculated from 5 ecohydrological metrics related to lateral connectivity with the floodplain. A separate cluster analysis of 5 geomorphologic variables associated with each of the 1,172 river segments also yielded 5 clusters. When the hydrologic variables were associated with corresponding segments, the cluster analysis yielded 8 hydrogeomorphic clusters that could be explained in terms of their contribution to floodplain connectivity. Although the clusters overlap considerably in principal component space, the resulting hydrogeomorphic classification leads to a physically reasonable distribution of classes. The resulting classification is intended to increase geographic awareness of the range of variation of connectivity potential among large rivers of the Upper Midwest, to increase understanding of the extent of alteration of these rivers, and potentially to serve as a template for stratifying study designs of large-river corridor ecological processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195132","usgsCitation":"Jacobson, R.B., Rohweder, J.J., and DeJager, N.R., 2019, A hydrogeomorphic classification of connectivity of large rivers of the Upper Midwest, United States: U.S. Geological Survey Scientific Investigations Report 2019–5132, 55 p., https://doi.org/10.3133/sir20195132.","productDescription":"Report: vi, 55 p.; 2 Data Releases","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-104678","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399611,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109562.htm"},{"id":370623,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5132/sir20195132.pdf","text":"Report","size":"9.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5132"},{"id":370625,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HFYOLO","text":"USGS data release","linkHelpText":"River valley boundaries and transects generated for select large rivers of the Upper Midwest, United States"},{"id":370624,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YGOKWZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Segment-scale classification, large rivers of the Upper Midwest United States"},{"id":370622,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5132/coverthb.jpg"}],"country":"United States","state":"Colorado, Illinois, Indiana, Iowa, Kansas, Kentucky, Minnesota, Missouri, Montana, Nebraska, New York, North Carolilna,North Dakota, Ohio, Pennsylvania, South Dakota, Tennessee, Virginia, West Virginia, Wisconsin, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.8251953125,\n 35.460669951495305\n ],\n [\n -80.5078125,\n 37.020098201368114\n ],\n [\n -80.15625,\n 35.60371874069731\n ],\n [\n -79.541015625,\n 37.43997405227057\n ],\n [\n -79.365234375,\n 39.87601941962116\n ],\n [\n -77.7392578125,\n 42.74701217318067\n ],\n [\n -82.265625,\n 40.91351257612758\n ],\n [\n -86.8359375,\n 41.73852846935917\n ],\n [\n -87.5830078125,\n 41.60722821271717\n ],\n [\n -88.9453125,\n 43.61221676817573\n ],\n [\n -89.4287109375,\n 43.45291889355465\n ],\n [\n -89.7802734375,\n 46.10370875598026\n ],\n [\n -90.439453125,\n 46.37725420510028\n ],\n [\n -93.42773437499999,\n 46.86019101567027\n ],\n [\n -94.9658203125,\n 47.57652571374621\n ],\n [\n -96.85546875,\n 45.55252525134013\n ],\n [\n -100.94238281249999,\n 48.42920055556841\n ],\n [\n -104.150390625,\n 49.210420445650286\n ],\n [\n -112.0166015625,\n 49.03786794532644\n ],\n [\n -113.99414062499999,\n 45.79816953017265\n ],\n [\n -112.9833984375,\n 44.5278427984555\n ],\n [\n -111.09374999999999,\n 44.77793589631623\n ],\n [\n -106.3916015625,\n 41.47566020027821\n ],\n [\n -105.6884765625,\n 39.67337039176558\n ],\n [\n -104.150390625,\n 38.75408327579141\n ],\n [\n -94.921875,\n 38.16911413556086\n ],\n [\n -91.93359375,\n 36.98500309285596\n ],\n [\n -89.736328125,\n 36.80928470205937\n ],\n [\n -85.8251953125,\n 35.460669951495305\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
North American sea ducks generally breed in mid- to northern-latitude regions and nearly all rely upon marine habitats for much of their annual cycle. Most sea duck species remained poorly studied until the 1990s when declines were noted in several species and populations. Subsequent research, much of which was funded by the Sea Duck Joint Venture, began in the late 1990s with an emphasis on defining use areas throughout the annual cycle, migration patterns, and determining if there were distinct populations, within species, across North America. These studies relied largely upon satellite telemetry information to identify winter, breeding, and molting areas of sea ducks. New information from band recovery and genetic markers was added, contributing to hypotheses and initial conclusions about population delineation. Information on population units across North America is critical for identifying appropriate scales for evaluating population status and trends through annual monitoring surveys, harvest assessments, habitat protection and measuring effectiveness of management applications. Previous descriptions of population segments were for single species or smaller groups of similar species. Here, we summarize current knowledge on the general distribution and population segments of 13 species of sea ducks in North America by comparing range maps to long-term band recovery, genetic, and satellite telemetry data to inform population delineation assessments and future research. These comparisons show a high degree of consistency in population patterns for most species across the independent data types. These maps provide a foundation for developing new hypothesis-driven research to address remaining knowledge gaps and questions about population differentiation, annual cycle distribution, habitat use, and harvest assessment.
","largerWorkTitle":"USGS Open File Report","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191142","collaboration":"Prepared in Cooperation with the Sea Duck Joint Venture Continental Technical Team","usgsCitation":"Pearce, J.M., Flint, P.L., Whalen, M.E., Sonsthagen, S.A., Stiller, J., Patil, V.P., Bowman, T., Boyd, S., Badzinski, S.S., Gilchrist, H.G., Gilliland, S.G., Lepage, C., Loring, P., McAuley, D., McLellan, N.R., Osenkowski, J., Reed, E.T., Roberts, A.J., Robertson, M.O., Rothe, T., Safine, D.E., Silverman, E.D., and Spragens, D., 2019, Visualizing populations of North American Sea Ducks—Maps to guide research and management planning: U.S. Geological Survey Open-File Report 2019-1142, 50 p., plus appendixes, https://doi.org/10.3133/ofr20191142.","productDescription":"vi, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-109661","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437250,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93EF3TH","text":"USGS data release","linkHelpText":"Tracking Data for Black Scoter (Melanitta americana)"},{"id":370662,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1142/ofr20191142.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1142"},{"id":370661,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1142/coverthb.jpg"}],"country":"Canada, Russia, United States","otherGeospatial":"North America","contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
In East Africa, accurate grain yield predictions can help save lives and protect livelihoods. Regional grain yield forecasts can inform decisions regarding the availability and prices of key staples, food aid, and large humanitarian responses. Here, we use earth observation (EO) products to develop and evaluate subnational grain yield forecasts for 56 regions located in two severely food insecure countries: Kenya and Somalia. We identify, for a given region and time of year, which, if any, product is the best indicator for end-of-season maize yields. Our analysis seeks to inform a real-world situation in which analysts have access to multiple regularly updated EO data products, but predictive skill corresponding to each may vary across these regions and throughout the season. We find that the most accurate predictions can be made for high-producing areas, but that the relationship between production and forecast accuracy diminishes in areas with yields averaging greater than one metric ton per hectare. However, while forecast accuracy is highest in high production areas, in many of these regions, the forecast accuracy of models using EO products is not better than a set of baseline models that do not use EO products. Overall, we find that rainfall is the best indicator in low-producing regions and that other EO products work best in areas where yields are relatively consistent, but production is still limited by environmental factors.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab5ccd","usgsCitation":"Davenport, F., Harrison, L., Shukla, S., Husak, G., Funk, C., and McNally, A., 2019, Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields: Environmental Research Letters, v. 14, no. 2, 124095, 13 p., https://doi.org/10.1088/1748-9326/ab5ccd.","productDescription":"124095, 13 p.","ipdsId":"IP-101895","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab5ccd","text":"Publisher Index Page"},{"id":372947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kenya, Somalia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 31.728515624999996,\n -5.615985819155327\n ],\n [\n 51.50390625,\n -5.615985819155327\n ],\n [\n 51.50390625,\n 10.833305983642491\n ],\n [\n 31.728515624999996,\n 10.833305983642491\n ],\n [\n 31.728515624999996,\n -5.615985819155327\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":783964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Laura","contributorId":192382,"corporation":false,"usgs":false,"family":"Harrison","given":"Laura","email":"","affiliations":[],"preferred":false,"id":784025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":783965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":783966,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":783963,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNally, Amy","contributorId":53225,"corporation":false,"usgs":true,"family":"McNally","given":"Amy","affiliations":[],"preferred":false,"id":784026,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70207599,"text":"70207599 - 2019 - Plot Locator: An app for locating plots in the field","interactions":[],"lastModifiedDate":"2019-12-30T16:26:13","indexId":"70207599","displayToPublicDate":"2019-12-20T16:25:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":826,"text":"Applications in Plant Science","active":true,"publicationSubtype":{"id":10}},"title":"Plot Locator: An app for locating plots in the field","docAbstract":"PREMISE: One of the challenges in field biology is locating previously sampled plots. The Plot Locator app was developed to assist field biologists with plot identification and location, with or without GPS or online connectivity.
METHODS AND RESULTS: The Plot Locator Android app helps users locate field plots by creating a searchable database that stores study area information, such as site/plot names and numbers, distances from landmarks, optional cardinal directions and GPS coordinates, and field notes. A GPS assist and Google Maps can also be used with the app when connectivity is available. All study location data and field notes are stored in a downloadable CSV file on the user’s device.
CONCLUSIONS: The Plot Locator app provides a comprehensive searchable database of study area information, plot location information, and location aids, which are easily accessed in the field.
","language":"English","publisher":"Wiley","doi":"10.1002/aps3.11311","usgsCitation":"Boudell, J., and Middleton, B., 2019, Plot Locator: An app for locating plots in the field: Applications in Plant Science, v. 7, no. 12, e11311, 6 p., https://doi.org/10.1002/aps3.11311.","productDescription":"e11311, 6 p.","ipdsId":"IP-084359","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458913,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/aps3.11311","text":"Publisher Index Page"},{"id":370878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"12","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Boudell, Jere","contributorId":221553,"corporation":false,"usgs":false,"family":"Boudell","given":"Jere","email":"","affiliations":[{"id":40405,"text":"Clayton State, Atlanta","active":true,"usgs":false}],"preferred":false,"id":778647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Beth A. 0000-0002-1220-2326","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":216869,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":778646,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196299,"text":"pp1836 - 2019 - Coal geology and assessment of resources and reserves in the Little Snake River Coal Field and Red Desert Assessment Area, Greater Green River Basin, Wyoming","interactions":[],"lastModifiedDate":"2023-06-26T19:55:03.421537","indexId":"pp1836","displayToPublicDate":"2019-12-19T11:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1836","displayTitle":"Coal Geology and Assessment of Resources and Reserves in the Little Snake River Coal Field and Red Desert Assessment Area, Greater Green River Basin, Wyoming","title":"Coal geology and assessment of resources and reserves in the Little Snake River Coal Field and Red Desert Assessment Area, Greater Green River Basin, Wyoming","docAbstract":"The U.S. Geological Survey is studying regional-scale assessments of resources and reserves of primary coal beds in the major coal bed basins in the United States to help formulate policy for Federal, State, and local energy and land use. This report summarizes the geology and coal resources and reserves in the Little Snake River coal field and Red Desert assessment area in the Greater Green River Basin, southwestern Wyoming. These areas are contiguous and referred to as the “assessment area” in this report. The assessment area covers about 2,300 square miles of the eastern section of the 15,400-square-mile Greater Green River Basin. This area was prioritized for assessment because no comprehensive resource assessment had previously been completed in the area; abundant, previously unavailable drill hole data from cooperators and stakeholders became available; and there are active coal mines in the Greater Green River Basin area producing from some of the same formations as those found in the assessment area.
Coal-bearing Eocene, Paleocene, and Upper Cretaceous formations have a composite thickness of more than 11,000 feet in the assessment area. Stratigraphic sequences that contain multiple coal beds within a formation or member are referred to as coal zones in this report. Paleogene coal beds are found within coal zones in the Eocene Wasatch Formation and the Paleocene Fort Union Formation. Cretaceous coal beds are within coal zones in the Lance, Almond, and Allen Ridge Formations.
A total of 4,214 drill holes and measured sections were used to construct a geologic database for this assessment. From these data, 7 coal zones containing 55 individual coal beds were identified. Not all 55 coal beds were assessed; only those beds that were at least 3 feet thick and had at least a 2-square-mile areal extent were considered. Using a geology-based assessment methodology, the U.S. Geological Survey estimated original, available, and recoverable coal resources for 33 coal beds that met those criteria.
An original resource of 73.2 billion short tons of coal was calculated for the 33 coal beds that met the criteria in the assessment area. To be considered extractable by surface mining methods, coal beds had to be equal to or greater than 3 feet thick and less than 300 feet deep. Of the 73.2 billion short tons (BST), 19.3 BST were determined to be recoverable resources, of which approximately 2.1 BST were considered as recoverable resources by surface mining methods at a stripping ratio of 10:1 or less. (defined as recoverable resources for this assessment, based on economic modeling and regional mining analogs). Recoverable resources for underground mining methods (coal 8 to 15 feet thick and between 300 and 3,000 feet deep) totaled 17.2 BST. Out of the 19.3 BST assessed as recoverable coal resources, approximately 167 million short tons (MST) were considered to be reserves.
Within the 7 coal zones, the Wasatch coal zone contains an original resource of about 6.8 BST billion short tons of coal, of which 2.6 BST are considered a recoverable resource and approximately 26.7 MST are considered reserves. The Overland coal zone, in the Fort Union Formation, contains an original resource of approximately 23 BST of coal, of which 8.4 BST are considered a recoverable resource and approximately 74 MST are considered reserves. The China Butte coal zone, in the Fort Union Formation, contains an original resource of 36.2 BST of coal, of which 6.3 BST are considered a recoverable resource and approximately 5.5 MST are considered reserves. The Almond coal zone, in the Almond Formation, contains an original resource of 7.0 BST, of which approximately 2.0 BST are considered a recoverable resource and 61 MST are considered reserves. Resources were not calculated for the coal zones within the Niland Tongue of the Wasatch Formation or the Cretaceous Lance and Allen Ridge Formations because the coal beds in those zones are relatively thin, discontinuous, and have a limited areal extent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1836","usgsCitation":"Scott, D.C., Shaffer, B.N., Haacke, J.E., Pierce, P.E., and Kinney, S.A., 2019, Coal geology and assessment of resources and reserves in the Little Snake River coal field and Red Desert assessment area, Greater Green River Basin, Wyoming: U.S. Geological Survey Professional Paper 1836, 169 p., https://doi.org/10.3133/pp1836.","productDescription":"xiii, 169 p.","onlineOnly":"Y","ipdsId":"IP-077210","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":370383,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20193053","text":"Assessment of Coal Resources and Reserves in the Little Snake River Coal Field and Red Desert Assessment Area, Greater Green River Basin, Wyoming"},{"id":356626,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1836/pp1836.pdf","text":"Report","size":"32.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1836"},{"id":356625,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1836/coverthb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.02783203125,\n 43.14909399920127\n ],\n [\n -111.07177734375,\n 41.02964338716638\n ],\n [\n -108.3251953125,\n 40.9964840143779\n ],\n [\n -106.8310546875,\n 41.02964338716638\n ],\n [\n -105.380859375,\n 41.04621681452063\n ],\n [\n -104.9853515625,\n 41.47566020027821\n ],\n [\n -107.24853515625,\n 42.90816007196054\n ],\n [\n -109.09423828125,\n 43.8028187190472\n ],\n [\n -110.9619140625,\n 43.8503744993026\n ],\n [\n -111.02783203125,\n 43.14909399920127\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The assessment of the Little Snake River coal field and Red Desert area covers approximately 2,300 square miles in the eastern portion of the Greater Green River Basin in south-central Wyoming. Coal-bearing formations are present throughout the Eocene, Paleocene, and Cretaceous strata in the assessment area. Paleogene-age coal beds are present in the Eocene Wasatch Formation and Paleocene Fort Union Formation. Cretaceous-age coal beds are present in the Lance, Almond, and Allen Ridge Formations. Utilizing over 4,000 data points, 55 individual coal beds were identified in the assessment area. Coal resources were calculated using geologic models generated from these data points, using criteria for minimum thickness and areal extent. The geologic modeling criteria indicated that 33 of the 55 individual coal beds had sufficient thickness and areal extent to be economically significant. Calculated original coal resources within the assessment area were approximately 73.2 billion short tons (BST). After excluding coal resources lost due to land use and technical restrictions, recoverable coal resources were approximately 19.37 BST, including 2.14 BST of coal resources that could be extracted using surface mining methods and 17.23 BST of coal resources that could be extracted using underground mining methods. Due to mining costs and projected low potential sales value of the coal resources, only approximately 167 million short tons (MST) can be classified as reserves, which is less than 1 percent of the recoverable coal resources
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193053","usgsCitation":"Shaffer, B.N., Pierce, P.E., Kinney, S.A., Olea, R., and Luppens, J.A., 2019, Assessment of coal resources and reserves in the Little Snake River coal field and Red Desert assessment area, Greater Green River Basin, Wyoming: U.S. Geological Survey Fact Sheet 2019–3053, 6 p., https://doi.org/10.3133/fs20193053.","productDescription":"6 p.","onlineOnly":"N","ipdsId":"IP-109360","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":370380,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3053/coverthb.jpg"},{"id":370382,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1836","text":"Coal Geology and Assessment of Resources and Reserves in the Little Snake River Coal Field and Red Desert Assessment Area, Greater Green River Basin, Wyoming"},{"id":370381,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3053/fs20193053.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3053"},{"id":399136,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109583.htm"}],"country":"United States","state":"Wyoming","otherGeospatial":"Little Snake River coal field, Red Desert assessment area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.35,\n 41\n ],\n [\n -107.3333,\n 41\n ],\n [\n -107.3333,\n 42.1078\n ],\n [\n -108.35,\n 42.1078\n ],\n [\n -108.35,\n 41\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Deformation associated with plate convergence at subduction zones is accommodated by a complex system involving fault slip and viscoelastic flow. These processes have proven difficult to disentangle. The 2010 Mw 8.8 Maule earthquake occurred close to the Chilean coast within a dense network of continuously recording Global Positioning System stations, which provide a comprehensive history of surface strain. We use these data to assemble a detailed picture of a structurally controlled megathrust fault frictional patchwork and the three-dimensional rheological and time-dependent viscosity structure of the lower crust and upper mantle, all of which control the relative importance of afterslip and viscoelastic relaxation during postseismic deformation. These results enhance our understanding of subduction dynamics including the interplay of localized and distributed deformation during the subduction zone earthquake cycle.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The California Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP) is a long-term cooperative project designed to assess the quality of groundwater resources used for public and domestic drinking water supplies in the State of California, to monitor and evaluate changes to that quality, to investigate the human and natural factors controlling water quality, and to improve the availability of comprehensive groundwater quality data and information. Between May 18, 2004, and May 3, 2018, the GAMA-PBP collected 3001 groundwater samples for analysis of pesticide constituents by the U.S. Geological Survey (USGS) National Water Quality Laboratory (NWQL)(note that ‘pesticide constituents’ includes parent compounds and degradates). Of these samples, 2994 were analyzed for pesticide constituents on schedules 2003, 2032, or 2033 (65 to 84 constituents), and 840 were analyzed for pesticide constituents on schedule 2060 (58 constituents). The original dataset reported by the NWQL to the USGS National Water Information System (NWIS) database contained a total of 2,688 detections of 78 pesticide constituents and 253,825 non-detections. In this original dataset, 33 percent of the 3,001 samples analyzed had reported detections of one or more pesticide constituents.
This report describes the GAMA-PBP data-quality objectives for pesticide data, the procedures used to establish study reporting limits, and use of those reporting limits to censor the data from the NWQL so that the final data published by the GAMA-PBP meet these data-quality objectives. The final GAMA-PBP dataset for samples collected from May 2004 to May 2018, after censoring, had a total of 1,632 detections of 37 pesticide constituents. In the final GAMA-PBP dataset, 25 percent of the 3,001 samples analyzed had detections of one or more pesticide constituents.
The presence of pesticides in groundwater is commonly evaluated by calculating detection frequencies. Detection frequencies for pesticides are sensitive to detection limits and method performance for concentrations near those limits; therefore, the two primary data quality issues addressed in the GAMA-PBP data-quality objectives for pesticides are (1) establishing criteria for classifying data from the laboratory as detections or non-detections for the purpose of data reporting by the project and (2) accounting for changes in analytical methods or method performance over time. The GAMA-PBP addresses these issues by developing study reporting limits that are used as the boundary between detections and non-detections for the reporting of GAMA-PBP results. These reporting limits are defined from method detection limits (MDLs) provided by the NWQL, unless examination of results from laboratory set blanks (LSBs) and GAMA-PBP field blanks indicates that a higher concentration censoring limit is warranted. The GAMA-PBP selected the MDL as the primary choice for defining study reporting limits for consistency with U.S. Environmental Protection Agency (EPA) guidelines for reporting detections of pesticides and other organic constituents.
A five-step procedure is used to develop study reporting limits and censor the GAMA-PBP dataset accordingly. The effect of the censoring at each step is described to provide information about the relative effect of each step on the overall censoring of the dataset. Steps 1 and 2 can be implemented at the time the data are received, whereas steps 3−5 require information accumulated over an extended period.
As of 2019, the USGS NWIS database does not have the capability to store both the original value reported by the NWQL and the final value published by the GAMA-PBP that reflects application of the quality-control censoring described in this report. In the interim, while this capability is developed, the 1,031 results censored in steps 2−5 are blocked from public release in NWIS, and the GAMA-PBP has published the original and final values in a USGS data release accompanying this report. The entire GAMA-PBP final dataset for pesticide constituents on schedules 2003, 2032, or 2033, or on schedule 2060 is publicly available in that USGS data release, through the USGS GAMA-PBP public web portal, and through the California State Water Resources Control Board GAMA public groundwater information system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195107","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Fram, M.S., and Stork, S.V., 2019, Determination of study reporting limits for pesticide constituent data for the California Groundwater Ambient Monitoring and Assessment Program Priority Basin Project, 2004–2018—Part 1: National Water Quality Schedules 2003, 2032, or 2033, and 2060: U.S. Geological Survey Scientific Investigations Report 2019–5107, 129 p., https://doi.org/10.3133/sir20195107.","productDescription":"Report: viii, 129 p.; Dataset","numberOfPages":"129","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101851","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":370195,"rank":3,"type":{"id":30,"text":"Data 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\"}}]}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Translocation of isolated species into suitable habitats may help secure vulnerable, geographically limited species. Due to the decline of Wyoming Hornyhead Chub Nocomis biguttatus, conservation actions such as translocation of populations within the plausible historical range are being considered to improve population redundancy and resiliency to disturbance events. Translocation of Wyoming Hornyhead Chub must be rigorously evaluated because a hatchery stock does not exist, so all fish used in translocations will come from the wild population. We present an approach to identify best available translocation sites prior to translocation efforts taking place. We evaluated fish community composition and habitat conditions at 54 potential translocation sites for Hornyhead Chub within 12 streams of the North Platte River Basin of Wyoming. We used two analyses to identify translocation sites most similar to currently occupied Hornyhead Chub sites on the Laramie River: hurdle models to predict hypothetical abundance of Hornyhead Chub at translocation sites and non-metric multidimensional scaling (NMDS) with fish community and habitat conditions. Presence and abundance of Hornyhead Chub was related to lack of nonnative predators and habitat features characteristic of backwater and velocity refuge habitats. We used a rank scoring system to weight the outcomes of each analysis and the highest ranking translocation sites occurred at a historical locality, the Sweetwater River. Our approach may be appropriate for other at-risk species with isolated distributions and little historical data.
","language":"English","doi":"10.1002/nafm.10261","usgsCitation":"Hickerson, B.T., and Walters, A.W., 2019, Evaluation of Potential Translocation Sites for an Imperiled Cyprinid, theHornyhead Chub: Transactions of the American Fisheries Society, v. 39, p. 205-218, https://doi.org/10.1002/nafm.10261.","productDescription":"14 p.","startPage":"205","endPage":"218","ipdsId":"IP-098524","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Laramie River, North Platte River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.38635253906249,\n 41.970722347928096\n ],\n [\n -104.54315185546875,\n 41.970722347928096\n ],\n [\n -104.54315185546875,\n 42.20817645934742\n ],\n [\n -105.38635253906249,\n 42.20817645934742\n ],\n [\n -105.38635253906249,\n 41.970722347928096\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2019-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hickerson, Brian T.","contributorId":275272,"corporation":false,"usgs":false,"family":"Hickerson","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":833909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833908,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215096,"text":"70215096 - 2019 - Time to branch out? Application of hierarchical survival models in plant phenology","interactions":[],"lastModifiedDate":"2020-10-08T11:54:37.37837","indexId":"70215096","displayToPublicDate":"2019-12-15T14:38:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Time to branch out? Application of hierarchical survival models in plant phenology","docAbstract":"The sensitivity of phenology to environmental drivers can vary across geography and species. As such, models developed to predict phenology are typically site- or taxon-specific. Generation of site- and taxon-specific models is limited by the intensive in-situ phenological monitoring effort required to generate sufficient data to parameterize each model. Where in-situ phenological observations exist, the data are often subject to analytical issues due to the limited duration of any individual monitoring program, spotty site- and species- level coverage, lack of standardized methodology, and infrequent or variable census intervals. Together, these characteristics constrain our ability to make phenological inferences outside of select sites and taxa where long-duration, intensive monitoring has occurred. In this study, we leveraged two national, standardized phenology datasets to develop a multi-species and multi-site state-space survival model of the onset of deciduous tree and shrub spring (leaf out) and fall (leaf-color) events across temperate ecoregions of the United States. We used data from two national-scale phenological databases, a 9-year, broadly distributed dataset from the USA National Phenology Network and a 4-year dataset from the National Ecological Observatory Network, to quantify regional and interspecific variation in sensitivity to environmental drivers for both spring and fall leaf phenophases. Spring leaf out was generally promoted by longer days, spring growing degree day accumulation, overwinter chilling, and was suppressed by frost events, whereas fall leaf color was promoted by shorter days and cold accumulation. The sensitivity to most environmental drivers tended to be more variable among species than among the regions as defined here (EPA ecoregions of North America, excluding desert and tropical areas). The results of this study lay the groundwork for incorporating the growing collection of phenological observations into a generalized framework for predicting the transition states for any species, in any location.","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2019.107694","usgsCitation":"Elmendorf, S., Crimmins, T., Gerst, K.L., and Weltzin, J., 2019, Time to branch out? Application of hierarchical survival models in plant phenology: Agricultural and Forest Meteorology, v. 279, 107694, 8 p., https://doi.org/10.1016/j.agrformet.2019.107694.","productDescription":"107694, 8 p.","ipdsId":"IP-107695","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":458954,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2019.107694","text":"Publisher Index Page"},{"id":379194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"279","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Elmendorf, Sarah","contributorId":147651,"corporation":false,"usgs":false,"family":"Elmendorf","given":"Sarah","affiliations":[{"id":16880,"text":"National Ecological Observatory Network (NEON), 1685 38th St., Boulder, CO 80301, USA","active":true,"usgs":false}],"preferred":false,"id":800827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crimmins, Theresa 0000-0001-9592-625X","orcid":"https://orcid.org/0000-0001-9592-625X","contributorId":222414,"corporation":false,"usgs":false,"family":"Crimmins","given":"Theresa","email":"","affiliations":[{"id":40537,"text":"USA National Phenology Network, National Coordinating Office; University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":800828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerst, Katharine L.","contributorId":175227,"corporation":false,"usgs":false,"family":"Gerst","given":"Katharine","email":"","middleInitial":"L.","affiliations":[{"id":27543,"text":"National Phenology Network, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":800829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weltzin, Jake 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":196323,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":800830,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216009,"text":"70216009 - 2019 - High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015","interactions":[],"lastModifiedDate":"2020-11-11T14:35:23.35128","indexId":"70216009","displayToPublicDate":"2019-12-15T07:30:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015","docAbstract":"Magma intrusion rate is a key parameter in eruption triggering but is poorly quantified in existing geodetic studies. Here we examine two episodes of rapid inflation in this context. Two noneruptive microseismic swarms were recorded at Semisopochnoi Volcano, Alaska in 2014–2015. We use differential SAR techniques and TerraSAR‐X images to document surface deformation from 2011 to 2015, which comprises island‐wide radial inflation totaling ~25 cm (+/−1 cm) line of sight displacement in 2014–2015. Multiple source geometries are tested in an inversion of the deformation data, and InSAR data are best fit by a spheroid trending to the northeast and plunging to the southeast, with a major axis of ~4 km and minor axes of ~1 km, directly under the central caldera of Semisopochnoi. In 2014, a modeled influx of 0.043 km3 of magma caused line of sight displacement of ~17 cm. This magma was stored at a depth of ~8 km, until 2015 when 0.029 km3 was added. Along with the definition of inflation source parameters, the recorded seismic events are relocated using differential travel times. These relocated events outline a linear aseismic area within a larger zone of shallow (<10 km) seismicity. This aseismic region aligns with the centroid of the deformation model. Based on these geodetic and seismic models, the plumbing system at Semisopochnoi is interpreted as a spheroidal magma storage zone at a depth of ˜8 km below a linear feature of partial melt. The observed deformation and seismicity appear to result from rapid injection into this main storage region.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GC008720","usgsCitation":"Degrandpre, K., Pesicek, J.D., Lu, Z., DeShon, H.R., and Roman, D., 2019, High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015: Journal of Geophysical Research, v. 20, no. 12, p. 6163-6186, https://doi.org/10.1029/2019GC008720.","productDescription":"24 p.","startPage":"6163","endPage":"6186","ipdsId":"IP-098799","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":380068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Semisopochnoi Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 179.47265625,\n 51.839171715043946\n ],\n [\n 179.77203369140625,\n 51.839171715043946\n ],\n [\n 179.77203369140625,\n 52.04742324502936\n ],\n [\n 179.47265625,\n 52.04742324502936\n ],\n [\n 179.47265625,\n 51.839171715043946\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Degrandpre, Kimberly","contributorId":244311,"corporation":false,"usgs":false,"family":"Degrandpre","given":"Kimberly","email":"","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":803746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":803747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lu, Zhong","contributorId":199794,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":803748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeShon, Heather R.","contributorId":244313,"corporation":false,"usgs":false,"family":"DeShon","given":"Heather","email":"","middleInitial":"R.","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":803749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":803777,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215201,"text":"70215201 - 2019 - Geometric targets for UAS Lidar","interactions":[],"lastModifiedDate":"2020-10-13T22:43:27.669449","indexId":"70215201","displayToPublicDate":"2019-12-14T11:14:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Geometric targets for UAS Lidar","docAbstract":"Lidar from small unoccupied aerial systems (UAS) is a viable method for collecting geospatial data associated with a wide variety of applications. Point clouds from UAS lidar require a means for accuracy assessment, calibration, and adjustment. In order to carry out these procedures, specific locations within the point cloud must be precisely found. To do this, artificial targets may be used for rural settings, or anywhere there is a lack of identifiable and measurable features in the scene. This paper presents the design of lidar targets for precise location based on geometric structure. The targets and associated mensuration algorithm were tested in two scenarios to investigate their performance under different point densities, and different levels of algorithmic rigor. The results show that the targets can be accurately located within point clouds from typical scanning parameters to <2 cm
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11242983","usgsCitation":"Palaseanu-Lovejoy, M., Bisson, M., Spinetti, C., Buongiorno, M.F., Alexandrov, O., and Cecere, T., 2019, High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data: Remote Sensing, v. 11, no. 24, 2983, 17 p., https://doi.org/10.3390/rs11242983.","productDescription":"2983, 17 p.","ipdsId":"IP-112349","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":458977,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11242983","text":"Publisher Index Page"},{"id":437261,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IGLDYE","text":"USGS data release","linkHelpText":"Digital Surface Model of Mt. Etna, Italy, derived from 2015 Pleiades Satellite Imagery"},{"id":371637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Etna","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.84733581542969,\n 37.62238973852369\n ],\n [\n 15.128173828125,\n 37.62238973852369\n ],\n [\n 15.128173828125,\n 37.84015683604136\n ],\n [\n 14.84733581542969,\n 37.84015683604136\n ],\n [\n 14.84733581542969,\n 37.62238973852369\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":780322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bisson, Marina 0000-0002-7104-9210","orcid":"https://orcid.org/0000-0002-7104-9210","contributorId":221724,"corporation":false,"usgs":false,"family":"Bisson","given":"Marina","email":"","affiliations":[{"id":40408,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Della Faggiola, Pisa, 56126, Italy","active":true,"usgs":false}],"preferred":false,"id":780323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spinetti, Claudia 0000-0002-1861-5666","orcid":"https://orcid.org/0000-0002-1861-5666","contributorId":221725,"corporation":false,"usgs":false,"family":"Spinetti","given":"Claudia","email":"","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":780324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buongiorno, Maria Fabrizia 0000-0002-6095-6974","orcid":"https://orcid.org/0000-0002-6095-6974","contributorId":221726,"corporation":false,"usgs":false,"family":"Buongiorno","given":"Maria","email":"","middleInitial":"Fabrizia","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":780325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alexandrov, Oleg","contributorId":167662,"corporation":false,"usgs":false,"family":"Alexandrov","given":"Oleg","email":"","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":780326,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cecere, Thomas 0000-0001-5254-8404 tcecere@usgs.gov","orcid":"https://orcid.org/0000-0001-5254-8404","contributorId":221727,"corporation":false,"usgs":true,"family":"Cecere","given":"Thomas","email":"tcecere@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":780327,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}