{"pageNumber":"6","pageRowStart":"125","pageSize":"25","recordCount":1873,"records":[{"id":70239747,"text":"sir20225107 - 2023 - Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021","interactions":[],"lastModifiedDate":"2026-02-23T19:32:52.536466","indexId":"sir20225107","displayToPublicDate":"2023-01-20T10:30:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5107","displayTitle":"Using Global Fiducials Library High-Resolution Imagery, Commercial Satellite Imagery, Landsat and Sentinel Satellite Imagery, and Aerial Photography to Monitor Change at East Timbalier Island, Louisiana, 1953–2021","title":"Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021","docAbstract":"This report documents morphological changes between 1953 and 2021 at East Timbalier Island, Louisiana, a Gulf of Mexico barrier island. East Timbalier Island, which was located west of the Mississippi River Delta at the front of Timbalier Bay, was one of the most rapidly changing barrier islands on Earth. Since aerial photographs were initially taken in 1953, the Island steadily lost length and area, finally eroding away by early summer 2021. After major storm events, sediment eroded from the Island and migrated hundreds of meters north. In August 1992, Hurricane Andrew breached the Island in several places, resulting in increased erosion and land loss. Until it completely eroded away, the Island underwent a cycle of washovers, vegetation removal, breaching, and erosion with sediment transport to the north. Satellite imagery shows that three such cycles occurred between 1992 and 2017, despite the partial restoration of the Island between 1998 and 2000. Each cycle increased the distance between the Island and the mainland to the east, reducing both the sediment supply from the east and the protection that Timbalier Bay and the adjacent coastal lands received from the barrier island.\n\nPreviously, the U.S. Geological Survey (USGS) National Civil Applications Center used 1-meter resolution imagery archived at the USGS Global Fiducials Library (GFL), collected between 2000 and 2010 by U.S. National Imaging Systems, to monitor the changes at the Island. New research expands this study retrospectively and prospectively using aerial photography collected from 1953 to 2012 and in 2020; declassified imagery collected in 1962, 1972, and 1975; DigitalGlobe satellite imagery collected since 2004; Landsat satellite imagery collected since 1972; Sentinel–2 satellite imagery collected since 2015; and GFL imagery collected from 1991 to 2020.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225107","isbn":"978-1-4113-4511-9","programNote":"Core Science Systems and the National Civil Applications Center","usgsCitation":"Fisher, G.B., Slonecker, E.T., Dilles, S.J., Molnia, B.F., and Angeli, K.M., 2023, Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021 (ver. 1.1, May 2023): U.S. Geological Survey Scientific Investigations Report 2022–5107, 61 p., https://doi.org/10.3133/sir20225107.","productDescription":"Report: vii, 61 p.; Data Release","numberOfPages":"61","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123372","costCenters":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":411976,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O71HYS","text":"USGS data release","linkHelpText":"Six decades of change at East Timbalier Island, Louisiana"},{"id":411971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5107/coverthb2.jpg"},{"id":416780,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5107/versionHist.txt","size":"4.31 KB","linkFileType":{"id":2,"text":"txt"}},{"id":411972,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5107/sir20225107.pdf","text":"Report","size":"150 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5107"},{"id":411975,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5107/images/"},{"id":500455,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114279.htm","linkFileType":{"id":5,"text":"html"}},{"id":411974,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5107/sir20225107.XML"}],"country":"United States","state":"Louisiana","otherGeospatial":"East Timbalier Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -90.4,\n              29.125\n            ],\n            [\n              -90.4,\n              29.033\n            ],\n            [\n              -90.233,\n              29.033\n            ],\n            [\n              -90.233,\n              29.125\n            ],\n            [\n              -90.4,\n              29.125\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","edition":"Version 1.0: January 2023; Version 1.1: May 2023","contact":"<p>Director, <a href=\"https://www.usgs.gov/programs/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/programs/national-land-imaging-program\">National Civil Applications Center</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive, MS 562<br>Reston, VA 20192</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Data and Methods</li><li>Results and Discussion</li><li>Conclusion</li><li>References Cited</li><li>Appendix 1. High-Resolution Imagery for East Timbalier Island, 1953–2021</li><li>Appendix 2. Historical Imagery Data</li><li>Appendix 3. Global Fiducials Library Imagery Dates</li><li>Appendix 4. DigitalGlobe Satellite Imagery Data</li><li>Appendix 5. Landsat Satellite Imagery Data</li><li>Appendix 6. Sentinel–2 Imagery Data</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-01-20","revisedDate":"2023-05-08","noUsgsAuthors":false,"publicationDate":"2023-01-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Fisher, Gary B. 0000-0001-8777-0216 gtfisher@usgs.gov","orcid":"https://orcid.org/0000-0001-8777-0216","contributorId":215627,"corporation":false,"usgs":true,"family":"Fisher","given":"Gary","email":"gtfisher@usgs.gov","middleInitial":"B.","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"preferred":true,"id":861731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dilles, Shawn J. 0000-0003-0341-7728","orcid":"https://orcid.org/0000-0003-0341-7728","contributorId":301012,"corporation":false,"usgs":true,"family":"Dilles","given":"Shawn","email":"","middleInitial":"J.","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"preferred":true,"id":861733,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Molnia, Bruce F. 0000-0001-8102-6269","orcid":"https://orcid.org/0000-0001-8102-6269","contributorId":301013,"corporation":false,"usgs":true,"family":"Molnia","given":"Bruce","email":"","middleInitial":"F.","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"preferred":true,"id":861734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Angeli, Kim M. 0000-0003-2427-3241 kangeli@usgs.gov","orcid":"https://orcid.org/0000-0003-2427-3241","contributorId":238809,"corporation":false,"usgs":true,"family":"Angeli","given":"Kim","email":"kangeli@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":861735,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70239758,"text":"70239758 - 2023 - Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California","interactions":[],"lastModifiedDate":"2023-01-18T14:25:55.153483","indexId":"70239758","displayToPublicDate":"2023-01-15T08:20:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California","docAbstract":"<p><span>In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and conservation actions to enhance wetland habitat. Taking advantage of Landsat satellite data and structured and community science bird survey data, we built species distribution models to describe how three duck species, the Northern Pintail (</span><i>Anas acuta</i><span>), Green-winged Teal (</span><i>Anas crecca</i><span>), and Northern Shoveler (</span><i>Anas clypeata</i><span>), respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models compared duck habitat suitability between the wettest and driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (1) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the water availability within the 16-day window of a Landsat observation, (2) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (3) long-term average covariates—the most common approach to species distribution modeling—in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to surface water availability. When models of habitat suitability based on long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly higher model performance. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4367","usgsCitation":"Conlisk, E.E., Byrd, K.B., Matchett, E., Lorenz, A., Casazza, M.L., Golet, G.H., Reynolds, M.D., Sesser, K.A., and Reiter, M.E., 2023, Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California: Ecosphere, v. 14, e4367, 19 p., https://doi.org/10.1002/ecs2.4367.","productDescription":"e4367, 19 p.","ipdsId":"IP-144890","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444827,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4367","text":"Publisher Index Page"},{"id":412024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -118.81873286495954,\n              35.04394124445325\n            ],\n            [\n              -118.8570190310794,\n              36.52123291574787\n            ],\n            [\n              -120.23927537042829,\n              37.988003366747364\n            ],\n            [\n              -121.61872476287942,\n              40.10174582877633\n            ],\n            [\n              -121.96284031570764,\n              40.846007013038246\n            ],\n            [\n              -123.06491347085935,\n              40.526780450482676\n         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E.","contributorId":301022,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":861775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":861777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lorenz, Austen 0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":861779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Golet, Gregory H.","contributorId":89844,"corporation":false,"usgs":false,"family":"Golet","given":"Gregory","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":861780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reynolds, Mark D.","contributorId":301023,"corporation":false,"usgs":false,"family":"Reynolds","given":"Mark","email":"","middleInitial":"D.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":861781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sesser, Kristin A.","contributorId":215294,"corporation":false,"usgs":false,"family":"Sesser","given":"Kristin","email":"","middleInitial":"A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":861782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reiter, Matthew E. 0000-0002-0587-786X","orcid":"https://orcid.org/0000-0002-0587-786X","contributorId":271031,"corporation":false,"usgs":false,"family":"Reiter","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":56258,"text":"Point Blue","active":true,"usgs":false}],"preferred":false,"id":861783,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70241048,"text":"70241048 - 2023 - National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images","interactions":[],"lastModifiedDate":"2023-11-08T16:49:21.339686","indexId":"70241048","displayToPublicDate":"2023-01-11T06:41:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images","docAbstract":"<div id=\"abstracts\" data-extent=\"frontmatter\"><div class=\"core-container\"><div>National Land Cover Database (NLCD) 2019 is a new epoch of national land cover products for the conterminous United States. Image quality is fundamental to the quality of any land cover product. Image preprocessing has often taken a considerable proportion of overall time and effort for this kind of national project. An approach to prepare image inputs for NLCD 2019 production was developed to ensure efficiency and quality of operational production. Here, we introduce a new and comprehensive strategy to produce clear Landsat composite images for NLCD 2019 production. First, we developed a new median-value compositing method. Second, we designed parameter settings for selecting images and pixels to generate 4 composite images (leaf-on, leaf-off, primary reference, and complementary reference) for a target year based on the US Landsat Analysis Ready Data surface reflectance dataset. Third, we developed a method, referred to as Detection and Filling with Simulated Image, to detect and replace clouds and cloud shadow pixels to produce the final clean leaf-on and leaf-off image composites. This image compositing and processing strategy was implemented for the entire conterminous United States to produce images for NLCD 2019. Our image results and NLCD 2019 change detection and land cover products, which were released in July 2021, showed this new strategy to be effective and efficient.</div></div></div>","language":"English","publisher":"AAAS","doi":"10.34133/remotesensing.0022","usgsCitation":"Jin, S., Dewitz, J., Danielson, P., Granneman, B., Costello, C., and Zhu, Z., 2023, National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images: ISPRS Journal of Photogrammetry and Remote Sensing, v. 3, 0022, 13 p., https://doi.org/10.34133/remotesensing.0022.","productDescription":"0022, 13 p.","ipdsId":"IP-130136","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":444871,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.34133/remotesensing.0022","text":"Publisher Index Page"},{"id":413845,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2023-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":865853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dewitz, Jon 0000-0002-0458-212X","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":215192,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":865854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danielson, Patrick 0000-0002-2990-2783","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":302925,"corporation":false,"usgs":false,"family":"Danielson","given":"Patrick","affiliations":[{"id":65584,"text":"KBR, contractor to the USGS EROS","active":true,"usgs":false}],"preferred":false,"id":865855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Granneman, Brian 0000-0002-1910-0955","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":302926,"corporation":false,"usgs":false,"family":"Granneman","given":"Brian","affiliations":[{"id":65584,"text":"KBR, contractor to the USGS EROS","active":true,"usgs":false}],"preferred":false,"id":865856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Costello, Catherine 0000-0001-7158-2675","orcid":"https://orcid.org/0000-0001-7158-2675","contributorId":223238,"corporation":false,"usgs":true,"family":"Costello","given":"Catherine","email":"","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":190828,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":865858,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70239173,"text":"70239173 - 2023 - Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment","interactions":[],"lastModifiedDate":"2023-01-02T19:07:29.676936","indexId":"70239173","displayToPublicDate":"2023-01-02T13:01:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9346,"text":"Science of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment","docAbstract":"<p>Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.srs.2022.100073","usgsCitation":"Gao, F., Jennewein, J., Hively, W.D., Soroka, A.M., Thieme, A., Bradley, D., Keppler, J., Mirsky, S., and Akumaga, U., 2023, Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment: Science of Remote Sensing, v. 7, 100073, 14 p., https://doi.org/10.1016/j.srs.2022.100073.","productDescription":"100073, 14 p.","ipdsId":"IP-144149","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":444975,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.srs.2022.100073","text":"Publisher Index Page"},{"id":411274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Delmarva Peninsula","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -76.069544467364,\n              37.94756618819288\n            ],\n            [\n              -75.94812876408099,\n              37.94131461385136\n            ],\n            [\n              -75.95701283993044,\n              37.899264516752396\n            ],\n            [\n              -75.79709947463141,\n              37.901601263962206\n            ],\n            [\n              -75.75564045399823,\n              37.94131461385136\n            ],\n            [\n              -75.6993746402825,\n              37.950655814583385\n            ],\n            [\n              -75.64014746794955,\n              37.94131461385136\n            ],\n            [\n              -75.61053388178237,\n              37.99734400246169\n            ],\n            [\n              -75.22555726161829,\n              38.02534265907727\n            ],\n            [\n              -75.08341204801894,\n              38.27684897319932\n            ],\n            [\n              -75.0389916687707,\n              38.447926991945224\n            ],\n            [\n              -75.69049056443372,\n              38.45952234969701\n            ],\n            [\n              -75.78229268154962,\n              39.723597608598226\n            ],\n            [\n              -75.88594023313227,\n              39.71676420384449\n            ],\n            [\n              -76.03993088119832,\n              39.44058615652014\n            ],\n            [\n              -76.16430794309719,\n              39.36507397284154\n            ],\n            [\n              -76.30053043946273,\n              39.1839704250678\n            ],\n            [\n              -76.3390281014787,\n              39.046110820719235\n            ],\n            [\n              -76.4219461427451,\n              38.850348316275074\n            ],\n            [\n              -76.36568032902868,\n              38.47343431903974\n            ],\n            [\n              -76.069544467364,\n              37.94756618819288\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":860675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":860676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":210993,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":860677,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soroka, Alexander M. 0000-0002-8002-5229","orcid":"https://orcid.org/0000-0002-8002-5229","contributorId":201664,"corporation":false,"usgs":true,"family":"Soroka","given":"Alexander","email":"","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860678,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thieme, Alison","contributorId":237963,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","email":"","affiliations":[{"id":47661,"text":"University of Maryland, Geographical Sciences","active":true,"usgs":false}],"preferred":false,"id":860679,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bradley, Dawn","contributorId":300533,"corporation":false,"usgs":false,"family":"Bradley","given":"Dawn","email":"","affiliations":[{"id":65189,"text":"Maryland Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":860680,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keppler, Jason","contributorId":218039,"corporation":false,"usgs":false,"family":"Keppler","given":"Jason","email":"","affiliations":[{"id":39731,"text":"Maryland Department of Agriculture, Office of Resource Conservation","active":true,"usgs":false}],"preferred":false,"id":860681,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mirsky, Steven","contributorId":292000,"corporation":false,"usgs":false,"family":"Mirsky","given":"Steven","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":860682,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Akumaga, Uvirkaa","contributorId":300534,"corporation":false,"usgs":false,"family":"Akumaga","given":"Uvirkaa","email":"","affiliations":[{"id":65190,"text":"USDA-ARS Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":860683,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70239356,"text":"70239356 - 2023 - Improving the operational simplified surface energy balance evapotranspiration model using the forcing and normalizing operation","interactions":[],"lastModifiedDate":"2023-01-10T13:18:25.464452","indexId":"70239356","displayToPublicDate":"2023-01-01T07:17:05","publicationYear":"2023","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":"Improving the operational simplified surface energy balance evapotranspiration model using the forcing and normalizing operation","docAbstract":"<div class=\"html-p\">Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (<span class=\"html-italic\">ETa</span>) estimates with AmeriFlux eddy covariance (EC) and water balance<span>&nbsp;</span><span class=\"html-italic\">ETa</span><span>&nbsp;</span>from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based<span>&nbsp;</span><span class=\"html-italic\">ETa</span><span>&nbsp;</span>bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration (<span class=\"html-italic\">ETr</span>) produced comparable<span>&nbsp;</span><span class=\"html-italic\">ETa</span><span>&nbsp;</span>results, justifying the use of climatology<span>&nbsp;</span><span class=\"html-italic\">ETr</span><span>&nbsp;</span>for the global SSEBop Landsat<span>&nbsp;</span><span class=\"html-italic\">ETa</span><span>&nbsp;</span>that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of<span>&nbsp;</span><span class=\"html-italic\">ETa</span><span>&nbsp;</span>for localized water budget applications.</div>","language":"English","publisher":"MDPI","doi":"10.3390/rs15010260","usgsCitation":"Senay, G.B., Parrish, G.E., Schauer, M., Friedrichs, M., Khand, K., Boiko, O., Kagone, S., Dittmeier, R., Arab, S., and Ji, L., 2023, Improving the operational simplified surface energy balance evapotranspiration model using the forcing and normalizing operation: Remote Sensing, v. 15, no. 1, 260, 25 p., https://doi.org/10.3390/rs15010260.","productDescription":"260, 25 p.","ipdsId":"IP-146439","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":444995,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs15010260","text":"Publisher Index Page"},{"id":435525,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NKWT3D","text":"USGS data release","linkHelpText":"Forcing and Normalizing Operation (FANO) method for the Operational Simplified Surface Energy Balance (SSEBop) ET model"},{"id":411621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":861239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parrish, Gabriel Edwin Lee 0000-0003-4078-3516","orcid":"https://orcid.org/0000-0003-4078-3516","contributorId":267751,"corporation":false,"usgs":false,"family":"Parrish","given":"Gabriel","email":"","middleInitial":"Edwin Lee","affiliations":[{"id":55490,"text":"Innovate! Inc., Contractor to the USGS EROS Center","active":true,"usgs":false}],"preferred":false,"id":861240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schauer, Matthew 0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":181608,"corporation":false,"usgs":false,"family":"Schauer","given":"Matthew","affiliations":[],"preferred":false,"id":861241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":199093,"corporation":false,"usgs":false,"family":"Friedrichs","given":"MacKenzie","affiliations":[],"preferred":false,"id":861242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Khand, Kul Bikram 0000-0002-1593-1508","orcid":"https://orcid.org/0000-0002-1593-1508","contributorId":259185,"corporation":false,"usgs":false,"family":"Khand","given":"Kul Bikram","affiliations":[{"id":52326,"text":"AFDS, Contractor to USGS ERSOS Center","active":true,"usgs":false}],"preferred":false,"id":861243,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boiko, Olena 0000-0002-2007-7852","orcid":"https://orcid.org/0000-0002-2007-7852","contributorId":272079,"corporation":false,"usgs":false,"family":"Boiko","given":"Olena","email":"","affiliations":[{"id":56343,"text":"KBR, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":861244,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":199091,"corporation":false,"usgs":false,"family":"Kagone","given":"Stefanie","affiliations":[],"preferred":false,"id":861245,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dittmeier, Ray","contributorId":299963,"corporation":false,"usgs":false,"family":"Dittmeier","given":"Ray","email":"","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":861246,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":299964,"corporation":false,"usgs":false,"family":"Arab","given":"Saeed","email":"","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":861247,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ji, Lei 0000-0002-6133-1036","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":272078,"corporation":false,"usgs":false,"family":"Ji","given":"Lei","affiliations":[{"id":56342,"text":"ASRC Federal Data Solutions, Contractor to USGS Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":861248,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70239219,"text":"70239219 - 2023 - Assessment of cropland inundation due to the operation of the Reelfoot Lake spillway in West Tennessee","interactions":[],"lastModifiedDate":"2023-08-07T16:55:22.918741","indexId":"70239219","displayToPublicDate":"2022-12-30T06:51:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of cropland inundation due to the operation of the Reelfoot Lake spillway in West Tennessee","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Running Reelfoot Bayou (RRB) is the outlet canal of Reelfoot Lake, the largest natural lake in Tennessee. RRB is not able to contain discharge from Reelfoot Lake greater than the bankfull discharge of 28 m<sup>3</sup>/s (1000 ft<sup>3</sup>/s), which typically occurs at the beginning of the growing season (April–June). Historically, the planting of crops has been delayed until flooding subsides and cropland has drained. The objective of this study is a preliminary quantification of cropland inundation to determine its spatial distribution in the RRB floodplain. Inundated croplands in the RRB floodplain were delineated over a range of spillway discharges from 2 to 57 m<sup>3</sup>/s (70–2000 ft<sup>3</sup>/s), using one-dimensional–two-dimensional hydrodynamic modeling and multispectral satellite images (Landsat 8 and Sentinel-2). The composite maps made by combining the simulated and image-derived flood maps were overlaid on the United States Department of Agriculture CropScape layer to determine the inundation of individual summer crops during the growing season. About 25% of the inundated croplands are flooded at discharges of RRB less than 28 m<sup>3</sup>/s, implying wetland hydrology. The results of this analysis can be used to inform operational management of the Reelfoot Lake spillway.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13092","usgsCitation":"Bhuyian, N., Lahiri, C., Diehl, T.H., and Heal, E., 2023, Assessment of cropland inundation due to the operation of the Reelfoot Lake spillway in West Tennessee: Journal of the American Water Resources Association, v. 59, no. 4, p. 855-873, https://doi.org/10.1111/1752-1688.13092.","productDescription":"19 p.","startPage":"855","endPage":"873","ipdsId":"IP-124997","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":445008,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13092","text":"Publisher Index Page"},{"id":411335,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -89.58348471060779,\n              36.42528433341498\n            ],\n            [\n              -89.58348471060779,\n              36.07800148864851\n            ],\n            [\n              -89.2636166393048,\n              36.07800148864851\n            ],\n            [\n              -89.2636166393048,\n              36.42528433341498\n            ],\n            [\n              -89.58348471060779,\n              36.42528433341498\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"59","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-12-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Bhuyian, N.M. 0000-0001-8101-8453","orcid":"https://orcid.org/0000-0001-8101-8453","contributorId":300553,"corporation":false,"usgs":false,"family":"Bhuyian","given":"N.M.","email":"","affiliations":[{"id":65197,"text":"Environmental Consultant 3, West Tennessee River Basin Authority","active":true,"usgs":false}],"preferred":false,"id":860798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lahiri, Chayan 0000-0002-7454-4196","orcid":"https://orcid.org/0000-0002-7454-4196","contributorId":300554,"corporation":false,"usgs":false,"family":"Lahiri","given":"Chayan","email":"","affiliations":[{"id":65199,"text":"Assistant Professor, Department of Biology and Geosciences, Adams State University","active":true,"usgs":false}],"preferred":false,"id":860799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diehl, Timothy H. 0000-0001-9691-2212 thdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9691-2212","contributorId":546,"corporation":false,"usgs":true,"family":"Diehl","given":"Timothy","email":"thdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heal, Elizabeth 0000-0002-1196-4708 eheal@usgs.gov","orcid":"https://orcid.org/0000-0002-1196-4708","contributorId":177003,"corporation":false,"usgs":true,"family":"Heal","given":"Elizabeth","email":"eheal@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":860801,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70239085,"text":"70239085 - 2022 - Riparian plant evapotranspiration and consumptive use for selected areas of the Little Colorado River watershed on the Navajo Nation","interactions":[],"lastModifiedDate":"2025-12-11T22:19:49.169866","indexId":"70239085","displayToPublicDate":"2022-12-26T10:55:03","publicationYear":"2022","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":"Riparian plant evapotranspiration and consumptive use for selected areas of the Little Colorado River watershed on the Navajo Nation","docAbstract":"<p><span>Estimates of riparian vegetation water use are important for hydromorphological assessment, partitioning within human and natural environments, and informing environmental policy decisions. The objectives of this study were to calculate the actual evapotranspiration (ETa) (mm/day and mm/year) and derive riparian vegetation annual consumptive use (CU) in acre-feet (AF) for select riparian areas of the Little Colorado River watershed within the Navajo Nation, in northeastern Arizona, USA. This was accomplished by first estimating the riparian land cover area for trees and shrubs using a 2019 summer scene from National Agricultural Imagery Program (NAIP) (1 m resolution), and then fusing the riparian delineation with Landsat-8 OLI (30-m) to estimate ETa for 2014–2020. We used indirect remote sensing methods based on gridded weather data, Daymet (1 km) and PRISM (4 km), and Landsat measurements of vegetation activity using the two-band Enhanced Vegetation Index (EVI2). Estimates of potential ET were calculated using Blaney-Criddle. Riparian ETa was quantified using the Nagler ET(EVI2) approach. Using both vector and raster estimates of tree, shrub, and total riparian area, we produced the first CU measurements for this region. Our best estimate of annual CU is 36,983 AF with a range between 31,648–41,585 AF and refines earlier projections of 25,387–46,397 AF.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs15010052","usgsCitation":"Nagler, P.L., Barreto-Muñoz, A., Sall, I., Lurtz, M.R., and Didan, K., 2022, Riparian plant evapotranspiration and consumptive use for selected areas of the Little Colorado River watershed on the Navajo Nation: Remote Sensing, v. 15, no. 1, 52, 37 p.; Data Release, https://doi.org/10.3390/rs15010052.","productDescription":"52, 37 p.; Data Release","ipdsId":"IP-143742","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445627,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs15010052","text":"Publisher Index Page"},{"id":435592,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EFZWPP","text":"USGS data release","linkHelpText":"Uncultivated plant water use (riparian evapotranspiration) and consumptive use data for selected areas of the Little Colorado River watershed on the Navajo Nation, Arizona"},{"id":411050,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Hopi Reservation, Little Colorado River Watershed, Navajo Nation","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -112.27451835472442,\n              37.936722934098526\n            ],\n            [\n              -112.27451835472442,\n              33.63417184178236\n            ],\n            [\n              -108.7808660109742,\n              33.63417184178236\n            ],\n            [\n              -108.7808660109742,\n              37.936722934098526\n            ],\n            [\n              -112.27451835472442,\n              37.936722934098526\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-22","publicationStatus":"PW","contributors":{"authors":[{"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":859997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":859998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":859999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lurtz, Matthew R.","contributorId":300337,"corporation":false,"usgs":false,"family":"Lurtz","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":65088,"text":"Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, 80523 USA","active":true,"usgs":false}],"preferred":false,"id":860000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":860001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70239014,"text":"fs20223086 - 2022 - Landsat Collection 2 Level-3 Fractional Snow Covered Area science product","interactions":[],"lastModifiedDate":"2023-06-28T14:35:51.745278","indexId":"fs20223086","displayToPublicDate":"2022-12-20T12:38:33","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3086","displayTitle":"Landsat Collection 2 Level-3 Fractional Snow Covered Area Science Product","title":"Landsat Collection 2 Level-3 Fractional Snow Covered Area science product","docAbstract":"<p>The Landsat Collection 2 Level-3 Fractional Snow Covered Area science product indicates the percentage of pixels covered by snow for Landsat 4–9 imagery. Landsat’s spatial resolution offers the capability to map snow cover patterns across topographically complex mountainous regions. Snow cover is spatially and temporally variable and is often concentrated in remote or inaccessible land regions, making spaceborne remote sensing the most feasible approach to measure and monitor snow cover change.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223086","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Fractional Snow Covered Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3086, 2 p., https://doi.org/10.3133/fs20223086.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139626","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418251,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3086/fs20223086.pdf","text":"Report","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3086"},{"id":410799,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418250,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3086/coverthb2.jpg"},{"id":418252,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3086/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: December 20, 2022; Version 1.1: June 21, 2023","contact":"<p><a href=\"mailto:custserv@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:custserv@usgs.gov\">Customer Services</a>,&nbsp;<a href=\"https://www.usgs.gov/centers/eros\" data-mce-href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p>","tableOfContents":"<ul><li>Product Availability</li><li>Product Improvements</li><li>Package Contents</li><li>Statistics Product</li><li>Data Access</li><li>Documentation</li><li>Citation Information</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-12-20","revisedDate":"2023-06-21","noUsgsAuthors":false,"publicationDate":"2022-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":859738,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70240649,"text":"70240649 - 2022 - Ecological Coastal Units – Standardized global shoreline characteristics","interactions":[],"lastModifiedDate":"2023-02-10T14:26:17.604346","indexId":"70240649","displayToPublicDate":"2022-12-19T08:19:59","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Ecological Coastal Units – Standardized global shoreline characteristics","docAbstract":"<p><span>A new set of resources is now available that describe global shoreline characteristics. High resolution (30 m), globally comprehensive Coastal Segment Units (CSUs) and Ecological Coastal Units (ECUs) were developed in a collaboration between the U.S. Geological Survey (USGS), Esri, and the Marine Biodiversity Observation Network (MBON). The data were produced from a segmentation and characterization of a global shoreline vector extracted from year 2014 Landsat imagery. A total of 4 million 1 km shoreline segments were attributed with values from ten variables which describe the ecological settings in which the coastline occurs, including water-side properties, landside properties, and properties of the coastline itself. These data were developed as part of a Group on Earth Observations (GEO) global ecosystem mapping initiative called GEO Ecosystems (GEO ECO). The development of the resource and its intended utility are reviewed herein.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Oceans 2022, Hampton Roads","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Oceans 2022, Hampton Roads","conferenceDate":"Oct 17-20, 2022","conferenceLocation":"Hampton Roads, VA","language":"English","publisher":"IEEE","doi":"10.1109/OCEANS47191.2022.9977390","usgsCitation":"Sayre, R., Butler, K., Van Graafeiland, K., Breyer, S., and Wright, D., 2022, Ecological Coastal Units – Standardized global shoreline characteristics, <i>in</i> Oceans 2022, Hampton Roads, Hampton Roads, VA, Oct 17-20, 2022, 4 p., https://doi.org/10.1109/OCEANS47191.2022.9977390.","productDescription":"4 p.","ipdsId":"IP-146446","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":412943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":302356,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":864110,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butler, Kevin","contributorId":267714,"corporation":false,"usgs":false,"family":"Butler","given":"Kevin","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":864111,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Graafeiland, Keith","contributorId":245012,"corporation":false,"usgs":false,"family":"Van Graafeiland","given":"Keith","affiliations":[],"preferred":false,"id":864112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Breyer, Sean","contributorId":267716,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":864113,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Dawn","contributorId":200268,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","affiliations":[],"preferred":false,"id":864114,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70238788,"text":"fs20223083 - 2022 - Landsat Collection 2 Level-3 Burned Area science product","interactions":[],"lastModifiedDate":"2023-06-28T14:33:11.943718","indexId":"fs20223083","displayToPublicDate":"2022-12-13T08:47:39","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3083","displayTitle":"Landsat Collection 2 Level-3 Burned Area Science Product","title":"Landsat Collection 2 Level-3 Burned Area science product","docAbstract":"<p>Accurate and complete data on fire locations and burned areas are needed to quantify trends and patterns of fire occurrence, characterize drivers of fire, project future fire pattern behavior, and help with assessments of fire effects on natural and social systems. The Landsat Collection 2 Level-3 Burned Area science product is designed to identify burned areas across all ecosystems (for example, forests, shrublands, and grasslands) for Landsat 4–9 data.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223083","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Burned Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3083, 2 p., https://doi.org/10.3133/fs20223083.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139624","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418240,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3083/coverthb2.jpg"},{"id":410296,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418243,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3083/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418241,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3083/fs20223083.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3038"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 20, 2023","contact":"<p><a data-mce-href=\"mailto:custserv@usgs.gov\" href=\"mailto:custserv@usgs.gov\">Customer Services</a>, <a data-mce-href=\"https://www.usgs.gov/centers/eros\" href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p>","tableOfContents":"<ul><li>Product Availability</li><li>Product Improvements</li><li>Product Content</li><li>Data Access</li><li>Documentation</li><li>Citation Information</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-12-13","revisedDate":"2023-06-20","noUsgsAuthors":false,"publicationDate":"2022-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128240,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":858725,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70238789,"text":"fs20223084 - 2022 - Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product","interactions":[],"lastModifiedDate":"2023-06-28T14:34:37.662328","indexId":"fs20223084","displayToPublicDate":"2022-12-13T08:20:53","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3084","displayTitle":"Landsat Collection 2 Level-3 Dynamic Surface Water Extent Science Product","title":"Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product","docAbstract":"<p>The Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product provides raster data that represent surface water inundation per pixel in Landsat 4–9 imagery. The Collection 2 Dynamic Surface Water Extent science product contains six acquisition-based raster products relating to surface water. Surface water extent is modulated by weather and climate, stream network hydrology, and geological processes such as isostatic rebound. Land use, ecosystem and service management, and overall water management also are affected by changes in surface water extent.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223084","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3084, 2 p., https://doi.org/10.3133/fs20223084.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139625","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":410308,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418247,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3084/coverthb2.jpg"},{"id":418249,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3084/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418248,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3084/fs20223084.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3084"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 21, 2023","contact":"<p><a href=\"mailto:custserv@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:custserv@usgs.gov\">Customer Services</a>,&nbsp;<a href=\"https://www.usgs.gov/centers/eros\" data-mce-href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p>","tableOfContents":"<ul><li>Product Improvements</li><li>Data Access</li><li>Documentation</li><li>Citation Information</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-12-13","revisedDate":"2023-06-21","noUsgsAuthors":false,"publicationDate":"2022-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128240,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":858726,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70238881,"text":"70238881 - 2022 - Optimizing Landsat Next shortwave infrared bands for crop residue characterization","interactions":[],"lastModifiedDate":"2022-12-15T13:48:36.566374","indexId":"70238881","displayToPublicDate":"2022-12-03T07:44:55","publicationYear":"2022","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":"Optimizing Landsat Next shortwave infrared bands for crop residue characterization","docAbstract":"<p><span>This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (</span><span class=\"html-italic\">f</span><sub>R</sub><span>) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured&nbsp;</span><span class=\"html-italic\">f</span><sub>R</sub><span>. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) &lt; 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on&nbsp;</span><span class=\"html-italic\">f</span><sub>R</sub><span>&nbsp;estimation. For the two-band wavelength shift analyses applied to the NDVI &lt; 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top&nbsp;</span><span class=\"html-italic\">f</span><sub>R</sub><span>&nbsp;estimation performance (</span><span class=\"html-italic\">R</span><sup>2</sup><span>&nbsp;= 0.8222;&nbsp;</span><span class=\"html-italic\">RMSE</span><span>&nbsp;= 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (</span><span class=\"html-italic\">R</span><sup>2</sup><span>&nbsp;= 0.8145;&nbsp;</span><span class=\"html-italic\">RMSE</span><span>&nbsp;= 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (</span><span class=\"html-italic\">R</span><sup>2</sup><span>&nbsp;= 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI &lt; 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (</span><span class=\"html-italic\">R</span><sup>2</sup><span>&nbsp;= 0.8397;&nbsp;</span><span class=\"html-italic\">RMSE</span><span>&nbsp;= 0.1231). Three-band indices with CAI-type wavelengths maintained top&nbsp;</span><span class=\"html-italic\">f</span><sub>R</sub><span>&nbsp;estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (</span><span class=\"html-italic\">R</span><sup>2</sup><span>&nbsp;= 0.7581;&nbsp;</span><span class=\"html-italic\">RMSE</span><span>&nbsp;= 0.1548). The 2036–2111–2217 nm band combination was also top performing in&nbsp;</span><span class=\"html-italic\">f</span><sub>R</sub><span>&nbsp;estimation (</span><span class=\"html-italic\">R</span><sup>2</sup><span>&nbsp;= 0.8690;&nbsp;</span><span class=\"html-italic\">RMSE</span><span>&nbsp;= 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for&nbsp;</span><span class=\"html-italic\">f</span><sub>R</sub><span>&nbsp;estimation.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs14236128","usgsCitation":"Lamb, B.T., Dennison, P., Hively, W.D., Kokaly, R.F., Serbin, G., Wu, Z., Dabney, P.W., Masek, J.G., Campbell, M., and Daughtry, C.S., 2022, Optimizing Landsat Next shortwave infrared bands for crop residue characterization: Remote Sensing, v. 14, no. 23, 6128, 29 p., https://doi.org/10.3390/rs14236128.","productDescription":"6128, 29 p.","ipdsId":"IP-144753","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":445721,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14236128","text":"Publisher Index Page"},{"id":410537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"23","noUsgsAuthors":false,"publicationDate":"2022-12-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dennison, Phillip 0000-0002-0241-1917","orcid":"https://orcid.org/0000-0002-0241-1917","contributorId":266031,"corporation":false,"usgs":false,"family":"Dennison","given":"Phillip","email":"","affiliations":[{"id":54865,"text":"Dept. Geography, Utah State University","active":true,"usgs":false}],"preferred":false,"id":859053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":859054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":859055,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Serbin, Guy 0000-0001-9345-1772","orcid":"https://orcid.org/0000-0001-9345-1772","contributorId":266030,"corporation":false,"usgs":false,"family":"Serbin","given":"Guy","email":"","affiliations":[{"id":54864,"text":"EOAnalytics","active":true,"usgs":false}],"preferred":false,"id":859056,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":859057,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dabney, Philip W.","contributorId":214572,"corporation":false,"usgs":false,"family":"Dabney","given":"Philip","email":"","middleInitial":"W.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":859058,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Masek, Jeffery G.","contributorId":294418,"corporation":false,"usgs":false,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":859059,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Campbell, Michael","contributorId":299937,"corporation":false,"usgs":false,"family":"Campbell","given":"Michael","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":859060,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Daughtry, Craig S. T.","contributorId":211093,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S. T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":859061,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70238161,"text":"70238161 - 2022 - Contemporary (1984–2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership","interactions":[],"lastModifiedDate":"2022-12-28T16:46:17.662436","indexId":"70238161","displayToPublicDate":"2022-11-02T06:34:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"Contemporary (1984–2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership","docAbstract":"<p><strong>Background:<span>&nbsp;</span></strong>Remotely sensed burned area products are critical to support fire modelling, policy, and management but often require further processing before use.</p><p><strong>Aim:<span>&nbsp;</span></strong>We calculated fire history metrics from the Landsat Burned Area Product (1984–2020) across the conterminous U.S. (CONUS) including (1) fire frequency, (2) time since last burn (TSLB), (3) year of last burn, (4) longest fire-free interval, (5) average fire interval length, and (6) contemporary fire return interval (cFRI).</p><p><strong>Methods:<span>&nbsp;</span></strong>Metrics were summarised by ecoregion and land ownership, and related to historical and cheatgrass datasets to demonstrate further applications of the products.</p><p><strong>Key results:<span>&nbsp;</span></strong>The proportion burned ranged from 0.7% in the Northeast Mixed Woods to 74.1% in the Kansas Flint Hills. The Flint Hills and Temperate Prairies showed the highest burn frequency, while the Flint Hills and the Sierra Nevada and Klamath Mountains showed the shortest TSLB. Compared to private, public land had greater burned area (19 of 31 ecoregions) and shorter cFRI (25 of 31 ecoregions).</p><p><strong>Conclusions:<span>&nbsp;</span></strong>Contemporary fire history metrics can help characterise recent fire regimes across CONUS.</p><p><strong>Implications:<span>&nbsp;</span></strong>In regions with frequent fire, comparison of contemporary with target fire regimes or invasive species datasets enables the efficient incorporation of burned area data into decision-making.</p>","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WF22044","usgsCitation":"Vanderhoof, M.K., Hawbaker, T., Teske, C., Noble, J., and Smith, J., 2022, Contemporary (1984–2020) fire history metrics for the conterminous United States and ecoregional differences by land ownership: International Journal of Wildland Fire, v. 31, no. 12, p. 1167-1183, https://doi.org/10.1071/WF22044.","productDescription":"17 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Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":857021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":857022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teske, Casey","contributorId":224732,"corporation":false,"usgs":false,"family":"Teske","given":"Casey","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":857023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noble, Joe","contributorId":257938,"corporation":false,"usgs":false,"family":"Noble","given":"Joe","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":857024,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Jim","contributorId":191054,"corporation":false,"usgs":false,"family":"Smith","given":"Jim","email":"","affiliations":[],"preferred":false,"id":857025,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70238665,"text":"70238665 - 2022 - November 22 Landsat update","interactions":[],"lastModifiedDate":"2022-12-02T13:33:34.777717","indexId":"70238665","displayToPublicDate":"2022-11-01T07:31:17","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10566,"text":"Landsat Update","active":true,"publicationSubtype":{"id":30}},"title":"November 22 Landsat update","docAbstract":"<p>No abstract available.</p>","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Hartpence, A., 2022, November 22 Landsat update: Landsat Update, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-146833","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":409991,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":409990,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/landsat-missions/news/november-2022-landsat-update"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hartpence, Anya 0000-0002-4510-3236","orcid":"https://orcid.org/0000-0002-4510-3236","contributorId":247379,"corporation":false,"usgs":false,"family":"Hartpence","given":"Anya","email":"","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":858228,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70238040,"text":"70238040 - 2022 - Landsat 9 cross calibration under-fly of Landsat 8: Planning, and execution","interactions":[],"lastModifiedDate":"2022-11-04T12:18:22.038051","indexId":"70238040","displayToPublicDate":"2022-10-28T07:16:12","publicationYear":"2022","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":"Landsat 9 cross calibration under-fly of Landsat 8: Planning, and execution","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">During the early post-launch phase of the Landsat 9 mission, the Landsat 8 and 9 mission teams conducted a successful under-fly of Landsat 8 by Landsat 9, allowing for the near-simultaneous data collection of common Earth targets by the on-board sensors for cross-calibration. This effort, coordinated by the Landsat Calibration and Validation team, required contributions from various entities across National Aeronautics and Space Administration and U.S. Geological Survey such as Flight Dynamics, Systems, Mission Planning, and Flight Operations teams, beginning about 18 months prior to launch. Plans existed to allow this under-fly for any possible launch date of Landsat 9. This included 16 ascent plans and 16 data acquisition plans, one for every day of the Landsat orbital repeat period, with a minimum of 5 days of useful coverage overlap between the sensors. After the Landsat 9 launch, the plan executed, and led to the acquisition of over 2000 partial to full overlapping scene pairs. Although containing less than the expected number of scenes, this dataset was larger than previous Landsat mission under-fly efforts and more than sufficient for performing cross-calibration of the Landsat 8 and Landsat 9 sensors. The details of the planning process and execution of this under-fly are presented.<span>&nbsp;</span></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs14215414","usgsCitation":"Kaita, E., Markham, B., Haque, M., Dichmann, D., Gerace, A., Leigh, L., Good, S., Schmidt, M., and Crawford, C., 2022, Landsat 9 cross calibration under-fly of Landsat 8: Planning, and execution: Remote Sensing, v. 14, no. 21, 5414, 15 p., https://doi.org/10.3390/rs14215414.","productDescription":"5414, 15 p.","ipdsId":"IP-144331","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":446006,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14215414","text":"Publisher Index Page"},{"id":409159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"21","noUsgsAuthors":false,"publicationDate":"2022-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaita, Edward","contributorId":298903,"corporation":false,"usgs":false,"family":"Kaita","given":"Edward","email":"","affiliations":[{"id":64728,"text":"Science Systems Applications Inc@NASA GSFC, Code 618, Greenbelt MD, 20771","active":true,"usgs":false}],"preferred":false,"id":856674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Markham, Brian 0000-0002-9612-8169","orcid":"https://orcid.org/0000-0002-9612-8169","contributorId":139286,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":856675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haque, Md Obaidul","contributorId":298904,"corporation":false,"usgs":false,"family":"Haque","given":"Md Obaidul","affiliations":[{"id":64729,"text":"KBR, Contractor to the U.S. Geological Survey Earth Resources Observation and Science Center, Sioux Falls, SD, 57198","active":true,"usgs":false}],"preferred":false,"id":856676,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dichmann, Donald","contributorId":298905,"corporation":false,"usgs":false,"family":"Dichmann","given":"Donald","email":"","affiliations":[{"id":64730,"text":"NASA GSFC, Code 595, Greenbelt MD, 20771","active":true,"usgs":false}],"preferred":false,"id":856677,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerace, Aaron","contributorId":199173,"corporation":false,"usgs":false,"family":"Gerace","given":"Aaron","email":"","affiliations":[],"preferred":false,"id":856678,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leigh, Lawrence","contributorId":298906,"corporation":false,"usgs":false,"family":"Leigh","given":"Lawrence","email":"","affiliations":[{"id":64731,"text":"Office of Engineering Research, College of Engineering, South Dakota State University  Brookings, SD 57007","active":true,"usgs":false}],"preferred":false,"id":856679,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Good, Susan","contributorId":298907,"corporation":false,"usgs":false,"family":"Good","given":"Susan","email":"","affiliations":[{"id":64732,"text":"A.I.Solutions@NASA GSFC, Code 595 Greenbelt, MD 20771","active":true,"usgs":false}],"preferred":false,"id":856680,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmidt, Michael","contributorId":298908,"corporation":false,"usgs":false,"family":"Schmidt","given":"Michael","affiliations":[{"id":64734,"text":"A.I.Solutions@NASA GSFC, Code 595 Greenbelt, MD 20771.","active":true,"usgs":false}],"preferred":false,"id":856681,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":856682,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70237677,"text":"ofr20221092 - 2022 - ECCOE Landsat Quarterly Calibration and Validation report—Quarter 2, 2022","interactions":[],"lastModifiedDate":"2022-10-20T10:57:08.281875","indexId":"ofr20221092","displayToPublicDate":"2022-10-19T14:35:42","publicationYear":"2022","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":"2022-1092","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 2, 2022","title":"ECCOE Landsat Quarterly Calibration and Validation report—Quarter 2, 2022","docAbstract":"<h1>Executive Summary</h1><p>The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.</p><p>This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 2 (April–June), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: <a data-mce-href=\"https://earthexplorer.usgs.gov\" href=\"https://earthexplorer.usgs.gov\">https://earthexplorer.usgs.gov</a>.</p><p>One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed throughout the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at the lower orbit of 697 kilometers on May 5, 2022, extending the science mission to allow for essential data to be acquired during the 2022 Northern Hemisphere fire and growing season. Additional information about the Landsat 7 orbit lowering is here: <br><a data-mce-href=\"https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&amp;text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers\" href=\"https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&amp;text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers\">https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&amp;text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers</a>.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221092","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report—Quarter 2, 2022: U.S. Geological Survey Open-File Report 2022–1092, 39 p., https://doi.org/10.3133/ofr20221092.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-143244","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":408547,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221092/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408512,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":408511,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1092/images"},{"id":408508,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1092/coverthb.jpg"},{"id":408509,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1092/ofr20221092.pdf","text":"Report","size":"4.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1092"},{"id":408510,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1092/ofr20221092.XML"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/eros\" data-mce-href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</li><li>Landsat 8 Radiometric Performance Summary</li><li>Landsat 8 Geometric Performance Summary</li><li>Landsat 7 Radiometric Performance Summary</li><li>Landsat 7 Geometric Performance Summary</li><li>Quarterly Level 2 Validation Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-10-19","noUsgsAuthors":false,"publicationDate":"2022-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":854982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110 rrengarajan@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":192376,"corporation":false,"usgs":true,"family":"Rengarajan","given":"Rajagopalan","email":"rrengarajan@contractor.usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":854983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lubke, Mark 0000-0002-7257-2337","orcid":"https://orcid.org/0000-0002-7257-2337","contributorId":261911,"corporation":false,"usgs":false,"family":"Lubke","given":"Mark","email":"","affiliations":[{"id":53079,"text":"KBR, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":854984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hasan, Nahid 0000-0002-0463-601X","orcid":"https://orcid.org/0000-0002-0463-601X","contributorId":292342,"corporation":false,"usgs":false,"family":"Hasan","given":"Nahid","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":854985,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shrestha, Ashish 0000-0002-9407-5462","orcid":"https://orcid.org/0000-0002-9407-5462","contributorId":298063,"corporation":false,"usgs":false,"family":"Shrestha","given":"Ashish","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":854986,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tuz Zafrin Tuli, Fatima 0000-0002-5225-8797","orcid":"https://orcid.org/0000-0002-5225-8797","contributorId":270395,"corporation":false,"usgs":false,"family":"Tuz Zafrin Tuli","given":"Fatima","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":854987,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaw, Jerad L. 0000-0002-8319-2778","orcid":"https://orcid.org/0000-0002-8319-2778","contributorId":270396,"corporation":false,"usgs":false,"family":"Shaw","given":"Jerad L.","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":854988,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Denevan, Alex 0000-0002-1215-3261","orcid":"https://orcid.org/0000-0002-1215-3261","contributorId":270398,"corporation":false,"usgs":false,"family":"Denevan","given":"Alex","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":854989,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Franks, Shannon 0000-0003-1335-5401","orcid":"https://orcid.org/0000-0003-1335-5401","contributorId":245457,"corporation":false,"usgs":false,"family":"Franks","given":"Shannon","email":"","affiliations":[{"id":49197,"text":"KBR, Contractor to NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":854990,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":854991,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":854992,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":854993,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thome, Kurt","contributorId":140792,"corporation":false,"usgs":false,"family":"Thome","given":"Kurt","email":"","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":854994,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Kaita, Ed","contributorId":251782,"corporation":false,"usgs":false,"family":"Kaita","given":"Ed","email":"","affiliations":[{"id":50397,"text":"SSAI","active":true,"usgs":false}],"preferred":false,"id":854995,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Barsi, Julia","contributorId":251781,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","affiliations":[{"id":50397,"text":"SSAI","active":true,"usgs":false}],"preferred":false,"id":854996,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Levy, Raviv","contributorId":131008,"corporation":false,"usgs":false,"family":"Levy","given":"Raviv","email":"","affiliations":[{"id":7209,"text":"SSAI / NASA / GSFC","active":true,"usgs":false}],"preferred":false,"id":854997,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Ong, Lawrence","contributorId":139287,"corporation":false,"usgs":false,"family":"Ong","given":"Lawrence","email":"","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":854998,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}}
,{"id":70237388,"text":"70237388 - 2022 - Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","interactions":[],"lastModifiedDate":"2022-10-17T16:42:25.152014","indexId":"70237388","displayToPublicDate":"2022-10-12T09:07:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","docAbstract":"This study investigates the applicability of the Landsat Dynamic Surface Water Extent (DSWE) science product for waterbird habitat modeling in multiple non-canopied habitat types. We compare surface water distribution estimates derived from DSWE to two site-specific survey methods: visual surveys and digitized aerial imagery. These site-specific surveys were conducted on Poplar Island, a restoration island project in the Chesapeake Bay, USA. Visual surveys were collected bimonthly from 2006 – 2013, and digitized aerial imagery was collected annually from 2006 – 2015. As a restoration island, Poplar Island presents a unique opportunity to analyze DSWE in a rapidly changing site. We structure our analysis based on the procedural development of individual sub-island cells developed from unconsolidated dredge material into fully restored wetlands that have independent hydrologic connection to the surrounding bay. Each development status is analyzed using our three DSWE classifications: Open Water (OW), a conservative estimate; Wetland Inclusive (WI), an aggressive estimate; and Development Dependent (DD), a landcover adaptive estimate. The OW classification consistently underestimates surface water coverage especially in the more complex, fully developed cells. The WI classification is better able to capture the tidal channels in these cells, but marginally overestimates surface water coverage in more sparsely vegetated cells. The DD classification does not significantly improve upon the estimations of the WI classification. Our data indicate that DSWE can be a capable alternative to our site-specific survey methods. However, the product is limited by Landsat’s 30 m spatial resolution, especially in more structurally complex wetlands. A recommended classification method for characterizing waterbird habitats would depend on the goals and targeted scale of analysis, for which DSWE may be a viable option.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2022.100845","usgsCitation":"Taylor, J., Sullivan, J.D., Teitelbaum, C.S., Reese, J.G., and Prosser, D., 2022, Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats: Remote Sensing Applications: Society and Environment, v. 28, 100845, 9 p., https://doi.org/10.1016/j.rsase.2022.100845.","productDescription":"100845, 9 p.","ipdsId":"IP-139932","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446139,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2022.100845","text":"Publisher Index Page"},{"id":435658,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SW505K","text":"USGS data release","linkHelpText":"Surface water estimates for a complex study site derived from traditional and emerging methods"},{"id":408211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Poplar Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.36236190795898,\n              38.74631848708898\n            ],\n            [\n              -76.36373519897461,\n              38.754886481591335\n            ],\n            [\n              -76.36905670166014,\n              38.7564928660758\n            ],\n            [\n              -76.37231826782227,\n              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38.74631848708898\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, John B.","contributorId":296294,"corporation":false,"usgs":false,"family":"Taylor","given":"John B.","affiliations":[{"id":64012,"text":"Chesapeake Bay Trust","active":true,"usgs":false}],"preferred":false,"id":854370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":854371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teitelbaum, Claire S. 0000-0001-5646-3184","orcid":"https://orcid.org/0000-0001-5646-3184","contributorId":255382,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":854372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reese, Jan G.","contributorId":296295,"corporation":false,"usgs":false,"family":"Reese","given":"Jan","email":"","middleInitial":"G.","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":854373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70237944,"text":"70237944 - 2022 - Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆","interactions":[],"lastModifiedDate":"2022-11-01T12:04:00.80791","indexId":"70237944","displayToPublicDate":"2022-10-01T06:59:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\">The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000&nbsp;km<sup>2</sup><span>&nbsp;of croplands, monitoring unintentional and intentional surface water inundation is important for&nbsp;<a class=\"topic-link\" title=\"Learn more about water resource management from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/water-resources-development\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/water-resources-development\">water resource management</a>&nbsp;and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to 2020 by intersecting monthly surface water maps (n&nbsp;=&nbsp;216 months) derived using two&nbsp;<a class=\"topic-link\" title=\"Learn more about satellite remote sensing from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/satellite-remote-sensing\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/satellite-remote-sensing\">satellite remote sensing</a>&nbsp;platforms (Landsat and&nbsp;<a class=\"topic-link\" title=\"Learn more about Moderate Resolution Imaging Spectroradiometer from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/modis\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/modis\">Moderate Resolution Imaging Spectroradiometer</a>&nbsp;[MODIS]) with a high-quality cropland map generated by the California Department of Water Resources. Surface water maps were produced using the Dynamic Surface Water Extent model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating monthly and annual occurrence of “high confidence” water for each satellite collection across eight cropland types and 58 counties. Results indicate that 49.9% (MODIS) to 56.4% (Landsat) of croplands were inundated at least once during the 18-year timespan. Rice crops, due to their unique need of consistent surface water and dominance as a crop type in CA, had the highest proportion of and mean annual inundation area, while citrus crops had the lowest. Mean monthly inundation patterns in most croplands followed California's precipitation patterns with high inundation during the winter and spring rainy season. At the county level, croplands in the southern Central Valley typically had high occurrences of inundation in conjunction with large crop areas. Exposure and sensitivity of inundation for three crop types (citrus, deciduous, and vineyards) that are typically less associated with intentional inundation were geographically variable, but overall were generally highest in counties in the southern Central Valley, California's primary agricultural region. Flood and precipitation related crop insurance claims indicated that rice had the highest mean indemnity payout for any month with damages typically occurring in March and April. Insurance claims were also high in deciduous fruit and nut crops, which had peak damages in February. A comparison between inundation results and insurance claims suggests that the inundation mapped by our process coincides with claim activity. These data elucidate water inundation patterns across the state that can serve to inform farmers, insurers, decision makers, resource managers, and flood mitigation professionals.</span></p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2022.100837","usgsCitation":"Smith, B.W., Soulard, C.E., Walker, J., and Wein, A., 2022, Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆: Remote Sensing Applications: Society and Environment, v. 28, 100837, 15 p., https://doi.org/10.1016/j.rsase.2022.100837.","productDescription":"100837, 15 p.","ipdsId":"IP-140896","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":435671,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EKSSXX","text":"USGS data release","linkHelpText":"County-level maps of cropland surface water inundation measured from Landsat and MODIS"},{"id":408970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -125.88667659285775,\n              42.496396873623155\n            ],\n            [\n              -125.88667659285775,\n              31.587887454403287\n            ],\n            [\n              -113.05464534285768,\n              31.587887454403287\n            ],\n            [\n              -113.05464534285768,\n              42.496396873623155\n            ],\n            [\n              -125.88667659285775,\n              42.496396873623155\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Britt Windsor 0000-0003-1556-2383","orcid":"https://orcid.org/0000-0003-1556-2383","contributorId":287481,"corporation":false,"usgs":true,"family":"Smith","given":"Britt","email":"","middleInitial":"Windsor","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":856293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":856294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":856295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":856296,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70270338,"text":"70270338 - 2022 - Early in mission Landsat 9 geometric performance","interactions":[],"lastModifiedDate":"2025-08-18T15:30:39.32726","indexId":"70270338","displayToPublicDate":"2022-09-30T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22173,"text":"SPIE Optics + Photonics 2022 - Conference Proceedings","active":true,"publicationSubtype":{"id":10}},"title":"Early in mission Landsat 9 geometric performance","docAbstract":"<table id=\"UsageTable0\" class=\"row mce-item-table\" border=\"0\"><thead><tr class=\"ArticleContentRow displayTableRow\"><td class=\"citationSection\"><div class=\"citationSectionDiv\">Landsat 9 (L9) was launched on September 27, 2021, from Vandenberg Space Force Base in California. The U. S. Geological Survey (USGS) released Level-1 data, geometrically orthorectified and radiometrically calibrated imagery in digital numbers that can be scaled to Top-of-Atmosphere reflectance, and Level-2 data, geometrically orthorectified and radiometrically calibrated surface reflectance imagery, to the public on February 10, 2022. From September 27, 2021 to early January of 2022, the satellite and its two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), were in their commissioning phase, updating key radiometric and geometric calibration parameters for both the spacecraft and the instruments. The data acquired during the commissioning phase of the spacecraft and instruments were reprocessed with the newly determined post-launch calibration parameters prior to the releasing of the data to the public. After the public release of the data, the calibration parameters of the sensors and the spacecraft continue to be monitored to ensure the data released to the public is of the same high quality as previous Landsat data products. This paper discusses three key geometric performance aspects of the L9 spacecraft and its instruments during its early mission time frame (September 27, 2021 to June 27, 2022) including geodetic accuracy, geometric accuracy, and within band registration accuracy of the L9 products generated.</div></td></tr></thead></table>","language":"English","publisher":"SPIE","doi":"https://doi.org/10.1117/12.2634253","usgsCitation":"Choate, M., Rengarajan, R., and Hasan, N., 2022, Early in mission Landsat 9 geometric performance: SPIE Optics + Photonics 2022 - Conference Proceedings, v. 12232, 122320V, https://doi.org/https://doi.org/10.1117/12.2634253.","productDescription":"122320V","ipdsId":"IP-144361","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12232","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":268248,"corporation":false,"usgs":true,"family":"Choate","given":"Michael J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":946117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":242014,"corporation":false,"usgs":false,"family":"Rengarajan","given":"Rajagopalan","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":946118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hasan, Nahid","contributorId":359709,"corporation":false,"usgs":false,"family":"Hasan","given":"Nahid","affiliations":[{"id":82380,"text":"KBR, Inc., contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":946119,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70236125,"text":"ofr20201143 - 2022 - Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010","interactions":[],"lastModifiedDate":"2022-09-26T15:51:47.675735","indexId":"ofr20201143","displayToPublicDate":"2022-09-23T13:19:03","publicationYear":"2022","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":"2020-1143","displayTitle":"Eelgrass (<em>Zostera marina</em>) and Seaweed Abundance Along the Coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010","title":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010","docAbstract":"<p>Eelgrass (&lt;em&gt;Zostera marina&lt;/em&gt;) is a highly productive seagrass that plays an essential role in the health of the estuarine and coastal ecosystems; however, information about its abundance and distribution is insufficient in the Bering Sea along the Yukon Delta National Wildlife Refuge. We inventoried the spatial extent and abundance of eelgrass and seaweed in Duchikthluk and Shoal bays on Nunivak Island in July 2010. Using Landsat Thematic Mapper imagery, we estimated the spatial extent of eelgrass to be 1,232 hectares in Duchikthluk Bay and 40 hectares in Shoal Bay. The overall accuracy of the assessments was high (86–87 percent) based on ground truthing using field reference points. We used point-sampling methodology to assess eelgrass abundance relative to the presence of associated seaweeds and selected macro-invertebrates within each of bays. Eelgrass was found at water depths ranging from 0.1 to 2.9 meters across both bays, but the greatest density (&gt;75 percent cover) occurred primarily in moderate to deep water (0.7–1.4 meters) in Duchikthluk Bay and deeper water (&gt;2 meters) in Shoal Bay. The mean aboveground biomass was 39.4±4.0 grams per meter squared in Duchikthluk Bay. The eelgrass biomass was greater (67.6±11.0 grams per meter squared) in Shoal Bay, but this estimate was based on a small sample size (n=3). Seaweeds, representing six species, occurred in low abundance across both bays and were primarily associated with eelgrass. Gastropods were the most common macro-invertebrate, occurring at 45 percent of field points in Duchikthluk Bay.<br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201143","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly, T.F., and Fairchild, L.L., 2022, Eelgrass (<em>Zostera marina</em>) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010: U.S. Geological Survey Open-File Report 2020–1143, 13 p., https://doi.org/10.3133/ofr20201143.","productDescription":"Report: v, 13 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-119381","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":405857,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Mapping data of eelgrass (<em>Zostera marina</em>) distribution, Alaska and Baja California, Mexico"},{"id":405858,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K1ZOMY","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data from eelgrass (<em>Zostera marina</em>), seaweeds and selected invertebrates at six embayments and two islands at the end of the Alaska Peninsula"},{"id":435680,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QI7RCQ","text":"USGS data release","linkHelpText":"Point Sampling for Eelgrass (Zostera marina) and Seaweeds in Duchikthluk and Shoal Bays of Nunivak Island, Alaska, 2010"},{"id":405855,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1143/coverthb.jpg"},{"id":405863,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (<em>Zostera marina</em>) and seaweeds at the end of the Alaska Peninsula, August–September 2012"},{"id":405861,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (<em>Zostera marina</em>) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":405860,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-01080","linkHelpText":"Distribution of eelgrass (<em>Zostera marina</em>) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"},{"id":405862,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (<em>Zostera marina</em>) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":405856,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1143/ofr20201143.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1143"},{"id":405859,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (<em>Zostera marina</em>) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"}],"country":"United States","state":"Alaska","otherGeospatial":"Nunivak Island, Yukon Delta National Wildlife Refuge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -167.67333984375,\n              59.517602921437295\n            ],\n            [\n              -164.06982421875,\n              59.517602921437295\n            ],\n            [\n              -164.06982421875,\n              60.60314950746827\n            ],\n            [\n              -167.67333984375,\n              60.60314950746827\n            ],\n            [\n              -167.67333984375,\n              59.517602921437295\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/asc/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/asc/\">Alaska Science Center</a><br>U.S. Geological Survey<br>4210 University Drive<br>Anchorage, Alaska 99508</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results and Discussion</li><li>Future Research Needs</li><li>References Cited</li></ul>","publishedDate":"2022-09-23","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":850165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogrefe, Kyle R. khogrefe@usgs.gov","contributorId":4264,"corporation":false,"usgs":true,"family":"Hogrefe","given":"Kyle","email":"khogrefe@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":850166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Tyrone F. tfdonnelly@usgs.gov","contributorId":4369,"corporation":false,"usgs":true,"family":"Donnelly","given":"Tyrone","email":"tfdonnelly@usgs.gov","middleInitial":"F.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":850167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fairchild, Lucretia L.","contributorId":295916,"corporation":false,"usgs":false,"family":"Fairchild","given":"Lucretia","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":850168,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70215230,"text":"ofr20201035 - 2022 - Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10","interactions":[],"lastModifiedDate":"2022-10-11T22:04:27.476609","indexId":"ofr20201035","displayToPublicDate":"2022-09-23T12:11:32","publicationYear":"2022","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":"2020-1035","displayTitle":"Abundance and Distribution of Eelgrass (<em>Zostera marina</em>) and Seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10","title":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10","docAbstract":"<p class=\"p1\">Eelgrass (<i>Zostera marina</i>) meadows are expansive along the lower Alaska Peninsula, supporting a rich diversity of marine life, yet little is known about their status and trends in the region. We tested techniques to inventory and monitor trends in the spatial extent and abundance of eelgrass in lagoons of the Izembek National Wildlife Refuge. We determined if Landsat imagery could be used to assess eelgrass spatial extent in shallow (less than 4 meter water depth) coastal waters of the refuge. We determined that this seagrass could be differentiated using Landsat imagery from other cover types (that is, channels and unvegetated tidal flats) with a high degree of accuracy (greater than 80 percent) in Izembek and Kinzarof Lagoons. Eelgrass meadows represented the largest cover type in Izembek (about 16,000 hectares) and Kinzarof (about 900 hectares) Lagoons, comprising between 45 and 50 percent of the spatial extent of these lagoons, respectively. When compared to estimates of spatial extent of eelgrass from previous studies, our results suggest little change in the spatial extent of eelgrass in Izembek Lagoon during the 28-year period 1978 through 2006. Preliminary mapping of eelgrass in other embayments indicated that this seagrass was also expansive in Big Lagoon (about 900 hectares; or 34 percent of the lagoon area) and Hook Bay (about 900 hectares; or 36 percent of the bay area) but not in Cold Bay (about 100 hectares; less than 5 percent of the bay area). We conducted an embayment-wide point sampling technique to assess aboveground biomass and distribution of eelgrass and seaweeds and presence of six macro-invertebrates during a 4-year period (2007–10). We determined that, when present, mean aboveground biomass of eelgrass was greater in Kinzarof Lagoon (182.5 plus or minus 12.1 grams dry weight per square meter) than in Izembek Lagoon (152.1 plus or minus 7.1 grams dry weight per square meter) in 2008–10, possibly reflecting the warmer sea temperatures and higher salinities found on the Gulf of Alaska side of the Alaska Peninsula. Seaweeds were more abundant in Kinzarof Lagoon than in Izembek Lagoon, surpassing aboveground biomass of eelgrass in both lagoons in 2008. Gastropods (4 percent of all points) and <i>Caprella</i> shrimp (25 percent) were the most common of the six macro-invertebrates surveyed in Izembek Lagoon, and Telmessus crab was the most common macro-invertebrate in Kinzarof Lagoon.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201035","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly,T.F., Fairchild, L.L., Sowl, K.M., and Lindstrom, S.C., 2022, Abundance and distribution of eelgrass (<em>Zostera marina</em>) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10: U.S. Geological Survey Open-File Report 2020–1035, 30 p., https://doi.org/10.3133/ofr20201035.","productDescription":"Report: vi, 30 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-112900","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZUDIOH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling for eelgrass (<em>Zostera marina</em>) and seaweeds in embayments adjacent to the Izembek National Wildlife Refuge, Alaska"},{"id":384515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Imagery and mapping data of eelgrass (<em>Zostera marina</em>) distribution, Alaska and Baja California, Mexico"},{"id":435682,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XNSWES","text":"USGS data release","linkHelpText":"Sampling Data for Eelgrass (Zostera marina) in Norma Bay, Izembek Lagoon, Alaska, 1987"},{"id":405752,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (<em>Zostera marina</em>) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405751,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (<em>Zostera marina</em>) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":379325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1035/ofr20201035.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1035"},{"id":405754,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (<em>Zostera marina</em>) and seaweeds at the end of the Alaska Peninsula, August–September 2012"},{"id":405753,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (<em>Zostera marina</em>) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1035/coverthb.jpg"},{"id":405750,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (<em>Zostera marina</em>) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"}],"country":"United States","state":"Alaska","otherGeospatial":"Izembek National Wildlife Refuge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -163.37081909179688,\n              55.00755132274014\n            ],\n            [\n              -162.77206420898438,\n              55.00755132274014\n            ],\n            [\n              -162.77206420898438,\n              55.2963199179754\n            ],\n            [\n              -163.37081909179688,\n              55.2963199179754\n            ],\n            [\n              -163.37081909179688,\n              55.00755132274014\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/asc/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/asc/\">Alaska Science Center</a><br>U.S. Geological Survey<br>4210 University Drive<br>Anchorage, Alaska 99508</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results and Discussion</li><li>Conclusions</li><li>References Cited</li></ul>","publishedDate":"2022-09-23","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":801233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogrefe, Kyle R. khogrefe@usgs.gov","contributorId":4264,"corporation":false,"usgs":true,"family":"Hogrefe","given":"Kyle","email":"khogrefe@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":801234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Tyronne F.","contributorId":242965,"corporation":false,"usgs":false,"family":"Donnelly","given":"Tyronne","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":801235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fairchild, Lucretia L.","contributorId":242966,"corporation":false,"usgs":false,"family":"Fairchild","given":"Lucretia L.","affiliations":[],"preferred":false,"id":801236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sowl, Kristine M.","contributorId":60372,"corporation":false,"usgs":false,"family":"Sowl","given":"Kristine","email":"","middleInitial":"M.","affiliations":[{"id":12598,"text":"Izembek National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":801237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindstrom, Sandra C.","contributorId":242967,"corporation":false,"usgs":false,"family":"Lindstrom","given":"Sandra","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":801238,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70236690,"text":"70236690 - 2022 - Development of the LCMAP annual land cover product across Hawai'i","interactions":[],"lastModifiedDate":"2023-11-08T16:45:41.692299","indexId":"70236690","displayToPublicDate":"2022-09-14T09:22:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Development of the LCMAP annual land cover product across Hawai'i","docAbstract":"<p><span>Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the&nbsp;U.S.&nbsp;Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full&nbsp;Landsat&nbsp;records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various&nbsp;remote sensing&nbsp;sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at</span><span>&nbsp;</span><a rel=\"noreferrer noopener\" href=\"https://doi.org/10.5066/P91E8M23\" target=\"_blank\" data-mce-href=\"https://doi.org/10.5066/P91E8M23\">https://doi.org/10.5066/P91E8M23</a><span>.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2022.103015","usgsCitation":"Li, C., Xian, G.Z., Wellington, D., Smith, K., Horton, J., and Zhou, Q., 2022, Development of the LCMAP annual land cover product across Hawai'i: International Journal of Applied Earth Observation and Geoinformation, v. 113, 103015, 17 p., https://doi.org/10.1016/j.jag.2022.103015.","productDescription":"103015, 17 p.","ipdsId":"IP-144117","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":446437,"rank":2,"type":{"id":40,"text":"Open Access 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,{"id":70237821,"text":"70237821 - 2022 - A reproducible and reusable pipeline for segmentation of geoscientific imagery","interactions":[],"lastModifiedDate":"2022-10-25T14:30:48.574122","indexId":"70237821","displayToPublicDate":"2022-09-09T09:27:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"A reproducible and reusable pipeline for segmentation of geoscientific imagery","docAbstract":"<p><span>Segmentation of Earth science imagery is an increasingly common task. Among modern techniques that use Deep Learning, the UNet architecture has been shown to be a reliable for segmenting a range of imagery. We developed software–Segmentation Gym–to implement a data-model pipeline for segmentation of scientific imagery using a family of UNet models. With an existing set of imagery and labels, the software uses a single configuration file that handles data set creation, as well as model setup and model training. Key benefits of this software are (a) the focus on reproducible data set creation and modeling, and (b) the ability for quick model experimentation through changes to a configuration file. Quick experimentation permits researchers to prototype different model architectures, sizes, and adjust common hyperparameters to find a suitable model. We demonstrate the use of the software using a data set of 419 labeled Landsat-8 scenes of coastal environments and compare results across two model architectures, five model sizes, and three loss functions. This demonstration highlights that our software enables rapid, reproducible experimentation to determine optimal hyperparameters for specific data sets and research questions.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022EA002332","usgsCitation":"Buscombe, D.D., and Goldstein, E.B., 2022, A reproducible and reusable pipeline for segmentation of geoscientific imagery: Earth and Space Science, v. 9, e2022EA002332, 11 p., https://doi.org/10.1029/2022EA002332.","productDescription":"e2022EA002332, 11 p.","ipdsId":"IP-136939","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446483,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022ea002332","text":"Publisher Index Page"},{"id":408698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":855767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldstein, Evan B. 0000-0001-9358-1016","orcid":"https://orcid.org/0000-0001-9358-1016","contributorId":184210,"corporation":false,"usgs":false,"family":"Goldstein","given":"Evan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":855768,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70236128,"text":"ofr20221072 - 2022 - ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022","interactions":[],"lastModifiedDate":"2022-09-27T12:21:24.033152","indexId":"ofr20221072","displayToPublicDate":"2022-08-31T06:56:32","publicationYear":"2022","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":"2022-1072","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 1, 2022","title":"ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022","docAbstract":"<h1>Executive Summary</h1><p>The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.</p><p>This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 1 (January–March), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: <a data-mce-href=\"https://earthexplorer.usgs.gov\" href=\"https://earthexplorer.usgs.gov\">https://earthexplorer.usgs.gov</a>.</p><p>One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: <a data-mce-href=\"https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold\" href=\"https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold\">https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold</a>.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221072","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022: U.S. Geological Survey Open-File Report 2022–1072, 39 p., https://doi.org/10.3133/ofr20221072.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-140787","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405985,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221072/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405885,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":405884,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1072/images"},{"id":405883,"rank":3,"type":{"id":31,"text":"Publication 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Summary</li><li>Quarterly Level 2 Validation Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-08-31","noUsgsAuthors":false,"publicationDate":"2022-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":850182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110 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0000-0002-8319-2778","orcid":"https://orcid.org/0000-0002-8319-2778","contributorId":270396,"corporation":false,"usgs":false,"family":"Shaw","given":"Jerad L.","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":850187,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Denevan, Alex 0000-0002-1215-3261","orcid":"https://orcid.org/0000-0002-1215-3261","contributorId":270398,"corporation":false,"usgs":false,"family":"Denevan","given":"Alex","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":850188,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Franks, Shannon 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,{"id":70235834,"text":"ofr20211030N - 2022 - System characterization report on the Amazônia-1 multispectral sensor","interactions":[{"subject":{"id":70235834,"text":"ofr20211030N - 2022 - System characterization report on the Amazônia-1 multispectral sensor","indexId":"ofr20211030N","publicationYear":"2022","noYear":false,"chapter":"N","displayTitle":"System Characterization Report on the Amazônia-1 Multispectral Sensor","title":"System characterization report on the Amazônia-1 multispectral 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 sensors"},"lastModifiedDate":"2024-11-06T13:31:15.277178","indexId":"ofr20211030N","displayToPublicDate":"2022-08-22T15:31:21","publicationYear":"2022","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":"N","displayTitle":"System Characterization Report on the Amazônia-1 Multispectral Sensor","title":"System characterization report on the Amazônia-1 multispectral sensor","docAbstract":"<h1>Executive Summary</h1><p>This report addresses system characterization of the Instituto Nacional de Pesquisas Espaciais Amazônia-1 satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.</p><p>Amazônia-1 is a four-band imager with a 64-meter (m) pixel ground sample distance. Amazônia-1 was launched in February 2021 into a Sun-synchronous orbit of 752 kilometers with an inclination of 98.4 degrees and a swath width of 850 kilometers. The satellite has an expected lifetime of about 4 years. More information on Amazônia-1 is available in the “Land Remote Sensing Satellites Online Compendium” (<a data-mce-href=\"https://calval.cr.usgs.gov/apps/compendium\" href=\"https://calval.cr.usgs.gov/apps/compendium\">https://calval.cr.usgs.gov/apps/compendium</a>).</p><p>The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the Amazônia-1 satellite has an interior geometric performance in the range of −3.584 m (−0.056 pixel) to 0.320 m (0.005 pixel) in easting and −1.984 m (−0.031 pixel) to 2.048 m (0.032 pixel) in northing in band-to-band registration, an exterior geometric performance of −37.256 m (−0.621 pixel) to 54.758 m (0.913 pixel) in easting and −12.684 m (−0.211 pixel) to 54.898 m (0.915 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of 0.030 to 0.143 in offset and 0.662 to 0.825 in slope, and a spatial performance in the range of 1.62 to 2.06 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.062 to 0.115.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030N","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., and Park, S., 2022, System characterization report on the Amazônia-1 multispectral sensor, chap. N of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 33 p., https://doi.org/10.3133/ofr20211030N.","productDescription":"v, 33 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-142103","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/n/ofr20211030n.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–N"},{"id":405397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/n/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/eros\" data-mce-href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science (EROS) Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</li><li>Purpose and Scope</li><li>System Description</li><li>Procedures</li><li>Measurements</li><li>Analysis</li><li>Summary and Conclusions</li><li>Selected References</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-08-22","noUsgsAuthors":false,"publicationDate":"2022-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Vrabel, James C. 0000-0002-0120-4721","orcid":"https://orcid.org/0000-0002-0120-4721","contributorId":264751,"corporation":false,"usgs":false,"family":"Vrabel","given":"James C.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":849495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":849496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":849497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christopherson, Jon 0000-0002-2472-0059 jonchris@usgs.gov","orcid":"https://orcid.org/0000-0002-2472-0059","contributorId":2552,"corporation":false,"usgs":true,"family":"Christopherson","given":"Jon","email":"jonchris@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":849498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":849499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Park, Seonkyung 0000-0003-3203-1998","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":223182,"corporation":false,"usgs":true,"family":"Park","given":"Seonkyung","email":"","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":849500,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
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