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And although California’s nickname is “The Golden State,\" it may also deserve the title of “The Extreme State.” Mount Whitney, the highest peak in the conterminous United States, stands at 14,494 feet above sea level in the Sierra Nevada mountain range. Just 2.5 hours away by car, Death Valley’s Badwater Basin boasts the lowest point in the United States: 282 feet below sea level. Although hundreds of inches of snow fall on Mount Whitney each year, the rainfall in Death Valley, commonly called the hottest place on Earth, averages less than 2 inches.</p><p>California is the most populous U.S. State with 39 million residents, 26 million of whom live near the coast. It’s also the most agriculturally productive State, raising a variety of fruits, vegetables, nuts, grains, grasses, and flowers in addition to dairy and livestock. 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 \"}}]}","edition":"Version 1.0: June 16, 2021; Version 1.1: January 30, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey<br>12201 Sunrise Valley Drive <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>Watching Where the Water Goes</li><li>Revealing More About Wildfires</li><li>Classifying Vegetation for More Than 40 Years</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-06-16","revisedDate":"2023-01-30","noUsgsAuthors":false,"publicationDate":"2021-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":817699,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70221096,"text":"fs20213031 - 2021 - Monitoring and assessing urban heat island variations and effects in the United States","interactions":[],"lastModifiedDate":"2021-06-15T10:08:30.944665","indexId":"fs20213031","displayToPublicDate":"2021-06-14T11:20:59","publicationYear":"2021","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":"2021-3031","displayTitle":"Monitoring and Assessing Urban Heat Island Variations and Effects in the United States","title":"Monitoring and assessing urban heat island variations and effects in the United States","docAbstract":"<p>Landsat surface temperature and land cover products have been used to estimate surface temperatures in urban and surrounding nonurban areas and to quantify urban heat island intensity. 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PSC"},"publishedDate":"2021-06-14","noUsgsAuthors":false,"publicationDate":"2021-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":816755,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70221211,"text":"ofr20211030B - 2021 - System characterization report on the Gaofen-1","interactions":[{"subject":{"id":70221211,"text":"ofr20211030B - 2021 - System characterization report on the Gaofen-1","indexId":"ofr20211030B","publicationYear":"2021","noYear":false,"chapter":"B","displayTitle":"System Characterization Report on the Gaofen-1","title":"System characterization report on the Gaofen-1"},"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":"2021-07-26T19:52:54.556607","indexId":"ofr20211030B","displayToPublicDate":"2021-06-07T09:22:57","publicationYear":"2021","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":"B","displayTitle":"System Characterization Report on the Gaofen-1","title":"System characterization report on the Gaofen-1","docAbstract":"<h1>Executive Summary</h1><p>This report addresses system characterization of Gaofen-1 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 in 2020. These reports present the detail 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>Gaofen represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellite is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at <a href=\"http://www.cnsageo.com/#/detailIndex?secondIndex=2&amp;id=3&amp;code=8\" data-mce-href=\"http://www.cnsageo.com/#/detailIndex?secondIndex=2&amp;id=3&amp;code=8\">http://www.cnsageo.com/#/detailIndex?secondIndex=2&amp;id=3&amp;code=8</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 Gaofen-1 has an interior geometric performance of −0.48 meter (m) (−0.03 pixel) northing and 0.42 m (0.03 pixel) easting offset for band 1, −0.99 m (−0.06 pixel) northing and −0.38 m (−0.02 pixel) easting offset for band 2, −0.45 m (−0.03) northing and 0.83 m (0.05 pixel) easting offset for band 3, −3.20 m (−0.20 pixel) northing and 1.44 m (0.09 pixel) easting offset for band 4 in band-to-band registration. Similarly, Gaofen-1 has an exterior geometric performance of 7.50 m (0.48 pixel) easting and 109.50 m (7.30 pixels) northing offset in comparison to the Landsat 8 Operational Land Imager; a radiometric performance in the range of −0.014 to 0.149 (absolute reflective difference); and a spatial performance in the range of 1.1 to 2.0 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.040 to 0.250.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030B","usgsCitation":"Shrestha, M., Sampath, A., Ramaseri Chandra, S.N., Christopherson, J.B., Shaw, J., Stensaas, G.L., and Anderson, C., 2021, System characterization report on the Gaofen-1, chap. B <i>of</i> Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 11 p., https://doi.org/10.3133/ofr20211030B.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-126808","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/b/coverthb.jpg"},{"id":386256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/b/ofr20211030b.pdf","text":"Report","size":"3.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030B"}],"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=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</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":"2021-06-07","noUsgsAuthors":false,"publicationDate":"2021-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Shrestha, Mahesh 0000-0002-8368-6399 mshrestha@contractor.usgs.gov","orcid":"https://orcid.org/0000-0002-8368-6399","contributorId":259303,"corporation":false,"usgs":false,"family":"Shrestha","given":"Mahesh","email":"mshrestha@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":817060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sampath, Aparajithan 0000-0002-6922-4913 asampath@usgs.gov","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":3622,"corporation":false,"usgs":true,"family":"Sampath","given":"Aparajithan","email":"asampath@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":817061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramaseri Chandra, Shankar N. 0000-0002-4434-4468","orcid":"https://orcid.org/0000-0002-4434-4468","contributorId":216043,"corporation":false,"usgs":true,"family":"Ramaseri Chandra","given":"Shankar","email":"","middleInitial":"N.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":817062,"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":817063,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shaw, Jerad 0000-0002-8319-2778 jshaw@usgs.gov","orcid":"https://orcid.org/0000-0002-8319-2778","contributorId":3564,"corporation":false,"usgs":true,"family":"Shaw","given":"Jerad","email":"jshaw@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":817064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":817065,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":817066,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70221207,"text":"ofr20211030A - 2021 - System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)","interactions":[{"subject":{"id":70221207,"text":"ofr20211030A - 2021 - System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)","indexId":"ofr20211030A","publicationYear":"2021","noYear":false,"chapter":"A","displayTitle":"System Characterization Report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)","title":"System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)"},"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":"2021-07-26T19:57:42.468861","indexId":"ofr20211030A","displayToPublicDate":"2021-06-07T09:22:32","publicationYear":"2021","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":"A","displayTitle":"System Characterization Report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)","title":"System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS)","docAbstract":"<h1>Executive Summary</h1><p>This report addresses system characterization of the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) 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 the methodology and procedures for characterization and the technical and operational information about the specific sensing system being evaluated. These reports also provide a description of data measurements, data retention practices, and data analysis results and provide system characterization conclusions.</p><p>In partnership with Teledyne Brown Engineering, DLR built the DESIS hyperspectral instrument, which Teledyne Brown Engineering then integrated onto its International Space Station-based imaging platform, the Multi-User System for Earth Sensing. DLR developed the processing software and, together with Innovative Imaging and Research, completes the validation and calibration of the data products. DESIS was launched in 2018, and the data are used for scientific research in atmospheric physics and Earth sciences. The DESIS sensor contributes to the scientific and commercial utilization of the International Space Station and helps to further hyperspectral remote sensing technologies for future satellites. More information on DLR satellites and sensors is included within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at <a data-mce-href=\"https://www.dlr.de/DE/Home/home_node.html\" href=\"https://www.dlr.de/DE/Home/home_node.html\">https://www.dlr.de/DE/Home/home_node.html</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 DESIS has an interior geometric performance of less than a 3.30-meter (less than 0.11 pixel) root mean square error in band-to-band registration, an exterior geometric performance in the range of a 2.40- (0.08 pixel) to 17.40-meter (0.58 pixel) offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.013 to 1.011 (offset and slope), and a spatial performance for band 130 of 1.5 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency of 0.167.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030A","usgsCitation":"Shrestha, M., Sampath, A., Ramaseri Chandra, S.N., Christopherson, J.B., Shaw, J., and Anderson, C., 2021, System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS), chap. A <i>of</i> Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 9 p., https://doi.org/10.3133/ofr20211030A.","productDescription":"iv, 9 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-126586","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/a/coverthb.jpg"},{"id":386253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/a/ofr20211030a.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030A"}],"contact":"<p>Director, <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><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</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":"2021-06-07","noUsgsAuthors":false,"publicationDate":"2021-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Shrestha, Mahesh 0000-0002-8368-6399 mshrestha@contractor.usgs.gov","orcid":"https://orcid.org/0000-0002-8368-6399","contributorId":259303,"corporation":false,"usgs":false,"family":"Shrestha","given":"Mahesh","email":"mshrestha@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":817049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sampath, Aparajithan 0000-0002-6922-4913 asampath@usgs.gov","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":3622,"corporation":false,"usgs":true,"family":"Sampath","given":"Aparajithan","email":"asampath@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":817050,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramaseri Chandra, Shankar N. 0000-0002-4434-4468","orcid":"https://orcid.org/0000-0002-4434-4468","contributorId":216043,"corporation":false,"usgs":true,"family":"Ramaseri Chandra","given":"Shankar","email":"","middleInitial":"N.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":817051,"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":817052,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shaw, Jerad 0000-0002-8319-2778 jshaw@usgs.gov","orcid":"https://orcid.org/0000-0002-8319-2778","contributorId":3564,"corporation":false,"usgs":true,"family":"Shaw","given":"Jerad","email":"jshaw@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":817053,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":817054,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70220514,"text":"70220514 - 2021 - Monitoring long-term riparian vegetation trends to inform local habitat management in a mountainous environment","interactions":[],"lastModifiedDate":"2021-05-19T12:04:52.977459","indexId":"70220514","displayToPublicDate":"2021-05-17T07:22:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring long-term riparian vegetation trends to inform local habitat management in a mountainous environment","docAbstract":"<div id=\"ab015\" class=\"abstract author\" lang=\"en\"><div id=\"as015\"><p id=\"sp0015\">Riparian ecosystems provide critical habitat for many species, yet assessment of vegetation condition at local scales is difficult to measure when considering large areas over long time periods. We present a framework to map and monitor two deciduous cover types, upland and riparian, occupying a small fraction of an expansive, mountainous landscape in north-central Wyoming. Initially, we developed broad-scale predictions of predominant woody vegetation types by integrating Landsat data into species distribution models and combining subsequent outputs into a synthesis map. Then, we evaluated a 35-year Landsat time series (1985–2019) using the Mann-Kendall test to identify significant trends in the condition of upland and riparian deciduous vegetation and assessed the rate and direction of change using the Theil-Sen estimator. Finally, we used plot level data to assess the utility of the framework to detect bottom-up controls (ungulate browse pressure and management actions) on vegetation condition. The synthesis map had an overall correct classification rate of 87% and field data indicated deciduous vegetation within 45&nbsp;m of coniferous forest faces increased pressure of conifer expansion. The trend assessment identified consistent patterns operating at the landscape scale across both upland and riparian deciduous vegetation; a predominant greening trend was observed for 12&nbsp;years followed by a 9-year browning trend, before switching back to a greening trend for the last 13&nbsp;years of the study. Our results indicate trends are driven by the climate of the measurement period at the landscape scale. Although we did not find conclusive evidence to establish a strong link between browse pressure and satellite data, we highlight examples where prevailing trends can be overridden by local disturbance or management intervention. This framework is transferable to other understudied riparian environments throughout western North America to provide insight on ecohydrological processes and assess global and local stressors across broad spatiotemporal scales.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107807","usgsCitation":"Assal, T.J., Steen, V., Caltrider, T., Cundy, T., Stewart, C., Manning, N., and Anderson, P.J., 2021, Monitoring long-term riparian vegetation trends to inform local habitat management in a mountainous environment: Ecological Indicators, v. 127, 107807, 11 p., https://doi.org/10.1016/j.ecolind.2021.107807.","productDescription":"107807, 11 p.","ipdsId":"IP-124139","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":452231,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107807","text":"Publisher Index Page"},{"id":385700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.8193359375,\n              42.261049162113856\n            ],\n            [\n              -106.083984375,\n              42.261049162113856\n            ],\n            [\n              -106.083984375,\n              45.02695045318546\n            ],\n            [\n              -109.8193359375,\n              45.02695045318546\n            ],\n            [\n              -109.8193359375,\n              42.261049162113856\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"127","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Assal, Timothy J. 0000-0001-6342-2954","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":258157,"corporation":false,"usgs":false,"family":"Assal","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":815864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steen, Valerie A. 0000-0002-1417-8139","orcid":"https://orcid.org/0000-0002-1417-8139","contributorId":205994,"corporation":false,"usgs":false,"family":"Steen","given":"Valerie A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":815865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caltrider, Todd","contributorId":258158,"corporation":false,"usgs":false,"family":"Caltrider","given":"Todd","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":815866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cundy, Travis","contributorId":258159,"corporation":false,"usgs":false,"family":"Cundy","given":"Travis","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":815867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stewart, Cheyenne","contributorId":258161,"corporation":false,"usgs":false,"family":"Stewart","given":"Cheyenne","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":815868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Manning, Nicholas","contributorId":258163,"corporation":false,"usgs":false,"family":"Manning","given":"Nicholas","email":"","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":815869,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":815870,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70220388,"text":"70220388 - 2021 - Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA","interactions":[],"lastModifiedDate":"2024-05-16T15:27:57.216807","indexId":"70220388","displayToPublicDate":"2021-05-08T06:59:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA","docAbstract":"<p><span>Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/fire4020026","usgsCitation":"Teske, C., Vanderhoof, M.K., Hawbaker, T., Noble, J., and Hires, J.K., 2021, Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA: Fire, v. 4, no. 2, 26, 21 p., https://doi.org/10.3390/fire4020026.","productDescription":"26, 21 p.","ipdsId":"IP-126697","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":452347,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire4020026","text":"Publisher Index Page"},{"id":385562,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Florida","otherGeospatial":"Florida Panhandle","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -87.64892578125,\n              29.554345125748267\n            ],\n            [\n              -83.43017578125,\n              29.554345125748267\n            ],\n            [\n              -83.43017578125,\n              30.939924331023445\n            ],\n            [\n              -87.64892578125,\n              30.939924331023445\n            ],\n            [\n              -87.64892578125,\n              29.554345125748267\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"4","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-05-08","publicationStatus":"PW","contributors":{"authors":[{"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":815369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":815372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":815371,"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":815370,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hires, J. 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 \"}}]}","edition":"Version 1.0: April 26, 2021; Version 1.1: January 18, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey<br>12201 Sunrise Valley Drive <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>Lowering Pollutants Entering Chesapeake Bay</li><li>Helping Farmers Reduce Runoff</li><li>Assessing Coastal Wetlands</li><li>Mapping High-Tide Flooding</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-04-26","revisedDate":"2023-01-18","noUsgsAuthors":false,"publicationDate":"2021-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":147999,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":814597,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70220156,"text":"fs20213020 - 2021 - New York and Landsat","interactions":[],"lastModifiedDate":"2023-01-24T11:51:39.97116","indexId":"fs20213020","displayToPublicDate":"2021-04-26T08:30:26","publicationYear":"2021","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":"2021-3020","displayTitle":"New York and Landsat","title":"New York and Landsat","docAbstract":"<p>From the iconic skyline of New York City to the forested landscapes of the Adirondack Mountains and the countryside of the Allegheny Plateau, the State of New York is overflowing with diversity and life. Bordered by the Atlantic Ocean on the east and two of the Great Lakes to the north and west, New York has more than 7,600 lakes, ponds, and reservoirs and more than 70,000 miles of rivers and streams. New York’s stewardship of its freshwater resources is fundamental to the health and well-being of all who work at, reside in, and visit the State’s landmarks and places.</p><p>Harmful algal blooms in the State’s waterbodies are a growing concern and threaten the health of the region and its inhabitants. Images and data from Landsat satellites continue to provide critical information to scientists, public health officials, and resource managers who are studying the effects and risks of the problem.</p><p>Here is a closer look at just a few examples of the value of Landsat to New York.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213020","usgsCitation":"U.S. Geological Survey, 2021, New York and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2021–3020, 2 p., https://doi.org/10.3133/fs20213020.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-126002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412235,"rank":6,"type":{"id":39,"text":"HTML 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York\",\"nation\":\"USA  \"}}]}","edition":"Version 1.0: April 26, 2021; Version 1.1: January 23, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey<br>12201 Sunrise Valley Drive <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>Urban Heat Equality Mapping</li><li>Harmful Algal Bloom Action Plans</li><li>Invasive Species in Hemlock Trees</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-04-26","revisedDate":"2023-01-23","noUsgsAuthors":false,"publicationDate":"2021-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":814598,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70230759,"text":"70230759 - 2021 - Geometric calibration updates to Landsat 7 ETM+ instrument for Landsat Collection 2 products","interactions":[],"lastModifiedDate":"2022-04-25T15:11:53.761629","indexId":"70230759","displayToPublicDate":"2021-04-22T10:03:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Geometric calibration updates to Landsat 7 ETM+ instrument for Landsat Collection 2 products","docAbstract":"<p><span>The Landsat 7 (L7) spacecraft and its instrument, the enhanced thematic mapper plus (ETM+), have been consistently characterized and calibrated since its launch in April of 1999. These performance metrics and calibration updates are determined through the U. S. Geological Survey (USGS) Landsat image assessment system (IAS), which has been performing this function since launch. Starting in 2016, the USGS adopted a tiered collection management structure for its Landsat data products that ensures a consistent method of processing for the Landsat archive within a given collection while allowing a set of calibration updates to be performed between any two given collections. The time frame between 2016 and the end of 2020 was part of the Landsat data Collection 1, in the middle of 2020 was the start of the Landsat Collection 2 data products. The start of a given collection initiates the reprocessing of the Landsat archive, which may involve one or more of a set of updated calibration parameters, improvements in the support data needed for product generation, and improved algorithms used in both the processing flow of products along with the characterization and calibration of the Landsat instruments and spacecraft. This paper discusses only the ETM+ geometric spacecraft and instrument calibration improvements for Collection 2. Three ETM+ calibration updates were made for the ETM+; updates to the thermal band odd-to-even detector alignment, sensor to attitude control system (ACS) alignment, and a cold-to-warm focal plane alignment adjustment. The sensor alignment updates impact only the accuracy of the systematic terrain products (L1GT), which are the products generated before applying any corrections based on the ground control used in registration. The band alignment changes impacted only bands 5, 6, and 7 within the focal plane. Other geometric calibration updates, such as scan mirror alignment, are done on a routine basis and are not part of the Collection 2 updates due to their more dynamic characteristics.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs13091638","usgsCitation":"Choate, M., Rengarajan, R., Storey, J.C., and Lubke, M., 2021, Geometric calibration updates to Landsat 7 ETM+ instrument for Landsat Collection 2 products: Remote Sensing, v. 13, no. 9, 1638, 20 p., https://doi.org/10.3390/rs13091638.","productDescription":"1638, 20 p.","ipdsId":"IP-128282","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13091638","text":"Publisher Index Page"},{"id":399585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-04-22","publicationStatus":"PW","contributors":{"authors":[{"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":841295,"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":841296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":841297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":841298,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70221215,"text":"70221215 - 2021 - Investigating vegetation responses to underground nuclear explosions through integrated analyses","interactions":[],"lastModifiedDate":"2021-06-07T13:08:15.404703","indexId":"70221215","displayToPublicDate":"2021-04-15T08:04:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Investigating vegetation responses to underground nuclear explosions through integrated analyses","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Vegetation has the potential to respond to underground nuclear explosions, yet these links have not been fully explored. Given the lack of previously described signatures, the changes in vegetation are possibly subtle. The integration of multiple different data streams is potentially a useful approach to improve signal detection. Here, we investigate whether semi-arid vegetation growth patterns responded to eight legacy underground nuclear tests at the Nevada National Security Site in southern Nevada, USA. We tested for spatial and temporal changes in vegetation cover, tree growth patterns, and tree leaf spectral properties using ground-based measurements, including those from tree-rings and hyperspectral surface vegetation reflectance, as well as space-based measurements of Normalized Difference Vegetation Index (NDVI) from Landsat. Multiple data streams suggest a localized (&lt;1.2&nbsp;km) spatial pattern whereby tree growth is enhanced closer to the source of the underground test relative to sites further away. We also observed a more regional (&gt;1.2–9&nbsp;km) pattern whereby tree growth is suppressed coincident with a drought beginning 1 year before the 1989 tests, but continuing in the 5 years following the tests, which is anomalous relative to what is expected based on the response of tree growth to previous droughts. Quantification of the relative effects of the tests on vegetation remains a challenge due to the coincident drought and the potential for other disturbances to have impacted tree growth at this time, but the integration of these data reveals a more nuanced growth response than any other one data set indicates alone.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1029/2020JG005831","usgsCitation":"Solander, K., Collins, A.D., Swanson, E., Margolis, E.Q., Crawford, B., Miller, E., Chen, M., Lavadie-Bulnes, A., Ryan, M., Borrego, I., Sevanto, S., and Schultz-Fellenz, E., 2021, Investigating vegetation responses to underground nuclear explosions through integrated analyses: Journal of Geophysical Research Biogeosciences, v. 126, no. 5, e2020JG005831, 23 p., https://doi.org/10.1029/2020JG005831.","productDescription":"e2020JG005831, 23 p.","ipdsId":"IP-118775","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":452677,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1786794","text":"External Repository"},{"id":386262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.927490234375,\n              36.39475669987386\n            ],\n            [\n              -115.587158203125,\n              36.39475669987386\n            ],\n            [\n              -115.587158203125,\n              37.16031654673677\n            ],\n            [\n              -116.927490234375,\n              37.16031654673677\n            ],\n            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National Laboratory","active":true,"usgs":false}],"preferred":false,"id":817083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":817084,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crawford, Brandon","contributorId":259308,"corporation":false,"usgs":false,"family":"Crawford","given":"Brandon","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":817085,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Elizabeth","contributorId":259309,"corporation":false,"usgs":false,"family":"Miller","given":"Elizabeth","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":817086,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Min","contributorId":56140,"corporation":false,"usgs":true,"family":"Chen","given":"Min","email":"","affiliations":[],"preferred":false,"id":817087,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lavadie-Bulnes, Anita","contributorId":259310,"corporation":false,"usgs":false,"family":"Lavadie-Bulnes","given":"Anita","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":817088,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ryan, Max","contributorId":207746,"corporation":false,"usgs":false,"family":"Ryan","given":"Max","email":"","affiliations":[{"id":37625,"text":"Earth and Environmental Sciences Division, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":817089,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Borrego, Isaac","contributorId":207748,"corporation":false,"usgs":false,"family":"Borrego","given":"Isaac","email":"","affiliations":[{"id":37625,"text":"Earth and Environmental Sciences Division, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":817090,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sevanto, Sanna","contributorId":150845,"corporation":false,"usgs":false,"family":"Sevanto","given":"Sanna","email":"","affiliations":[],"preferred":false,"id":817091,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Schultz-Fellenz, Emily","contributorId":259311,"corporation":false,"usgs":false,"family":"Schultz-Fellenz","given":"Emily","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":817092,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70219528,"text":"fs20213021 - 2021 - Oregon and Landsat","interactions":[],"lastModifiedDate":"2023-01-24T11:50:53.369746","indexId":"fs20213021","displayToPublicDate":"2021-04-13T12:42:54","publicationYear":"2021","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":"2021-3021","displayTitle":"Oregon and Landsat","title":"Oregon and Landsat","docAbstract":"<p>Oregon’s landscape is as complex and diverse as it is beautiful. Mountain peaks in the Cascade Range soar higher than 10,000 feet. Crater Lake sinks to a depth of 1,943 feet, making it the deepest lake in the United States. Oregon’s lands feature forests, farm fields, grasslands, ocean coastline, rivers, a semidesert, and mountain ranges that stretch across the State. A wide range of birds, animals, and fish—including 16 federally endangered species—share this space with more than 4 million people.</p><p>With Oregon’s economy tied to these natural resources, industries like agriculture, timber, and fishing interlace with the well-being of wildlife and residents. Landsat data and imagery are one of many U.S. Geological Survey tools used by State resource managers and scientists to help achieve a natural balance and provide information about forests, habitats, and much more to Oregon decision makers. This will become even more important as research indicates climate change will make extreme weather more likely, leading to the likely increase in droughts, infestation, wildfires, and other natural hazards.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213021","usgsCitation":"U.S. Geological Survey, 2021, Oregon and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2021–3021, 2 p., https://doi.org/10.3133/fs20213021.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-127726","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":385031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3021/coverthb2.jpg"},{"id":412185,"rank":4,"type":{"id":31,"text":"Publication 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 \"}}]}","edition":"Version 1.0: April 13, 2021; Version 1.1: January 23, 2023","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey<br>12201 Sunrise Valley Drive <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>Landsat Helps Characterize the Changing Landscape of the Willamette Valley</li><li>Taking the Mystery out of Irrigation</li><li>Offering a Clearer Picture of Forests</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-04-13","revisedDate":"2023-01-23","noUsgsAuthors":false,"publicationDate":"2021-04-13","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":147999,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":814060,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70219529,"text":"70219529 - 2021 - The effects of urban land cover dynamics on urban heat Island intensity and temporal trends","interactions":[],"lastModifiedDate":"2021-06-30T18:29:22.544721","indexId":"70219529","displayToPublicDate":"2021-04-12T08:25:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8118,"text":"GIScience & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"The effects of urban land cover dynamics on urban heat Island intensity and temporal trends","docAbstract":"<div class=\"hlFld-Abstract test\"><div class=\"abstractSection abstractInFull\"><p>Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of Landsat land surface temperature products and annual land change information. We analyzed UHI distribution and change in Sioux Falls, South Dakota, in the north-central United States and found that the mean UHI intensity in the region was as large as 2.2°C during the period 1986–2017 with an increasing trend of 0.02°C per year within the area with a 5-km non-urban extent. The UHI intensity associated with high intensity urban land cover usually is stronger than with low intensity urban land cover. We evaluated the impact of different non-urban reference extents on UHI variation using different non-urban buffers. The result also suggests that the overall temporal trends of UHI intensity are almost the same when using a 5-km or 10-km non-urban buffer surrounding the urban core. The prototype approach provides a framework to consistently quantify UHI and monitor its change to a large geographic extent.</p></div></div>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2021.1903282","usgsCitation":"Xian, G.Z., Shi, H., Auch, R.F., Gallo, K., Zhou, Q., Wu, Z., and Kolian, M., 2021, The effects of urban land cover dynamics on urban heat Island intensity and temporal trends: GIScience & Remote Sensing, v. 58, no. 4, p. 501-515, https://doi.org/10.1080/15481603.2021.1903282.","productDescription":"15 p.","startPage":"501","endPage":"515","ipdsId":"IP-121520","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499916,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/b74f3081aca04fe1b1b49320295d5bfd","text":"External 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Center","active":true,"usgs":true}],"preferred":true,"id":814062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","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":814063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gallo, Kevin 0000-0001-9162-5011","orcid":"https://orcid.org/0000-0001-9162-5011","contributorId":257326,"corporation":false,"usgs":false,"family":"Gallo","given":"Kevin","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":814064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":223103,"corporation":false,"usgs":true,"family":"Zhou","given":"Qiang","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814065,"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":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":814066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kolian, Michael 0000-0002-7134-8317","orcid":"https://orcid.org/0000-0002-7134-8317","contributorId":257327,"corporation":false,"usgs":false,"family":"Kolian","given":"Michael","email":"","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":814067,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70224551,"text":"70224551 - 2021 - Riparian area changes in greenness and water use on the Lower Colorado River in the USA from 2000-2020","interactions":[],"lastModifiedDate":"2025-12-11T22:15:33.73473","indexId":"70224551","displayToPublicDate":"2021-03-31T07:26:26","publicationYear":"2021","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 area changes in greenness and water use on the Lower Colorado River in the USA from 2000-2020","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Declines in riparian ecosystem greenness and water use have been observed in the delta of the Lower Colorado River (LCR) since 2000. The purpose of our case study was to measure these metrics on the U.S. side of the border between Hoover and Morelos Dams to see if declining greenness was unique to the portion of the river in Mexico. In this case study, five riparian reaches of the LCR from Hoover to Morelos Dam since 2000 were studied to evaluate trends in riparian ecosystem health. We measure these riparian woodlands using remotely sensed measurements of the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness); daily evapotranspiration (ET; mmd<sup>−1</sup>) using EVI2 (ET(EVI2)); and an annualized ET based on EVI2, the Phenology Assessment Metric (PAM ET), an annualized ET using Landsat time-series. A key finding is that riparian health and its water use has been in decline since 2000 on the U.S. portion of the LCR, depicting a loss of green vegetation over the last two decades. EVI2 results show a decline of −13.83%, while average daily ET(EVI2) between the first and last decade had a decrease of over 1 mmd<sup>−1</sup><span>&nbsp;</span>(−27.30%) and the respective average PAM ET losses were 170.91 mmyr<sup>−1</sup><span>&nbsp;</span>(−17.95%). The difference between the first and last five-year periods, 2000–2005 and 2016–2020, showed the largest decrease in daily ET(EVI) of 1.24 mmd<sup>−1</sup><span>&nbsp;</span>(−32.61%). These declines come from a loss in healthy, green, riparian plant-cover, not a change in plant water use efficiency nor efficient use of managed water resources. Our results suggest further deterioration of biodiversity, wildlife habitat and other key ecosystem services on the U.S. portion of the LCR.<span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span></span></span></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs13071332","usgsCitation":"Nagler, P.L., Barreto-Muñoz, A., Borujeni, S.C., Nouri, H., Jarchow, C., and Didan, K., 2021, Riparian area changes in greenness and water use on the Lower Colorado River in the USA from 2000-2020: Remote Sensing, v. 13, no. 7, 1332, 48 p.; Data Release, https://doi.org/10.3390/rs13071332.","productDescription":"1332, 48 p.; Data Release","ipdsId":"IP-125535","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":452865,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13071332","text":"Publisher Index Page"},{"id":436426,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MIPBRP","text":"USGS data release","linkHelpText":"Colorado River Project: A compilation of vegetation indices, phenology assessment metrics, estimates of evapotranspiration and change maps for five reaches between Hoover and Morelos Dams on the Lower Colorado River, for nearly the last two decades"},{"id":389803,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California","otherGeospatial":"Lower Colorado River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.9169921875,\n              32.509761735919426\n            ],\n            [\n              -113.90625,\n              32.509761735919426\n            ],\n            [\n              -113.90625,\n              35.38904996691167\n            ],\n            [\n              -114.9169921875,\n              35.38904996691167\n            ],\n            [\n              -114.9169921875,\n              32.509761735919426\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-03-31","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":824040,"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":824041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borujeni, Sattar Chavoshi","contributorId":240671,"corporation":false,"usgs":false,"family":"Borujeni","given":"Sattar","email":"","middleInitial":"Chavoshi","affiliations":[],"preferred":false,"id":824042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nouri, Hamideh","contributorId":178847,"corporation":false,"usgs":false,"family":"Nouri","given":"Hamideh","affiliations":[],"preferred":false,"id":824043,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":824044,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":824045,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70219174,"text":"fs20213017 - 2021 - Texas and Landsat","interactions":[],"lastModifiedDate":"2025-03-20T14:26:43.368165","indexId":"fs20213017","displayToPublicDate":"2021-03-30T10:39:13","publicationYear":"2021","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":"2021-3017","displayTitle":"Texas and Landsat","title":"Texas and Landsat","docAbstract":"<p>The State of Texas has the largest land area of any in the contiguous United States, and its sprawling landscapes show rich geographic diversity. The Lone Star State has cactus flats in the high plains of its far western panhandle, rolling hills in its western Trans-Pecos region, farms and ranchlands stretching across central Texas, thick forests and swamplands spread through the east, and 3,359 miles of Gulf of America coastline. The consistent, reliable, and historically unique Landsat data archive provides an important tool for Texans to track landscape changes and enhance their economy and environment.&nbsp;</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213017","usgsCitation":"U.S. Geological Survey, 2021, Texas and Landsat (ver. 1.2, March 2025): U.S. Geological Survey Fact Sheet 2021–3017, 2 p., https://doi.org/10.3133/fs20213017.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-126698","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":408400,"rank":4,"type":{"id":31,"text":"Publication 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 \"}}]}","edition":"Version 1.0: March 30, 2021; Version 1.1: October 17, 2022; Version 1.2: March 19, 2025","contact":"<p>Program Coordinator, <a href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\" data-mce-href=\"https://www.usgs.gov/core-science-systems/national-land-imaging-program\">National Land Imaging Program</a> <br>U.S. Geological Survey<br>12201 Sunrise Valley Drive <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>Mapping Change to Texas Coastlines</li><li>Tracking Urban Heat from Above</li><li>Measuring and Managing Water Use</li><li>Landsat—Critical Information Infrastructure for the Nation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-03-30","revisedDate":"2025-03-19","noUsgsAuthors":false,"publicationDate":"2021-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":813135,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70217666,"text":"sim3466 - 2021 - Improved Automated Identification and Mapping of Iron Sulfate Minerals, Other Mineral Groups, and Vegetation using Landsat 8 Operational Land Imager Data, San Juan Mountains, Colorado, and Four Corners Region","interactions":[],"lastModifiedDate":"2021-03-04T16:52:50.265664","indexId":"sim3466","displayToPublicDate":"2021-02-24T13:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3466","title":"Improved Automated Identification and Mapping of Iron Sulfate Minerals, Other Mineral Groups, and Vegetation using Landsat 8 Operational Land Imager Data, San Juan Mountains, Colorado, and Four Corners Region","docAbstract":"<p>Multispectral remote sensing data acquired by the Landsat 8 Operational Land Imager (OLI) sensor were analyzed using a new, automated technique to generate a map of exposed mineral and vegetation groups in the western San Juan Mountains, Colo., and the Four Corners Region of the United States. Band ratio results were combined into displayed mineral and vegetation groups using Boolean algebra. New analysis logic has been implemented to exploit the coastal aerosol band in Landsat 8 OLI data and identify concentrations of iron sulfate minerals. These may indicate the presence of near-surface pyrite, which can be a potential nonpoint source of acid rock drainage. Zoned occurrences of iron sulfate minerals in areas surrounding and down gradient of known sources of pyrite have been mapped using this technique. They show high correlation with occurrences of jarosite-bearing mineral assemblages, as mapped using airborne imaging spectrometer data and supporting field verification surveys. Mapping the occurrence of iron sulfate minerals produced by the weathering and oxidation of pyrite in exposed hydrothermally altered rocks can also provide an important indicator of the genetic environment of alteration and the associated mineral deposit type. The automated analysis methodology is being employed to rapidly and cost-effectively generate maps of large regions of the United States in support of U.S. Geological Survey mineral resource and mineral-environmental assessments. This map, which includes the ERDAS IMAGINE thematic raster format in the data release, has been attributed by pixel value with mineral and vegetation group classification data, which can be queried in most image processing and GIS software packages.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3466","usgsCitation":"Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improved automated identification and mapping of iron sulfate minerals, other mineral groups, and vegetation using Landsat 8 Operational Land Imager data, San Juan Mountains, Colorado, and Four Corners Region: U.S. Geological Survey Scientific Investigations Map 3466, 1 sheet, scale 1:325,000, 37-p. pamphlet, https://doi.org/10.3133/sim3466.","productDescription":"Pamphlet: v, 37 p.; 1 Sheet: 38.72 x 37.71 inches; 2 Data Releases: Read Me","onlineOnly":"Y","ipdsId":"IP-079681","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":382731,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ONOKKX","text":"USGS data release","linkHelpText":"Digital map of iron sulfate minerals, other mineral groups, and vegetation of the San Juan Mountains, Colorado, and Four Corners Region derived from automated analysis of Landsat 8 satellite data"},{"id":383537,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BYV5H4","text":"USGS data release","linkHelpText":"Digital map of iron sulfate minerals, other mineral groups, and vegetation of the western United States derived from automated analysis of Landsat 8 satellite data"},{"id":382727,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3466/coverthb2.jpg"},{"id":382729,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3466/sim3466_sheet1.pdf","text":"Sheet 1. Improved Automated Identification and Mapping of Iron Sulfate Minerals, Other Mineral Groups,  and Vegetation using Landsat 8 Operational Land Imager Data, San Juan Mountains, Colorado, and Four Corners Region","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3466 map sheet","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."},{"id":382728,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3466/sim3466_pamphlet.pdf","text":"Report","size":"2.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3466 pamphlet"},{"id":382730,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3466/ReadMe.txt","text":"Read Me","size":"12.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3466 map read me file"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Four Corners region, San Juan Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.4737548828125,\n              36.70365959719456\n            ],\n            [\n              -106.2872314453125,\n              38.50948995925553\n            ],\n            [\n              -109.039306640625,\n              39.10022600175347\n            ],\n            [\n              -110.31372070312499,\n              36.87522650673951\n            ],\n            [\n              -107.4737548828125,\n              36.70365959719456\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"http:/www.usgs.gov/centers/gggsc/\" data-mce-href=\"http:/www.usgs.gov/centers/gggsc/\">Geology, Geophysics, and Geochemistry Science Center</a><br>U.S. Geological Survey<br>Box 25046, MS-973<br>Denver, CO 80225-0046</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Descriptions of Map Sheet and Associated ERDAS Imagine Raster Image File Included in This Publication</li><li>Description of Automated Analysis Methodology</li><li>Deficiencies of the Automated Analysis Methodology</li><li>Conclusions</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishedDate":"2021-02-24","noUsgsAuthors":false,"publicationDate":"2021-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Rockwell, Barnaby W. 0000-0002-9549-0617","orcid":"https://orcid.org/0000-0002-9549-0617","contributorId":203924,"corporation":false,"usgs":true,"family":"Rockwell","given":"Barnaby W.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":809205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gnesda, William R. 0000-0001-8968-2260","orcid":"https://orcid.org/0000-0001-8968-2260","contributorId":225039,"corporation":false,"usgs":true,"family":"Gnesda","given":"William","email":"","middleInitial":"R.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809203,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":809204,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70218477,"text":"70218477 - 2021 - Rangeland fractional components across the western United States from 1985 to 2018","interactions":[],"lastModifiedDate":"2022-02-03T17:59:45.727597","indexId":"70218477","displayToPublicDate":"2021-02-23T10:18:19","publicationYear":"2021","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":"Rangeland fractional components across the western United States from 1985 to 2018","docAbstract":"<p><span>Monitoring temporal dynamics of rangelands to detect and understand change in vegetation cover and composition provides a wealth of information to improve management and sustainability. Remote sensing allows the evaluation of both abrupt and gradual rangeland change at unprecedented spatial and temporal extents. Here, we describe the production of the National Land Cover Database (NLCD) Back in Time (BIT) dataset which quantified the percent cover of rangeland components (bare ground, herbaceous, annual herbaceous, litter, shrub, and sagebrush (</span><span class=\"html-italic\">Artemisia</span><span>&nbsp;spp. Nutt.) across the western United States using Landsat imagery from 1985 to 2018. We evaluate the relationships of component trends with climate drivers at an ecoregion scale, describe the nature of landscape change, and demonstrate several case studies related to changes in grazing management, prescribed burns, and vegetation treatments. Our results showed the net cover of shrub, sagebrush, and litter significantly (</span><i><span class=\"html-italic\">p</span></i><span>&nbsp;&lt; 0.01) decreased, bare ground and herbaceous cover had no significant change, and annual herbaceous cover significantly (</span><i><span class=\"html-italic\">p</span></i><span>&nbsp;&lt; 0.05) increased. Change was ubiquitous, with a mean of 92% of pixels with some change and 38% of pixels with significant change (</span><i><span class=\"html-italic\">p</span></i><span>&nbsp;&lt; 0.10). However, most change was gradual, well over half of pixels have a range of less than 10%, and most change occurred outside of known disturbances. The BIT data facilitate a comprehensive assessment of rangeland condition, evaluation of past management actions, understanding of system variability, and opportunities for future planning.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs13040813","usgsCitation":"Rigge, M.B., Homer, C., Shi, H., Meyer, D., Bunde, B., Granneman, B.J., Postma, K., Danielson, P., Case, A., and Xian, G.Z., 2021, Rangeland fractional components across the western United States from 1985 to 2018: Remote Sensing, v. 13, no. 4, 813, 24 p., https://doi.org/10.3390/rs13040813.","productDescription":"813, 24 p.","ipdsId":"IP-119778","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453335,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13040813","text":"Publisher Index Page"},{"id":436491,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ODAZHC","text":"USGS data release","linkHelpText":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2021"},{"id":384677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395378,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95IQ4BT","text":"USGS data release","description":"USGS data release","linkHelpText":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2020"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Kansas, Montana, Nebraska, Nevada, New Mexico, North Dakota, Oklahoma, Oregon, South Dakota, Texas, Utah, Washington, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -101.4697265625,\n              29.53522956294847\n            ],\n            [\n              -102.6123046875,\n              32.58384932565662\n            ],\n            [\n              -104.1064453125,\n              34.488447837809304\n            ],\n            [\n              -100.0634765625,\n              37.020098201368114\n            ],\n            [\n              -103.095703125,\n              39.50404070558415\n            ],\n            [\n              -104.23828125,\n              40.38002840251183\n            ],\n            [\n              -104.2822265625,\n              41.672911819602085\n            ],\n            [\n              -101.162109375,\n              43.58039085560784\n            ],\n            [\n              -102.3486328125,\n              48.60385760823255\n            ],\n            [\n              -104.0185546875,\n              48.951366470947725\n            ],\n            [\n              -121.025390625,\n              48.980216985374994\n            ],\n            [\n              -122.25585937500001,\n              43.58039085560784\n            ],\n            [\n              -123.04687499999999,\n              39.232253141714885\n            ],\n            [\n              -121.81640624999999,\n              36.27970720524017\n            ],\n            [\n              -120.58593749999999,\n              34.27083595165\n            ],\n            [\n              -116.98242187499999,\n              32.54681317351514\n            ],\n            [\n              -114.697265625,\n              32.69486597787505\n            ],\n            [\n              -114.78515624999999,\n              32.39851580247402\n            ],\n            [\n              -111.97265625,\n              31.690781806136822\n            ],\n            [\n              -111.1376953125,\n              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]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin 0000-0003-4755-8135","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":252931,"corporation":false,"usgs":false,"family":"Homer","given":"Collin","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":811153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192361,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bunde, Brett 0000-0003-0228-779X brett.bunde.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":198821,"corporation":false,"usgs":true,"family":"Bunde","given":"Brett","email":"brett.bunde.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":811156,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Granneman, Brian J. 0000-0002-1910-0955 grann@usgs.gov","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":4209,"corporation":false,"usgs":true,"family":"Granneman","given":"Brian","email":"grann@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811157,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":252852,"corporation":false,"usgs":true,"family":"Postma","given":"Kory","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":811158,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Danielson, Patrick 0000-0002-2990-2783 pdanielson@usgs.gov","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":3551,"corporation":false,"usgs":true,"family":"Danielson","given":"Patrick","email":"pdanielson@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":811159,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Case, Adam 0000-0002-6342-5853","orcid":"https://orcid.org/0000-0002-6342-5853","contributorId":252932,"corporation":false,"usgs":true,"family":"Case","given":"Adam","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":811160,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":811161,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70218279,"text":"70218279 - 2021 - Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires","interactions":[],"lastModifiedDate":"2021-02-24T13:02:35.006174","indexId":"70218279","displayToPublicDate":"2021-02-19T07:02:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Background</h3><p>The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant<span>&nbsp;</span><i>Artemisia</i><span>&nbsp;</span>L<i>.</i><span>&nbsp;</span>species, primarily<span>&nbsp;</span><i>A. tridentata</i><span>&nbsp;</span>Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusions</h3><p>While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations.</p>","language":"English","publisher":"Springer","doi":"10.1186/s42408-021-00091-7","usgsCitation":"Applestein, C., and Germino, M., 2021, Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires: Fire Ecology, v. 17, no. 5, 11 p., https://doi.org/10.1186/s42408-021-00091-7.","productDescription":"11 p.","ipdsId":"IP-121781","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-021-00091-7","text":"Publisher Index Page"},{"id":383586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Oregon, Idaho, Nevada, Utah","otherGeospatial":"Sagebrush steppe of the Great Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.35546875000001,\n              40.84706035607122\n            ],\n            [\n              -111.357421875,\n              40.84706035607122\n            ],\n            [\n              -111.357421875,\n              44.15068115978094\n            ],\n            [\n              -119.35546875000001,\n              44.15068115978094\n            ],\n            [\n              -119.35546875000001,\n              40.84706035607122\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":218003,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":810810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":810811,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70220098,"text":"70220098 - 2021 - Landsat 8 thermal infrared sensor scene select mechanism open loop operations","interactions":[],"lastModifiedDate":"2021-04-19T12:55:23.904501","indexId":"70220098","displayToPublicDate":"2021-02-09T07:52:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7131,"text":"MDPI Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat 8 thermal infrared sensor scene select mechanism open loop operations","docAbstract":"The Landsat 8 (L8) spacecraft and its two instruments, the operational land imager (OLI) and thermal infrared sensor (TIRS), have been consistently characterized and calibrated since its launch in February 2013. These performance metrics and calibration updates are determined through the United States Geological Survey (USGS) Landsat image assessment system (IAS), which has been performing this function since its launch. The TIRS on-orbit geometric calibration procedures in-clude TIRS-to-OLI alignment, TIRS sensor chip assembly (SCA) alignment, and TIRS band align-ment. In December 2014, the TIRS instrument experienced an anomalous condition related to the instrument’s ability to accurately measure the location of the scene select mechanism (SSM). The SSM is a rotating mirror that allows the instrument’s field of view to be pointed at the Earth, for normal imaging, or at either deep space or an onboard black body, for radiometric calibration purposes. This anomalous condition in the SSM’s position sensor made it necessary to implement a new mode of operation for this mirror, termed mode-0. Mode-0 involves operating the mirror in an open-loop control state during normal mission operations when acquiring Earth data. Closed-loop mode-4 is needed for directing the mirror towards the radiometric calibration targets and is used approximately once every two weeks to collect radiometric calibration data. Mode-0 is used for most operational imaging because it does not require SSM encoder data, thereby allowing the SSM en-coder electronics to remain unpowered most of the time, reducing its use throughout the lifetime of the TIRS instrument, thus helping to preserve its nominal behavior during it use. This paper dis-cusses the geometric calibration of the SSM mirror during its current normal mode-0 set of image operations, as its open-loop control allows the mirror to drift over time in its uncontrolled state and its impacts on products. The results shown in this paper demonstrate that the ability to have on-going updates to the modeling of the TIRS SSM mirror model, in both an automated fashion and with a set of more manual operations, allows accuracy that approaches mode-4 results within days from the start of a mode-0 event.","language":"English","publisher":"MDPI","doi":"10.3390/rs13040617","usgsCitation":"Choate, M.J., Rengarajan, R., Storey, J.C., and Beckmann, T., 2021, Landsat 8 thermal infrared sensor scene select mechanism open loop operations: MDPI Remote Sensing, v. 13, no. 4, 617, 15 p., https://doi.org/10.3390/rs13040617.","productDescription":"617, 15 p.","ipdsId":"IP-124617","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13040617","text":"Publisher Index Page"},{"id":385187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":216866,"corporation":false,"usgs":true,"family":"Choate","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, R. 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":56036,"corporation":false,"usgs":true,"family":"Rengarajan","given":"R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":814473,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beckmann, Tim 0000-0002-2557-0638","orcid":"https://orcid.org/0000-0002-2557-0638","contributorId":87995,"corporation":false,"usgs":true,"family":"Beckmann","given":"Tim","affiliations":[],"preferred":false,"id":814474,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70218770,"text":"70218770 - 2021 - Ground‐penetrating radar, electromagnetic induction, terrain, and vegetation observations coupled with machine learning to map permafrost distribution at Twelvemile Lake, Alaska","interactions":[],"lastModifiedDate":"2021-08-17T16:09:40.432278","indexId":"70218770","displayToPublicDate":"2021-02-05T08:03:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3032,"text":"Permafrost and Periglacial Processes","active":true,"publicationSubtype":{"id":10}},"title":"Ground‐penetrating radar, electromagnetic induction, terrain, and vegetation observations coupled with machine learning to map permafrost distribution at Twelvemile Lake, Alaska","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>We collected ground‐penetrating radar (GPR) and frequency‐domain electromagnetic induction (FDEM) profiles in 2011 and 2012 to identify the extent of permafrost relative to surface biomass and solar insolation around Twelvemile Lake near Fort Yukon, Alaska. We compared a Landsat‐derived biomass estimate and modeled solar insolation from a digital elevation model to the geophysical measurements. We show correspondence between vegetation type and biomass relative to permafrost extent and seasonal freeze–thaw. Thicker permafrost (≥25 m) was covered by greater biomass, and seasonal thaw depths in these regions were minimal (1 m). Shallow (1–3 m depth) and thin (20–50 cm) newly forming permafrost or frozen layers from the previous winter occurred below northward oriented slopes with thin biomass cover. South‐facing slopes exhibited permafrost when there was enough biomass to shield incoming solar energy. We developed an artificial neural network to predict permafrost extent across the broader region by mapping GPR‐observed instances of permafrost to FDEM, biomass, and terrain observations with 90.2% accuracy. We identified a strong linear correlation (<i>r</i><span>&nbsp;</span>= −0.77) between permafrost probability and seasonal thaw depth, indicating that our models may also be used to explore thaw patterns and variability in active layer thickness. This study highlights the combined influence of biomass and terrain on the presence of permafrost and the value of evaluating such parameters via remote sensing to predict permafrost spatial or temporal variability. Incorporating diverse geophysical datasets with in‐situ validation into machine learning models demonstrates a useful approach to upscale estimated permafrost extent across large Arctic expanses.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/ppp.2100","usgsCitation":"Campbell, S., Briggs, M.A., Roy, S., Douglas, T.A., and Saari, S., 2021, Ground‐penetrating radar, electromagnetic induction, terrain, and vegetation observations coupled with machine learning to map permafrost distribution at Twelvemile Lake, Alaska: Permafrost and Periglacial Processes, v. 32, no. 3, p. 407-426, https://doi.org/10.1002/ppp.2100.","productDescription":"10 p.","startPage":"407","endPage":"426","ipdsId":"IP-124412","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453564,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ppp.2100","text":"Publisher Index Page"},{"id":384349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Twelvemile Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -145.50241470336914,\n              66.45051486808394\n            ],\n            [\n              -145.5571746826172,\n              66.46772395915804\n            ],\n            [\n              -145.58670043945312,\n              66.45366961339475\n            ],\n            [\n              -145.5420684814453,\n              66.43569595053626\n            ],\n            [\n              -145.51202774047852,\n              66.43981319835001\n            ],\n            [\n              -145.50241470336914,\n              66.45051486808394\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"32","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Campbell, S.","contributorId":255084,"corporation":false,"usgs":false,"family":"Campbell","given":"S.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":811771,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":811772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, S.P.","contributorId":37465,"corporation":false,"usgs":false,"family":"Roy","given":"S.P.","email":"","affiliations":[],"preferred":false,"id":811773,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas, T. A.","contributorId":200579,"corporation":false,"usgs":false,"family":"Douglas","given":"T.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":811774,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Saari, S.","contributorId":255086,"corporation":false,"usgs":false,"family":"Saari","given":"S.","email":"","affiliations":[{"id":51414,"text":"U.S. Army Cold Regions Research and Engineering Laboratory; Fort Wainwright","active":true,"usgs":false}],"preferred":false,"id":811775,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70218778,"text":"70218778 - 2021 - Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty","interactions":[],"lastModifiedDate":"2021-05-13T15:53:26.945858","indexId":"70218778","displayToPublicDate":"2021-01-28T07:10:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty","docAbstract":"<ol class=\"\"><li>Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales.</li><li>We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data.</li><li>Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product.</li><li>The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.</li></ol>","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.13564","usgsCitation":"Allred, B.W., Bestelmeyer, B.T., Boyd, C.S., Brown, C., Davies, K.W., Duniway, M.C., Ellsworth, L.M., Erickson, T.A., Fuhlendorf, S.D., Griffiths, T.V., Jansen, V., Jones, M.O., Karl, J.W., Knight, A.C., Maestas, J.D., Maynard, J.J., McCord, S.E., Naugle, D., Starns, H.D., Twidwell, D., and Uden, D.R., 2021, Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty: Methods in Ecology and Evolution, v. 12, no. 5, p. 841-849, https://doi.org/10.1111/2041-210X.13564.","productDescription":"9 p.","startPage":"841","endPage":"849","ipdsId":"IP-122860","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453676,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/2041-210x.13564","text":"External Repository"},{"id":384299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Allred, Brady W.","contributorId":255105,"corporation":false,"usgs":false,"family":"Allred","given":"Brady","email":"","middleInitial":"W.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":811805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bestelmeyer, Brandon T.","contributorId":26180,"corporation":false,"usgs":false,"family":"Bestelmeyer","given":"Brandon","email":"","middleInitial":"T.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":811806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Chad S.","contributorId":255106,"corporation":false,"usgs":false,"family":"Boyd","given":"Chad","email":"","middleInitial":"S.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":811807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Christopher","contributorId":255107,"corporation":false,"usgs":false,"family":"Brown","given":"Christopher","affiliations":[{"id":51434,"text":"Google, Inc., Mountain View, CA 94043, USA","active":true,"usgs":false}],"preferred":false,"id":811808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davies, Kirk W.","contributorId":255108,"corporation":false,"usgs":false,"family":"Davies","given":"Kirk","email":"","middleInitial":"W.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":811809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811810,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ellsworth, Lisa M.","contributorId":255109,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":51436,"text":"Fisheries and Wildlife Department, Oregon State University, Corvallis, Oregon 97331 USA","active":true,"usgs":false}],"preferred":false,"id":811811,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Erickson, Tyler A.","contributorId":255110,"corporation":false,"usgs":false,"family":"Erickson","given":"Tyler","email":"","middleInitial":"A.","affiliations":[{"id":51434,"text":"Google, Inc., Mountain View, CA 94043, USA","active":true,"usgs":false}],"preferred":false,"id":811812,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fuhlendorf, Samuel D.","contributorId":171488,"corporation":false,"usgs":false,"family":"Fuhlendorf","given":"Samuel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":811813,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Griffiths, Timothy V.","contributorId":255111,"corporation":false,"usgs":false,"family":"Griffiths","given":"Timothy","email":"","middleInitial":"V.","affiliations":[{"id":51437,"text":"USDA Natural Resources Conservation Service, Landscape Initiatives Team, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":811814,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jansen, Vincent","contributorId":255112,"corporation":false,"usgs":false,"family":"Jansen","given":"Vincent","email":"","affiliations":[{"id":51438,"text":"Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID 83844, USA","active":true,"usgs":false}],"preferred":false,"id":811815,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Matthew O.","contributorId":169805,"corporation":false,"usgs":false,"family":"Jones","given":"Matthew","email":"","middleInitial":"O.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":811816,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Karl, Jason W.","contributorId":191703,"corporation":false,"usgs":false,"family":"Karl","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":7045,"text":"USDA-ARS Jornada Experimental Range ","active":true,"usgs":false}],"preferred":false,"id":811817,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811818,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Maestas, Jeremy D.","contributorId":219258,"corporation":false,"usgs":false,"family":"Maestas","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":39978,"text":"USDA Natural Resources Conservation Service, Redmond, OR","active":true,"usgs":false}],"preferred":false,"id":811819,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Maynard, Jonathan J.","contributorId":216782,"corporation":false,"usgs":false,"family":"Maynard","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[{"id":39514,"text":"USDA-Agricultural Resource Service, Jornada Experimental Range, Las Cruces, NM 88003, USA","active":true,"usgs":false}],"preferred":false,"id":811820,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"McCord, Sarah E.","contributorId":195931,"corporation":false,"usgs":false,"family":"McCord","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":811821,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Naugle, David E.","contributorId":255114,"corporation":false,"usgs":false,"family":"Naugle","given":"David E.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":811822,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Starns, Heath D.","contributorId":131091,"corporation":false,"usgs":false,"family":"Starns","given":"Heath","email":"","middleInitial":"D.","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":811823,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Twidwell, Dirac","contributorId":187431,"corporation":false,"usgs":false,"family":"Twidwell","given":"Dirac","email":"","affiliations":[],"preferred":false,"id":811824,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Uden, Daniel R.","contributorId":219904,"corporation":false,"usgs":false,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":40095,"text":"Nebraska Cooperative Fish and Wildlife Unit, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE","active":true,"usgs":false}],"preferred":false,"id":811825,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}}
,{"id":70217127,"text":"fs20213002 - 2021 - Landsat collection 2","interactions":[],"lastModifiedDate":"2021-01-19T18:23:15.774882","indexId":"fs20213002","displayToPublicDate":"2021-01-07T13:10:00","publicationYear":"2021","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":"2021-3002","displayTitle":"Landsat Collection 2","title":"Landsat collection 2","docAbstract":"<p>Landsat Collections ensure that all Landsat Level-1 data are consistently calibrated and processed and retain traceability of data quality provenance. Landsat Collection 2 introduces improvements that harness recent advancements in data processing, algorithm development, data access, and distribution capabilities. Collection 2 includes Landsat Level-1 data for all sensors since 1972 and global Level-2 surface reflectance and surface temperature scene-based products for data acquired since 1982 starting with the Landsat Thematic Mapper sensor era.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213002","usgsCitation":"U.S. Geological Survey, 2021, Landsat Collection 2 (ver. 1.1, January 15, 2021): U.S. Geological Survey Fact Sheet 2021–3002, 4 p., https://doi.org/10.3133/fs20213002.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-123860","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":382187,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2021/3002/versionHist.txt","text":"Version History","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"FS 2021-3002 Version History"},{"id":381947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3002/coverthb2.jpg"},{"id":381948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3002/fs20213002.pdf","text":"Report","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3002 Version 1.1"}],"edition":"Version 1.0: January 7, 2021; Version 1.1: January 15, 2021","contact":"<p>Director, <a href=\"http://www.usgs.gov/centers/eros/\" data-mce-href=\"http://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>Geometric Accuracy</li><li>Digital Elevation Models</li><li>Radiometric Calibration</li><li>Quality Assessment Bands</li><li>Metadata Files</li><li>Cloud Optimized File Format</li><li>Collection Tier Structure</li><li>Data Access</li></ul>","publishedDate":"2021-01-07","revisedDate":"2021-01-15","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":210377,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":808440,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70218684,"text":"70218684 - 2021 - Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability","interactions":[],"lastModifiedDate":"2021-03-05T14:07:30.426022","indexId":"70218684","displayToPublicDate":"2021-01-04T08:03:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.<span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span></span></span></div>","language":"English","publisher":"MDPI","doi":"10.3390/land10010035","usgsCitation":"Xing, D., Stehman, S.V., Foody, G.M., and Pengra, B., 2021, Comparison of simple averaging and latent class modeling to estimate the area of land cover in the presence of reference data variability: Land, v. 10, no. 1, 35, 17 p., https://doi.org/10.3390/land10010035.","productDescription":"35, 17 p.","ipdsId":"IP-124326","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453953,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land10010035","text":"Publisher Index Page"},{"id":384067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Lowlands Eco-Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.33251953125,\n              45.69083283645816\n            ],\n            [\n              -121.4208984375,\n              45.69083283645816\n            ],\n            [\n              -121.4208984375,\n              48.40003249610685\n            ],\n            [\n              -123.33251953125,\n              48.40003249610685\n            ],\n            [\n              -123.33251953125,\n              45.69083283645816\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Xing, Dingfan 0000-0003-1233-7260","orcid":"https://orcid.org/0000-0003-1233-7260","contributorId":254318,"corporation":false,"usgs":false,"family":"Xing","given":"Dingfan","email":"","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":811346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stehman, Stephen V. 0000-0001-5234-2027","orcid":"https://orcid.org/0000-0001-5234-2027","contributorId":216812,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":811347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foody, Giles M 0000-0001-6464-3054","orcid":"https://orcid.org/0000-0001-6464-3054","contributorId":254319,"corporation":false,"usgs":false,"family":"Foody","given":"Giles","email":"","middleInitial":"M","affiliations":[{"id":51079,"text":"School of Geography, University of Nottingham, Sir Clive Granger Building, University Park, Nottingham, NG7 2RD, UK","active":true,"usgs":false}],"preferred":false,"id":811348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811349,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70254305,"text":"70254305 - 2021 - Regional crop water use assessment using Landsat-derived evapotranspiration","interactions":[],"lastModifiedDate":"2024-05-17T14:43:46.916845","indexId":"70254305","displayToPublicDate":"2021-01-01T09:40:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7176,"text":"Hydrologic Processes","active":true,"publicationSubtype":{"id":10}},"title":"Regional crop water use assessment using Landsat-derived evapotranspiration","docAbstract":"<p><span>Reliable information on water use and availability at basin and field scales are important to ensure the optimized constructive uses of available water resources. This study was conducted with the specific objective to estimate Landsat-based actual evapotranspiration (ETa) using the Operational Simplified Surface Energy Balance (SSEBop) model across the state of South Dakota (SD), USA for the 1986–2018 (33-year) period. Validated ETa estimations (r</span><sup>2</sup><span>&nbsp;= 0.91, PBIAS = −4%, and %RMSE = 11.8%) were further used to understand the crop water-use characteristics and existing historic mono-directional (increasing/decreasing) trends over the eastern (ESD) and western (WSD) regions of SD. The crop water-use characteristics indicated that the annual cropland water uses across the ESD and WSD were more or less met by the precipitation amounts in the area. The ample water supply and distribution have led to high rainfed and low percentage of irrigated cropland (~2.5%) in the state. The WSD faced greater crop-water use reductions than the ESD during drought periods. The landscape ETa responses across the state were found to be more sensitive than precipitation for the drought impact assessments. The Mann Kendall trend analysis revealed the absence of a significant trend (</span><i>p</i><span> &gt; 0.05) in annual ETa at a regional scale due to the varying weather conditions in the state. However, about 12% and 9% cropland areas in the ESD and WSD, respectively, revealed a significant mono-directional trend at pixel scale ETa. Most of the pixels under significant trend showed an increasing trend that can be explained by the shift in agricultural practices, increased irrigated cropland area, higher productions, moisture regime shifts, and decreased risk of farming in the dry areas. The decreasing trend pixels were clustered in mid-eastern SD and could be the result of dynamic conversion of wetlands to croplands and decreased irrigation practices in the region. This study also demonstrates the tremendous potential and robustness of the SSEBop model, Landsat imagery, and remote sensing-based ETa modelling approaches in estimating consistent spatially distributed evapotranspiration.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14015","usgsCitation":"Bawa, A., Senay, G.B., and Kumar, S., 2021, Regional crop water use assessment using Landsat-derived evapotranspiration: Hydrologic Processes, v. 35, no. 1, e14015, 13 p., https://doi.org/10.1002/hyp.14015.","productDescription":"e14015, 13 p.","ipdsId":"IP-124142","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":428803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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,{"id":70215542,"text":"70215542 - 2021 - Investigation of land surface phenology detections in shrublands using multiple scale satellite data","interactions":[],"lastModifiedDate":"2024-05-17T15:52:29.667553","indexId":"70215542","displayToPublicDate":"2021-01-01T09:35:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Investigation of land surface phenology detections in shrublands using multiple scale satellite data","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0065\">Shrublands occupy about 13% of the global land surface, contain about one-third of the biodiversity, store about half of the global terrestrial carbon, and provide many ecosystem services to a large amount of world's human population and livestock. Because phenology is a sensitive indicator of the response of shrubland ecosystems to climate change, the alteration of ecosystems following species invasions, and the dynamics of shrubland ecosystem function, biodiversity, and carbon budgets, it is critical to monitor and assess phenological dynamics in shrubland ecosystems. However, most current land surface phenology (LSP) products derived from satellite data do not provide phenology detections in some semiarid shrublands where the amplitude of seasonal vegetation greenness is small. Therefore, we investigated the LSP detection using multiple spatial resolution satellite data and examined the impacts of spatial scales and shrubland ecosystem components (shrub and herb cover) on LSP detections over the western United States. Specifically, greenup onset date (GUD) in shrublands was detected from 30&nbsp;m Harmonized Landsat and Sentinel-2 (HLS) data and 500&nbsp;m Visible Infrared Imaging Radiometer Suite (VIIRS) data to quantify scale effects. The GUD spatial patterns were explored with 30&nbsp;m pixel variations in shrubland ecosystem components. The results show that GUD values varied with percent vegetation cover and shifted to earlier dates with increasing vegetation cover, demonstrating that satellite observations were not able to capture greenup onset until a threshold of green vegetation cover is reached. GUD was mostly undetectable from both HLS and VIIRS pixels with vegetation cover less than 10% and became fully detectable with vegetation covers larger than 50%. Similarly, the differences of GUD between HLS and VIIRS detections also decreased with increased vegetation cover. As a result of high shrubland heterogeneity, GUD from 30&nbsp;m HLS pixels could be partially detected within a 500&nbsp;m pixel despite GUD being undetectable from VIIRS time series. Moreover, vegetation cover heterogeneity also made it difficult for GUD at 30&nbsp;m to be aggregated to coarse scales (such as to 500&nbsp;m VIIRS pixels). These findings have significant implications to the detection and characterization of shrubland LSP responses to environmental and climate changes.</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.rse.2020.112133","usgsCitation":"Peng, D., Wang, Y., Xian, G.Z., Huete, A.R., Huang, W., Shen, M., Wang, F., Yu, L., Liu, L., Xie, Q., Liu, L., and Zhang, X., 2021, Investigation of land surface phenology detections in shrublands using multiple scale satellite data: Remote Sensing of Environment, v. 252, 112133, 18 p., https://doi.org/10.1016/j.rse.2020.112133.","productDescription":"112133, 18 p.","ipdsId":"IP-122229","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453963,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":802643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huete, Alfredo R","contributorId":243589,"corporation":false,"usgs":false,"family":"Huete","given":"Alfredo","email":"","middleInitial":"R","affiliations":[{"id":48742,"text":"School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia","active":true,"usgs":false}],"preferred":false,"id":802644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huang, Wenjiang","contributorId":243591,"corporation":false,"usgs":false,"family":"Huang","given":"Wenjiang","email":"","affiliations":[{"id":48743,"text":"Key Laboratory of Digital Earth Science, 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