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Understanding current and future user needs is crucial to informing the design of Landsat missions beyond Landsat 9. The U.S. Geological Survey (USGS) initiated a user needs collection process to document needs from U.S. Federal civil subject matter experts who rely on moderate-resolution land imaging data across a diverse range of scientific research and application domains. In total, 379 moderate-resolution land imaging user needs were collected through structured interviews. The findings indicate that, at present, users need continuity in Landsat capabilities with free and open data access. Improvements to future Landsat systems should include 10 m spatial resolution and at least weekly cloud-free observation frequency. Spectral enhancements should include the addition of red edge bands, and multiple, narrower visible, near infrared, shortwave infrared, and thermal infrared bands. Ideally, a variety of applications need continuous, full-spectrum coverage in 10 nm-wide bands spanning the visible to shortwave infrared (VSWIR) region (400–2500 nm) and 5 to 8 multispectral thermal infrared bands. Non-Federal (state, local, commercial, academic, and international) sources found similar results, but a more comprehensive comparison across these communities through a broader survey may provide additional insights. USGS-collected moderate-resolution land imaging user needs are an input to the Landsat 10 Architecture Study to develop and assess feasible Landsat 10 mission architectures.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111214","usgsCitation":"Wu, Z., Snyder, G., Vadnais, C.M., Arora, R., Babcock, M., Stensaas, G.L., Doucette, P., and Newman, T., 2019, User needs for future Landsat missions: Remote Sensing of Environment, v. 231, 111214, 13 p., https://doi.org/10.1016/j.rse.2019.111214.","productDescription":"111214, 13 p.","ipdsId":"IP-101509","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"links":[{"id":468145,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111214","text":"Publisher Index Page"},{"id":364583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"231","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":763997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snyder, Gregory 0000-0001-8644-6334 gsnyder@usgs.gov","orcid":"https://orcid.org/0000-0001-8644-6334","contributorId":216150,"corporation":false,"usgs":true,"family":"Snyder","given":"Gregory","email":"gsnyder@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":764002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vadnais, Carolyn M. 0000-0002-5357-5217","orcid":"https://orcid.org/0000-0002-5357-5217","contributorId":216149,"corporation":false,"usgs":false,"family":"Vadnais","given":"Carolyn","email":"","middleInitial":"M.","affiliations":[{"id":39372,"text":"Integrity Applications Incorporated","active":true,"usgs":false}],"preferred":false,"id":764001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arora, Rohit 0000-0001-8714-3044","orcid":"https://orcid.org/0000-0001-8714-3044","contributorId":216148,"corporation":false,"usgs":false,"family":"Arora","given":"Rohit","email":"","affiliations":[{"id":39372,"text":"Integrity Applications Incorporated","active":true,"usgs":false}],"preferred":false,"id":764000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Babcock, Michael 0000-0002-4097-7997","orcid":"https://orcid.org/0000-0002-4097-7997","contributorId":216151,"corporation":false,"usgs":false,"family":"Babcock","given":"Michael","email":"","affiliations":[{"id":39372,"text":"Integrity Applications Incorporated","active":true,"usgs":false}],"preferred":false,"id":764004,"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":764003,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Doucette, Peter 0000-0002-8162-7897","orcid":"https://orcid.org/0000-0002-8162-7897","contributorId":216147,"corporation":false,"usgs":true,"family":"Doucette","given":"Peter","email":"","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":763999,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Newman, Timothy 0000-0001-9712-1009 tnewman@usgs.gov","orcid":"https://orcid.org/0000-0001-9712-1009","contributorId":216146,"corporation":false,"usgs":true,"family":"Newman","given":"Timothy","email":"tnewman@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":763998,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70270060,"text":"70270060 - 2018 - Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization","interactions":[],"lastModifiedDate":"2025-08-08T14:25:23.619796","indexId":"70270060","displayToPublicDate":"2025-08-08T09:20:53","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization","docAbstract":"<p><span>Landsat-8 has been operating on-orbit for 5+ years. Its two sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), are continuing to produce high quality data. The OLI has been radiometrically stable at the better than 0.3% level on a band average basis for all but the shortest wavelength (443 nm) band, which has degraded about 1.3% since launch. All on-board calibration devices continue to perform well and consistently. No gaps in across track coverage exist as 100% operability of the detectors is maintained. The variability over time of detector responsivity within a band relative to the average is better than 0.05% (1 sigma), though there are occasional detectors that jump up to 1.5% in response in the Short-Wave InfraRed (SWIR) bands. Signal-to-Noise performance continues at 2-3x better than requirements, with a small degradation in the 443 nm band commensurate with the loss in sensitivity. Pre-launch error analysis, combined with the stability of the OLI indicates that the absolute reflectance calibration uncertainty is better than 3%; comparisons to ground measurements and comparisons to other sensors are consistent with this. The Landsat-8 TIRS is similarly radiometrically stable, showing changes of at most 0.3% over the mission. The uncertainty in the absolute calibration as well as the detector to detector variability are largely driven by the stray light response of TIRS. The current processing corrects most of the stray light effects, resulting in absolute uncertainties of ~1% and reduced striping. Efforts continue to further reduce the striping. Noise equivalent delta temperature is about 50 mK at typical temperatures and 100% detector operability is maintained. Landsat-9 is currently under development with a launch no earlier than December 2020. The nearly identical OLI-2 and upgraded TIRS-2 sensors have completed integration and are in the process of instrument level performance characterization including spectral, spatial, radiometric and geometric testing. Component and assembly level measurements of the OLI-2, which include spectral response, radiometric response and stray light indicate comparable performance to OLI. The first functional tests occurred in July 2018 and spatial performance testing in vacuum is scheduled for August 2018. Similarly, for TIRS-2, partially integrated instrument level testing indicated spectral and spatial responses comparable to TIRS, with stray light reduced by approximately an order of magnitude from TIRS.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SPIE","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Society of Photo-Optical Instrumentation Engineers (SPIE)","doi":"10.1117/12.2324715","usgsCitation":"Markham, B., Barsi, J., Montanaro, M., McCorkel, J., Gerace, A., Pedelty, J., Hook, S., Raqueno, N., Anderson, C., and Haque, O., 2018, Landsat-8 on-orbit and Landsat-9 pre-launch sensor radiometric characterization, <i>in</i> Proceedings of SPIE, v. 10781, 1078104, https://doi.org/10.1117/12.2324715.","productDescription":"1078104","ipdsId":"IP-101030","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494179,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2060/20190002076","text":"External Repository"},{"id":493833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10781","noUsgsAuthors":false,"publicationDate":"2018-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Markham, Brian L.","contributorId":359395,"corporation":false,"usgs":false,"family":"Markham","given":"Brian L.","affiliations":[{"id":79115,"text":"NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":945252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barsi, Julia A","contributorId":359397,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia A","affiliations":[{"id":85786,"text":"SSAI, NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":945253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montanaro, Matthew","contributorId":251778,"corporation":false,"usgs":false,"family":"Montanaro","given":"Matthew","affiliations":[{"id":32390,"text":"Rochester Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":945254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCorkel, Joel T","contributorId":359399,"corporation":false,"usgs":false,"family":"McCorkel","given":"Joel T","affiliations":[{"id":79115,"text":"NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":945255,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerace, Aaron","contributorId":199173,"corporation":false,"usgs":false,"family":"Gerace","given":"Aaron","email":"","affiliations":[],"preferred":false,"id":945256,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pedelty, Jeffrey A.","contributorId":359400,"corporation":false,"usgs":false,"family":"Pedelty","given":"Jeffrey A.","affiliations":[{"id":79115,"text":"NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":945257,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hook, Simon J","contributorId":359401,"corporation":false,"usgs":false,"family":"Hook","given":"Simon J","affiliations":[{"id":35444,"text":"NASA/JPL","active":true,"usgs":false}],"preferred":false,"id":945258,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raqueno, Nina G.","contributorId":359402,"corporation":false,"usgs":false,"family":"Raqueno","given":"Nina G.","affiliations":[{"id":35443,"text":"RIT","active":true,"usgs":false}],"preferred":false,"id":945259,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":945260,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":945261,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70201780,"text":"70201780 - 2018 - Landsat benefiting society for fifty years","interactions":[{"subject":{"id":70268821,"text":"70268821 - 2018 - Addressing the water consumption riddle","indexId":"70268821","publicationYear":"2018","noYear":false,"title":"Addressing the water consumption riddle"},"predicate":"IS_PART_OF","object":{"id":70201780,"text":"70201780 - 2018 - Landsat benefiting society for fifty years","indexId":"70201780","publicationYear":"2018","noYear":false,"title":"Landsat benefiting society for fifty years"},"id":1},{"subject":{"id":70268822,"text":"70268822 - 2018 - After the fire: Landsat helps map the way forward","indexId":"70268822","publicationYear":"2018","noYear":false,"title":"After the fire: Landsat helps map the way forward"},"predicate":"IS_PART_OF","object":{"id":70201780,"text":"70201780 - 2018 - Landsat benefiting society for fifty years","indexId":"70201780","publicationYear":"2018","noYear":false,"title":"Landsat benefiting society for fifty years"},"id":2}],"lastModifiedDate":"2025-07-08T14:19:17.321439","indexId":"70201780","displayToPublicDate":"2019-02-01T13:24:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Landsat benefiting society for fifty years","docAbstract":"<p>Since 1972, data acquired by the Landsat series of satellites have become integral to land management for both government and the private sector, providing scientists and decision makers with key information about agricultural productivity, ice sheet dynamics, urban growth, forest monitoring, natural resource management, water quality, and supporting disaster response. </p><p>Landsat 9 continues the mission of unrivaled space-based Earth observation and will lead the Landsat program into its second half century of Earth imagery provided to users, worldwide, at no charge. More than 8 million Landsat scenes held in the USGS archive to date are used in conjunction with advanced geographic information systems, image processing software, and cloud computing capabilities to enable Landsat users to study changes on the Earth’s surface across continental regions and extended time periods. </p><p>The Operational Land Imager 2 (OLI-2) and the Thermal Infrared Sensor 2 (TIRS-2) instruments onboard Landsat 9 —which replicate the technologically-advanced instruments introduced onboard Landsat 8—allow for the collection of continuous high-quality data required for advancing Earth applications, including our ability to map surface temperature and surface water quality. </p><p>Landsat 9 will build on the Landsat legacy, achieving a half-century record of global Earth observations.</p>","language":"English","publisher":"NASA","usgsCitation":"Rocchio, L., Connot, P., Young, S., Ramsayer, K., Owen, L., Bouchard, M., and Barnes, C., 2018, Landsat benefiting society for fifty years, 60 p.","productDescription":"60 p.","onlineOnly":"Y","ipdsId":"IP-103742","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":361030,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399495,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://landsat.gsfc.nasa.gov/wp-content/uploads/2019/02/Case_Studies_Book2018_Landsat_Final_12x9web.pdf","linkFileType":{"id":1,"text":"pdf"}}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rocchio, Laura E. P.","contributorId":212822,"corporation":false,"usgs":false,"family":"Rocchio","given":"Laura E. P.","affiliations":[],"preferred":false,"id":756680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connot, Peggy 0000-0002-8474-8096","orcid":"https://orcid.org/0000-0002-8474-8096","contributorId":212823,"corporation":false,"usgs":true,"family":"Connot","given":"Peggy","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":756681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Steve 0000-0002-7904-9696","orcid":"https://orcid.org/0000-0002-7904-9696","contributorId":212824,"corporation":false,"usgs":true,"family":"Young","given":"Steve","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":756682,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ramsayer, Kate","contributorId":212825,"corporation":false,"usgs":false,"family":"Ramsayer","given":"Kate","email":"","affiliations":[],"preferred":false,"id":756683,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Owen, Linda 0000-0002-1734-5406","orcid":"https://orcid.org/0000-0002-1734-5406","contributorId":212826,"corporation":false,"usgs":true,"family":"Owen","given":"Linda","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":756684,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bouchard, Michelle 0000-0002-6353-3491 mbouchard@usgs.gov","orcid":"https://orcid.org/0000-0002-6353-3491","contributorId":3765,"corporation":false,"usgs":true,"family":"Bouchard","given":"Michelle","email":"mbouchard@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":756685,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barnes, Christopher 0000-0002-4608-4364 christopher.barnes.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-4608-4364","contributorId":198908,"corporation":false,"usgs":true,"family":"Barnes","given":"Christopher","email":"christopher.barnes.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":756686,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70268821,"text":"70268821 - 2018 - Addressing the water consumption riddle","interactions":[{"subject":{"id":70268821,"text":"70268821 - 2018 - Addressing the water consumption riddle","indexId":"70268821","publicationYear":"2018","noYear":false,"title":"Addressing the water consumption riddle"},"predicate":"IS_PART_OF","object":{"id":70201780,"text":"70201780 - 2018 - Landsat benefiting society for fifty years","indexId":"70201780","publicationYear":"2018","noYear":false,"title":"Landsat benefiting society for fifty years"},"id":1}],"isPartOf":{"id":70201780,"text":"70201780 - 2018 - Landsat benefiting society for fifty years","indexId":"70201780","publicationYear":"2018","noYear":false,"title":"Landsat benefiting society for fifty years"},"lastModifiedDate":"2025-07-08T13:59:09.032588","indexId":"70268821","displayToPublicDate":"2019-02-01T08:54:44","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Addressing the water consumption riddle","docAbstract":"<p>No abstract available.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Landsat benefiting society for fifty years","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"NASA","usgsCitation":"Connot, P., and Young, S., 2018, Addressing the water consumption riddle, 8 p.","productDescription":"8 p.","startPage":"25","endPage":"32","ipdsId":"IP-103651","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":491775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Connot, Peggy 0000-0002-8474-8096","orcid":"https://orcid.org/0000-0002-8474-8096","contributorId":212823,"corporation":false,"usgs":true,"family":"Connot","given":"Peggy","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":942242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Young, Steve 0000-0002-7904-9696","orcid":"https://orcid.org/0000-0002-7904-9696","contributorId":212824,"corporation":false,"usgs":true,"family":"Young","given":"Steve","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":942241,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70268822,"text":"70268822 - 2018 - After the fire: Landsat helps map the way forward","interactions":[{"subject":{"id":70268822,"text":"70268822 - 2018 - After the fire: Landsat helps map the way forward","indexId":"70268822","publicationYear":"2018","noYear":false,"title":"After the fire: Landsat helps map the way forward"},"predicate":"IS_PART_OF","object":{"id":70201780,"text":"70201780 - 2018 - Landsat benefiting society for fifty years","indexId":"70201780","publicationYear":"2018","noYear":false,"title":"Landsat benefiting society for fifty years"},"id":1}],"isPartOf":{"id":70201780,"text":"70201780 - 2018 - Landsat benefiting society for fifty years","indexId":"70201780","publicationYear":"2018","noYear":false,"title":"Landsat benefiting society for fifty years"},"lastModifiedDate":"2025-07-08T13:54:06.755447","indexId":"70268822","displayToPublicDate":"2019-02-01T08:51:24","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"After the fire: Landsat helps map the way forward","docAbstract":"<p>No abstract available.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Landsat benefiting society for fifty years","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"NASA","usgsCitation":"Young, S., and Connot, P., 2018, After the fire: Landsat helps map the way forward, 5 p.","productDescription":"5 p.","startPage":"51","endPage":"55","ipdsId":"IP-103652","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":491774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Steve 0000-0002-7904-9696","orcid":"https://orcid.org/0000-0002-7904-9696","contributorId":212824,"corporation":false,"usgs":true,"family":"Young","given":"Steve","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":942243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connot, Peggy 0000-0002-8474-8096","orcid":"https://orcid.org/0000-0002-8474-8096","contributorId":212823,"corporation":false,"usgs":true,"family":"Connot","given":"Peggy","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":942244,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70199962,"text":"ds1098 - 2018 - Interior Least Tern sandbar nesting habitat measurements from Landsat Thematic Mapper imagery","interactions":[],"lastModifiedDate":"2019-01-28T10:50:50","indexId":"ds1098","displayToPublicDate":"2018-12-21T17:19:46","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1098","displayTitle":"Interior Least Tern Sandbar Nesting Habitat Measurements from Landsat Thematic Mapper Imagery","title":"Interior Least Tern sandbar nesting habitat measurements from Landsat Thematic Mapper imagery","docAbstract":"<p>Sandbars of large sand-bedded rivers of the central United States serve important ecological functions to many species, including the endangered Interior Least Tern (<i>Sternula antillarum</i>, ILT). The ILT is a colonial bird that feeds on fish and nests primarily on riverine sandbars during its annual breeding season of around May through July, depending on region. During this time, ILTs require bare sand of sufficient elevation so as not to be inundated between nest initiation and fledging of hatchlings. Partly because of decreases in available sandbar habitat from river channelization and impoundment, ILTs were listed as endangered in 1985.</p><p>Sandbars used by ILTs in central United States rivers are highly dynamic and undergo substantive changes across a wide range of temporal and spatial scales. River hydrology is the primary driver of sandbar morphodynamics in these systems. Better characterization of sandbar area with time, accounting for varying flow regimes, allows for a better understanding of landscape-scale ecology for sandbar-dependent species such as the ILT. This work uses remote-sensing techniques to quantify sandbar area that may be used by ILTs at the land-scape scale and how it has changed with time. The assessment of landscape-scale trends in sandbar area with time requires datasets with high temporal resolution and long record periods covering large geographic areas. Evaluation of remotely sensed datasets requires consideration of river stage fluctuations. To make this assessment, we developed land-cover classification datasets within active channel masks using all available images from the Landsat Thematic Mapper series of satellites meeting cloud-free (40 percent or less) and ice-free criteria. Landsat imagery was selected because of its long record period, spatial coverage, and regular reimaging cycle, making it well suited to monitor ILT sandbar habitat with time. We also attributed each scene with discharge or stage using a new database integrating U.S. Geological Survey and U.S. Army Corps of Engineers river data with Landsat metadata. This report documents development of these riverine classification datasets with a focus on applicability to the ILT. This framework may be used to continue monitoring the ILT sandbar nesting habitat or to evaluate other aquatic and terrestrial species whose life cycles are related to sandbars and channel complexity.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1098","collaboration":"Prepared in cooperation with the American Bird Conservancy","usgsCitation":"Bulliner, E.A., Elliott, C.M., Jacobson, R.B., and Lott, C., 2018, Interior Least Tern sandbar nesting habitat measurements from Landsat Thematic Mapper imagery: U.S. Geological Survey Data Series 1098, 32 p., https://doi. org/10.3133/ds1098. ","productDescription":"Report: v, 32 p.; Tables 9–12; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066937","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":360602,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1098/ds1098_tables9-12.xlsx","text":"Tables 9–12","size":"28.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Tables 9–12"},{"id":360653,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CV4GNG","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Interior least tern sandbar nesting habitat measurements from Landsat TM imagery"},{"id":360600,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1098/coverthb.jpg"},{"id":360601,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1098/ds1098.pdf","text":"Report","size":"1.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1098"}],"contact":"<p>Director, <a href=\"http://www.usgs.gov/centers/cerc/\" data-mce-href=\"http://www.usgs.gov/centers/cerc/\">Columbia Environmental Research Center</a><br>U.S. Geological Survey<br>4200 New Haven Road<br>Columbia, MO 65201</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Approach and Methods</li><li>Product Descriptions</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-12-21","noUsgsAuthors":false,"publicationDate":"2018-12-21","publicationStatus":"PW","scienceBaseUri":"5c1e0a2ce4b0708288cb01f3","contributors":{"authors":[{"text":"Bulliner, Edward A. 0000-0002-2774-9295 ebulliner@usgs.gov","orcid":"https://orcid.org/0000-0002-2774-9295","contributorId":4983,"corporation":false,"usgs":true,"family":"Bulliner","given":"Edward","email":"ebulliner@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":747495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elliott, Caroline M. 0000-0002-9190-7462 celliott@usgs.gov","orcid":"https://orcid.org/0000-0002-9190-7462","contributorId":2380,"corporation":false,"usgs":true,"family":"Elliott","given":"Caroline","email":"celliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":747496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jacobson, Robert B. 0000-0002-8368-2064 rjacobson@usgs.gov","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":1289,"corporation":false,"usgs":true,"family":"Jacobson","given":"Robert","email":"rjacobson@usgs.gov","middleInitial":"B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":747497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lott, Casey","contributorId":211742,"corporation":false,"usgs":false,"family":"Lott","given":"Casey","affiliations":[],"preferred":false,"id":754765,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70199787,"text":"70199787 - 2018 - Crop water productivity estimation with hyperspectral remote sensing","interactions":[],"lastModifiedDate":"2020-05-27T15:58:19.713875","indexId":"70199787","displayToPublicDate":"2018-12-11T10:48:12","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"5","title":"Crop water productivity estimation with hyperspectral remote sensing","docAbstract":"<p><span>Crop water productivity (CWP) is the ratio of accumulated crop biomass or yield (Y) to the water utilized to produce it, which is typically estimated using transpiration (ET</span><sub>C</sub><span>). CWP is an important metric to test and monitor water-saving strategies in agroecosystems across the globe. Red and near-infrared broadbands have been used to estimate CWP, because they capture biophysical constraints based on crop-light interaction principles at pixel level (e.g., 30-meter resolution) over large areas through time. Hyperspectral remote sensing, which allows for the more precise measurement of crop-light interactions at higher spectral resolution, should in theory provide higher accuracy in CWP estimation but has been underutilized by the remote sensing community due to computational challenges and lack of availability. In this study, a simple methodology is presented to demonstrate how CWP could be estimated using hyperspectral remote sensing. Due to a lack of hyperspectral data, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data were used for the demonstration. Landsat is a broadband sensor that provides considerable spectral information for CWP estimation. New bands were identified in the workflow outside the typical Landsat bands used to estimate CWP and its components (Y and ET</span><sub>C</sub><span>). Landsat bands 1 and 3 were the most effective at estimating CWP and Y with an R</span><sup>2</sup><span>&nbsp;of 0.72 (RMSE = 0.50 kg m</span><sup>−3</sup><span>) and 0.64 (RMSE = 0.31 kg m</span><sup>−2</sup><span>), respectively. All of the bands were poor at estimating ET</span><sub>C</sub><span>, with Landsat bands 1 and 7 being the most highly correlated (R</span><sup>2</sup><span>&nbsp;= 0.13, RMSE = 0.08 m). Future work should train models with multiple estimates of CWP and Y over the growing season, while ET</span><sub>C</sub><span>&nbsp;may be better estimated with thermal infrared bands not considered in this study. Finally, studies should also consider estimating CWP categorically, instead of continuously, if the same objectives of testing and monitoring are met.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Hyperspectral remote sensing of vegetation: Advanced applications in remote Sensing of agricultural crops and natural vegetation","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","usgsCitation":"Marshall, M., Aneece, I.P., Foley, D., Xueliang, C., and Biggs, T., 2018, Crop water productivity estimation with hyperspectral remote sensing, chap. 5 <i>of</i> Hyperspectral remote sensing of vegetation: Advanced applications in remote Sensing of agricultural crops and natural vegetation, v. 4, p. 79-96.","productDescription":"18 p.","startPage":"79","endPage":"96","ipdsId":"IP-097174","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":375087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375086,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.taylorfrancis.com/books/9780429431166/chapters/10.1201/9780429431166-5"}],"volume":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Michael","contributorId":145855,"corporation":false,"usgs":false,"family":"Marshall","given":"Michael","affiliations":[{"id":16265,"text":"Dept. of Geography, UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":746604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foley, Daniel 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":208266,"corporation":false,"usgs":true,"family":"Foley","given":"Daniel","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":746605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xueliang, Cai","contributorId":208267,"corporation":false,"usgs":false,"family":"Xueliang","given":"Cai","email":"","affiliations":[],"preferred":false,"id":746606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Biggs, Trent","contributorId":208268,"corporation":false,"usgs":false,"family":"Biggs","given":"Trent","affiliations":[],"preferred":false,"id":746607,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70201790,"text":"70201790 - 2018 - Analysis ready data: Enabling analysis of the Landsat archive","interactions":[],"lastModifiedDate":"2021-04-02T14:39:41.314848","indexId":"70201790","displayToPublicDate":"2018-12-10T12:28:37","publicationYear":"2018","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":"Analysis ready data: Enabling analysis of the Landsat archive","docAbstract":"<div id=\"container\"><div class=\"off-canvas-wrap\" data-offcanvas=\"\"><div class=\"inner-wrap\"><div id=\"content\"><div class=\"row full-width\"><div id=\"middle-column\" class=\"large-60 medium-6 middle-bordered small-12 columns\"><div class=\"top-border\"><div id=\"main_midcol\" class=\"maincol-midcol\"><div id=\"abstract\" class=\"abstract_div\"><div id=\"page-tab\"><div id=\"tabs-0\" class=\"ui-tabs-panel\"><div class=\"art-abstract in-tab hypothesis_container\"><span>Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and appropriate metadata to enable further processing while retaining traceability of data provenance.</span></div></div></div></div></div></div></div></div></div></div></div></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs10091363","usgsCitation":"Dwyer, J.L., Roy, D.P., Sauer, B., Jenkerson, C.B., Zhang, H.K., and Lymburner, L., 2018, Analysis ready data: Enabling analysis of the Landsat archive: Remote Sensing, v. 10, no. 9, 1363, 19 p., https://doi.org/10.3390/rs10091363.","productDescription":"1363, 19 p.","ipdsId":"IP-100589","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468193,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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(Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":755383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Hankui K.","contributorId":211965,"corporation":false,"usgs":false,"family":"Zhang","given":"Hankui","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":755384,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lymburner, Leo","contributorId":190978,"corporation":false,"usgs":false,"family":"Lymburner","given":"Leo","email":"","affiliations":[],"preferred":false,"id":755385,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70201227,"text":"70201227 - 2018 - Analysis of multi-decadal wetland changes, and cumulative impact of multiple storms 1984 to 2017","interactions":[],"lastModifiedDate":"2018-12-07T13:41:19","indexId":"70201227","displayToPublicDate":"2018-12-07T13:41:14","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of multi-decadal wetland changes, and cumulative impact of multiple storms 1984 to 2017","docAbstract":"<p><span>Land-cover classification analysis using Landsat satellite imagery acquired between 1984 and 2017 quantified short- (post-Hurricane Sandy) and long-term wetland-change trends along the Maryland and Virginia coasts between Metompkin Bay, VA and Ocean City, MD. Although there are limited options for upland migration of wetlands in the study area, regression analysis showed that wetland area increased slightly between 1984 and 2011, indicating that marsh aggradation rates were sufficient to maintain wetland elevation relative to mean sea level. Following Hurricane Irene (August 2011), the Halloween Nor’Easter (October 2011), and Hurricane Sandy (October 2012), wetland area decreased by more than 7&nbsp;km</span><sup>2</sup><span>&nbsp;compared with average pre-storm extents. We assume that Hurricane Sandy had the greatest impact due to the size and intensity of the storm. However, the cumulative effects of multiple storms within a short time period likely contributed to the greater observed losses in coastal wetlands relative to earlier periods. Five years after Hurricane Sandy, wetland area had not significantly recovered, but more time may be necessary to assess if the observed wetland losses will persist or if new growth within flooded marsh areas will be sufficient for the wetlands to recover to pre-storm extents. Comparisons of long-term and storm-driven wetland changes can lead to improved accuracy of habitat vulnerability models and greater understanding of potential impacts of future storms and SLR to coastal wetlands.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s11273-018-9635-6","usgsCitation":"Douglas, S.H., Bernier, J., and Smith, K., 2018, Analysis of multi-decadal wetland changes, and cumulative impact of multiple storms 1984 to 2017: Wetlands Ecology and Management, v. 26, no. 6, p. 1121-1142, https://doi.org/10.1007/s11273-018-9635-6.","productDescription":"22 p.","startPage":"1121","endPage":"1142","ipdsId":"IP-074495","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468199,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11273-018-9635-6","text":"Publisher Index Page"},{"id":360056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.57769775390625,\n              37.73162487017297\n            ],\n            [\n              -75.04486083984375,\n              37.73162487017297\n            ],\n            [\n              -75.04486083984375,\n              38.352426464461445\n            ],\n            [\n              -75.57769775390625,\n              38.352426464461445\n            ],\n            [\n              -75.57769775390625,\n              37.73162487017297\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"26","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-09","publicationStatus":"PW","scienceBaseUri":"5c0b957de4b0c53ecb2aca88","contributors":{"authors":[{"text":"Douglas, Steven H. 0000-0001-9078-538X sdouglas@usgs.gov","orcid":"https://orcid.org/0000-0001-9078-538X","contributorId":182361,"corporation":false,"usgs":true,"family":"Douglas","given":"Steven","email":"sdouglas@usgs.gov","middleInitial":"H.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernier, Julie 0000-0002-9918-5353 jbernier@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-5353","contributorId":3549,"corporation":false,"usgs":true,"family":"Bernier","given":"Julie","email":"jbernier@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Kathryn E.L. 0000-0002-7521-7875 kelsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-7521-7875","contributorId":173264,"corporation":false,"usgs":true,"family":"Smith","given":"Kathryn","email":"kelsmith@usgs.gov","middleInitial":"E.L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753333,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70201660,"text":"70201660 - 2018 - Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico","interactions":[],"lastModifiedDate":"2018-12-21T11:42:18","indexId":"70201660","displayToPublicDate":"2018-12-01T11:42:13","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5272,"text":"Proceedings of the International Association of Hydrological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico","docAbstract":"<p><span>During the spring of 2014, 130&nbsp;million m</span><span class=\"inline-formula\"><sup>3</sup></span><span>&nbsp;of water were released from the United States' Morelos Dam on the lower Colorado River to Mexico, allowing water to reach the Gulf of California for the first time in 13&nbsp;years. Our study assessed the effects of water transfer or ecological environmental flows from one nation to another, using remote sensing. Spatial applications for water resource evaluation are important for binational, integrated water resources management and planning for the Colorado River, which includes seven basin states in the US plus two states in Mexico. Our study examined the effects of the historic binational experiment (the Minute 319 agreement) on vegetative response along the riparian corridor. We used 250 m Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and 30 m Landsat 8 satellite imagery to track evapotranspiration (ET) and the normalized difference vegetation index (NDVI). Our analysis showed an overall increase in NDVI and evapotranspiration (ET) in the year following the 2014 pulse, which reversed a decline in those metrics since the last major flood in 2000. NDVI and ET levels decreased in 2015, but were still significantly higher (</span><span class=\"inline-formula\"><i>P</i></span><span> &lt; 0.001) than pre-pulse (2013) levels. Preliminary findings show that the decline in 2015 persisted into 2016 and 2017. We continue to analyse results for 2018 in comparison to short-term (2013–2018) and long-term (2000–2018) trends. Our results support the conclusion that these environmental flows from the US to Mexico via the Minute 319 “pulse” had a positive, but short-lived (1&nbsp;year), impact on vegetation growth in the delta.</span></p>","language":"English","publisher":"International Association of Hydrological Sciences","doi":"10.5194/piahs-380-45-2018","usgsCitation":"Nagler, P.L., Jarchow, C., and Glenn, E., 2018, Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico: Proceedings of the International Association of Hydrological Sciences, v. 380, p. 45-54, https://doi.org/10.5194/piahs-380-45-2018.","productDescription":"10 p.","startPage":"45","endPage":"54","ipdsId":"IP-097590","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468221,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/piahs-380-45-2018","text":"Publisher Index Page"},{"id":360670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","otherGeospatial":" Colorado River Delta","volume":"380","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-18","publicationStatus":"PW","scienceBaseUri":"5c1e0a30e4b0708288cb021b","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":754756,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":754757,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":754758,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70201809,"text":"70201809 - 2018 - Estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data","interactions":[],"lastModifiedDate":"2019-01-30T16:10:17","indexId":"70201809","displayToPublicDate":"2018-11-30T15:18:46","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2319,"text":"Journal of Geophysical Research G: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data","docAbstract":"<p>Landscape carbon (C) flux estimates are necessary for assessing the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO2) emissions. Advances in remote sensing have allowed for coarse-scale estimates of gross primary productivity (GPP) (e.g., MODIS 17), yet efforts to assess spatial patterns in respiration lag behind those of GPP. Here, we demonstrate a method to predict growing season soil respiration at a regional scale in a forested ecosystem. We related field measurements (n=144) of growing season soil respiration across subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors with a Random Forest model (30 m pixel size). We found that Landsat Enhanced Vegetation Index (EVI), growing season AI, temperature, precipitation, elevation, and slope aspect explained spatiotemporal variability in soil respiration. Our model had a psuedo-r2 of 0.45 and root mean squared error (RMSE) of roughly one-quarter of the mean value of respiration. Predicted growing season soil respiration across the region was remarkably consistent across 2004, 2005 and 2006 (150-d averages of 542.8, 544.3, and 536.5 g C m-2, respectively). Yet, we observed substantial variability in spatial patterns of soil respiration predictions that varied between years, suggesting that our method is sensitive to changes in respiration drivers. We compared our estimates to MODIS GPP and nocturnal net ecosystem exchange (NEE) derived from eddy covariance towers as a proxy for ecosystem respiration. Averaged across the predictive region, mean predicted growing season soil respiration was 73% of MODIS GPP, while predicted soil respiration was generally within 20% of nocturnal NEE from eddy covariance towers. This study demonstrated that geospatial and remotely-sensed datasets can be used in a statistical modeling framework to estimate soil respiration at landscape scales. </p>","language":"English","publisher":"AGU","doi":"10.1029/2018JG004613","usgsCitation":"Berryman, E.M., Vanderhoof, M.K., Bradford, J.B., Hawbaker, T., Henne, P., Burns, S.P., Frank, J.M., Birdsey, R.A., and Ryan, M.G., 2018, Estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data: Journal of Geophysical Research G: Biogeosciences, v. 123, no. 10, p. 3231-3249, https://doi.org/10.1029/2018JG004613.","productDescription":"19 p.","startPage":"3231","endPage":"3249","ipdsId":"IP-097679","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437667,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99TRHPB","text":"USGS data release","linkHelpText":"Data release for estimating soil respiration in a subalpine landscape using point, terrain, climate and greenness data"},{"id":360845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -108,\n              39\n            ],\n            [\n              -104,\n              39\n            ],\n            [\n              -104,\n              41.5\n            ],\n            [\n              -108,\n              41.5\n            ],\n            [\n              -108,\n              39\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"123","issue":"10","noUsgsAuthors":false,"publicationDate":"2018-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Berryman, Erin Michele 0000-0001-8699-2474 eberryman@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-2474","contributorId":5765,"corporation":false,"usgs":true,"family":"Berryman","given":"Erin","email":"eberryman@usgs.gov","middleInitial":"Michele","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755441,"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":755442,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":755443,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":755444,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henne, Paul D. 0000-0003-1211-5545 phenne@usgs.gov","orcid":"https://orcid.org/0000-0003-1211-5545","contributorId":169166,"corporation":false,"usgs":true,"family":"Henne","given":"Paul D.","email":"phenne@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755445,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burns, Sean P.","contributorId":98921,"corporation":false,"usgs":true,"family":"Burns","given":"Sean","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":755446,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frank, John M.","contributorId":11969,"corporation":false,"usgs":true,"family":"Frank","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":755447,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Birdsey, Richard A.","contributorId":17751,"corporation":false,"usgs":true,"family":"Birdsey","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":755448,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ryan, Michael G.","contributorId":202371,"corporation":false,"usgs":false,"family":"Ryan","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":33176,"text":"Rocky Mountain Research Station, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":755449,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70201202,"text":"70201202 - 2018 - Status of tidal marsh mapping for blue carbon inventories","interactions":[],"lastModifiedDate":"2018-12-06T11:40:57","indexId":"70201202","displayToPublicDate":"2018-11-20T11:40:51","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Status of tidal marsh mapping for blue carbon inventories","docAbstract":"<p>Remote-sensing-based maps of tidal marshes, both of their extents and carbon stocks, will play a key role in conducting greenhouse gas (GHG) inventories.</p><p>The U.N. Environment Programme World Conservation Monitoring Centre has produced a new Global Distribution of Salt Marsh dataset that estimates global salt marsh area at 5.5 Mha.</p><p>A Tier 1–2 GHG Inventory of U.S. Coastal Wetlands has been developed using the NOAA Coastal-Change Analysis Program Landsat-based land cover maps as a primary dataset.</p><p>180Successful mapping of tidal marsh biomass with optical satellite images provides opportunity to improve GHG Inventories.</p><p>Further work is needed to map tidal marsh salinity gradients, the extent of tidal vs. non-tidal marshes, methane emissions, and high-resolution elevation.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"A blue carbon primer: The state of coastal wetland carbon science, practice and policy","language":"English","publisher":"CRC Press","doi":"10.1201/9780429435362-14","usgsCitation":"Byrd, K.B., Mcowen, C., Weatherdon, L., Holmquist, J., and Crooks, S., 2018, Status of tidal marsh mapping for blue carbon inventories, chap. <i>of</i> A blue carbon primer: The state of coastal wetland carbon science, practice and policy, 17 p., https://doi.org/10.1201/9780429435362-14.","productDescription":"17 p.","ipdsId":"IP-079453","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":359982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c0a4357e4b0815414d2812e","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":753197,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mcowen, Chris","contributorId":211096,"corporation":false,"usgs":false,"family":"Mcowen","given":"Chris","email":"","affiliations":[{"id":38181,"text":"U.N. Environment Programme World Conservation Monitoring Programme","active":true,"usgs":false}],"preferred":false,"id":753198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weatherdon, Lauren","contributorId":211097,"corporation":false,"usgs":false,"family":"Weatherdon","given":"Lauren","affiliations":[{"id":38181,"text":"U.N. Environment Programme World Conservation Monitoring Programme","active":true,"usgs":false}],"preferred":false,"id":753199,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holmquist, James","contributorId":204126,"corporation":false,"usgs":false,"family":"Holmquist","given":"James","affiliations":[{"id":36858,"text":"Smithsonian","active":true,"usgs":false}],"preferred":false,"id":753200,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crooks, Stephen","contributorId":211098,"corporation":false,"usgs":false,"family":"Crooks","given":"Stephen","affiliations":[{"id":38182,"text":"Silvestrum Climate Associates","active":true,"usgs":false}],"preferred":false,"id":753201,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70200967,"text":"70200967 - 2018 - Landscape topoedaphic features create refugia from drought and insect disturbance in a lodgepole and whitebark pine forest","interactions":[],"lastModifiedDate":"2018-11-21T14:52:38","indexId":"70200967","displayToPublicDate":"2018-11-19T10:07:47","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Landscape topoedaphic features create refugia from drought and insect disturbance in a lodgepole and whitebark pine forest","docAbstract":"<p><span>Droughts and insect outbreaks are primary disturbance processes linking climate change to tree mortality in western North America. Refugia from these disturbances—locations where impacts are less severe relative to the surrounding landscape—may be priorities for conservation, restoration, and monitoring. In this study, hypotheses concerning physical and biological processes supporting refugia were investigated by modelling the landscape controls on disturbance refugia that were identified using remotely sensed vegetation indicators. Refugia were identified at 30-m resolution using anomalies of Landsat-derived Normalized Difference Moisture Index in lodgepole and whitebark pine forests in southern Oregon, USA, in 2001 (a single-year drought with no insect outbreak) and 2009 (during a multi-year drought and severe outbreak of mountain pine beetle). Landscape controls on refugia (topographic, soil, and forest characteristics) were modeled using boosted regression trees. Landscape characteristics better explained and predicted refugia locations in 2009, when forest impacts were greater, than in 2001. Refugia in lodgepole and whitebark pine forests were generally associated with topographically shaded slopes, convergent environments such as valleys, areas of relatively low soil bulk density, and in thinner forest stands. In whitebark pine forest, refugia were associated with riparian areas along headwater streams. Spatial patterns in evapotranspiration, snowmelt dynamics, soil water storage, and drought-tolerance and insect-resistance abilities may help create refugia from drought and mountain pine beetle. Identification of the landscape characteristics supporting refugia can help forest managers target conservation resources in an era of climate-change exacerbation of droughts and insect outbreaks.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/f9110715","usgsCitation":"Cartwright, J.M., 2018, Landscape topoedaphic features create refugia from drought and insect disturbance in a lodgepole and whitebark pine forest: Forests, v. 9, no. 11, p. 1-35, https://doi.org/10.3390/f9110715.","productDescription":"Article 715; 35 p.","startPage":"1","endPage":"35","ipdsId":"IP-090482","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":460809,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f9110715","text":"Publisher Index Page"},{"id":437682,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74Q7SWX","text":"USGS data release","linkHelpText":"Analysis of remotely-sensed vegetation conditions during droughts and a mountain pine beetle outbreak, Gearhart Mountain Wilderness, Oregon"},{"id":359541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Gearhart Mountain Wilderness","volume":"9","issue":"11","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-18","publicationStatus":"PW","scienceBaseUri":"5bf3d9efe4b045bfcae0c9ad","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":751469,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70212586,"text":"70212586 - 2018 - Analysis of different sensor performances in impervious surface mapping","interactions":[],"lastModifiedDate":"2020-08-25T15:23:02.103013","indexId":"70212586","displayToPublicDate":"2018-11-05T10:18:26","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Analysis of different sensor performances in impervious surface mapping","docAbstract":"<p><span>The U.S. Geological Survey (USGS) has developed the National Land Cover Database (NLCD) to provide consistent land cover and land cover change products for the nation since 2001. As one of products in the NLCD, the percent impervious surface area (ISA), which was estimated with Landsat imagery, represents the fraction of human-made impervious area in a 30-m grid and has been used to quantify urban land cover types and extents for the United States. However, it is still a challenge to clearly determine urban land cover intensity and extents using remote sensing data with spatial and spectral resolutions similar to Landsat in part because of highly heterogeneous features of urban land cover. Most urban areas, especially in low intensity development areas, exhibit sub-pixel characteristics that mix impervious surface with other land covers (e.g., grass and trees) in the 30-m resolution satellite imagery. Furthermore, the influence of highly heterogeneous features in many urban areas and how they alter the spectral signature of urban landscapes has not yet been fully studied. Recent advances in remote sensing technology have provided multiple spectral and spatial resolution data from several satellites including WorldView (WV), Sentinel-2, and the Landsat Operational Land Imager (OLI). Remote sensing images having different spectral bands and high spatial resolution provide the potential to derive detailed information on the nature and properties of different surface materials on the urban ground. This study focuses on performance of mapping impervious surface using data collected from WorldView-3, Sentinel-2, and Landsat OLI. We compared ISA results estimated from these sensors and evaluated benefits and limitations of radiometric and spatial resolutions for mapping impervious surface in a study area on the Eastern corridor between Washington, D.C., and Baltimore, where developed impervious surface containing both residential housings, office buildings, and roads, in the United States. The impact of different band combinations in Sentinel-2 imagery on mapping urban impervious surface and urban land cover was also evaluated.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"Jul 22-27, 2018","conferenceLocation":"Valencia, Spain","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS.2018.8518013","usgsCitation":"Xian, G.Z., Shi, H., Dewitz, J., and Wu, Z., 2018, Analysis of different sensor performances in impervious surface mapping, <i>in</i> IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, Jul 22-27, 2018, p. 8189-8192, https://doi.org/10.1109/IGARSS.2018.8518013.","productDescription":"4 p.","startPage":"8189","endPage":"8192","ipdsId":"IP-093474","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":377825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":796922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":796923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewitz, Jon 0000-0002-0458-212X dewitz@usgs.gov","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":2401,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","email":"dewitz@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":797261,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796924,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204939,"text":"70204939 - 2018 - It matters when you measure it: Using snow-cover Normalised Difference Vegetation Index (NDVI) to isolate post-fire conifer regeneration","interactions":[],"lastModifiedDate":"2019-08-23T15:06:29","indexId":"70204939","displayToPublicDate":"2018-10-31T14:57:44","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"It matters when you measure it: Using snow-cover Normalised Difference Vegetation Index (NDVI) to isolate post-fire conifer regeneration","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Landsat Normalised Difference Vegetation Index (NDVI) is commonly used to monitor post-fire green-up; however, most studies do not distinguish new growth of conifer from deciduous or herbaceous species, despite potential consequences for local climate, carbon and wildlife. We found that dual season (growing and snow cover) NDVI improved our ability to distinguish conifer tree presence and density. We then examined the post-fire pattern (1984–2017) in Landsat NDVI for fires that occurred a minimum of 20 years ago (1986–1997). Points were classified into four categories depending on whether NDVI, 20 years post-fire, had returned to pre-fire values in only the growing season, only under snow cover, in both seasons or neither. We found that each category of points showed distinct patterns of NDVI change that could be used to characterise the average pre-fire and post-fire vegetation condition Of the points analysed, 43% showed a between-season disagreement if NDVI had returned to pre-fire values, suggesting that using dual-season NDVI can modify our interpretations of post-fire conditions. We also found an improved correlation between 5- and 20-year NDVI change under snow cover, potentially attributable to snow masking fast-growing herbaceous vegetation. This study suggests that snow-cover Landsat imagery can enhance characterisations of forest recovery following fire.</span></span><br data-mce-bogus=\"1\"></p>","language":"English","publisher":"CSIRO","doi":"10.1071/WF18075","usgsCitation":"Vanderhoof, M.K., and Hawbaker, T., 2018, It matters when you measure it: Using snow-cover Normalised Difference Vegetation Index (NDVI) to isolate post-fire conifer regeneration: International Journal of Wildland Fire, v. 27, no. 12, p. 815-830, https://doi.org/10.1071/WF18075.","productDescription":"16 p.","startPage":"815","endPage":"830","ipdsId":"IP-096952","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437702,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UOBL50","text":"USGS data release","linkHelpText":"Data release for it matters when you measure it: using snow-cover Normalised Difference Vegetation Index (NDVI) to isolate post-fire conifer regeneration"},{"id":366872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Idaho, Montana, Utah, Washington, Wyoming","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.640625,\n              34.63320791137959\n            ],\n            [\n              -104.23828125,\n              34.63320791137959\n            ],\n            [\n              -104.23828125,\n              48.8936153614802\n            ],\n            [\n              -121.640625,\n              48.8936153614802\n            ],\n            [\n              -121.640625,\n              34.63320791137959\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"27","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":769169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":769170,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70249718,"text":"70249718 - 2018 - Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska","interactions":[],"lastModifiedDate":"2023-10-25T11:51:50.384966","indexId":"70249718","displayToPublicDate":"2018-10-25T06:48:05","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"ab010\" class=\"abstract author\" lang=\"en\"><div id=\"as010\"><p id=\"sp0010\">A methodology based on general hydraulic relations for rivers has been developed to estimate the discharge (flow rate) of rivers using satellite remote sensing observations. The estimates of discharge, flow depth, and flow velocity are derived from remotely observed water surface area, water surface slope, and water surface height, and demonstrated for two reaches of the Yukon River in Alaska, at Eagle (reach length 34.7 km) and near Stevens Village (reach length 38.3 km). The method is based on fundamental equations of hydraulic flow resistance in rivers, including the Manning equation and the Prandtl-von Karman universal velocity distribution equation. The method employs some new hydraulic relations to help define flow resistance and height of the zero flow boundary in the channel. Estimates are made both with and without calibration. The water surface area of the river reach is measured by using a provisional version of the U.S. Geological Survey (USGS) Landsat based product named Dynamic Surface Water Extent (DSWE). The water surface height and slope measurements require a self-consistent datum, and are derived from observations from the Jason-2 satellite altimeter mission. At both reach locations, the Jason-2 radar altimeter non-winter heights consistently tracked the stage recorded at USGS streamgages with a standard deviation of differences (error) during the non-winter periods of less than 7%. Part of the error may be due to differences in the gage and altimeter crossing locations with respect to the range of stage change and the response to changes in discharge at the upstream and downstream locations. For the non-winter periods, the radar derived slope estimates (mean = 0.0003) were constant over the mission lifetime, and in agreement with previously measured USGS water surface slopes and slopes determined from USGS topographic maps. The accuracy of the mean of the uncalibrated daily estimates of discharge varied between reaches, ranging from 13% near Stevens Village (N = 90) to −21% at Eagle (N = 246) based on the absolute error, and 5% to −6% based on the error of the log of the estimates. Calibrating to the mean of USGS daily discharge estimates from the streamflow rating for the same period of record at each streamgage resulted in mean absolute errors ranging from 1% to 2%, and log errors ranging from 1% or less. The error pattern of the estimates shows that without calibration, even though the mean is well simulated, the high and low end values over the range of estimates may have significant bias.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.04.005","usgsCitation":"Bjerklie, D.M., Birkett, C.M., Jones, J., Carabajal, C.C., Rover, J., Fulton, J.W., and Garambois, P., 2018, Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska: Journal of Hydrology, v. 561, p. 1000-1018, https://doi.org/10.1016/j.jhydrol.2018.04.005.","productDescription":"19 p.","startPage":"1000","endPage":"1018","ipdsId":"IP-085646","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":468292,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-02362515","text":"External Repository"},{"id":422090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -141.01012063275317,\n              64.05851086216975\n            ],\n            [\n              -141.01012063275317,\n              67.4123449375727\n            ],\n            [\n              -156.8963511015032,\n              67.4123449375727\n            ],\n            [\n              -156.8963511015032,\n              64.05851086216975\n            ],\n            [\n              -141.01012063275317,\n              64.05851086216975\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"561","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bjerklie, David M. 0000-0002-9890-4125 dmbjerkl@usgs.gov","orcid":"https://orcid.org/0000-0002-9890-4125","contributorId":3589,"corporation":false,"usgs":true,"family":"Bjerklie","given":"David","email":"dmbjerkl@usgs.gov","middleInitial":"M.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886841,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birkett, Charon M.","contributorId":331162,"corporation":false,"usgs":false,"family":"Birkett","given":"Charon","email":"","middleInitial":"M.","affiliations":[{"id":79138,"text":"University of Maryland ESSIC","active":true,"usgs":false}],"preferred":false,"id":886842,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":886843,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carabajal, Claudia C.","contributorId":265505,"corporation":false,"usgs":false,"family":"Carabajal","given":"Claudia","email":"","middleInitial":"C.","affiliations":[{"id":54699,"text":"SSAI Inc.","active":true,"usgs":false}],"preferred":false,"id":886844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":886845,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886846,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garambois, Pierre-Andre","contributorId":331163,"corporation":false,"usgs":false,"family":"Garambois","given":"Pierre-Andre","affiliations":[{"id":79140,"text":"ICUBE-UMR 7357, Fluid Mechanucs Team, INSA Strasbourg","active":true,"usgs":false}],"preferred":false,"id":886847,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70201200,"text":"70201200 - 2018 - Mapping crop residue and tillage intensity using WorldView-3 satellite shortwave infrared residue indices","interactions":[],"lastModifiedDate":"2018-12-06T11:24:25","indexId":"70201200","displayToPublicDate":"2018-10-18T11:24:19","publicationYear":"2018","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":"Mapping crop residue and tillage intensity using WorldView-3 satellite shortwave infrared residue indices","docAbstract":"<p><span>Crop residues serve many important functions in agricultural conservation including preserving soil moisture, building soil organic carbon, and preventing erosion. Percent crop residue cover on a field surface reflects the outcome of tillage intensity and crop management practices. Previous studies using proximal hyperspectral remote sensing have demonstrated accurate measurement of percent residue cover using residue indices that characterize cellulose and lignin absorption features found between 2100 nm and 2300 nm in the shortwave infrared (SWIR) region of the electromagnetic spectrum. The 2014 launch of the WorldView-3 (WV3) satellite has now provided a space-borne platform for the collection of narrow band SWIR reflectance imagery capable of measuring these cellulose and lignin absorption features. In this study, WorldView-3 SWIR imagery (14 May 2015) was acquired over farmland on the Eastern Shore of Chesapeake Bay (Maryland, USA), was converted to surface reflectance, and eight different SWIR reflectance indices were calculated. On-farm photographic sampling was used to measure percent residue cover at a total of 174 locations in 10 agricultural fields, ranging from plow-till to continuous no-till management, and these in situ measurements were used to develop percent residue cover prediction models from the SWIR indices using both polynomial and linear least squares regressions. Analysis was limited to agricultural fields with minimal green vegetation (Normalized Difference Vegetation Index &lt; 0.3) due to expected interference of vegetation with the SWIR indices. In the resulting residue prediction models, spectrally narrow residue indices including the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Lignin Cellulose Absorption Index (LCA) were determined to be more accurate than spectrally broad Landsat-compatible indices such as the Normalized Difference Tillage Index (NDTI), as determined by respective R</span><sup>2</sup><span>&nbsp;values of 0.94, 0.92, and 0.84 and respective residual mean squared errors (RMSE) of 7.15, 8.40, and 12.00. Additionally, SINDRI and LCA were more resistant to interference from low levels of green vegetation. The model with the highest correlation (2nd order polynomial SINDRI, R</span><sup>2</sup><span>&nbsp;= 0.94) was used to convert the SWIR imagery into a map of crop residue cover for non-vegetated agricultural fields throughout the imagery extent, describing the distribution of tillage intensity within the farm landscape. WorldView-3 satellite imagery provides spectrally narrow SWIR reflectance measurements that show utility for a robust mapping of crop residue cover.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs10101657","usgsCitation":"Hively, W.D., Lamb, B.T., Daughtry, C.S., Shermeyer, J., McCarty, G.W., and Quemada, M., 2018, Mapping crop residue and tillage intensity using WorldView-3 satellite shortwave infrared residue indices: Remote Sensing, v. 10, no. 10, p. 1-22, https://doi.org/10.3390/rs10101657.","productDescription":"Article 1657; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-090230","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":468309,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs10101657","text":"Publisher Index Page"},{"id":437715,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7930SDB","text":"USGS data release","linkHelpText":"WorldView-3 satellite imagery and crop residue field data collection, Talbot County, MD, May 2015"},{"id":359980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Choptank River watershed","volume":"10","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-18","publicationStatus":"PW","scienceBaseUri":"5c0a4357e4b0815414d28132","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":753190,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Brian T.","contributorId":211092,"corporation":false,"usgs":false,"family":"Lamb","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":753191,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daughtry, Craig S. T.","contributorId":211093,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S. T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":753192,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shermeyer, Jacob 0000-0002-8143-2790 jshermeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8143-2790","contributorId":211095,"corporation":false,"usgs":true,"family":"Shermeyer","given":"Jacob","email":"jshermeyer@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":753195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":753193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Quemada, Miguel","contributorId":211094,"corporation":false,"usgs":false,"family":"Quemada","given":"Miguel","email":"","affiliations":[{"id":38180,"text":"School of Agricultural Engineering and CEIGRAM, Technical University of Madrid","active":true,"usgs":false}],"preferred":false,"id":753194,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70201693,"text":"70201693 - 2018 - A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","interactions":[],"lastModifiedDate":"2018-12-21T13:38:22","indexId":"70201693","displayToPublicDate":"2018-10-01T13:38:15","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","docAbstract":"<p><span>Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands&nbsp;</span><i>versus</i><span>&nbsp;non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R</span><sup>2</sup><span>&nbsp;value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at&nbsp;</span><a rel=\"noreferrer noopener\" href=\"http://www.croplands.org/\" target=\"_blank\" data-mce-href=\"http://www.croplands.org/\">www.croplands.org</a><span>&nbsp;and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC)&nbsp;</span><a rel=\"noreferrer noopener\" href=\"https://www.lpdaac.usgs.gov/node/1282\" target=\"_blank\" data-mce-href=\"https://www.lpdaac.usgs.gov/node/1282\">https://www.lpdaac.usgs.gov/node/1282</a><span>.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2018.07.017","usgsCitation":"Teluguntla, P., Thenkabail, P.S., Oliphant, A., Xiong, J., Gumma, M.K., Congalton, R.G., Yadav, K., and Huete, A., 2018, A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform: ISPRS Journal of Photogrammetry and Remote Sensing, v. 144, p. 325-340, https://doi.org/10.1016/j.isprsjprs.2018.07.017.","productDescription":"16 p.","startPage":"325","endPage":"340","ipdsId":"IP-095003","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468349,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2018.07.017","text":"Publisher Index Page"},{"id":360683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia, 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,{"id":70227947,"text":"70227947 - 2018 - A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies","interactions":[],"lastModifiedDate":"2023-07-24T18:21:04.356707","indexId":"70227947","displayToPublicDate":"2018-09-13T10:28:16","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies","docAbstract":"<p><span>The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing&nbsp;<a class=\"topic-link\" title=\"Learn more about Landsat from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat\" data-mce-href=\"https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat\">Landsat</a>&nbsp;imagery and geospatial ancillary datasets; a multi-source integrated training data development and&nbsp;</span><a class=\"topic-link\" title=\"Learn more about Decision Trees from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/computer-science/decision-trees\" data-mce-href=\"https://www.sciencedirect.com/topics/computer-science/decision-trees\">decision-tree</a><span>&nbsp;based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2018.09.006","usgsCitation":"Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Bender, S.M., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Granneman, B.J., Liknes, G.C., Rigge, M.B., and Xian, G.Z., 2018, A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108-123, https://doi.org/10.1016/j.isprsjprs.2018.09.006.","productDescription":"16 p.; Data release","startPage":"108","endPage":"123","ipdsId":"IP-098281","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468406,"rank":5,"type":{"id":40,"text":"Open Access Publisher Index 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,{"id":70198609,"text":"fs20183053 - 2018 - U.S. Landsat Analysis Ready Data","interactions":[],"lastModifiedDate":"2018-09-06T10:26:54","indexId":"fs20183053","displayToPublicDate":"2018-09-05T13:55:49","publicationYear":"2018","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":"2018-3053","title":"U.S. Landsat Analysis Ready Data","docAbstract":"<p>U.S. Landsat Analysis Ready Data (ARD) are a revolutionary new U.S.&nbsp;Geological Survey science product that allows the Landsat archive to be more accessible and easier to analyze and reduces the amount of time users spend on data processing for monitoring and assessing landscape change. U.S.&nbsp;Landsat ARD are Level-2 products derived from Landsat Collections Level-1 precision and terrain-corrected scenes that are processed and arranged in spatially consistent tiles and dense temporal stacks for immediate use in monitoring and assessing landscape change.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183053","usgsCitation":"U.S. Geological Survey, 2018, U.S. Landsat Analysis Ready Data: U.S. Geological Survey Fact Sheet 2018–3053, 2 p., https://doi.org/10.3133/fs20183053.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-098255","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":357027,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3053/coverthb.jpg"},{"id":357028,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3053/fs20183053.pdf","text":"Report","size":"579 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018–3053"}],"contact":"<p>Director, <a data-mce-href=\"https://eros.usgs.gov/\" href=\"https://eros.usgs.gov/\">Earth Resources Observation and Science (EROS) Center</a> <br>U.S. Geological Survey<br>47914 252d Street <br>Sioux Falls, SD 57198</p>","tableOfContents":"<p><br data-mce-bogus=\"1\"></p>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2018-09-05","noUsgsAuthors":false,"publicationDate":"2018-09-05","publicationStatus":"PW","scienceBaseUri":"5b98a267e4b0702d0e842e72","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128240,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":742138,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70273758,"text":"70273758 - 2018 - An overview of USGS-NASA Landsat Science activities during 2018","interactions":[],"lastModifiedDate":"2026-01-28T15:01:45.383245","indexId":"70273758","displayToPublicDate":"2018-09-01T08:59:09","publicationYear":"2018","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":17130,"text":"The Earth Observer","active":true,"publicationSubtype":{"id":30}},"title":"An overview of USGS-NASA Landsat Science activities during 2018","docAbstract":"Two meetings of the U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) took place in 2018. The USGS Earth Resources Observation and Science (EROS) Center hosted the winter meeting, which took place February 21-22 in Sioux Falls, SD. The University of Colorado-Boulder hosted the summer meeting, which was held August 8-10 in Boulder, CO.","language":"English","publisher":"NASA","usgsCitation":"Crawford, C., Loveland, T.R., Masek, J.G., and Wulder, M.A., 2018, An overview of USGS-NASA Landsat Science activities during 2018: The Earth Observer, v. 30, no. 5, p. 19-26.","productDescription":"8 p.","startPage":"19","endPage":"26","ipdsId":"IP-179703","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":499144,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://science.nasa.gov/earth-science/the-earth-observer/archives/"}],"volume":"30","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":954589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":365671,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","middleInitial":"R.","affiliations":[{"id":36625,"text":"Emeritus","active":true,"usgs":false}],"preferred":false,"id":954590,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masek, Jeffery G.","contributorId":365672,"corporation":false,"usgs":false,"family":"Masek","given":"Jeffery","middleInitial":"G.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":954591,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wulder, Michael A.","contributorId":365673,"corporation":false,"usgs":false,"family":"Wulder","given":"Michael","middleInitial":"A.","affiliations":[{"id":13540,"text":"Canadian Forest Service","active":true,"usgs":false}],"preferred":false,"id":954592,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70199020,"text":"70199020 - 2018 - Sensitivity of mangrove range limits to climate variability","interactions":[],"lastModifiedDate":"2018-08-29T15:22:30","indexId":"70199020","displayToPublicDate":"2018-08-29T15:22:07","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of mangrove range limits to climate variability","docAbstract":"<div id=\"geb12751-sec-0001\" class=\"article-section__content\"><p class=\"article-section__sub-title section1\"><strong>Aim</strong></p><p>Correlative distribution models have been used to identify potential climatic controls of mangrove range limits, but there is still uncertainty about the relative importance of these factors across different regions. To provide insights into the strength of climatic control of different mangrove range limits, we tested whether temporal variability in mangrove abundance increases near range limits and whether this variability is correlated with climatic factors thought to control large‐scale mangrove distributions.</p></div><div id=\"geb12751-sec-0002\" class=\"article-section__content\"><p class=\"article-section__sub-title section1\"><strong>Location</strong></p><p>North and South America.</p></div><div id=\"geb12751-sec-0003\" class=\"article-section__content\"><p class=\"article-section__sub-title section1\"><strong>Time period</strong></p><p>1984–2011.</p></div><div id=\"geb12751-sec-0004\" class=\"article-section__content\"><p class=\"article-section__sub-title section1\"><strong>Major taxa studied</strong></p><p><i>Avicennia germinans</i>,<span>&nbsp;</span><i>Avicennia schuaeriana</i>,<span>&nbsp;</span><i>Rhizophora mangle</i>,<span>&nbsp;</span><i>Laguncularia racemosa</i>.</p></div><div id=\"geb12751-sec-1000\" class=\"article-section__content\"><p class=\"article-section__sub-title section1\"><strong>Methods</strong></p><p>We characterized temporal variability in the enhanced vegetation index (EVI) at mangrove range limits using Landsat satellite imagery collected between 1984–2011. We characterized greening trends at each range limit, examined variability in EVI along latitudinal gradients near each range limit, and assessed correlations between changes in EVI and temperature and precipitation.</p></div><div id=\"geb12751-sec-1460\" class=\"article-section__content\"><p class=\"article-section__sub-title section1\"><strong>Results</strong></p><p>Spatial variability in mean EVI was generally correlated with temperature and precipitation, but the relationships were region specific. Greening trends were most pronounced at range limits in eastern North America. In these regions variability in EVI increased toward the range limit and was sensitive to climatic factors. In contrast, EVI at range limits on the Pacific coast of North America and both coasts of South America was relatively stable and less sensitive to climatic variability.</p></div><div id=\"geb12751-sec-0005\" class=\"article-section__content\"><p class=\"article-section__sub-title section1\"><strong>Main conclusions</strong></p><p>Our results suggest that range limits in eastern North America are strongly controlled by climate factors. Mangrove expansion in response to future warming is expected to be rapid in regions that are highly sensitive to climate variability (e.g. eastern North America), but the response in other range limits (e.g. South America) is likely to be more complex and modulated by additional factors such as dispersal limitation, habitat constraints, and/or changing climatic means rather than just extremes.</p></div>","language":"English","publisher":"Wiley","doi":"10.1111/geb.12751","usgsCitation":"Cavanaugh, K.C., Osland, M.J., Bardou, R., Hinojosa-Arango, G., Lopez-Vivas, J.M., Parker, J.D., and Rovai, A.S., 2018, Sensitivity of mangrove range limits to climate variability: Global Ecology and Biogeography, v. 27, no. 8, p. 925-935, https://doi.org/10.1111/geb.12751.","productDescription":"11 p.","startPage":"925","endPage":"935","ipdsId":"IP-086658","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":356928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-24","publicationStatus":"PW","scienceBaseUri":"5b98a270e4b0702d0e842ec0","contributors":{"authors":[{"text":"Cavanaugh, Kyle C.","contributorId":149015,"corporation":false,"usgs":false,"family":"Cavanaugh","given":"Kyle","email":"","middleInitial":"C.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":743797,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osland, Michael J. 0000-0001-9902-8692 mosland@usgs.gov","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":3080,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","email":"mosland@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":743796,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bardou, Remi","contributorId":207414,"corporation":false,"usgs":false,"family":"Bardou","given":"Remi","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":743802,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hinojosa-Arango, Gustavo","contributorId":207412,"corporation":false,"usgs":false,"family":"Hinojosa-Arango","given":"Gustavo","email":"","affiliations":[{"id":37534,"text":"Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional Unidad Oaxaca","active":true,"usgs":false}],"preferred":false,"id":743798,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lopez-Vivas, Juan M.","contributorId":207413,"corporation":false,"usgs":false,"family":"Lopez-Vivas","given":"Juan","email":"","middleInitial":"M.","affiliations":[{"id":37535,"text":"Universidad Autónoma de Baja California Sur","active":true,"usgs":false}],"preferred":false,"id":743799,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parker, John D.","contributorId":207430,"corporation":false,"usgs":false,"family":"Parker","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":743800,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rovai, Andre S.","contributorId":167671,"corporation":false,"usgs":false,"family":"Rovai","given":"Andre","email":"","middleInitial":"S.","affiliations":[{"id":24801,"text":"Federal University of Santa Catarina, Dept. Ecology and Zoology, Brazil","active":true,"usgs":false}],"preferred":false,"id":743801,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70270641,"text":"70270641 - 2018 - Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites","interactions":[],"lastModifiedDate":"2025-08-21T15:07:51.023919","indexId":"70270641","displayToPublicDate":"2018-08-27T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16883,"text":"European Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites","docAbstract":"<p><span>The Sentinel-2A and Landsat-8 satellites carry on-board moderate resolution multispectral imagers for the purpose of documenting the Earth’s changing surface. Though they are independently built and managed, users will certainly take advantage of the opportunity to have higher temporal coverage by combining the datasets. Thus it is important for the radiometric and geometric calibration of the MultiSpectral Instrument (MSI) and the Operational Land Imager (OLI) to be compatible. Cross-calibration of MSI to OLI has been accomplished using multiple techniques involving the use of pseudo-invariant calibration sites (PICS) using direct comparisons as well as through use of PICS models predicting top-of-atmosphere reflectance. A team from the University of Arizona is acquiring field data under both instruments for vicarious calibration of the sensors. This paper shows that the work done to date by the Landsat and Sentinel-2 calibration teams has resulted in stable radiometric calibration for each instrument and consistency to ~2.5% between the instruments for all the spectral bands that the instruments have in common.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/22797254.2018.1507613","usgsCitation":"Barsi, J., Alhammoud, B., Czapla-Myers, J., Gascon, F., Haque, O., Kaewmanee, M., Leigh, L., and Markham, B., 2018, Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites: European Journal of Remote Sensing, v. 51, no. 1, p. 822-837, https://doi.org/10.1080/22797254.2018.1507613.","productDescription":"16 p.","startPage":"822","endPage":"837","ipdsId":"IP-088432","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494460,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/22797254.2018.1507613","text":"Publisher Index Page"},{"id":494383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Barsi, Julia","contributorId":251781,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","affiliations":[{"id":50397,"text":"SSAI","active":true,"usgs":false}],"preferred":false,"id":946725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alhammoud, Bahjat","contributorId":360058,"corporation":false,"usgs":false,"family":"Alhammoud","given":"Bahjat","affiliations":[{"id":85960,"text":"ARGANS Limited","active":true,"usgs":false}],"preferred":false,"id":946726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Czapla-Myers, Jeffrey","contributorId":360059,"corporation":false,"usgs":false,"family":"Czapla-Myers","given":"Jeffrey","affiliations":[{"id":85963,"text":"College of Optical Sciences, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":946727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gascon, Ferran","contributorId":360060,"corporation":false,"usgs":false,"family":"Gascon","given":"Ferran","affiliations":[{"id":85964,"text":"ESA/ESRIN","active":true,"usgs":false}],"preferred":false,"id":946728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":946729,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kaewmanee, Morakot","contributorId":360061,"corporation":false,"usgs":false,"family":"Kaewmanee","given":"Morakot","affiliations":[{"id":85965,"text":"IP Lab, SDSU","active":true,"usgs":false}],"preferred":false,"id":946730,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Leigh, Larry","contributorId":360062,"corporation":false,"usgs":false,"family":"Leigh","given":"Larry","affiliations":[{"id":85965,"text":"IP Lab, SDSU","active":true,"usgs":false}],"preferred":false,"id":946731,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Markham, Brian","contributorId":360063,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","affiliations":[{"id":79115,"text":"NASA/GSFC","active":true,"usgs":false}],"preferred":false,"id":946732,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70212568,"text":"70212568 - 2018 - Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake Bay region, USA","interactions":[],"lastModifiedDate":"2021-07-06T22:56:20.260535","indexId":"70212568","displayToPublicDate":"2018-08-21T09:01:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake Bay region, USA","docAbstract":"The National Land Cover Database (NLCD) contains three eras (2001, 2006, 2011) of percentage urban impervious cover (%IC) at the native pixel size (30 m-x-30 m) of the Landsat Thematic Mapper satellite.  These data are potentially valuable to environmental managers and stakeholders because of the utility of %IC as an indicator of watershed and aquatic condition, but lack an accuracy assessment because of the absence of suitable reference data.  Recently developed 1 m2 land cover data for the Chesapeake Bay region makes it possible to assess NLCD %IC accuracy for a 262,000 km2 region based on a census rather than a sample of reference data.  We report agreement between the two %IC datasets for watersheds and the riparian zones within watersheds and four additional square units.  The areas of the six assessment units were 40 ha cell, 433 ha (riparian unit average), 2,756 ha cell, 5,626 ha cell, 8,569 ha (watershed unit average) and 22,500 ha cell.  Mean Absolute Deviation (MAD) was ≤ 1.6% for each of the six assessment units and Mean Deviation (MD) was only slightly less, indicating NLCD reliably reproduced %IC from the 1 m2 data with a small (≤ 1.6%) and consistent tendency for underestimation.  Results were sensitive to assessment unit choice.  The results for the four largest assessment units had very similar regression parameters, R2 values, and patterns of bias.  Results for the riparian assessment were different from those for the watershed unit and the other three larger units. MAD was about 50% less for the riparian zones than it was for the watersheds, the direction of bias was less consistent, and NLCD %IC was uniformly higher than 1 m2 %IC in urbanized riparian zones.  For the smallest unit, bias patterns were more similar to the riparian unit and regression results were more similar to the four larger units.  MAD and MD were also sensitive to the amount of urbanization, increasing as NLCD %IC increased.  The low overall bias and positive relationship between bias and level of urbanization suggest that the benefits of obtaining 1 m2 IC data outside of urban areas may not outweigh the costs of obtaining such data.","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2018.09.010","usgsCitation":"Wickham, J., Herold, N., Stehman, S.V., Homer, C., Xian, G.Z., and Claggett, P., 2018, Accuracy assessment of NLCD 2011 impervious cover data for the Chesapeake Bay region, USA: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 151-160, https://doi.org/10.1016/j.isprsjprs.2018.09.010.","productDescription":"10 p.","startPage":"151","endPage":"160","ipdsId":"IP-099047","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468486,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2018.09.010","text":"Publisher Index Page"},{"id":377722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, New York, Pennsylvania, Virginia, West Virginia","otherGeospatial":"Chesapeake Bay region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.1904296875,\n              38.41916639395372\n            ],\n            [\n              -75.223388671875,\n              38.64261790634527\n            ],\n            [\n              -75.35522460937499,\n              38.79690830348427\n            ],\n            [\n              -75.498046875,\n              38.87392853923629\n            ],\n            [\n              -75.5419921875,\n              39.0533181067413\n            ],\n            [\n     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