{"pageNumber":"14","pageRowStart":"325","pageSize":"25","recordCount":1869,"records":[{"id":70208398,"text":"70208398 - 2020 - Multi-decadal patterns of vegetation succession after tundra fire on the Yukon-Kuskokwim Delta, Alaska","interactions":[],"lastModifiedDate":"2020-02-09T13:41:53","indexId":"70208398","displayToPublicDate":"2020-01-30T13:39:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Multi-decadal patterns of vegetation succession after tundra fire on the Yukon-Kuskokwim Delta, Alaska","docAbstract":"Alaska’s Yukon-Kuskokwim Delta (YKD) is one of the warmest parts of the\nArctic tundra biome and tundra fires are common in its upland areas. Here we combine\nfield measurements, Landsat observations, and quantitative cover maps for tundra plant\nfunctional types (PFTs) to characterize multi-decadal succession and landscape change\nafter fire in lichen-dominated upland tundra of the YKD, where extensive wildfires\noccurred in 1971–1972, 1985, 2006–2007, and 2015. Unburned tundra was\ncharacterized by abundant lichens and low lichen cover was consistently associated\nwith historical fire. While we observed some successional patterns that were consistent\nwith earlier work in Alaskan tussock tundra, other patterns were not. In the landscape\nwe studied, a large proportion of pre-fire moss cover and surface peat tended to survive\nfire, which favors survival of existing vascular plants and limits opportunities for seed\nrecruitment. Although shrub cover was much higher in 1985 and 1971–1972 burns than\nin unburned tundra, tall shrubs (>0.5 m height) were rare and the PFT maps indicate\nhigh landscape-scale variability in the degree and persistence of shrub increase after\nfire. Fire has induced persistent changes in species composition and structure of upland\ntundra on the YKD, but the lichen-dominated fuels and thick surface peat appear to\nhave limited the potential for severe fire and accompanying edaphic changes. Soil thaw\ndepths were about 10 cm deeper in 2006–2007 burns than in unburned tundra, but\nwere similar to unburned tundra in 1985 and 1971–1972 burns. Historically, repeat fire\nhas been rare on the YKD, and the functional diversity of vegetation has recovered\nwithin several decades post-fire. Our findings provide a basis for predicting and\nmonitoring post-fire tundra succession on the YKD and elsewhere.","language":"English","publisher":"IOPScience","doi":"10.1088/1748-9326/ab5f49","usgsCitation":"Frost, G., Loehman, R.A., Saperstein, L., Macander, M.J., Nelson, P., Paradis, D., and Natali, S.M., 2020, Multi-decadal patterns of vegetation succession after tundra fire on the Yukon-Kuskokwim Delta, Alaska: Environmental Research Letters, no. 2, 14 p., https://doi.org/10.1088/1748-9326/ab5f49.","productDescription":"14 p.","ipdsId":"IP-112003","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":457945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab5f49","text":"Publisher Index Page"},{"id":372177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-Kuskokwim Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -167.958984375,\n              58.619777025081675\n            ],\n            [\n              -157.52197265625,\n              58.619777025081675\n            ],\n            [\n              -157.52197265625,\n              63.30281270313518\n            ],\n            [\n              -167.958984375,\n              63.30281270313518\n            ],\n            [\n              -167.958984375,\n              58.619777025081675\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","issue":"2","edition":"15","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Frost, Gerald","contributorId":222261,"corporation":false,"usgs":false,"family":"Frost","given":"Gerald","email":"","affiliations":[{"id":40510,"text":"ABR, Inc","active":true,"usgs":false}],"preferred":false,"id":781726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":781725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saperstein, Lisa","contributorId":218974,"corporation":false,"usgs":false,"family":"Saperstein","given":"Lisa","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":781727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Macander, Matthew J.","contributorId":203639,"corporation":false,"usgs":false,"family":"Macander","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36669,"text":"ABR, Inc.—Environmental Research & Services","active":true,"usgs":false}],"preferred":false,"id":781728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, Peter","contributorId":198617,"corporation":false,"usgs":false,"family":"Nelson","given":"Peter","affiliations":[],"preferred":false,"id":781729,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Paradis, David","contributorId":222262,"corporation":false,"usgs":false,"family":"Paradis","given":"David","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":781730,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Natali, Sue M.","contributorId":204028,"corporation":false,"usgs":false,"family":"Natali","given":"Sue","email":"","middleInitial":"M.","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":781731,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70208311,"text":"70208311 - 2020 - Evaluation of hydrologic impact of an irrigation curtailment program in the Upper Klamath Lake Basin using Landsat satellite data","interactions":[],"lastModifiedDate":"2020-05-05T16:44:59.832545","indexId":"70208311","displayToPublicDate":"2020-01-22T07:27:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of hydrologic impact of an irrigation curtailment program in the Upper Klamath Lake Basin using Landsat satellite data","docAbstract":"Upper Klamath Lake (UKL) is the source of the Klamath river that flows through southern Oregon and northern California. The UKL basin is home to two endangered species and provides water for 81,000+ ha (200,000+ acres) of irrigation on the United States Bureau of Reclamation (USBR) Klamath Project located downstream of the UKL basin. Irrigated agriculture also occurs along the tributaries to UKL. During 2013–2016, water right calls resulted in various levels of curtailment of irrigation diversions from the tributaries to UKL. However, information on the extent of curtailment, how much irrigation water was saved, and its impact on the UKL is unknown. In this study, we combined Landsat-based actual evapotranspiration (ETa) data obtained from the Operational Simplified Surface Energy Balance (SSEBop) model with gridded precipitation and USGS station discharge data to evaluate the hydrologic impact of the curtailment program. Analysis was performed for five base years (2004, 2006, 2008-2010) and four target years (2013-2016) over irrigated areas above UKL. Our results indicated that the impact of the curtailment program over the June to September time-period was highest during 2013 and declined in each of the following years. The total on-field water savings were approximately 60 hm3 in 2013 and 2014, 44 hm3 in 2015, and 32 hm3 in 2016. The instream water flow change or extra water available (EWA) were found at 92, 68, 45, and 26 hm3 respectively for 2013, 2014, 2015 and 2016. Most water savings came from pasture and wetlands. Alfalfa showed the most decline in water use among grain crops. The resulting EWA from the curtailment contributed to a maximum of 19% of the lake inflows and 50% of the lake volume. This study presents the use of Landsat-based ETa and other remote sensing datasets for evaluating water-related impacts of the irrigation curtailment program.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13708","usgsCitation":"Velpuri, N., Senay, G., Schauer, M., Garcia, C.A., Singh, R., Friedrichs, M., Bohms, S., Haynes, J.V., and Conlon, T.D., 2020, Evaluation of hydrologic impact of an irrigation curtailment program in the Upper Klamath Lake Basin using Landsat satellite data: Hydrological Processes, v. 34, no. 8, p. 1697-1713, https://doi.org/10.1002/hyp.13708.","productDescription":"17 p.","startPage":"1697","endPage":"1713","ipdsId":"IP-111134","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":458053,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13708","text":"Publisher Index Page"},{"id":437147,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BC38CL","text":"USGS data release","linkHelpText":"Assessing the impact of irrigation curtailment using Landsat satellite data: A case study in the Upper Klamath Lake basin"},{"id":371987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"California, Oregon","otherGeospatial":"Upper Klamath Lake Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.42041015624999,\n              40.76806170936614\n            ],\n            [\n              -119.94323730468749,\n              40.76806170936614\n            ],\n            [\n              -119.94323730468749,\n              43.205175817237304\n            ],\n            [\n              -123.42041015624999,\n              43.205175817237304\n            ],\n            [\n              -123.42041015624999,\n              40.76806170936614\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"34","issue":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Velpuri, Naga Manohar  0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":216911,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga Manohar ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":781360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel 0000-0002-8810-8539","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":216910,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":781361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schauer, Matthew 0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":216909,"corporation":false,"usgs":true,"family":"Schauer","given":"Matthew","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":781362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garcia, C. Amanda 0000-0003-3776-3565 cgarcia@usgs.gov","orcid":"https://orcid.org/0000-0003-3776-3565","contributorId":1899,"corporation":false,"usgs":true,"family":"Garcia","given":"C.","email":"cgarcia@usgs.gov","middleInitial":"Amanda","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Ramesh  0000-0002-8164-3483","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":216912,"corporation":false,"usgs":false,"family":"Singh","given":"Ramesh ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":781364,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":216914,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":781365,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bohms, Stefanie 0000-0002-2979-4655 sbohms@usgs.gov","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":3148,"corporation":false,"usgs":true,"family":"Bohms","given":"Stefanie","email":"sbohms@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":781359,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haynes, Jonathan V. 0000-0001-6530-6252 jhaynes@usgs.gov","orcid":"https://orcid.org/0000-0001-6530-6252","contributorId":3113,"corporation":false,"usgs":true,"family":"Haynes","given":"Jonathan","email":"jhaynes@usgs.gov","middleInitial":"V.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781366,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Conlon, Terrence D. 0000-0002-5899-7187 tdconlon@usgs.gov","orcid":"https://orcid.org/0000-0002-5899-7187","contributorId":819,"corporation":false,"usgs":true,"family":"Conlon","given":"Terrence","email":"tdconlon@usgs.gov","middleInitial":"D.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781367,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70249384,"text":"70249384 - 2020 - Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data","interactions":[],"lastModifiedDate":"2024-05-16T14:17:08.092399","indexId":"70249384","displayToPublicDate":"2020-01-20T07:00:38","publicationYear":"2020","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":"Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data","docAbstract":"<div class=\"html-p\">Quantifying western U.S. rangelands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. Nine rangeland ecosystem components, including percent shrub, sagebrush (<span class=\"html-italic\">Artemisia</span>), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30 m resolution. Extensive ground measurements, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree models were used to create component predictions. In the mapped area (2,993,655 km²), bare ground averaged 45.5%, shrub 15.2%, sagebrush 4.3%, big sagebrush 2.9%, herbaceous 23.0%, annual herbaceous 4.2%, and litter 15.8%. Component accuracies using independent validation across all components averaged<span>&nbsp;</span><span class=\"html-italic\">R</span><sup>2</sup><span>&nbsp;</span>values of 0.46 and an root mean squared error (RMSE) of 10.37, and cross-validation averaged<span>&nbsp;</span><span class=\"html-italic\">R</span><sup>2</sup><span>&nbsp;</span>values of 0.72 and an RMSE of 5.09. Component composition strongly varies by Environmental Protection Agency (EPA) level III ecoregions (<span class=\"html-italic\">n</span><span>&nbsp;</span>= 32): 17 are bare ground dominant, 11 herbaceous dominant, and four shrub dominant. Sagebrush physically covers 90,950 km², or 4.3%, of our study area, but is present in 883,449 km², or 41.5%, of the mapped portion of our study area.</div>","language":"English","publisher":"MDPI","doi":"10.3390/rs12030412","usgsCitation":"Rigge, M., Homer, C., Cleeves, L., Meyer, D., Bunde, B., Shi, H., Xian, G.Z., and Bobo, M.R., 2020, Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data: Remote Sensing, v. 12, no. 3, 412, 26 p., https://doi.org/10.3390/rs12030412.","productDescription":"412, 26 p.","ipdsId":"IP-097596","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458091,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12030412","text":"Publisher Index Page"},{"id":421669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Texas, Washington, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -126.64737991483358,\n              50.2281194336924\n            ],\n            [\n              -126.64737991483358,\n              28.766650583257572\n            ],\n            [\n              -100.88632287724539,\n              28.766650583257572\n            ],\n            [\n              -100.88632287724539,\n              50.2281194336924\n            ],\n            [\n              -126.64737991483358,\n              50.2281194336924\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew 0000-0003-4471-8009","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":221482,"corporation":false,"usgs":false,"family":"Rigge","given":"Matthew","affiliations":[{"id":40392,"text":"Contractor; Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":885423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin 0000-0003-4755-8135","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":238918,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cleeves, Lauren","contributorId":221860,"corporation":false,"usgs":false,"family":"Cleeves","given":"Lauren","email":"","affiliations":[{"id":12586,"text":"Consultant","active":true,"usgs":false}],"preferred":false,"id":885425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Deb 0000-0002-8841-697X","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":288363,"corporation":false,"usgs":false,"family":"Meyer","given":"Deb","affiliations":[{"id":61730,"text":"Retired, KBR","active":true,"usgs":false}],"preferred":false,"id":885426,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":885427,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":302265,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":885428,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885429,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bobo, Matthew R","contributorId":217910,"corporation":false,"usgs":false,"family":"Bobo","given":"Matthew","email":"","middleInitial":"R","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":885430,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70249354,"text":"70249354 - 2020 - Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires","interactions":[],"lastModifiedDate":"2023-10-05T00:14:25.937585","indexId":"70249354","displayToPublicDate":"2020-01-10T12:32:34","publicationYear":"2020","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":"Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires","docAbstract":"<p><span>Fire Radiative Power (FRP) is related to fire combustion rates and is used to quantify the atmospheric emissions of greenhouse gases and aerosols. FRP over gas flares and wildfires can be retrieved remotely using satellites that observe in shortwave infrared (SWIR) to middle infrared (MIR) wavelengths. Heritage techniques to retrieve FRP developed for wildland fires using the MIR 4 μm radiances have been adapted for the hotter burning gas flares using the SWIR 2 μm observations. Effects of atmosphere, including smoke and aerosols, are assumed to be minimal in these algorithms because of the use of longer than visual wavelengths. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS) and Landsat 8 observations acquired before and during emergency oil and gas flaring in eastern Saudi Arabia to show that dark, sooty smoke affects both 4 μm and 2 μm observations. While the 2 μm observations used to retrieve gas FRP may be reliable during clear atmospheric conditions, performance is severely impacted by dark smoke. Global remote sensing-based inventories of wildfire and gas flaring need to consider the possibility that soot and dark smoke can potentially lead to an underestimation of FRP over fires.</span></p>","language":"English","publisher":"MPDI","doi":"10.3390/rs12020238","usgsCitation":"Kumar, S.S., Hult, J.E., Picotte, J., and Peterson, B., 2020, Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires: Remote Sensing, v. 12, no. 2, 238, 9 p., https://doi.org/10.3390/rs12020238.","productDescription":"238, 9 p.","ipdsId":"IP-113025","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458161,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12020238","text":"Publisher Index Page"},{"id":421611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Saudi Arabia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[42.77933,16.34789],[42.64957,16.77464],[42.34799,17.07581],[42.27089,17.47472],[41.75438,17.83305],[41.22139,18.6716],[40.93934,19.48649],[40.24765,20.17463],[39.80168,20.33886],[39.1394,21.2919],[39.0237,21.98688],[39.06633,22.57966],[38.49277,23.68845],[38.02386,24.07869],[37.48363,24.28549],[37.15482,24.85848],[37.20949,25.08454],[36.93163,25.60296],[36.6396,25.82623],[36.24914,26.57014],[35.64018,27.37652],[35.13019,28.06335],[34.63234,28.05855],[34.78778,28.60743],[34.83222,28.95748],[34.95604,29.35655],[36.06894,29.19749],[36.50121,29.50525],[36.74053,29.86528],[37.50358,30.00378],[37.66812,30.33867],[37.99885,30.5085],[37.00217,31.50841],[39.00489,32.01022],[39.19547,32.16101],[40.39999,31.88999],[41.88998,31.19001],[44.7095,29.17889],[46.56871,29.09903],[47.45982,29.00252],[47.70885,28.52606],[48.41609,28.552],[48.80759,27.68963],[49.29955,27.46122],[49.47091,27.11],[50.15242,26.68966],[50.21294,26.27703],[50.1133,25.94397],[50.23986,25.60805],[50.52739,25.32781],[50.66056,24.9999],[50.81011,24.75474],[51.11242,24.55633],[51.38961,24.62739],[51.57952,24.2455],[51.61771,24.01422],[52.00073,23.00115],[55.0068,22.49695],[55.20834,22.70833],[55.66666,22],[54.99998,19.99999],[52.00001,19],[49.11667,18.61667],[48.18334,18.16667],[47.46669,17.11668],[47,16.95],[46.74999,17.28334],[46.36666,17.23332],[45.4,17.33334],[45.21665,17.43333],[44.06261,17.41036],[43.79152,17.31998],[43.38079,17.57999],[43.1158,17.08844],[43.21838,16.66689],[42.77933,16.34789]]]},\"properties\":{\"name\":\"Saudi Arabia\"}}]}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Sanath S. 0000-0003-4067-4926","orcid":"https://orcid.org/0000-0003-4067-4926","contributorId":330540,"corporation":false,"usgs":true,"family":"Kumar","given":"Sanath","email":"","middleInitial":"S.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hult, John Edward 0000-0001-8895-3727","orcid":"https://orcid.org/0000-0001-8895-3727","contributorId":330551,"corporation":false,"usgs":true,"family":"Hult","given":"John","email":"","middleInitial":"Edward","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Picotte, Joshua J. 0000-0002-4021-4623","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":202800,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":885284,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Birgit 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":192353,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885285,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70209758,"text":"70209758 - 2020 - Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics","interactions":[],"lastModifiedDate":"2020-04-28T14:24:02.273893","indexId":"70209758","displayToPublicDate":"2019-12-24T08:13:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics","docAbstract":"<p><span>Understanding the effectiveness of environmental flow deliveries along rivers requires monitoring vegetation. Monitoring data are often collected at multiple spatial scales. For riparian vegetation, optical remote sensing methods can estimate growth responses at the riparian corridor scale, and field‐based measures can quantify species composition; however, the extent to which these different measures are duplicative or complementary is important to understand when planning monitoring programmes with limited resources. In this study, we analysed riparian vegetation growth in the delta of the Colorado River in response to an experimental pulse flow. Our goal was to compare ground‐based measurements of vegetation structure and composition with satellite‐based Landsat radiometric variables, such as the normalized difference vegetation index (NDVI). We made this comparison in 21 transects following the delivery of 131.8 million cubic meters (mcm) of water in the stream channel during the spring of 2014 as a pulse flow and 38.4 mcm as base flows. Vegetation cover increased 14% and NDVI increased 0.02 (15%) by October 2015, and both variables returned to pre‐pulse flow values in October 2016. Observed changes in vegetation structure and composition did not persist after the second year. The highest increase in vegetation cover in October 2014 and October 2015 resulted from species that could respond rapidly to additional water such as reeds (</span><i>Arundo donax</i><span>&nbsp;and&nbsp;</span><i>Phragmites australis</i><span>), cattail (</span><i>Typha domingensis</i><span>), and herbaceous plants. Dominant shrubs, saltcedar (</span><i>Tamarix</i><span>&nbsp;spp.) and arrowweed (</span><i>Pluchea sericea</i><span>), both indicative of nonrestored habitats showed variable increases in cover, and native trees (</span><i>Salicaceae</i><span>&nbsp;family) presented low increases (1%). The strong NDVI–vegetation cover relationship indicates that NDVI is appropriate to detect changes at the riparian corridor scale but needs to be complemented with ground data to determine the contributions by different species to the observed trends.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13689","collaboration":"","usgsCitation":"Gomez-Sapiens, M.M., Jarchow, C., Flessa, K.W., Shafroth, P.B., Glenn, E., and Nagler, P.L., 2020, Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics: Hydrological Processes, v. 34, no. 8, p. 1682-1696, https://doi.org/10.1002/hyp.13689.","productDescription":"15 p.","startPage":"1682","endPage":"1696","ipdsId":"IP-109952","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":488909,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/659868","text":"External Repository"},{"id":374314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.3070068359375,\n              31.5691754490709\n            ],\n            [\n              -114.70275878906249,\n              31.5691754490709\n            ],\n            [\n              -114.70275878906249,\n              32.708733368521585\n            ],\n            [\n              -115.3070068359375,\n              32.708733368521585\n            ],\n            [\n              -115.3070068359375,\n              31.5691754490709\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"34","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomez-Sapiens, Martha M.","contributorId":58172,"corporation":false,"usgs":true,"family":"Gomez-Sapiens","given":"Martha","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":787897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":787898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flessa, Karl W.","contributorId":175308,"corporation":false,"usgs":false,"family":"Flessa","given":"Karl","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":787899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":787900,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":787901,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":787902,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70206881,"text":"70206881 - 2020 - Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","interactions":[],"lastModifiedDate":"2020-04-06T21:07:55.202827","indexId":"70206881","displayToPublicDate":"2019-11-22T06:58:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud","docAbstract":"The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2019.1690780","usgsCitation":"Gumma, M.K., Thenkabail, P., Pardhasaradhi Teluguntla, and Oliphant, A., 2020, Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud: GIScience and Remote Sensing, v. 57, no. 3, p. 302-322, https://doi.org/10.1080/15481603.2019.1690780.","productDescription":"21 p.","startPage":"302","endPage":"322","ipdsId":"IP-111091","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":458465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2019.1690780","text":"Publisher Index Page"},{"id":369607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India, Pakistan, Bangladesh, Nepal, Sri Lanka, Bhutan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[77.83745,35.49401],[78.91227,34.32194],[78.81109,33.5062],[79.20889,32.99439],[79.17613,32.48378],[78.45845,32.61816],[78.73889,31.51591],[79.72137,30.88271],[81.11126,30.18348],[81.5258,30.42272],[82.32751,30.11527],[83.33712,29.46373],[83.89899,29.32023],[84.23458,28.83989],[85.01164,28.64277],[85.82332,28.20358],[86.95452,27.97426],[88.12044,27.87654],[88.73033,28.08686],[88.81425,27.29932],[89.47581,28.04276],[90.01583,28.29644],[90.73051,28.06495],[91.25885,28.04061],[91.69666,27.77174],[92.50312,27.89688],[93.41335,28.64063],[94.56599,29.27744],[95.4048,29.03172],[96.11768,29.4528],[96.58659,28.83098],[96.24883,28.41103],[97.32711,28.26158],[97.40256,27.88254],[97.05199,27.69906],[97.134,27.08377],[96.41937,27.26459],[95.12477,26.57357],[95.15515,26.00131],[94.60325,25.1625],[94.55266,24.67524],[94.10674,23.85074],[93.32519,24.07856],[93.28633,23.04366],[93.06029,22.70311],[93.16613,22.27846],[92.67272,22.04124],[92.65226,21.32405],[92.30323,21.47549],[92.36855,20.67088],[92.08289,21.1922],[92.02522,21.70157],[91.83489,22.18294],[91.41709,22.76502],[90.49601,22.80502],[90.58696,22.39279],[90.27297,21.83637],[89.84747,22.03915],[89.70205,21.85712],[89.41886,21.96618],[89.03196,22.05571],[88.88877,21.69059],[88.2085,21.70317],[86.9757,21.49556],[87.03317,20.74331],[86.49935,20.15164],[85.06027,19.47858],[83.94101,18.30201],[83.18922,17.67122],[82.19279,17.01664],[82.19124,16.55666],[81.69272,16.31022],[80.792,15.95197],[80.3249,15.89918],[80.02507,15.13641],[80.23327,13.83577],[80.28629,13.00626],[79.86255,12.05622],[79.858,10.35728],[79.34051,10.30885],[78.88535,9.54614],[79.18972,9.21654],[78.27794,8.93305],[77.94117,8.25296],[77.5399,7.96553],[76.59298,8.89928],[76.13006,10.29963],[75.74647,11.30825],[75.3961,11.78125],[74.86482,12.74194],[74.61672,13.99258],[74.44386,14.61722],[73.5342,15.99065],[73.11991,17.92857],[72.82091,19.20823],[72.82448,20.4195],[72.63053,21.35601],[71.17527,20.75744],[70.47046,20.87733],[69.16413,22.0893],[69.64493,22.45077],[69.3496,22.84318],[68.17665,23.69197],[67.44367,23.94484],[67.14544,24.66361],[66.37283,25.42514],[64.53041,25.23704],[62.9057,25.21841],[61.49736,25.07824],[61.87419,26.23997],[63.31663,26.75653],[63.2339,27.21705],[62.75543,27.37892],[62.72783,28.25964],[61.77187,28.69933],[61.36931,29.30328],[60.87425,29.82924],[62.54986,29.31857],[63.55026,29.46833],[64.148,29.34082],[64.35042,29.56003],[65.04686,29.47218],[66.34647,29.88794],[66.38146,30.7389],[66.93889,31.30491],[67.68339,31.30315],[67.79269,31.58293],[68.55693,31.71331],[68.92668,31.62019],[69.31776,31.90141],[69.26252,32.50194],[69.68715,33.1055],[70.32359,33.35853],[69.93054,34.02012],[70.8818,33.98886],[71.15677,34.34891],[71.11502,34.73313],[71.61308,35.1532],[71.49877,35.65056],[71.26235,36.07439],[71.84629,36.50994],[72.92002,36.72001],[74.06755,36.83618],[74.57589,37.02084],[75.15803,37.13303],[75.8969,36.66681],[76.19285,35.8984],[77.83745,35.49401]]],[[[81.78796,7.52306],[81.63732,6.48178],[81.21802,6.19714],[80.34836,5.96837],[79.87247,6.76346],[79.69517,8.20084],[80.1478,9.82408],[80.83882,9.26843],[81.30432,8.56421],[81.78796,7.52306]]]]},\"properties\":{\"name\":\"India\"}}]}","volume":"57","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Gumma, Murali Krishna","contributorId":127590,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali","email":"","middleInitial":"Krishna","affiliations":[{"id":7069,"text":"International Crops Research Institute for the Semi Arid Tropics (ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":776137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":776136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pardhasaradhi Teluguntla 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":214457,"corporation":false,"usgs":false,"family":"Pardhasaradhi Teluguntla","affiliations":[{"id":39046,"text":"Bay Area Environmental Research Institute at USGS","active":true,"usgs":false}],"preferred":false,"id":776138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":776139,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70206828,"text":"70206828 - 2020 - Using full and partial unmixing algorithms to estimate the inundation extent of small, isolated stock ponds in an arid landscape","interactions":[],"lastModifiedDate":"2020-08-27T15:29:37.417951","indexId":"70206828","displayToPublicDate":"2019-07-30T06:48:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Using full and partial unmixing algorithms to estimate the inundation extent of small, isolated stock ponds in an arid landscape","docAbstract":"<p><span>Many natural wetlands around the world have disappeared or been replaced, resulting in the dependence of many wildlife species on small, artificial earthen stock ponds. These ponds provide critical wildlife habitat, such that the accurate detection of water and assessment of inundation extent is required. We applied a full (linear spectral mixture analysis; LSMA) and partial (matched filtering; MF) spectral unmixing algorithm to a 2007 Landsat 5 and a 2014 Landsat 8 satellite image to determine the ability of a time-intensive (i.e., more spectral input; LSMA) vs. a more efficient (less spectral input; MF) spectral unmixing approach to detect and estimate surface water area of stock ponds in southern Arizona, USA and northern Sonora, Mexico. Spearman rank correlations (</span><i>r</i><sub>s</sub><span>) between modeled and actual inundation areas less than a single Landsat pixel (&lt; 900 m</span><sup>2</sup><span>) were low for both techniques (</span><i>r</i><sub>s</sub><span>&nbsp;range = 0.22 to 0.62), but improved for inundation areas &gt;900&nbsp;m</span><sup>2</sup><span>&nbsp;(</span><i>r</i><sub>s</sub><span>&nbsp;range = 0.34 to 0.70). Our results demonstrate that the MF approach can model ranked inundation extent of known pond locations with results comparable to or better than LSMA, but further refinement is required for estimating absolute inundation areas and mapping wetlands &lt;1 Landsat pixel.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s13157-019-01201-7","usgsCitation":"Jarchow, C., Sigafus, B.H., Muths, E.L., and Hossack, B.R., 2020, Using full and partial unmixing algorithms to estimate the inundation extent of small, isolated stock ponds in an arid landscape: Wetlands, v. 40, p. 563-575, https://doi.org/10.1007/s13157-019-01201-7.","productDescription":"13 p.","startPage":"563","endPage":"575","ipdsId":"IP-092489","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437220,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95ZFPT1","text":"USGS data release","linkHelpText":"Surface water data for isolated stock ponds in southern Arizona, USA and northern Sonora, Mexico"},{"id":369519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":775953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":775952,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":775954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":775955,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70206108,"text":"gip194 - 2019 - Earth as art 6","interactions":[],"lastModifiedDate":"2019-12-24T10:38:32","indexId":"gip194","displayToPublicDate":"2019-12-23T18:25:14","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"194","displayTitle":"Earth As Art 6","title":"Earth as art 6","docAbstract":"<p>Earth has a stunning variety of landscapes. The colors, patterns, textures, and shapes all make for intriguing artwork as seen from the perspective of space.</p><p>Earth As Art shows not only what satellites capture in the visible wavelengths of light you and I can see, but also what’s hiding in the invisible wavelengths that Landsat sensors can detect in the infrared part of the electromagnetic spectrum. Those combinations can bring out much more scientific value, but also can produce imagery of breathtaking beauty.</p><p>Earth As Art 6 even includes images from U.S. Geological Survey (USGS) Unmanned Aircraft Systems (UAS), commonly known as drones. Sensors attached to a UAS also capture visible and infrared light and have proven their value at monitoring change over time alongside their spaceborne partners. Besides, their images look great, too. Enjoy the latest from Earth As Art!</p><p><a href=\"https://eros.usgs.gov/image-gallery/earth-art-6\" data-mce-href=\"https://eros.usgs.gov/image-gallery/earth-art-6\">https://eros.usgs.gov/image-gallery/earth-art-6</a><br></p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip194","usgsCitation":"U.S. Geological Survey, 2019, Earth as art 6—A unique and unconventional perspective of the Earth’s geographic attributes: U.S. Geological Survey General Information Product 194, 42 p., https://doi.org/10.3133/gip194.","productDescription":"42 p.","numberOfPages":"48","onlineOnly":"N","ipdsId":"IP-111803","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":370620,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0194/coverthb.jpg"},{"id":370621,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0194/gip194.pdf","text":"Report","size":"19.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 194"}],"contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/eros\" href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science (EROS) Center</a><br>U.S. Geological Survey<br>47914 252nd Street <br>Sioux Falls, SD 57198–0001<br> </p>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-12-23","noUsgsAuthors":false,"publicationDate":"2019-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128037,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":773609,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70207382,"text":"70207382 - 2019 - Validating a landsat time-series of fractional component cover across western U.S. Rangelands","interactions":[],"lastModifiedDate":"2022-02-16T21:32:14.39285","indexId":"70207382","displayToPublicDate":"2019-12-13T19:22:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Validating a landsat time-series of fractional component cover across western U.S. Rangelands","docAbstract":"Western U.S. rangelands have been quantified as six fractional cover (0%–100%) components\nover the Landsat archive (1985–2018) at a 30 m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. Here, we used field data collected concurrently with high-resolution satellite (HRS) images over multiple locations (n = 42) and years. Field observations were used to train regression tree models, predicting the component cover across each HRS image. Our objectives were to evaluate the spatial and temporal relationships between HRS and BIT component cover and compare spatio-temporal climate responses. First, for each HRS site-year (n = 77) we averaged both the HRS and BIT predictions within each site separately and regressed the averages to quantify the temporal accuracy. Next, we regressed individual pixel values of corresponding HRS and BIT predictions to quantify the spatio-temporal accuracy. Results showed strong temporal correlations with an average R2 of 0.63 and Root Mean Square Error (RMSE) of 5.47% as well as strong spatio-temporal correlations with an average R2 of 0.52 and RMSE of 7.89% across components. Our approach increased the validation sample size relative to direct comparison of field observations. Validation results showed robust spatio-temporal relationships between HRS and BIT data, providing increased user confidence in the data.","language":"English","publisher":"MPDI","doi":"10.3390/rs11243009","usgsCitation":"Rigge, M.B., Homer, C.G., Shi, H., and Meyer, D.K., 2019, Validating a landsat time-series of fractional component cover across western U.S. Rangelands: Remote Sensing, v. 11, no. 24, 3009, 16 p.; Data release, https://doi.org/10.3390/rs11243009.","productDescription":"3009, 16 p.; Data release","ipdsId":"IP-113763","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458961,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11243009","text":"Publisher Index Page"},{"id":370436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396049,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90Q8BCP","text":"USGS data release","description":"USGS data release","linkHelpText":"Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands"}],"country":"Unites States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.05859375,\n              38.89103282648846\n            ],\n            [\n              -115.31249999999999,\n              38.89103282648846\n            ],\n            [\n              -115.31249999999999,\n              42.09822241118974\n            ],\n            [\n              -120.05859375,\n              42.09822241118974\n            ],\n            [\n              -120.05859375,\n              38.89103282648846\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.533203125,\n              41.04621681452063\n            ],\n            [\n              -103.974609375,\n              41.04621681452063\n            ],\n            [\n              -103.974609375,\n              45.213003555993964\n            ],\n            [\n              -111.533203125,\n              45.213003555993964\n            ],\n            [\n              -111.533203125,\n              41.04621681452063\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"24","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","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":777870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":777871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Debra K. 0000-0002-8841-697X dkmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":3145,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra","email":"dkmeyer@usgs.gov","middleInitial":"K.","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":777872,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70245784,"text":"70245784 - 2019 - Overall methodology design for the United States National Land Cover Database 2016 products","interactions":[],"lastModifiedDate":"2023-06-27T12:07:26.372706","indexId":"70245784","displayToPublicDate":"2019-12-11T07:05:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Overall methodology design for the United States National Land Cover Database 2016 products","docAbstract":"<div class=\"html-p\">The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download.</div>","language":"English","publisher":"MDPI","doi":"10.3390/rs11242971","usgsCitation":"Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., Zhu, Z., Xian, G.Z., and Howard, D., 2019, Overall methodology design for the United States National Land Cover Database 2016 products: Remote Sensing, v. 11, no. 24, 2971, 32 p., https://doi.org/10.3390/rs11242971.","productDescription":"2971, 32 p.","ipdsId":"IP-106705","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458982,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11242971","text":"Publisher Index Page"},{"id":418501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"24","noUsgsAuthors":false,"publicationDate":"2019-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":876322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin 0000-0003-4755-8135","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":238918,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":876323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Limin 0000-0002-2843-6944","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":313589,"corporation":false,"usgs":false,"family":"Yang","given":"Limin","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":876324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Danielson, Patrick 0000-0002-2990-2783 pdanielson@usgs.gov","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":3551,"corporation":false,"usgs":true,"family":"Danielson","given":"Patrick","email":"pdanielson@usgs.gov","affiliations":[{"id":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":876325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dewitz, Jon 0000-0002-0458-212X dewitz@usgs.gov","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":313590,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","email":"dewitz@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":876326,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":876327,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zhu, Zhe 0000-0003-4716-2309","orcid":"https://orcid.org/0000-0003-4716-2309","contributorId":272038,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":876328,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":876329,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176973,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":876334,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70206894,"text":"70206894 - 2019 - Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires","interactions":[],"lastModifiedDate":"2019-12-09T15:04:14","indexId":"70206894","displayToPublicDate":"2019-11-26T08:49:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires","docAbstract":"Post-fire recovery trajectories in ponderosa pine (Pinus ponderosa Laws.) forests of the US Southwest are increasingly shifting away from pre-burn vegetation communities.  This study investigated whether phenological metrics derived from a multi-decade remotely sensed imagery time-series could differentiate among grass, evergreen shrub, deciduous, or conifer-dominated replacement pathways.  We focused on 10 fires that burned ponderosa pine forests in Arizona and New Mexico, USA before the year 2000.  A total of 29 sites with discernable post-fire recovery signals were selected within high-severity burn areas.  At each site, we used Google Earth Engine to derive time-series of normalized difference vegetation index (NDVI) signals from Landsat Thematic Mapper, Enhanced Thematic Mapper+, and Operational Line Imager data from 1984 to 2017.  We aggregated values to 8- and 16-day intervals, fit Savitzy-Golay filters to each sequence, and extracted annual phenology metrics of amplitude, base value, peak value, and timing of peak value in the Timesat analysis package. Results show that relative to post-fire conditions, pre-burn ponderosa pine forests exhibit significantly lower mean NDVI amplitude (0.14 vs. 0.21), higher mean base NDVI (0.47 vs. 0.22), higher mean peak NDVI (0.60 vs. 0.43), and later mean peak NDVI (day of year 277 vs. 237).  Vegetation succession exhibits distinct phenometric characteristics as early as year five (amplitude) and as late as year 20 (timing of peak NDVI).  This study confirms the feasibility of leveraging phenology metrics derived from long-term imagery time series to identify and monitor ecological outcomes. This information may be of benefit to land resource managers who seek indicators of future landscape composition to inform management strategies.","language":"English","publisher":"MDPI","doi":"10.3390/rs11232782","usgsCitation":"Walker, J.J., and Soulard, C.E., 2019, Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires: Remote Sensing, v. 11, no. 23, 2782, https://doi.org/10.3390/rs11232782.","productDescription":"2782","ipdsId":"IP-112858","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":459110,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11232782","text":"Publisher Index Page"},{"id":437276,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y1Z03F","text":"USGS data release","linkHelpText":"Phenology pattern data indicating recovery trajectories of ponderosa pine forests after 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,{"id":70202385,"text":"70202385 - 2019 - The U. S. Geological Survey’s approach to analysis ready data","interactions":[],"lastModifiedDate":"2020-05-27T17:14:07.429313","indexId":"70202385","displayToPublicDate":"2019-11-14T11:59:57","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The U. S. Geological Survey’s approach to analysis ready data","docAbstract":"<p><span>Analysis Ready Data (ARD) is a recent concept in Earth observing remote sensing which encompasses many different initiatives by individual imagery providers and collaborative international organizations working towards easing/minimizing data preprocessing required by users. This allows users to spend more time on analysis and less time on downloading, formatting, and ingesting. The U. S. Geological Survey (USGS), the primary provider of Landsat image data, has been making internal strides to provide ARD: moving towards Level-2 surface reflectance and surface temperature as standard products. External cooperation, working toward a common ARD definition, has also been a focus of the USGS by working directly with other governmental or commercial providers, both national and international, or through organizations such as the Committee on Earth Observation Satellites (CEOS) and the Joint Agency Commercial Imagery Evaluation (JACIE) workshop. The USGS is determined to provide users with the most accurate and easy to use data.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"Jul 28-Aug 2, 2019","conferenceLocation":"Yokohama, Japan","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS.2019.8899216","usgsCitation":"Anderson, C., Labahn, S., Helder, D., Stensaas, G.L., Engebretson, C., Crawford, C., Jenkerson, C.B., and Barnes, C., 2019, The U. S. Geological Survey’s approach to analysis ready data, <i>in</i> IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, Jul 28-Aug 2, 2019, p. 5541-5544, https://doi.org/10.1109/IGARSS.2019.8899216.","productDescription":"3 p.","startPage":"5541","endPage":"5544","ipdsId":"IP-105685","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":375094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":758132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Labahn, Steven 0000-0002-9258-2890","orcid":"https://orcid.org/0000-0002-9258-2890","contributorId":213605,"corporation":false,"usgs":true,"family":"Labahn","given":"Steven","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":758133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":213606,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":758135,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Engebretson, Christopher 0000-0003-1012-8684","orcid":"https://orcid.org/0000-0003-1012-8684","contributorId":224985,"corporation":false,"usgs":true,"family":"Engebretson","given":"Christopher","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":758136,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":758137,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jenkerson, Calli B. 0000-0002-3780-9175 jenkerson@usgs.gov","orcid":"https://orcid.org/0000-0002-3780-9175","contributorId":469,"corporation":false,"usgs":true,"family":"Jenkerson","given":"Calli","email":"jenkerson@usgs.gov","middleInitial":"B.","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":758138,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":758139,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70207518,"text":"70207518 - 2019 - Pre‐fire vegetation drives post‐fire outcomes in sagebrush ecosystems: Evidence from field and remote sensing data","interactions":[],"lastModifiedDate":"2020-02-21T06:15:50","indexId":"70207518","displayToPublicDate":"2019-11-12T10:32:04","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Pre‐fire vegetation drives post‐fire outcomes in sagebrush ecosystems: Evidence from field and remote sensing data","docAbstract":"Understanding the factors that influence vegetation responses to disturbance is important because vegetation is the foundation of food resources, wildlife habitat, and ecosystem properties and processes. We integrated vegetation cover data derived from field plots and remotely sensed Landsat images in two focal areas over a 37‐yr period (1979–2016) to investigate how historical changes to community composition influence contemporary responses of vegetation to fire in sagebrush ecosystems in the Great Basin. Our objectives were (1) to quantify the magnitude and direction of change in the cover of native and exotic plant functional groups in relation to their exposure to fire; (2) to relate plant community changes to their historical composition, exposure to fire, and environmental conditions; and (3) to test for consistency of trends revealed by vegetation cover data derived from field plots and Landsat images. Historical (1979–1981) field data originated from 298 locations, Landsat‐derived data and contemporary (2011–2016) field data originated from 448 locations, and an expanded set of locations were included in some analyses of Landsat‐derived data. We found that areas burned by fire since the 1980s had higher annual herbaceous cover than unburned areas both historically and contemporarily. Models revealed a significant interaction between historical community composition and exposure to fire, which suggests that plots with historically high herbaceous cover were more susceptible to burning. Trends revealed by field and Landsat‐derived cover data were only partially consistent, potentially due in part to methods used to predict cover values from Landsat images, and the time period over which each data set was collected. Our results suggest that burned areas historically occupied by sagebrush‐dominated plant communities may have been invaded by exotic annuals prior to burning, possibly because of prior land uses, and after burning, have now transitioned to a persistent herbaceous‐dominated state. This type of state transition has important consequences for forage quality, wildlife habitat, soil nutrients, and future disturbances, such as drought and wildfire.","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.2929","usgsCitation":"Barker, B., Pilliod, D.S., Rigge, M., and Homer, C.G., 2019, Pre‐fire vegetation drives post‐fire outcomes in sagebrush ecosystems: Evidence from field and remote sensing data: Ecosphere, v. 10, no. 11, e02929, https://doi.org/10.1002/ecs2.2929.","productDescription":"e02929","ipdsId":"IP-101852","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":459199,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2929","text":"Publisher Index Page"},{"id":370602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Nevada ","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.76171875,\n              40.78054143186033\n            ],\n            [\n              -116.5869140625,\n              40.78054143186033\n            ],\n            [\n              -116.5869140625,\n              43.16512263158296\n            ],\n            [\n              -120.76171875,\n              43.16512263158296\n            ],\n            [\n              -120.76171875,\n              40.78054143186033\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"11","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Barker, Brittany S. 0000-0002-2198-8287","orcid":"https://orcid.org/0000-0002-2198-8287","contributorId":221481,"corporation":false,"usgs":false,"family":"Barker","given":"Brittany S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":778343,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rigge, Matthew 0000-0003-4471-8009","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":221482,"corporation":false,"usgs":false,"family":"Rigge","given":"Matthew","affiliations":[{"id":40392,"text":"Contractor; Earth Resources Observation and Science Center","active":true,"usgs":false}],"preferred":false,"id":778344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","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":778345,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70212579,"text":"70212579 - 2019 - Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping","interactions":[],"lastModifiedDate":"2020-08-21T14:56:33.711489","indexId":"70212579","displayToPublicDate":"2019-11-06T09:50:31","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping","docAbstract":"Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.","language":"English","publisher":"Remote Sensing","doi":"10.3390/rs11222603","usgsCitation":"Xian, G.Z., Shi, H., Anderson, C., and Wu, Z., 2019, Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping: Remote Sensing, v. 11, no. 22, 2603, 23 p., https://doi.org/10.3390/rs11222603.","productDescription":"2603, 23 p.","ipdsId":"IP-113657","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":459244,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11222603","text":"Publisher Index Page"},{"id":377729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"22","noUsgsAuthors":false,"publicationDate":"2019-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796912,"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":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":796913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Cody 0000-0001-5612-1889","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":238942,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":796914,"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":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":796915,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70206442,"text":"70206442 - 2019 - Landsat time series assessment of invasive annual grasses following energy development","interactions":[],"lastModifiedDate":"2019-11-05T06:33:48","indexId":"70206442","displayToPublicDate":"2019-10-30T12:39:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat time series assessment of invasive annual grasses following energy development","docAbstract":"Invasive annual grasses are of concern in many areas of the Western United States because they tolerate resource variability and have high reproductive capacity, with propagules that are readily dispersed in disturbed areas like those created and maintained for energy development. Early-season invasive grasses “green up” earlier than the most native plants, producing a distinct pulse of greenness in the early spring that can be exploited to identify their location using multi-date imagery. To determine if invasive annual grasses increased around energy development areas after the construction phase, we calculated an invasive index using Landsat TM and ETM+ imagery for a 34-year time period (1985–2018) and assessed trends for 1755 wind turbines installed between 1988 and 2013 in the Southern California Desert. The index uses the maximum normalized difference vegetation index (NDVI) for early-season greenness (January–June) and mean NDVI (July–October) for the later dry season. We estimated the relative cover of invasive annuals each year at turbine locations and control sites, and tested for changes before and after each turbine was installed. The time series was also mapped across the region and temporal trends were assessed relative to seasonal precipitation. The results showed an increase in early-season invasives at turbine sites after installation, but also an increase in many of the surrounding control areas. Maps of the invasive index show a region-wide increase starting at around 1998, and a great deal of the increase occurred in areas surrounding wind development sites. These results suggest that invasions around the energy developments occurred within the context of a larger regional invasion, and while the development did not necessarily initiate the invasion, annual grasses were more prevalent around the development areas.","language":"English","publisher":"MDPI","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs11212553","collaboration":"WGSC","usgsCitation":"Villarreal, M.L., Soulard, C.E., and Waller, E., 2019, Landsat time series assessment of invasive annual grasses following energy development: Remote Sensing, v. 11, no. 21, p. 1-18, https://doi.org/10.3390/rs11212553.","productDescription":"2533, 18p.","startPage":"1","endPage":"18","ipdsId":"IP-111295","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":459316,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11212553","text":"Publisher Index Page"},{"id":368926,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Colorado Desert, Mojave Desert","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.54296874999999,\n              33.61461929233378\n            ],\n            [\n              -114.47753906249999,\n              33.61461929233378\n            ],\n            [\n              -114.47753906249999,\n              34.66032236481892\n            ],\n            [\n              -116.54296874999999,\n              34.66032236481892\n            ],\n            [\n              -116.54296874999999,\n              33.61461929233378\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.378173828125,\n              35.71083783530009\n            ],\n            [\n              -117.90527343750001,\n              37.055177106660814\n            ],\n            [\n              -119.53125,\n              35.92464453144099\n            ],\n            [\n              -117.7734375,\n              34.379712580462204\n            ],\n            [\n              -116.378173828125,\n              35.71083783530009\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"21","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":774555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":774556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waller, Eric 0000-0002-9169-9210","orcid":"https://orcid.org/0000-0002-9169-9210","contributorId":220101,"corporation":false,"usgs":false,"family":"Waller","given":"Eric","affiliations":[],"preferred":false,"id":774557,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70207025,"text":"70207025 - 2019 - Influence of multi-decadal land use, irrigation practices and climate on riparian corridors across the Upper Missouri River Headwaters Basin, Montana","interactions":[],"lastModifiedDate":"2019-12-03T11:57:49","indexId":"70207025","displayToPublicDate":"2019-10-23T11:54:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Influence of multi-decadal land use, irrigation practices and climate on riparian corridors across the Upper Missouri River Headwaters Basin, Montana","docAbstract":"The Upper Missouri River Headwaters Basin (36,400 km2) depends on its river corridors to support irrigated agriculture and world-class trout fisheries. We evaluated trends (1984-2016) in riparian wetness, an indicator of riparian condition, in peak irrigation months (June, July, August) for 158 km2 of riparian area across the basin using the Landsat Normalized Difference Wetness Index (NDWI). We found that 8 of the 19 riparian reaches across the basin showed a significant drying trend over this period, including all three basin outlet reaches along the Jefferson, Madison and Gallatin Rivers. The influence of upstream climate was quantified using per reach random forest regressions. Much of the interannual variability in the NDWI was explained by climate, especially by drought indices and annual precipitation, but the significant temporal drying trends persisted in the NDWI-climate model residuals, indicating that trends were not entirely attributable to climate. Over the same period we documented a basin-wide shift from 9% of agriculture irrigated with center pivot irrigation to 50% irrigated with center pivot irrigation. Riparian reaches with a drying trend had a greater increase in the total area with center pivot irrigation (within-reach and upstream from the reach) relative to riparian reaches without such a trend (p<0.05). The drying trend, however, did not extend to river discharge. Over the same period, stream gages (n=7) showed a positive correlation with riparian wetness (p<0.05), but no trend in summer river discharge, suggesting that riparian areas may be more sensitive to changes in irrigation return flows, relative to river discharge. Identifying trends in riparian vegetation is a critical precursor to enhancing the resiliency of river systems and associated riparian corridors.","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-23-4269-2019","usgsCitation":"Vanderhoof, M.K., Christensen, J., and Alexander, L.C., 2019, Influence of multi-decadal land use, irrigation practices and climate on riparian corridors across the Upper Missouri River Headwaters Basin, Montana: Hydrology and Earth System Sciences, v. 23, no. 10, p. 4269-4292, https://doi.org/10.5194/hess-23-4269-2019.","productDescription":"24 p.","startPage":"4269","endPage":"4292","ipdsId":"IP-104946","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":459393,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-23-4269-2019","text":"Publisher Index Page"},{"id":437294,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P976LZ2G","text":"USGS data release","linkHelpText":"Data release for Influence of multi-decadal land use, irrigation practices and climate on riparian corridors across the Upper Missouri River headwaters basin, Montana"},{"id":369872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Upper Missouri River headwaters basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -113.73046875,\n              44.84029065139799\n            ],\n            [\n              -109.5556640625,\n              44.84029065139799\n            ],\n            [\n              -109.5556640625,\n              46.46813299215554\n            ],\n            [\n              -113.73046875,\n              46.46813299215554\n            ],\n            [\n              -113.73046875,\n              44.84029065139799\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"23","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-23","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":776552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, J.R.","contributorId":204058,"corporation":false,"usgs":false,"family":"Christensen","given":"J.R.","email":"","affiliations":[{"id":36813,"text":"U.S. EPA Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":776553,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Laurie C.","contributorId":196285,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":776554,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70206089,"text":"70206089 - 2019 - Assessing plant production responses to climate across water-limited regions using Google Earth Engine","interactions":[],"lastModifiedDate":"2019-10-22T06:32:15","indexId":"70206089","displayToPublicDate":"2019-10-21T13:41:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Assessing plant production responses to climate across water-limited regions using Google Earth Engine","docAbstract":"(Munson) Climate variability and change acting at broad scales can lead to divergent changes in plant production at local scales. Quantifying how production responds to variation in climate at local scales is essential to understand underlying ecological processes and inform land management decision-making, but has historically been limited in spatiotemporal scale based on the use of discrete ground-based measurements or coarse resolution satellite observations. With the advent of cloud-based computing through Google Earth Engine (GEE), production responses to climate can be evaluated across broad landscapes though time at a resolution useful for ecological and land management applications. Here, GEE was employed to synthesize a multi-platform Landsat time series (1988 – 2014) and evaluate relationships between the soil-adjusted vegetation index (a proxy for plant production) and climate across deserts and plant communities of the southwestern U.S. A “climate pivot point” approach was adopted in GEE to assess the trade-off between production responses to increasing wetness and resistances to drought at 30-m resolution. Consistent with a long-term seasonal climate gradient, production was most related to climate variance during the cool-season in the western deserts, during the warm-season in the eastern deserts, and equally related to both seasons within several desert areas. Communities dominated by grasses and deciduous trees displayed large production responses to an increase in wetness and low resistances to water deficit, while shrublands and evergreen woodlands had variable responses and high drought resistances. Production in plant communities that spanned multiple deserts responded differently to seasonal climate variability in each desert. Defining these plant production sensitivities to climate at 30-m resolution in GEE advances forecasts of how long-term climate trajectories may affect carbon storage, wildlife habitat, and the vulnerability of water-limited ecosystems.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111379","collaboration":"None.","usgsCitation":"Bunting, E., Munson, S.M., and Bradford, J., 2019, Assessing plant production responses to climate across water-limited regions using Google Earth Engine: Remote Sensing of Environment, v. 233, p. 1-15, https://doi.org/10.1016/j.rse.2019.111379.","productDescription":"1113792, 15p.","startPage":"1","endPage":"15","ipdsId":"IP-093613","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":459429,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111379","text":"Publisher Index Page"},{"id":437297,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98ZCJBI","text":"USGS data release","linkHelpText":"Dataset for plant production responses to climate across water-limited regions"},{"id":368461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368458,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0034425719303980?via%3Dihub"}],"country":"United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Texas, Utah","otherGeospatial":"Chihuahuan, Colorado Plateau, Great Basin, Mojave, Sonoran ","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.09374999999999,\n              41.705728515237524\n            ],\n 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bunting, Erin L.","contributorId":208169,"corporation":false,"usgs":false,"family":"Bunting","given":"Erin L.","affiliations":[{"id":37758,"text":"Michigan State University, East Lansing, MI USA","active":true,"usgs":false}],"preferred":false,"id":773528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":773527,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":773529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205687,"text":"ofr20191112 - 2019 - Economic valuation of Landsat imagery","interactions":[],"lastModifiedDate":"2025-08-12T18:44:44.520151","indexId":"ofr20191112","displayToPublicDate":"2019-10-16T10:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1112","displayTitle":"Economic Valuation of Landsat Imagery","title":"Economic valuation of Landsat imagery","docAbstract":"<p>Landsat satellites have been operating since 1972, providing a continuous global record of the Earth’s land surface. The imagery is currently available at no cost through the U.S. Geological Survey (USGS). A previous USGS study estimated that Landsat imagery provided users an annual benefit of <abbr>$</abbr>2.19 billion in 2011, with U.S. users accounting for <abbr>$</abbr>1.79 billion of those benefits. That study, published in 2013, surveyed users in 2012 about Landsat imagery they retrieved in 2011. But since then, many changes have altered the demand for and supply of remotely sensed imagery and have made the analysis complex. This report updates these estimates, surveying users in 2018 about Landsat images they retrieved in 2017. The report discusses changes in the value per scene in 2017 when compared to 2011 and analyzes the potential consequences of charging fees. Landsat imagery has been available at no cost to the public since 2008, resulting in the distribution of millions of scenes each subsequent year. In addition, tens of thousands of Landsat users have registered with the USGS to access the data. Considering the number of Landsat data users worldwide and the broad range of Landsat data applications, it is difficult to quantify the cascading benefits to society provided by Landsat imagery. The value of Landsat imagery to these users was demonstrated by the substantial aggregated annual economic benefit from the imagery. Landsat imagery provided domestic and international users an estimated <abbr>$</abbr>3.45 billion in benefits in 2017 compared to <abbr>$</abbr>2.19 billion in 2011, with U.S. users accounting for <abbr>$</abbr>2.06 billion of those benefits. Much of the societal value of Landsat stems from the free and open data policy that allows users to access as much imagery as is necessary for their analysis at no cost. Charging even small fees would result in a loss of users and, most likely, a steep decline in the amount of imagery downloaded. It is reasonable that more than 50 percent of users will decline to pay. The consequences of charging for Landsat imagery would be felt by downstream users as well, through increased prices for value-added products as well as more intangible effects, such as reduced monitoring of environmental hazards.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191112","usgsCitation":"Straub, C.L., Koontz, S.R., and Loomis, J.B., 2019, Economic valuation of Landsat imagery: U.S. Geological Survey Open-File Report 2019–1112, 13 p., https://doi.org/10.3133/ofr20191112.","productDescription":"iv, 13 p.","onlineOnly":"Y","ipdsId":"IP-110993","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":368280,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1112/coverthb.jpg"},{"id":368281,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1112/ofr20191112.pdf","text":"Report","size":"3.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1112"}],"contact":"<p>Director,&nbsp;<a href=\"https://www.usgs.gov/fort/\" data-mce-href=\"https://www.usgs.gov/fort/\">Fort Collins Science Center</a><br>U.S. Geological Survey<br>2150 Centre Ave., Building C<br>Fort Collins, CO 80526-8118</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Valuation Method</li><li>Sample Frame and Sample Design</li><li>Survey Implementation</li><li>Results</li><li>Annual Value of Landsat</li><li>Conclusion</li><li>Acknowledgments</li><li>References</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2019-10-16","noUsgsAuthors":false,"publicationDate":"2019-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Straub, Crista L. 0000-0001-7828-3328","orcid":"https://orcid.org/0000-0001-7828-3328","contributorId":219353,"corporation":false,"usgs":true,"family":"Straub","given":"Crista","email":"","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":773075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koontz, Stephen R.","contributorId":69272,"corporation":false,"usgs":true,"family":"Koontz","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":773226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loomis, John B.","contributorId":197268,"corporation":false,"usgs":false,"family":"Loomis","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":772072,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70205929,"text":"70205929 - 2019 - Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California","interactions":[],"lastModifiedDate":"2022-07-21T13:48:01.291782","indexId":"70205929","displayToPublicDate":"2019-10-10T13:39:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California","docAbstract":"Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflow volume measurements could be integrated with satellite data to fill gaps in monthly surface water chronologies for the Central Valley region of California, USA, from 1984 to 2015.  We aggregated measurements of maximum monthly water extent within each of the study area’s 50 8-digit hydrologic unit code [HUC] watersheds from two Landsat-derived datasets: the European Commission’s Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface Water Extent (DSWE).  We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflow discharge data.  Linear regression fits of the water extent/streamflow data pairs with the highest correlations served as the basis for interpolation of missing imagery surface water values on a HUC-wise basis.  Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface water extents and elucidate flooding or drought events over the period of data collection.  Results of a non-parametric trend analysis of the long-term time series on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface water over the past 30 years.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2019.101973","usgsCitation":"Walker, J., Soulard, C.E., and Petrakis, R.E., 2019, Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California: International Journal of Applied Earth Observation and Geoinformation, v. 84, 101973, 13 p.; Data Release, https://doi.org/10.1016/j.jag.2019.101973.","productDescription":"101973, 13 p.; Data Release","ipdsId":"IP-110207","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":459569,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doaj.org/article/4da62e7b2b8b4e95ab645ffcc5de6106","text":"Publisher Index Page"},{"id":368237,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":404209,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XPA5AK"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.87158203125,\n              41.5579215778042\n            ],\n            [\n              -123.53027343749999,\n              41.52502957323801\n            ],\n            [\n              -123.24462890625,\n              39.57182223734374\n            ],\n            [\n              -122.2119140625,\n              37.70120736474139\n            ],\n            [\n              -120.38818359375,\n              36.491973470593685\n            ],\n            [\n              -119.24560546875001,\n              34.70549341022544\n            ],\n            [\n              -116.65283203124999,\n              35.94243575255426\n            ],\n            [\n              -119.42138671875,\n              38.11727165830543\n            ],\n            [\n              -120.43212890625,\n              39.53793974517628\n            ],\n            [\n              -120.38818359375,\n              41.0130657870063\n            ],\n            [\n              -120.87158203125,\n              41.5579215778042\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"84","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":772925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":772926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petrakis, Roy E. 0000-0001-8932-077X","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":219707,"corporation":false,"usgs":false,"family":"Petrakis","given":"Roy","email":"","middleInitial":"E.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":772927,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256187,"text":"70256187 - 2019 - Landsat 1-5 Multispectral Scanner System (MSS) sensors radiometric calibration update","interactions":[],"lastModifiedDate":"2024-07-25T15:46:01.649986","indexId":"70256187","displayToPublicDate":"2019-09-25T10:41:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat 1-5 Multispectral Scanner System (MSS) sensors radiometric calibration update","docAbstract":"First launched in 1972, the Landsat satellite sensors have provided the longest continuous record of high quality images of the Earth’s surface that are used in both civilian and military applications. The Landsat Multispectral Scanner (MSS) sensor was on-board Landsat-1 through Landsat-5. In fact, the MSS sensors provide the only systematic global multispectral space-based imagery of the Earth’s surface from 1972 to 1982. This paper focuses on the radiometric calibration update of the Landsat 1-5 MSS sensors. The radiometric calibration was performed in both radiance and reflectance-based scales through cross-calibration approach. Simultaneous or a near simultaneous image collections were available for MSS sensor pairs and used for the cross-calibration. The estimated uncertainties for this calibration update exhibit progressively decreasing calibration accuracy, ranging from 5.1% for MSS 5 to 8.8% for MSS-1 in the Green spectral band, for example. Lastly, the new radiometric calibration coefficients were validated through the use of pseudo invariant calibration sites (PICS). The temporal TOA Radiance and TOA Reflectance over the Sonoran Desert were plotted with the purpose of verifying the lifetime radiometric stability of MSS sensors. With the previous calibration, the agreement between the measurements of TOA Reflectance over the Sonoran Desert was around 7.5% for all spectral bands. With the calibration update implemented in this study, the agreement between MSS sensors is 3.6%, 2.9%, 3.5% and 5.9% for the Green, Red, NIR-1 and NIR 2 spectral bands, respectively. This study ties all the Landsat legacy instruments from Landsat-1 MSS through Landsat-8 OLI to a consistent radiometric scale.","language":"English","doi":"10.1109/TGRS.2019.2913106","usgsCitation":"Pinto, C.T., Haque, O., Micijevic, E., and Helder, D., 2019, Landsat 1-5 Multispectral Scanner System (MSS) sensors radiometric calibration update: IEEE Transactions on Geoscience and Remote Sensing, v. 57, no. 10, p. 7378-7394, https://doi.org/10.1109/TGRS.2019.2913106.","productDescription":"8718016, 17 p.","startPage":"7378","endPage":"7394","ipdsId":"IP-100424","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":431444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pinto, Cibele Teixeira","contributorId":340389,"corporation":false,"usgs":false,"family":"Pinto","given":"Cibele","email":"","middleInitial":"Teixeira","affiliations":[{"id":81601,"text":"Image Processing Laboratory, South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907034,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":907035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":907036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":213606,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":907037,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70207203,"text":"70207203 - 2019 - Assessing beach and island habitat loss in the Chesapeake Bay and Delmarva coastal bay region, USA, through processing of Landsat TM and OLI imagery: A case study","interactions":[],"lastModifiedDate":"2019-12-13T06:21:24","indexId":"70207203","displayToPublicDate":"2019-09-16T09:52:30","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Assessing beach and island habitat loss in the Chesapeake Bay and Delmarva coastal bay region, USA, through processing of Landsat TM and OLI imagery: A case study","docAbstract":"Beaches and islands provide economic value to humans and critical habitat for breeding and foraging wildlife. These ecosystems, however, are being severely impacted by global climate change and sea level rise through increased erosion and frequency of inundation. The case study presented here aimed to document island loss in the Chesapeake Bay and Delmarva coastal bay region of the United States using image processing techniques within a GIS from 1986 to 2016. Satellite imagery from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) sensors were processed within ArcMap 10.5 to determine spatial and temporal trends in island and beach habitat. Calculation of unweighted Cohen’s Kappa showed that classified scenes were, on average, within the range of moderate agreement between the classified Landsat scenes and the validation imagery within Google Earth (0.539). Recommendations regarding existing beach habitat management and future supplementation were created based on these results. From 1986 to 2016, island area declined by over 1,200 hectares (ha) with agriculture/open field (all open vegetated spaces) declining by nearly 82% and beach, surprisingly, increasing nearly 2%. This study was the first to document Chesapeake Bay region-wide island loss beyond the mid-2000s. The accuracy of this study was limited slightly by the 30 m spatial resolution of the imagery used. This technique may be best suited for documenting trends on large islands and along the mainland coastline.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2019.100265","usgsCitation":"Marban, P., Mullinax, J.M., Resop, J.P., and Prosser, D.J., 2019, Assessing beach and island habitat loss in the Chesapeake Bay and Delmarva coastal bay region, USA, through processing of Landsat TM and OLI imagery: A case study: Remote Sensing Applications: Society and Environment, v. 16, 100265, 10 p., https://doi.org/10.1016/j.rsase.2019.100265.","productDescription":"100265, 10 p.","ipdsId":"IP-106458","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":370203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay, Delmarva Coastal Bays","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.0638427734375,\n              39.65222681530652\n            ],\n            [\n              -76.6790771484375,\n              39.2832938689385\n            ],\n            [\n              -77.14599609375,\n              38.272688535980976\n            ],\n            [\n              -76.607666015625,\n              36.83127162140714\n            ],\n            [\n              -75.9814453125,\n              36.787291466820015\n            ],\n            [\n              -74.8553466796875,\n              38.40194908237822\n            ],\n            [\n              -75.0640869140625,\n              38.84826438869913\n            ],\n            [\n              -76.0638427734375,\n              39.65222681530652\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"16","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marban, Paul R.","contributorId":221168,"corporation":false,"usgs":false,"family":"Marban","given":"Paul R.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":777269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":777270,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Resop, Jonathan P.","contributorId":221169,"corporation":false,"usgs":false,"family":"Resop","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":777271,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":777268,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70212582,"text":"70212582 - 2019 - Performances of WorldView-3, Sentinel-2, and Landsat-8 data in mapping impervious surface","interactions":[],"lastModifiedDate":"2020-08-24T12:37:52.256233","indexId":"70212582","displayToPublicDate":"2019-08-31T09:38:43","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Performances of WorldView-3, Sentinel-2, and Landsat-8 data in mapping impervious surface","docAbstract":"<p><span>Many efforts have been made to map developed impervious surface from remotely sensed information in the last two decades. The U.S. Geological Survey (USGS) developed the National Land Cover Database (NLCD) to provide consistent land cover and change products for the Nation since 2001. Percent impervious surface area (ISA), one of the products in NLCD as a continuous field and estimated with Landsat imagery, represents the fraction of human-made impervious area in a 30 m resolution grid. ISA is used to map urban land cover types and extents for the United States. However, it is still a challenge to quantify highly heterogeneous features in many urban areas using remotely sensed data with spatial and spectral resolutions similar to Landsat and to determine the impacts of remotely sensed data characteristics on ISA mapping.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2019.100246","usgsCitation":"Xian, G.Z., Shi, H., Dewitz, J., and Wu, Z., 2019, Performances of WorldView-3, Sentinel-2, and Landsat-8 data in mapping impervious surface: Remote Sensing Applications: Society and Environment, v. 15, 100246, 11 p., https://doi.org/10.1016/j.rsase.2019.100246.","productDescription":"100246, 11 p.","ipdsId":"IP-102241","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2019.100246","text":"Publisher Index Page"},{"id":377728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Texas","city":"San Francisco, Dallas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.9150390625,\n              37.47485808497102\n            ],\n            [\n              -121.904296875,\n              37.47485808497102\n            ],\n            [\n              -121.904296875,\n              38.13455657705411\n            ],\n            [\n              -122.9150390625,\n              38.13455657705411\n            ],\n            [\n              -122.9150390625,\n              37.47485808497102\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -96.94335937499999,\n              32.58384932565662\n            ],\n            [\n              -96.5478515625,\n              32.58384932565662\n            ],\n            [\n              -96.5478515625,\n              32.95336814579932\n            ],\n            [\n              -96.94335937499999,\n              32.95336814579932\n            ],\n            [\n              -96.94335937499999,\n              32.58384932565662\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796916,"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":796917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewitz, Jon 0000-0002-0458-212X","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":215192,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796918,"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":796919,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204702,"text":"70204702 - 2019 - Mapping crop residue by combining Landsat and WorldView-3 satellite imagery","interactions":[],"lastModifiedDate":"2019-08-09T12:33:40","indexId":"70204702","displayToPublicDate":"2019-08-09T12:27:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping crop residue by combining Landsat and WorldView-3 satellite imagery","docAbstract":"A unique, multi-tiered approach was applied to map crop-residue cover on the Eastern Shore of the Chesapeake Bay, USA. Field measurements of crop-residue cover were used to calibrate residue mapping using shortwave infrared (SWIR) indices derived from WorldView-3 imagery for an 8-km x 8-km footprint. The resulting map was then used to calibrate and subsequently classify residue mapping of Landsat imagery at a larger spatial resolution and extent. This manuscript describes how the method was applied and presents results in the form of crop-residue cover maps, validation statistics, and quantification of conservation tillage implementation in the agricultural landscape. Overall accuracy for maps derived from Landsat 7 (ETM+) and Landsat 8 (OLI) were comparable at roughly 92% (+/- 10%). Tillage class specific accuracy was also strong and ranged from 75% to 99%. The approach, which employed a 12-band image stack of six tillage spectral indices and six individual Landsat bands, was shown to be adaptable to variable soil-moisture conditions: under dry conditions (Landsat 7, May 14, 2015) the majority of predictive power was attributed to SWIR indices, and under wet conditions (Landsat 8, May 22, 2015) single band reflectance values were more effective at explaining variability in residue cover. Summary statistics of resulting tillage class occurrence matched closely with conservation tillage implementation totals reported by Maryland and Delaware to the Chesapeake Bay Program. This hybrid method combining WorldView-3 and Landsat imagery sources shows promise for monitoring progress in the adoption of conservation tillage practices and for describing crop-residue outcomes associated with a variety of agricultural management practices.","language":"English","publisher":"MDPI","doi":"10.3390/rs11161857","usgsCitation":"Hively, W.D., Shermeyer, J., Lamb, B.T., Daughtry, C.S., Quemada, M., and Keppler, J., 2019, Mapping crop residue by combining Landsat and WorldView-3 satellite imagery: Remote Sensing, v. 11, no. 16, 1857, 21 p., https://doi.org/10.3390/rs11161857.","productDescription":"1857, 21 p.","ipdsId":"IP-090242","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":467379,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11161857","text":"Publisher Index Page"},{"id":366446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Talbot County","otherGeospatial":"Choptank River Watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -76.19842529296875,\n              38.565347844885466\n            ],\n            [\n              -75.728759765625,\n              38.565347844885466\n            ],\n            [\n              -75.728759765625,\n              39.02345139405935\n            ],\n            [\n              -76.19842529296875,\n              39.02345139405935\n            ],\n            [\n              -76.19842529296875,\n              38.565347844885466\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}\n","volume":"11","issue":"16","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-09","publicationStatus":"PW","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":768123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shermeyer, Jacob 0000-0002-8143-2790","orcid":"https://orcid.org/0000-0002-8143-2790","contributorId":218038,"corporation":false,"usgs":true,"family":"Shermeyer","given":"Jacob","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":768124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":768125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daughtry, Craig S.T.","contributorId":214079,"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":768126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":768127,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keppler, Jason","contributorId":218039,"corporation":false,"usgs":false,"family":"Keppler","given":"Jason","email":"","affiliations":[{"id":39731,"text":"Maryland Department of Agriculture, Office of Resource Conservation","active":true,"usgs":false}],"preferred":false,"id":768128,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70204623,"text":"70204623 - 2019 - Remote sensing as the foundation for high-resolution United States landscape projections – The Land Change Monitoring, assessment, and projection (LCMAP) initiative","interactions":[],"lastModifiedDate":"2019-08-07T09:37:29","indexId":"70204623","displayToPublicDate":"2019-07-31T09:35:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing as the foundation for high-resolution United States landscape projections – The Land Change Monitoring, assessment, and projection (LCMAP) initiative","docAbstract":"<p><span>The Land Change Monitoring, Assessment, and Projection (LCMAP) initiative uses temporally dense Landsat data and time series analyses to characterize landscape change in the United States from 1985 to present. LCMAP will be used to explain how past, present, and future landscape change affects society and natural systems. Here, we describe a modeling framework for producing high-resolution (spatial and thematic) landscape projections at a national scale, using a unique parcel-based modeling framework. The methodology was tested by modeling 11 land use scenarios and 3 climate realizations for the U.S. Great Plains. Results demonstrate 1) an ability to balance competing land-use demands from quite variable, complex scenarios, 2) urban growth that matches theoretical future patterns, 3) the value of remote sensing data sources for model parameterization and for deriving landscape parcels, and 4) a pragmatic approach that facilitates the development of high thematic- and spatial-resolution projections at a national scale.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2019.104495","usgsCitation":"Sohl, T.L., Dornbierer, J., Wika, S., and Robison, C., 2019, Remote sensing as the foundation for high-resolution United States landscape projections – The Land Change Monitoring, assessment, and projection (LCMAP) initiative: Environmental Modelling and Software, v. 120, 104495, 17 p., https://doi.org/10.1016/j.envsoft.2019.104495.","productDescription":"104495, 17 p.","ipdsId":"IP-110128","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467406,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2019.104495","text":"Publisher Index Page"},{"id":366326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"120","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","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":767809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dornbierer, Jordan 0000-0003-2099-5095","orcid":"https://orcid.org/0000-0003-2099-5095","contributorId":213067,"corporation":false,"usgs":false,"family":"Dornbierer","given":"Jordan","affiliations":[{"id":38270,"text":"SGT Inc., contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":767810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wika, Steve 0000-0001-9992-8973","orcid":"https://orcid.org/0000-0001-9992-8973","contributorId":213068,"corporation":false,"usgs":false,"family":"Wika","given":"Steve","affiliations":[{"id":38700,"text":"SGT Inc.","active":true,"usgs":false}],"preferred":false,"id":767811,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robison, Charles 0000-0002-7623-2380","orcid":"https://orcid.org/0000-0002-7623-2380","contributorId":217916,"corporation":false,"usgs":false,"family":"Robison","given":"Charles","email":"","affiliations":[{"id":39714,"text":"SGT Inc. (USGS Contractor)","active":true,"usgs":false}],"preferred":false,"id":767812,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204576,"text":"70204576 - 2019 - Characterizing crop water use dynamics in the Central Valley of California using landsat-derived evapotranspiration","interactions":[],"lastModifiedDate":"2019-08-07T08:59:41","indexId":"70204576","displayToPublicDate":"2019-07-30T12:20:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing crop water use dynamics in the Central Valley of California using landsat-derived evapotranspiration","docAbstract":"Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and agricultural production. The main objective of this paper is to characterize the spatiotemporal dynamics of crop water use in the Central Valley of California using Landsat-based annual actual evapotranspiration (ETa) from 2008 to 2018 derived from the Operational Simplified Surface Energy Balance (SSEBop) model. Crop water use for 10 crops is characterized at multiple scales. The Mann–Kendall trend analysis revealed a significant increase in area cultivated with almonds and their water use, with an annual rate of change of 16,327 ha in area and 13,488 ha-m in water use. Conversely, alfalfa showed a significant decline with 12,429 ha in area and 13,901 ha-m in water use per year during the same period. A pixel-based Mann–Kendall trend analysis showed the changing crop type and water use at the level of individual fields for all of Kern County in the Central Valley. This study demonstrates the useful application of historical Landsat ET to produce relevant water management information. Similar studies can be conducted at regional and global scales to understand and quantify the relationships between land cover change and its impact on water use.","language":"English","publisher":"MDPI","doi":"10.3390/rs11151782","usgsCitation":"Schauer, M., and Senay, G., 2019, Characterizing crop water use dynamics in the Central Valley of California using landsat-derived evapotranspiration: Remote Sensing, v. 15, no. 11, 22 p., https://doi.org/10.3390/rs11151782.","productDescription":"22 p.","ipdsId":"IP-085933","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467408,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11151782","text":"Publisher Index Page"},{"id":366308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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