This 30-meter data set represents land use and land cover for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System (see http://water.usgs.gov/GIS/browse/nlcd01-partition.jpg). The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004), (see: http://www.mrlc.gov/mrlc2k.asp). The NLCD 2001 was created by partitioning the United States into mapping zones. A total of 68 mapping zones (see http://water.usgs.gov/GIS/browse/nlcd01-mappingzones.jpg), were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.
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Landsat: Comparison with in situ data from the Everglades Depth Estimation Network","docAbstract":"The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management.
","language":"English","publisher":"MDPI","doi":"10.3390/rs70912503","usgsCitation":"Jones, J., 2015, Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network: Remote Sensing, v. 9, no. 7, p. 12503-12538, https://doi.org/10.3390/rs70912503.","productDescription":"36 p.","startPage":"12503","endPage":"12538","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066317","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471512,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70912503","text":"Publisher Index Page"},{"id":314188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.331787109375,\n 24.831610355586918\n ],\n [\n -80.31280517578125,\n 24.831610355586918\n ],\n [\n -80.31280517578125,\n 26.561506704037942\n ],\n [\n -81.331787109375,\n 26.561506704037942\n ],\n [\n -81.331787109375,\n 24.831610355586918\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-23","publicationStatus":"PW","scienceBaseUri":"5694d22de4b039675d005dc0","contributors":{"authors":[{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":588290,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70190299,"text":"70190299 - 2015 - The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps","interactions":[],"lastModifiedDate":"2017-08-24T12:12:24","indexId":"70190299","displayToPublicDate":"2015-12-31T00:00:00","publicationYear":"2015","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}},"displayTitle":"The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps","title":"The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps","docAbstract":"Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map.
Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors.
We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped.
We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps.
Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2014.09.025","usgsCitation":"Ackers, S.H., Davis, R.J., Olsen, K., and Dugger, K., 2015, The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps: Remote Sensing of Environment, v. 156, p. 361-373, https://doi.org/10.1016/j.rse.2014.09.025.","productDescription":"13 p.","startPage":"361","endPage":"373","ipdsId":"IP-049418","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":345111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"156","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"599fe5bae4b038630d022104","contributors":{"authors":[{"text":"Ackers, Steven H.","contributorId":36065,"corporation":false,"usgs":true,"family":"Ackers","given":"Steven","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":708389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Raymond J.","contributorId":150574,"corporation":false,"usgs":false,"family":"Davis","given":"Raymond","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":708390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olsen, K.","contributorId":61570,"corporation":false,"usgs":true,"family":"Olsen","given":"K.","email":"","affiliations":[],"preferred":false,"id":708391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":708356,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","interactions":[{"subject":{"id":70156593,"text":"ofr20131280D - 2015 - Database creation, data quality assessment, and geochemical maps (phase V, deliverable 59)—Final report on compilation and validation of geochemical data","indexId":"ofr20131280D","publicationYear":"2015","noYear":false,"chapter":"D","title":"Database creation, data quality assessment, and geochemical maps (phase V, deliverable 59)—Final report on compilation and validation of geochemical data"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":1},{"subject":{"id":70156594,"text":"ofr20131280J - 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2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":3},{"subject":{"id":70156596,"text":"ofr20131280K1 - 2012 - Permissive tracts for sediment-hosted copper deposits in Mauritania (phase V, deliverable 74): Chapter K1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)","indexId":"ofr20131280K1","publicationYear":"2012","noYear":false,"chapter":"K1","title":"Permissive tracts for sediment-hosted copper deposits in Mauritania (phase V, deliverable 74): Chapter K1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 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2015 - Mineral potential for volcanogenic massive sulfide deposits in the Islamic Republic of Mauritania, (phase V, deliverable 77)","indexId":"ofr20131280L","publicationYear":"2015","noYear":false,"chapter":"L","title":"Mineral potential for volcanogenic massive sulfide deposits in the Islamic Republic of Mauritania, (phase V, deliverable 77)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":6},{"subject":{"id":70156599,"text":"ofr20131280O - 2015 - Algoma-, Superior-, and oolitic-type iron deposits of the Islamic Republic of Mauritania (phase V, deliverable 83)","indexId":"ofr20131280O","publicationYear":"2015","noYear":false,"chapter":"O","title":"Algoma-, Superior-, and oolitic-type iron deposits of the Islamic Republic of Mauritania (phase V, deliverable 83)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":7},{"subject":{"id":70156600,"text":"ofr20131280R - 2015 - Reported industrial minerals occurrences and permissive areas for other occurrences in the Islamic Republic of Mauritania, (phase V, deliverable 89)","indexId":"ofr20131280R","publicationYear":"2015","noYear":false,"chapter":"R","title":"Reported industrial minerals occurrences and permissive areas for other occurrences in the Islamic Republic of Mauritania, (phase V, deliverable 89)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":8},{"subject":{"id":70156601,"text":"ofr20131280L1 - 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2012 - Permissive tracts for algoma-, superior-, and oolitic-type iron deposits in Mauritania (phase V, deliverable 82): Chapter O1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)","indexId":"ofr20131280O1","publicationYear":"2012","noYear":false,"chapter":"O1","title":"Permissive tracts for algoma-, superior-, and oolitic-type iron deposits in Mauritania (phase V, deliverable 82): Chapter O1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":10},{"subject":{"id":70156603,"text":"ofr20131280R1 - 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In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.
\nTo achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.
\nThis report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.
","language":"English, French","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131280","collaboration":"Prepared in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania","usgsCitation":"Taylor, C.D., ed., 2015, Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II) phase V: U.S. Geological Survey Open-File Report 2013‒1280, 20 chaps., pages variable, pls. variable, https://dx.doi.org/10.3133/ofr20131280. [In English and French.]","productDescription":"Report: 20 Chapters; Readme; GIS and Maps; Publication Index","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071408","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":312690,"rank":35,"type":{"id":6,"text":"Chapter"},"url":"https://pubs.usgs.gov/of/2013/1280/GIS_and_Maps/Chapter_Q1_deliverable_86-Incompatible_element_deposits_hosted_in_pegmatities_alkaline rocks_and_carbonatities","text":"Chapter Q1--Permissive Tracts for Incompatible Element Deposits Hosted in Pegmatities, Alkaline Rocks, and Carbonatities in Mauritania","size":"2.39 MB","description":"OFR 2013-1280 Chapter Q1","linkHelpText":"Director, Central Mineral and Environmental Resources Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225
http://minerals.cr.usgs.gov/
The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. These studies, however, have traditionally focused on sandy shorelines and sandy barrier-island systems, without consideration of impacts to coastal wetlands. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, including erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments.
\nThis report serves as an archive of data that were derived from Landsat 5 and Landsat 8 imagery from 1984 to 2014, including wetland and terrestrial habitat extents; open-ocean, back-barrier, and estuarine mainland shoreline positions; and sand-line positions along the estuarine mainland and barrier shorelines from Assateague Island, Maryland to Metompkin Island, Virginia. The geographic information system data files with accompanying formal Federal Geographic Data Committee metadata can be downloaded from the Data Downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds968","usgsCitation":"Bernier, J.C., Douglas, S.H., Terrano, J.F., Barras, J.A., Plant, N.G., and Smith, C.G., 2015, Land-cover types, shoreline positions, and sand extents derived from Landsat satellite imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2014: U.S. Geological Survey Data Series 968, https://dx.doi.org/10.3133/ds968.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1984-01-01","temporalEnd":"2014-12-31","ipdsId":"IP-065873","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":312270,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0968/index.html","text":"Report (HTML format)","description":"DS 968"},{"id":312269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0968/images/coverthb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island, Metompkin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.10528564453125,\n 38.33303882235456\n ],\n [\n -75.25360107421875,\n 38.23925875585244\n ],\n [\n -75.5474853515625,\n 37.8065289741725\n ],\n [\n -75.30990600585938,\n 37.801103690609615\n ],\n [\n -75.02975463867188,\n 38.33088431959968\n ],\n [\n -75.10528564453125,\n 38.33303882235456\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
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600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models. We estimated up to 90% of variation in foliar N concentration using partial least squares (PLS) regression of full-spectrum field spectrometer reflectance data. Landsat 7 reflectance data explained up to 70% of % foliar N and 67% of aboveground biomass. Spectrally estimated foliar N or aboveground biomass had negative relationships with belowground biomass and root:shoot ratio in both Schoenoplectus acutus and Typha, consistent with a balanced growth model, which suggests plants only allocate growth belowground when additional nutrients are necessary to support shoot development. Hybrid models explained up to 76% of variation in belowground biomass and 86% of variation in root:shoot ratio. Our modeling approach provides a method for developing maps of spatial variation in wetland belowground biomass.
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","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs71215837","usgsCitation":"Jessica L. O'Connell, Byrd, K.B., and Kelly, M., 2015, A hybrid model for mapping relative differences in belowground biomass and root: Shoot ratios using spectral reflectance, foliar N and plant biophysical data within coastal marsh: Remote Sensing, v. 12, no. 7, p. 16480-16503, https://doi.org/10.3390/rs71215837.","productDescription":"24 p.","startPage":"16480","endPage":"16503","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059688","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471578,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs71215837","text":"Publisher Index Page"},{"id":318288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mayberry Slough, Sacramento-San Joaquin River Delta, Sherman Island, Twitchell Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6,\n 38.2\n ],\n [\n -121.6,\n 38\n ],\n [\n -121.8,\n 38\n ],\n [\n -121.8,\n 38.2\n ],\n [\n -121.6,\n 38.2\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-05","publicationStatus":"PW","scienceBaseUri":"56cc3f3ce4b059daa47e4388","contributors":{"authors":[{"text":"Jessica L. 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Engineering","active":true,"usgs":false}],"preferred":false,"id":621139,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159914,"text":"70159914 - 2015 - Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations","interactions":[],"lastModifiedDate":"2017-01-18T09:55:38","indexId":"70159914","displayToPublicDate":"2015-12-03T15:00:00","publicationYear":"2015","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":"Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations","docAbstract":"Accurately estimating aboveground vegetation biomass productivity is essential for local ecosystem assessment and best land management practice. Satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. A 250-m grassland biomass productivity map for the Greater Platte River Basin had been developed based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) GSN and Soil Survey Geographic (SSURGO) annual grassland productivity. However, the 250-m MODIS grassland biomass productivity map does not capture detailed ecological features (or patterns) and may result in only generalized estimation of the regional total productivity. Developing a high or moderate spatial resolution (e.g., 30-m) productivity map to better understand the regional detailed vegetation condition and ecosystem services is preferred. The 30-m Landsat data provide spatial detail for characterizing human-scale processes and have been successfully used for land cover and land change studies. The main goal of this study is to develop a 30-m grassland biomass productivity estimation map for central Nebraska, leveraging 250-m MODIS GSN and 30-m Landsat data. A rule-based piecewise regression GSN model based on MODIS and Landsat (r = 0.91) was developed, and a 30-m MODIS equivalent GSN map was generated. Finally, a 30-m grassland biomass productivity estimation map, which provides spatially detailed ecological features and conditions for central Nebraska, was produced. The resulting 30-m grassland productivity map was generally supported by the SSURGO biomass production map and will be useful for regional ecosystem study and local land management practices.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2015.10.018","usgsCitation":"Gu, Y., and Wylie, B.K., 2015, Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations: Remote Sensing of Environment, v. 171, p. 291-298, https://doi.org/10.1016/j.rse.2015.10.018.","productDescription":"8 p.","startPage":"291","endPage":"298","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068578","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":311883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.162353515625,\n 41.00477542222949\n ],\n [\n -100.162353515625,\n 42.52879629320373\n ],\n [\n -96.624755859375,\n 42.52879629320373\n ],\n [\n -96.624755859375,\n 41.00477542222949\n ],\n [\n -100.162353515625,\n 41.00477542222949\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"171","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"566167b8e4b06a3ea36c565b","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@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":581015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@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":581016,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159774,"text":"fs20153081 - 2015 - Landsat—Earth observation satellites","interactions":[{"subject":{"id":70038609,"text":"fs20123072 - 2012 - Landsat: A global land-imaging mission","indexId":"fs20123072","publicationYear":"2012","noYear":false,"title":"Landsat: A global land-imaging mission"},"predicate":"SUPERSEDED_BY","object":{"id":70159774,"text":"fs20153081 - 2015 - Landsat—Earth observation satellites","indexId":"fs20153081","publicationYear":"2015","noYear":false,"title":"Landsat—Earth observation satellites"},"id":1},{"subject":{"id":70047982,"text":"fs20133060 - 2013 - Landsat 8","indexId":"fs20133060","publicationYear":"2013","noYear":false,"title":"Landsat 8"},"predicate":"SUPERSEDED_BY","object":{"id":70159774,"text":"fs20153081 - 2015 - Landsat—Earth observation satellites","indexId":"fs20153081","publicationYear":"2015","noYear":false,"title":"Landsat—Earth observation satellites"},"id":2}],"lastModifiedDate":"2022-08-24T16:34:32.839169","indexId":"fs20153081","displayToPublicDate":"2015-11-25T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-3081","displayTitle":"Landsat—Earth Observation Satellites","title":"Landsat—Earth observation satellites","docAbstract":"Since 1972, Landsat satellites have continuously acquired space-based images of the Earth’s land surface, providing data that serve as valuable resources for land use/land change research. The data are useful to a number of applications including forestry, agriculture, geology, regional planning, and education. Landsat is a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). NASA develops remote sensing instruments and the spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and data distribution. The result of this program is an unprecedented continuing record of natural and human-induced changes on the global landscape.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153081","usgsCitation":"U.S. Geological Survey, 2015, Landsat—Earth observation satellites (ver. 1.4, August 2022): U.S. Geological Survey Fact Sheet 2015–3081, 4 p., https://doi.org/10.3133/fs20153081.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068422","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":311694,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3081/coverthb5.jpg"},{"id":405531,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3081/fs20153081.pdf","text":"Report","size":"4.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015–3081"},{"id":405532,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2015/3081/versionHist.txt","text":"Version History","size":"13.9 kB","linkFileType":{"id":2,"text":"txt"},"description":"FS 2015–3081 Version History"}],"edition":"Version 1.0: November 25, 2015; Version 1.1: August 22, 2016; Version 1.2: April 8, 2020; Version 1.3: August 3, 2022; Version 1.4: August 24, 2022","contact":"Earth Resources Observation and Science (EROS) Center
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In the Chesapeake Bay Watershed, winter cereal cover crops are often planted in rotation with summer crops to reduce the loss of nutrients and sediment from agricultural systems. Cover crops can also improve soil health, control weeds and pests, supplement forage needs, and support resilient cropping systems. In southeastern Pennsylvania, cover crops can be successfully established following corn (Zea mays L.) silage harvest and are strongly promoted for use in this niche. They are also planted following corn grain, soybean (Glycine max L.), and vegetable harvest. In Pennsylvania, the use of winter cover crops for agricultural conservation has been supported through a combination of outreach, regulation, and incentives. On-farm implementation is thought to be increasing, but the actual extent of cover crops is not well quantified. Satellite imagery can be used to map green winter cover crop vegetation on agricultural fields and, when integrated with additional remote sensing data products, can be used to evaluate wintertime vegetative groundcover following specific summer crops. This study used Landsat and SPOT (System Probatoire d’ Observation de la Terre) satellite imagery, in combination with the USDA National Agricultural Statistics Service Cropland Data Layer, to evaluate the extent and amount of green wintertime vegetation on agricultural fields in four Pennsylvania counties (Berks, Lebanon, Lancaster, and York) from 2010 to 2013. In December of 2010, a windshield survey was conducted to collect baseline data on winter cover crop implementation, with particular focus on identifying corn harvested for silage (expected earlier harvest date and lower levels of crop residue), versus for grain (expected later harvest date and higher levels of crop residue). Satellite spectral indices were successfully used to detect both the amount of green vegetative groundcover and the amount of crop residue on the surveyed fields. Analysis of wintertime satellite imagery showed consistent increases in vegetative groundcover over the four-year study period and determined that trends did not result from annual weather variability, indicating that farmers are increasing adoption of practices such as cover cropping that promote wintertime vegetation. Between 2010 and 2013, the occurrence of wintertime vegetation on agricultural fields increased from 36% to 67% of corn fields in Berks County, from 53% to 75% in Lancaster County, from 42% to 65% in Lebanon County, and from 26% to 52% in York County. Apparently, efforts to promote cover crop use in the Chesapeake Bay Watershed have coincided with a rapid increase in the occurrence of wintertime vegetation following corn harvest in southeastern Pennsylvania. However, despite these increases, between 25% and 48% of corn fields remained without substantial green vegetation over the wintertime, indicating further opportunity for cover crop adoption.
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.70.6.340","usgsCitation":"Hively, W., Duiker, S., Greg McCarty, and Prabhakara, K., 2015, Remote sensing to monitor cover crop adoption in southeastern Pennsylvania: Journal of Soil and Water Conservation, v. 70, no. 6, p. 340-352, https://doi.org/10.2489/jswc.70.6.340.","productDescription":"13 p.","startPage":"340","endPage":"352","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061440","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.70.6.340","text":"Publisher Index Page"},{"id":311303,"type":{"id":15,"text":"Index Page"},"url":"https://www.jswconline.org/content/70/6/340.full.pdf"},{"id":311321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Southeastern and Central Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2176513671875,\n 39.73253798438173\n ],\n [\n -76.48681640625,\n 40.0360265298117\n ],\n [\n -76.22314453125,\n 40.12429084831405\n ],\n [\n -76.3275146484375,\n 40.32141999593439\n ],\n [\n -76.08032226562499,\n 40.35073056591789\n ],\n [\n -76.08032226562499,\n 40.32560799973207\n ],\n [\n -75.78369140625,\n 40.41767833585551\n ],\n [\n -75.5474853515625,\n 40.27533480732468\n ],\n [\n -75.860595703125,\n 39.757879992021756\n ],\n [\n -75.8660888671875,\n 39.72831341029745\n ],\n [\n -76.2176513671875,\n 39.73253798438173\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.9754638671875,\n 41.31907562295136\n ],\n [\n -77.9425048828125,\n 40.61812224225511\n ],\n [\n -77.0306396484375,\n 40.6723059714534\n ],\n [\n -77.0965576171875,\n 41.36031866306708\n ],\n [\n -77.9754638671875,\n 41.31907562295136\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"70","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-06","publicationStatus":"PW","scienceBaseUri":"564717d7e4b0e2669b313129","contributors":{"authors":[{"text":"Hively, Wells whively@usgs.gov","contributorId":149843,"corporation":false,"usgs":true,"family":"Hively","given":"Wells","email":"whively@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":579787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duiker, Sjoerd","contributorId":149844,"corporation":false,"usgs":false,"family":"Duiker","given":"Sjoerd","email":"","affiliations":[{"id":17838,"text":"Dep. of Crop and Soil Sciences, The Pennsylvania State University, 116 ASI Building, University Park, PA 16802-3504","active":true,"usgs":false}],"preferred":false,"id":579788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greg McCarty","contributorId":149845,"corporation":false,"usgs":false,"family":"Greg McCarty","affiliations":[{"id":17839,"text":"USDA-Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Building 007 Room 104 BARC-West, 10300 Baltimore Avenue, Beltsville, MD","active":true,"usgs":false}],"preferred":false,"id":579789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prabhakara, Kusuma","contributorId":6313,"corporation":false,"usgs":true,"family":"Prabhakara","given":"Kusuma","email":"","affiliations":[],"preferred":false,"id":579790,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70147802,"text":"70147802 - 2015 - Landsat Science Team meeting: Winter 2015","interactions":[],"lastModifiedDate":"2017-04-21T15:47:52","indexId":"70147802","displayToPublicDate":"2015-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3555,"text":"The Earth Observer","active":true,"publicationSubtype":{"id":10}},"title":"Landsat Science Team meeting: Winter 2015","docAbstract":"The summer meeting of the joint U.S. Geological Survey (USGS)–NASA Landsat Science Team (LST) was held at the USGS’s Earth Resources Observation and Science (EROS) Center July 7-9, 2015, in Sioux Falls, SD. The LST co-chairs, Tom Loveland [EROS—Senior Scientist] and Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Landsat 8 Project Scientist], opened the three-day meeting on an upbeat note following the recent successful launch of the European Space Agency’s Sentinel-2 mission on June 23, 2015 (see image on page 14), and the news that work on Landsat 9 has begun, with a projected launch date of 2023.
With over 60 participants in attendance, this was the largest LST meeting ever held. Meeting topics on the first day included Sustainable Land Imaging and Landsat 9 development, Landsat 7 and 8 operations and data archiving, the Landsat 8 Thermal Infrared Sensor (TIRS) stray-light issue, and the successful Sentinel-2 launch. In addition, on days two and three the LST members presented updates on their Landsat science and applications research. All presentations are available at landsat.usgs.gov/science_LST_Team_ Meetings.php.
","language":"English","publisher":"NASA","usgsCitation":"Schroeder, T.A., Loveland, T., Wulder, M.A., and Irons, J.R., 2015, Landsat Science Team meeting: Winter 2015: The Earth Observer, v. 27, no. 6, p. 12-17.","productDescription":"6 p.","startPage":"12","endPage":"17","ipdsId":"IP-065489","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":340093,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://eospso.nasa.gov/sites/default/files/eo_pdfs/Nov%20Dec%202015_508_col.pdf"}],"volume":"27","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4ee4b0c3010a8087d1","contributors":{"authors":[{"text":"Schroeder, Todd A. taschroeder@fs.fed.us","contributorId":190802,"corporation":false,"usgs":false,"family":"Schroeder","given":"Todd","email":"taschroeder@fs.fed.us","middleInitial":"A.","affiliations":[],"preferred":false,"id":692438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":546324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":546325,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":546326,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70158955,"text":"70158955 - 2015 - Landsat science team meeting: Summer 2015","interactions":[],"lastModifiedDate":"2017-01-18T09:56:24","indexId":"70158955","displayToPublicDate":"2015-11-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3555,"text":"The Earth Observer","active":true,"publicationSubtype":{"id":10}},"title":"Landsat science team meeting: Summer 2015","docAbstract":"The summer meeting of the joint U.S. Geological Survey (USGS)–NASA Landsat Science Team (LST) was held at the USGS’s Earth Resources Observation and Science (EROS) Center July 7-9, 2015, in Sioux Falls, SD. The LST co-chairs, Tom Loveland [EROS—Senior Scientist] and Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Landsat 8 Project Scientist], opened the three-day meeting on an upbeat note following the recent successful launch of the European Space Agency’s Sentinel-2 mission on June 23, 2015 (see image on page 14), and the news that work on Landsat 9 has begun, with a projected launch date of 2023.
\nWith over 60 participants in attendance, this was the largest LST meeting ever held. Meeting topics on the first day included Sustainable Land Imaging and Landsat 9 development, Landsat 7 and 8 operations and data archiving, the Landsat 8 Thermal Infrared Sensor (TIRS) stray-light issue, and the successful Sentinel-2 launch. In addition, on days two and three the LST members presented updates on their Landsat science and applications research. All presentations are available at landsat.usgs.gov/science_LST_Team_ Meetings.php.
","language":"English","publisher":"NASA","usgsCitation":"Schroeder, T., Loveland, T., Wulder, M.A., and Irons, J.R., 2015, Landsat science team meeting: Summer 2015: The Earth Observer, v. 27, no. 6, p. 12-17.","productDescription":"6 p.","startPage":"12","endPage":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068875","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":324704,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://eospso.nasa.gov/sites/default/files/eo_pdfs/Nov%20Dec%202015_508_col.pdf"},{"id":324705,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57779432e4b07dd077c905f2","contributors":{"authors":[{"text":"Schroeder, Todd tschroeder@usgs.gov","contributorId":149137,"corporation":false,"usgs":true,"family":"Schroeder","given":"Todd","email":"tschroeder@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":577035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":577036,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":577037,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":577038,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70158607,"text":"ofr20151179 - 2015 - Barrier Island Shorelines Extracted from Landsat Imagery","interactions":[],"lastModifiedDate":"2015-12-15T10:18:16","indexId":"ofr20151179","displayToPublicDate":"2015-10-13T15:45:00","publicationYear":"2015","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":"2015-1179","title":"Barrier Island Shorelines Extracted from Landsat Imagery","docAbstract":"Changes to barrier islands occur at time scales that vary from the few hours it takes an individual storm to pass (Morton, 2008) to the millennia it takes for coastal systems to undergo geologic evolution. Developing an understanding of how barrier islands will respond to climate change, sea level rise, and major storms over a range of time scales is relevant to studies of physical, geological, ecological, and societal processes and will help to guide and improve management of our coastal resources (Sallenger and others, 1987). Observations of coastal processes made over a range of spatial and temporal scales and from a variety of instrument platforms (for example, in situ and remote remote sensing) are required to understand and eventually predict the evolution of coastal systems.
\nThe deployment of Landsat and other earth-observing satellites within the last few decades has provided an opportunity to observe barrier islands at frequent intervals, often many times a year. This sample frequency is much higher and the spatial coverage much greater than most routine high-resolution topographic surveys (Guy and others, 2014). In addition, the historical record of these datasets have become long enough to document shorter- (that is, annual) and longer-term (that is, decadal) changes from a single data source. Certain aspects of barrier island morphology, such as island size, shape, and position, can be determined from these images and can indicate erosion, land loss, and island breakup (McBride and others, 1989; Plant and Guy, 2013).
\nThe shoreline is a common variable used as a metric for coastal erosion or change (Himmelstoss and others, 2010). Although shorelines are often extracted from topographic data (for example, ground-based surveys and light detection and ranging [lidar]), image-based shorelines, corrected for their inherent uncertainties (Moore and others, 2006), have provided much of our understanding of long-term shoreline change because they pre-date routine lidar elevation survey methods. Image-based shorelines continue to be valuable because of their higher temporal resolution compared to costly airborne lidar surveys. A method for extracting sandy shorelines from 30-meter (m) resolution Landsat imagery is presented here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20151179","usgsCitation":"Guy, K.K., 2015, Barrier island shorelines extracted from Landsat imagery: U.S. Geological Survey Open-File Report 2015–1179, 3 p., https://dx.doi.org/10.3133/ofr20151179.","productDescription":"Report: iv, 3 p.; Spatial Data","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067042","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438679,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7028PMP","text":"USGS data release","linkHelpText":"Shorelines Extracted from Landsat Imagery: Dauphin Island, Alabama"},{"id":312288,"rank":4,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F7DZ06CD","text":"Shorelines Extracted from Landsat Imagery: Ship Island, Mississippi","description":"OFR 2015-1179"},{"id":312289,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F7JQ0Z3R","text":"Shorelines Extracted from Landsat Imagery: Cat Island, Mississippi","description":"OFR 2015-1179"},{"id":312290,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F7XW4GVG","text":"Shorelines Extracted from Landsat Imagery: Horn Island, Mississippi","description":"OFR 2015-1179"},{"id":312291,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F72N509Q","text":"Shorelines Extracted from Landsat Imagery: Petit Bois Island, Mississippi","description":"OFR 2015-1179"},{"id":309457,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1179/coverthb.jpg"},{"id":309459,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://dx.doi.org/10.5066/F7028PMP","text":"Shorelines Extracted from Landsat Imagery: Dauphin Island, Alabama","description":"OFR 2015-1179"},{"id":309458,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1179/ofr20151179.pdf","text":"Report","size":"242 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1179"}],"country":"United States","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5–31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3–33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the “red-edge” spectral range (700–740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400–2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice (R2 = 0.91); 925 and 1104 nm for alfalfa (R2 = 0.81); 722 and 732 nm for cotton (R2 = 0.97); and 529 and 895 nm for maize (R2 = 0.94). The higher spectral resolution of the EO-1 Hyperion hyperspectral sensor and the ability of users to choose distinct HNBs for improved crop biomass estimation outweigh the benefits that come with higher spatial resolution of MSBBs.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.isprsjprs.2015.08.001","usgsCitation":"Marshall, M.T., and Thenkabail, P.S., 2015, Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation: ISPRS Journal of Photogrammetry and Remote Sensing, v. 108, p. 205-218, https://doi.org/10.1016/j.isprsjprs.2015.08.001.","productDescription":"14 p.","startPage":"205","endPage":"218","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060745","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471737,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2015.08.001","text":"Publisher Index Page"},{"id":313981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05810546875,\n 40.730608477796636\n ],\n [\n -122.82714843749999,\n 40.3130432088809\n ],\n [\n -122.56347656249999,\n 39.90973623453719\n ],\n [\n -122.62939453125001,\n 39.38526381099774\n ],\n [\n -122.23388671874999,\n 38.496593518947556\n ],\n [\n -121.70654296874999,\n 37.78808138412046\n ],\n [\n -121.17919921875001,\n 37.43997405227057\n ],\n [\n -121.00341796874999,\n 36.96744946416934\n ],\n [\n -120.60791015625,\n 36.4566360115962\n ],\n [\n -120.16845703125,\n 36.01356058518153\n ],\n [\n -119.7509765625,\n 35.35321610123821\n ],\n [\n -119.39941406249999,\n 34.95799531086792\n ],\n [\n -118.93798828125,\n 34.97600151317591\n ],\n [\n -118.63037109375,\n 35.15584570226544\n ],\n [\n -118.63037109375,\n 35.764343479667176\n ],\n [\n -118.89404296875,\n 36.24427318493909\n ],\n [\n -119.39941406249999,\n 36.84446074079564\n ],\n [\n -119.88281249999999,\n 37.23032838760387\n ],\n [\n -120.41015624999999,\n 37.80544394934274\n ],\n [\n -120.84960937499999,\n 38.34165619279595\n ],\n [\n -121.28906250000001,\n 38.89103282648849\n ],\n [\n -121.61865234375,\n 39.52099229357195\n ],\n [\n -121.9921875,\n 39.9434364619742\n ],\n [\n -122.05810546875,\n 40.730608477796636\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"108","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"568e48dee4b0e7a44bc4186b","contributors":{"authors":[{"text":"Marshall, Michael T. mmarshall@usgs.gov","contributorId":5480,"corporation":false,"usgs":true,"family":"Marshall","given":"Michael","email":"mmarshall@usgs.gov","middleInitial":"T.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":581537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":581536,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194287,"text":"70194287 - 2015 - Reconstructing turbidity in a glacially influenced lake using the Landsat TM and ETM+ surface reflectance climate data record archive, Lake Clark, Alaska","interactions":[],"lastModifiedDate":"2017-11-21T16:37:41","indexId":"70194287","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","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":"Reconstructing turbidity in a glacially influenced lake using the Landsat TM and ETM+ surface reflectance climate data record archive, Lake Clark, Alaska","docAbstract":"Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased glacial runoff have been shown to reduce salmon production via reduced abundance of zooplankton and macroinvertebrates. In this study, we reconstruct long-term, lake-wide water clarity for Lake Clark using the Landsat TM and ETM+ surface reflectance products (1985–2014) and in situwater clarity data collected between 2009 and 2013. Analysis of a Landsat scene acquired in 2009, coincident with in situ measurements in the lake, and uncertainty analysis with four scenes acquired within two weeks of field data collection showed that Band 3 surface reflectance was the best indicator of turbidity (r2 = 0.55,RMSE << 0.01). We then processed 151 (98 partial- and 53 whole-lake) Landsat scenes using this relation and detected no significant long-term trend in mean turbidity for Lake Clark between 1991 and 2014. We did, however, detect interannual variation that exhibited a non-significant (r2 = 0.20) but positive correlation (r = 0.20) with regional mean summer air temperature and found the month of May exhibited a significant positive trend (r2 = 0.68, p = 0.02) in turbidity between 2000 and 2014. This study demonstrates the utility of hindcasting turbidity in a glacially influenced lake using the Landsat surface reflectance products. It may also help land and resource managers reconstruct turbidity records for lakes that lack in situ monitoring, and may be useful in predicting future water clarity conditions based on projected climate scenarios.
","language":"English","publisher":"MDPI","doi":"10.3390/rs71013692","usgsCitation":"Baughman, C., Jones, B.M., Bartz, K.K., Young, D.B., and Zimmerman, C.E., 2015, Reconstructing turbidity in a glacially influenced lake using the Landsat TM and ETM+ surface reflectance climate data record archive, Lake Clark, Alaska: Remote Sensing, v. 7, no. 10, p. 13692-13710, https://doi.org/10.3390/rs71013692.","productDescription":"19 p.","startPage":"13692","endPage":"13710","ipdsId":"IP-066580","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":471755,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs71013692","text":"Publisher Index Page"},{"id":349242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lake Clark","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.84954833984375,\n 60.0113438097352\n ],\n [\n -153.57513427734375,\n 60.0113438097352\n ],\n [\n -153.57513427734375,\n 60.45992621736877\n ],\n [\n -154.84954833984375,\n 60.45992621736877\n ],\n [\n -154.84954833984375,\n 60.0113438097352\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-20","publicationStatus":"PW","scienceBaseUri":"5a60fe67e4b06e28e9c252f3","contributors":{"authors":[{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":723094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":723095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartz, Krista K.","contributorId":200705,"corporation":false,"usgs":false,"family":"Bartz","given":"Krista","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":723097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Daniel","contributorId":58468,"corporation":false,"usgs":false,"family":"Young","given":"Daniel","affiliations":[{"id":35763,"text":"National Park Service, Lake Clark National Park and Preserve, Port Alsworth, AK","active":true,"usgs":false}],"preferred":false,"id":723098,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":723096,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184225,"text":"70184225 - 2015 - Landsat-8: Status and on-orbit performance","interactions":[],"lastModifiedDate":"2017-05-31T16:19:11","indexId":"70184225","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-8: Status and on-orbit performance","docAbstract":"Landsat 8 and its two Earth imaging sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) have been operating on-orbit for 2 ½ years. Landsat 8 has been acquiring substantially more images than initially planned, typically around 700 scenes per day versus a 400 scenes per day requirement, acquiring nearly all land scenes. Both the TIRS and OLI instruments are exceeding their SNR requirements by at least a factor of 2 and are very stable, degrading by at most 1% in responsivity over the mission to date. Both instruments have 100% operable detectors covering their cross track field of view using the redundant detectors as necessary. The geometric performance is excellent, meeting or exceeding all performance requirements. One anomaly occurred with the TIRS Scene Select Mirror (SSM) encoder that affected its operation, though by switching to the side B electronics, this was fully recovered. The one challenge is with the TIRS stray light, which affects the flat fielding and absolute calibration of the TIRS data. The error introduced is smaller in TIRS band 10. Band 11 should not currently be used in science applications.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proc. SPIE 9639, Sensors, Systems, and Next-Generation Satellites XIX","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Sensors, Systems, and Next-Generation Satellites XIX","conferenceDate":"September 21, 2015","conferenceLocation":"Toulouse, France","language":"English","publisher":"SPIE","doi":"10.1117/12.2194905","usgsCitation":"Markham, B.L., Barsi, J.A., Morfitt, R., Choate, M., Montanaro, M., Arvidson, T., and Irons, J.R., 2015, Landsat-8: Status and on-orbit performance, in Proc. SPIE 9639, Sensors, Systems, and Next-Generation Satellites XIX, v. 9639, Toulouse, France, September 21, 2015, 963908, https://doi.org/10.1117/12.2194905.","productDescription":"963908","ipdsId":"IP-068625","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":337714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9639","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58cba41ce4b0849ce97dc754","contributors":{"authors":[{"text":"Markham, Brian L. 0000-0002-9612-8169","orcid":"https://orcid.org/0000-0002-9612-8169","contributorId":121488,"corporation":false,"usgs":true,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":680629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barsi, Julia A.","contributorId":71822,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":680630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morfitt, Ron 0000-0002-4777-4877 rmorfitt@usgs.gov","orcid":"https://orcid.org/0000-0002-4777-4877","contributorId":4097,"corporation":false,"usgs":true,"family":"Morfitt","given":"Ron","email":"rmorfitt@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":680628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":684707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Montanaro, Matthew","contributorId":147004,"corporation":false,"usgs":false,"family":"Montanaro","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":680631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Arvidson, Terry","contributorId":97801,"corporation":false,"usgs":true,"family":"Arvidson","given":"Terry","email":"","affiliations":[],"preferred":false,"id":684708,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":684709,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188043,"text":"70188043 - 2015 - Characterization of shrubland ecosystem components as continuous fields in the northwest United States","interactions":[],"lastModifiedDate":"2018-03-08T13:04:23","indexId":"70188043","displayToPublicDate":"2015-10-01T00:00:00","publicationYear":"2015","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":"Characterization of shrubland ecosystem components as continuous fields in the northwest United States","docAbstract":"Accurate and consistent estimates of shrubland ecosystem components are crucial to a better understanding of ecosystem conditions in arid and semiarid lands. An innovative approach was developed by integrating multiple sources of information to quantify shrubland components as continuous field products within the National Land Cover Database (NLCD). The approach consists of several procedures including field sample collections, high-resolution mapping of shrubland components using WorldView-2 imagery and regression tree models, Landsat 8 radiometric balancing and phenological mosaicking, medium resolution estimates of shrubland components following different climate zones using Landsat 8 phenological mosaics and regression tree models, and product validation. Fractional covers of nine shrubland components were estimated: annual herbaceous, bare ground, big sagebrush, herbaceous, litter, sagebrush, shrub, sagebrush height, and shrub height. Our study area included the footprint of six Landsat 8 scenes in the northwestern United States. Results show that most components have relatively significant correlations with validation data, have small normalized root mean square errors, and correspond well with expected ecological gradients. While some uncertainties remain with height estimates, the model formulated in this study provides a cross-validated, unbiased, and cost effective approach to quantify shrubland components at a regional scale and advances knowledge of horizontal and vertical variability of these components.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2015.07.014","usgsCitation":"Xian, G.Z., Homer, C.G., Rigge, M.B., Shi, H., and Meyer, D., 2015, Characterization of shrubland ecosystem components as continuous fields in the northwest United States: Remote Sensing of Environment, v. 168, p. 286-300, https://doi.org/10.1016/j.rse.2015.07.014.","productDescription":"15 p.","startPage":"286","endPage":"300","ipdsId":"IP-061128","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2015.07.014","text":"Publisher Index Page"},{"id":341880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 39\n ],\n [\n -116,\n 39\n ],\n [\n -116,\n 44\n ],\n [\n -122,\n 44\n ],\n [\n -122,\n 39\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"168","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84bbe4b092b266f10d3f","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":696303,"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":696304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":696305,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":696306,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192028,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[],"preferred":false,"id":696307,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156634,"text":"sir20155118 - 2015 - Evaluation and comparison of methods to estimate irrigation withdrawal for the National Water Census Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia","interactions":[],"lastModifiedDate":"2017-01-18T13:22:16","indexId":"sir20155118","displayToPublicDate":"2015-09-30T11:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-5118","title":"Evaluation and comparison of methods to estimate irrigation withdrawal for the National Water Census Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia","docAbstract":"Methods to estimate irrigation withdrawal using nationally available datasets and techniques that are transferable to other agricultural regions were evaluated by the U.S. Geological Survey as part of the Apalachicola-Chattahoochee-Flint (ACF) River Basin focus area study of the National Water Census (ACF–FAS). These methods investigated the spatial, temporal, and quantitative distributions of water withdrawal for irrigation in the southwestern Georgia region of the ACF–FAS, filling a vital need to inform science-based decisions regarding resource management and conservation. The crop– demand method assumed that only enough water is pumped onto a crop to satisfy the deficit between evapotranspiration and precipitation. A second method applied a geostatistical regimen of variography and conditional simulation to monthly metered irrigation withdrawal to estimate irrigation withdrawal where data do not exist. A third method analyzed Landsat satellite imagery using an automated approach to generate monthly estimates of irrigated lands. These methods were evaluated independently and compared collectively with measured water withdrawal information available in the Georgia part of the ACF–FAS, principally in the Chattahoochee-Flint River Basin. An assessment of each method’s contribution to the National Water Census program was also made to identify transfer value of the methods to the national program and other water census studies. None of the three methods evaluated represent a turnkey process to estimate irrigation withdrawal on any spatial (local or regional) or temporal (monthly or annual) extent. Each method requires additional information on agricultural practices during the growing season to complete the withdrawal estimation process. Spatial and temporal limitations inherent in identifying irrigated acres during the growing season, and in designing spatially and temporally representative monitor (meter) networks, can belie the ability of the methods to produce accurate irrigation-withdrawal estimates that can be used to produce dependable and consistent assessments of water availability and use for the National Water Census. Emerging satellite-data products and techniques for data analysis can generate high spatial-resolution estimates of irrigated-acres distributions with near-term temporal frequencies compatible with the needs of the ACF–FAS and the National Water Census.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155118","collaboration":"Prepared in cooperation with the National Water Census Program","usgsCitation":"Painter, J.A., Torak, L.J., and Jones, J.W., 2015, Evaluation and comparison of methods to estimate irrigation withdrawal for the National Water Census Focus Area Study of the Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia, U.S. Geological Survey Scientific Investigations ReportDirector, South Atlantic Water Science Center
North Carolina–South Carolina–Georgia
720 Gracern Road, Suite 129
Columbia, SC 29210
Phone: (803) 750-6100
http://ga.water.usgs.gov/
This document provides an overview of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) supplemental algorithms in conjunction with the reuse of Landsat geometric algorithms modified by the National Aeronautics and Space Administration (NASA Land Processes Distributed Active Archive Center (LP DAAC) to create an ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) product. Implementation of these algorithms occurs within the AST_L1T product generation executable (PGE) as part of the open source Simple, Scalable, Script-based Science Processor for Missions (S4PM) processing software subsystem.
The AST_L1T algorithms include the following:
Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198
http://eros.usgs.gov/
Aim. Studies of fire activity along environmental gradients have been undertaken, but the results of such studies have yet to be integrated with fire-regime analysis. We characterize fire-regime components along climate gradients and a gradient of human influence.
Location. We focus on a climatically diverse region of north-western North America extending from northern British Columbia, Canada, to northern Utah and Colorado, USA.
Methods. We used a multivariate framework to collapse 12 climatic variables into two major climate gradients and binned them into 73 discrete climate domains. We examined variation in fire-regime components (frequency, size, severity, seasonality and cause) across climate domains. Fire-regime attributes were compiled from existing databases and Landsat imagery for 1897 large fires. Relationships among the fire-regime components, climate gradients and human influence were examined through bivariate regressions. The unique contribution of human influence was also assessed.
Results. A primary climate gradient of temperature and summer precipitation and a secondary gradient of continentality and winter precipitation in the study area were identified. Fire occupied a distinct central region of such climate space, within which fire-regime components varied considerably. We identified significant interrelations between fire-regime components of fire size, frequency, burn severity and cause. The influence of humans was apparent in patterns of burn severity and ignition cause.
Main conclusions. Wildfire activity is highest where thermal and moisture gradients converge to promote fuel production, flammability and ignitions. Having linked fire-regime components to large-scale climate gradients, we show that fire regimes – like the climate that controls them – are a part of a continuum, expanding on models of varying constraints on fire activity. The observed relationships between fire-regime components, together with the distinct role of climatic and human influences, generate variation in biotic communities. Thus, future changes to climate may lead to ecological changes through altered fire regimes.
The methodology for selection, creation, and application of a global remote sensing validation dataset using high resolution commercial satellite data is presented. High resolution data are obtained for a stratified random sample of 500 primary sampling units (5 km × 5 km sample blocks), where the stratification based on Köppen climate classes is used to distribute the sample globally among biomes. The high resolution data are classified to categorical land cover maps using an analyst mediated classification workflow. Our initial application of these data is to evaluate a global 30 m Landsat-derived, continuous field tree cover product. For this application, the categorical reference classification produced at 2 m resolution is converted to percent tree cover per 30 m pixel (secondary sampling unit)for comparison to Landsat-derived estimates of tree cover. We provide example results (based on a subsample of 25 sample blocks in South America) illustrating basic analyses of agreement that can be produced from these reference data. Commercial high resolution data availability and data quality are shown to provide a viable means of validating continuous field tree cover. When completed, the reference classifications for the full sample of 500 blocks will be released for public use.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2015.01.018","usgsCitation":"Pengra, B., Long, J., Dahal, D., Stehman, S.V., and Loveland, T.R., 2015, A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data: Remote Sensing of Environment, v. 165, p. 234-248, https://doi.org/10.1016/j.rse.2015.01.018.","productDescription":"15 p.; Data Release","startPage":"234","endPage":"248","ipdsId":"IP-058560","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397411,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96FKANW","text":"USGS data release","description":"USGS data release","linkHelpText":"A circa 2010 global land cover reference dataset from commercial high resolution satellite data"}],"otherGeospatial":"South America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.6875,\n -54.77534585936447\n ],\n [\n -68.90625,\n -51.39920565355377\n ],\n [\n -64.3359375,\n -48.69096039092549\n ],\n [\n -56.953125,\n -37.71859032558814\n ],\n [\n -46.7578125,\n -28.92163128242129\n ],\n [\n -37.265625,\n -21.289374355860424\n ],\n [\n -34.1015625,\n -5.9657536710655235\n ],\n [\n -59.0625,\n 10.833305983642491\n ],\n [\n -74.53125,\n 12.897489183755892\n ],\n [\n -80.15625,\n 10.487811882056695\n ],\n [\n -83.3203125,\n -7.710991655433217\n ],\n [\n -72.421875,\n -19.31114335506464\n ],\n [\n -77.34374999999999,\n -47.27922900257082\n ],\n [\n -75.9375,\n -52.05249047600098\n ],\n [\n -69.9609375,\n -56.55948248376223\n ],\n [\n -64.6875,\n -54.77534585936447\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"165","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593e26bfe4b0764e6c61b75b","contributors":{"authors":[{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@usgs.gov","affiliations":[{"id":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":695525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Jordan 0000-0002-4814-464X jlong@usgs.gov","orcid":"https://orcid.org/0000-0002-4814-464X","contributorId":3609,"corporation":false,"usgs":true,"family":"Long","given":"Jordan","email":"jlong@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@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":695527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stehman, Stephen V.","contributorId":77283,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":695528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":695529,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191854,"text":"70191854 - 2015 - Valuing geospatial information: Using the contingent valuation method to estimate the economic benefits of Landsat satellite imagery","interactions":[],"lastModifiedDate":"2017-10-18T14:05:33","indexId":"70191854","displayToPublicDate":"2015-08-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Valuing geospatial information: Using the contingent valuation method to estimate the economic benefits of Landsat satellite imagery","docAbstract":"While the U.S. government does not charge for downloading Landsat images, the images have value to users. This paper demonstrates a method that can value Landsat and other imagery to users. A survey of downloaders of Landsat images found: (a) established US users have a mean value of $912 USD per scene; (b) new US users and users returning when imagery became free have a mean value of $367 USD per scene. Total US user benefits for the 2.38 million scenes downloaded is $1.8 billion USD. While these benefits indicate a high willingness-to-pay among many Landsat downloaders, it would be economically inefficient for the US government to charge for Landsat imagery. Charging a price of $100 USD a scene would result in an efficiency loss of $37.5 million a year. This economic information should be useful to policy-makers who must decide about the future of this and similar remote sensing programs.
","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.81.8.647","usgsCitation":"Loomis, J.B., Koontz, S., Miller, H., and Richardson, L.A., 2015, Valuing geospatial information: Using the contingent valuation method to estimate the economic benefits of Landsat satellite imagery: Photogrammetric Engineering and Remote Sensing, v. 81, no. 8, p. 647-656, https://doi.org/10.14358/PERS.81.8.647.","productDescription":"10 p.","startPage":"647","endPage":"656","ipdsId":"IP-064207","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471913,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.81.8.647","text":"Publisher Index Page"},{"id":346871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e8683be4b05fe04cd4d22e","contributors":{"authors":[{"text":"Loomis, John B.","contributorId":197268,"corporation":false,"usgs":false,"family":"Loomis","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":713405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koontz, Steve","contributorId":197401,"corporation":false,"usgs":false,"family":"Koontz","given":"Steve","email":"","affiliations":[],"preferred":false,"id":713406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Holly M. 0000-0003-0914-7570 millerh@usgs.gov","orcid":"https://orcid.org/0000-0003-0914-7570","contributorId":4577,"corporation":false,"usgs":true,"family":"Miller","given":"Holly M.","email":"millerh@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":713407,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richardson, Leslie A. lrichardson@usgs.gov","contributorId":4810,"corporation":false,"usgs":true,"family":"Richardson","given":"Leslie","email":"lrichardson@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713404,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70155894,"text":"70155894 - 2015 - A landsat data tiling and compositing approach optimized for change detection in the conterminous United States","interactions":[],"lastModifiedDate":"2017-08-29T09:41:46","indexId":"70155894","displayToPublicDate":"2015-07-01T11:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A landsat data tiling and compositing approach optimized for change detection in the conterminous United States","docAbstract":"Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels was defined for CONUS and adjusted to consolidate smaller tiles along national borders, resulting in 98 non-overlapping tiles. Data from Landsat-5,-7, and -8 were re-projected to the tile extents, masked to remove clouds, shadows, water, and snow/ice, then composited using a cosine similarity approach. The resultant images were used in a change detection algorithm to determine areas of vegetation change. This approach enabled more efficient processing compared to using single Landsat scenes, by taking advantage of overlap between adjacent paths, and allowed an automated system to be developed for the entire process.
","language":"English","publisher":"American Society of Photogrammetry","publisherLocation":"Falls Church, VA","doi":"10.14358/PERS.81.7.573","usgsCitation":"Nelson, K., and Steinwand, D.R., 2015, A landsat data tiling and compositing approach optimized for change detection in the conterminous United States: Photogrammetric Engineering and Remote Sensing, v. 81, no. 7, p. 573-586, https://doi.org/10.14358/PERS.81.7.573.","productDescription":"14 p.","startPage":"573","endPage":"586","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055870","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488385,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.81.7.573","text":"Publisher Index Page"},{"id":306782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}\n","volume":"81","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55d305aae4b0518e35468cd1","contributors":{"authors":[{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":566669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steinwand, Daniel R. steinwand@usgs.gov","contributorId":3224,"corporation":false,"usgs":true,"family":"Steinwand","given":"Daniel","email":"steinwand@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":566670,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70182467,"text":"70182467 - 2015 - Tree mortality predicted from drought-induced vascular damage","interactions":[],"lastModifiedDate":"2017-02-23T12:46:12","indexId":"70182467","displayToPublicDate":"2015-07-01T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Tree mortality predicted from drought-induced vascular damage","docAbstract":"The projected responses of forest ecosystems to warming and drying associated with twenty-first-century climate change vary widely from resiliency to widespread tree mortality1, 2, 3. Current vegetation models lack the ability to account for mortality of overstorey trees during extreme drought owing to uncertainties in mechanisms and thresholds causing mortality4, 5. Here we assess the causes of tree mortality, using field measurements of branch hydraulic conductivity during ongoing mortality in Populus tremuloides in the southwestern United States and a detailed plant hydraulics model. We identify a lethal plant water stress threshold that corresponds with a loss of vascular transport capacity from air entry into the xylem. We then use this hydraulic-based threshold to simulate forest dieback during historical drought, and compare predictions against three independent mortality data sets. The hydraulic threshold predicted with 75% accuracy regional patterns of tree mortality as found in field plots and mortality maps derived from Landsat imagery. In a high-emissions scenario, climate models project that drought stress will exceed the observed mortality threshold in the southwestern United States by the 2050s. Our approach provides a powerful and tractable way of incorporating tree mortality into vegetation models to resolve uncertainty over the fate of forest ecosystems in a changing climate.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/NGEO2400","usgsCitation":"Anderegg, W.R., Flint, A.L., Huang, C., Flint, L.E., Berry, J.A., Davis, F., Sperry, J.S., and Field, C.B., 2015, Tree mortality predicted from drought-induced vascular damage: Nature Geoscience, v. 8, p. 367-371, https://doi.org/10.1038/NGEO2400.","productDescription":"5 p.","startPage":"367","endPage":"371","ipdsId":"IP-059538","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":336106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-30","publicationStatus":"PW","scienceBaseUri":"58b002c7e4b01ccd54fb27d1","contributors":{"authors":[{"text":"Anderegg, William R.L.","contributorId":147089,"corporation":false,"usgs":false,"family":"Anderegg","given":"William","email":"","middleInitial":"R.L.","affiliations":[{"id":16784,"text":"Princeton U.","active":true,"usgs":false}],"preferred":false,"id":671207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":671208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, Cho-ying","contributorId":182348,"corporation":false,"usgs":false,"family":"Huang","given":"Cho-ying","email":"","affiliations":[],"preferred":false,"id":671209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":671206,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berry, Joseph A.","contributorId":182349,"corporation":false,"usgs":false,"family":"Berry","given":"Joseph","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":671210,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davis, Frank W.","contributorId":127849,"corporation":false,"usgs":false,"family":"Davis","given":"Frank W.","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":671211,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sperry, John S.","contributorId":182350,"corporation":false,"usgs":false,"family":"Sperry","given":"John","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":671212,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Field, Christopher B.","contributorId":182351,"corporation":false,"usgs":false,"family":"Field","given":"Christopher","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":671213,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70150362,"text":"70150362 - 2015 - Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru","interactions":[],"lastModifiedDate":"2015-06-24T10:14:50","indexId":"70150362","displayToPublicDate":"2015-06-24T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2172,"text":"Journal of Applied Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru","docAbstract":"Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.
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