{"pageNumber":"31","pageRowStart":"750","pageSize":"25","recordCount":1873,"records":[{"id":70038646,"text":"sim3207 - 2012 - Land area change analysis following hurricane impacts in Delacroix, Louisiana, 2004--2009","interactions":[],"lastModifiedDate":"2012-06-09T01:01:37","indexId":"sim3207","displayToPublicDate":"2012-06-08T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3207","title":"Land area change analysis following hurricane impacts in Delacroix, Louisiana, 2004--2009","docAbstract":"The purpose of this project is to provide improved estimates of Louisiana wetland land loss due to hurricane impacts between 2004 and 2009 based upon a change detection mapping analysis that incorporates pre- and post-landfall (Hurricanes Katrina, Rita, Gustav, and Ike) fractional water classification of a combination of high resolution (QuickBird, IKONOS and Geoeye-1) and medium resolution (Landsat) satellite imagery. This second dataset focuses on Hurricanes Katrina and Gustav, which made landfall on August 29, 2005, and September 1, 2008, respectively. The study area is an approximately 1208-square-kilometer region surrounding Delacroix, Louisiana, in the eastern Delta Plain. Overall, 77 percent of the area remained unchanged between 2004 and 2009, and over 11 percent of the area was changed permanently by Hurricane Katrina (including both land gain and loss). Less than 3 percent was affected, either temporarily or permanently, by Hurricane Gustav. A related dataset (SIM 3141) focused on Hurricane Rita, which made landfall on the Louisiana/Texas border on September 24, 2005, as a Category 3 hurricane.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3207","usgsCitation":"Palaseanu-Lovejoy, M., Kranenburg, C., and Brock, J., 2012, Land area change analysis following hurricane impacts in Delacroix, Louisiana, 2004--2009: U.S. Geological Survey Scientific Investigations Map 3207, ii, 9 p.; PDF Download of Map: 48.01 x 36.01 inches; ZIP Download of Data Files; General Metadata File; Readme File, https://doi.org/10.3133/sim3207.","productDescription":"ii, 9 p.; PDF Download of Map: 48.01 x 36.01 inches; ZIP Download of Data Files; General Metadata File; Readme File","startPage":"i","endPage":"9","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2004-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":257375,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3207/","linkFileType":{"id":5,"text":"html"}},{"id":257376,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3207/pdf/SIM_3207_poster.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":257386,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3207.bmp"}],"country":"United States","state":"Louisiana","city":"Delacroix","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a419ee4b0c8380cd6566d","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":464590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. ckranenburg@usgs.gov","contributorId":3924,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine J.","email":"ckranenburg@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":464591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brock, John 0000-0002-5289-9332 jbrock@usgs.gov","orcid":"https://orcid.org/0000-0002-5289-9332","contributorId":2261,"corporation":false,"usgs":true,"family":"Brock","given":"John","email":"jbrock@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":464589,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70038466,"text":"70038466 - 2012 - Pattern and process of prescribed fires influence effectiveness at reducing wildfire severity in dry coniferous forests","interactions":[],"lastModifiedDate":"2012-06-12T01:01:51","indexId":"70038466","displayToPublicDate":"2012-06-06T10:27:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Pattern and process of prescribed fires influence effectiveness at reducing wildfire severity in dry coniferous forests","docAbstract":"We examined the effects of three early season (spring) prescribed fires on burn severity patterns of summer wildfires that occurred 1–3 years post-treatment in a mixed conifer forest in central Idaho. Wildfire and prescribed fire burn severities were estimated as the difference in normalized burn ratio (dNBR) using Landsat imagery. We used GIS derived vegetation, topography, and treatment variables to generate models predicting the wildfire burn severity of 1286–5500 30-m pixels within and around treated areas. We found that wildfire severity was significantly lower in treated areas than in untreated areas and significantly lower than the potential wildfire severity of the treated areas had treatments not been implemented. At the pixel level, wildfire severity was best predicted by an interaction between prescribed fire severity, topographic moisture, heat load, and pre-fire vegetation volume. Prescribed fire severity and vegetation volume were the most influential predictors. Prescribed fire severity, and its influence on wildfire severity, was highest in relatively warm and dry locations, which were able to burn under spring conditions. In contrast, wildfire severity peaked in cooler, more mesic locations that dried later in the summer and supported greater vegetation volume. We found considerable evidence that prescribed fires have landscape-level influences within treatment boundaries; most notable was an interaction between distance from the prescribed fire perimeter and distance from treated patch edges, which explained up to 66% of the variation in wildfire severity. Early season prescribed fires may not directly target the locations most at risk of high severity wildfire, but proximity of these areas to treated patches and the discontinuity of fuels following treatment may influence wildfire severity and explain how even low severity treatments can be effective management tools in fire-prone landscapes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Forest Ecology and Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.foreco.2012.04.002","usgsCitation":"Arkle, R., Pilliod, D., and Welty, J., 2012, Pattern and process of prescribed fires influence effectiveness at reducing wildfire severity in dry coniferous forests: Forest Ecology and Management, v. 276, p. 174-184, https://doi.org/10.1016/j.foreco.2012.04.002.","productDescription":"11 p.l","startPage":"174","endPage":"184","numberOfPages":"11","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":257437,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":257420,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.foreco.2012.04.002","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a75b2e4b0c8380cd77cb3","contributors":{"authors":[{"text":"Arkle, Robert S.","contributorId":55679,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert S.","affiliations":[],"preferred":false,"id":464291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S.","contributorId":101760,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[],"preferred":false,"id":464293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welty, Justin L.","contributorId":80558,"corporation":false,"usgs":true,"family":"Welty","given":"Justin L.","affiliations":[],"preferred":false,"id":464292,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70038609,"text":"fs20123072 - 2012 - Landsat: A global land-imaging mission","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}],"supersededBy":{"id":70159774,"text":"fs20153081 - 2015 - Landsat—Earth observation satellites","indexId":"fs20153081","publicationYear":"2015","noYear":false,"title":"Landsat—Earth observation satellites"},"lastModifiedDate":"2017-03-28T11:08:59","indexId":"fs20123072","displayToPublicDate":"2012-06-06T00:00:00","publicationYear":"2012","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":"2012-3072","title":"Landsat: A global land-imaging mission","docAbstract":"<p>Across four decades since 1972, Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. NASA develops remote-sensing instruments and 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 distribution. The result of this program is a long-term record of natural and human induced changes on the global landscape.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123072","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2012, Landsat: A global land-imaging mission: U.S. Geological Survey Fact Sheet 2012-3072, 4 p., https://doi.org/10.3133/fs20123072.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":257250,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3072.gif"},{"id":299698,"rank":101,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2012/3072/fs2012-3072.pdf","text":"Report","size":"3.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":257249,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3072/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a43f3e4b0c8380cd6670d","contributors":{"authors":[{"text":"Water Resources Division, U.S. Geological Survey","contributorId":128075,"corporation":true,"usgs":false,"organization":"Water Resources Division, U.S. Geological Survey","id":535188,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70038426,"text":"fs20123066 - 2012 - Landsat Data Continuity Mission","interactions":[],"lastModifiedDate":"2012-05-26T01:01:37","indexId":"fs20123066","displayToPublicDate":"2012-05-25T00:00:00","publicationYear":"2012","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":"2012-3066","title":"Landsat Data Continuity Mission","docAbstract":"The Landsat Data Continuity Mission (LDCM) is a partnership formed between the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) to place the next Landsat satellite in orbit in January 2013. The Landsat era that began in 1972 will become a nearly 41-year global land record with the successful launch and operation of the LDCM. The LDCM will continue the acquisition, archiving, and distribution of multispectral imagery affording global, synoptic, and repetitive coverage of the Earth's land surfaces at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time.\r\nThe mission objectives of the LDCM are to (1) collect and archive medium resolution (30-meter spatial resolution) multispectral image data affording seasonal coverage of the global landmasses for a period of no less than 5 years; (2) ensure that LDCM data are sufficiently consistent with data from the earlier Landsat missions in terms of acquisition geometry, calibration, coverage characteristics, spectral characteristics, output product quality, and data availability to permit studies of landcover and land-use change over time; and (3) distribute LDCM data products to the general public on a nondiscriminatory basis at no cost to the user.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123066","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2012, Landsat Data Continuity Mission: U.S. Geological Survey Fact Sheet 2012-3066, 4 p., https://doi.org/10.3133/fs20123066.","productDescription":"4 p.","onlineOnly":"Y","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":256974,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3066.gif"},{"id":256968,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3066/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a43cfe4b0c8380cd66631","contributors":{"authors":[{"text":"Water Resources Division, U.S. Geological Survey","contributorId":128075,"corporation":true,"usgs":false,"organization":"Water Resources Division, U.S. Geological Survey","id":535185,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70007255,"text":"70007255 - 2012 - Limitations and potential of satellite imagery to monitor environmental response to coastal flooding","interactions":[],"lastModifiedDate":"2017-04-06T14:37:14","indexId":"70007255","displayToPublicDate":"2012-05-14T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Limitations and potential of satellite imagery to monitor environmental response to coastal flooding","docAbstract":"Storm-surge flooding and marsh response throughout the coastal wetlands of Louisiana were mapped using several types of remote sensing data collected before and after Hurricanes Gustav and Ike in 2008. These included synthetic aperture radar (SAR) data obtained from the (1) C-band advance SAR (ASAR) aboard the Environmental Satellite, (2) phased-array type L-band SAR (PALSAR) aboard the Advanced Land Observing Satellite, and (3) optical data obtained from Thematic Mapper (TM) sensor aboard the Land Satellite (Landsat). In estuarine marshes, L-band SAR and C-band ASAR provided accurate flood extent information when depths averaged at least 80 cm, but only L-band SAR provided consistent subcanopy detection when depths averaged 50 cm or less. Low performance of inundation mapping based on C-band ASAR was attributed to an apparent inundation detection limit (>30 cm deep) in tall Spartina alterniflora marshes, a possible canopy collapse of shoreline fresh marsh exposed to repeated storm-surge inundations, wind-roughened water surfaces where water levels reached marsh canopy heights, and relatively high backscatter in the near-range portion of the SAR imagery. A TM-based vegetation index of live biomass indicated that the severity of marsh dieback was linked to differences in dominant species. The severest impacts were not necessarily caused by longer inundation but rather could be caused by repeated exposure of the palustrine marsh to elevated salinity floodwaters. Differential impacts occurred in estuarine marshes. The more brackish marshes on average suffered higher impacts than the more saline marshes, particularly the nearshore coastal marshes occupied by S. alterniflora.","language":"English","publisher":"Coastal Education and Research Foundation","publisherLocation":"West Palm Beach, FL","doi":"10.2112/JCOASTRES-D-11-00052.1","usgsCitation":"Ramsey, E., Werle, D., Suzuoki, Y., Rangoonwala, A., and Lu, Z., 2012, Limitations and potential of satellite imagery to monitor environmental response to coastal flooding: Journal of Coastal Research, v. 28, no. 2, p. 457-476, https://doi.org/10.2112/JCOASTRES-D-11-00052.1.","productDescription":"20 p.","startPage":"457","endPage":"476","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":254772,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":254765,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2112/JCOASTRES-D-11-00052.1","linkFileType":{"id":5,"text":"html"}}],"volume":"28","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a4788e4b0c8380cd678b3","contributors":{"authors":[{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":72769,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah W.","suffix":"III","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":356189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Werle, Dirk","contributorId":82167,"corporation":false,"usgs":true,"family":"Werle","given":"Dirk","email":"","affiliations":[],"preferred":false,"id":356190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suzuoki, Yukihiro","contributorId":25283,"corporation":false,"usgs":true,"family":"Suzuoki","given":"Yukihiro","email":"","affiliations":[],"preferred":false,"id":356188,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":356187,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lu, Zhong 0000-0001-9181-1818 lu@usgs.gov","orcid":"https://orcid.org/0000-0001-9181-1818","contributorId":901,"corporation":false,"usgs":true,"family":"Lu","given":"Zhong","email":"lu@usgs.gov","affiliations":[],"preferred":true,"id":356186,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70038353,"text":"sir20125025 - 2012 - Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods","interactions":[],"lastModifiedDate":"2017-12-27T15:03:56","indexId":"sir20125025","displayToPublicDate":"2012-05-11T00:00:00","publicationYear":"2012","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":"2012-5025","title":"Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods","docAbstract":"We evaluated how well three leading information-extraction software programs (eCognition, Feature Analyst, Feature Extraction) and manual hand digitization interpreted information from remotely sensed imagery of a visually complex gas field in Wyoming. Specifically, we compared how each mapped the area of and classified the disturbance features present on each of three remotely sensed images, including 30-meter-resolution Landsat, 10-meter-resolution SPOT (Satellite Pour l'Observation de la Terre), and 0.6-meter resolution pan-sharpened QuickBird scenes. Feature Extraction mapped the spatial area of disturbance features most accurately on the Landsat and QuickBird imagery, while hand digitization was most accurate on the SPOT imagery. Footprint non-overlap error was smallest on the Feature Analyst map of the Landsat imagery, the hand digitization map of the SPOT imagery, and the Feature Extraction map of the QuickBird imagery. When evaluating feature classification success against a set of ground-truthed control points, Feature Analyst, Feature Extraction, and hand digitization classified features with similar success on the QuickBird and SPOT imagery, while eCognition classified features poorly relative to the other methods. All maps derived from Landsat imagery classified disturbance features poorly. Using the hand digitized QuickBird data as a reference and making pixel-by-pixel comparisons, Feature Extraction classified features best overall on the QuickBird imagery, and Feature Analyst classified features best overall on the SPOT and Landsat imagery. Based on the entire suite of tasks we evaluated, Feature Extraction performed best overall on the Landsat and QuickBird imagery, while hand digitization performed best overall on the SPOT imagery, and eCognition performed worst overall on all three images. Error rates for both area measurements and feature classification were prohibitively high on Landsat imagery, while QuickBird was time and cost prohibitive for mapping large spatial extents. The SPOT imagery produced map products that were far more accurate than Landsat and did so at a far lower cost than QuickBird imagery. Consideration of degree of map accuracy required, costs associated with image acquisition, software, operator and computation time, and tradeoffs in the form of spatial extent versus resolution should all be considered when evaluating which combination of imagery and information-extraction method might best serve any given land use mapping project. When resources permit, attaining imagery that supports the highest classification and measurement accuracy possible is recommended.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125025","usgsCitation":"Germaine, S., O’Donnell, M.S., Aldridge, C.L., Baer, L., Fancher, T.S., McBeth, J., McDougal, R., Waltermire, R., Bowen, Z.H., Diffendorfer, J., Garman, S., and Hanson, L., 2012, Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods: U.S. Geological Survey Scientific Investigations Report 2012-5025, iv, 42 p., https://doi.org/10.3133/sir20125025.","productDescription":"iv, 42 p.","startPage":"i","endPage":"42","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":254735,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5025.png"},{"id":254729,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5025/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5077e4b0c8380cd6b6dd","contributors":{"authors":[{"text":"Germaine, Stephen S.","contributorId":40305,"corporation":false,"usgs":true,"family":"Germaine","given":"Stephen S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":463939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":3351,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":463935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":463940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baer, Lori","contributorId":69028,"corporation":false,"usgs":true,"family":"Baer","given":"Lori","affiliations":[],"preferred":false,"id":463942,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fancher, Tammy S. 0000-0002-1318-3614 fanchert@usgs.gov","orcid":"https://orcid.org/0000-0002-1318-3614","contributorId":3788,"corporation":false,"usgs":true,"family":"Fancher","given":"Tammy","email":"fanchert@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":463936,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McBeth, Jamie","contributorId":79770,"corporation":false,"usgs":true,"family":"McBeth","given":"Jamie","affiliations":[],"preferred":false,"id":463943,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McDougal, Robert R.","contributorId":53418,"corporation":false,"usgs":true,"family":"McDougal","given":"Robert R.","affiliations":[],"preferred":false,"id":463941,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Waltermire, Robert","contributorId":18644,"corporation":false,"usgs":true,"family":"Waltermire","given":"Robert","affiliations":[],"preferred":false,"id":463937,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bowen, Zachary H. 0000-0002-8656-1831 bowenz@usgs.gov","orcid":"https://orcid.org/0000-0002-8656-1831","contributorId":821,"corporation":false,"usgs":true,"family":"Bowen","given":"Zachary","email":"bowenz@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":463933,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Diffendorfer, James","contributorId":35610,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James","affiliations":[],"preferred":false,"id":463938,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Garman, Steven","contributorId":105981,"corporation":false,"usgs":true,"family":"Garman","given":"Steven","affiliations":[],"preferred":false,"id":463944,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hanson, Leanne hansonl@usgs.gov","contributorId":3231,"corporation":false,"usgs":true,"family":"Hanson","given":"Leanne","email":"hansonl@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":463934,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70038324,"text":"fs20123055 - 2012 - Landsat's international partners","interactions":[],"lastModifiedDate":"2012-10-25T17:16:18","indexId":"fs20123055","displayToPublicDate":"2012-05-08T00:00:00","publicationYear":"2012","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":"2012-3055","title":"Landsat's international partners","docAbstract":"Since the launch of the first Landsat satellite 40 years ago, International Cooperators (ICs) have formed a key strategic alliance with the U.S. Geological Survey (USGS) to not only engage in Landsat data downlink services but also to enable a foundation for scientific and technical collaboration.\r\nThe map below shows the locations of all ground stations operated by the United States and IC ground station network for the direct downlink and distribution of Landsat 5 (L5) and Landsat 7 (L7) image data. The circles show the approximate area over which each station has the capability for direct reception of Landsat data. The red circles show the components of the L5 ground station network, the green circles show components of the L7 station network, and the dashed circles show stations with dual (L5 and L7) status. The yellow circles show L5 short-term (\"campaign\") stations that contribute to the USGS Landsat archive. \r\nGround stations in South Dakota and Australia currently serve as the primary data capture facilities for the USGS Landsat Ground Network (LGN). The Landsat Ground Station (LGS) is located at the USGS Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. The Alice Springs (ASN) ground station is located at the Geoscience Australia facility in Alice Springs, Australia. These sites receive the image data, via X-band Radio Frequency (RF) link, and the spacecraft housekeeping data, via S-band RF link. LGS also provides tracking services and a command link to the spacecrafts.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123055","usgsCitation":"Byrnes, R.A., 2012, Landsat's international partners: U.S. Geological Survey Fact Sheet 2012-3055, 2 p., https://doi.org/10.3133/fs20123055.","productDescription":"2 p.","onlineOnly":"Y","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":254710,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3055.gif"},{"id":262789,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3055/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Argentina;Australia;Brazil;Canada;China;Germany;Indonesia;Italy;Japan;Kenya;Mexico;Russia;South Africa;Thailand","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a43efe4b0c8380cd666ec","contributors":{"authors":[{"text":"Byrnes, Raymond A. rbyrnes@usgs.gov","contributorId":4779,"corporation":false,"usgs":true,"family":"Byrnes","given":"Raymond","email":"rbyrnes@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":463883,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70038274,"text":"fs20123057 - 2012 - Landsat: a global land imaging program","interactions":[],"lastModifiedDate":"2012-05-05T01:01:37","indexId":"fs20123057","displayToPublicDate":"2012-05-03T00:00:00","publicationYear":"2012","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":"2012-3057","title":"Landsat: a global land imaging program","docAbstract":"Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs across four decades. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. In practice, NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123057","usgsCitation":"Byrnes, R.A., 2012, Landsat: a global land imaging program: U.S. Geological Survey Fact Sheet 2012-3057, 2 p., https://doi.org/10.3133/fs20123057.","productDescription":"2 p.","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":254668,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3057.gif"},{"id":254667,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3057/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a43f5e4b0c8380cd6671f","contributors":{"authors":[{"text":"Byrnes, Raymond A. rbyrnes@usgs.gov","contributorId":4779,"corporation":false,"usgs":true,"family":"Byrnes","given":"Raymond","email":"rbyrnes@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":463779,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70193784,"text":"70193784 - 2012 - Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity","interactions":[],"lastModifiedDate":"2017-11-08T14:35:14","indexId":"70193784","displayToPublicDate":"2012-04-13T00:00:00","publicationYear":"2012","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":"Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity","docAbstract":"<p><span>Water clarity is a reliable indicator of lake productivity and an ideal metric of regional water quality. Clarity is an indicator of other water quality variables including chlorophyll-a, total phosphorus and trophic status; however, unlike these metrics, clarity can be accurately and efficiently estimated remotely on a regional scale. Remote sensing is useful in regions containing a large number of lakes that are cost prohibitive to monitor regularly using traditional field methods. Field-assessed lakes generally are easily accessible and may represent a spatially irregular, non-random sample of a region. We developed a remote monitoring program for Maine lakes &gt;</span><span>8</span><span>&nbsp;</span><span>ha (1511 lakes) to supplement existing field monitoring programs. We combined Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) brightness values for TM bands 1 (blue) and 3 (red) to estimate water clarity (secchi disk depth) during 1990–2010. Although similar procedures have been applied to Minnesota and Wisconsin lakes, neither state incorporates physical lake variables or watershed characteristics that potentially affect clarity into their models. Average lake depth consistently improved model fitness, and the proportion of wetland area in lake watersheds also explained variability in clarity in some cases. Nine regression models predicted water clarity (R</span><sup>2</sup><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>0.69–0.90) during 1990–2010, with separate models for eastern (TM path 11; four models) and western Maine (TM path 12; five models that captured differences in topography and landscape disturbance. Average absolute difference between model-estimated and observed secchi depth ranged 0.65–1.03</span><span>&nbsp;</span><span>m. Eutrophic and mesotrophic lakes consistently were estimated more accurately than oligotrophic lakes. Our results show that TM bands 1 and 3 can be used to estimate regional lake water clarity outside the Great Lakes Region and that the accuracy of estimates is improved with additional model variables that reflect physical lake characteristics and watershed conditions.</span></p>","language":"English","publisher":"Elsevier ","doi":"10.1016/j.rse.2012.03.006","usgsCitation":"McCullough, I.M., Loftin, C., and Sader, S., 2012, Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity: Remote Sensing of Environment, v. 123, p. 109-115, https://doi.org/10.1016/j.rse.2012.03.006.","productDescription":"7 p.","startPage":"109","endPage":"115","ipdsId":"IP-033562","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -69.32373046875,\n              48.980216985374994\n            ],\n            [\n              -72.94921875,\n              43.56447158721811\n            ],\n            [\n              -69.169921875,\n              42.147114459220994\n            ],\n            [\n              -65.6103515625,\n              47.84265762816538\n            ],\n            [\n              -69.32373046875,\n              48.980216985374994\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"123","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425f1e4b0dc0b45b456e5","contributors":{"authors":[{"text":"McCullough, Ian M.","contributorId":149952,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":720505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sader, Steven A.","contributorId":112282,"corporation":false,"usgs":true,"family":"Sader","given":"Steven A.","affiliations":[],"preferred":false,"id":721312,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70038040,"text":"sir20125057 - 2012 - Approximating tasseled cap values to evaluate brightness, greenness, and wetness for the Advanced Land Imager (ALI)","interactions":[],"lastModifiedDate":"2012-04-30T16:43:33","indexId":"sir20125057","displayToPublicDate":"2012-04-12T00:00:00","publicationYear":"2012","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":"2012-5057","title":"Approximating tasseled cap values to evaluate brightness, greenness, and wetness for the Advanced Land Imager (ALI)","docAbstract":"The Tasseled Cap transformation is a method of image band conversion to enhance spectral information. It primarily is used to detect vegetation using the derived brightness, greenness, and wetness bands. An approximation of Tasseled Cap values for the Advanced Land Imager was investigated and compared to the Landsat Thematic Mapper Tasseled Cap values. Despite sharing similar spectral, temporal, and spatial resolution, the two systems are not interchangeable with regard to Tasseled Cap matrices.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125057","usgsCitation":"Yamamoto, K.H., and Finn, M.P., 2012, Approximating tasseled cap values to evaluate brightness, greenness, and wetness for the Advanced Land Imager (ALI): U.S. Geological Survey Scientific Investigations Report 2012-5057, iv, 10 p., https://doi.org/10.3133/sir20125057.","productDescription":"iv, 10 p.","onlineOnly":"Y","costCenters":[{"id":425,"text":"National Geospatial Technical Operations Center","active":false,"usgs":true}],"links":[{"id":254507,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5057.gif"},{"id":254502,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5057/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California;Colorado;Georgia;Missouri;West Virginia","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -130,27 ], [ -130,40 ], [ -70,40 ], [ -70,27 ], [ -130,27 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ed02e4b0c8380cd49581","contributors":{"authors":[{"text":"Yamamoto, Kristina H. khyamamoto@usgs.gov","contributorId":4490,"corporation":false,"usgs":true,"family":"Yamamoto","given":"Kristina","email":"khyamamoto@usgs.gov","middleInitial":"H.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":463320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, Michael P. 0000-0003-0415-2194 mfinn@usgs.gov","orcid":"https://orcid.org/0000-0003-0415-2194","contributorId":2657,"corporation":false,"usgs":true,"family":"Finn","given":"Michael","email":"mfinn@usgs.gov","middleInitial":"P.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":463319,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70007275,"text":"70007275 - 2012 - Landsat Data Continuity Mission (LDCM) space to ground mission data architecture","interactions":[],"lastModifiedDate":"2017-01-18T13:34:26","indexId":"70007275","displayToPublicDate":"2012-04-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat Data Continuity Mission (LDCM) space to ground mission data architecture","docAbstract":"<p><span>The Landsat Data Continuity Mission (LDCM) is a scientific endeavor to extend the longest continuous multi-spectral imaging record of Earth's land surface. The observatory consists of a spacecraft bus integrated with two imaging instruments; the Operational Land Imager (OLI), built by Ball Aerospace &amp; Technologies Corporation in Boulder, Colorado, and the Thermal Infrared Sensor (TIRS), an in-house instrument built at the Goddard Space Flight Center (GSFC). Both instruments are integrated aboard a fine-pointing, fully redundant, spacecraft bus built by Orbital Sciences Corporation, Gilbert, Arizona. The mission is scheduled for launch in January 2013. This paper will describe the innovative end-to-end approach for efficiently managing high volumes of simultaneous realtime and playback of image and ancillary data from the instruments to the reception at the United States Geological Survey's (USGS) Landsat Ground Network (LGN) and International Cooperator (IC) ground stations. The core enabling capability lies within the spacecraft Command and Data Handling (C&amp;DH) system and Radio Frequency (RF) communications system implementation. Each of these systems uniquely contribute to the efficient processing of high speed image data (up to 265Mbps) from each instrument, and provide virtually error free data delivery to the ground. Onboard methods include a combination of lossless data compression, Consultative Committee for Space Data Systems (CCSDS) data formatting, a file-based/managed Solid State Recorder (SSR), and Low Density Parity Check (LDPC) forward error correction. The 440 Mbps wideband X-Band downlink uses Class 1 CCSDS File Delivery Protocol (CFDP), and an earth coverage antenna to deliver an average of 400 scenes per day to a combination of LGN and IC ground stations. This paper will also describe the integrated capabilities and processes at the LGN ground stations for data reception using adaptive filtering, and the mission operations approach fro- the LDCM Mission Operations Center (MOC) to perform the CFDP accounting, file retransmissions, and management of the autonomous features of the SSR.</span></p>","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceTitle":"Aerospace Conference, 2012 IEEE","conferenceDate":"March 3-10, 2012","conferenceLocation":"Big Sky, MT","language":"English","publisher":"IEEE","doi":"10.1109/AERO.2012.6187391","usgsCitation":"Nelson, J.L., Ames, J., Williams, J., Patschke, R., Mott, C., Joseph, J., Garon, H., and Mah, G., 2012, Landsat Data Continuity Mission (LDCM) space to ground mission data architecture, Aerospace Conference, 2012 IEEE, Big Sky, MT, March 3-10, 2012, p. 1-13, https://doi.org/10.1109/AERO.2012.6187391.","productDescription":"13 p.","startPage":"1","endPage":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-025263","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":307741,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"UNITED STATES","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"560bb6c2e4b058f706e53d21","contributors":{"authors":[{"text":"Nelson, Jack L.","contributorId":41671,"corporation":false,"usgs":true,"family":"Nelson","given":"Jack","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":570790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ames, J.A.","contributorId":15139,"corporation":false,"usgs":true,"family":"Ames","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":570791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, J.","contributorId":76270,"corporation":false,"usgs":true,"family":"Williams","given":"J.","affiliations":[],"preferred":false,"id":570792,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patschke, R.","contributorId":147220,"corporation":false,"usgs":false,"family":"Patschke","given":"R.","email":"","affiliations":[],"preferred":false,"id":570793,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mott, C.","contributorId":147221,"corporation":false,"usgs":false,"family":"Mott","given":"C.","email":"","affiliations":[],"preferred":false,"id":570794,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Joseph, J.","contributorId":14555,"corporation":false,"usgs":true,"family":"Joseph","given":"J.","email":"","affiliations":[],"preferred":false,"id":570795,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garon, H.","contributorId":147222,"corporation":false,"usgs":false,"family":"Garon","given":"H.","email":"","affiliations":[],"preferred":false,"id":570796,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mah, G.","contributorId":147223,"corporation":false,"usgs":false,"family":"Mah","given":"G.","email":"","affiliations":[],"preferred":false,"id":570797,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70037783,"text":"70037783 - 2012 - Land change variability and human-environment dynamics in the United States Great Plains","interactions":[],"lastModifiedDate":"2017-04-06T14:22:19","indexId":"70037783","displayToPublicDate":"2012-03-20T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2599,"text":"Land Use Policy","active":true,"publicationSubtype":{"id":10}},"title":"Land change variability and human-environment dynamics in the United States Great Plains","docAbstract":"<p><span>Land use and land cover changes have complex linkages to climate variability and change, biophysical resources, and socioeconomic driving forces. To assess these land change dynamics and their causes in the Great Plains, we compare and contrast contemporary changes across 16 ecoregions using Landsat satellite data and statistical analysis. Large-area change analysis of agricultural regions is often hampered by change detection error and the tendency for land conversions to occur at the local-scale. To facilitate a regional-scale analysis, a statistical sampling design of randomly selected 10&nbsp;km&nbsp;×&nbsp;10&nbsp;km blocks is used to efficiently identify the types and rates of land conversions for four time intervals between 1973 and 2000, stratified by relatively homogenous ecoregions. Nearly 8% of the overall Great Plains region underwent land-use and land-cover change during the study period, with a substantial amount of ecoregion variability that ranged from less than 2% to greater than 13%. Agricultural land cover declined by more than 2% overall, with variability contingent on the differential characteristics of regional human–environment systems. A large part of the Great Plains is in relatively stable land cover. However, other land systems with significant biophysical and climate limitations for agriculture have high rates of land change when pushed by economic, policy, technology, or climate forcing factors. The results indicate the regionally based potential for land cover to persist or fluctuate as land uses are adapted to spatially and temporally variable forcing factors.</span></p>","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.landusepol.2011.11.007","usgsCitation":"Drummond, M.A., Auch, R.F., Karstensen, K.A., Sayler, K., Taylor, J., and Loveland, T., 2012, Land change variability and human-environment dynamics in the United States Great Plains: Land Use Policy, v. 29, no. 3, p. 710-723, https://doi.org/10.1016/j.landusepol.2011.11.007.","productDescription":"14 p.","startPage":"710","endPage":"723","numberOfPages":"14","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"links":[{"id":246779,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado;Iowa;Kansas;Minnesota;Missouri;Montana;Nebraska;New Mexico;North Dakota;Oklahoma;South Dakota;Texas;Wyoming","otherGeospatial":"Great Plains","volume":"29","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a41a0e4b0c8380cd6567d","contributors":{"authors":[{"text":"Drummond, Mark A. 0000-0001-7420-3503 madrummond@usgs.gov","orcid":"https://orcid.org/0000-0001-7420-3503","contributorId":3053,"corporation":false,"usgs":true,"family":"Drummond","given":"Mark","email":"madrummond@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":462723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":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":462720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karstensen, Krista A. kkarstensen@usgs.gov","contributorId":286,"corporation":false,"usgs":true,"family":"Karstensen","given":"Krista","email":"kkarstensen@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":462719,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sayler, Kristi L. 0000-0003-2514-242X sayler@usgs.gov","orcid":"https://orcid.org/0000-0003-2514-242X","contributorId":2988,"corporation":false,"usgs":true,"family":"Sayler","given":"Kristi","email":"sayler@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":462721,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, Janis L. 0000-0002-9418-5215","orcid":"https://orcid.org/0000-0002-9418-5215","contributorId":33409,"corporation":false,"usgs":true,"family":"Taylor","given":"Janis L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":462724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":3005,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":462722,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70009697,"text":"sir20115236 - 2012 - An analytical method for predicting postwildfire peak discharges","interactions":[],"lastModifiedDate":"2012-03-09T18:33:47","indexId":"sir20115236","displayToPublicDate":"2012-03-09T00:00:00","publicationYear":"2012","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":"2011-5236","title":"An analytical method for predicting postwildfire peak discharges","docAbstract":"An analytical method presented here that predicts postwildfire peak discharge was developed from analysis of paired rainfall and runoff measurements collected from selected burned basins. Data were collected from 19 mountainous basins burned by eight wildfires in different hydroclimatic regimes in the western United States (California, Colorado, Nevada, New Mexico, and South Dakota). Most of the data were collected for the year of the wildfire and for 3 to 4 years after the wildfire. These data provide some estimate of the changes with time of postwildfire peak discharges, which are known to be transient but have received little documentation. The only required inputs for the analytical method are the burned area and a quantitative measure of soil burn severity (change in the normalized burn ratio), which is derived from Landsat reflectance data and is available from either the U.S. Department of Agriculture Forest Service or the U.S. Geological Survey. The method predicts the postwildfire peak discharge per unit burned area for the year of a wildfire, the first year after a wildfire, and the second year after a wildfire. It can be used at three levels of information depending on the data available to the user; each subsequent level requires either more data or more processing of the data. Level 1 requires only the burned area. Level 2 requires the burned area and the basin average value of the change in the normalized burn ratio. Level 3 requires the burned area and the calculation of the hydraulic functional connectivity, which is a variable that incorporates the sequence of soil burn severity along hillslope flow paths within the burned basin.\r\nMeasurements indicate that the unit peak discharge response increases abruptly when the 30-minute maximum rainfall intensity is greater than about 5 millimeters per hour (0.2 inches per hour). This threshold may relate to a change in runoff generation from saturated-excess to infiltration-excess overland flow. The threshold value was about 7.6 millimeters per hour for the year of the wildfire and the first year after the wildfire, and it was about 11.1 millimeters per hour for the second year after the wildfire.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115236","usgsCitation":"Moody, J.A., 2012, An analytical method for predicting postwildfire peak discharges: U.S. Geological Survey Scientific Investigations Report 2011-5236, vii, 29 p.; Appendices, https://doi.org/10.3133/sir20115236.","productDescription":"vii, 29 p.; Appendices","onlineOnly":"Y","costCenters":[{"id":145,"text":"Branch of Regional Research-Central Region","active":false,"usgs":true}],"links":[{"id":204874,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2011_5236.png"},{"id":204873,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2011/5236/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California;Colorado;Nevada;New Mexico;South Dakota","city":"Los Alamos;Boulder;Denver","otherGeospatial":"San Dimas;Galena;Bear Gulch;Buffalo Creek;Spring Creek;Cerro Grande;Bobcat Gulch;Jug Gulch;Fourmile Canyon","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059e9f8e4b0c8380cd48570","contributors":{"authors":[{"text":"Moody, John A. 0000-0003-2609-364X jamoody@usgs.gov","orcid":"https://orcid.org/0000-0003-2609-364X","contributorId":771,"corporation":false,"usgs":true,"family":"Moody","given":"John","email":"jamoody@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":356870,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70007490,"text":"fs20123020 - 2012 - The National Land Cover Database","interactions":[],"lastModifiedDate":"2018-03-08T14:29:48","indexId":"fs20123020","displayToPublicDate":"2012-02-23T00:00:00","publicationYear":"2012","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":"2012-3020","title":"The National Land Cover Database","docAbstract":"The National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution, land cover database for the Nation. NLCD provides spatial reference and descriptive data for characteristics of the land surface such as thematic class (for example, urban, agriculture, and forest), percent impervious surface, and percent tree canopy cover. NLCD supports a wide variety of Federal, State, local, and nongovernmental applications that seek to assess ecosystem status and health, understand the spatial patterns of biodiversity, predict effects of climate change, and develop land management policy. NLCD products are created by the Multi-Resolution Land Characteristics (MRLC) Consortium, a partnership of Federal agencies led by the U.S. Geological Survey. All NLCD data products are available for download at no charge to the public from the MRLC Web site: http://www.mrlc.gov.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123020","usgsCitation":"Homer, C.G., Fry, J.A., and Barnes, C., 2012, The National Land Cover Database: U.S. Geological Survey Fact Sheet 2012-3020, 4 p., https://doi.org/10.3133/fs20123020.","productDescription":"4 p.","onlineOnly":"Y","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":116329,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3020.gif"},{"id":115883,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3020/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n  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         48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505ba82de4b08c986b321a69","contributors":{"authors":[{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":356476,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fry, Joyce A. 0000-0002-8466-9582","orcid":"https://orcid.org/0000-0002-8466-9582","contributorId":69293,"corporation":false,"usgs":true,"family":"Fry","given":"Joyce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":356474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnes, Christopher A. 0000-0002-4608-4364","orcid":"https://orcid.org/0000-0002-4608-4364","contributorId":92793,"corporation":false,"usgs":true,"family":"Barnes","given":"Christopher A.","affiliations":[],"preferred":false,"id":356475,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70007512,"text":"70007512 - 2012 - A remote sensing approach for estimating the location and rate of urban irrigation in semi-arid climates","interactions":[],"lastModifiedDate":"2021-03-25T16:51:15.491091","indexId":"70007512","displayToPublicDate":"2012-02-19T18:15:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A remote sensing approach for estimating the location and rate of urban irrigation in semi-arid climates","docAbstract":"<p>Urban irrigation is an important component of the hydrologic cycle in many areas of the arid and semiarid western United States. This paper describes a new approach that uses readily available datasets to estimate the location and rate of urban irrigation. The approach provides a repeatable methodology at 1/3 km<sup>2</sup> resolution across a large urbanized area (500 km<sup>2</sup>). For this study, Landsat Thematic Mapper satellite imagery, air photos, climatic records, and a land-use map were used to: (1) identify the fraction of irrigated landscaping in urban areas, and (2) estimate the monthly rate of irrigation being applied to those areas. The area chosen for this study was the San Fernando Valley in Southern California.</p>\n<br/>\n<p>Identifying irrigated areas involved the use of 29 satellite images, air photos, and a land-use map. The fraction of a pixel that consists of irrigated landscaping (F<sub>irr</sub>) was estimated using a linear-mixture model of two land-cover endmembers (selected pixels within a satellite image that represent a targeted land-cover). The two endmembers were impervious and fully-irrigated landscaping. In the San Fernando Valley, we used airport buildings, runways, and pavement to represent the impervious endmember; golf courses and parks were used to represent the fully irrigated endmember. The average F<sup>irr</sup> using all 29 satellite scenes was 44%. F<sub>irr</sub> calculated from hand-digitizing using air photos for 13 randomly selected single-family-residential neighborhoods showed similar results (42%).</p>\n<br/>\n<p>Estimating the rate of irrigation required identification of a third endmember: areas that consisted of urban vegetation but were not irrigated. This \"nonirrigated\" endmember was used to compute a Normalized Difference Vegetation Index (NDVI) surplus, defined as the difference between the NDVI signals of the irrigated and nonirrigated endmembers. The NDVI signals from irrigated areas remains relatively constant throughout the year, whereas the signal from nonirrigated areas rises and falls seasonally due to precipitation. The areas between airport runways were chosen to represent the nonirrigated endmember. Water-delivery records from 65 spatially-distributed single-family neighborhoods, consisting of nearly 1800 homes, were correlated with the NDVI surplus. The results show a strong exponential correlation (<i>r</i><sup>2</sup> = 0.94).</p>\n<br/>\n<p>In the absence of water-delivery records, which can be difficult to obtain, a surrogate was identified: the landscape evapotranspiration rate (ET<sub>L</sub>). ET<sub>L</sub> was used to scale NDVI surplus (which is dimensionless) to irrigation rates using an exponential scaling function. The monthly irrigation rates calculated from satellite and climatic data compared well with irrigation rates calculated from actual water-delivery data using a paired Wilcoxan signed-rank test (<i>p</i> = 0.0063).</p>\n<br/>\n<p>Identification of F<sub>irr</sub> at the pixel scale, along with identification of the irrigation rate for a fully-irrigated pixel, allows for mapping of urban irrigation over large areas. Maps showing the location and rate of monthly irrigation for the San Fernando study area were computed for January and August 1997.</p>","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.jhydrol.2011.10.016","usgsCitation":"Johnson, T., and Belitz, K., 2012, A remote sensing approach for estimating the location and rate of urban irrigation in semi-arid climates: Journal of Hydrology, v. 414-415, p. 86-98, https://doi.org/10.1016/j.jhydrol.2011.10.016.","productDescription":"13 p.","startPage":"86","endPage":"98","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":204733,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Fernando Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -118.608119,34.038848 ], [ -118.608119,34.287715 ], [ -118.280568,34.287715 ], [ -118.280568,34.038848 ], [ -118.608119,34.038848 ] ] ] } } ] }","volume":"414-415","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059e546e4b0c8380cd46c61","contributors":{"authors":[{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":64366,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[],"preferred":false,"id":356553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":356552,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70043303,"text":"70043303 - 2012 - Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring","interactions":[],"lastModifiedDate":"2018-03-08T13:02:00","indexId":"70043303","displayToPublicDate":"2012-02-01T10:35:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring","docAbstract":"agebrush ecosystems in North America have experienced extensive degradation since European settlement. Further degradation continues from exotic invasive plants, altered fire frequency, intensive grazing practices, oil and gas development, and climate change – adding urgency to the need for ecosystem-wide understanding. Remote sensing is often identified as a key information source to facilitate ecosystem-wide characterization, monitoring, and analysis; however, approaches that characterize sagebrush with sufficient and accurate local detail across large enough areas to support this paradigm are unavailable. We describe the development of a new remote sensing sagebrush characterization approach for the state of Wyoming, U.S.A. This approach integrates 2.4 m QuickBird, 30 m Landsat TM, and 56 m AWiFS imagery into the characterization of four primary continuous field components including percent bare ground, percent herbaceous cover, percent litter, and percent shrub, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata Wyomingensis), and shrub height using a regression tree. According to an independent accuracy assessment, primary component root mean square error (RMSE) values ranged from 4.90 to 10.16 for 2.4 m QuickBird, 6.01 to 15.54 for 30 m Landsat, and 6.97 to 16.14 for 56 m AWiFS. Shrub and herbaceous components outperformed the current data standard called LANDFIRE, with a shrub RMSE value of 6.04 versus 12.64 and a herbaceous component RMSE value of 12.89 versus 14.63. This approach offers new advancements in sagebrush characterization from remote sensing and provides a foundation to quantitatively monitor these components into the future.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Applied Earth Observation and Geoinformation","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2011.09.012","usgsCitation":"Homer, C.G., Aldridge, C.L., Meyer, D., and Schell, S., 2012, Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring: International Journal of Applied Earth Observation and Geoinformation, v. 14, no. 1, p. 233-244, https://doi.org/10.1016/j.jag.2011.09.012.","productDescription":"12 p.","startPage":"233","endPage":"244","numberOfPages":"12","ipdsId":"IP-014786","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":281235,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jag.2011.09.012"},{"id":281236,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -111.0569,40.9947 ], [ -111.0569,45.0059 ], [ -104.0522,45.0059 ], [ -104.0522,40.9947 ], [ -111.0569,40.9947 ] ] ] } } ] }","volume":"14","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd685ae4b0b29085101fb5","contributors":{"authors":[{"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":473345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":473342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Debra K. 0000-0002-8841-697X","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":72282,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra K.","affiliations":[],"preferred":false,"id":473344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schell, Spencer J.","contributorId":50432,"corporation":false,"usgs":true,"family":"Schell","given":"Spencer J.","affiliations":[],"preferred":false,"id":473343,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70007140,"text":"fs20123006 - 2012 - Monitoring floods and fires during the summer of 2011--The value of the Landsat satellite 40-year archives","interactions":[],"lastModifiedDate":"2012-02-02T00:16:01","indexId":"fs20123006","displayToPublicDate":"2012-01-18T00:00:00","publicationYear":"2012","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":"2012-3006","title":"Monitoring floods and fires during the summer of 2011--The value of the Landsat satellite 40-year archives","docAbstract":"The summer of 2011 proved to be a season of extreme events. Heavy snowfall in the western mountains and excessive spring rains caused flooding along the Missouri and Mississippi Rivers; whereas extended dry conditions enabled fires to rage out of control from Alaska and Canada, south to Texas, Arizona, New Mexico, Georgia, and Mexico. The Landsat archive holds nearly 40 years of continuous global earth observation data. Landsat data are used by emergency responders to monitor change and damage caused by natural and man-made disasters. Decision makers rely on Landsat as they create plans for future environmental concerns.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123006","collaboration":"N","usgsCitation":"Owen, L., 2012, Monitoring floods and fires during the summer of 2011--The value of the Landsat satellite 40-year archives: U.S. Geological Survey Fact Sheet 2012-3006, 2 p., https://doi.org/10.3133/fs20123006.","productDescription":"2 p.","startPage":"1","endPage":"2","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":116443,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3006.jpg"},{"id":112504,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3006/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"North America","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5da7e4b0c8380cd704ec","contributors":{"authors":[{"text":"Owen, Linda 0000-0002-1734-5406 jonescheit@usgs.gov","orcid":"https://orcid.org/0000-0002-1734-5406","contributorId":478,"corporation":false,"usgs":true,"family":"Owen","given":"Linda","email":"jonescheit@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":355920,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70118134,"text":"70118134 - 2012 - Assessing long-term variations in sagebrush habitat: characterization of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data","interactions":[],"lastModifiedDate":"2017-12-27T15:07:51","indexId":"70118134","displayToPublicDate":"2012-01-01T16:16:11","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Assessing long-term variations in sagebrush habitat: characterization of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data","docAbstract":"<p><span>An approach that can generate sagebrush habitat change estimates for monitoring large-area sagebrush ecosystems has been developed and tested in southwestern Wyoming, USA. This prototype method uses a satellite-based image change detection algorithm and regression models to estimate sub-pixel percentage cover for five sagebrush habitat components: bare ground, herbaceous, litter, sagebrush and shrub. Landsat images from three different months in 1988, 1996 and 2006 were selected to identify potential landscape change during these time periods using change vector (CV) analysis incorporated with an image normalization algorithm. Regression tree (RT) models were used to estimate percentage cover for five components on all change areas identified in 1988 and 1996, using unchanged 2006 baseline data as training for both estimates. Over the entire study area (24 950 km</span><sup>2</sup><span>), a net increase of 98.83 km</span><sup>2</sup><span>, or 0.7%, for bare ground was measured between 1988 and 2006. Over the same period, the other four components had net losses of 20.17 km</span><sup>2</sup><span>, or 0.6%, for herbaceous vegetation; 30.16 km</span><sup>2</sup><span>, or 0.7%, for litter; 32.81 km</span><sup>2</sup><span>, or 1.5%, for sagebrush; and 33.34 km</span><sup>2</sup><span>, or 1.2%, for shrubs. The overall accuracy for shrub vegetation change between 1988 and 2006 was 89.56%. Change patterns within sagebrush habitat components differ spatially and quantitatively from each other, potentially indicating unique responses by these components to disturbances imposed upon them.</span></p>","language":"English","publisher":"Taylor & Francis","publisherLocation":"London, England","doi":"10.1080/01431161.2011.605085","usgsCitation":"Xian, G., Homer, C.G., and Aldridge, C.L., 2012, Assessing long-term variations in sagebrush habitat: characterization of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data: International Journal of Remote Sensing, v. 33, no. 7, p. 2034-2058, https://doi.org/10.1080/01431161.2011.605085.","productDescription":"25 p.","startPage":"2034","endPage":"2058","numberOfPages":"25","ipdsId":"IP-021130","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":291062,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431161.2011.605085"},{"id":291063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"7","noUsgsAuthors":false,"publicationDate":"2011-10-18","publicationStatus":"PW","scienceBaseUri":"57f7f545e4b0bc0bec0a153d","contributors":{"authors":[{"text":"Xian, George 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":76589,"corporation":false,"usgs":true,"family":"Xian","given":"George","affiliations":[],"preferred":false,"id":496427,"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":496426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":496425,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70046361,"text":"70046361 - 2012 - Upper Klamath Basin Landsat Image for September 30, 2004: Path 44 Row 31","interactions":[],"lastModifiedDate":"2013-06-10T13:05:19","indexId":"70046361","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"title":"Upper Klamath Basin Landsat Image for September 30, 2004: Path 44 Row 31","docAbstract":"This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70046361","usgsCitation":"Snyder, D.T., 2012, Upper Klamath Basin Landsat Image for September 30, 2004: Path 44 Row 31, Dataset, https://doi.org/10.3133/70046361.","productDescription":"Dataset","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":273537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":273536,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/erosl1t_09302004_p44r31_l5_usgs_NAD83.xml"}],"country":"United States","state":"Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -123.382600,41.991760 ], [ -123.382600,43.492919 ], [ -120.601579,43.492919 ], [ -120.601579,41.991760 ], [ -123.382600,41.991760 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51b6f571e4b0097a7158e643","contributors":{"authors":[{"text":"Snyder, Daniel T. dtsnyder@usgs.gov","contributorId":820,"corporation":false,"usgs":true,"family":"Snyder","given":"Daniel","email":"dtsnyder@usgs.gov","middleInitial":"T.","affiliations":[],"preferred":true,"id":479554,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70046656,"text":"70046656 - 2012 - Upper Klamath Basin Landsat Image for September 27, 2006: Path 45 Rows 30 and 31","interactions":[],"lastModifiedDate":"2013-06-18T12:56:07","indexId":"70046656","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"title":"Upper Klamath Basin Landsat Image for September 27, 2006: Path 45 Rows 30 and 31","docAbstract":"This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70046656","usgsCitation":"Snyder, D.T., 2012, Upper Klamath Basin Landsat Image for September 27, 2006: Path 45 Rows 30 and 31, Dataset, https://doi.org/10.3133/70046656.","productDescription":"Dataset","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":273933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":273932,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/l5045030_03120060927_klamath_nad83.xml"}],"country":"United States","state":"Oregon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -123.382600,41.991760 ], [ -123.382600,43.492919 ], [ -120.601579,43.492919 ], [ -120.601579,41.991760 ], [ -123.382600,41.991760 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51c18170e4b0dd0e00d9223d","contributors":{"authors":[{"text":"Snyder, Daniel T. dtsnyder@usgs.gov","contributorId":820,"corporation":false,"usgs":true,"family":"Snyder","given":"Daniel","email":"dtsnyder@usgs.gov","middleInitial":"T.","affiliations":[],"preferred":true,"id":479941,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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