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,{"id":70157505,"text":"70157505 - 2011 - Recent wetland land loss due to hurricanes: Improved estimates based upon multiple source images","interactions":[],"lastModifiedDate":"2021-10-27T11:49:51.248497","indexId":"70157505","displayToPublicDate":"2011-05-06T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Recent wetland land loss due to hurricanes: Improved estimates based upon multiple source images","docAbstract":"<p><span>The objective of this study was to provide a moderate resolution 30-m fractional water map of the Chenier Plain for 2003, 2006 and 2009 by using information contained in high-resolution satellite imagery of a subset of the study area. Indices and transforms pertaining to vegetation and water were created using the high-resolution imagery, and a threshold was applied to obtain a categorical land/water map. The high-resolution data was used to train a decision-tree classifier to estimate percent water in a lower resolution (Landsat) image. Two new water indices based on the tasseled cap transformation were proposed for IKONOS imagery in wetland environments and more than 700 input parameter combinations were considered for each Landsat image classified. Final selection and thresholding of the resulting percent water maps involved over 5,000 unambiguous classified random points using corresponding 1-m resolution aerial photographs, and a statistical optimization procedure to determine the threshold at which the maximum Kappa coefficient occurs. Each selected dataset has a Kappa coefficient, percent correctly classified (PCC) water, land and total greater than 90%. An accuracy assessment using 1,000 independent random points was performed. Using the validation points, the PCC values decreased to around 90%. The time series change analysis indicated that due to Hurricane Rita, the study area lost 6.5% of marsh area, and transient changes were less than 3% for either land or water. Hurricane Ike resulted in an additional 8% land loss, although not enough time has passed to discriminate between persistent and transient changes.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Coastal Sediments 2011","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2011","conferenceDate":"May 2-6, 2011","conferenceLocation":"Miami, Florida","language":"English","publisher":"World Scientific","doi":"10.1142/9789814355537_0169","usgsCitation":"Palaseanu-Lovejoy, M., Kranenburg, C.J., Brock, J., and Barras, J., 2011, Recent wetland land loss due to hurricanes: Improved estimates based upon multiple source images, <i>in</i> Proceedings of the Coastal Sediments 2011, Miami, Florida, May 2-6, 2011, p. 2253-2270, https://doi.org/10.1142/9789814355537_0169.","productDescription":"18 p.","startPage":"2253","endPage":"2270","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-025853","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":311664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisana","otherGeospatial":"Chenier Plain","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.1148681640625,\n              29.587788659909958\n            ],\n            [\n              -92.1478271484375,\n              29.77391386999227\n            ],\n            [\n              -91.91162109375,\n              29.859701442126756\n            ],\n            [\n              -92.1917724609375,\n              30.774878871959746\n            ],\n            [\n              -93.5321044921875,\n              30.869225348040825\n            ],\n            [\n              -93.7518310546875,\n              30.4060442699695\n            ],\n            [\n              -93.702392578125,\n              30.09286062952815\n            ],\n            [\n              -93.9166259765625,\n              29.821582720575016\n            ],\n            [\n              -93.900146484375,\n              29.654642479663647\n            ],\n            [\n              -93.53759765625,\n              29.754839972510933\n            ],\n            [\n              -92.867431640625,\n              29.654642479663647\n            ],\n            [\n              -92.21923828124999,\n              29.511330027309146\n            ],\n            [\n              -92.1258544921875,\n              29.56867942523516\n            ],\n            [\n              -92.1148681640625,\n              29.587788659909958\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationDate":"2012-06-07","publicationStatus":"PW","scienceBaseUri":"565446c5e4b071e7ea53d4d8","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":580496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. ckranenburg@usgs.gov","contributorId":140083,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine","email":"ckranenburg@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":580497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brock, John","contributorId":39011,"corporation":false,"usgs":true,"family":"Brock","given":"John","affiliations":[],"preferred":false,"id":580498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barras, John","contributorId":24437,"corporation":false,"usgs":true,"family":"Barras","given":"John","affiliations":[],"preferred":false,"id":580499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70156826,"text":"70156826 - 2011 - Landsat science team meeting summary","interactions":[],"lastModifiedDate":"2017-04-25T16:30:26","indexId":"70156826","displayToPublicDate":"2011-05-01T00:00:00","publicationYear":"2011","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 summary","docAbstract":"<p>The Landsat Science Team sponsored by the U.S. Geo- logical Survey (USGS) and NASA met in Mesa, AZ, from March 1-3, 2011. The team met in Mesa so that they could receive briefings and tours of the Landsat Data Continuity Mission (LDCM) spacecraft that is being developed by Orbital Sciences Corporation in nearby Gilbert, AZ.</p>","language":"English","publisher":"NASA","usgsCitation":"Loveland, T., Maiersperger, T., Irons, J.R., and Woodcock, C.E., 2011, Landsat science team meeting summary: The Earth Observer, v. 23, no. 3, p. 32-35.","productDescription":"4 p.","startPage":"32","endPage":"35","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-029157","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://eospso.nasa.gov/sites/default/files/eo_pdfs/Nov_Dec_2011_col_508.pdf"},{"id":307700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55e18635e4b05561fa206ac9","contributors":{"authors":[{"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":570719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maiersperger, Tom 0000-0003-3132-6997 tmaiersperger@usgs.gov","orcid":"https://orcid.org/0000-0003-3132-6997","contributorId":3693,"corporation":false,"usgs":true,"family":"Maiersperger","given":"Tom","email":"tmaiersperger@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":570720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":570721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodcock, C. E.","contributorId":93696,"corporation":false,"usgs":false,"family":"Woodcock","given":"C.","email":"","middleInitial":"E.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":570722,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":99214,"text":"ofr20111057 - 2011 - U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center-fiscal year 2010 annual report","interactions":[],"lastModifiedDate":"2012-02-02T00:15:50","indexId":"ofr20111057","displayToPublicDate":"2011-04-22T00:00:00","publicationYear":"2011","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":"2011-1057","title":"U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center-fiscal year 2010 annual report","docAbstract":"The Earth Resources Observation and Science (EROS) Center is a U.S. Geological Survey (USGS) facility focused on providing science and imagery to better understand our Earth. The work of the Center is shaped by the earth sciences, the missions of our stakeholders, and implemented through strong program and project management, and application of state-of-the-art information technologies. Fundamentally, EROS contributes to the understanding of a changing Earth through 'research to operations' activities that include developing, implementing, and operating remote-sensing-based terrestrial monitoring capabilities needed to address interdisciplinary science and applications objectives at all levels-both nationally and internationally.\r\n\r\nThe Center's programs and projects continually strive to meet, and where possible exceed, the changing needs of the USGS, the Department of the Interior, our Nation, and international constituents. The Center's multidisciplinary staff uses their unique expertise in remote sensing science and technologies to conduct basic and applied research, data acquisition, systems engineering, information access and management, and archive preservation to address the Nation's most critical needs. Of particular note is the role of EROS as the primary provider of Landsat data, the longest comprehensive global land Earth observation record ever collected.\r\n\r\nThis report is intended to provide an overview of the scientific and engineering achievements and illustrate the range and scope of the activities and accomplishments at EROS throughout fiscal year (FY) 2010. Additional information concerning the scientific, engineering, and operational achievements can be obtained from the scientific papers and other documents published by EROS staff or by visiting our web site at http://eros.usgs.gov.\r\n\r\nWe welcome comments and follow-up questions on any aspect of this Annual Report and invite any of our customers or partners to contact us at their convenience. To communicate with us, or for more information about EROS, contact: Communications and Outreach, USGS EROS Center, 47914 252nd Street, Sioux Falls, South Dakota 57198, jsnelson@usgs.gov, http://eros.usgs.gov/.","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20111057","usgsCitation":"Nelson, J.S., 2011, U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center-fiscal year 2010 annual report: U.S. Geological Survey Open-File Report 2011-1057, xx, 118 p., https://doi.org/10.3133/ofr20111057.","productDescription":"xx, 118 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":116111,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2011_1057.jpg"},{"id":14627,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2011/1057/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a2ce4b07f02db613a7a","contributors":{"authors":[{"text":"Nelson, Janice S. jsnelson@usgs.gov","contributorId":113,"corporation":false,"usgs":true,"family":"Nelson","given":"Janice","email":"jsnelson@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":307793,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70156666,"text":"70156666 - 2011 - Developing climate data records and essential climate variables from landsat data","interactions":[],"lastModifiedDate":"2017-01-18T13:45:01","indexId":"70156666","displayToPublicDate":"2011-04-15T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Developing climate data records and essential climate variables from landsat data","docAbstract":"<p><span>The series of Landsat missions has compiled the longest record of satellite observation of the Earth&rsquo;s land surface, extending for more than 38 years for most areas of the globe. Landsat data are particularly important as long term climate data records because the scale of observation is sufficient to differentiate between natural and human drivers of land cover change. The USGS has established consistent radiometric calibration across the Landsat TM and ETM+ sensors, and have extended the calibration back to the earlier MSS sensors. The USGS is developing capabilities to create fundamental climate data records (FCDRs), thematic climate data records (TCDRs), and essential climate variables (ECVs) from the Landsat data archive. Two high priority TCDRs were identified: surface reflectance and land surface temperature because they have direct application or are required as input to the generation of ECVs. We will focus development on a few of the terrestrial ECVs that have a high potential for being derived from Landsat data, that include land cover, albedo, fire disturbance, surface water, snow and ice, and leaf area index (LAI). We are collaborating with scientists who have demonstrated successful algorithm development and application of these science products to develop a framework of processing capabilities to support research projects and land management applications, along with an independent strategy for product validation. Our goal is to scale the creation and validation of these products from specific sites in the conterminous U.S. and Alaska, for extension to continental and global scales.</span></p>","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"34th International Symposium on Remote Sensing of Environment: the GEOSS era : towards operational environmental monitoring : April 10-15, 2011, Sydney, Australia : proceedings.","conferenceTitle":"34th International Symposium on Remote Sensing of Environment: the GEOSS era : towards operational environmental monitoring","conferenceDate":"April 10-15 2011","conferenceLocation":"Sydney, Australia","language":"English","publisher":"International Symposium for Remote Sensing of the Environment","usgsCitation":"Dwyer, J., Dinardo, T.P., and Muchoney, D.M., 2011, Developing climate data records and essential climate variables from landsat data, <i>in</i> 34th International Symposium on Remote Sensing of Environment: the GEOSS era : towards operational environmental monitoring : April 10-15, 2011, Sydney, Australia : proceedings., Sydney, Australia, April 10-15 2011, 3 p.","productDescription":"3 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":307457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":307456,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.isprs.org/proceedings/2011/ISRSE-34/"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55dd91b1e4b0518e354dd14e","contributors":{"authors":[{"text":"Dwyer, John","contributorId":45042,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","affiliations":[],"preferred":false,"id":569862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dinardo, Thomas P. tpdinardo@usgs.gov","contributorId":4165,"corporation":false,"usgs":true,"family":"Dinardo","given":"Thomas","email":"tpdinardo@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":569863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muchoney, Douglas M. dmuchoney@usgs.gov","contributorId":4592,"corporation":false,"usgs":true,"family":"Muchoney","given":"Douglas","email":"dmuchoney@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":569864,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":99128,"text":"sim3155 - 2011 - Terrestrial essential climate variables (ECVs) at a glance","interactions":[],"lastModifiedDate":"2017-03-29T14:21:14","indexId":"sim3155","displayToPublicDate":"2011-03-26T00:00:00","publicationYear":"2011","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":"3155","title":"Terrestrial essential climate variables (ECVs) at a glance","docAbstract":"The Global Terrestrial Observing System, Global Climate Observing System, World Meteorological Organization, and Committee on Earth Observation Satellites all support consistent global land observations and measurements. To accomplish this goal, the Global Terrestrial Observing System defined 'essential climate variables' as measurements of atmosphere, oceans, and land that are technically and economically feasible for systematic observation and that are needed to meet the United Nations Framework Convention on Climate Change and requirements of the Intergovernmental Panel on Climate Change. The following are the climate variables defined by the Global Terrestrial Observing System that relate to terrestrial measurements. Several of them are currently measured most appropriately by in-place observations, whereas others are suitable for measurement by remote sensing technologies. The U.S. Geological Survey is the steward of the Landsat archive, satellite imagery collected from 1972 to the present, that provides a potential basis for deriving long-term, global-scale, accurate, timely and consistent measurements of many of these essential climate variables.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3155","usgsCitation":"Stitt, S., Dwyer, J., Dye, D., and Josberger, E., 2011, Terrestrial essential climate variables (ECVs) at a glance: U.S. Geological Survey Scientific Investigations Map 3155, 60.0 x 55.0 inches, https://doi.org/10.3133/sim3155.","productDescription":"60.0 x 55.0 inches","onlineOnly":"Y","additionalOnlineFiles":"N","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":116204,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3155.gif"},{"id":14577,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3155/","linkFileType":{"id":5,"text":"html"}},{"id":338661,"rank":1,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3155/pdf/SIM11-3155.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ad9e4b07f02db6850f3","contributors":{"authors":[{"text":"Stitt, Susan susan_stitt@usgs.gov","contributorId":1410,"corporation":false,"usgs":true,"family":"Stitt","given":"Susan","email":"susan_stitt@usgs.gov","affiliations":[],"preferred":true,"id":307642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dwyer, John","contributorId":45042,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","affiliations":[],"preferred":false,"id":307644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dye, Dennis","contributorId":54159,"corporation":false,"usgs":true,"family":"Dye","given":"Dennis","affiliations":[],"preferred":false,"id":307645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Josberger, Edward","contributorId":30733,"corporation":false,"usgs":true,"family":"Josberger","given":"Edward","affiliations":[],"preferred":false,"id":307643,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70043495,"text":"70043495 - 2011 - Enhancing the Simplified Surface Energy Balance (SSEB) Approach for Estimating Landscape ET: Validation with the METRIC model","interactions":[],"lastModifiedDate":"2013-02-15T13:58:50","indexId":"70043495","displayToPublicDate":"2011-03-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Enhancing the Simplified Surface Energy Balance (SSEB) Approach for Estimating Landscape ET: Validation with the METRIC model","docAbstract":"Evapotranspiration (ET) can be derived from satellite data using surface energy balance principles. METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) is one of the most widely used models available in the literature to estimate ET from satellite imagery. The Simplified Surface Energy Balance (SSEB) model is much easier and less expensive to implement. The main purpose of this research was to present an enhanced version of the Simplified Surface Energy Balance (SSEB) model and to evaluate its performance using the established METRIC model. In this study, SSEB and METRIC ET fractions were compared using 7 Landsat images acquired for south central Idaho during the 2003 growing season. The enhanced SSEB model compared well with the METRIC model output exhibiting an r2 improvement from 0.83 to 0.90 in less complex topography (elevation less than 2000 m) and with an improvement of r2 from 0.27 to 0.38 in more complex (mountain) areas with elevation greater than 2000 m. Independent evaluation showed that both models exhibited higher variation in complex topographic regions, although more with SSEB than with METRIC. The higher ET fraction variation in the complex mountainous regions highlighted the difficulty of capturing the radiation and heat transfer physics on steep slopes having variable aspect with the simple index model, and the need to conduct more research. However, the temporal consistency of the results suggests that the SSEB model can be used on a wide range of elevation (more successfully up 2000 m) to detect anomalies in space and time for water resources management and monitoring such as for drought early warning systems in data scarce regions. SSEB has a potential for operational agro-hydrologic applications to estimate ET with inputs of surface temperature, NDVI, DEM and reference ET.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Agricultural Water Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.agwat.2010.10.014","usgsCitation":"Senay, G.B., Budde, M.E., and Verdin, J.P., 2011, Enhancing the Simplified Surface Energy Balance (SSEB) Approach for Estimating Landscape ET: Validation with the METRIC model: Agricultural Water Management, v. 98, no. 4, p. 606-618, https://doi.org/10.1016/j.agwat.2010.10.014.","startPage":"606","endPage":"618","ipdsId":"IP-016651","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":267573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":267572,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.agwat.2010.10.014"}],"country":"United States","volume":"98","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"511f6714e4b03b29402c5dd3","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":473711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budde, Michael E. 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":3007,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","middleInitial":"E.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":473710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":473709,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":99068,"text":"ofr20111031 - 2011 - The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States-Executive report","interactions":[],"lastModifiedDate":"2012-02-02T00:15:19","indexId":"ofr20111031","displayToPublicDate":"2011-02-26T00:00:00","publicationYear":"2011","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":"2011-1031","title":"The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States-Executive report","docAbstract":"Moderate-resolution imagery (MRI), such as that provided by the Landsat satellites, provides unique spatial information for use by many people both within and outside of the United States (U.S.). However, exactly who these users are, how they use the imagery, and the value and benefits derived from the information are, to a large extent, unknown. To explore these issues, social scientists at the USGS Fort Collins Science Center conducted a study of U.S.-based MRI users from 2008 through 2010 in two parts: 1) a user identification and 2) a user survey. The objectives for this study were to: 1) identify and classify U.S.-based users of this imagery; 2) better understand how and why MRI, and specifically Landsat, is being used; and 3) qualitatively and quantitatively measure the value and societal benefits of MRI (focusing on Landsat specifically). The results of the survey revealed that respondents from multiple sectors use Landsat imagery in many different ways, as demonstrated by the breadth of project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance placed on the imagery, the numerous benefits received from projects using Landsat imagery, the negative impacts if Landsat imagery was no longer available, and the substantial willingness to pay for replacement imagery in the event of a data gap. The survey collected information from users who are both part of and apart from the known user community. The diversity of the sample delivered results that provide a baseline of knowledge about the users, uses, and value of Landsat imagery. While the results supply a wealth of information on their own, they can also be built upon through further research to generate a more complete picture of the population of Landsat users as a whole.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20111031","usgsCitation":"Miller, H.M., Sexton, N.R., Koontz, L., Loomis, J., Koontz, S.R., and Hermans, C., 2011, The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States-Executive report: U.S. Geological Survey Open-File Report 2011-1031, v, 42 p. , https://doi.org/10.3133/ofr20111031.","productDescription":"v, 42 p. ","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":126194,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2011_1031.bmp"},{"id":14515,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2011/1031/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a59e4b07f02db62f606","contributors":{"authors":[{"text":"Miller, Holly M. 0000-0003-0914-7570 millerh@usgs.gov","orcid":"https://orcid.org/0000-0003-0914-7570","contributorId":29544,"corporation":false,"usgs":true,"family":"Miller","given":"Holly","email":"millerh@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":false,"id":307454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sexton, Natalie R.","contributorId":82750,"corporation":false,"usgs":true,"family":"Sexton","given":"Natalie","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":307458,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koontz, Lynne koontzl@usgs.gov","contributorId":2174,"corporation":false,"usgs":false,"family":"Koontz","given":"Lynne","email":"koontzl@usgs.gov","affiliations":[{"id":7016,"text":"Environmental Quality Division, National Park Service, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":307453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loomis, John","contributorId":60746,"corporation":false,"usgs":true,"family":"Loomis","given":"John","affiliations":[],"preferred":false,"id":307456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koontz, Stephen R.","contributorId":69272,"corporation":false,"usgs":true,"family":"Koontz","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":307457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hermans, Caroline","contributorId":42680,"corporation":false,"usgs":true,"family":"Hermans","given":"Caroline","affiliations":[],"preferred":false,"id":307455,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":99062,"text":"sir20115007 - 2011 - Predicting lake trophic state by relating Secchi-disk transparency measurements to Landsat-satellite imagery for Michigan inland lakes, 2003-05 and 2007-08","interactions":[],"lastModifiedDate":"2016-09-22T16:12:15","indexId":"sir20115007","displayToPublicDate":"2011-02-19T00:00:00","publicationYear":"2011","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":"2011-5007","title":"Predicting lake trophic state by relating Secchi-disk transparency measurements to Landsat-satellite imagery for Michigan inland lakes, 2003-05 and 2007-08","docAbstract":"<p>Inland lakes are an important economic and environmental resource for Michigan. The U.S. Geological Survey and the Michigan Department of Natural Resources and Environment have been cooperatively monitoring the quality of selected lakes in Michigan through the Lake Water Quality Assessment program. Sampling for this program began in 2001; by 2010, 730 of Michigan’s 11,000 inland lakes are expected to have been sampled once. Volunteers coordinated by the Michigan Department of Natural Resources and Environment began sampling lakes in 1974 and continue to sample (in 2010) approximately 250 inland lakes each year through the Michigan Cooperative Lakes Monitoring Program. Despite these sampling efforts, it still is impossible to physically collect measurements for all Michigan inland lakes; however, Landsat-satellite imagery has been used successfully in Minnesota, Wisconsin, Michigan, and elsewhere to predict the trophic state of unsampled inland lakes greater than 20 acres by producing regression equations relating in-place Secchi-disk measurements to Landsat bands. This study tested three alternatives to methods previously used in Michigan to improve results for predicted statewide Trophic State Index (TSI) computed from Secchi-disk transparency (TSI (SDT)). The alternative methods were used on 14 Landsat-satellite scenes with statewide TSI (SDT) for two time periods (2003– 05 and 2007–08). Specifically, the methods were (1) satellitedata processing techniques to remove areas affected by clouds, cloud shadows, haze, shoreline, and dense vegetation for inland lakes greater than 20 acres in Michigan; (2) comparison of the previous method for producing a single open-water predicted TSI (SDT) value (which was based on an area of interest (AOI) and lake-average approach) to an alternative Gethist method for identifying open-water areas in inland lakes (which follows the initial satellite-data processing and targets the darkest pixels, representing the deepest water, before regression equations are created); and (3) checking to see whether the predicted TSI (SDT) values compared well between two regression equations, one previously used in Michigan and an alternative equation from the hydrologic literature. </p><p>The combination of improved satellite-data processing techniques and the Gethist method to identify open-water areas in inland lakes during 2003–05 and 2007–08 provided a stronger relation and statistical significance between predicted TSI (SDT) and measured TSI than did the AOI lake-average method; differences in results for the two methods were significant at the 99-percent confidence level. With regard to the comparison of the regression equations, there were no statistically significant differences at the 95-percent confidence level between results from the two equations. The previously used equation, in combination with the Gethist method, yielded coefficient of determination (R<sup>2</sup>) values of 0.71 and 0.77 for the periods 2003–05 and 2007–08, respectively. The alternative equation, in combination with the Gethist method, yielded R<sup>2</sup> values of 0.74 and 0.75 for 2003–05 and 2007–08, respectively. Predicted TSI (SDT) and measured TSI (SDT) values for lakes used in the regression equations compared well, with R<sup>2</sup> values of 0.95 and 0.96 for predicted TSI (SDT) for 2003–05 and 2007–08, respectively. The R<sup>2</sup> values for statewide predicted TSI (SDT) for all inland lakes with available open-water areas for 2003–05 and 2007–08 were 0.91 and 0.93, respectively. Although the two equations predicted similar trophic-state classes, the alternative equation is planned to be used for future prediction of TSI (SDT) values for Michigan inland lakes, to promote consistency in comparing predicted values between States and for potential use in trend analysis.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115007","collaboration":"In cooperation with the Michigan Department of Natural Resources and Environment","usgsCitation":"Fuller, L.M., Jodoin, R., and Minnerick, R., 2011, Predicting lake trophic state by relating Secchi-disk transparency measurements to Landsat-satellite imagery for Michigan inland lakes, 2003-05 and 2007-08: U.S. Geological Survey Scientific Investigations Report 2011-5007, Report: viii, 18 p.; Appendixes 1 and 2, https://doi.org/10.3133/sir20115007.","productDescription":"Report: viii, 18 p.; Appendixes 1 and 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M.","contributorId":97987,"corporation":false,"usgs":true,"family":"Fuller","given":"L.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":307439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jodoin, R.S.","contributorId":19681,"corporation":false,"usgs":true,"family":"Jodoin","given":"R.S.","email":"","affiliations":[],"preferred":false,"id":307437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minnerick, R. J.","contributorId":52255,"corporation":false,"usgs":true,"family":"Minnerick","given":"R. J.","affiliations":[],"preferred":false,"id":307438,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":9000594,"text":"ofr20101302 - 2011 - DESI-Detection of early-season invasives (software-installation manual and user's guide version 1.0)","interactions":[],"lastModifiedDate":"2012-02-02T00:04:06","indexId":"ofr20101302","displayToPublicDate":"2011-02-14T00:00:00","publicationYear":"2011","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":"2010-1302","title":"DESI-Detection of early-season invasives (software-installation manual and user's guide version 1.0)","docAbstract":"This report describes a software system for detecting early-season invasive plant species, such as cheatgrass. The report includes instructions for installing the software and serves as a user's guide in processing Landsat satellite remote sensing data to map the distributions of cheatgrass and other early-season invasive plants. The software was developed for application to the semi-arid regions of southern Utah; however, the detection parameters can be altered by the user for application to other areas.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20101302","usgsCitation":"Kokaly, R., 2011, DESI-Detection of early-season invasives (software-installation manual and user's guide version 1.0): U.S. Geological Survey Open-File Report 2010-1302, iv, 26 p.; Appendices; Downloads Directory, https://doi.org/10.3133/ofr20101302.","productDescription":"iv, 26 p.; Appendices; Downloads Directory","additionalOnlineFiles":"Y","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":116018,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1302.png"},{"id":14493,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1302/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4acce4b07f02db67ea5d","contributors":{"authors":[{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":344340,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":99040,"text":"ofr20101282 - 2011 - Analysis of change in marsh types of coastal Louisiana, 1978-2001","interactions":[],"lastModifiedDate":"2012-02-02T00:04:05","indexId":"ofr20101282","displayToPublicDate":"2011-02-10T00:00:00","publicationYear":"2011","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":"2010-1282","title":"Analysis of change in marsh types of coastal Louisiana, 1978-2001","docAbstract":"Scientists and geographers have provided multiple datasets and maps to document temporal changes in vegetation types and land-water relationships in coastal Louisiana. Although these maps provide useful historical information, technological limitations prevented these and other mapping efforts from providing sufficiently detailed calculations of areal changes and shifts in habitat coverage. The current analysis of habitat change draws upon these past mapping efforts but is based on an advanced, geographic information system dataset that was created by using Landsat 5 Thematic Mapper imagery and digital orthophoto quarter quadrangles. The objective of building this dataset was to more specifically define land-water relationships over time in coastal Louisiana, and it provides the most detailed analysis of vegetation shifts to date. In the current study, we have attempted to explain these vegetation shifts by interpreting them in the context of rainfall records, data from the Palmer Drought Severity Index, and salinity data.\r\nDuring the 23 years we analyzed, total marsh acreage decreased, with conversion of marsh to open water. Furthermore, the general trend across coastal Louisiana was a shift to increasingly fresh marsh types. Although fresh marsh remained almost the same during the 1978-88 study period, there were greater increases during the 1988-2001 study periods. Intermediate marsh followed the same pattern, whereas brackish marsh showed a reverse (decreasing) pattern. Changes in saline (saltwater) marsh were minimal.\r\nInterpreting shifts in marsh vegetation types by using climate and salinity data provides better understanding of factors influencing these changes and, therefore, can improve our ability to make predictions about future marsh loss related to vegetation changes. Results of our study indicate that precipitation fluctuations prior to vegetation surveys impacted salinities differently across the coast. For example, a wet 6 months prior to the survey may or may not have made up for a dry period during the earlier 12 months. More research is needed to better understand rainfall periods and how they affect salinity changes.\r\nThe ability to understand past dynamics and to anticipate future trends in vegetation change and related land loss in the coastal region of Louisiana is a vital part of ongoing and future efforts to conserve its critical wetland ecosystem. With the loss of marsh and resultant changes in hydrology, it is likely that changes in marsh type may show greater variation in the future, even if given only minor changes in precipitation levels. ","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101282","usgsCitation":"Linscombe, R.G., and Hartley, S.B., 2011, Analysis of change in marsh types of coastal Louisiana, 1978-2001: U.S. Geological Survey Open-File Report 2010-1282, viii, 52 p., https://doi.org/10.3133/ofr20101282.","productDescription":"viii, 52 p.","additionalOnlineFiles":"N","temporalStart":"1978-01-01","temporalEnd":"2001-12-31","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":126199,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1282.png"},{"id":14480,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1282/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4acfe4b07f02db6806a1","contributors":{"authors":[{"text":"Linscombe, Robert G.","contributorId":36886,"corporation":false,"usgs":true,"family":"Linscombe","given":"Robert","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":307362,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartley, Stephen B. 0000-0003-1380-2769 hartleys@usgs.gov","orcid":"https://orcid.org/0000-0003-1380-2769","contributorId":4164,"corporation":false,"usgs":true,"family":"Hartley","given":"Stephen","email":"hartleys@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":307361,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":99036,"text":"ofr20101327 - 2011 - Detecting Cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software","interactions":[],"lastModifiedDate":"2012-02-02T00:05:15","indexId":"ofr20101327","displayToPublicDate":"2011-02-08T00:00:00","publicationYear":"2011","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":"2010-1327","title":"Detecting Cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software","docAbstract":"Invasive plant species disrupt native ecosystems and cause economic harm to public lands. In this report, an example of applying the Detection of Early Season Invasives software to mapping cheatgrass infestations is given. A discussion of each step of the DESI process is given, including selection of Landsat images. Tutorial data, covering a semi-arid area in southern Utah, are distributed with this report. Tips on deriving the inputs required to run DESI are provided. An approach for evaluating and adjusting detection parameters by examining interim products of DESI is discussed. ","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101327","usgsCitation":"Kokaly, R., 2011, Detecting Cheatgrass on the Colorado Plateau using Landsat data: A tutorial for the DESI software: U.S. Geological Survey Open-File Report 2010-1327, vii, 81 p.; Appendices; Downloads Directory, https://doi.org/10.3133/ofr20101327.","productDescription":"vii, 81 p.; Appendices; Downloads Directory","additionalOnlineFiles":"Y","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":126203,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1327.bmp"},{"id":14476,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1327/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4aa8e4b07f02db667b22","contributors":{"authors":[{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":307348,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70208557,"text":"70208557 - 2011 - Assessment of mangrove forests in the Pacific region using Landsat imagery","interactions":[],"lastModifiedDate":"2020-02-20T10:01:40","indexId":"70208557","displayToPublicDate":"2011-01-01T15:54:17","publicationYear":"2011","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":"Assessment of mangrove forests in the Pacific region using Landsat imagery","docAbstract":"<p><span>The information on the mangrove forests for the Pacific region is scarce or outdated. A regional assessment based on a consistent methodology and data sources was needed to understand their true extent. Our investigation offers a regionally consistent, high resolution (30 m), and the most comprehensive mapping of mangrove forests on the islands of American Samoa, Fiji, French Polynesia, Guam, Hawaii, Kiribati, Marshall Islands, Micronesia, Nauru, New Caledonia, Northern Mariana Islands, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Wallis and Futuna Islands for the year 2000. We employed a hybrid supervised and unsupervised image classification technique on a total of 128 Landsat scenes gathered between 1999 and 2004, and validated the results using existing geographic information science (GIS) datasets, high resolution imagery, and published literature. We also draw a comparative analysis with the mangrove forests inventory published by the Food and Agriculture Association (FAO) of the United Nations. Our estimate shows a total of 623755 hectares of mangrove forests in the Pacific region; an increase of 18% from FAO's estimates. Although mangrove forests are disproportionately distributed toward a few larger islands on the western Pacific, they are also significant in many smaller islands.</span></p>","language":"English","publisher":"SPIE","doi":"10.1117/1.3563584","usgsCitation":"Bhattarai, B., and Giri, C., 2011, Assessment of mangrove forests in the Pacific region using Landsat imagery: Journal of Applied Remote Sensing, v. 5, no. 1, 053509, https://doi.org/10.1117/1.3563584.","productDescription":"053509","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":372370,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"American Samoa, Fiji, French Polynesia, Guam, Hawaii, Kiribati, Marshall Islands, Micronesia, Nauru, New Caledonia, Northern Mariana Islands, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Wallis and Futuna Islands ","volume":"5","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bhattarai, Bibek","contributorId":222541,"corporation":false,"usgs":false,"family":"Bhattarai","given":"Bibek","email":"","affiliations":[],"preferred":false,"id":782470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782471,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70043624,"text":"70043624 - 2011 - Status and distribution of mangrove forests of the world using earth observation satellite data","interactions":[],"lastModifiedDate":"2022-01-07T17:13:21.708647","indexId":"70043624","displayToPublicDate":"2011-01-01T13:05:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Status and distribution of mangrove forests of the world using earth observation satellite data","docAbstract":"<b>Aim</b>  Our scientific understanding of the extent and distribution of mangrove forests of the world is inadequate. The available global mangrove databases, compiled using disparate geospatial data sources and national statistics, need to be improved. Here, we mapped the status and distributions of global mangroves using recently available Global Land Survey (GLS) data and the Landsat archive.\n<br>\n<br>\n<b>Methods</b>  We interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Each image was normalized for variation in solar angle and earth–sun distance by converting the digital number values to the top-of-the-atmosphere reflectance. Ground truth data and existing maps and databases were used to select training samples and also for iterative labelling. Results were validated using existing GIS data and the published literature to map ‘true mangroves’.\n<br>\n<br>\n<b>Results</b>  The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude.\n<br>\n<br>\n<b>Main conclusions</b>  We report that the remaining area of mangrove forest in the world is less than previously thought. Our estimate is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. We present the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created. We developed and used better mapping techniques and data sources and mapped mangroves with better spatial and thematic details than previous studies.","language":"English","publisher":"Wiley","doi":"10.1111/j.1466-8238.2010.00584.x","usgsCitation":"Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., Masek, J.G., and Duke, N., 2011, Status and distribution of mangrove forests of the world using earth observation satellite data: Global Ecology and Biogeography, v. 20, no. 1, p. 154-159, https://doi.org/10.1111/j.1466-8238.2010.00584.x.","productDescription":"6 p.","startPage":"154","endPage":"159","numberOfPages":"6","ipdsId":"IP-018403","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":275454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2010-08-17","publicationStatus":"PW","scienceBaseUri":"51f39a67e4b0a32220222fa7","contributors":{"authors":[{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":2403,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":473992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ochieng, E.","contributorId":94888,"corporation":false,"usgs":true,"family":"Ochieng","given":"E.","email":"","affiliations":[],"preferred":false,"id":473999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tieszen, Larry L. tieszen@usgs.gov","contributorId":2831,"corporation":false,"usgs":true,"family":"Tieszen","given":"Larry","email":"tieszen@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":true,"id":473993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhi-Liang","contributorId":70726,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhi-Liang","affiliations":[],"preferred":false,"id":473997,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Ashbindu singh@usgs.gov","contributorId":5410,"corporation":false,"usgs":true,"family":"Singh","given":"Ashbindu","email":"singh@usgs.gov","affiliations":[],"preferred":true,"id":473995,"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":473994,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":473998,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duke, Norm","contributorId":17897,"corporation":false,"usgs":true,"family":"Duke","given":"Norm","email":"","affiliations":[],"preferred":false,"id":473996,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70118894,"text":"70118894 - 2011 - Landsat imagery: a unique resource","interactions":[],"lastModifiedDate":"2014-07-31T08:41:27","indexId":"70118894","displayToPublicDate":"2011-01-01T08:38:58","publicationYear":"2011","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Landsat imagery: a unique resource","docAbstract":"Landsat satellites provide high-quality, multi-spectral imagery of the surface of the Earth. These moderate-resolution, remotely sensed images are not just pictures, but contain many layers of data collected at different points along the visible and invisible light spectrum. These data can be manipulated to reveal what the Earth’s surface looks like, including what types of vegetation are present or how a natural disaster has impacted an area (Fig. 1).","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","usgsCitation":"Miller, H., Sexton, N., and Koontz, L., 2011, Landsat imagery: a unique resource, 1 p.","productDescription":"1 p.","numberOfPages":"1","costCenters":[],"links":[{"id":291435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53db5843e4b0fba533fa3586","contributors":{"authors":[{"text":"Miller, H.","contributorId":57009,"corporation":false,"usgs":true,"family":"Miller","given":"H.","affiliations":[],"preferred":false,"id":497346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sexton, N.","contributorId":61519,"corporation":false,"usgs":true,"family":"Sexton","given":"N.","affiliations":[],"preferred":false,"id":497347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koontz, L.","contributorId":54538,"corporation":false,"usgs":true,"family":"Koontz","given":"L.","email":"","affiliations":[],"preferred":false,"id":497345,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70035456,"text":"70035456 - 2011 - Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis program, Landsat, and LANDFIRE","interactions":[],"lastModifiedDate":"2018-02-23T11:45:44","indexId":"70035456","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis program, Landsat, and LANDFIRE","docAbstract":"<p><span>The concentrations of CO</span><sub>2</sub><span> and other greenhouse gases in the atmosphere have been increasing and greatly affecting global climate and socio-economic systems. Actively growing forests are generally considered to be a major carbon sink, but forest wildfires lead to large releases of biomass carbon into the atmosphere. Aboveground forest biomass carbon (AFBC), an important ecological indicator, and fire-induced carbon emissions at regional scales are highly relevant to forest sustainable management and climate change. It is challenging to accurately estimate the spatial distribution of AFBC across large areas because of the spatial heterogeneity of forest cover types and canopy structure. In this study, Forest Inventory and Analysis (FIA) data, Landsat, and Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) data were integrated in a regression tree model for estimating AFBC at a 30-m resolution in the Utah High Plateaus. AFBC were calculated from 225 FIA field plots and used as the dependent variable in the model. Of these plots, 10% were held out for model evaluation with stratified random sampling, and the other 90% were used as training data to develop the regression tree model. Independent variable layers included Landsat imagery and the derived spectral indicators, digital elevation model (DEM) data and derivatives, biophysical gradient data, existing vegetation cover type and vegetation structure. The cross-validation correlation coefficient (</span><i>r</i><span> value) was 0.81 for the training model. Independent validation using withheld plot data was similar with </span><i>r</i><span> value of 0.82. This validated regression tree model was applied to map AFBC in the Utah High Plateaus and then combined with burn severity information to estimate loss of AFBC in the Longston fire of Zion National Park in 2001. The final dataset represented 24 forest cover types for a 4 million ha forested area. We estimated a total of 353 Tg AFBC with an average of 87 MgC/ha in the Utah High Plateaus. We also estimated that 8054 Mg AFBC were released from 2.24&nbsp;km</span><sup>2</sup><span> burned forest area in the Longston fire. These results demonstrate that an AFBC spatial map and estimated biomass carbon consumption can readily be generated using existing database. The methodology provides a consistent, practical, and inexpensive way for estimating AFBC at 30-m resolution over large areas throughout the United States.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2009.03.013","issn":"1470160X","usgsCitation":"Chen, X., Liu, S., Zhu, Z., Vogelmann, J., Li, Z., and Ohlen, D.O., 2011, Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis program, Landsat, and LANDFIRE: Ecological Indicators, v. 11, no. 1, p. 140-148, https://doi.org/10.1016/j.ecolind.2009.03.013.","productDescription":"9 p.","startPage":"140","endPage":"148","numberOfPages":"9","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":243341,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":215530,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolind.2009.03.013"}],"volume":"11","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a0b07e4b0c8380cd5251c","contributors":{"authors":[{"text":"Chen, Xuexia","contributorId":140368,"corporation":false,"usgs":false,"family":"Chen","given":"Xuexia","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":450748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Shuguang 0000-0002-6027-3479 sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":450750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":450745,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vogelmann, James E. 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":649,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":450747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Zhen","contributorId":200957,"corporation":false,"usgs":false,"family":"Li","given":"Zhen","affiliations":[],"preferred":false,"id":450746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ohlen, Donald O. ohlen@usgs.gov","contributorId":3779,"corporation":false,"usgs":true,"family":"Ohlen","given":"Donald","email":"ohlen@usgs.gov","middleInitial":"O.","affiliations":[],"preferred":true,"id":450749,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70036930,"text":"70036930 - 2011 - Mapping the Philippines' mangrove forests using Landsat imagery","interactions":[],"lastModifiedDate":"2017-04-06T13:28:33","indexId":"70036930","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3380,"text":"Sensors","active":true,"publicationSubtype":{"id":10}},"title":"Mapping the Philippines' mangrove forests using Landsat imagery","docAbstract":"<p><span>Current, accurate, and reliable information on the areal extent and spatial distribution of mangrove forests in the Philippines is limited. Previous estimates of mangrove extent do not illustrate the spatial distribution for the entire country. This study, part of a global assessment of mangrove dynamics, mapped the spatial distribution and areal extent of the Philippines’ mangroves circa 2000. We used publicly available Landsat data acquired primarily from the Global Land Survey to map the total extent and spatial distribution. ISODATA clustering, an unsupervised classification technique, was applied to 61 Landsat images. Statistical analysis indicates the total area of mangrove forest cover was approximately 256,185 hectares circa 2000 with overall classification accuracy of 96.6% and a kappa coefficient of 0.926. These results differ substantially from most recent estimates of mangrove area in the Philippines. The results of this study may assist the decision making processes for rehabilitation and conservation efforts that are currently needed to protect and restore the Philippines’ degraded mangrove forests. </span></p>","language":"English","publisher":"MDPI","doi":"10.3390/s110302972","issn":"14248220","usgsCitation":"Long, J., and Giri, C., 2011, Mapping the Philippines' mangrove forests using Landsat imagery: Sensors, v. 11, no. 3, p. 2972-2981, https://doi.org/10.3390/s110302972.","productDescription":"10 p.","startPage":"2972","endPage":"2981","numberOfPages":"10","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":475281,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s110302972","text":"Publisher Index Page"},{"id":245806,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":217834,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3390/s110302972"}],"volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2011-03-07","publicationStatus":"PW","scienceBaseUri":"505a507be4b0c8380cd6b6fa","contributors":{"authors":[{"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":458517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":458518,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70036469,"text":"70036469 - 2011 - Characterizing fragmentation of the collective forests in southern China from multitemporal Landsat imagery: A case study from Kecheng district of Zhejiang province","interactions":[],"lastModifiedDate":"2017-04-06T13:23:44","indexId":"70036469","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":836,"text":"Applied Geography","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing fragmentation of the collective forests in southern China from multitemporal Landsat imagery: A case study from Kecheng district of Zhejiang province","docAbstract":"<p><span>Tropical and subtropical forests provide important ecosystem goods and services including carbon sequestration and biodiversity conservation. These forests are facing increasing socioeconomic pressures and are rapidly being degraded and fragmented. This analysis focuses on the rate of change and patterns of fragmentation in a collective forest area in Zhejiang province, China, during the time period 1988–2005. The research consisted of two parts. The first was the development of general land cover maps and the identification of land cover changes by interpreting Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) time series imagery. The second part involved the computation and analysis of forest fragmentation metrics. For this portion of the study, fragmentation statistics were analyzed, and images were developed to depict forest fragmentation patterns and trends. Results revealed that there was a net loss of 7.8% in forest coverage, dropping from 66.8% in 1988 to 59.0% in 2005, primarily caused by agricultural expansion and poor forest management practices. An acceleration of forest fragmentation was also witnessed during the time intervals, which was evidenced by a decreasing trend in interior forest (57.2% in 1988, 55.0% in 1996 and 54.8% in 2005 respectively) coupled with the scales of the selected geospatial metrics. Continued forest loss and fragmentation are closely correlated with the existing political, educational, institutional and economic processes of contemporary China. To unlock the developmental potentials of the collective forests and to effectively mitigate the rate of forest loss and fragmentation, reforms of forest tenure and ecological immigration practices are recognized as a prospective alternative. The produced fragmentation maps further illustrates the importance of assessing landscape change history, especially the spatiotemporal patterns of forest fragments, when developing landscape level plans for biodiversity conservation, land use management and ecologically sustainable forestry.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeog.2011.02.004","issn":"01436228","usgsCitation":"Li, M., Zhu, Z., Vogelmann, J., Xu, D., Wen, W., and Liu, A., 2011, Characterizing fragmentation of the collective forests in southern China from multitemporal Landsat imagery: A case study from Kecheng district of Zhejiang province: Applied Geography, v. 31, no. 3, p. 1026-1035, https://doi.org/10.1016/j.apgeog.2011.02.004.","productDescription":"10 p.","startPage":"1026","endPage":"1035","numberOfPages":"10","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488024,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeog.2011.02.004","text":"Publisher Index Page"},{"id":218586,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.apgeog.2011.02.004"},{"id":246612,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059f4fee4b0c8380cd4c014","contributors":{"authors":[{"text":"Li, M.","contributorId":97246,"corporation":false,"usgs":true,"family":"Li","given":"M.","email":"","affiliations":[],"preferred":false,"id":456298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Z.","contributorId":10898,"corporation":false,"usgs":true,"family":"Zhu","given":"Z.","email":"","affiliations":[],"preferred":false,"id":456293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogelmann, James E. 0000-0002-0804-5823","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":16604,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","affiliations":[],"preferred":false,"id":456294,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xu, D.","contributorId":41231,"corporation":false,"usgs":true,"family":"Xu","given":"D.","email":"","affiliations":[],"preferred":false,"id":456296,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wen, W.","contributorId":17866,"corporation":false,"usgs":true,"family":"Wen","given":"W.","email":"","affiliations":[],"preferred":false,"id":456295,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, A.","contributorId":90110,"corporation":false,"usgs":true,"family":"Liu","given":"A.","email":"","affiliations":[],"preferred":false,"id":456297,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70036234,"text":"70036234 - 2011 - Fire frequency, area burned, and severity: A quantitative approach to defining a normal fire year","interactions":[],"lastModifiedDate":"2021-01-20T21:13:11.940359","indexId":"70036234","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Fire frequency, area burned, and severity: A quantitative approach to defining a normal fire year","docAbstract":"<p><span>Fire frequency, area burned, and fire severity are important attributes of a fire regime, but few studies have quantified the interrelationships among them in evaluating a fire year. Although area burned is often used to summarize a fire season, burned area may not be well correlated with either the number or ecological effect of fires. Using the Landsat data archive, we examined all 148 wildland fires (prescribed fires and wildfires) &gt;40 ha from 1984 through 2009 for the portion of the Sierra Nevada centered on Yosemite National Park, California, USA. We calculated mean fire frequency and mean annual area burned from a combination of field- and satellite-derived data. We used the continuous probability distribution of the differenced Normalized Burn Ratio (dNBR) values to describe fire severity. For fires &gt;40 ha, fire frequency, annual area burned, and cumulative severity were consistent in only 13 of 26 years (50 %), but all pair-wise comparisons among these fire regime attributes were significant. Borrowing from long-established practice in climate science, we defined “fire normals” to be the 26 year means of fire frequency, annual area burned, and the area under the cumulative probability distribution of dNBR. Fire severity normals were significantly lower when they were aggregated by year compared to aggregation by area. Cumulative severity distributions for each year were best modeled with Weibull functions (all 26 years, r</span><sup>2</sup><span>&nbsp;≥ 0.99;&nbsp;</span><i>P</i><span>&nbsp;&lt; 0.001). Explicit modeling of the cumulative severity distributions may allow more comprehensive modeling of climate-severity and area-severity relationships. Together, the three metrics of number of fires, size of fires, and severity of fires provide land managers with a more comprehensive summary of a given fire year than any single metric.</span></p>","language":"English","publisher":"Springer Link","doi":"10.4996/fireecology.0702051","issn":"19339747","usgsCitation":"Lutz, J., Key, C.H., Kolden, C., Kane, J., and Van Wagtendonk, J.W., 2011, Fire frequency, area burned, and severity: A quantitative approach to defining a normal fire year: Fire Ecology, v. 7, no. 2, p. 51-65, https://doi.org/10.4996/fireecology.0702051.","productDescription":"15 p.","startPage":"51","endPage":"65","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":475064,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4996/fireecology.0702051","text":"Publisher Index Page"},{"id":246469,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":218459,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.4996/fireecology.0702051"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yosemite","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.03881835937499,\n              44.040218713142146\n            ],\n            [\n              -109.57763671875,\n              44.040218713142146\n            ],\n            [\n              -109.57763671875,\n              44.99199795382439\n            ],\n            [\n              -111.03881835937499,\n              44.99199795382439\n            ],\n            [\n              -111.03881835937499,\n              44.040218713142146\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"7","issue":"2","noUsgsAuthors":false,"publicationDate":"2011-08-01","publicationStatus":"PW","scienceBaseUri":"505a103ee4b0c8380cd53bb8","contributors":{"authors":[{"text":"Lutz, J.A.","contributorId":71792,"corporation":false,"usgs":true,"family":"Lutz","given":"J.A.","email":"","affiliations":[],"preferred":false,"id":455019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Key, Carl H. carl_key@usgs.gov","contributorId":4138,"corporation":false,"usgs":true,"family":"Key","given":"Carl","email":"carl_key@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":455020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kolden, C.A.","contributorId":54449,"corporation":false,"usgs":true,"family":"Kolden","given":"C.A.","affiliations":[],"preferred":false,"id":455018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kane, J.T.","contributorId":44779,"corporation":false,"usgs":true,"family":"Kane","given":"J.T.","email":"","affiliations":[],"preferred":false,"id":455017,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Wagtendonk, Jan W. jan_van_wagtendonk@usgs.gov","contributorId":2648,"corporation":false,"usgs":true,"family":"Van Wagtendonk","given":"Jan","email":"jan_van_wagtendonk@usgs.gov","middleInitial":"W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":455021,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70035835,"text":"70035835 - 2011 - Continuity of Landsat observations: Short term considerations","interactions":[],"lastModifiedDate":"2017-04-06T13:48:49","indexId":"70035835","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","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":"Continuity of Landsat observations: Short term considerations","docAbstract":"<p><span>As of writing in mid-2010, both Landsat-5 and -7 continue to function, with sufficient fuel to enable data collection until the launch of the Landsat Data Continuity Mission (LDCM) scheduled for December of 2012. Failure of one or both of Landsat-5 or -7 may result in a lack of Landsat data for a period of time until the 2012 launch. Although the potential risk of a component failure increases the longer the sensor's design life is exceeded, the possible gap in Landsat data acquisition is reduced with each passing day and the risk of Landsat imagery being unavailable diminishes for all except a handful of applications that are particularly data demanding. Advances in Landsat data compositing and fusion are providing opportunities to address issues associated with Landsat-7 SLC-off imagery and to mitigate a potential acquisition gap through the integration of imagery from different sensors. The latter will likely also provide short-term, regional solutions to application-specific needs for the continuity of Landsat-like observations. Our goal in this communication is not to minimize the community's concerns regarding a gap in Landsat observations, but rather to clarify how the current situation has evolved and provide an up-to-date understanding of the circumstances, implications, and mitigation options related to a potential gap in the Landsat data record.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2010.11.002","issn":"00344257","usgsCitation":"Wulder, M.A., White, J.C., Masek, J.G., Dwyer, J.L., and Roy, D.P., 2011, Continuity of Landsat observations: Short term considerations: Remote Sensing of Environment, v. 115, no. 2, p. 747-751, https://doi.org/10.1016/j.rse.2010.11.002.","productDescription":"5 p.","startPage":"747","endPage":"751","numberOfPages":"5","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":244247,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":216383,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2010.11.002"}],"volume":"115","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059fa56e4b0c8380cd4da5f","contributors":{"authors":[{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":452661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Joanne C.","contributorId":63362,"corporation":false,"usgs":true,"family":"White","given":"Joanne","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":452663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":452665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","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":452664,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roy, David P.","contributorId":71083,"corporation":false,"usgs":true,"family":"Roy","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":452662,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70035776,"text":"70035776 - 2011 - Estimation of land surface evapotranspiration with A satellite remote sensing procedure","interactions":[],"lastModifiedDate":"2018-02-21T15:22:24","indexId":"70035776","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1859,"text":"Great Plains Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimation of land surface evapotranspiration with A satellite remote sensing procedure","docAbstract":"<p><span>There are various methods available for estimating magnitude and trends of evapotranspiration. Bowen ratio energy balance system and eddy correlation techniques offer powerful alternatives for measuring land surface evapotranspiration. In spite of the elegance, high accuracy, and theoretical attractions of these techniques for measuring evapotranspiration, their practical use over large areas can be limited due to the number of sites needed and the related expense. Application of evapotranspiration mapping from satellite measurements can overcome the limitations. The objective of this study was to utilize the METRIC</span><sup>TM</sup><span> (Mapping Evapotranspiration at High Resolution using Internalized Calibration) model in Great Plains environmental settings to understand water use in managed ecosystems on a regional scale. We investigated spatiotemporal distribution of a fraction of reference evapotranspiration (ETrF) using eight Landsat 5 images during the 2005 and 2006 growing season for path 29, row 32. The ETrF maps generated by METRIC</span><sup>TM</sup><span> allowed us to follow the magnitude and trend in ETrF for major land-use classes during the growing season. The ETrF was lower early in the growing season for agricultural crops and gradually increased as the normalized difference vegetation index of crops increased, thus presenting more surface area over which water could transpire toward the midseason. Comparison of predictions with Bowen ratio energy balance system measurements at Clay Center, NE, showed that METRIC</span><sup>TM</sup><span> performed well at the field scale for predicting evapotranspiration from a cornfield. If calibrated properly, the model could be a viable tool to estimate water use in managed ecosystems in subhumid climates at a large scale.</span></p>","language":"English","issn":"10525165","usgsCitation":"Irmak, A., Ratcliffe, I., Ranade, P., Hubbard, K., Singh, R.K., Kamble, B., and Kjaersgaard, J., 2011, Estimation of land surface evapotranspiration with A satellite remote sensing procedure: Great Plains Research, v. 21, no. 1, p. 73-88.","productDescription":"16 p.","startPage":"73","endPage":"88","numberOfPages":"16","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":244272,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a0b96e4b0c8380cd527b1","contributors":{"authors":[{"text":"Irmak, A.","contributorId":101473,"corporation":false,"usgs":true,"family":"Irmak","given":"A.","email":"","affiliations":[],"preferred":false,"id":452317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ratcliffe, I.","contributorId":69812,"corporation":false,"usgs":true,"family":"Ratcliffe","given":"I.","email":"","affiliations":[],"preferred":false,"id":452314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ranade, P.","contributorId":34744,"corporation":false,"usgs":true,"family":"Ranade","given":"P.","email":"","affiliations":[],"preferred":false,"id":452312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hubbard, K.G.","contributorId":74224,"corporation":false,"usgs":true,"family":"Hubbard","given":"K.G.","email":"","affiliations":[],"preferred":false,"id":452315,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Ramesh K. 0000-0002-8164-3483","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":85424,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":452316,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kamble, B.","contributorId":30071,"corporation":false,"usgs":true,"family":"Kamble","given":"B.","email":"","affiliations":[],"preferred":false,"id":452311,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kjaersgaard, J.","contributorId":39608,"corporation":false,"usgs":true,"family":"Kjaersgaard","given":"J.","affiliations":[],"preferred":false,"id":452313,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70035119,"text":"70035119 - 2011 - Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data","interactions":[],"lastModifiedDate":"2013-03-18T13:20:59","indexId":"70035119","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1942,"text":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data","docAbstract":"LANDFIRE is a large interagency project designed to provide nationwide spatial data for fire management applications. As part of the effort, many 2000 vintage Landsat Thematic Mapper and Enhanced Thematic Mapper plus data sets were used in conjunction with a large volume of field information to generate detailed vegetation type and structure data sets for the entire United States. In order to keep these data sets current and relevant to resource managers, there was strong need to develop an approach for updating these products. We are using three different approaches for these purposes. These include: 1) updating using Landsat-derived historic and current fire burn information derived from the Monitoring Trends in Burn Severity project; 2) incorporating vegetation disturbance information derived from time series Landsat data analysis using the Vegetation Change Tracker; and 3) developing data products that capture subtle intra-state disturbance such as those related to insects and disease using either Landsat or the Moderate Resolution Imaging Spectroradiometer (MODIS). While no one single approach provides all of the land cover change and update information required, we believe that a combination of all three captures most of the disturbance conditions taking place that have relevance to the fire community.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Institute of Electrical and Electronics Engineers","publisherLocation":"New York, NY","doi":"10.1109/JSTARS.2010.2044478","usgsCitation":"Vogelmann, J., Kost, J.R., Tolk, B., Howard, S.M., Short, K., Chen, X., Huang, C., Pabst, K., and Rollins, M.G., 2011, Monitoring landscape change for LANDFIRE using multi-temporal satellite imagery and ancillary data: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 4, no. 2, p. 252-264, https://doi.org/10.1109/JSTARS.2010.2044478.","startPage":"252","endPage":"264","numberOfPages":"13","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":475055,"rank":10000,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.473.2217","text":"External Repository"},{"id":215417,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/JSTARS.2010.2044478"},{"id":243223,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5dbae4b0c8380cd7056f","contributors":{"authors":[{"text":"Vogelmann, James E. 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":649,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":449369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kost, Jay R. jkost@usgs.gov","contributorId":3931,"corporation":false,"usgs":true,"family":"Kost","given":"Jay","email":"jkost@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":449371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tolk, Brian 0000-0002-9060-0266","orcid":"https://orcid.org/0000-0002-9060-0266","contributorId":62426,"corporation":false,"usgs":true,"family":"Tolk","given":"Brian","affiliations":[],"preferred":false,"id":449377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Stephen M. 0000-0001-5255-5882 smhoward@usgs.gov","orcid":"https://orcid.org/0000-0001-5255-5882","contributorId":3483,"corporation":false,"usgs":true,"family":"Howard","given":"Stephen","email":"smhoward@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":449370,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Short, Karen","contributorId":33940,"corporation":false,"usgs":true,"family":"Short","given":"Karen","affiliations":[],"preferred":false,"id":449375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Xuexia","contributorId":14213,"corporation":false,"usgs":true,"family":"Chen","given":"Xuexia","affiliations":[],"preferred":false,"id":449373,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Chengquan","contributorId":25378,"corporation":false,"usgs":true,"family":"Huang","given":"Chengquan","affiliations":[],"preferred":false,"id":449374,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pabst, Kari","contributorId":12284,"corporation":false,"usgs":true,"family":"Pabst","given":"Kari","email":"","affiliations":[],"preferred":false,"id":449372,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rollins, Matthew G.","contributorId":54695,"corporation":false,"usgs":true,"family":"Rollins","given":"Matthew","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":449376,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70034729,"text":"70034729 - 2011 - A simple and effective method for filling gaps in Landsat ETM+ SLC-off images","interactions":[],"lastModifiedDate":"2018-02-21T15:33:06","indexId":"70034729","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","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":"A simple and effective method for filling gaps in Landsat ETM+ SLC-off images","docAbstract":"The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in 2003, resulting in about 22% of the pixels per scene not being scanned. The SLC failure has seriously limited the scientific applications of ETM+ data. While there have been a number of methods developed to fill in the data gaps, each method has shortcomings, especially for heterogeneous landscapes. Based on the assumption that the same-class neighboring pixels around the un-scanned pixels have similar spectral characteristics, and that these neighboring and un-scanned pixels exhibit similar patterns of spectral differences between dates, we developed a simple and effective method to interpolate the values of the pixels within the gaps. We refer to this method as the Neighborhood Similar Pixel Interpolator (NSPI). Simulated and actual SLC-off ETM+ images were used to assess the performance of the NSPI. Results indicate that NSPI can restore the value of un-scanned pixels very accurately, and that it works especially well in heterogeneous regions. In addition, it can work well even if there is a relatively long time interval or significant spectral changes between the input and target image. The filled images appear reasonably spatially continuous without obvious striping patterns. Supervised classification using the maximum likelihood algorithm was done on both gap-filled simulated SLC-off data and the original \"gap free\" data set, and it was found that classification results, including accuracies, were very comparable. This indicates that gap-filled products generated by NSPI will have relevance to the user community for various land cover applications. In addition, the simple principle and high computational efficiency of NSPI will enable processing large volumes of SLC-off ETM+ data.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.rse.2010.12.010","issn":"00344257","usgsCitation":"Chen, J., Zhu, X., Vogelmann, J., Gao, F., and Jin, S., 2011, A simple and effective method for filling gaps in Landsat ETM+ SLC-off images: Remote Sensing of Environment, v. 115, no. 4, p. 1053-1064, https://doi.org/10.1016/j.rse.2010.12.010.","productDescription":"12 p.","startPage":"1053","endPage":"1064","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":243518,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":215697,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2010.12.010"}],"volume":"115","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059e58ae4b0c8380cd46df1","contributors":{"authors":[{"text":"Chen, Jin","contributorId":202654,"corporation":false,"usgs":false,"family":"Chen","given":"Jin","email":"","affiliations":[],"preferred":false,"id":447245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Xiaolin","contributorId":202655,"corporation":false,"usgs":false,"family":"Zhu","given":"Xiaolin","email":"","affiliations":[],"preferred":false,"id":447242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogelmann, James E. 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":649,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":447241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":447244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":447243,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70034448,"text":"70034448 - 2011 - Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota","interactions":[],"lastModifiedDate":"2018-02-21T10:53:22","indexId":"70034448","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","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":"Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota","docAbstract":"<p><span>Spatiotemporal variations of wetland water in the Prairie Pothole Region are controlled by many factors; two of them are temperature and precipitation that form the basis of the Palmer Drought Severity Index (PDSI). Taking the 196</span><span>&nbsp;</span><span>km</span><sup>2</sup><span><span>&nbsp;</span>Cottonwood Lake area in North Dakota as our pilot study site, we integrated PDSI, Landsat images, and aerial photography records to simulate monthly water surface. First, we developed a new Wetland Water Area Index (WWAI) from PDSI to predict water surface area. Second, we developed a water allocation model to simulate the spatial distribution of water bodies at a resolution of 30</span><span>&nbsp;</span><span>m. Third, we used an additional procedure to model the small wetlands (less than 0.8</span><span>&nbsp;</span><span>ha) that could not be detected by Landsat. Our results showed that i) WWAI was highly correlated with water area with an R</span><sup>2</sup><span><span>&nbsp;</span>of 0.90, resulting in a simple regression prediction of monthly water area to capture the intra- and inter-annual water change from 1910 to 2009; ii) the spatial distribution of water bodies modeled from our approach agreed well with the water locations visually identified from the aerial photography records; and iii) the R</span><sup>2</sup><span><span>&nbsp;</span>between our modeled water bodies (including both large and small wetlands) and those from aerial photography records could be up to 0.83 with a mean average error of 0.64</span><span>&nbsp;</span><span>km</span><sup>2</sup><span><span>&nbsp;</span>within the study area where the modeled wetland water areas ranged from about 2 to 14</span><span>&nbsp;</span><span>km</span><sup>2</sup><span>. These results indicate that our approach holds great potential to simulate major changes in wetland water surface for ecosystem service; however, our products could capture neither the short-term water change caused by intensive rainstorm events nor the wetland change caused by human activities.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2011.08.002","issn":"00344257","usgsCitation":"Huang, S., Dahal, D., Young, C., Chander, G., and Liu, S., 2011, Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota: Remote Sensing of Environment, v. 115, no. 12, p. 3377-3389, https://doi.org/10.1016/j.rse.2011.08.002.","productDescription":"13 p.","startPage":"3377","endPage":"3389","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":216832,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2011.08.002"},{"id":244727,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Cottonwood Lake","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -104.05,45.9351 ], [ -104.05,49.0007 ], [ -96.5545,49.0007 ], [ -96.5545,45.9351 ], [ -104.05,45.9351 ] ] ] } } ] }","volume":"115","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a3c88e4b0c8380cd62dff","contributors":{"authors":[{"text":"Huang, Shengli shuang@usgs.gov","contributorId":1926,"corporation":false,"usgs":true,"family":"Huang","given":"Shengli","email":"shuang@usgs.gov","affiliations":[],"preferred":true,"id":445835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":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":445834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Claudia 0000-0002-0859-7206 claudia.young.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-0859-7206","contributorId":191382,"corporation":false,"usgs":true,"family":"Young","given":"Claudia","email":"claudia.young.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":445836,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":445837,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, Shuguang 0000-0002-6027-3479 sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":445838,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70034420,"text":"70034420 - 2011 - Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project","interactions":[],"lastModifiedDate":"2017-04-06T12:35:54","indexId":"70034420","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3251,"text":"Remote Sensing Letters","active":true,"publicationSubtype":{"id":10}},"title":"Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project","docAbstract":"<p><span>Vegetation Continuous Field (VCF) layers of 30&nbsp;m percent tree cover, bare ground, other vegetation and probability of water were derived for the conterminous United States (CONUS) using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data sets from the Web-Enabled Landsat Data (WELD) project. Turnkey approaches to land cover characterization were enabled due to the systematic WELD Landsat processing, including conversion of digital numbers to calibrated top of atmosphere reflectance and brightness temperature, cloud masking, reprojection into a continental map projection and temporal compositing. Annual, seasonal and monthly WELD composites for 2008 were used as spectral inputs to a bagged regression and classification tree procedure using a large training data set derived from very high spatial resolution imagery and available ancillary data. The results illustrate the ability to perform Landsat land cover characterizations at continental scales that are internally consistent while retaining local spatial and thematic detail.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2010.519002","issn":"2150704X","usgsCitation":"Hansen, M., Egorov, A., Roy, D.P., Potapov, P., Ju, J., Turubanova, S., Kommareddy, I., and Loveland, T., 2011, Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project: Remote Sensing Letters, v. 2, no. 4, p. 279-288, https://doi.org/10.1080/01431161.2010.519002.","productDescription":"10 p.","startPage":"279","endPage":"288","numberOfPages":"10","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":244725,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":216830,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431161.2010.519002"}],"volume":"2","issue":"4","noUsgsAuthors":false,"publicationDate":"2010-11-06","publicationStatus":"PW","scienceBaseUri":"5059fa5ae4b0c8380cd4da7a","contributors":{"authors":[{"text":"Hansen, M.C.","contributorId":69690,"corporation":false,"usgs":false,"family":"Hansen","given":"M.C.","email":"","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false}],"preferred":false,"id":445684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Egorov, Alexey","contributorId":81719,"corporation":false,"usgs":false,"family":"Egorov","given":"Alexey","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":445685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, David P.","contributorId":54761,"corporation":false,"usgs":false,"family":"Roy","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":33433,"text":"University of Maryland, College Park","active":true,"usgs":false},{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false},{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":445682,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Potapov, P.","contributorId":39921,"corporation":false,"usgs":true,"family":"Potapov","given":"P.","email":"","affiliations":[],"preferred":false,"id":445681,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ju, J.","contributorId":85801,"corporation":false,"usgs":false,"family":"Ju","given":"J.","email":"","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":445686,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Turubanova, S.","contributorId":21375,"corporation":false,"usgs":true,"family":"Turubanova","given":"S.","affiliations":[],"preferred":false,"id":445680,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kommareddy, I.","contributorId":65693,"corporation":false,"usgs":true,"family":"Kommareddy","given":"I.","email":"","affiliations":[],"preferred":false,"id":445683,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":106125,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":445687,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
]}