{"pageNumber":"49","pageRowStart":"1200","pageSize":"25","recordCount":1869,"records":[{"id":70024908,"text":"70024908 - 2003 - Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges","interactions":[],"lastModifiedDate":"2017-04-10T10:33:21","indexId":"70024908","displayToPublicDate":"2003-01-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges","docAbstract":"Effective May 5, 2003, Landsat-5 (L5) Thematic Mapper (TM) data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation System (EROS) Data Center (EDC) will be radiometrically calibrated using a new procedure and revised calibration parameters. This change will improve absolute calibration accuracy, consistency over time, and consistency with Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) data. Users will need to use new parameters to convert the calibrated data products to radiance. The new procedure for the reflective bands (1-5,7) is based on a lifetime radiometric calibration curve for the instrument derived from the instrument's internal calibrator, cross-calibration with the ETM+, and vicarious measurements. The thermal band will continue to be calibrated using the internal calibrator. Further updates to improve the relative detector-to-detector calibration and thermal band calibration are being investigated, as is the calibration of the Landsat-4 (L4) TM.","language":"English","publisher":"IEEE","doi":"10.1109/TGRS.2003.818464","issn":"01962892","usgsCitation":"Chander, G., and Markham, B., 2003, Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges: IEEE Transactions on Geoscience and Remote Sensing, v. 41, no. 11, p. 2674-2677, https://doi.org/10.1109/TGRS.2003.818464.","productDescription":"4 p.","startPage":"2674","endPage":"2677","numberOfPages":"4","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":232790,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":207655,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/TGRS.2003.818464"}],"volume":"41","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505aacaae4b0c8380cd86d97","contributors":{"authors":[{"text":"Chander, G.","contributorId":51449,"corporation":false,"usgs":true,"family":"Chander","given":"G.","affiliations":[],"preferred":false,"id":403082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Markham, B.","contributorId":70563,"corporation":false,"usgs":true,"family":"Markham","given":"B.","affiliations":[],"preferred":false,"id":403083,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70024872,"text":"70024872 - 2003 - Data specifications for the Landsat Data Continuity Mission","interactions":[],"lastModifiedDate":"2022-05-06T16:47:25.778607","indexId":"70024872","displayToPublicDate":"2003-01-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Data specifications for the Landsat Data Continuity Mission","docAbstract":"

The National Aeronautics and Space Administration (NASA) plans to procure data from a privately-owned and commercially-operated remote sensing system for the next Landsat Mission, the Landsat Data Continuity Mission (LDCM).Data requirements are documented in an LDCM Data Specification. The specifications require delivery of data covering 250 Landsat scenes on a daily basis. The data are to be acquired in a manner that affords seasonal coverage of the global land mass. Data are required for the heritage reflective Thematic Mapper (TM) spectral bands plus two new bands, a blue band for coastal zone observations and a short wave infrared band for cirrus cloud detection. The specifications do not require thermal data, representing a departure from the TM heritage. The specification also requires data providing a 30 m ground sample distance for each of the spectral bands with the exception of the new cirrus cloud band at 120 m. An absolute uncertainty of 5 percent or less is required for radiometrically corrected LDCM data and the commercial operator is required to deliver data that can be registered to a cartographic projection with an uncertainty of 65 m or less. Procuring data from a commercial operator represents a new approach for the 30-year old Landsat program. The LDCM Data Specification ensures that the procured data provides continuity of the Landsat data stream and advances the mission.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"International Geoscience and Remote Sensing Symposium (IGARSS)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2003 IGARSS: Learning From Earth's Shapes and Colours","conferenceDate":"Jul 21-25, 2003","conferenceLocation":"Toulouse, France","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS.2003.1294100","usgsCitation":"Irons, J.R., Speciale, N., Douglas, M.J., Masek, J.G., Markham, B.L., Storey, J.C., Lencioni, D.E., and Ryan, R.E., 2003, Data specifications for the Landsat Data Continuity Mission, in International Geoscience and Remote Sensing Symposium (IGARSS), v. 2, Toulouse, France, Jul 21-25, 2003, p. 1335-1337, https://doi.org/10.1109/IGARSS.2003.1294100.","productDescription":"3 p.","startPage":"1335","endPage":"1337","numberOfPages":"3","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":232827,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059fd68e4b0c8380cd4e804","contributors":{"authors":[{"text":"Irons, J. R.","contributorId":67694,"corporation":false,"usgs":true,"family":"Irons","given":"J.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":402959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Speciale, N.J.","contributorId":64848,"corporation":false,"usgs":true,"family":"Speciale","given":"N.J.","email":"","affiliations":[],"preferred":false,"id":402958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, McCuistion J.","contributorId":80041,"corporation":false,"usgs":true,"family":"Douglas","given":"McCuistion","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":402960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Masek, J. G.","contributorId":105883,"corporation":false,"usgs":true,"family":"Masek","given":"J.","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":402964,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Markham, B. L.","contributorId":88872,"corporation":false,"usgs":true,"family":"Markham","given":"B.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":402962,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Storey, James C. 0000-0002-6664-7232","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":35505,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":402957,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lencioni, D. E.","contributorId":82893,"corporation":false,"usgs":true,"family":"Lencioni","given":"D.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":402961,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ryan, R. E.","contributorId":98082,"corporation":false,"usgs":true,"family":"Ryan","given":"R.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":402963,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70024871,"text":"70024871 - 2003 - Multi-site evaluation of IKONOS data for classification of tropical coral reef environments","interactions":[],"lastModifiedDate":"2012-03-12T17:20:11","indexId":"70024871","displayToPublicDate":"2003-01-01T00:00:00","publicationYear":"2003","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":"Multi-site evaluation of IKONOS data for classification of tropical coral reef environments","docAbstract":"Ten IKONOS images of different coral reef sites distributed around the world were processed to assess the potential of 4-m resolution multispectral data for coral reef habitat mapping. Complexity of reef environments, established by field observation, ranged from 3 to 15 classes of benthic habitats containing various combinations of sediments, carbonate pavement, seagrass, algae, and corals in different geomorphologic zones (forereef, lagoon, patch reef, reef flats). Processing included corrections for sea surface roughness and bathymetry, unsupervised or supervised classification, and accuracy assessment based on ground-truth data. IKONOS classification results were compared with classified Landsat 7 imagery for simple to moderate complexity of reef habitats (5-11 classes). For both sensors, overall accuracies of the classifications show a general linear trend of decreasing accuracy with increasing habitat complexity. The IKONOS sensor performed better, with a 15-20% improvement in accuracy compared to Landsat. For IKONOS, overall accuracy was 77% for 4-5 classes, 71% for 7-8 classes, 65% in 9-11 classes, and 53% for more than 13 classes. The Landsat classification accuracy was systematically lower, with an average of 56% for 5-10 classes. Within this general trend, inter-site comparisons and specificities demonstrate the benefits of different approaches. Pre-segmentation of the different geomorphologic zones and depth correction provided different advantages in different environments. Our results help guide scientists and managers in applying IKONOS-class data for coral reef mapping applications. ?? 2003 Elsevier Inc. All rights reserved.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1016/j.rse.2003.04.005","issn":"00344257","usgsCitation":"Andrefouet, S., Kramer, P., Torres-Pulliza, D., Joyce, K., Hochberg, E., Garza-Perez, R., Mumby, P., Riegl, B., Yamano, H., White, W.H., Zubia, M., Brock, J.C., Phinn, S., Naseer, A., Hatcher, B., and Muller-Karger, F., 2003, Multi-site evaluation of IKONOS data for classification of tropical coral reef environments: Remote Sensing of Environment, v. 88, no. 1-2, p. 128-143, https://doi.org/10.1016/j.rse.2003.04.005.","startPage":"128","endPage":"143","numberOfPages":"16","costCenters":[],"links":[{"id":232789,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":207654,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2003.04.005"}],"volume":"88","issue":"1-2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5fc7e4b0c8380cd71123","contributors":{"authors":[{"text":"Andrefouet, S.","contributorId":43134,"corporation":false,"usgs":true,"family":"Andrefouet","given":"S.","affiliations":[],"preferred":false,"id":402949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, Philip","contributorId":35911,"corporation":false,"usgs":false,"family":"Kramer","given":"Philip","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":402947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torres-Pulliza, D.","contributorId":87721,"corporation":false,"usgs":true,"family":"Torres-Pulliza","given":"D.","email":"","affiliations":[],"preferred":false,"id":402953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joyce, K.E.","contributorId":19334,"corporation":false,"usgs":true,"family":"Joyce","given":"K.E.","email":"","affiliations":[],"preferred":false,"id":402941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hochberg, E.J.","contributorId":32706,"corporation":false,"usgs":true,"family":"Hochberg","given":"E.J.","email":"","affiliations":[],"preferred":false,"id":402945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Garza-Perez, R.","contributorId":98917,"corporation":false,"usgs":true,"family":"Garza-Perez","given":"R.","email":"","affiliations":[],"preferred":false,"id":402955,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mumby, P.J.","contributorId":70963,"corporation":false,"usgs":true,"family":"Mumby","given":"P.J.","email":"","affiliations":[],"preferred":false,"id":402950,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Riegl, Bernhard","contributorId":20942,"corporation":false,"usgs":true,"family":"Riegl","given":"Bernhard","affiliations":[],"preferred":false,"id":402942,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yamano, H.","contributorId":24135,"corporation":false,"usgs":true,"family":"Yamano","given":"H.","email":"","affiliations":[],"preferred":false,"id":402943,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"White, W. H.","contributorId":24793,"corporation":false,"usgs":true,"family":"White","given":"W.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":402944,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zubia, M.","contributorId":77705,"corporation":false,"usgs":true,"family":"Zubia","given":"M.","email":"","affiliations":[],"preferred":false,"id":402951,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Brock, J. C.","contributorId":36095,"corporation":false,"usgs":true,"family":"Brock","given":"J.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":402948,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Phinn, S.R.","contributorId":97677,"corporation":false,"usgs":true,"family":"Phinn","given":"S.R.","affiliations":[],"preferred":false,"id":402954,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Naseer, A.","contributorId":35509,"corporation":false,"usgs":true,"family":"Naseer","given":"A.","email":"","affiliations":[],"preferred":false,"id":402946,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hatcher, B.G.","contributorId":104252,"corporation":false,"usgs":true,"family":"Hatcher","given":"B.G.","email":"","affiliations":[],"preferred":false,"id":402956,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Muller-Karger, F. 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Seagrasses are critically important components of many marine coastal and estuarine ecosystems, but are declining worldwide. Spatial change in distribution of eelgrass, Zostera marina L., was assessed at Bahía San Quintín, Baja California, Mexico, using a map to map comparison of data interpreted from a 1987 Satellite Pour l'Observation de la Terre multispectral satellite image and a 2000 Landsat Enhanced Thematic Mapping image. Eelgrass comprised 49% and 43% of the areal extent of the bay in 1987 and 2000, respectively. Spatial extent of eelgrass was 13% less (-321 ha) in 2000 than in 1987 with most losses occurring in subtidal areas. Over the 13-yr study period, there was a 34% loss of submerged eelgrass (-457 ha) and a 13% (+136 ha) gain of intertidal eelgrass. Within the two types of intertidal eelgrass, the patchy cover class (<85% cover) expanded (+250 ha) and continuous cover class (≥85% cover) declined (-114 ha). Most eelgrass losses were likely the result of sediment loading and turbidity caused by a single flooding event in winter of 1992-1993. Recent large-scale agricultural development of adjacent uplands may have exacerbated the effects of the flood. Oyster farming was not associated with any detectable losses in eelgrass spatial extent, despite the increase in number of oyster racks from 57 to 484 over the study period.

","language":"English","publisher":"Springer","doi":"10.1007/BF02803661","issn":"01608347","usgsCitation":"Ward, D.H., Morton, A., Tibbitts, T.L., Douglas, D.C., and Carrera-Gonzalez, E., 2003, Long-term change in eelgrass distribution at Bahía San Quintín, Baja California, Mexico, using satellite imagery: Estuaries, v. 26, no. 6, p. 1529-1539, https://doi.org/10.1007/BF02803661.","productDescription":"11 p.","startPage":"1529","endPage":"1539","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":232878,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Baja California","otherGeospatial":"Bahía San Quintín","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.06712341308594,\n 30.34384300675069\n ],\n [\n -115.89065551757811,\n 30.34384300675069\n ],\n [\n -115.89065551757811,\n 30.542156206995088\n ],\n [\n -116.06712341308594,\n 30.542156206995088\n ],\n [\n -116.06712341308594,\n 30.34384300675069\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a4972e4b0c8380cd685f3","contributors":{"authors":[{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":401823,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morton, Alexandra","contributorId":42323,"corporation":false,"usgs":true,"family":"Morton","given":"Alexandra","email":"","affiliations":[],"preferred":false,"id":401822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tibbitts, T. Lee 0000-0002-0290-7592 ltibbitts@usgs.gov","orcid":"https://orcid.org/0000-0002-0290-7592","contributorId":140455,"corporation":false,"usgs":true,"family":"Tibbitts","given":"T.","email":"ltibbitts@usgs.gov","middleInitial":"Lee","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":401821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":401820,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carrera-Gonzalez, Eduardo","contributorId":65638,"corporation":false,"usgs":true,"family":"Carrera-Gonzalez","given":"Eduardo","email":"","affiliations":[],"preferred":false,"id":401824,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":69808,"text":"i2600F - 2003 - Coastal-change and glaciological map of the Bakutis Coast, Antarctica: 1972-2002","interactions":[{"subject":{"id":67577,"text":"i2600F_ED1 - 1997 - Coastal-change and glaciological map of the Bakutis Coast, Antarctica","indexId":"i2600F_ED1","publicationYear":"1997","noYear":false,"title":"Coastal-change and glaciological map of the Bakutis Coast, Antarctica"},"predicate":"SUPERSEDED_BY","object":{"id":69808,"text":"i2600F - 2003 - Coastal-change and glaciological map of the Bakutis Coast, Antarctica: 1972-2002","indexId":"i2600F","publicationYear":"2003","noYear":false,"chapter":"F","title":"Coastal-change and glaciological map of the Bakutis Coast, Antarctica: 1972-2002"},"id":1}],"lastModifiedDate":"2019-11-14T16:13:09","indexId":"i2600F","displayToPublicDate":"1997-11-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":320,"text":"IMAP","code":"I","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2600","chapter":"F","title":"Coastal-change and glaciological map of the Bakutis Coast, Antarctica: 1972-2002","docAbstract":"Changes in the area and volume of the polar ice sheets are intricately linked to changes in global climate, and the resulting changes in sea level may severely impact the densely populated coastal regions on Earth. Loss of the West Antarctic part of the Antarctic ice sheet alone could cause a sea-level rise of approximately 6 m. The potential sea-level rise after melting of the entire Antarctic ice sheet is estimated to be 65 m to 73 m. In spite of its importance, the mass balance (the net volumetric gain or loss) of the Antarctic ice sheet is poorly known; it is not known whether the ice sheet is growing or shrinking. As a result, measurement of changes in the Antarctic ice sheet was given a very high priority in recommendations by the Polar Research Board of the National Research Council (1986), by the Scientific Committee on Antarctic Research (SCAR) (1989), and by the National Science Foundation's (1990) Division of Polar Programs. An archive of early 1970's Landsat 1, 2, and 3 Multispectral Scanner (MSS) images of Antarctica and the fact that the repeat coverage with satellite images provided an excellent means of documenting changes in the coastline of Antarctica provided the impetus for carrying out a comprehensive analysis of the glaciological features of the coastal regions and changes in ice fronts of Antarctica. The project was later modified to include Landsat 4 and 5 MSS and Thematic Mapper (TM) and RADARSAT images to compare changes over a 20- to 25- year time interval and to prepare a series of 24 1:1,000,000-scale and 1 1:5,000,000-scale U.S. Geological Survey Geologic Investigations Series Maps ('I-Maps') (Williams and others, 1995; Williams and Ferrigno, 1998; and Ferrigno and others, 2002) in both paper and digital format.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Coastal-change and glaciological maps of Antarctica","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/i2600F","usgsCitation":"Swithinbank, C., Williams, R., Ferrigno, J.G., Foley, K.M., and Rosanova, C.E., 2003, Coastal-change and glaciological map of the Bakutis Coast, Antarctica: 1972-2002 (2nd Edition; Version 1.0): U.S. Geological Survey IMAP 2600, Report: 10 p.; 1 Plate: 42.70 x 28.94 inches, https://doi.org/10.3133/i2600F.","productDescription":"Report: 10 p.; 1 Plate: 42.70 x 28.94 inches","temporalStart":"1972-01-01","temporalEnd":"2002-12-31","costCenters":[],"links":[{"id":6166,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/imap/2600/F/","linkFileType":{"id":5,"text":"html"}},{"id":115633,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/imap/2600f-ed1/report.pdf","size":"2018","linkFileType":{"id":1,"text":"pdf"}},{"id":115634,"rank":400,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/imap/2600f-ed1/plate-1.pdf","size":"9285","linkFileType":{"id":1,"text":"pdf"}},{"id":189090,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/imap/2600f-ed1/report-thumb.jpg"}],"scale":"1000000","projection":"Polar stereographic, MSL","otherGeospatial":"Antarctica","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.890625,\n -70.95969716686398\n ],\n [\n 156.4453125,\n -70.95969716686398\n ],\n [\n 156.4453125,\n -67.74275906666388\n ],\n [\n -87.890625,\n -67.74275906666388\n ],\n [\n -87.890625,\n -70.95969716686398\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"2nd Edition; Version 1.0","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b24e4b07f02db6aea05","contributors":{"authors":[{"text":"Swithinbank, Charles","contributorId":26368,"corporation":false,"usgs":true,"family":"Swithinbank","given":"Charles","email":"","affiliations":[],"preferred":false,"id":281296,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, Richard S. Jr.","contributorId":90679,"corporation":false,"usgs":true,"family":"Williams","given":"Richard S.","suffix":"Jr.","affiliations":[],"preferred":false,"id":281299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrigno, Jane G. jferrign@usgs.gov","contributorId":39825,"corporation":false,"usgs":true,"family":"Ferrigno","given":"Jane","email":"jferrign@usgs.gov","middleInitial":"G.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":281297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":281295,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosanova, Christine E.","contributorId":77239,"corporation":false,"usgs":true,"family":"Rosanova","given":"Christine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":281298,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":53247,"text":"ofr2003478 - 2003 - Interim Progress Report on the Application of an Independent Components Analysis-based Spectral Unmixing Algorithm to Beowulf Computers","interactions":[],"lastModifiedDate":"2012-04-15T17:28:14","indexId":"ofr2003478","displayToPublicDate":"1994-01-01T00:00:00","publicationYear":"2003","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":"2003-478","title":"Interim Progress Report on the Application of an Independent Components Analysis-based Spectral Unmixing Algorithm to Beowulf Computers","docAbstract":"This report describes work done to implement an independent-components-analysis (ICA) -based blind unmixing algorithm on the Eastern Region Geography (ERG) Beowulf computer cluster. It gives a brief description of blind spectral unmixing using ICA-based techniques and a preliminary example of unmixing results for Landsat-7 Thematic Mapper multispectral imagery using a recently reported1,2,3 unmixing algorithm. Also included are computer performance data. The final phase of this work, the actual implementation of the unmixing algorithm on the Beowulf cluster, was not completed this fiscal year and is addressed elsewhere. It is noted that study of this algorithm and its application to land-cover mapping will continue under another research project in the Land Remote Sensing theme into fiscal year 2004.","language":"ENGLISH","publisher":"Geological Survey (U.S.)","doi":"10.3133/ofr2003478","usgsCitation":"Lemeshewsky, G., 2003, Interim Progress Report on the Application of an Independent Components Analysis-based Spectral Unmixing Algorithm to Beowulf Computers: U.S. Geological Survey Open-File Report 2003-478, 5 p., https://doi.org/10.3133/ofr2003478.","productDescription":"5 p.","costCenters":[{"id":247,"text":"Eastern Region Geography","active":false,"usgs":true}],"links":[{"id":11544,"rank":300,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2003/0478/","linkFileType":{"id":5,"text":"html"}},{"id":176993,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ae0e4b07f02db687fd4","contributors":{"authors":[{"text":"Lemeshewsky, George","contributorId":97134,"corporation":false,"usgs":true,"family":"Lemeshewsky","given":"George","affiliations":[],"preferred":false,"id":247045,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70263764,"text":"70263764 - 2002 - Development of a circa 2000 land cover database for the United States","interactions":[],"lastModifiedDate":"2025-02-21T16:32:22.580936","indexId":"70263764","displayToPublicDate":"2002-12-01T10:31:36","publicationYear":"2002","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Development of a circa 2000 land cover database for the United States","docAbstract":"

Multi-Resolution Land Characterization 2000 (MRLC 2000) is a second-generation federal consortium to create an updated pool of nation-wide Landsat 7 imagery, and derive a second-generation National Land Cover Database (NLCD 2000). This multi-layer, multisource database will include a suite of 30-meter resolution data that will serve as standardized ingredients for the production of land cover – both nationally and locally. This database will also provide the framework to allow flexibility in developing and applying suites of independent data layers. These nationally standardized independent data layers or components, will be useful not only within the land-cover classification but as data themes for other applications. This database will consist of the following components: (1) normalized tasseled cap (TC) transformations of Landsat 7 imagery for three time periods per scene (early, peak and late), (2) ancillary data layers, including 30m DEM derivatives of slope, aspect and elevation and three STATSCO soil derivatives, (4) image shape and texture information, (5) image derivatives of percent imperviousness and percent tree canopy per-pixel, (6) classified land-cover data derived from the Tassel Capped imagery, ancillary data and derivatives, (7) classification rules and metadata from the land cover classification, allowing future users the potential to modify rules to derive land cover products tailored to their specific local applications. In a pilot study application of the database concept, two mapping zones (Utah and Virginia) were selected for full generation of the above data components. Three derivative layers including, per-pixel imperviousness, per-pixel canopy and land cover were classified from the database. Cross validation accuracies for land cover ranged from 65-82%, and mean absolute error values of 10-15% were reported for percent tree canopy and imperviousness.

","conferenceTitle":"ACSM–ASPRS Conference and Technology Exhibition, Annual Conference","conferenceDate":"April 19-26, 2002","conferenceLocation":"Washington D.C.","language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","usgsCitation":"Homer, C.G., Huang, C., Yang, L., and Wylie, B., 2002, Development of a circa 2000 land cover database for the United States, ACSM–ASPRS Conference and Technology Exhibition, Annual Conference, Washington D.C., April 19-26, 2002, 13 p.","productDescription":"13 p.","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":482340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n 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Bruce 0000-0002-7374-1083","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":201929,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":928186,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259448,"text":"70259448 - 2002 - Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping","interactions":[],"lastModifiedDate":"2024-10-08T15:34:21.894862","indexId":"70259448","displayToPublicDate":"2002-12-01T10:18:16","publicationYear":"2002","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NC-230","title":"Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest 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FIA plot data were used to assist in classifying forest land cover from Landsat imagery and relevant ancillary data in two regions of the U.S.: one around the Chesapeake Bay area and the other around Utah. The overall accuracies for the forest/nonforest classification were over 90 percent and about 80 percent, respectively, in the two regions. The accuracies for deciduous/evergreen/mixed and forest type group classifications were around 80 percent and 65 percent, respectively, and were consistent in the two regions. These results suggest that use of FIA plot data together with satellite imagery and relevant ancillary data may substantially improve the efficiency, accuracy, and consistency of large area forest land cover mapping.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Proceedings of the third annual forest inventory and analysis symposium, General Technical Report NC-230","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"conferenceTitle":"Third Annual Forest Inventory and Analysis Sumposium","conferenceDate":"October 17-19, 2001","conferenceLocation":"Traverse City, MI","language":"English","publisher":"USDA Forest Service","usgsCitation":"Huang, C., Yang, L., Homer, C.G., Coan, M., Rykhus, R.P., Zhang, Z., Wylie, B., Hegge, K., Zhu, Z., Lister, A., Hoppus, M., Tymcio, R., DeBlander, L., Cooke, W., McRoberts, R., Wendt, D., and Weyermann, D., 2002, Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping: General Technical Report NC-230, 6 p.","productDescription":"6 p.","startPage":"50","endPage":"55","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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area mapping using satellite imagery and forest inventory data","interactions":[],"lastModifiedDate":"2024-09-16T15:17:17.792099","indexId":"70258388","displayToPublicDate":"2002-12-01T10:13:38","publicationYear":"2002","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data","docAbstract":"

Accurate, current, and cost-effective fire fuel data are required by management and fire science communities for use in reducing wildland fire hazards over large areas. In this paper we present results of applying decision-tree techniques to mapping vegetation parameters (such as vegetation types and canopy structure classification) required for fire fuel characterization. Specifically, we present preliminary results of mapping forest types and average basal area by different forest types at 30-meter resolution. Input data into the decision tree model included Landsat-7 ETM+ spring, summer and fall greenness, brightness and wetness of the tasseled cap transformation, topographic data layers such as slope and elevation, and forest variables measured on inventory plots in the Mid-Atlantic region. Using decision-tree models, eight forest types were successfully identified in training cases and mapped for the entire mapping area. Forest basal area per unit area (conifer and deciduous) was estimated as well using regression tree models. Cross-validation conducted for both forest types and basal area showed that discrete forest type estimation error was 35% and continuous basal area relative errors were between 58 and 72%. Accuracy was higher in homogeneous forested lands and lower in areas with fragmented forest cover. The study demonstrated that decision tree and regression tree methods are efficient for large-area vegetation mapping if sufficient large-amount of reference data are available.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Integrated remote sensing at the global, regional, and local scale","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"ISPRS","usgsCitation":"Xian, G.Z., Zhu, Z., Hoppus, M., and Fleming, M., 2002, Application of decision-tree techniques to forest group and basal area mapping using satellite imagery and forest inventory data, in Integrated remote sensing at the global, regional, and local scale, 8 p.","productDescription":"8 p.","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":434777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":434776,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.isprs.org/proceedings/XXXIV/part1/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":913154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":913155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoppus, Michael","contributorId":344203,"corporation":false,"usgs":false,"family":"Hoppus","given":"Michael","email":"","affiliations":[],"preferred":false,"id":913156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fleming, Michael","contributorId":11687,"corporation":false,"usgs":true,"family":"Fleming","given":"Michael","affiliations":[],"preferred":false,"id":913157,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159718,"text":"70159718 - 2002 - Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.","interactions":[],"lastModifiedDate":"2015-11-18T11:23:40","indexId":"70159718","displayToPublicDate":"2002-01-01T12:30:00","publicationYear":"2002","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1753,"text":"Geocarto International","active":true,"publicationSubtype":{"id":10}},"title":"Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.","docAbstract":"

Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.

","language":"English","publisher":"Taylor and Francis","publisherLocation":"Hong Kong","doi":"10.1080/10106040208542226","usgsCitation":"White, J., Running, S.W., Ryan, K.C., and Key, C.H., 2002, Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.: Geocarto International, v. 17, no. 1, p. 61-68, https://doi.org/10.1080/10106040208542226.","productDescription":"8 p.","startPage":"61","endPage":"68","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":311494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"North fork of the Flathead River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.77691650390625,\n 48.53843177405044\n ],\n [\n -114.77691650390625,\n 48.99824008113872\n ],\n [\n -113.6810302734375,\n 48.99824008113872\n ],\n [\n -113.6810302734375,\n 48.53843177405044\n ],\n [\n -114.77691650390625,\n 48.53843177405044\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564daf4de4b0112df6c62e19","contributors":{"authors":[{"text":"White, Joseph D.","contributorId":56077,"corporation":false,"usgs":true,"family":"White","given":"Joseph D.","affiliations":[],"preferred":false,"id":580175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Running, Steven W. 0000-0001-6906-3841","orcid":"https://orcid.org/0000-0001-6906-3841","contributorId":53258,"corporation":false,"usgs":false,"family":"Running","given":"Steven","email":"","middleInitial":"W.","affiliations":[{"id":7089,"text":"University of Montana, Missoula, MT","active":true,"usgs":false}],"preferred":false,"id":580176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryan, Kevin C.","contributorId":149962,"corporation":false,"usgs":false,"family":"Ryan","given":"Kevin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":580177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":580178,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70025017,"text":"70025017 - 2002 - Operating the EOSDIS at the land processes DAAC managing expectations, requirements, and performance across agencies, missions, instruments, systems, and user communities","interactions":[],"lastModifiedDate":"2022-05-09T11:13:22.901459","indexId":"70025017","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Operating the EOSDIS at the land processes DAAC managing expectations, requirements, and performance across agencies, missions, instruments, systems, and user communities","docAbstract":"NASA developed the Earth Observing System (EOS) during the 1990'S. At the Land Processes Distributed Active Archive Center (LP DAAC), located at the USGS EROS Data Center, the EOS Data and Information System (EOSDIS) is required to support heritage missions as well as Landsat 7, Terra, and Aqua. The original system concept of the early 1990'S changed as each community had its say - first the managers, then engineers, scientists, developers, operators, and then finally the general public. The systems at the LP DAAC - particularly the largest single system, the EOSDIS Core System (ECS) - are changing as experience accumulates, technology changes, and each user group gains influence. The LP DAAC has adapted as contingencies were planned for, requirements and therefore plans were modified, and expectations changed faster than requirements could hope to be satisfied. Although not responsible for Quality Assurance of the science data, the LP DAAC works to ensure the data are accessible and useable by influencing systems, capabilities, and data formats where possible, and providing tools and user support as necessary. While supporting multiple missions and instruments, the LP DAAC also works with and learns from multiple management and oversight groups as they review mission requirements, system capabilities, and the overall operation of the LP DAAC. Stakeholders, including the Land Science community, are consulted regularly to ensure that the LP DAAC remains cognizant and responsive to the evolving needs of the user community. Today, the systems do not look or function as originally planned, but they do work, and they allow customers to search and order of an impressive amount of diverse data.","conferenceTitle":"Earth Observing Systems VII","conferenceDate":"July 7-10, 2002","conferenceLocation":"Seattle, WA","language":"English","publisher":"SPIE","doi":"10.1117/12.451678","issn":"0277786X","usgsCitation":"Kalvelage, T.A., 2002, Operating the EOSDIS at the land processes DAAC managing expectations, requirements, and performance across agencies, missions, instruments, systems, and user communities, Earth Observing Systems VII, v. 4814, Seattle, WA, July 7-10, 2002, p. 380-391, https://doi.org/10.1117/12.451678.","productDescription":"12 p.","startPage":"380","endPage":"391","numberOfPages":"12","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":233223,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4814","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a6e75e4b0c8380cd75662","contributors":{"editors":[{"text":"Barnes W.L.","contributorId":128354,"corporation":true,"usgs":false,"organization":"Barnes W.L.","id":536544,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Kalvelage, T. A.","contributorId":74548,"corporation":false,"usgs":true,"family":"Kalvelage","given":"T.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":403457,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":1013155,"text":"1013155 - 2002 - Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data","interactions":[],"lastModifiedDate":"2018-05-06T12:39:18","indexId":"1013155","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","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":"Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data","docAbstract":"

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms.

","language":"English","publisher":"Elsevier","doi":"10.1016/S0034-4257(01)00333-9","usgsCitation":"Belchansky, G.I., and Douglas, D.C., 2002, Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data: Remote Sensing of Environment, v. 81, no. 1, p. 67-81, https://doi.org/10.1016/S0034-4257(01)00333-9.","productDescription":"15 p.","startPage":"67","endPage":"81","costCenters":[{"id":106,"text":"Alaska Biological Science Center","active":false,"usgs":true}],"links":[{"id":129548,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49e2e4b07f02db5e4ddc","contributors":{"authors":[{"text":"Belchansky, Gennady I.","contributorId":71471,"corporation":false,"usgs":true,"family":"Belchansky","given":"Gennady","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":318525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":318524,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70023929,"text":"70023929 - 2002 - Landsat-7 ETM+ radiometric stability and absolute calibration","interactions":[],"lastModifiedDate":"2022-05-06T16:36:54.304804","indexId":"70023929","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-7 ETM+ radiometric stability and absolute calibration","docAbstract":"Launched in April 1999, the Landsat-7 ETM+ instrument is in its fourth year of operation. The quality of the acquired calibrated imagery continues to be high, especially with respect to its three most important radiometric performance parameters: reflective band instrument stability to better than ??1%, reflective band absolute calibration to better than ??5%, and thermal band absolute calibration to better than ??0.6 K. The ETM+ instrument has been the most stable of any of the Landsat instruments, in both the reflective and thermal channels. To date, the best on-board calibration source for the reflective bands has been the Full Aperture Solar Calibrator, which has indicated changes of at most -1.8% to -2.0% (95% C.I.) change per year in the ETM+ gain (band 4). However, this change is believed to be caused by changes in the solar diffuser panel, as opposed to a change in the instrument's gain. This belief is based partially on ground observations, which bound the changes in gain in band 4 at -0.7% to +1.5%. Also, ETM+ stability is indicated by the monitoring of desert targets. These image-based results for four Saharan and Arabian sites, for a collection of 35 scenes over the three years since launch, bound the gain change at -0.7% to +0.5% in band 4. Thermal calibration from ground observations revealed an offset error of +0.31 W/m 2 sr um soon after launch. This offset was corrected within the U. S. ground processing system at EROS Data Center on 21-Dec-00, and since then, the band 6 on-board calibration has indicated changes of at most +0.02% to +0.04% (95% C.I.) per year. The latest ground observations have detected no remaining offset error with an RMS error of ??0.6 K. The stability and absolute calibration of the Landsat-7 ETM+ sensor make it an ideal candidate to be used as a reference source for radiometric cross-calibrating to other land remote sensing satellite systems.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SPIE - The International Society for Optical Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Sensors, Systems, and Next-Generation Satellites VI","conferenceDate":"Sep 23-26, 2002","conferenceLocation":"Agia Pelagia, Crete, Greece","language":"English","publisher":"SPIE","doi":"10.1117/12.462998","usgsCitation":"Markham, B.L., Barker, J.L., Barsi, J., Kaita, E., Thome, K.J., Helder, D., Palluconi, F.D., Schott, J.R., and Scaramuzza, P., 2002, Landsat-7 ETM+ radiometric stability and absolute calibration, in Proceedings of SPIE - The International Society for Optical Engineering, v. 4881, Agia Pelagia, Crete, Greece, Sep 23-26, 2002, p. 308-318, https://doi.org/10.1117/12.462998.","productDescription":"11 p.","startPage":"308","endPage":"318","numberOfPages":"11","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":231707,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4881","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a43f2e4b0c8380cd66707","contributors":{"editors":[{"text":"Fujisada H.Lurie J.B.Aten M.L.Weber K.","contributorId":128398,"corporation":true,"usgs":false,"organization":"Fujisada H.Lurie J.B.Aten M.L.Weber K.","id":536522,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Markham, B. L.","contributorId":88872,"corporation":false,"usgs":true,"family":"Markham","given":"B.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":399379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barker, J. L.","contributorId":83518,"corporation":false,"usgs":true,"family":"Barker","given":"J.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":399377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barsi, J. A.","contributorId":24085,"corporation":false,"usgs":true,"family":"Barsi","given":"J. A.","affiliations":[],"preferred":false,"id":399373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaita, E.","contributorId":73777,"corporation":false,"usgs":true,"family":"Kaita","given":"E.","email":"","affiliations":[],"preferred":false,"id":399375,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thome, K. J.","contributorId":88099,"corporation":false,"usgs":true,"family":"Thome","given":"K.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":399378,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Helder, D. L. 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":51496,"corporation":false,"usgs":true,"family":"Helder","given":"D. L.","affiliations":[],"preferred":false,"id":399374,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Palluconi, Frank Don","contributorId":14952,"corporation":false,"usgs":true,"family":"Palluconi","given":"Frank","email":"","middleInitial":"Don","affiliations":[],"preferred":false,"id":399371,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schott, J. R.","contributorId":16613,"corporation":false,"usgs":true,"family":"Schott","given":"J.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":399372,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scaramuzza, Pat 0000-0002-2616-8456","orcid":"https://orcid.org/0000-0002-2616-8456","contributorId":80035,"corporation":false,"usgs":true,"family":"Scaramuzza","given":"Pat","affiliations":[],"preferred":false,"id":399376,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70156733,"text":"70156733 - 2002 - Using satellite data in map design and production","interactions":[],"lastModifiedDate":"2017-04-10T10:18:14","indexId":"70156733","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3924,"text":"Bulletin of the Society of Cartographers","active":true,"publicationSubtype":{"id":10}},"title":"Using satellite data in map design and production","docAbstract":"

Satellite image maps have been produced by the U.S. Geological Survey (USGS) since shortly after the launch of the first Landsat satellite in 1972. Over the years, the use of image data to design and produce maps has developed from a manual and photographic process to one that incorporates geographic information systems, desktop publishing, and digital prepress techniques. At the same time, the content of most image-based maps produced by the USGS has shifted from raw image data to land cover or other information layers derived from satellite imagery, often portrayed in combination with shaded relief.

","language":"English","publisher":"Society of Cartographers","usgsCitation":"Hutchinson, J.A., 2002, Using satellite data in map design and production: Bulletin of the Society of Cartographers, v. 36, no. 1, p. 1-9.","productDescription":"9 p.","startPage":"1","endPage":"9","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":308194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg","text":"https://pubs.er.usgs.gov/manager/#cataloging-pane"},{"id":307601,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://soc.org.uk/bulletin/"}],"volume":"36","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55fa92d6e4b05d6c4e501ae8","contributors":{"authors":[{"text":"Hutchinson, John A. 0000-0002-9595-5648 hutch@usgs.gov","orcid":"https://orcid.org/0000-0002-9595-5648","contributorId":4466,"corporation":false,"usgs":true,"family":"Hutchinson","given":"John","email":"hutch@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":570306,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70024817,"text":"70024817 - 2002 - The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments","interactions":[],"lastModifiedDate":"2012-03-12T17:20:10","indexId":"70024817","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments","docAbstract":"The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LM-NRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1007/s00267-001-0048-5","issn":"0364152X","usgsCitation":"Ramsey, E., Nelson, G., Echols, D., and Sapkota, S., 2002, The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments: Environmental Management, v. 29, no. 5, p. 703-715, https://doi.org/10.1007/s00267-001-0048-5.","startPage":"703","endPage":"715","numberOfPages":"13","costCenters":[],"links":[{"id":207745,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s00267-001-0048-5"},{"id":232927,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505ba83fe4b08c986b321ad9","contributors":{"authors":[{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":72769,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah W.","suffix":"III","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":402724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, G.A.","contributorId":17687,"corporation":false,"usgs":true,"family":"Nelson","given":"G.A.","email":"","affiliations":[],"preferred":false,"id":402722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Echols, D.","contributorId":88127,"corporation":false,"usgs":true,"family":"Echols","given":"D.","email":"","affiliations":[],"preferred":false,"id":402725,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sapkota, S.K.","contributorId":24434,"corporation":false,"usgs":true,"family":"Sapkota","given":"S.K.","email":"","affiliations":[],"preferred":false,"id":402723,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70024815,"text":"70024815 - 2002 - Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance","interactions":[],"lastModifiedDate":"2018-02-23T13:34:51","indexId":"70024815","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance","docAbstract":"A new tasselled cap transformation based on Landsat 7 at-satellite reflectance was developed. This transformation is most appropriate for regional applications where atmospheric correction is not feasible. The brightness, greenness and wetness of the derived transformation collectively explained over 97% of the spectral variance of the individual scenes used in this study.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431160110106113","issn":"01431161","usgsCitation":"Huang, C., Wylie, B.K., Yang, L., Homer, C.G., and Zylstra, G., 2002, Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance: International Journal of Remote Sensing, v. 23, no. 8, p. 1741-1748, https://doi.org/10.1080/01431160110106113.","productDescription":"8 p.","startPage":"1741","endPage":"1748","numberOfPages":"8","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":232895,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":207722,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431160110106113"}],"volume":"23","issue":"8","noUsgsAuthors":false,"publicationDate":"2010-11-25","publicationStatus":"PW","scienceBaseUri":"5059fedce4b0c8380cd4ef70","contributors":{"authors":[{"text":"Huang, Chengquan","contributorId":25378,"corporation":false,"usgs":true,"family":"Huang","given":"Chengquan","affiliations":[],"preferred":false,"id":402717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":402716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":402714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":402715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zylstra, G.","contributorId":70564,"corporation":false,"usgs":true,"family":"Zylstra","given":"G.","email":"","affiliations":[],"preferred":false,"id":402718,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70025074,"text":"70025074 - 2002 - Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study","interactions":[],"lastModifiedDate":"2017-04-10T10:12:05","indexId":"70025074","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","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":"Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study","docAbstract":"

Quantification of biophysical parameters is needed by terrestrial process modeling and other applications. A study testing the role of multispectral data for monitoring biophysical parameters was conducted over a network of grassland field sites in the Great Plains of North America. Grassland biophysical parameters [leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fPAR), and biomass] and their relationships with ground radiometer normalized difference vegetation index (NDVI) were established in this study (r2=.66–.85) from data collected across the central and northern Great Plains in 1995. These spectral/biophysical relationships were compared to 1996 field data from the Tallgrass Prairie Preserve in northeastern Oklahoma and showed no consistent biases, with most regression estimates falling within the respective 95% confidence intervals. Biophysical parameters were estimated for 21 “ground pixels” (grids) at the Tallgrass Prairie Preserve in 1996, representing three grazing/burning treatments. Each grid was 30×30 m in size and was systematically sampled with ground radiometer readings. The radiometric measurements were then converted to biophysical parameters and spatially interpolated using geostatistical kriging. Grid-based biophysical parameters were monitored through the growing season and regressed against Landsat Thematic Mapper (TM) NDVI (r2=.92–.94). These regression equations were used to estimate biophysical parameters for grassland TM pixels over the Tallgrass Prairie Preserve in 1996. This method maintained consistent regression development and prediction scales and attempted to minimize scaling problems associated with mixed land cover pixels. A method for scaling Landsat biophysical parameters to coarser resolution satellite data sets (1 km2) was also investigated.

","language":"English","publisher":"Elsevier","doi":"10.1016/S0034-4257(01)00278-4","issn":"00344257","usgsCitation":"Wylie, B., Meyer, D.J., Tieszen, L., and Mannel, S., 2002, Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study: Remote Sensing of Environment, v. 79, no. 2-3, p. 266-278, https://doi.org/10.1016/S0034-4257(01)00278-4.","productDescription":"13 p.","startPage":"266","endPage":"278","numberOfPages":"13","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":235762,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":209384,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/S0034-4257(01)00278-4"}],"volume":"79","issue":"2-3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b86dfe4b08c986b316194","contributors":{"authors":[{"text":"Wylie, B.K. 0000-0002-7374-1083","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":24877,"corporation":false,"usgs":true,"family":"Wylie","given":"B.K.","affiliations":[],"preferred":false,"id":403707,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, D. J.","contributorId":46721,"corporation":false,"usgs":true,"family":"Meyer","given":"D.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":403708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tieszen, L.L.","contributorId":24046,"corporation":false,"usgs":true,"family":"Tieszen","given":"L.L.","email":"","affiliations":[],"preferred":false,"id":403706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mannel, S.","contributorId":65647,"corporation":false,"usgs":true,"family":"Mannel","given":"S.","email":"","affiliations":[],"preferred":false,"id":403709,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70024784,"text":"70024784 - 2002 - Monitoring the recovery of Juncus roemerianus marsh burns with the normalized difference vegetation index and Landsat Thematic Mapper data","interactions":[],"lastModifiedDate":"2012-03-12T17:20:08","indexId":"70024784","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring the recovery of Juncus roemerianus marsh burns with the normalized difference vegetation index and Landsat Thematic Mapper data","docAbstract":"Nine atmospherically corrected Landsat Thematic Mapper images were used to generate mean normalized difference vegetation indices (NDVI) at 11 burn sites throughout a coastal Juncus roemerianus marsh in St. Marks National Wildlife Refuge, Florida. Time-since-burn, the time lapse from the date of burn to the date of image collection, was related to variation in mean NDVI over time. Regression analysis showed that NDVI increased for about 300 to 400 days immediately after the burn, overshooting the typical mean NDVI of a nonburned marsh. For about another 500 to 600 days NDVI decreased until reaching a nearly constant NDVI of about 0.40. During the phase of increasing NDVI the ability to predict time-since-burn was within about ??60 days. Within the decreasing phase this dropped to about ??88 days. Examination of each burn site revealed some nonburn related influences on NDVI (e.g., seasonality). Normalization of burn NDVI by site-specific nonburn control NDVI eliminated most influences. However, differential responses at the site-specific level remained related to either storm impacts or secondary burning. At these sites, collateral data helped clarify the abnormal changes in NDVI. Accounting for these abnormalities, site-specific burn recovery trends could be broadly standardized into four general phases: Phase 1-preburn, Phase 2-initial recovery (increasing NDVI), Phase 3-late recovery (decreasing NDVI), and Phase 4-final coalescence (unchanging NDVI). Phase 2 tended to last about 300 to 500 days, Phase 3 an additional 500 to 600 days, and finally reaching Phase 4, 900 to 1,000 days after burn.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Wetlands Ecology and Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1023/A:1014362616119","issn":"09234861","usgsCitation":"Ramsey, E., Sapkota, S., Barnes, F., and Nelson, G., 2002, Monitoring the recovery of Juncus roemerianus marsh burns with the normalized difference vegetation index and Landsat Thematic Mapper data: Wetlands Ecology and Management, v. 10, no. 1, p. 85-96, https://doi.org/10.1023/A:1014362616119.","startPage":"85","endPage":"96","numberOfPages":"12","costCenters":[],"links":[{"id":233068,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":207831,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1023/A:1014362616119"}],"volume":"10","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5df0e4b0c8380cd706cb","contributors":{"authors":[{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":72769,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah W.","suffix":"III","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":402612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sapkota, S.K.","contributorId":24434,"corporation":false,"usgs":true,"family":"Sapkota","given":"S.K.","email":"","affiliations":[],"preferred":false,"id":402611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnes, F.G.","contributorId":20943,"corporation":false,"usgs":true,"family":"Barnes","given":"F.G.","email":"","affiliations":[],"preferred":false,"id":402610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, G.A.","contributorId":17687,"corporation":false,"usgs":true,"family":"Nelson","given":"G.A.","email":"","affiliations":[],"preferred":false,"id":402609,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70024244,"text":"70024244 - 2002 - Assessing state-wide biodiversity in the Florida Gap analysis project","interactions":[],"lastModifiedDate":"2018-01-12T12:41:29","indexId":"70024244","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Assessing state-wide biodiversity in the Florida Gap analysis project","docAbstract":"The Florida Gap (FI-Gap) project provides an assessment of the degree to which native animal species and natural communities are or are not represented in existing conservation lands. Those species and communities not adequately represented in areas being managed for native species constitute 'gaps' in the existing network of conservation lands. The United States Geological Survey Gap Analysis Program is a national effort and so, eventually, all 50 states will have completed it. The objective of FI-Gap was to provide broad geographic information on the status of terrestrial vertebrates, butterflies, skippers and ants and their respective habitats to address the loss of biological diversity. To model the distributions and potential habitat of all terrestrial species of mammals, breeding birds, reptiles, amphibians, butterflies, skippers and ants in Florida, natural land cover was mapped to the level of dominant or co-dominant plant species. Land cover was classified from Landsat Thematic Mapper (TM) satellite imagery and auxiliary data such as the national wetlands inventory (NWI), soils maps, aerial imagery, existing land use/land cover maps, and on-the-ground surveys, Wildlife distribution models were produced by identifying suitable habitat for each species within that species' range, Mammalian models also assessed a minimum critical area required for sustainability of the species' population. Wildlife species richness was summarized against land stewardship ranked by an area's mandates for conservation protection. ?? 2002 Elsevier Science Ltd. All rights reserved.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Environmental Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1006/jema.2002.0551","issn":"03014797","usgsCitation":"Pearlstine, L., Smith, S.E., Brandt, L., Allen, C.R., Kitchens, W., and Stenberg, J., 2002, Assessing state-wide biodiversity in the Florida Gap analysis project: Journal of Environmental Management, v. 66, no. 2, p. 127-144, https://doi.org/10.1006/jema.2002.0551.","startPage":"127","endPage":"144","numberOfPages":"18","costCenters":[],"links":[{"id":207136,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1006/jema.2002.0551"},{"id":231807,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"66","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ede2e4b0c8380cd49a99","contributors":{"authors":[{"text":"Pearlstine, L.G.","contributorId":56000,"corporation":false,"usgs":true,"family":"Pearlstine","given":"L.G.","email":"","affiliations":[],"preferred":false,"id":400532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, S. E.","contributorId":46120,"corporation":false,"usgs":true,"family":"Smith","given":"S.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":400531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, L.A.","contributorId":67690,"corporation":false,"usgs":true,"family":"Brandt","given":"L.A.","email":"","affiliations":[],"preferred":false,"id":400533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":400535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kitchens, W.M.","contributorId":87647,"corporation":false,"usgs":true,"family":"Kitchens","given":"W.M.","affiliations":[],"preferred":false,"id":400534,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stenberg, J.","contributorId":24127,"corporation":false,"usgs":true,"family":"Stenberg","given":"J.","email":"","affiliations":[],"preferred":false,"id":400530,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70024295,"text":"70024295 - 2002 - A strategy for estimating the rates of recent United States land-cover changes","interactions":[],"lastModifiedDate":"2017-04-10T10:12:59","indexId":"70024295","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A strategy for estimating the rates of recent United States land-cover changes","docAbstract":"Information on the rates of land-use and land-cover change is important in addressing issues ranging from the health of aquatic resources to climate change. Unfortunately, there is a paucity of information on land-use and land-cover change except at very local levels. We describe a strategy for estimating land-cover change across the conterminous United States over the past 30 years. Change rates are estimated for 84 ecoregions using a sampling procedure and five dates of Landsat imagery. We have applied this methodology to six eastern U.S. ecoregions. Results show very high rates of change in the Plains ecoregions, high to moderate rates in the Piedmont ecoregions, and moderate to low rates in the Appalachian ecoregions. This indicates that ecoregions are appropriate strata for capturing unique patterns of land-cover change. The results of the study are being applied as we undertake the mapping of the rest of the conterminous United States.","language":"English","issn":"00991112","usgsCitation":"Loveland, T., Sohl, T.L., Stehman, S., Gallant, A.L., Sayler, K., and Napton, D., 2002, A strategy for estimating the rates of recent United States land-cover changes: Photogrammetric Engineering and Remote Sensing, v. 68, no. 10, p. 1091-1099.","productDescription":"9 p.","startPage":"1091","endPage":"1099","numberOfPages":"9","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":231996,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","issue":"10","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059e5b5e4b0c8380cd46f1f","contributors":{"authors":[{"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":400755,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sohl, Terry L. 0000-0002-9771-4231","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":76419,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":400752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stehman, S.V.","contributorId":91974,"corporation":false,"usgs":false,"family":"Stehman","given":"S.V.","email":"","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":400754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gallant, Alisa L. 0000-0002-3029-6637","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":23508,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":400750,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sayler, K. L. 0000-0003-2514-242X sayler@usgs.gov","orcid":"https://orcid.org/0000-0003-2514-242X","contributorId":88122,"corporation":false,"usgs":true,"family":"Sayler","given":"K. L.","email":"sayler@usgs.gov","affiliations":[],"preferred":false,"id":400753,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Napton, D.E.","contributorId":23720,"corporation":false,"usgs":true,"family":"Napton","given":"D.E.","affiliations":[],"preferred":false,"id":400751,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70024268,"text":"70024268 - 2002 - Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project","interactions":[],"lastModifiedDate":"2012-03-12T17:20:16","indexId":"70024268","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","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":"Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project","docAbstract":"Landsat Thematic Mapper (TM) data were the basis in production of a statewide land cover data set for Wisconsin, undertaken in partnership with U.S. Geological Survey's (USGS) Gap Analysis Program (GAP). The data set contained seven classes comparable to Anderson Level I and 24 classes comparable to Anderson Level II/III. Twelve scenes of dual-date TM data were processed with methods that included principal components analysis, stratification into spectrally consistent units, separate classification of upland, wetland, and urban areas, and a hybrid supervised/unsupervised classification called \"guided clustering.\" The final data had overall accuracies of 94% for Anderson Level I upland classes, 77% for Level II/III upland classes, and 84% for Level II/III wetland classes. Classification accuracies for deciduous and coniferous forest were 95% and 93%, respectively, and forest species' overall accuracies ranged from 70% to 84%. Limited availability of acceptable imagery necessitated use of an early May date in a majority of scene pairs, perhaps contributing to lower accuracy for upland deciduous forest species. The mixed deciduous/coniferous forest class had the lowest accuracy, most likely due to distinctly classifying a purely mixed class. Mixed forest signatures containing oak were often confused with pure oak. Guided clustering was seen as an efficient classification method, especially at the tree species level, although its success relied in part on image dates, accurate ground troth, and some analyst intervention. ?? 2002 Elsevier Science Inc. All rights reserved.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1016/S0034-4257(02)00039-1","issn":"00344257","usgsCitation":"Reese, H., Lillesand, T.M., Nagel, D., Stewart, J., Goldmann, R., Simmons, T., Chipman, J., and Tessar, P., 2002, Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project: Remote Sensing of Environment, v. 82, no. 2-3, p. 224-237, https://doi.org/10.1016/S0034-4257(02)00039-1.","startPage":"224","endPage":"237","numberOfPages":"14","costCenters":[],"links":[{"id":478712,"rank":10000,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pub.epsilon.slu.se/3340/1/Reese_et_al_080630.pdf","text":"External Repository"},{"id":207029,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/S0034-4257(02)00039-1"},{"id":231573,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"2-3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b96f0e4b08c986b31b7c3","contributors":{"authors":[{"text":"Reese, H.M.","contributorId":90498,"corporation":false,"usgs":true,"family":"Reese","given":"H.M.","email":"","affiliations":[],"preferred":false,"id":400639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lillesand, T. M.","contributorId":24126,"corporation":false,"usgs":true,"family":"Lillesand","given":"T.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":400634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nagel, D.E.","contributorId":89303,"corporation":false,"usgs":true,"family":"Nagel","given":"D.E.","email":"","affiliations":[],"preferred":false,"id":400638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, J.S.","contributorId":65890,"corporation":false,"usgs":true,"family":"Stewart","given":"J.S.","email":"","affiliations":[],"preferred":false,"id":400636,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldmann, R.A.","contributorId":13779,"corporation":false,"usgs":true,"family":"Goldmann","given":"R.A.","email":"","affiliations":[],"preferred":false,"id":400633,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Simmons, T.E.","contributorId":9031,"corporation":false,"usgs":true,"family":"Simmons","given":"T.E.","email":"","affiliations":[],"preferred":false,"id":400632,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chipman, J.W.","contributorId":27639,"corporation":false,"usgs":true,"family":"Chipman","given":"J.W.","email":"","affiliations":[],"preferred":false,"id":400635,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tessar, P.A.","contributorId":80032,"corporation":false,"usgs":true,"family":"Tessar","given":"P.A.","affiliations":[],"preferred":false,"id":400637,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70159620,"text":"70159620 - 2002 - Landsat-4/5 Band 6 relative radiometry","interactions":[],"lastModifiedDate":"2015-11-13T10:06:01","indexId":"70159620","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat-4/5 Band 6 relative radiometry","docAbstract":"

Relative radiometric responses for the thematic mapper (TM) band 6 data from Landsat-4 and Landsat-5 were analyzed, and an algorithm has been developed that significantly reduces the striping in Band 6 images due to detector mismatch. The TM internal calibration system as originally designed includes a DC restore circuit, which acts as a feedback system designed to keep detector bias at a constant value. There is a strong indication that the DC restore circuitry implemented in Band 6 does not function as it had been designed to. It operates as designed only during a portion of the calibration interval and not at all during acquisition of scene data. This renders the data acquired during the calibration shutter interval period virtually useless for correction of the individual responses of the four detectors in Band 6. It was observed and statistically quantified that the relative response of each of the detectors to the band average is stable over the dynamic range and throughout the lifetime of the instrument. This allows an alternate approach to relative radiometric correction of TM Band 6 images

","language":"English","publisher":"IEEE","doi":"10.1109/36.981362","usgsCitation":"Chander, G., Helder, D., and Boncyk, W.C., 2002, Landsat-4/5 Band 6 relative radiometry: IEEE Transactions on Geoscience and Remote Sensing, v. 40, no. 1, p. 206-210, https://doi.org/10.1109/36.981362.","productDescription":"5 p.","startPage":"206","endPage":"210","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":311292,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564717cce4b0e2669b31311a","contributors":{"authors":[{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":579745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helder, D. L. 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":51496,"corporation":false,"usgs":true,"family":"Helder","given":"D. L.","affiliations":[],"preferred":false,"id":579746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boncyk, Wayne C.","contributorId":46707,"corporation":false,"usgs":true,"family":"Boncyk","given":"Wayne","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":579747,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70025025,"text":"70025025 - 2002 - Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges","interactions":[],"lastModifiedDate":"2012-03-12T17:20:09","indexId":"70025025","displayToPublicDate":"2002-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges","docAbstract":"Enhanced false color images from mid-IR, near-IR (NIR), and visible bands of the Landsat thematic mapper (TM) are commonly used for visually interpreting land cover type. Described here is a technique for sharpening or fusion of NIR with higher resolution panchromatic (Pan) that uses a shift-invariant implementation of the discrete wavelet transform (SIDWT) and a reported pixel-based selection rule to combine coefficients. There can be contrast reversals (e.g., at soil-vegetation boundaries between NIR and visible band images) and consequently degraded sharpening and edge artifacts. To improve performance for these conditions, I used a local area-based correlation technique originally reported for comparing image-pyramid-derived edges for the adaptive processing of wavelet-derived edge data. Also, using the redundant data of the SIDWT improves edge data generation. There is additional improvement because sharpened subband imagery is used with the edge-correlation process. A reported technique for sharpening three-band spectral imagery used forward and inverse intensity, hue, and saturation transforms and wavelet-based sharpening of intensity. This technique had limitations with opposite contrast data, and in this study sharpening was applied to single-band multispectral-Pan image pairs. Sharpening used simulated 30-m NIR imagery produced by degrading the spatial resolution of a higher resolution reference. Performance, evaluated by comparison between sharpened and reference image, was improved when sharpened subband data were used with the edge correlation.","largerWorkTitle":"Proceedings of SPIE - The International Society for Optical Engineering","conferenceTitle":"Visual Information Processing XI","conferenceDate":"4 April 2002 through 4 April 2002","conferenceLocation":"Orlando, FL","language":"English","doi":"10.1117/12.477580","issn":"0277786X","usgsCitation":"Lemeshewsky, G., 2002, Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges, in Proceedings of SPIE - The International Society for Optical Engineering, v. 4736, Orlando, FL, 4 April 2002 through 4 April 2002, p. 189-200, https://doi.org/10.1117/12.477580.","startPage":"189","endPage":"200","numberOfPages":"12","costCenters":[],"links":[{"id":207996,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1117/12.477580"},{"id":233335,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4736","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a60a2e4b0c8380cd715c5","contributors":{"editors":[{"text":"Rahman, Z.-U.","contributorId":112042,"corporation":false,"usgs":true,"family":"Rahman","given":"Z.-U.","email":"","affiliations":[],"preferred":false,"id":508797,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Schowengerdt, R.A.","contributorId":83707,"corporation":false,"usgs":true,"family":"Schowengerdt","given":"R.A.","affiliations":[],"preferred":false,"id":508796,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Reichenbach, S.E.","contributorId":113015,"corporation":false,"usgs":true,"family":"Reichenbach","given":"S.E.","email":"","affiliations":[],"preferred":false,"id":508798,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Lemeshewsky, G.P.","contributorId":106927,"corporation":false,"usgs":true,"family":"Lemeshewsky","given":"G.P.","affiliations":[],"preferred":false,"id":403477,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":53686,"text":"ofr2002441 - 2002 - Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992","interactions":[],"lastModifiedDate":"2017-04-26T11:26:21","indexId":"ofr2002441","displayToPublicDate":"1994-01-01T00:00:00","publicationYear":"2002","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":"2002-441","title":"Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992","docAbstract":"Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land overlying the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a water-quality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated-land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres).","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr2002441","usgsCitation":"Qi, S.L., Konduris, A., Litke, D.W., and Dupree, J., 2002, Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992 (Version 1.0): U.S. Geological Survey Open-File Report 2002-441, raster digital data set, https://doi.org/10.3133/ofr2002441.","productDescription":"raster digital data set","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":177395,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":340298,"rank":2,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2002/0441/ofr2002441_metadata.html","text":"OFR 2002-441 Metadata","linkFileType":{"id":5,"text":"html"},"description":"OFR 2002-441 Metadata"}],"edition":"Version 1.0","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a69e4b07f02db63be6b","contributors":{"authors":[{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":248079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konduris, Alexandria","contributorId":53459,"corporation":false,"usgs":true,"family":"Konduris","given":"Alexandria","email":"","affiliations":[],"preferred":false,"id":248082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Litke, David W.","contributorId":19145,"corporation":false,"usgs":true,"family":"Litke","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":248080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dupree, Jean","contributorId":43428,"corporation":false,"usgs":true,"family":"Dupree","given":"Jean","affiliations":[],"preferred":false,"id":248081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":44924,"text":"wri024236 - 2002 - Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992","interactions":[],"lastModifiedDate":"2012-02-02T00:10:11","indexId":"wri024236","displayToPublicDate":"1994-01-01T00:00:00","publicationYear":"2002","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2002-4236","title":"Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992","docAbstract":"Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land across the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a waterquality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres). This information was also compared to a similar data set based on 1980 imagery. The 1980 data classified 13.7 million acres as irrigated. Although the change in the amount of irrigated land between the two times was not substantial, the location of the irrigated land did shift from areas where there were large ground-water-level declines to other areas where ground-water levels were static or rising.","language":"ENGLISH","doi":"10.3133/wri024236","usgsCitation":"Qi, S.L., Konduris, A., Litke, D.W., and Dupree, J., 2002, Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992: U.S. Geological Survey Water-Resources Investigations Report 2002-4236, vi, 31 p. : col. ill., col. maps ; 28 cm., https://doi.org/10.3133/wri024236.","productDescription":"vi, 31 p. : col. ill., col. maps ; 28 cm.","costCenters":[],"links":[{"id":3801,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri024236/","linkFileType":{"id":5,"text":"html"}},{"id":162169,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49d6e4b07f02db5de4cd","contributors":{"authors":[{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":230692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konduris, Alexandria","contributorId":53459,"corporation":false,"usgs":true,"family":"Konduris","given":"Alexandria","email":"","affiliations":[],"preferred":false,"id":230695,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Litke, David W.","contributorId":19145,"corporation":false,"usgs":true,"family":"Litke","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":230693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dupree, Jean","contributorId":43428,"corporation":false,"usgs":true,"family":"Dupree","given":"Jean","affiliations":[],"preferred":false,"id":230694,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}