{"pageNumber":"28","pageRowStart":"675","pageSize":"25","recordCount":1869,"records":[{"id":70043210,"text":"ds709Q - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Takhar mineral district in Afghanistan: Chapter Q in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-07T13:45:33","indexId":"ds709Q","displayToPublicDate":"2013-02-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"Q","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Takhar mineral district in Afghanistan: Chapter Q in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Takhar mineral district, which has industrial evaporite deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA, 2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Takhar) and the WGS84 datum. The final image mosaics for the Takhar area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. 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The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the South Helmand mineral district, which has travertine deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA, 2008, 2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (41 for South Helmand) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the South Helmand area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709O","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter O in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. 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The LANDFIRE project produces such data nationwide based on a collection of field observations, Landsat imagery, and other geospatial data. Where field data are not available, alternate strategies are being investigated. In this study, vegetation structure data available from GLAS were used to fill this data gap for the Yukon Flats Ecoregion of interior Alaska. The GLAS-derived structure and fuel layers and the original LANDFIRE layers were subsequently used as inputs into a fire behavior model to determine what effect the revised inputs would have on the model outputs. The outputs showed that inclusion of the GLAS data enabled better landscape-level characterization of\nvegetation structure and therefore enabled a broader wildland fire modeling capability. The results of this work underscore how GLAS data can be incorporated into LANDFIRE canopy structure and fuel mapping.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Photogrammetric Engineering and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society for Photogrammetry","usgsCitation":"Peterson, B.E., Nelson, K., and Wylie, B., 2013, Towards integration of GLAS data into a national fuels mapping program: Photogrammetric Engineering and Remote Sensing, v. 79, no. 2, p. 175-183.","productDescription":"9 p.","startPage":"175","endPage":"183","ipdsId":"IP-038047","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":273015,"type":{"id":11,"text":"Document"},"url":"https://www.conservationgateway.org/ConservationPractices/FireLandscapes/LANDFIRE/Documents/Peterson%20et%20all%20GLAS%20and%20Fuel%20Mapping.pdf"},{"id":273016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats Ecoregion","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -149.55,65.47 ], [ -149.55,67.47 ], [ -142.43,67.47 ], [ -142.43,65.47 ], [ -149.55,65.47 ] ] ] } } ] }","volume":"79","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51a874ece4b082d85d5ed90a","contributors":{"authors":[{"text":"Peterson, Birgit E. 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":3599,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","middleInitial":"E.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":473475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":473476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce 0000-0002-7374-1083","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":107996,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","affiliations":[],"preferred":false,"id":473477,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70043032,"text":"ds709L - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Nalbandon mineral district in Afghanistan: Chapter L in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:11:56","indexId":"ds709L","displayToPublicDate":"2013-01-31T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"L","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Nalbandon mineral district in Afghanistan: Chapter L in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Nalbandon mineral district, which has lead and zinc deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA, 2007, 2008, 2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (41 for Nalbandon) and the WGS84 datum. The final image mosaics were subdivided into ten overlapping tiles or quadrants because of the large size of the target area. The ten image tiles (or quadrants) for the Nalbandon area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Nalbandon study area, two subareas were designated for detailed field investigations (that is, the Nalbandon District and Gharghananaw-Gawmazar subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709L","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter L in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. 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The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Panjsher Valley mineral district, which has emerald and silver-iron deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA, 2009, 2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Panjsher Valley) and the WGS84 datum. The final image mosaics were subdivided into two overlapping tiles or quadrants because of the large size of the target area. The two image tiles (or quadrants) for the Panjsher Valley area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Panjsher Valley study area, two subareas were designated for detailed field investigations (that is, the Emerald and Silver-Iron subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709M","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter M in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. 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The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Katawas mineral district, which has gold deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©AXA, 2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Katawas) and the WGS84 datum. The final image mosaics are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Katawas study area, one subarea was designated for detailed field investigation (that is, the Gold subarea); this subarea was extracted from the area's image mosaic and is provided as a separate embedded geotiff image.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709N","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter N in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. 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The hydrothermally altered rocks mapped in this study include (1) hydrothermal silica-rich rocks (hydrous quartz, chalcedony, opal, and amorphous silica), (2) propylitic rocks (calcite-dolomite and epidote-chlorite mapped as separate mineral groups), (3) argillic rocks (alunite-pyrophyllite-kaolinite), and (4) phyllic rocks (sericite-muscovite). A series of hydrothermal alteration maps, which identify the potential locations of hydrothermal silica-rich, propylitic, argillic, and phyllic rocks on Landsat Thematic Mapper (TM) band 7 orthorectified images, and geographic information systems shape files of hydrothermal alteration units are provided in this study.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131139","usgsCitation":"Mars, J.L., 2013, Hydrothermal alteration maps of the central and southern Basin and Range province of the United States compiled from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (Originally posted November 21, 2013; Revised April 8, 2014): U.S. Geological Survey Open-File Report 2013-1139, Report: iv, 6 p.; 13 Maps: 52.00 x 52.00 inches; Downloads Directory, https://doi.org/10.3133/ofr20131139.","productDescription":"Report: iv, 6 p.; 13 Maps: 52.00 x 52.00 inches; Downloads Directory","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-042460","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":279387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131139.jpg"},{"id":279069,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1139/"},{"id":279370,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate3d.pdf"},{"id":279371,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate3e.pdf"},{"id":279368,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate3b.pdf"},{"id":279369,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate3c.pdf"},{"id":279372,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate3f.pdf"},{"id":279385,"type":{"id":23,"text":"Spatial Data"},"url":"https://mrdata.usgs.gov/surficial-mineralogy/ofr-2013-1139/"},{"id":279360,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate2c.pdf"},{"id":279342,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1139/of2013-1139.pdf"},{"id":279358,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate2a.pdf"},{"id":279356,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate1.pdf"},{"id":279359,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate2b.pdf"},{"id":279367,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate3a.pdf"},{"id":279362,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate2d.pdf"},{"id":279363,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate2e.pdf"},{"id":279365,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2013/1139/plates/of2013-1139_plate2f.pdf"}],"projection":"Universal Transverse Mercator projection, zone 11N","datum":"1927 North American Datum","country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -0.01611111111111111,8.333333333333334E-4 ], [ -0.01611111111111111,0.0011111111111111111 ], [ -0.016666666666666666,0.0011111111111111111 ], [ -0.016666666666666666,8.333333333333334E-4 ], [ -0.01611111111111111,8.333333333333334E-4 ] ] ] } } ] }","edition":"Originally posted November 21, 2013; Revised April 8, 2014","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"528f53ffe4b0660d392bedf5","contributors":{"authors":[{"text":"Mars, John L. jmars@usgs.gov","contributorId":3428,"corporation":false,"usgs":true,"family":"Mars","given":"John","email":"jmars@usgs.gov","middleInitial":"L.","affiliations":[],"preferred":false,"id":486267,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70042686,"text":"tm11C7 - 2013 - Landsat surface reflectance quality assurance extraction (version 1.7)","interactions":[],"lastModifiedDate":"2017-03-29T14:30:20","indexId":"tm11C7","displayToPublicDate":"2013-01-17T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-C7","title":"Landsat surface reflectance quality assurance extraction (version 1.7)","docAbstract":"The U.S. Geological Survey (USGS) Land Remote Sensing Program is developing an operational capability to produce Climate Data Records (CDRs) and Essential Climate Variables (ECVs) from the Landsat Archive to support a wide variety of science and resource management activities from regional to global scale. The USGS Earth Resources Observation and Science (EROS) Center is charged with prototyping systems and software to generate these high-level data products. Various USGS Geographic Science Centers are charged with particular ECV algorithm development and (or) selection as well as the evaluation and application demonstration of various USGS CDRs and ECVs. Because it is a foundation for many other ECVs, the first CDR in development is the Landsat Surface Reflectance Product (LSRP). The LSRP incorporates data quality information in a bit-packed structure that is not readily accessible without postprocessing services performed by the user. This document describes two general methods of LSRP quality-data extraction for use in image processing systems. Helpful hints for the installation and use of software originally developed for manipulation of Hierarchical Data Format (HDF) produced through the National Aeronautics and Space Administration (NASA) Earth Observing System are first provided for users who wish to extract quality data into separate HDF files. Next, steps follow to incorporate these extracted data into an image processing system. Finally, an alternative example is illustrated in which the data are extracted within a particular image processing system.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Geographic Information Systems tools and applications in Book 11 Collection and Delineation of Spatial Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11C7","usgsCitation":"Jones, J.W., Starbuck, M., and Jenkerson, C.B., 2013, Landsat surface reflectance quality assurance extraction (version 1.7): U.S. Geological Survey Techniques and Methods 11-C7, iv, 9 p., https://doi.org/10.3133/tm11C7.","productDescription":"iv, 9 p.","numberOfPages":"15","onlineOnly":"Y","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":265816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm_11_c7.gif"},{"id":265814,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/11/c07/"},{"id":265815,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/c07/pdf/tm11-c7.pdf"}],"country":"United States","publicComments":"This report is Chapter 7 of Section C: Geographic Information Systems tools and applications in Book 11 Collection and Delineation of Spatial Data.","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50f91d6fe4b0727905955f1c","contributors":{"authors":[{"text":"Jones, J. W.","contributorId":89233,"corporation":false,"usgs":true,"family":"Jones","given":"J.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":472063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Starbuck, M.J.","contributorId":86243,"corporation":false,"usgs":true,"family":"Starbuck","given":"M.J.","email":"","affiliations":[],"preferred":false,"id":472062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenkerson, Calli B. 0000-0002-3780-9175","orcid":"https://orcid.org/0000-0002-3780-9175","contributorId":24958,"corporation":false,"usgs":true,"family":"Jenkerson","given":"Calli","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":472061,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042679,"text":"fs20133003 - 2013 - What is the economic value of satellite imagery?","interactions":[],"lastModifiedDate":"2013-01-17T10:50:06","indexId":"fs20133003","displayToPublicDate":"2013-01-17T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-3003","title":"What is the economic value of satellite imagery?","docAbstract":"Does remote-sensing information, such as that from Landsat and similar Earth-observing satellites, provide economic benefits to society, and can this value be estimated? Using satellite data for northeastern Iowa, U.S. Geological Survey scientists modeled the relations among land uses, agricultural production, and dynamic nitrate (NO3-) contamination of aquifers. They demonstrated that information from such modeling can allow more efficient management of agricultural production without sacrificing groundwater quality. Just for northeastern Iowa, the value of such remote-sensing information was shown to be as much as $858 million ± $197 million per year, which corresponds to a current value of $38.1 billion ± $8.8 billion for that flow of benefits into the foreseeable future.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133003","usgsCitation":"Raunikar, R.P., Forney, W.M., and Benjamin, S.P., 2013, What is the economic value of satellite imagery?: U.S. Geological Survey Fact Sheet 2013-3003, 2 p., https://doi.org/10.3133/fs20133003.","productDescription":"2 p.","additionalOnlineFiles":"N","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":265801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2013_3003.gif"},{"id":265799,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3003/"},{"id":265800,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3003/fs2013-3003.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50f91d72e4b0727905955f28","contributors":{"authors":[{"text":"Raunikar, Ronald P.","contributorId":101535,"corporation":false,"usgs":true,"family":"Raunikar","given":"Ronald","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":472042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forney, William M.","contributorId":43490,"corporation":false,"usgs":true,"family":"Forney","given":"William","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":472041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benjamin, Susan P. sbenjamin@usgs.gov","contributorId":354,"corporation":false,"usgs":true,"family":"Benjamin","given":"Susan","email":"sbenjamin@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":472040,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042440,"text":"70042440 - 2013 - Accuracy assessment of NLCD 2006 land cover and impervious surface","interactions":[],"lastModifiedDate":"2013-01-09T10:23:55","indexId":"70042440","displayToPublicDate":"2013-01-09T00:00:00","publicationYear":"2013","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":"Accuracy assessment of NLCD 2006 land cover and impervious surface","docAbstract":"Release of NLCD 2006 provides the first wall-to-wall land-cover change database for the conterminous United States from Landsat Thematic Mapper (TM) data. Accuracy assessment of NLCD 2006 focused on four primary products: 2001 land cover, 2006 land cover, land-cover change between 2001 and 2006, and impervious surface change between 2001 and 2006. The accuracy assessment was conducted by selecting a stratified random sample of pixels with the reference classification interpreted from multi-temporal high resolution digital imagery. The NLCD Level II (16 classes) overall accuracies for the 2001 and 2006 land cover were 79% and 78%, respectively, with Level II user's accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates. Level I (8 classes) accuracies were 85% for NLCD 2001 and 84% for NLCD 2006. The high overall and user's accuracies for the individual dates translated into high user's accuracies for the 2001–2006 change reporting themes water gain and loss, forest loss, urban gain, and the no-change reporting themes for water, urban, forest, and agriculture. The main factor limiting higher accuracies for the change reporting themes appeared to be difficulty in distinguishing the context of grass. We discuss the need for more research on land-cover change accuracy assessment.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.rse.2012.12.001","usgsCitation":"Wickham, J.D., Stehman, S.V., Gass, L., Dewitz, J., Fry, J.A., and Wade, T., 2013, Accuracy assessment of NLCD 2006 land cover and impervious surface: Remote Sensing of Environment, v. 130, p. 294-304, https://doi.org/10.1016/j.rse.2012.12.001.","productDescription":"11 p.","startPage":"294","endPage":"304","ipdsId":"IP-040031","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":265420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":265419,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2012.12.001"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.383333 ], [ -66.95,49.383333 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","volume":"130","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ee916de4b0160a2d0ee327","chorus":{"doi":"10.1016/j.rse.2012.12.001","url":"http://dx.doi.org/10.1016/j.rse.2012.12.001","publisher":"Elsevier BV","authors":"Wickham James D., Stehman Stephen V., Gass Leila, Dewitz Jon, Fry Joyce A., Wade Timothy G.","journalName":"Remote Sensing of Environment","publicationDate":"3/2013"},"contributors":{"authors":[{"text":"Wickham, James D.","contributorId":72278,"corporation":false,"usgs":false,"family":"Wickham","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":471534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stehman, Stephen V.","contributorId":77283,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":471535,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":471531,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dewitz, Jon 0000-0002-0458-212X dewitz@usgs.gov","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":2401,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","email":"dewitz@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":471530,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fry, Joyce A. 0000-0002-8466-9582","orcid":"https://orcid.org/0000-0002-8466-9582","contributorId":69293,"corporation":false,"usgs":true,"family":"Fry","given":"Joyce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471533,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wade, Timothy G.","contributorId":48845,"corporation":false,"usgs":true,"family":"Wade","given":"Timothy G.","affiliations":[],"preferred":false,"id":471532,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70121475,"text":"70121475 - 2013 - Monitoring vegetation response to episodic disturbance events by using multitemporal vegetation indices","interactions":[],"lastModifiedDate":"2019-07-01T11:46:55","indexId":"70121475","displayToPublicDate":"2013-01-01T09:51:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring vegetation response to episodic disturbance events by using multitemporal vegetation indices","docAbstract":"

Normalized Difference Vegetation Index (NDVI) derived from MODerate-resolution Imaging Spectroradiometer (MODIS) satellite imagery and land/water assessments from Landsat Thematic Mapper (TM) imagery were used to quantify the extent and severity of damage and subsequent recovery after Hurricanes Katrina and Rita of 2005 within the vegetation communities of Louisiana's coastal wetlands. Field data on species composition and total live cover were collected from 232 unique plots during multiple time periods to corroborate changes in NDVI values over time. Aprehurricane 5-year baseline time series clearly identified NDVI values by habitat type, suggesting the sensitivity of NDVI to assess and monitor phenological changes in coastal wetland habitats. Monthly data from March 2005 to November 2006 were compared to the baseline average to create a departure from average statistic. Departures suggest that over 33% (4,714 km2) of the prestorm, coastal wetlands experienced a substantial decline in the density and vigor of vegetation by October 2005 (poststorm), mostly in the east and west regions, where landfalls of Hurricanes Katrina and Rita occurred. The percentage of area of persistent vegetation damage due to long-lasting formation of new open water was 91.8% in the east and 81.0% and 29.0% in the central and west regions, respectively. Although below average NDVI values were observed in most marsh communities through November 2006, recovery of vegetation was evident. Results indicated that impacts and recovery from large episodic disturbance events that influence multiple habitat types can be accurately determined using NDVI, especially when integrated with assessments of physical landscape changes and field verifications.

","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/SI63-011.1","usgsCitation":"Steyer, G.D., Couvillion, B.R., and Barras, J., 2013, Monitoring vegetation response to episodic disturbance events by using multitemporal vegetation indices: Journal of Coastal Research, no. 63, p. 118-130, https://doi.org/10.2112/SI63-011.1.","productDescription":"13 p.","startPage":"118","endPage":"130","numberOfPages":"13","ipdsId":"IP-035355","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":292831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.0434,28.9254 ], [ -94.0434,30.5829 ], [ -88.8162,30.5829 ], [ -88.8162,28.9254 ], [ -94.0434,28.9254 ] ] ] } } ] }","issue":"63","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53f85975e4b03f038c5c1872","contributors":{"authors":[{"text":"Steyer, Gregory D. 0000-0001-7231-0110 steyerg@usgs.gov","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":2856,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","email":"steyerg@usgs.gov","middleInitial":"D.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":499102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Couvillion, Brady R. 0000-0001-5323-1687 couvillionb@usgs.gov","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":3829,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","email":"couvillionb@usgs.gov","middleInitial":"R.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":499101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barras, John A. jbarras@usgs.gov","contributorId":2425,"corporation":false,"usgs":true,"family":"Barras","given":"John A.","email":"jbarras@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":499103,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70128318,"text":"70128318 - 2013 - Landsat Data Continuity Mission, now Landsat-8: six months on-orbit","interactions":[],"lastModifiedDate":"2017-04-21T16:07:17","indexId":"70128318","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat Data Continuity Mission, now Landsat-8: six months on-orbit","docAbstract":"

The Landsat Data Continuity Mission (LDCM) with two pushbroom Earth-imaging sensors, the Operational Land Imager (OLI) and the Thermal InfraRed Sensor (TIRS), was launched on February 11, 2013. Its on-orbit check out period or commissioning phase lasted about 90 days. During this phase the spacecraft and its instruments were activated, operationally tested and their performance verified. In addition, during this period, the spacecraft was temporarily placed in an intermediary orbit where it drifted relative to the Landsat-7 spacecraft, providing near simultaneous imaging for about 3 days, allowing data comparison and cross calibration. After this tandem-imaging period, LDCM was raised to its final altitude and placed in the position formerly occupied by Landsat-5, i.e., 8 days out of phase with Landsat-7, with about a 10:10 AM equatorial crossing time. At the end of commissioning, the satellite was transferred to the United States Geological Survey (USGS), officially renamed Landsat-8 and declared operational. Data were made available to the public beginning May 31, 2013. The performance of the satellite and two instruments has generally been excellent as evidenced in the quality of the distributed data products. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SPIE 8866, Earth Observing Systems","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Earth Observing Systems XVIII","conferenceDate":"August 25, 2013","conferenceLocation":"San Diego, CA","language":"English","publisher":"SPIE","doi":"10.1117/12.2025290","usgsCitation":"Markham, B.L., Storey, J.C., and Irons, J.R., 2013, Landsat Data Continuity Mission, now Landsat-8: six months on-orbit, in SPIE 8866, Earth Observing Systems, v. 88661B, San Diego, CA, August 25, 2013, https://doi.org/10.1117/12.2025290.","ipdsId":"IP-050323","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88661B","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a50e4b0c3010a8087dd","contributors":{"authors":[{"text":"Markham, Brian L.","contributorId":90482,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":519713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","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":519711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":519712,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188029,"text":"70188029 - 2013 - Land use and carbon dynamics in the southeastern United States from 1992 to 2050","interactions":[],"lastModifiedDate":"2017-05-31T15:26:34","indexId":"70188029","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Land use and carbon dynamics in the southeastern United States from 1992 to 2050","docAbstract":"

Land use and land cover change (LUCC) plays an important role in determining the spatial distribution, magnitude, and temporal change of terrestrial carbon sources and sinks. However, the impacts of LUCC are not well understood and quantified over large areas. The goal of this study was to quantify the spatial and temporal patterns of carbon dynamics in various terrestrial ecosystems in the southeastern United States from 1992 to 2050 using a process-based modeling system and then to investigate the impacts of LUCC. Spatial LUCC information was reconstructed and projected using the FOREcasting SCEnarios of future land cover (FORE-SCE) model according to information derived from Landsat observations and other sources. Results indicated that urban expansion (from 3.7% in 1992 to 9.2% in 2050) was expected to be the primary driver for other land cover changes in the region, leading to various declines in forest, cropland, and hay/pasture. The region was projected to be a carbon sink of 60.4 gC m−2 yr−1 on average during the study period, primarily due to the legacy impacts of large-scale conversion of cropland to forest that happened since the 1950s. Nevertheless, the regional carbon sequestration rate was expected to decline because of the slowing down of carbon accumulation in aging forests and the decline of forest area.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/8/4/044022","usgsCitation":"Zhao, S., Liu, S., Sohl, T.L., Young, C., and Werner, J.M., 2013, Land use and carbon dynamics in the southeastern United States from 1992 to 2050: Environmental Research Letters, v. 8, p. 1-9, https://doi.org/10.1088/1748-9326/8/4/044022.","productDescription":"Article 044022; 9 p.","startPage":"1","endPage":"9","ipdsId":"IP-051075","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":474044,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/8/4/044022","text":"Publisher Index Page"},{"id":341960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.40576171875,\n 25.06569718553588\n ],\n [\n -75.30029296875,\n 25.06569718553588\n ],\n [\n -75.30029296875,\n 36.58024660149866\n ],\n [\n -93.40576171875,\n 36.58024660149866\n ],\n [\n -93.40576171875,\n 25.06569718553588\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2013-10-30","publicationStatus":"PW","scienceBaseUri":"592fd641e4b0e9bd0ea89713","contributors":{"authors":[{"text":"Zhao, Shuqing","contributorId":9152,"corporation":false,"usgs":true,"family":"Zhao","given":"Shuqing","email":"","affiliations":[],"preferred":false,"id":696832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Shuguang 0000-0002-6027-3479 sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696249,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Claudia 0000-0002-0859-7206 claudia.young.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-0859-7206","contributorId":192026,"corporation":false,"usgs":true,"family":"Young","given":"Claudia","email":"claudia.young.ctr@usgs.gov","affiliations":[],"preferred":false,"id":696834,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Werner, Jeremy M.","contributorId":192558,"corporation":false,"usgs":false,"family":"Werner","given":"Jeremy","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":696835,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187685,"text":"70187685 - 2013 - Multitemporal cross-calibration of the Terra MODIS and Landsat 7 ETM+ reflective solar bands","interactions":[],"lastModifiedDate":"2017-05-15T14:42:57","indexId":"70187685","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","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":"Multitemporal cross-calibration of the Terra MODIS and Landsat 7 ETM+ reflective solar bands","docAbstract":"

In recent years, there has been a significant increase in the use of remotely sensed data to address global issues. With the open data policy, the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Enhanced Thematic Mapper Plus (ETM+) sensors have become a critical component of numerous applications. These two sensors have been operational for more than a decade, providing a rich archive of multispectral imagery for analysis of mutitemporal remote sensing data. This paper focuses on evaluating the radiometric calibration agreement between MODIS and ETM+ using the near-simultaneous and cloud-free image pairs over an African pseudo-invariant calibration site, Libya 4. To account for the combined uncertainties in the top-of-atmosphere (TOA) reflectance due to surface and atmospheric bidirectional reflectance distribution function (BRDF), a semiempirical BRDF model was adopted to normalize the TOA reflectance to the same illumination and viewing geometry. In addition, the spectra from the Earth Observing-1 (EO-1) Hyperion were used to compute spectral corrections between the corresponding MODIS and ETM+ spectral bands. As EO-1 Hyperion scenes were not available for all MODIS and ETM+ data pairs, MODerate resolution atmospheric TRANsmission (MODTRAN) 5.0 simulations were also used to adjust for differences due to the presence or lack of absorption features in some of the bands. A MODIS split-window algorithm provides the atmospheric water vapor column abundance during the overpasses for the MODTRAN simulations. Additionally, the column atmospheric water vapor content during the overpass was retrieved using the MODIS precipitable water vapor product. After performing these adjustments, the radiometric cross-calibration of the two sensors was consistent to within 7%. Some drifts in the response of the bands are evident, with MODIS band 3 being the largest of about 6% over 10 years, a change that will be corrected in Collection 6 MODIS processing.

","language":"English","publisher":"IEEE","doi":"10.1109/TGRS.2012.2235448","usgsCitation":"Angal, A., Xiong, X., Wu, A., Chander, G., and Choi, T., 2013, Multitemporal cross-calibration of the Terra MODIS and Landsat 7 ETM+ reflective solar bands: IEEE Transactions on Geoscience and Remote Sensing, v. 51, no. 4, p. 1870-1882, https://doi.org/10.1109/TGRS.2012.2235448.","productDescription":"13 p.","startPage":"1870","endPage":"1882","ipdsId":"IP-043734","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":474036,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2060/20140013366","text":"External Repository"},{"id":341240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5916c9b5e4b044b359e486a0","contributors":{"authors":[{"text":"Angal, Amit","contributorId":67394,"corporation":false,"usgs":true,"family":"Angal","given":"Amit","email":"","affiliations":[],"preferred":false,"id":695066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xiong, Xiaoxiong","contributorId":15088,"corporation":false,"usgs":true,"family":"Xiong","given":"Xiaoxiong","email":"","affiliations":[],"preferred":false,"id":695067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wu, Aisheng","contributorId":65362,"corporation":false,"usgs":true,"family":"Wu","given":"Aisheng","email":"","affiliations":[],"preferred":false,"id":695068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":695065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Choi, Taeyoung","contributorId":146955,"corporation":false,"usgs":false,"family":"Choi","given":"Taeyoung","email":"","affiliations":[],"preferred":false,"id":695069,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70173624,"text":"70173624 - 2013 - Lakes without Landsat? Implications of scale and an alternative approach to regional remote lake monitoring using MODIS 250 m imagery","interactions":[],"lastModifiedDate":"2021-04-02T16:42:48.793655","indexId":"70173624","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Lakes without Landsat? Implications of scale and an alternative approach to regional remote lake monitoring using MODIS 250 m imagery","docAbstract":"

We evaluated use of MODIS 250 m imagery for remote lake monitoring in Maine. Despite limited spectral resolution (visible red and near infrared bands), the twice daily image capture has a potential advantage over conventionally used, often cloudy Landsat imagery (16 day interval) when short time windows are of interest. We analyzed 364 eligible (≥100 ha) Maine lakes during late summer (Aug–early Sep) 2000–2011. The red band was strongly correlated with natural log-transformed Secchi depth (SD), and the addition of ancillary lake and watershed variables explained some variability in ln(SD) (R2 = 0.68–0.85; 9 models). Weak spectral resolution and variable lake conditions limited accurate lake monitoring to relatively productive periods in late summer, as indicated by inconsistent, sometimes weak regressions during June and July when lakes were clearer and less stable (R2 = 0.19–0.74; 8 models). Additionally, SD estimates derived from 2 sets of concurrent MODIS and Landsat imagery generally did not agree unless Landsat imagery (30 m) was resampled to 250 m, likely owing to various factors related to scale. Average MODIS estimates exceeded those of Landsat by 0.35 and 0.49 m on the 2 dates. Overall, MODIS 250 m imagery are potentially useful for remote lake monitoring during productive periods when Landsat data are unavailable; however, analyses must occur when algal communities are stable and well-developed, are biased toward large lakes, may overestimate SD, and accuracy may be unreliable without non-spectral lake predictors.

","language":"English","publisher":"Taylor & Francis","doi":"10.1080/10402381.2013.778926","usgsCitation":"Ian M. McCullough, Loftin, C., and Sader, S.A., 2013, Lakes without Landsat? 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McCullough","affiliations":[{"id":25572,"text":"University of Maine, Orono","active":true,"usgs":false}],"preferred":false,"id":637414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":637413,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sader, Steven A.","contributorId":171436,"corporation":false,"usgs":false,"family":"Sader","given":"Steven","email":"","middleInitial":"A.","affiliations":[{"id":25572,"text":"University of Maine, Orono","active":true,"usgs":false}],"preferred":false,"id":637415,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188071,"text":"70188071 - 2013 - Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data","interactions":[],"lastModifiedDate":"2017-05-30T12:53:54","indexId":"70188071","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","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":"Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data","docAbstract":"

This study provides a baseline quality check on provisional Landsat Surface Reflectance (SR) products as generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software. Characterization of the Landsat SR products leveraged comparisons between aerosol optical thickness derived from LEDAPS and measured by Aerosol Robotic Network (AERONET), as well as reflectance correlations with field spectrometer and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Results consistently indicated similarity between LEDAPS and alternative data products in longer wavelengths over vegetated areas with no adjacent water, while less reliable performance was observed in shorter wavelengths and sparsely vegetated areas. This study demonstrates the strengths and weaknesses of the atmospheric correction methodology used in LEDAPS, confirming its successful implementation to generate Landsat SR products.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.04.007","usgsCitation":"Maiersperger, T., Scaramuzza, P., Leigh, L., Shrestha, S., Gallo, K., Jenkerson, C.B., and Dwyer, J.L., 2013, Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data: Remote Sensing of Environment, v. 136, p. 1-13, https://doi.org/10.1016/j.rse.2013.04.007.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-039343","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"136","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84c9e4b092b266f10dcd","contributors":{"authors":[{"text":"Maiersperger, Tom 0000-0003-3132-6997 tmaiersperger@usgs.gov","orcid":"https://orcid.org/0000-0003-3132-6997","contributorId":3693,"corporation":false,"usgs":true,"family":"Maiersperger","given":"Tom","email":"tmaiersperger@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scaramuzza, Pat 0000-0002-2616-8456 pscar@usgs.gov","orcid":"https://orcid.org/0000-0002-2616-8456","contributorId":3970,"corporation":false,"usgs":true,"family":"Scaramuzza","given":"Pat","email":"pscar@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leigh, Larry","contributorId":192383,"corporation":false,"usgs":false,"family":"Leigh","given":"Larry","email":"","affiliations":[],"preferred":false,"id":696394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shrestha, S.","contributorId":182437,"corporation":false,"usgs":false,"family":"Shrestha","given":"S.","email":"","affiliations":[],"preferred":false,"id":696395,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallo, Kevin 0000-0001-9162-5011 kgallo@usgs.gov","orcid":"https://orcid.org/0000-0001-9162-5011","contributorId":192334,"corporation":false,"usgs":true,"family":"Gallo","given":"Kevin","email":"kgallo@usgs.gov","affiliations":[],"preferred":true,"id":696391,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jenkerson, Calli B. 0000-0002-3780-9175 jenkerson@usgs.gov","orcid":"https://orcid.org/0000-0002-3780-9175","contributorId":469,"corporation":false,"usgs":true,"family":"Jenkerson","given":"Calli","email":"jenkerson@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696390,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696389,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70187701,"text":"70187701 - 2013 - Eighth Landsat satellite becomes operational","interactions":[],"lastModifiedDate":"2017-05-15T14:34:24","indexId":"70187701","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2013","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":"Eighth Landsat satellite becomes operational","docAbstract":"

No abstract available.

","language":"English","publisher":"ASPRS","usgsCitation":"Irons, J.R., and Loveland, T.R., 2013, Eighth Landsat satellite becomes operational: Photogrammetric Engineering and Remote Sensing, v. 79, no. 5, p. 398-401.","productDescription":"4 p.","startPage":"398","endPage":"401","ipdsId":"IP-045042","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"79","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591abe3ae4b0a7fdb43c8c07","contributors":{"authors":[{"text":"Irons, J. R.","contributorId":67694,"corporation":false,"usgs":true,"family":"Irons","given":"J.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":695193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":695194,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70040323,"text":"70040323 - 2012 - Landsat Update: Volume 6 Issue 4","interactions":[],"lastModifiedDate":"2018-03-27T13:29:31","indexId":"70040323","displayToPublicDate":"2015-07-06T09:15:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5658,"text":"Landsat Update","active":true,"publicationSubtype":{"id":10}},"title":"Landsat Update: Volume 6 Issue 4","docAbstract":"

No abstract available.

","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"U.S. Geological Survey, 2012, Landsat Update: Volume 6 Issue 4: Landsat Update, v. 6, no. 4, 6 p.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-041157","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":310902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":310901,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://landsat.usgs.gov/about_LU_Vol_6_Issue_4.php"}],"volume":"6","issue":"4","tableOfContents":"

News
LDCM News
Landsat Stories
Upcoming Meetings
Tips and Tricks – 'Clear List' feature released in GloVis
EROS Authors in Recent Publications
Landsat Image of Interest – Mississippi River Level Dropping

","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56389758e4b0d6133fe72fc9"} ,{"id":70041259,"text":"70041259 - 2012 - Landsat Update: Volume 6 Issue 3","interactions":[],"lastModifiedDate":"2017-01-18T13:32:39","indexId":"70041259","displayToPublicDate":"2015-06-15T09:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"seriesTitle":{"id":5124,"text":"Landsat Update","active":true,"publicationSubtype":{"id":6}},"title":"Landsat Update: Volume 6 Issue 3","docAbstract":"

No abstract available.

","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/70041259","usgsCitation":"U.S. Geological Survey, 2012, Landsat Update: Volume 6 Issue 3: Landsat Update, v. 6, no. 3, 4 p., https://doi.org/10.3133/70041259.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-039455","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":310772,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":310771,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://landsat.usgs.gov/documents/about_LU_Vol_6_Issue_3.pdf"}],"volume":"6","issue":"3","tableOfContents":"

Landsat 40th Anniversary
The LandsatLook Viewer Now Available
Landsat Stories – The Uses and Benefits of Landsat Data
Upcoming Meetings
Tips and Tricks – Sample LDCM Data Available
EROS Authors in Recent Publications
Landsat Image of Interest – Western Wildfires

","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5633433de4b048076347eecf"} ,{"id":70159523,"text":"ofr20131280Q1 - 2012 - Permissive tracts for incompatible element deposits hosted in pegmatities, alkaline rocks, and carbonatities in Mauritania (phase V, deliverable 86), Chapter Q1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)","interactions":[{"subject":{"id":70159523,"text":"ofr20131280Q1 - 2012 - Permissive tracts for incompatible element deposits hosted in pegmatities, alkaline rocks, and carbonatities in Mauritania (phase V, deliverable 86), Chapter Q1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)","indexId":"ofr20131280Q1","publicationYear":"2012","noYear":false,"chapter":"Q1","title":"Permissive tracts for incompatible element deposits hosted in pegmatities, alkaline rocks, and carbonatities in Mauritania (phase V, deliverable 86), Chapter Q1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":1}],"isPartOf":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"lastModifiedDate":"2016-03-21T15:52:36","indexId":"ofr20131280Q1","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2012","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":"2013-1280","chapter":"Q1","title":"Permissive tracts for incompatible element deposits hosted in pegmatities, alkaline rocks, and carbonatities in Mauritania (phase V, deliverable 86), Chapter Q1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)","docAbstract":"

In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.

\n

To achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.

\n

This report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II) (Open File Report 2013-1280)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131280Q1","collaboration":"Prepared in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania","usgsCitation":"Taylor, C.D., and Horton, J.D., 2012, Permissive tracts for incompatible element deposits hosted in pegmatities, alkaline rocks, and carbonatities in Mauritania (phase V, deliverable 86), Chapter Q1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II): U.S. Geological Survey Open-File Report 2013-1280, Plate: 54.0 x 60.0 inches; Data; Metadata, https://doi.org/10.3133/ofr20131280Q1.","productDescription":"Plate: 54.0 x 60.0 inches; Data; Metadata","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056916","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":319135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131280Q1.PNG"},{"id":319134,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1280/GIS_and_Maps/Chapter_Q1_deliverable_86-Incompatible_element_deposits_hosted_in_pegmatities_alkaline%20rocks_and_carbonatities/","text":"Map, Data, and Metadata"},{"id":318737,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/publication/ofr20131280"}],"country":"Mauritania","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-12.17075,14.61683],[-12.83066,15.30369],[-13.43574,16.03938],[-14.09952,16.3043],[-14.57735,16.59826],[-15.13574,16.58728],[-15.62367,16.36934],[-16.12069,16.45566],[-16.4631,16.13504],[-16.54971,16.67389],[-16.27055,17.16696],[-16.14635,18.10848],[-16.25688,19.09672],[-16.37765,19.59382],[-16.27784,20.09252],[-16.53632,20.56787],[-17.06342,20.99975],[-16.84519,21.33332],[-12.9291,21.32707],[-13.11875,22.77122],[-12.87422,23.28483],[-11.93722,23.37459],[-11.96942,25.93335],[-8.68729,25.88106],[-8.6844,27.39574],[-4.92334,24.97457],[-6.45379,24.95659],[-5.97113,20.64083],[-5.48852,16.3251],[-5.31528,16.20185],[-5.53774,15.50169],[-9.55024,15.4865],[-9.70026,15.26411],[-10.08685,15.33049],[-10.65079,15.13275],[-11.3491,15.41126],[-11.66608,15.38821],[-11.83421,14.7991],[-12.17075,14.61683]]]},\"properties\":{\"name\":\"Mauritania\"}}]}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f11b65e4b0f59b85ddc4a0","contributors":{"authors":[{"text":"Taylor, Cliff D. 0000-0001-6376-6298 ctaylor@usgs.gov","orcid":"https://orcid.org/0000-0001-6376-6298","contributorId":1283,"corporation":false,"usgs":true,"family":"Taylor","given":"Cliff","email":"ctaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622260,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horton, John D. 0000-0003-2969-9073 jhorton@usgs.gov","orcid":"https://orcid.org/0000-0003-2969-9073","contributorId":1227,"corporation":false,"usgs":true,"family":"Horton","given":"John","email":"jhorton@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622261,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159518,"text":"ofr20131280G1 - 2012 - Permissive tracts for nickel, copper, platinum group elements (PGE), and chromium deposits of Mauritania (phase V, deliverable 66): Chapter G1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)","interactions":[{"subject":{"id":70159518,"text":"ofr20131280G1 - 2012 - Permissive tracts for nickel, copper, platinum group elements (PGE), and chromium deposits of Mauritania (phase V, deliverable 66): Chapter G1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)","indexId":"ofr20131280G1","publicationYear":"2012","noYear":false,"chapter":"G1","title":"Permissive tracts for nickel, copper, platinum group elements (PGE), and chromium deposits of Mauritania (phase V, deliverable 66): Chapter G1 in Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 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In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.

\n

To achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.

\n

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In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.

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To achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.

\n

This report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.

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In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.

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To achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.

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In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.

\n

To achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.

\n

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In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.

\n

To achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.

\n

This report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.

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