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USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghunday-Achin mineral district in Afghanistan, in Davis, P.A, compiler, Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

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 Ghunday-Achin mineral district, which has magnesite and talc 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,2008,2009), 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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was 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 (42 for Ghunday-Achin) and the WGS84 datum. The final image mosaics were subdivided into six overlapping tiles or quadrants because of the large size of the target area. The six image tiles (or quadrants) for the Ghunday-Achin 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 Ghunday-Achin study area, two subareas were designated for detailed field investigations (that is, the Achin-Magnesite and Ghunday-Mamahel subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.

Data Series↗
USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Nuristan mineral district in Afghanistan

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 Nuristan mineral district, which has gem, lithium, and cesium 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,2008,2009), 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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was 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. All available panchromatic images for this area had significant cloud and snow cover that precluded their use for resolution enhancement of the multispectral image data. Each of the four-band images within the 10-m 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 (42 for Nuristan) and the WGS84 datum. The final image mosaics for the Nuristan 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.

Data Series↗
USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ahankashan mineral district in Afghanistan

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 Ahankashan mineral district, which has copper and 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 (©JAXA,2007,2008, 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 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 Ahankashan) and the WGS84 datum. The final image mosaics were subdivided into five overlapping tiles or quadrants because of the large size of the target area. The five image tiles (or quadrants) for the Ahankashan 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.

Ahankashan Mineral District↗
USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan

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 Kandahar mineral district, which has bauxite 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 ((c)JAXA,2006,2007,2008), 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 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 (42 for Kandahar) 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 Kandahar 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 Kandahar study area, two subareas were designated for detailed field investigations (that is, the Obatu-Shela and Sekhab-Zamto Kalay subareas); these subareas were extracted from the area's image mosaic and are provided as separate embedded geotiff images.

Kandahar Mineral District↗
USGS2013

Natural-color and color-infrared image mosaics of the Colorado River corridor in Arizona derived from the May 2009 airborne image collection

The Grand Canyon Monitoring and Research Center (GCMRC) of the U.S. Geological Survey (USGS) periodically collects airborne image data for the Colorado River corridor within Arizona (fig. 1) to allow scientists to study the impacts of Glen Canyon Dam water release on the corridor’s natural and cultural resources. These data are collected from just above Glen Canyon Dam (in Lake Powell) down to the entrance of Lake Mead, for a total distance of 450 kilometers (km) and within a 500-meter (m) swath centered on the river’s mainstem and its seven main tributaries (fig. 1). The most recent airborne data collection in 2009 acquired image data in four wavelength bands (blue, green, red, and near infrared) at a spatial resolution of 20 centimeters (cm). The image collection used the latest model of the Leica ADS40 airborne digital sensor (the SH52), which uses a single optic for all four bands and collects and stores band radiance in 12-bits. Davis (2012) reported on the performance of the SH52 sensor and on the processing steps required to produce the nearly flawless four-band image mosaic (sectioned into map tiles) for the river corridor. The final image mosaic has a total of only 3 km of surface defects in addition to some areas of cloud shadow because of persistent inclement weather during data collection. The 2009 four-band image mosaic is perhaps the best image dataset that exists for the entire Arizona part of the Colorado River. Some analyses of these image mosaics do not require the full 12-bit dynamic range or all four bands of the calibrated image database, in which atmospheric scattering (or haze) had not been removed from the four bands. To provide scientists and the general public with image products that are more useful for visual interpretation, the 12-bit image data were converted to 8-bit natural-color and color-infrared images, which also removed atmospheric scattering within each wavelength-band image. The conversion required an evaluation of the histograms of each band’s digital-number population within each map tile throughout the corridor and the determination of the digital numbers corresponding to the lower and upper one percent of the picture-element population within each map tile. Visual examination of the image tiles that were given a 1-percent stretch (whereby the lower 1- percent 12-bit digital number is assigned an 8-bit value of zero and the upper 1-percent 12-bit digital number is assigned an 8-bit value of 255) indicated that this stretch sufficiently removed atmospheric scattering, which provided improved image clarity and true natural colors for all surface materials. The lower and upper 1-percent, 12-bit digital numbers for each wavelength-band image in the image tiles exhibit erratic variations along the river corridor; the variations exhibited similar trends in both the lower and upper 1-percent digital numbers for all four wavelength-band images (figs. 2–5). The erratic variations are attributed to (1) daily variations in atmospheric water-vapor content due to monsoonal storms, (2) variations in channel water color due to variable sediment input from tributaries, and (3) variations in the amount of topographic shadows within each image tile, in which reflectance is dominated by atmospheric scattering. To make the surface colors of the stretched, 8-bit images consistent among adjacent image tiles, it was necessary to average both the lower and upper 1-percent digital values for each wavelength-band image over 20 river miles to subdue the erratic variations. The average lower and upper 1-percent digital numbers for each image tile (figs. 2–5) were used to convert the 12-bit image values to 8-bit values and the resulting 8-bit four-band images were stored as natural-color (red, green, and blue wavelength bands) and color-infrared (near-infrared, red, and green wavelength bands) images in embedded geotiff format, 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. All image data are projected in the State Plane (SP) map projection using the central Arizona zone (202) and the North American Datum of 1983 (NAD83). The map-tile scheme used to segment the corridor image mosaic followed the standard USGS quarter-quadrangle (QQ) map borders, but the high resolution (20 cm) of the images required further quarter segmentation (QQQ) of the standard QQ tiles, where the image mosaic covered a large fraction of a QQ map tile (segmentation shown in (figure 6), where QQ_1 to QQ_4 shows the number convention used to designate a quarter of a QQ tile). To minimize the size of each image tile, each image or map tile was subset to only include that part of the tile that had image data. In addition, some QQQ image tiles within a QQ tile were combined when adjacent QQQ map tiles were small. Thus, some image tiles consist of combinations of QQQ map tiles, some consist of an entire QQ map tile, and some consist of two adjoining QQ map tiles. The final image tiles number 143, which is a large number of files to list on the Internet for both the natural-color and color-infrared images. Thus, the image tiles were placed in seven file folders based on the one-half-degree geographic boundaries within the study area (fig. 7). The map tiles in each file folder were compressed to minimize folder size for more efficient downloading. The file folders are sequentially referred to as zone 1 through zone 7, proceeding down river (fig. 7). The QQ designations of the image tiles contained within each folder or zone are shown on the index map for each respective zone (figs. 8–14).

Arizona↗
USGS2013

Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Parwan mineral district in Afghanistan: Chapter CC in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan

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 Parwan mineral district, which has gold and copper 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,2006, 2007), 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. 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 (42 for Parwan) 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 North Bamyan 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.

Parwan Mineral District↗
USGS2008

Geolocation of man-made reservoirs across terrains of varying complexity using GIS

The Reservoir Sedimentation Survey Information System (RESIS) is one of the world's most comprehensive databases of reservoir sedimentation rates, comprising nearly 6000 surveys for 1819 reservoirs across the continental United States. Sediment surveys in the database date from 1904 to 1999, though more than 95% of surveys were entered prior to 1980, making RESIS largely a historical database. The use of this database for large-scale studies has been limited by the lack of precise coordinates for the reservoirs. Many of the reservoirs are relatively small structures and do not appear on current USGS topographic maps. Others have been renamed or have only approximate (i.e. township and range) coordinates. This paper presents a method scripted in ESRI's ARC Macro Language (AML) to locate the reservoirs on digital elevation models using information available in RESIS. The script also delineates the contributing watersheds and compiles several hydrologically important parameters for each reservoir. Evaluation of the method indicates that, for watersheds larger than 5 km2, the correct outlet is identified over 80% of the time. The importance of identifying the watershed outlet correctly depends on the application. Our intent is to collect spatial data for watersheds across the continental United States and describe the land use, soils, and topography for each reservoir's watershed. Because of local landscape similarity in these properties, we show that choosing the incorrect watershed does not necessarily mean that the watershed characteristics will be misrepresented. We present a measure termed terrain complexity and examine its relationship to geolocation success rate and its influence on the similarity of nearby watersheds. ?? 2008 Elsevier Ltd. All rights reserved.

Computers & Geosciences↗
USGS2008

GIS Representation of Coal-Bearing Areas in Africa

The African continent contains approximately 5 percent of the world's proven recoverable reserves of coal (World Energy Council, 2007). Energy consumption in Africa is projected to grow at an annual rate of 2.3 percent from 2004 through 2030, while average consumption in first-world nations is expected to rise at 1.4 percent annually (Energy Information Administration, 2007). Coal reserves will undoubtedly continue to be part of Africa's energy portfolio as it grows in the future. A review of academic and industrial literature indicates that 27 nations in Africa contain coal-bearing rock. South Africa accounts for 96 percent of Africa's total proven recoverable coal reserves, ranking it sixth in the world. This report is a digital compilation of information on Africa's coal-bearing geology found in the literature and is intended to be used in small scale spatial investigations in a Geographic Information System (GIS) and as a visual aid for the discussion of Africa's coal resources. Many maps of African coal resources often include points for mine locations or regional scale polygons with generalized borders depicting basin edges. Point locations are detailed but provide no information regarding extent, and generalized polygons do not have sufficient detail. In this dataset, the polygons are representative of the actual coal-bearing lithology both in location and regional extent. Existing U.S. Geological Survey (USGS) digital geology datasets provide the majority of the base geologic polygons. Polygons for the coal-bearing localities were clipped from the base geology that represented the age and extent of the coal deposit as indicated in the literature. Where the 1:5,000,000-scale geology base layer's ages conflicted with those in the publications, polygons were generated directly from the regional African coal maps (1:500,000 scale, approximately) in the published material. In these cases, coal-bearing polygons were clipped to the literature's indicated coal extent, without regard to the underlying geology base or topographic constraints. Indication of the presence of African coal is based on multiple sources. However, the quality of the sources varies and there is often disagreement in the literature. This dataset includes the rank, age, and location of coal in Africa as well as the detailed source information responsible for each coal-bearing polygon. The dataset is not appropriate for use in resource assessments of any kind. Attributes necessary for tasks, such as number of coal seams, thickness of seams, and depth to coal are rarely provided in the literature and accordingly not represented in this data set. Small-scale investigations, representations and display uses are most appropriate for this product. This product is the first to show coal distribution as bounded by actual geologic contacts for the entire African continent. In addition to the spatial component of this dataset, complete references to source material are provided for each polygon, making this product a useful first step resource in African coal research. Greater detail regarding the creation of this dataset as well as the sources used is provided in the metadata file for the Africa_coal.shp file.

Open-File Report↗
USGS2004

Converting analog interpretive data to digital formats for use in database and GIS applications

There is a growing need by researchers and managers for comprehensive and unified nationwide datasets of scientific data. These datasets must be in a digital format that is easily accessible using database and GIS applications, providing the user with access to a wide variety of current and historical information. Although most data currently being collected by scientists are already in a digital format, there is still a large repository of information in the literature and paper archive. Converting this information into a format accessible by computer applications is typically very difficult and can result in loss of data. However, since scientific data are commonly collected in a repetitious, concise matter (i.e., forms, tables, graphs, etc.), these data can be recovered digitally by using a conversion process that relates the position of an attribute in two-dimensional space to the information that the attribute signifies. For example, if a table contains a certain piece of information in a specific row and column, then the space that the row and column occupies becomes an index of that information. An index key is used to identify the relation between the physical location of the attribute and the information the attribute contains. The conversion process can be achieved rapidly, easily and inexpensively using widely available digitizing and spreadsheet software, and simple programming code. In the geological sciences, sedimentary character is commonly interpreted from geophysical profiles and descriptions of sediment cores. In the field and laboratory, these interpretations were typically transcribed to paper. The information from these paper archives is still relevant and increasingly important to scientists, engineers and managers to understand geologic processes affecting our environment. Direct scanning of this information produces a raster facsimile of the data, which allows it to be linked to the electronic world. But true integration of the content with database and GIS software as point, vector or text information is commonly lost. Sediment core descriptions and interpretation of geophysical profiles are usually portrayed as lines, curves, symbols and text information. They have vertical and horizontal dimensions associated with depth, category, time, or geographic position. These dimensions are displayed in consistent positions, which can be digitized and converted to a digital format, such as a spreadsheet. Once this data is in a digital, tabulated form it can easily be made available to a wide variety of imaging and data manipulation software for compilation and world-wide dissemination.

Open-File Report↗
USGS2008

GIS Representation of Coal-Bearing Areas in North, Central, and South America

Worldwide coal consumption and international coal trade are projected to increase in the next several decades (Energy Information Administration, 2007). A search of existing literature indicates that in the Western Hemisphere, coal resources are known to occur in about 30 countries. The need exists to be able to depict these areas in a digital format for use in Geographic Information System (GIS) applications at small scales (large areas) and in visual presentations. Existing surficial geology GIS layers of the appropriate geologic age have been used as an approximation to depict the extent of coal-bearing areas in North, Central, and South America, as well as Greenland (fig. 1). Global surficial geology GIS data were created by the U.S. Geological Survey (USGS) for use in world petroleum assessments (Hearn and others, 2003). These USGS publications served as the major sources for the selection and creation of polygons to represent coal-bearing areas. Additional publications and maps by various countries and agencies were also used as sources of coal locations. GIS geologic polygons were truncated where literature or hardcopy maps did not indicate the presence of coal. The depicted areas are not adequate for use in coal resource calculations, as they were not adjusted for geologic structure and do not include coal at depth. Additionally, some coal areas in Central America could not be represented by the mapped surficial geology and are shown only as points based on descriptions or depictions from scientific publications or available maps. The provided GIS files are intended to serve as a backdrop for display of coal information. Three attributes of the coal that are represented by the polygons or points include geologic age (or range of ages), published rank (or range of ranks), and information source (published sources for age, rank, or physical location, or GIS geology base).

Open-File Report↗
USGS2001

Habitat evaluation using GIS a case study applied to the San Joaquin Kit Fox

Concern over the fate of plant and animal species throughout the world has accelerated over recent decades. Habitat loss is considered the main culprit in reducing many species' abundance and range, leading to numerous efforts to plan and manage habitat preservation. Our work uses Geographic Information Systems (GIS) data and modeling to define a spatially explicit analysis of habitat value, using the San Joaquin Kit Fox (Vulpes macrotis mutica) of California (USA) as an example. Over the last 30 years, many field studies and surveys have enhanced our knowledge of the life history, behavior, and needs of the kit fox, which has been proposed as an umbrella or indicator species for grassland habitat in the San Joaquin Valley of California. There has yet been no attempt to convert much of this field knowledge into a model of spatial habitat value useful for planning purposes. This is a significant omission given the importance and visibility of the imperiled kit fox and increasing trends toward spatially explicit modeling and planning. In this paper we apply data from northern California to derive a small-cell GIS raster of habitat value for the kit fox that incorporates both intrinsic habitat quality and neighborhood context, as well the effects of barriers such as roads. Such a product is a useful basis for assessing the presence and amounts of good (and poor) quality habitat and for eventually constructing GIS representations of viable animal territories that could be included in future reserves. ?? 2001 Elsevier Science B.V.

Landscape and Urban Planning↗
USGS2008

TerraLook: GIS-Ready Time-Series of Satellite Imagery for Monitoring Change

TerraLook is a joint project of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) with a goal of providing satellite images that anyone can use to see changes in the Earth's surface over time. Each TerraLook product is a user-specified collection of satellite images selected from imagery archived at the USGS Earth Resources Observation and Science (EROS) Center. Images are bundled with standards-compliant metadata, a world file, and an outline of each image's ground footprint, enabling their use in geographic information systems (GIS), image processing software, and Web mapping applications. TerraLook images are available through the USGS Global Visualization Viewer (http://glovis.usgs.gov).

Fact Sheet↗
USGS2009

Carbonatites of the world, explored deposits of Nb and REE— Database and grade and tonnage models

This report is based on published tonnage and grade data on 58 Nb- and rare-earth-element (REE)-bearing carbonatite deposits that are mostly well explored and are partially mined or contain resources of these elements. The deposits represent only a part of the known 527 carbonatites around the world, but they are characterized by reliable quantitative data on ore tonnages and grades of niobium and REE. Grade and tonnage models are an important component of mineral resource assessments. Carbonatites present one of the main natural sources of niobium and rare-earth elements, the economic importance of which grows consistently. A purpose of this report is to update earlier publications. New information about known deposits, as well as data on new deposits published during the last decade, are incorporated in the present paper. The compiled database (appendix 1; linked to right) contains 60 explored Nb- and REE-bearing carbonatite deposits - resources of 55 of these deposits are taken from publications. In the present updated grade-tonnage model we have added 24 deposits comparing with the previous model of Singer (1998). Resources of most deposits are residuum ores in the upper part of carbonatite bodies. Mineral-deposit models are important in exploration planning and quantitative resource assessments for two reasons: (1) grades and tonnages among deposit types vary significantly, and (2) deposits of different types are present in distinct geologic settings that can be identified from geologic maps. Mineral-deposit models combine the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Globally based deposit models allow recognition of important features and demonstrate how common different features are. Well-designed deposit models allow geologists to deduce possible mineral-deposit types in a given geologic environment, and the grade and tonnage models allow economists to estimate the possible economic viability of these resources. Thus, mineral-deposit models play a central role in presenting geoscience information in a useful form to policy makers. The foundation of mineral-deposit models is information about known deposits. This publication presents the latest geologic information and newly developed grade and tonnage models for Nb- and REE-carbonatite deposits in digital form. The publication contains computer files with information on deposits from around the world. It also contains a text file allowing locations of all deposits to be plotted in geographic information system (GIS) programs. The data are presented in FileMaker Pro as well as in .xls and text files to make the information available to a broadly based audience. The value of this information and any derived analyses depends critically on the consistent manner of data gathering. For this reason, we first discuss the rules used in this compilation. Next, the fields of the database are explained. Finally, we provide new grade and tonnage models and analysis of the information in the file.

Open-File Report↗
USGS2002

Spatial digital database for the tectonic map of southeast Arizona

A spatial database was created for Drewes' (1980) tectonic map of southeast Arizona: this database supercedes Drewes and others (2001, ver. 1.0). Staff and a contractor at the U.S. Geological Survey in Tucson, Arizona completed an interim digital geologic map database for the east part of the map in 2001, made revisions to the previously released digital data for the west part of the map (Drewes and others, 2001, ver. 1.0), merged data files for the east and west parts, and added additional data not previously captured. Digital base map data files (such as topography, roads, towns, rivers and lakes) are not included: they may be obtained from a variety of commercial and government sources. This digital geospatial database is one of many being created by the U.S. Geological Survey as an ongoing effort to provide geologic information in a geographic information system (GIS) for use in spatial analysis. The resulting digital geologic map database can be queried in many ways to produce a variety of geologic maps and derivative products. Because Drewes' (1980) map sheets include additional text and graphics that were not included in this report, scanned images of his maps (i1109_e.jpg, i1109_w.jpg) are included as a courtesy to the reader. This database should not be used or displayed at any scale larger than 1:125,000 (for example, 1:100,000 or 1:24,000). The digital geologic map plot files (i1109_e.pdf and i1109_w.pdf) that are provided herein are representations of the database (see Appendix A). The map area is located in southeastern Arizona (fig. 1). This report describes the map units (from Drewes, 1980), the methods used to convert the geologic map data into a digital format, the ArcInfo GIS file structures and relationships, and explains how to download the digital files from the U.S. Geological Survey public access World Wide Web site on the Internet. The manuscript and digital data review by Helen Kayser (Information Systems Support, Inc.) is greatly appreciated.

Arizona↗
USGS2000

Digital geologic map of the Coeur d'Alene 1:100,000 quadrangle, Idaho and Montana

Between 1961 and 1969, Alan Griggs and others conducted fieldwork to prepare a geologic map of the Spokane 1:250,000 map (Griggs, 1973). Their field observations were posted on paper copies of 15-minute quadrangle maps. In 1999, the USGS contracted with the Idaho Geological Survey to prepare a digital version of the Coeur d’Alene 1:100,000 quadrangle. To facilitate this work, the USGS obtained the field maps prepared by Griggs and others from the USGS Field Records Library in Denver, Colorado. The Idaho Geological Survey (IGS) digitized these maps and used them in their mapping program. The mapping focused on field checks to resolve problems in poorly known areas and in areas of disagreement between adjoining maps. The IGS is currently in the process of preparing a final digital spatial database for the Coeur d’Alene 1:100,000 quadrangle. However, there was immediate need for a digital version of the geologic map of the Coeur d’Alene 1:100,000 quadrangle and the data from the field sheets along with several other sources were assembled to produce this interim product. This interim product is the digital geologic map of the Coeur d’Alene 1:100,000 quadrangle, Idaho and Montana. It was compiled from the preliminary digital files prepared by the Idaho Geological, and supplemented by data from Griggs (1973) and from digital databases by Bookstrom and others (1999) and Derkey and others (1996). The resulting digital geologic map (GIS) database can be queried in many ways to produce a variety of geologic maps. Digital base map data files (topography, roads, towns, rivers and lakes, etc.) are not included: they may be obtained from a variety of commercial and government sources. This database is not meant to be used or displayed at any scale larger than 1:100,000 (e.g., 1:62,500 or 1:24,000). The digital geologic map graphics (of00-135_map.pdf) that are provided are representations of the digital database. The map area is located in north Idaho. This open-file report describes the geologic map units, the methods used to convert the geologic map data into a digital format, the ArcInfo GIS file structures and relationships, and explains how to download the digital files from the U.S. Geological Survey public access World Wide Web site on the Internet.

Idaho, Montana↗
USGS2002

Spatial digital database of the geologic map of Catalina Core Complex and San Pedro Trough, Pima, Pinal, Gila, Graham, and Cochise Counties, Arizona

The geologic map of Catalina Core Complex and San Pedro Trough by Dickinson (1992) was digitized for input into a geographic information system (GIS) by the U.S. Geological Survey staff and contractors in 2000-2001. This digital geospatial database is one of many being created by the U.S. Geological Survey as an ongoing effort to provide geologic information in a geographic information system (GIS) for use in spatial analysis. The resulting digital geologic map database data can be queried in many ways to produce a variety of geologic maps and derivative products. Digital base map data (topography, roads, towns, rivers, lakes, and so forth) are not included; they may be obtained from a variety of commercial and government sources. This database is not meant to be used or displayed at any scale larger than 1:125,000 (for example, 1:100,000 or 1:24,000). The digital geologic map plot files that are provided herein are representations of the database. The map area is located in southern Arizona. This report lists the geologic map units, the methods used to convert the geologic map data into a digital format, the ArcInfo GIS file structures and relationships, and explains how to download the digital files from the U.S. Geological Survey public access World Wide Web site on the Internet. The manuscript and digital data review by Lorre Moyer (USGS) is greatly appreciated.

Arizona↗
USGS2003

Spatial digital database for the geologic map of Oregon

Introduction This report describes and makes available a geologic digital spatial database (orgeo) representing the geologic map of Oregon (Walker and MacLeod, 1991). The original paper publication was printed as a single map sheet at a scale of 1:500,000, accompanied by a second sheet containing map unit descriptions and ancillary data. A digital version of the Walker and MacLeod (1991) map was included in Raines and others (1996). The dataset provided by this open-file report supersedes the earlier published digital version (Raines and others, 1996). This digital spatial database is one of many being created by the U.S. Geological Survey as an ongoing effort to provide geologic information for use in spatial analysis in a geographic information system (GIS). This database can be queried in many ways to produce a variety of geologic maps. This database is not meant to be used or displayed at any scale larger than 1:500,000 (for example, 1:100,000). This report describes the methods used to convert the geologic map data into a digital format, describes the ArcInfo GIS file structures and relationships, and explains how to download the digital files from the U.S. Geological Survey public access World Wide Web site on the Internet. Scanned images of the printed map (Walker and MacLeod, 1991), their correlation of map units, and their explanation of map symbols are also available for download.

Oregon↗
USGS2006

The World Coal Quality Inventory: South America

Executive Summary-Introduction: The concepts of a global environment and economy are strongly and irrevocably linked to global energy issues. Worldwide coal production and international coal trade are projected to increase during the next several decades in an international energy mix that is still strongly dependent on fossil fuels. Therefore, worldwide coal use will play an increasingly visible role in global environmental, economic, and energy forums. Policy makers require information on coal, including coal quality data, to make informed decisions regarding domestic coal resource allocation, import needs and export opportunities, foreign policy objectives, technology transfer policies, foreign investment prospects, environmental and health assessments, and byproduct use and disposal issues. The development of a worldwide, reliable, coal quality database would help ensure the most economically and environmentally efficient global use of coal. The U.S. Geological Survey (USGS), in cooperation with many agencies and scientists from the world's coal producing countries, originally undertook a project to obtain representative samples of coal from most of the world's producing coal provinces during a limited period of time (roughly 1998-2005), which is called the World Coal Quality Inventory (WoCQI). The multitude of producing coal mines, coal occurrences, or limited accessibility to sites in some countries can preclude collecting more than a single sample from a mine. In some areas, a single sample may represent an entire coal mining region or basin. Despite these limitations in sampling and uneven distribution of sample collection, the analytical results can still provide a general overview of world coal quality. The USGS intends to present the WoCQI data in reports and, when possible, in Geographic Information System (GIS) products that cover important coal bearing and producing regions.

Open-File Report↗