Land surface change caused by fires and succession is confounded by many site-specific factors and requires further study. The objective of this study was to reveal the spatially explicit land surface change by minimizing the confounding factors of weather variability, seasonal offset, topography, land cover, and drainage. In a pilot study of the Yukon River Basin of interior Alaska, we retrieved Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) from a postfire Landsat image acquired on August 5th, 2004. With a Landsat reference image acquired on June 26th, 1986, we reconstructed NDVI, albedo, and LST of 1987–2004 fire scars for August 5th, 2004, assuming that these fires had not occurred. The difference between actual postfire and assuming-no-fire scenarios depicted the fires and succession impact. Our results demonstrated the following: (1) NDVI showed an immediate decrease after burning but gradually recovered to prefire levels in the following years, in which burn severity might play an important role during this process; (2) Albedo showed an immediate decrease after burning but then recovered and became higher than prefire levels; and (3) Most fires caused surface warming, but cooler surfaces did exist; time-since-fire affected the prefire and postfire LST difference but no absolute trend could be found. Our approach provided spatially explicit land surface change rather than average condition, enabling a better understanding of fires and succession impact on ecological consequences at the pixel level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2013.02.010","usgsCitation":"Huang, S., Jin, S., Dahal, D., Chen, X., Young, C., Liu, H., and Liu, S., 2013, Reconstructing satellite images to quantify spatially explicit land surface change caused by fires and succession: A demonstration in the Yukon River Basin of interior Alaska: ISPRS Journal of Photogrammetry and Remote Sensing, v. 79, p. 94-105, https://doi.org/10.1016/j.isprsjprs.2013.02.010.","productDescription":"12 p.","startPage":"94","endPage":"105","ipdsId":"IP-039018","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":342155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"79","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf30e4b0f6c2d0d9c78f","contributors":{"authors":[{"text":"Huang, Shengli shuang@usgs.gov","contributorId":1926,"corporation":false,"usgs":true,"family":"Huang","given":"Shengli","email":"shuang@usgs.gov","affiliations":[],"preferred":true,"id":697254,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":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":697255,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":192023,"corporation":false,"usgs":false,"family":"Dahal","given":"Devendra","affiliations":[],"preferred":false,"id":697258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Xuexia","contributorId":14213,"corporation":false,"usgs":true,"family":"Chen","given":"Xuexia","affiliations":[],"preferred":false,"id":697279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, Claudia 0000-0002-0859-7206 claudia.young.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-0859-7206","contributorId":191382,"corporation":false,"usgs":true,"family":"Young","given":"Claudia","email":"claudia.young.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":697253,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Heping","contributorId":192024,"corporation":false,"usgs":false,"family":"Liu","given":"Heping","email":"","affiliations":[],"preferred":false,"id":697259,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":697256,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70045743,"text":"ds709Y - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ahankashan mineral district in Afghanistan","interactions":[],"lastModifiedDate":"2013-05-01T21:37:12","indexId":"ds709Y","displayToPublicDate":"2013-05-01T00:00:00","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":"Y","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ahankashan mineral district 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 Ahankashan mineral district, which has copper and gold deposits.\n\nALOS 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.\n\nThe 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).\n\nAll 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.","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/ds709Y","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey","usgsCitation":"Davis, P.A., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ahankashan mineral district in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme; 4 Index Maps; 10 Image Files; 10 Metadata; Shapefiles, https://doi.org/10.3133/ds709Y.","productDescription":"HTML Document; Readme; 4 Index Maps; 10 Image Files; 10 Metadata; Shapefiles","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":271706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709Y.png"},{"id":271700,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/y/"},{"id":271701,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/y/1_readme.txt"},{"id":271702,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/y/index_maps/index_maps.html"},{"id":271703,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/y/image_files/image_files.html"},{"id":271704,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/y/metadata/metadata.html"},{"id":271705,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/y/shapefiles/shapefiles.html"}],"country":"Afghanistan","otherGeospatial":"Ahankashan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51822b6be4b04bbc6ead2702","contributors":{"editors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":509319,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":478226,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70045602,"text":"ofr20131057 - 2013 - Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description","interactions":[],"lastModifiedDate":"2013-04-25T14:02:16","indexId":"ofr20131057","displayToPublicDate":"2013-04-25T00:00:00","publicationYear":"2013","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":"2013-1057","title":"Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description","docAbstract":"The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from LandsatThematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product.The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record.This report discusses the LEDAPS algorithm, which was implemented by the USGS.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131057","usgsCitation":"Schmidt, G., Jenkerson, C.B., Masek, J., Vermote, E., and Gao, F., 2013, Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description: U.S. Geological Survey Open-File Report 2013-1057, vi, 19 p., https://doi.org/10.3133/ofr20131057.","productDescription":"vi, 19 p.","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":271479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131057.gif"},{"id":271477,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1057/"},{"id":271478,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1057/ofr13_1057.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"517a425ce4b072c16ef14ae7","contributors":{"authors":[{"text":"Schmidt, Gail 0000-0002-9684-8158","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":29086,"corporation":false,"usgs":true,"family":"Schmidt","given":"Gail","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":477944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":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":477942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Masek, Jeffrey","contributorId":89783,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffrey","affiliations":[],"preferred":false,"id":477946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vermote, Eric","contributorId":15498,"corporation":false,"usgs":true,"family":"Vermote","given":"Eric","email":"","affiliations":[],"preferred":false,"id":477943,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":477945,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188332,"text":"70188332 - 2013 - Radiometric cross-calibration of EO-1 ALI with L7 ETM+ and Terra MODIS sensors using near-simultaneous desert observations","interactions":[],"lastModifiedDate":"2017-06-06T14:29:04","indexId":"70188332","displayToPublicDate":"2013-04-24T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1942,"text":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Radiometric cross-calibration of EO-1 ALI with L7 ETM+ and Terra MODIS sensors using near-simultaneous desert observations","docAbstract":"The Earth Observing-1 (EO-1) satellite was launched on November 21, 2000, as part of a one-year technology demonstration mission. The mission was extended because of the value it continued to add to the scientific community. EO-1 has now been operational for more than a decade, providing both multispectral and hyperspectral measurements. As part of the EO-1 mission, the Advanced Land Imager (ALI) sensor demonstrates a potential technological direction for the next generation of Landsat sensors. To evaluate the ALI sensor capabilities as a precursor to the Operational Land Imager (OLI) onboard the Landsat Data Continuity Mission (LDCM, or Landsat 8 after launch), its measured top-of-atmosphere (TOA) reflectances were compared to the well-calibrated Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors in the reflective solar bands (RSB). These three satellites operate in a near-polar, sun-synchronous orbit 705 km above the Earth's surface. EO-1 was designed to fly one minute behind L7 and approximately 30 minutes in front of Terra. In this configuration, all the three sensors can view near-identical ground targets with similar atmospheric, solar, and viewing conditions. However, because of the differences in the relative spectral response (RSR), the measured physical quantities can be significantly different while observing the same target. The cross-calibration of ALI with ETM+ and MODIS was performed using near-simultaneous surface observations based on image statistics from areas observed by these sensors over four desert sites (Libya 4, Mauritania 2, Arabia 1, and Sudan 1). The differences in the measured TOA reflectances due to RSR mismatches were compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of each sensor. For this study, the spectral profile of the target comes from the near-simultaneous EO-1 Hyperion data over these sites. The results indicate that the TOA reflectance measurements for ALI agree with those of ETM+ and MODIS to within 5% after the application of SBAF.
","language":"English","publisher":"IEEE","doi":"10.1109/JSTARS.2013.2251999","usgsCitation":"Chander, G., Angal, A., Choi, T., and Xiong, X., 2013, Radiometric cross-calibration of EO-1 ALI with L7 ETM+ and Terra MODIS sensors using near-simultaneous desert observations: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 6, no. 2, p. 386-399, https://doi.org/10.1109/JSTARS.2013.2251999.","productDescription":"14 p.","startPage":"386","endPage":"399","ipdsId":"IP-040530","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":342161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Libya, Mauritania, Sudan","otherGeospatial":"Arabia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": 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Taeyoung","contributorId":146955,"corporation":false,"usgs":false,"family":"Choi","given":"Taeyoung","email":"","affiliations":[],"preferred":false,"id":697314,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xiong, Xiaoxiong","contributorId":15088,"corporation":false,"usgs":true,"family":"Xiong","given":"Xiaoxiong","email":"","affiliations":[],"preferred":false,"id":697315,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045509,"text":"70045509 - 2013 - Wetland fire scar monitoring and analysis using archival Landsat data for the Everglades","interactions":[],"lastModifiedDate":"2013-04-19T21:06:46","indexId":"70045509","displayToPublicDate":"2013-04-19T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Wetland fire scar monitoring and analysis using archival Landsat data for the Everglades","docAbstract":"The ability to document the frequency, extent, and severity of fires in wetlands, as well as the dynamics of post-fire wetland land cover, informs fire and wetland science, resource management, and ecosystem protection. Available information on Everglades burn history has been based on field data collection methods that evolved through time and differ by land management unit. Our objectives were to (1) design and test broadly applicable and repeatable metrics of not only fire scar delineation but also post-fire land cover dynamics through exhaustive use of the Landsat satellite data archives, and then (2) explore how those metrics relate to various hydrologic and anthropogenic factors that may influence post-fire land cover dynamics. Visual interpretation of every Landsat scene collected over the study region during the study time frame produced a new, detailed database of burn scars greater than 1.6 ha in size in the Water Conservation Areas and post-fire land cover dynamics for Everglades National Park fires greater than 1.6 ha in area. Median burn areas were compared across several landscape units of the Greater Everglades and found to differ as a function of administrative unit and fire history. Some burned areas transitioned to open water, exhibiting water depths and dynamics that support transition mechanisms proposed in the literature. Classification tree techniques showed that time to green-up and return to pre-burn character were largely explained by fire management practices and hydrology. Broadly applicable as they use data from the global, nearly 30-year-old Landsat archive, these methods for documenting wetland burn extent and post-fire land cover change enable cost-effective collection of new data on wetland fire ecology and independent assessment of fire management practice effectiveness.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Fire Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Association for Fire Ecology","publisherLocation":"Eugene, OR","doi":"10.4996/fireecology.0901133","usgsCitation":"Jones, J., Hall, A.E., Foster, A.M., and Smith, T.J., 2013, Wetland fire scar monitoring and analysis using archival Landsat data for the Everglades: Fire Ecology, v. 9, no. 1, p. 133-150, https://doi.org/10.4996/fireecology.0901133.","productDescription":"18 p.","startPage":"133","endPage":"150","ipdsId":"IP-040357","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":473873,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4996/fireecology.0901133","text":"Publisher Index Page"},{"id":271273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271272,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.4996/fireecology.0901133"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -81.5205,24.851 ], [ -81.5205,25.8915 ], [ -80.3887,25.8915 ], [ -80.3887,24.851 ], [ -81.5205,24.851 ] ] ] } } ] }","volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-04-01","publicationStatus":"PW","scienceBaseUri":"5172595ee4b0c173799e78fa","contributors":{"authors":[{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":477670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Annette E. ahall@usgs.gov","contributorId":4791,"corporation":false,"usgs":true,"family":"Hall","given":"Annette","email":"ahall@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":477672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foster, Ann M. amfoster@usgs.gov","contributorId":3545,"corporation":false,"usgs":true,"family":"Foster","given":"Ann","email":"amfoster@usgs.gov","middleInitial":"M.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":477671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Thomas J. III tom_j_smith@usgs.gov","contributorId":1615,"corporation":false,"usgs":true,"family":"Smith","given":"Thomas","suffix":"III","email":"tom_j_smith@usgs.gov","middleInitial":"J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":477669,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70118022,"text":"70118022 - 2013 - Modeling mountain pine beetle disturbance in Glacier National Park using multiple lines of evidence","interactions":[],"lastModifiedDate":"2014-07-25T09:24:04","indexId":"70118022","displayToPublicDate":"2013-04-13T09:10:24","publicationYear":"2013","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Modeling mountain pine beetle disturbance in Glacier National Park using multiple lines of evidence","docAbstract":"Temperate forest ecosystems are subject to various disturbances which contribute to ecological legacies that can have profound effects on the structure of the ecosystem. Impacts of disturbance can vary widely in extent, duration and severity over space and time. Given that global climate change is expected to increase rates of forest disturbance, an understanding of these events are critical in the interpretation of contemporary forest patterns and those of the near future. We seek to understand the impact of the 1970s mountain pine beetle outbreak on the landscape of Glacier National Park and investigate any connection between this event and subsequent decades of extensive wildfire. The lack of spatially explicit data on the mountain pine beetle disturbance represents a major data gap and inhibits our ability to test for correlations between outbreak severity and fire severity. To overcome this challenge, we utilized multiple lines of evidence to model forest canopy mortality as a proxy for outbreak severity. We used historical aerial and landscape photos, reports, aerial survey data, a six year collection of Landsat imagery and abiotic data in combination with regression analysis. The use of remotely sensed data is critical in large areas where subsequent disturbance (fire) has erased some of the evidence from the landscape. Results indicate that this method is successful in capturing the spatial heterogeneity of the outbreak in a topographically complex landscape. Furthermore, this study provides an example on the use of existing data to reduce levels of uncertainty associated with an historic disturbance.","conferenceTitle":"Association of American Geographers Annual Meeting","conferenceDate":"2013-04-13T00:00:00","conferenceLocation":"Chicago, IL","language":"English","publisher":"Association of American Geographers","publisherLocation":"Washington, D.C.","usgsCitation":"Assal, T., and Sibold, J., 2013, Modeling mountain pine beetle disturbance in Glacier National Park using multiple lines of evidence, Association of American Geographers Annual Meeting, Chicago, IL, 2013-04-13T00:00:00.","costCenters":[],"links":[{"id":290971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7f314e4b0bc0bec0a0779","contributors":{"authors":[{"text":"Assal, Timothy","contributorId":87864,"corporation":false,"usgs":true,"family":"Assal","given":"Timothy","affiliations":[],"preferred":false,"id":496140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sibold, Jason","contributorId":10724,"corporation":false,"usgs":false,"family":"Sibold","given":"Jason","affiliations":[],"preferred":false,"id":496139,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70044142,"text":"70044142 - 2013 - Assessment of spectral band impact on intercalibration over desert sites using simulation based on EO-1 Hyperion data","interactions":[],"lastModifiedDate":"2013-04-09T13:47:15","indexId":"70044142","displayToPublicDate":"2013-04-09T00:00:00","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":"Assessment of spectral band impact on intercalibration over desert sites using simulation based on EO-1 Hyperion data","docAbstract":"Since the beginning of the 1990s, stable desert sites have been used for the calibration monitoring of many different sensors. Many attempts at sensor intercalibration have been also conducted using these stable desert sites. As a result, site characterization techniques and the quality of intercalibration techniques have gradually improved over the years. More recently, the Committee on Earth Observation Satellites has recommended a list of reference pseudo-invariant calibration sites for frequent image acquisition by multiple agencies. In general, intercalibration should use well-known or spectrally flat reference. The reflectance profile of desert sites, however, might not be flat or well characterized (from a fine spectral point of view). The aim of this paper is to assess the expected accuracy that can be reached when using desert sites for intercalibration. In order to have a well-mastered estimation of different errors or error sources, this study is performed with simulated data from a hyperspectral sensor. Earth Observing-1 Hyperion images are chosen to provide the simulation input data. Two different cases of intercalibration are considered, namely, Landsat 7 Enhanced Thematic Mapper Plus with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Environmental Satellite MEdium Resolution Imaging Spectrometer (MERIS) with Aqua MODIS. The simulation results have confirmed that intercalibration accuracy of 1% to 2% can be achieved between sensors, provided there are a sufficient number of available measurements. The simulated intercalibrations allow explaining results obtained during real intercalibration exercises and to establish some recommendations for the use of desert sites for intercalibration.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Transactions on Geoscience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","publisherLocation":"Washington, D.C.","doi":"10.1109/TGRS.2012.2228210","usgsCitation":"Henry, P., Chander, G., Fougnie, B., Thomas, C., and Xiong, X., 2013, Assessment of spectral band impact on intercalibration over desert sites using simulation based on EO-1 Hyperion data: IEEE Transactions on Geoscience and Remote Sensing, v. 51, no. 3, p. 1297-1308, https://doi.org/10.1109/TGRS.2012.2228210.","productDescription":"12 p.","startPage":"1297","endPage":"1308","ipdsId":"IP-040537","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":270700,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/TGRS.2012.2228210"},{"id":270701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51652a52e4b077fa94dadf3b","contributors":{"authors":[{"text":"Henry, P.","contributorId":91599,"corporation":false,"usgs":true,"family":"Henry","given":"P.","email":"","affiliations":[],"preferred":false,"id":474889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chander, G.","contributorId":51449,"corporation":false,"usgs":true,"family":"Chander","given":"G.","affiliations":[],"preferred":false,"id":474888,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fougnie, B.","contributorId":12346,"corporation":false,"usgs":true,"family":"Fougnie","given":"B.","email":"","affiliations":[],"preferred":false,"id":474886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thomas, C.","contributorId":7443,"corporation":false,"usgs":true,"family":"Thomas","given":"C.","affiliations":[],"preferred":false,"id":474885,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xiong, Xiaoxiong","contributorId":15088,"corporation":false,"usgs":true,"family":"Xiong","given":"Xiaoxiong","email":"","affiliations":[],"preferred":false,"id":474887,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70043720,"text":"70043720 - 2013 - Automated cloud and shadow detection and filling using two-date Landsat imagery in the United States","interactions":[],"lastModifiedDate":"2013-04-09T20:09:58","indexId":"70043720","displayToPublicDate":"2013-04-09T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated cloud and shadow detection and filling using two-date Landsat imagery in the United States","docAbstract":"A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates (a target image and a reference image). These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group (SSG), which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor & Francis","publisherLocation":"Philadelphia, PA","doi":"10.1080/01431161.2012.720045","usgsCitation":"Jin, S., Homer, C.G., Yang, L., Xian, G., Fry, J., Danielson, P., and Townsend, P., 2013, Automated cloud and shadow detection and filling using two-date Landsat imagery in the United States: International Journal of Remote Sensing, v. 34, no. 5, p. 1540-1560, https://doi.org/10.1080/01431161.2012.720045.","productDescription":"21 p.","startPage":"1540","endPage":"1560","ipdsId":"IP-024783","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":270762,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":270761,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431161.2012.720045"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 172.5,18.9 ], [ 172.5,71.4 ], [ -66.9,71.4 ], [ -66.9,18.9 ], [ 172.5,18.9 ] ] ] } } ] }","volume":"34","issue":"5","noUsgsAuthors":false,"publicationDate":"2012-10-16","publicationStatus":"PW","scienceBaseUri":"51652a5de4b077fa94dadf43","contributors":{"authors":[{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":474167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":474163,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":474166,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xian, George 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":76589,"corporation":false,"usgs":true,"family":"Xian","given":"George","affiliations":[],"preferred":false,"id":474169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fry, Joyce 0000-0002-8466-9582 jfry@usgs.gov","orcid":"https://orcid.org/0000-0002-8466-9582","contributorId":3147,"corporation":false,"usgs":true,"family":"Fry","given":"Joyce","email":"jfry@usgs.gov","affiliations":[],"preferred":true,"id":474164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Danielson, Patrick 0000-0002-2990-2783 pdanielson@usgs.gov","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":3551,"corporation":false,"usgs":true,"family":"Danielson","given":"Patrick","email":"pdanielson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":474165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Townsend, Philip A.","contributorId":47664,"corporation":false,"usgs":true,"family":"Townsend","given":"Philip A.","affiliations":[],"preferred":false,"id":474168,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70044140,"text":"70044140 - 2013 - Absolute radiometric calibration of Landsat using a pseudo invariant calibration site","interactions":[],"lastModifiedDate":"2013-04-06T20:43:45","indexId":"70044140","displayToPublicDate":"2013-04-06T00:00:00","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":"Absolute radiometric calibration of Landsat using a pseudo invariant calibration site","docAbstract":"Pseudo invariant calibration sites (PICS) have been used for on-orbit radiometric trending of optical satellite systems for more than 15 years. This approach to vicarious calibration has demonstrated a high degree of reliability and repeatability at the level of 1-3% depending on the site, spectral channel, and imaging geometries. A variety of sensors have used this approach for trending because it is broadly applicable and easy to implement. Models to describe the surface reflectance properties, as well as the intervening atmosphere have also been developed to improve the precision of the method. However, one limiting factor of using PICS is that an absolute calibration capability has not yet been fully developed. Because of this, PICS are primarily limited to providing only long term trending information for individual sensors or cross-calibration opportunities between two sensors. This paper builds an argument that PICS can be used more extensively for absolute calibration. To illustrate this, a simple empirical model is developed for the well-known Libya 4 PICS based on observations by Terra MODIS and EO-1 Hyperion. The model is validated by comparing model predicted top-of-atmosphere reflectance values to actual measurements made by the Landsat ETM+ sensor reflective bands. Following this, an outline is presented to develop a more comprehensive and accurate PICS absolute calibration model that can be Système international d'unités (SI) traceable. These initial concepts suggest that absolute calibration using PICS is possible on a broad scale and can lead to improved on-orbit calibration capabilities for optical satellite sensors.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Transactions on Geoscience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","publisherLocation":"Washington, D.C.","doi":"10.1109/TGRS.2013.2243738","usgsCitation":"Helder, D., Thome, K.J., Mishra, N., Chander, G., Xiong, X., Angal, A., and Choi, T., 2013, Absolute radiometric calibration of Landsat using a pseudo invariant calibration site: IEEE Transactions on Geoscience and Remote Sensing, v. 51, no. 3, p. 1360-1369, https://doi.org/10.1109/TGRS.2013.2243738.","productDescription":"10 p.","startPage":"1360","endPage":"1369","ipdsId":"IP-040531","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":270617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":270616,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/TGRS.2013.2243738"}],"volume":"51","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"516135d6e4b022d43fdfaa19","contributors":{"authors":[{"text":"Helder, D. 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":15490,"corporation":false,"usgs":true,"family":"Helder","given":"D.","affiliations":[],"preferred":false,"id":474869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thome, K. J.","contributorId":88099,"corporation":false,"usgs":true,"family":"Thome","given":"K.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":474873,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mishra, N.","contributorId":67379,"corporation":false,"usgs":true,"family":"Mishra","given":"N.","email":"","affiliations":[],"preferred":false,"id":474872,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chander, G.","contributorId":51449,"corporation":false,"usgs":true,"family":"Chander","given":"G.","affiliations":[],"preferred":false,"id":474870,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xiong, Xiaoxiong","contributorId":15088,"corporation":false,"usgs":true,"family":"Xiong","given":"Xiaoxiong","email":"","affiliations":[],"preferred":false,"id":474868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Angal, A.","contributorId":52716,"corporation":false,"usgs":true,"family":"Angal","given":"A.","affiliations":[],"preferred":false,"id":474871,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Choi, Tae-young","contributorId":89036,"corporation":false,"usgs":true,"family":"Choi","given":"Tae-young","affiliations":[],"preferred":false,"id":474874,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70098949,"text":"70098949 - 2013 - Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data","interactions":[],"lastModifiedDate":"2017-02-13T14:53:37","indexId":"70098949","displayToPublicDate":"2013-04-01T14:42:31","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1942,"text":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data","docAbstract":"The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy ~70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using ~20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was ~ 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysi- al characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI.","language":"English","publisher":"Institute of Electrical and Electronics Engineers","publisherLocation":"New York, NY","doi":"10.1109/JSTARS.2013.2252601","usgsCitation":"Thenkabail, P., Mariotto, I., Gumma, M., Middleton, E., Landis, D., and Huemmrich, K., 2013, Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 6, no. 2, p. 427-439, https://doi.org/10.1109/JSTARS.2013.2252601.","productDescription":"13 p.","startPage":"427","endPage":"439","numberOfPages":"13","ipdsId":"IP-037139","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":473885,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/11603/31506","text":"External Repository"},{"id":284275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":284273,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/JSTARS.2013.2252601"}],"country":"India","otherGeospatial":"Africa;Central Asia;Middle East","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -32.7,-40.6 ], [ -32.7,46.9 ], [ 100.0,46.9 ], [ 100.0,-40.6 ], [ -32.7,-40.6 ] ] ] } } ] }","volume":"6","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd7255e4b0b29085108408","contributors":{"authors":[{"text":"Thenkabail, P.S.","contributorId":66071,"corporation":false,"usgs":true,"family":"Thenkabail","given":"P.S.","email":"","affiliations":[],"preferred":false,"id":491784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mariotto, I.","contributorId":47285,"corporation":false,"usgs":true,"family":"Mariotto","given":"I.","affiliations":[],"preferred":false,"id":491783,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gumma, M.K.","contributorId":12286,"corporation":false,"usgs":true,"family":"Gumma","given":"M.K.","email":"","affiliations":[],"preferred":false,"id":491781,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Middleton, E.M.","contributorId":107656,"corporation":false,"usgs":true,"family":"Middleton","given":"E.M.","email":"","affiliations":[],"preferred":false,"id":491786,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landis, D.R.","contributorId":25454,"corporation":false,"usgs":true,"family":"Landis","given":"D.R.","email":"","affiliations":[],"preferred":false,"id":491782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Huemmrich, K.F.","contributorId":105632,"corporation":false,"usgs":true,"family":"Huemmrich","given":"K.F.","email":"","affiliations":[],"preferred":false,"id":491785,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70045178,"text":"ofr20131075 - 2013 - Change in the length of the middle section of the Chandeleur Islands oil berm, November 17, 2010, through September 6, 2011","interactions":[],"lastModifiedDate":"2013-04-01T14:06:26","indexId":"ofr20131075","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","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":"2013-1075","title":"Change in the length of the middle section of the Chandeleur Islands oil berm, November 17, 2010, through September 6, 2011","docAbstract":"On April 20, 2010, an explosion on the Deepwater Horizon oil rig drilling at the Macondo Prospect site in the Gulf of Mexico resulted in a marine oil spill that continued to flow through July 15, 2010. One of the affected areas was the Breton National Wildlife Refuge, which consists of a chain of low-lying islands, including Breton Island and the Chandeleur Islands, and their surrounding waters. The island chain is located approximately 115-150 kilometers north-northwest of the spill site. A sand berm was constructed seaward of, and on, the island chain. Construction began at the northern end of the Chandeleur Islands in June 2010 and ended in April 2011. The berm consisted of three distinct sections based on where the berm was placed relative to the islands. The northern section of the berm was built in open water on a submerged portion of the Chandeleur Islands platform. The middle section was built approximately 70-90 meters seaward of the Chandeleur Islands. The southern section was built on the islands' beaches. Repeated Landsat and SPOT satellite imagery and airborne lidar were used to observe the disintegration of the berm over time. The methods used to analyze the remotely sensed data and the resulting, derived data for the middle section are described in this report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131075","usgsCitation":"Plant, N., and Guy, K.K., 2013, Change in the length of the middle section of the Chandeleur Islands oil berm, November 17, 2010, through September 6, 2011: U.S. Geological Survey Open-File Report 2013-1075, iii, 8 p., https://doi.org/10.3133/ofr20131075.","productDescription":"iii, 8 p.","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-11-17","temporalEnd":"2011-09-06","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":270427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131075.gif"},{"id":270426,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1075/"},{"id":270425,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1075/pdf/ofr2013-1075.pdf"}],"country":"United States","state":"Alabama;Louisiana;Mississippi","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.5605,28.8206 ], [ -89.5605,30.4794 ], [ -88.0417,30.4794 ], [ -88.0417,28.8206 ], [ -89.5605,28.8206 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515a9e4fe4b0105540728a16","contributors":{"authors":[{"text":"Plant, N.G.","contributorId":94023,"corporation":false,"usgs":true,"family":"Plant","given":"N.G.","email":"","affiliations":[],"preferred":false,"id":476993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, K. K.","contributorId":24393,"corporation":false,"usgs":true,"family":"Guy","given":"K.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":476992,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045175,"text":"ofr20131074 - 2013 - Change in the length of the northern section of the Chandeleur Islands oil berm, September 5, 2010, through September 3, 2012","interactions":[],"lastModifiedDate":"2013-04-01T13:38:52","indexId":"ofr20131074","displayToPublicDate":"2013-04-01T00:00:00","publicationYear":"2013","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":"2013-1074","title":"Change in the length of the northern section of the Chandeleur Islands oil berm, September 5, 2010, through September 3, 2012","docAbstract":"On April 20, 2010, an explosion on the Deepwater Horizon oil rig drilling at the Macondo Prospect site in the Gulf of Mexico resulted in a marine oil spill that continued to flow through July 15, 2010. One of the affected areas was the Breton National Wildlife Refuge, which consists of a chain of low-lying islands, including Breton Island and the Chandeleur Islands, and their surrounding waters. The island chain is located approximately 115–150 kilometers north-northwest of the spill site. A sand berm was constructed seaward of, and on, the island chain. Construction began at the northern end of the Chandeleur Islands in June 2010 and ended in April 2011. The berm consisted of three distinct sections based on where the berm was placed relative to the islands. The northern section of the berm was built in open water on a submerged portion of the Chandeleur Islands platform. The middle section was built approximately 70–90 meters seaward of the Chandeleur Islands. The southern section was built on the islands’ beaches. Repeated Landsat and SPOT satellite imagery and airborne lidar were used to observe the disintegration of the berm over time. The methods used to analyze the remotely sensed data and the resulting, derived data for the northern section are described in this report.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131074","usgsCitation":"Plant, N., and Guy, K.K., 2013, Change in the length of the northern section of the Chandeleur Islands oil berm, September 5, 2010, through September 3, 2012: U.S. Geological Survey Open-File Report 2013-1074, iii, 9 p., https://doi.org/10.3133/ofr20131074.","productDescription":"iii, 9 p.","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2010-09-05","temporalEnd":"2012-09-03","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":270421,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1074/pdf/ofr2013-1074.pdf"},{"id":270422,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1074/"},{"id":270423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131074.gif"}],"country":"United States","state":"Alabama;Louisiana;Mississippi","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.560547,29.084977 ], [ -89.560547,30.47945 ], [ -88.041687,30.47945 ], [ -88.041687,29.084977 ], [ -89.560547,29.084977 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"515a9e5de4b0105540728a1a","contributors":{"authors":[{"text":"Plant, N.G.","contributorId":94023,"corporation":false,"usgs":true,"family":"Plant","given":"N.G.","email":"","affiliations":[],"preferred":false,"id":476991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, K. K.","contributorId":24393,"corporation":false,"usgs":true,"family":"Guy","given":"K.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":476990,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70044648,"text":"ds709X - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Nuristan mineral district in Afghanistan","interactions":[],"lastModifiedDate":"2013-03-19T10:25:22","indexId":"ds709X","displayToPublicDate":"2013-03-19T00:00:00","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":"X","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Nuristan mineral district 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 Nuristan mineral district, which has gem, lithium, and cesium deposits.\n\nALOS 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.\n\nThe 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).\n\nAll 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. 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Two work activities were completed in FY2011: (1) the assessment of rancher perceptions of energy development in Southwest Wyoming and (2) mapping aspen stands and conifer encroachment using classification and regression tree (CART) analysis for effectiveness monitoring. The USGS continued to compile data, develop geospatial products, and upgrade Web-based products in support of both individual and overall WLCI efforts, including (1) ranking and prioritizing proposed conservation projects, (2) developing the WLCI integrated assessment, (3) developing the WLCI 5-year Conservation Action Plan, and (4) continuing to upgrade the content and improve the functionality of the WLCI Web site. For the WLCI FY2012 annual report, a decision was made to greatly reduce the overall length of the annual report, which will be accomplished by simplifying the report format and focusing on the take-home messages of each work activity for WLCI partners.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131033","usgsCitation":"Bowen, Z.H., Aldridge, C.L., Anderson, P.J., Assal, T.J., Biewick, L., Blecker, S.W., Boughton, G.K., Carr, N.B., Chalfoun, A., Chong, G.W., Clark, M.L., Diffendorfer, J.E., Fedy, B.C., Foster, K., Garman, S.L., Germaine, S., Hethcoat, M.G., Holloway, J., Homer, C.G., Kauffman, M., Keinath, D., Latysh, N., Manier, D.J., McDougal, R., Melcher, C.P., Miller, K.A., Montag, J., Olexa, E.M., Potter, C.J., Schell, S., Shafer, S., Smith, D., Stillings, L., Sweat, M.J., Tuttle, M., and Wilson, A.B., 2013, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative: 2011 annual report: U.S. 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Afghanistan","interactions":[],"lastModifiedDate":"2013-03-17T20:55:47","indexId":"ds709W","displayToPublicDate":"2013-03-17T00:00:00","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":"W","title":"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","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 Ghunday-Achin mineral district, which has magnesite and talc deposits.\n\nALOS 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.\n\nThe 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).\n\nAll 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.","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/ds709W","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 W in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2013, 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: U.S. Geological Survey Data Series 709, HTML Document; Readme; 4 Index Maps: 71 x 33 inches; 16 Image Files; 16 Metadata; Shapefiles, https://doi.org/10.3133/ds709W.","productDescription":"HTML Document; Readme; 4 Index Maps: 71 x 33 inches; 16 Image Files; 16 Metadata; Shapefiles","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":269545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709w.png"},{"id":269539,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/w/"},{"id":269543,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/w/metadata/metadata.html"},{"id":269540,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/w/1_readme.txt"},{"id":269541,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/w/index_maps/index_maps.html"},{"id":269542,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/w/image_files/image_files.html"},{"id":269544,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/w/shapefiles/shapefiles.html"}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 58.0,28.0 ], [ 58.0,40.0 ], [ 78.0,40.0 ], [ 78.0,28.0 ], [ 58.0,28.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5146d7d2e4b0694ee75ad3d0","contributors":{"editors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":509264,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":476086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":476088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":476087,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70044606,"text":"ds709V - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Uruzgan mineral district in Afghanistan: Chapter V in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-03-14T20:50:12","indexId":"ds709V","displayToPublicDate":"2013-03-14T00:00:00","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":"V","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Uruzgan mineral district in Afghanistan: Chapter V 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 Uruzgan mineral district, which has tin and tungsten deposits.\n\nALOS 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.\n\nThe 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).\n\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Uruzgan) 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 Uruzgan 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/ds709V","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey","usgsCitation":"Davis, P.A., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Uruzgan mineral district in Afghanistan: Chapter V in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme; 4 Index Maps: 66 x 59 inches; 16 Image Files; 16 Metadata; 1 Shapefile, https://doi.org/10.3133/ds709V.","productDescription":"HTML Document; Readme; 4 Index Maps: 66 x 59 inches; 16 Image Files; 16 Metadata; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":269393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709v.png"},{"id":269389,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/v/index_maps/index_maps.html"},{"id":269390,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/v/image_files/image_files.html"},{"id":269391,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/v/metadata/metadata.html"},{"id":269392,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/v/shapefiles/shapefiles.html"},{"id":269387,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/v/"},{"id":269388,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/v/1_readme.txt"}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 58.0,28.0 ], [ 58.0,40.0 ], [ 78.0,40.0 ], [ 78.0,28.0 ], [ 58.0,28.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5142e35ae4b073a963ff6531","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":475992,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70042437,"text":"70042437 - 2013 - Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images","interactions":[],"lastModifiedDate":"2013-03-12T13:21:57","indexId":"70042437","displayToPublicDate":"2013-03-12T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images","docAbstract":"Routine acquisition of Landsat 5 Thematic Mapper (TM) data was discontinued recently and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) has an ongoing problem with the scan line corrector (SLC), thereby creating spatial gaps when covering images obtained during the process. Since temporal and spatial discontinuities of Landsat data are now imminent, it is therefore important to investigate other potential satellite data that can be used to replace Landsat data. We thus cross-compared two near-simultaneous images obtained from Landsat 5 TM and the Indian Remote Sensing (IRS)-P6 Advanced Wide Field Sensor (AWiFS), both captured on 29 May 2007 over Los Angeles, CA. TM and AWiFS reflectances were compared for the green, red, near-infrared (NIR), and shortwave infrared (SWIR) bands, as well as the normalized difference vegetation index (NDVI) based on manually selected polygons in homogeneous areas. All R2 values of linear regressions were found to be higher than 0.99. The temporally invariant cluster (TIC) method was used to calculate the NDVI correlation between the TM and AWiFS images. The NDVI regression line derived from selected polygons passed through several invariant cluster centres of the TIC density maps and demonstrated that both the scene-dependent polygon regression method and TIC method can generate accurate radiometric normalization. A scene-independent normalization method was also used to normalize the AWiFS data. Image agreement assessment demonstrated that the scene-dependent normalization using homogeneous polygons provided slightly higher accuracy values than those obtained by the scene-independent method. Finally, the non-normalized and relatively normalized ‘Landsat-like’ AWiFS 2007 images were integrated into 1984 to 2010 Landsat time-series stacks (LTSS) for disturbance detection using the Vegetation Change Tracker (VCT) model. Both scene-dependent and scene-independent normalized AWiFS data sets could generate disturbance maps similar to what were generated using the LTSS data set, and their kappa coefficients were higher than 0.97. These results indicate that AWiFS can be used instead of Landsat data to detect multitemporal disturbance in the event of Landsat data discontinuity.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor & Francis","publisherLocation":"Philadelphia, PA","doi":"10.1080/01431161.2012.743690","usgsCitation":"Chen, X., Vogelmann, J., Chander, G., Ji, L., Tolk, B., Huang, C., and Rollins, M., 2013, Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images: International Journal of Remote Sensing, v. 34, no. 7, p. 2432-2453, https://doi.org/10.1080/01431161.2012.743690.","productDescription":"22 p.","startPage":"2432","endPage":"2453","ipdsId":"IP-022909","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":269159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":269158,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431161.2012.743690"}],"volume":"34","issue":"7","noUsgsAuthors":false,"publicationDate":"2012-12-12","publicationStatus":"PW","scienceBaseUri":"5140407ee4b089809dbf43e7","contributors":{"authors":[{"text":"Chen, Xuexia","contributorId":14213,"corporation":false,"usgs":true,"family":"Chen","given":"Xuexia","affiliations":[],"preferred":false,"id":471526,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vogelmann, James E. 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":649,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":471523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":471525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":2832,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":471524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tolk, Brian 0000-0002-9060-0266","orcid":"https://orcid.org/0000-0002-9060-0266","contributorId":62426,"corporation":false,"usgs":true,"family":"Tolk","given":"Brian","affiliations":[],"preferred":false,"id":471528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Huang, Chengquan","contributorId":25378,"corporation":false,"usgs":true,"family":"Huang","given":"Chengquan","affiliations":[],"preferred":false,"id":471527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rollins, Matthew","contributorId":72347,"corporation":false,"usgs":true,"family":"Rollins","given":"Matthew","affiliations":[],"preferred":false,"id":471529,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70044556,"text":"ds709U - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Bakhud mineral district in Afghanistan: Chapter U in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-03-12T18:57:26","indexId":"ds709U","displayToPublicDate":"2013-03-12T00:00:00","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":"U","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Bakhud mineral district in Afghanistan: Chapter U 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 Bakhud mineral district, which has industrial fluorite deposits.\n\nALOS 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, 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.\n\nThe 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).\n\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (41 for Bakhud) and the WGS84 datum. The final image mosaics were subdivided into nine overlapping tiles or quadrants because of the large size of the target area. The nine image tiles (or quadrants) for the Bakhud 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/ds709U","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 U in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","usgsCitation":"Davis, P.A., and Cagney, L.E., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Bakhud mineral district in Afghanistan: Chapter U in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme; 4 Index Maps: 37 x 39 inches; 18 Image Files; Metadata; 1 Shapefile, https://doi.org/10.3133/ds709U.","productDescription":"HTML Document; Readme; 4 Index Maps: 37 x 39 inches; 18 Image Files; Metadata; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":269192,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709U.png"},{"id":269187,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/u/index_maps/index_maps.html"},{"id":269188,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/u/image_files/image_files.html"},{"id":269185,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/u/"},{"id":269186,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/u/1_readme.txt"},{"id":269189,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/u/metadata/metadata.html"},{"id":269190,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/u/shapefiles/shapefiles.html"},{"id":269191,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 58.0,28.0 ], [ 58.0,40.0 ], [ 78.0,40.0 ], [ 78.0,28.0 ], [ 58.0,28.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5140407fe4b089809dbf43eb","contributors":{"editors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":509263,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":475873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":475874,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70044170,"text":"ds709R - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dudkash mineral district in Afghanistan: Chapter R in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-27T16:10:20","indexId":"ds709R","displayToPublicDate":"2013-02-27T00:00:00","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":"R","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dudkash mineral district in Afghanistan: Chapter R 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 Dudkash mineral district, which has industrial mineral 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,2006,2007,2008,2009), 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 Dudkash) 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 Dudkash 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/ds709R","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 R in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709-R)","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dudkash mineral district in Afghanistan: Chapter R in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme; 4 Index Maps; 16 Image Files; 16 Metadata Files; 1 Shapefile, https://doi.org/10.3133/ds709R.","productDescription":"HTML Document; Readme; 4 Index Maps; 16 Image Files; 16 Metadata Files; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":268489,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/r/1_readme.txt"},{"id":268490,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/r/index_maps/index_maps.html"},{"id":268491,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/r/image_files/image_files.html"},{"id":268492,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/r/metadata/metadata.html"},{"id":268493,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/r/shapefiles/shapefiles.html"},{"id":268488,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/r/"},{"id":268494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_R.png"}],"country":"Afghanistan","otherGeospatial":"Dudkash Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 58.0,29.0 ], [ 58.0,40.0 ], [ 77.0,40.0 ], [ 77.0,29.0 ], [ 58.0,29.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2afbe4b0cad81a732d7b","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":474970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":474972,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":474971,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70044146,"text":"70044146 - 2013 - Assessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study","interactions":[],"lastModifiedDate":"2017-05-10T15:48:47","indexId":"70044146","displayToPublicDate":"2013-02-27T00:00:00","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":"Assessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study","docAbstract":"Cross-calibration of satellite sensors permits the quantitative comparison of measurements obtained from different Earth Observing (EO) systems. Cross-calibration studies usually use simultaneous or near-simultaneous observations from several spaceborne sensors to develop band-by-band relationships through regression analysis. The investigation described in this paper focuses on evaluation of the uncertainties inherent in the cross-calibration process, including contributions due to different spectral responses, spectral resolution, spectral filter shift, geometric misregistrations, and spatial resolutions. The hyperspectral data from the Environmental Satellite SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY and the EO-1 Hyperion, along with the relative spectral responses (RSRs) from the Landsat 7 Enhanced Thematic Mapper (TM) Plus and the Terra Moderate Resolution Imaging Spectroradiometer sensors, were used for the spectral uncertainty study. The data from Landsat 5 TM over five representative land cover types (desert, rangeland, grassland, deciduous forest, and coniferous forest) were used for the geometric misregistrations and spatial-resolution study. The spectral resolution uncertainty was found to be within 0.25%, spectral filter shift within 2.5%, geometric misregistrations within 0.35%, and spatial-resolution effects within 0.1% for the Libya 4 site. The one-sigma uncertainties presented in this paper are uncorrelated, and therefore, the uncertainties can be summed orthogonally. Furthermore, an overall total uncertainty was developed. In general, the results suggested that the spectral uncertainty is more dominant compared to other uncertainties presented in this paper. Therefore, the effect of the sensor RSR differences needs to be quantified and compensated to avoid large uncertainties in cross-calibration results.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Transactions on Geoscience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","publisherLocation":"Washington, D.C.","doi":"10.1109/TGRS.2012.2228008","usgsCitation":"Chander, G., Helder, D., Aaron, D., Mishra, N., and Shrestha, A., 2013, Assessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study: IEEE Transactions on Geoscience and Remote Sensing, v. 51, no. 3, p. 1282-1296, https://doi.org/10.1109/TGRS.2012.2228008.","productDescription":"15 p.","startPage":"1282","endPage":"1296","ipdsId":"IP-039167","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2af9e4b0cad81a732d77","contributors":{"authors":[{"text":"Chander, G.","contributorId":51449,"corporation":false,"usgs":true,"family":"Chander","given":"G.","affiliations":[],"preferred":false,"id":474896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helder, D. L. 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":51496,"corporation":false,"usgs":true,"family":"Helder","given":"D. L.","affiliations":[],"preferred":false,"id":474897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aaron, David","contributorId":83809,"corporation":false,"usgs":false,"family":"Aaron","given":"David","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":474899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mishra, N.","contributorId":67379,"corporation":false,"usgs":true,"family":"Mishra","given":"N.","email":"","affiliations":[],"preferred":false,"id":474898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shrestha, A.K.","contributorId":104783,"corporation":false,"usgs":true,"family":"Shrestha","given":"A.K.","email":"","affiliations":[],"preferred":false,"id":474900,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70044171,"text":"ds709S - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kunduz mineral district in Afghanistan: Chapter S in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-27T16:24:33","indexId":"ds709S","displayToPublicDate":"2013-02-27T00:00:00","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":"S","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kunduz mineral district in Afghanistan: Chapter S 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 Kunduz mineral district, which has celestite 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), 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 Kunduz) 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 Kunduz 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-S)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709S","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey; Chapter S in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kunduz mineral district in Afghanistan: Chapter S in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme; 4 Index Maps; 16 Image Files; 16 Metadata Files; 1 Shapefile, https://doi.org/10.3133/ds709S.","productDescription":"HTML Document; Readme; 4 Index Maps; 16 Image Files; 16 Metadata Files; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":268501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_S.png"},{"id":268498,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/s/image_files/image_files.html"},{"id":268499,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/s/metadata/metadata.html"},{"id":268500,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/s/shapefiles/shapefiles.html"},{"id":268495,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/s/"},{"id":268496,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/s/1_readme.txt"},{"id":268497,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/s/index_maps/index_maps.html"}],"country":"Afghanistan","otherGeospatial":"Kunduz Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 58.0,29.0 ], [ 58.0,40.0 ], [ 77.0,40.0 ], [ 77.0,29.0 ], [ 58.0,29.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2afde4b0cad81a732d7f","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":474973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":474975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":474974,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70044136,"text":"70044136 - 2013 - Applications of spectral band adjustment factors (SBAF) for cross-calibration","interactions":[],"lastModifiedDate":"2013-02-27T17:44:54","indexId":"70044136","displayToPublicDate":"2013-02-27T00:00:00","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":"Applications of spectral band adjustment factors (SBAF) for cross-calibration","docAbstract":"To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earth's surface acquired from multiple spaceborne imaging sensors. However, an integrated global observation framework requires an understanding of how land surface processes are seen differently by various sensors. This is particularly true for sensors acquiring data in spectral bands whose relative spectral responses (RSRs) are not similar and thus may produce different results while observing the same target. The intrinsic offsets between two sensors caused by RSR mismatches can be compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of the two sensors. The motivation of this work comes from the need to compensate the spectral response differences of multispectral sensors in order to provide a more accurate cross-calibration between the sensors. In this paper, radiometric cross-calibration of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors was performed using near-simultaneous observations over the Libya 4 pseudoinvariant calibration site in the visible and near-infrared spectral range. The RSR differences of the analogous ETM+ and MODIS spectral bands provide the opportunity to explore, understand, quantify, and compensate for the measurement differences between these two sensors. The cross-calibration was initially performed by comparing the top-of-atmosphere (TOA) reflectances between the two sensors over their lifetimes. The average percent differences in the long-term trends ranged from $-$5% to $+$6%. The RSR compensated ETM+ TOA reflectance (ETM+$^{ast}$) measurements were then found to agree with MODIS TOA reflectance to within 5% for all bands when Earth Observing-1 Hy- erion hyperspectral data were used to produce the SBAFs. These differences were later reduced to within 1% for all bands (except band 2) by using Environmental Satellite Scanning Imaging Absorption Spectrometer for Atmospheric Cartography hyperspectral data to produce the SBAFs.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Transactions on Geoscience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","publisherLocation":"Washington, D.C.","doi":"10.1109/TGRS.2012.2228007","usgsCitation":"Chander, G., 2013, Applications of spectral band adjustment factors (SBAF) for cross-calibration: IEEE Transactions on Geoscience and Remote Sensing, v. 51, no. 3, p. 1267-1281, https://doi.org/10.1109/TGRS.2012.2228007.","productDescription":"15 p.","startPage":"1267","endPage":"1281","ipdsId":"IP-037261","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268516,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/TGRS.2012.2228007"}],"volume":"51","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f2adfe4b0cad81a732d73","contributors":{"authors":[{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":474863,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70043719,"text":"70043719 - 2013 - A comprehensive change detection method for updating the National Land Cover Database to circa 2011","interactions":[],"lastModifiedDate":"2013-02-26T12:57:26","indexId":"70043719","displayToPublicDate":"2013-02-26T00:00:00","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":"A comprehensive change detection method for updating the National Land Cover Database to circa 2011","docAbstract":"The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on different landscapes.","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.2013.01.012","usgsCitation":"Jin, S., Yang, L., Danielson, P., Homer, C.G., Fry, J., and Xian, G., 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159-175, https://doi.org/10.1016/j.rse.2013.01.012.","productDescription":"17 p.","startPage":"159","endPage":"175","ipdsId":"IP-041925","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268380,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2013.01.012"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 172.5,18.9 ], [ 172.5,71.4 ], [ -66.9,71.4 ], [ -66.9,18.9 ], [ 172.5,18.9 ] ] ] } } ] }","volume":"132","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd49a9e4b0b290850ef516","chorus":{"doi":"10.1016/j.rse.2013.01.012","url":"http://dx.doi.org/10.1016/j.rse.2013.01.012","publisher":"Elsevier BV","authors":"Jin Suming, Yang Limin, Danielson Patrick, Homer Collin, Fry Joyce, Xian George","journalName":"Remote Sensing of Environment","publicationDate":"5/2013","auditedOn":"4/22/2016"},"contributors":{"authors":[{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":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":474161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":474160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danielson, Patrick 0000-0002-2990-2783 pdanielson@usgs.gov","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":3551,"corporation":false,"usgs":true,"family":"Danielson","given":"Patrick","email":"pdanielson@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":474159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":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":474157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fry, Joyce 0000-0002-8466-9582 jfry@usgs.gov","orcid":"https://orcid.org/0000-0002-8466-9582","contributorId":3147,"corporation":false,"usgs":true,"family":"Fry","given":"Joyce","email":"jfry@usgs.gov","affiliations":[],"preferred":true,"id":474158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xian, George 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":76589,"corporation":false,"usgs":true,"family":"Xian","given":"George","affiliations":[],"preferred":false,"id":474162,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197217,"text":"70197217 - 2013 - Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes","interactions":[],"lastModifiedDate":"2018-05-23T11:00:04","indexId":"70197217","displayToPublicDate":"2013-02-13T00:00:00","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":"Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes","docAbstract":"We applied a spectroscopic analysis to Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) data collected from low and medium altitudes during and after the Deepwater Horizon oil spill to delineate the distribution of oil-damaged canopies in the marshes of Barataria Bay, Louisiana. Spectral feature analysis compared the AVIRIS data to reference spectra of oiled marsh by using absorption features centered near 1.7 and 2.3 μm, which arise from CH bonds in oil. AVIRIS-derived maps of oiled shorelines from the individual dates of July 31, September 14, and October 4, 2010, were 89.3%, 89.8%, and 90.6% accurate, respectively. A composite map at 3.5 m grid spacing, accumulated from the three dates, was 93.4% accurate in detecting oiled shorelines. The composite map had 100% accuracy for detecting damaged plant canopy in oiled areas that extended more than 1.2 m into the marsh. Spatial resampling of the AVIRIS data to 30 m reduced the accuracy to 73.6% overall. However, detection accuracy remained high for oiled canopies that extended more than 4 m into the marsh (23 of 28 field reference points with oil were detected). Spectral resampling of the 3.5 m AVIRIS data to Landsat Enhanced Thematic Mapper (ETM) spectral response greatly reduced the detection of oil spectral signatures. With spatial resampling of simulated Landsat ETM data to 30 m, oil signatures were not detected. Overall, ~ 40 km of coastline, marsh comprised mainly of Spartina alterniflora and Juncus roemerianus, were found to be oiled in narrow zones at the shorelines. Zones of oiled canopies reached on average 11 m into the marsh, with a maximum reach of 21 m. The field and airborne data showed that, in many areas, weathered oil persisted in the marsh from the first field survey, July 10, to the latest airborne survey, October 4, 2010. The results demonstrate the applicability of high spatial resolution imaging spectrometer data to identifying contaminants in the environment for use in evaluating ecosystem disturbance and response.","largerWorkTitle":"Remote Sensing of Environment","language":"English","publisher":"Elsevier ","doi":"10.1016/j.rse.2012.10.028","usgsCitation":"Kokaly, R.F., Couvillion, B., Holloway, J.M., Roberts, D.A., Ustin, S.L., Peterson, S.H., Khanna, S., and Piazza, S.C., 2013, Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes: Remote Sensing of Environment, v. 129, p. 210-230, https://doi.org/10.1016/j.rse.2012.10.028.","productDescription":"21 p.","startPage":"210","endPage":"230","ipdsId":"IP-029498","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":473952,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2012.10.028","text":"Publisher Index Page"},{"id":354412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.9454116821289,\n 29.420460341013133\n ],\n [\n -89.9454116821289,\n 29.513421462044942\n ],\n [\n -89.81494903564453,\n 29.513421462044942\n ],\n [\n -89.81494903564453,\n 29.420460341013133\n ],\n [\n -89.9454116821289,\n 29.420460341013133\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b156714e4b092d9651e1d21","contributors":{"authors":[{"text":"Kokaly, Raymond F. 0000-0003-0276-7101 raymond@usgs.gov","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":150717,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"raymond@usgs.gov","middleInitial":"F.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":736265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Couvillion, Brady 0000-0001-5323-1687 couvillionb@usgs.gov","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":146832,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","email":"couvillionb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research 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Susan L.","contributorId":52878,"corporation":false,"usgs":false,"family":"Ustin","given":"Susan","email":"","middleInitial":"L.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":736269,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, Seth H.","contributorId":139568,"corporation":false,"usgs":false,"family":"Peterson","given":"Seth","email":"","middleInitial":"H.","affiliations":[{"id":12804,"text":"Univ. of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":736267,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Khanna, Shruti","contributorId":205167,"corporation":false,"usgs":false,"family":"Khanna","given":"Shruti","email":"","affiliations":[{"id":37041,"text":"Department of Land, Air, and Water Resources, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":736270,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Piazza, Sarai C. 0000-0001-6962-9008 piazzas@usgs.gov","orcid":"https://orcid.org/0000-0001-6962-9008","contributorId":466,"corporation":false,"usgs":true,"family":"Piazza","given":"Sarai","email":"piazzas@usgs.gov","middleInitial":"C.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":736264,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70043200,"text":"ds709P - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Baghlan mineral district in Afghanistan: Chapter P in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-07T13:47:41","indexId":"ds709P","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":"P","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Baghlan mineral district in Afghanistan: Chapter P 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 Baghlan mineral district, which has industrial clay and gypsum 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, 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 Baghlan) 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 Baghlan 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/ds709P","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 P in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709","usgsCitation":"Davis, P.A., and Cagney, L.E., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Baghlan mineral district in Afghanistan: Chapter P in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme; 2 Maps; 4 Image Files; 4 Metadata; 1 Shapefile, https://doi.org/10.3133/ds709P.","productDescription":"HTML Document; Readme; 2 Maps; 4 Image Files; 4 Metadata; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":267118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_p.png"},{"id":267113,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/p/index_maps/Baghlan_Area-of-Interest_Index_Map.pdf"},{"id":267111,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/p/"},{"id":267112,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/p/1_readme.txt"},{"id":267114,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/p/index_maps/Baghlan_Image_Index_Map.pdf"},{"id":267115,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/p/image_files/image_files.html"},{"id":267116,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/p/metadata/metadata.html"},{"id":267117,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/p/shapefiles/shapefiles.html"}],"country":"Afghanistan","otherGeospatial":"Baghlan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5114cd05e4b0ca7af0743adf","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":473151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":473152,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}