Kokaly, R.F. Johnson, M.R. Graham, G.E. Hoefen, T.M Kelley, K.D. Hubbard, B.E. 20180209 Version 1.1 remote-sensing image Denver, CO U.S. Geological Survey Additional information about Originator: Kokaly, R.F., https://orcid.org/0000-0003-0276-7101; Johnson, M.R., https://orcid.org/0000-0001-6133-0247; Graham, G.E., https://orcid.org/0000-0003-0657-0365;, Hoefen, T.M., https://orcid.org/0000-0002-3083-5987; Kelley, K.D., https://orcid.org/0000-0002-3232-5809; Hubbard, B.E., https://orcid.org/0000-0002-9315-2032 https://doi.org/10.5066/F7NV9H6F Reflectance data from HyMap™ were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011), programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). The HyMap reflectance data are provided and described in this data release. MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals, vegetation, water, and other materials. Linear continuum removal is a technique to isolate an absorption feature from background spectral variations (Clark and Roush, 1984). Following continuum removal of a spectral feature in a reference spectrum and the corresponding channels in an imaging spectrometer pixel, the coefficient of determination (r2) of a linear regression of these continuum-removed values is used as the metric to judge the degree of match (or fit) between the unknown and reference spectra. MICA analysis is controlled by a command file, which lists the reference spectra for comparison to imaging spectrometer pixel spectra, the wavelength regions for continuum removal and spectral feature comparison, and other parameters (see Kokaly, 2011). For each pixel, the reference spectrum with the highest fit value identifies the predominant mineral class. The reference spectra used in this MICA analysis are available to the public in the USGS spectral library (Kokaly and others, 2017). The MICA command file used in this study was adapted from that used to process HyMap data covering Afghanistan (Kokaly and others, 2013). The MICA command file is provided in this data release and also in the digital appendix of Graham and others (2018). The imaging spectrometer data were collected and processed as one component of a U.S. Geological Survey (USGS) mineral resource project with the goals of enhancing geologic mapping and developing methods to identify and characterize mineral deposits elsewhere in Alaska. Hyperspectral surveying is one method that can be used to rapidly acquire information about the distributions of surficial materials, including different types of bedrock and ground cover. References Cited Clark, R.N., and Roush, T., 1984, Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications: Journal of Geophysical Research, v. 89, p. 6329–6340. Clark, R.N., 1993, SPECtrum Processing Routines user’s manual version 3 (program SPECPR): U.S. Geological Survey Open File Report 93–595, 210 p., software online, accessed November 3, 2016, at http://speclab.cr.usgs.gov/software.html. Cocks, T., Jenssen, R., Stewart, A., Wilson, I., and Shields, T., 1998, The HyMap airborne hyperspectral sensor: The system, calibration and performance, in Schaepman, M., Schlapfer, and D., and Itten, K.I., eds., Proceedings of 1998 EARSeL Workshop on Imaging Spectroscopy, Zurich, Sweden, 6–8 October 1998; p. 37–43. Graham, G.E., Kokaly, R.F., Kelley, K.D, Hoefen, T.M. Johnson, M.R., and Hubbard, B.E., in press, Application of imaging spectroscopy for mineral exploration in Alaska: A study over porphyry Cu deposits in the eastern Alaska Range, submitted to Economic Geology. Kokaly, R.F., 2011, PRISM: Processing routines in IDL for spectroscopic measurements (installation manual and user's guide, version 1.0): U.S. Geological Survey Open-File Report 2011–1155, 432 p., available at https://pubs.usgs.gov/of/2011/1155/. Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., Driscoll, R.L., and Klein, A.J., 2017, USGS Spectral Library Version 7: U.S. Geological Survey Data Series 1035, 61 p., available at https://doi.org/10.3133/ds1035. Kokaly, R.F., King, T.V.V., and Hoefen, T.M., 2013, Surface mineral maps of Afghanistan derived from HyMap imaging spectrometer data, version 2: U.S. Geological Survey Data Series 787, 29 p., available at https://pubs.usgs.gov/ds/787/. Kokaly, R.F., and Skidmore, A.K., 2015, Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm, International Journal of Applied Earth Observation and Geoinformation, v. 43, p. 55-83. Kokaly, R.F., Hoefen, T.M., King, T.V.V., and Johnson, M.R., 2017, Airborne imaging spectroscopy data collected for characterizing mineral resources near Nabesna, Alaska, 2014, U.S. Geological Survey Data Release, available at http://dx.doi.org/10.5066/F7DN435W. 20140714 20160721 ground condition Planned As needed -143.385752 -140.943323 62.4499567 61.9420478 USGS Thesaurus mineralogy mineral resources hyperspectral imaging remote sensing infrared imaging ISO 19115 Topic Category economy geoscientificInformation imageryBaseMapsEarthCover None spectroscopy HyMap imaging spectroscopy shortwave infrared SWIR visible-near infrared VNIR Environment for Visualizing Images ENVI Mineral Resources Program MRP USGS Metadata Identifier USGS:59ee7d13e4b0220bbd976365 Geographic Names Information System (GNIS) Alaska Orange Hill Bond Creek Nabesna Alaska Range Wrangell–Saint Elias National Preserve Wrangell Mountains Nutzotin Mountains Nikonda Creek Common geographic areas Canada Alaska Yukon Territory Valdez-Cordova Nabesna A-2 quadrangle Nabesna A-4 quadrangle Nabesna B-4 quadrangle none There is no guarantee concerning the accuracy of the data. Any user who modifies the data is obligated to describe the types of modifications they perform. Data have been checked to ensure the accuracy. If any errors are detected, please notify the originating office. The U.S. Geological Survey strongly recommends that careful attention be paid to the metadata file associated with these data. Acknowledgment of the U.S. Geological Survey would be appreciated in products derived from these data. User specifically agrees not to misrepresent the data, nor to imply that changes made were approved or endorsed by the U.S. Geological Survey. Please refer to http://www.usgs.gov/privacy.html for the USGS disclaimer. Raymond Kokaly U.S. Geological Survey Research Geophysicist mailing address

Mail Stop 964, W 6th Ave Kipling St

Lakewood CO 80225 United States 720-352-7414 303-236-1425 raymond@usgs.gov This release of the dataset was funded by the Mineral Resources Program (MRP) and collected with the HyVista Corporation (HyMap™ hyperspectral scanner manufactured by Integrated Spectronics Pty Ltd). Environment as of Metadata Creation: Microsoft [Unknown] Version 6.2 (Build 9200) ; Esri ArcGIS 10.5 (Build 6491) Service Pack N/A (Build N/A) Attribute fields and values were reviewed and checked for accuracy and consistency of terms. No formal logical accuracy tests were conducted Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. The root mean square error (RMSE) for this dataset was assessed at +/-14m based on a sample size of 710 ground control points. The value was calculated as the average of the RMSE of each individual point's x-error and y-error. Not applicable. Kokaly, R.F., Hoefen, T.M., King, T.V.V., and Johnson, M.R. 2017 Raster Digital Data Set Denver, CO U.S. Geological Survey https://dx.doi.org/10.5066/F7DN435W Digital and/or Hardcopy Resources 20140714 20140721 ground condition AirborneHyMap2014 Source information used in support of the development of the data set. IMAGING SPECTROMETER REFECTANCE DATA Data were delivered by the operators of the sensor (HyVista Corp., Australia) in units of radiance (data are available in Kokaly and others, 2017). Radiance data were converted to reflectance with procedures adapted from Kokaly and others (2013). First, the radiance data were converted to apparent surface reflectance using a radiative transfer program, Atmospheric and Topographic Correction for airborne imagery (ATCOR®-4), in rugged terrain mode (ReSe Applications, Zurich, Switzerland). The ATCOR-4 rugged terrain mode utilizes a surface elevation model to adjust illumination levels. Apparent surface reflectance values from the ATCOR-4 processing were empirically adjusted using ground-based reflectance measurements from calibration sites measured with an Analytical Spectral Devices FieldSpec® 4 (ASD FS4; ASD Inc., a Malvern PANalytical Company, Longmont, Colorado) standard resolution field spectrometer. Following the procedures adapted from Clark and others (2002), ASD FS4 data were collected from four sites in broad alluvial-fluvial gravel bars that were minimally vegetated and mostly lichen-free. The four sites covered areas of 0.31, 0.36, 0.51 and 0.76 ha. In the HyMap data, 86, 101, 143, and 210 HyMap pixels covered these areas, respectively. Although the rocks in these areas were mixed and varied at the fine spatial scale, at the HyMap 6-m pixel scale the calibration areas were spectrally homogeneous. The bare fiber optic of the ASD was held at shoulder height (~1.4 m) while walking around the calibration site and recording measurements of reflected sunlight relative to a Spectralon® white reference panel. The integration times for dark current and white reference panel were set to 10 and 24 seconds, respectively. The ASD was configured for 6-second averages for each recording of surface reflectance. A great number of ASD recordings were made in each calibration site: 455, 319, 420, and 310, respectively. Subsequently, the relative reflectance measurements at each site were averaged. The average relative reflectance was converted to absolute reflectance by correcting for the absorption properties of Spectralon (see the discussion of processing ASD spectra in Kokaly and Skidmore, 2015). Furthermore, offsets in reflectance between the three ASD detectors were rectified using a procedure in the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011) programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). PRISM functions were also used to compute multiplicative correction factors to convert HyMap apparent surface reflectance to ground-calibrated surface reflectance. Because flight lines were designed with substantial overlap, the four calibration sites could be used to directly calibrate eight of the nine flight lines. For the remaining flight line, the cross-calibration procedure of Kokaly and others (2013) was used to compute an empirical correction factor using a non-vegetated and topographically flat area overlapping with an adjacent flight line. Each flight line was geometrically-corrected using data provided by HyVista Corp. (see files provided in Kokaly and others, 2017). The images were mosaicked together using the mosaic function in ENVI (ENvironment for Visualizing Images; Harris Geospatial Solutions, Broomfield, Colorado). To improve the quality of the mosaic image, each flight line was subject to masking for clouds and cloud shadows as well as pixel averaging in areas of poorly illuminated steep terrain (steep slopes facing away from the sun’s postion in the sky). A pixel containing cloud or cloud shadow was determined by comparing radiance and reflectance levels for five HyMap channels (5, 40, 55, 74, and 105, corresponding to wavelength positions 515, 1020, 1238, 1547, and 2129 nm, respectively) against threshold values. In a pixel, if radiance or reflectance levels of any of these five channels exceeded the threshold values for cloud, the pixel was masked; thus, bright pixels were identified as cloud contaminated. If any of the these five channels were below the threshold values for cloud shadow, the pixel was masked; thus, dark pixels were identified as shadowed. The cloud and cloud shadow pixels were combined into a single mask for each flight line. Spatial filtering in ENVI was applied using clumping and sieving functions in order to add a buffer of 1 pixel around the identified cloud and cloud shadow pixels, thereby, masking out adjacent pixels that might also be affected by cloud or cloud shadowing. In the mosaicking procedure, the masked pixels are often filled in with non-masked data from adjacent flight lines because clouds and cloud shadows shifted in the time that elapsed between aircraft passes. In addition to masking for clouds and cloud shadows, flight lines were adjusted in areas of poorly illuminated terrain. Poorly-illuminated pixels were identified using the illumination output image (defining the local solar zenith angle of each pixel) of the ATCOR-4 program, which accounts for the angle of the sun at the time of the hyperspectral image collection and topographic slope and aspect to create an image of the relative illumination of each pixel. In areas with local solar zenith angles greater than 75.5 degrees, a 3x3 pixel averaging was applied to increase the signal-to-noise ratio of reflectance. As a result of the masking, pixel averaging, and mosaicking, the user may see some artifacts of the mosaic process, including: 1) areas of differening reflectance where cloud holes in one flight line were filled by data from an adjacent flight line, sometimes a single dark pixels can outline the filled-in cloud holes, and 2) areas that appear more “pixelized” (coarser spatial resolution) in regions where a steep slope was facing away from the sun’s position in the sky. 2016 MINERAL PREDOMINANCE MAP Reflectance data from HyMap were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011), programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals, vegetation, water, and other materials. Linear continuum removal is a technique to isolate an absorption feature from background spectral variations (Clark and Roush, 1984). Following continuum removal of a spectral feature in a reference spectrum and the corresponding channels in an imaging spectrometer pixel, the coefficient of determination (r^2) of a linear regression of these continuum-removed values is used as the metric to judge the degree of match (or fit) between the two spectra. MICA analysis is controlled by a command file, which lists the reference spectra for comparison to imaging spectrometer pixels, the wavelength regions for continuum removal and spectral feature comparison, and other parameters (see Kokaly, 2011). For each pixel, the reference spectrum with the highest fit value identifies the predominant mineral class. A lookup table, mica_colors_group2_hymap2_2014_v6.txt, is used to assign class image values and colors to the mineral identifications made by the MICA algorithm (Kokaly, 2011). The reference spectra used in this MICA analysis are available to the public in the USGS spectral library (Kokaly and others, 2017). The USGS spectral library version 6 after convolution to HyMap spectral characteristics is provided here, splib06b_cv_hymap2_2014_126ch, in SPECPR file format (Clark, 1993; Kokaly, 2011). The MICA command file used in this study was adapted from that used to process HyMap data covering Afghanistan (Kokaly and others, 2013). The MICA command file, mica_cmds_group2_hymap2_2014_v6a_FOR_RELEASE.mcf, is provided with this data release, and changes from the command file documented and described in Kokaly, 2011 are also provided; LIST_CHANGES_MICAcmdfile_ver4_vs_ver6a.csv. The material classes in the predominance map are listed by value in a separate data table with the source spectra (Kokaly and others, 2017) that were used in identification of the material; HyMap2014_mineral_predominance_classes_and_descriptions.csv. 2017 GEOTIFF FORMAT The mineral predominance dataset, *.dat, was exported to geotiff format. The values in the geotiff are described in the class description files (*.csv) for the dataset. 2017 Raster Grid Cell 8113 18821 1 Universal Transverse Mercator 7 0.9996 -141.0 0.0 500000.0 0.0 row and column 6.7 6.7 Meter D_WGS_1984 WGS_1984 6378137.0 298.257223563 MINERAL PREDOMINANCE DATA - HyMap2014_mineral_predominance_classes_and_descriptions.csv This is a CSV format file containing the raster values (0-78), surficial material classes, and the USGS Spectral Library version 7 reference spectra (Kokaly and others, 2017) that were used identify the material classes. U.S. Geological Survey Value Integer representing each unique MICA summary class. U.S. Geological Survey 0 78 none MaterialPredominance MICA summary classes with diagnostic absorptions at visible and short-wave infrared wavelengths. U.S. Geological Survey MICA summary class names USGSSpecLibv7 Identifying spectra from USGS Spectral Library Version 7 (Kokaly and others, 2017). U.S. Geological Survey Spectrum title MINERAL PREDOMINANCE DATA - LIST_CHANGES_MICAcmdfile_ver4_vs_ver6.csv This is a CSV format file documenting changes in the MICA file procedures from the file used to process the HyMap data covering Afghanistan (Kokaly and others, 2013). U.S. Geological Survey VERSION6aMICAclassValue Integer representing each unique MICA material class. U.S. Geological Survey 0 77 none VERSION6aMICAclassName MICA summary classes with diagnostic absorptions at visible and short-wave infrared wavelengths. U.S. Geological Survey MICA summary class names ChangesToVersion6aFromVersion4 Description of changes from the command file (version 4) documented in the Afghanistan Data Series (Kokaly and others, 2013) to the version documented here. U.S. Geological Survey Short text description summarizing changes. VERSION4MICAclassValue Integer representing each unique MICA material class from version 4 (Kokaly and others, 2013); 'na' was used to indicate where version 6a had no matching value with version 4. U.S. Geological Survey 0 61 none VERSION4MICAclassName MICA summary classes with diagnostic absorptions at visible and short-wave infrared wavelengths from version4 (Kokaly and others 2013); 'na' was used to indicate where version 6a had no matching class with version 4. U.S. Geological Survey MICA summary class names MINERAL PREDOMINANCE DATA - ENVI file - mineral_predominance_hymap_nabesna_ak_july2014.dat This is an ENVI format file containing the predominant mineral or surficial material class with the highest fit to reference spectra for each pixel in the area covering parts of the Wrangell and Nutzotin Mountains in the eastern Alaska Range near Nabesna, Alaska. The file lists the material associated with each raster value, 0-78. The data file is available for download here: https://www.sciencebase.gov/catalog/item/59ee7d13e4b0220bbd976365. The extension, *.dat, added onto the file aids in opening the file format in Esri GIS software products. MINERAL PREDOMINANCE DATA - ENVI header file - mineral_predominance_hymap_nabesna_ak_july2014.hdr This is an ENVI format header file containing descriptive and location information associated with the *.dat file of the same name. MINERAL PREDOMINANCE DATA - GEOTIFF file - mineral_predominance_hymap_nabesna_ak_july2014.tif This is a geotiff format file of the predominant minerals or surficial materials for each pixel in the area covering parts of the Wrangell and Nutzotin Mountains in the eastern Alaska Range near Nabesna, Alaska. The raster values (0-78) and associated descriptive class information are provided in the CSV file, HyMap2014_mineral_predominance_classes_and_descriptions.csv. The file is in RGB format and is available for download here: https://www.sciencebase.gov/catalog/item/59ee7d13e4b0220bbd976365. MINERAL PREDOMINANCE DATA - GEOTIFF world file - mineral_predominance_hymap_nabesna_ak_july2014.tfw This is a geotiff world file containing the location information associated with the *.tif file of the same name. MINERAL PREDOMINANCE DATA - colormap file - mineral_predominance_hymap_nabesna_ak_july2014.clr This is a text file containing the RGB color information associated with the *.tif and *.dat files of the same name. The colormap file each mineral class value and associated color as red (R), green (G), and blue (B). Esri GIS software uses the colormap file to symbolize the geotiff. MINERAL PREDOMINANCE DATA - MICA command file - mica_cmds_group2_hymap2_2014_v6a_FOR_RELEASE.mcf This is the text format MICA command file that was used to process the HyMap data in this data release. Further description is available in Kokaly, 2011. MINERAL PREDOMINANCE DATA - Lookup table - mica_colors_group2_hymap2_2014_v6.txt This text file is used to assign class image values and colors to the mineral identifications made by the MICA algorithm. Further description is available in Kokaly, 2011. MINERAL PREDOMINANCE DATA - USGS spectral library version 6 - splib06b_cv_hymap2_2014_126ch This is the USGS spectral library version 6 after convolution to HyMap spectral characteristics in SPECPR file format. Further description is available in Kokaly, 2011; Clark, 1993. ScienceBase U.S. Geological Survey Mailing and Physical

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Denver CO 80225 United States 1-888-275-874 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty remote-sensing image https://doi.org/10.5066/F7NV9H6F None. No fees are applicable for obtaining the data set. 20200929 Michaela R Johnson U.S. Geological Survey Physical Scientist Mailing and Physical

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