Hydrothermal Alteration Maps of the Central and Southern Basin and Range Province of the United States Compiled From Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Data

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Frequently anticipated questions:


What does this data set describe?

Title:
Hydrothermal Alteration Maps of the Central and Southern Basin and Range Province of the United States Compiled From Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Data
Abstract:
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and Interactive Data Language (IDL) logical operator algorithms were used to map hydrothermally altered rocks in the central and southern parts of the Basin and Range province of the United States. The hydrothermally altered rocks mapped in this study include (1) hydrothermal silica-rich rocks (hydrous quartz, chalcedony, opal, and amorphous silica), (2) propylitic rocks (calcite-dolomite and epidote-chlorite mapped as separate mineral groups), (3) argillic rocks (alunite-pyrophyllite-kaolinite), and (4) phyllic rocks (sericite-muscovite). A series of hydrothermal alteration maps, which identify the potential locations of hydrothermal silica-rich, propylitic, argillic, and phyllic rocks on Landsat Thematic Mapper (TM) band 7 orthorectified images, and shape files of hydrothermal alteration units are provided.
  1. How might this data set be cited?
    Mars, John C., 2013, Hydrothermal Alteration Maps of the Central and Southern Basin and Range Province of the United States Compiled From Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Data: Open-File Report 2013-1139, U.S. Geological Survey, Reston, VA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -120.40976898312
    East_Bounding_Coordinate: -107.40390063535
    North_Bounding_Coordinate: 42.3918816756081
    South_Bounding_Coordinate: 30.6523919922748
  3. What does it look like?
    https://mrdata.usgs.gov/surficial-mineralogy/ofr-2013-1139/ofr-2013-1139.png (PNG)
    Reduced-size map showing extent of these data with US state boundaries, (770 x 514 pixels)
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2013
    Currentness_Reference:
    publication date
  5. What is the general form of this data set?
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Vector data set. It contains the following vector data types (SDTS terminology):
      • G-Polygon (5180485)
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.0008. Longitudes are given to the nearest 0.0009. Latitude and longitude values are specified in decimal degrees. The horizontal datum used is World Geodetic System 1984.
      The ellipsoid used is WGS 84.
      The semi-major axis of the ellipsoid used is 6378137.
      The flattening of the ellipsoid used is 1/298.257.
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    Data are arranged as collections of polygons having the same alteration type, so there are five separate packages or layers: argillic, carbonate, epi_chlor, hydro_silica, and phyllic. With this separation of alteration types, it is not necessary to code the alteration type explicitly in the data files themselves, so the alteration type is a characteristic of each whole package or layer.

    The report text refers to propylitic alteration; in the data files this is labeled epi_chlor to refer to epidote-chlorite-albite alteration.
    Entity_and_Attribute_Detail_Citation: https://pubs.usgs.gov/of/2013/1139/pdf/of2013-1139.pdf

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Mars, John C.
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    John C Mars
    USGS Midwest Area
    Research Geologist
    Mail Stop 954
    12201 Sunrise Valley Dr
    Reston, VA
    USA

    703-648-6302 (voice)
    703-648-6383 (FAX)
    jmars@usgs.gov

Why was the data set created?

Economic deposits such as gold and copper are associated with hydrothermally altered rocks, which typically consist of one or more hydrous zones of alteration minerals containing at least one mineral that exhibits diagnostic spectral absorption features in the VNIR through the SWIR or the thermal-infrared TIR regions. ASTER bands are positioned to define and map the diagnostic VNIR, SWIR, and TIR spectral absorption features of hydrothermal alteration minerals. These data are available with resolutions from 15m to 90m, giving a level of detail sufficient to help locate mineral deposits.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 2012 (process 1 of 1)
    The ASTER dataset used to cover the central and southern parts of the Basin and Range province in the United States consists of 247 ASTER_Level 1B scenes. The ASTER_Level 1B radiance data consist of all VNIR, SWIR and TIR bands, including the backward-looking VNIR band. The VNIR and SWIR radiance data were calibrated to the reflectance data using Moderate Resolution Imaging Spectrometer (MODIS) water-vapor data, radiometric "crosstalk" correction software, radiance "gain" correction coefficients, and Atmospheric Correction Now (ACORN) software. The TIR radiance data were calibrated to emissivity using atmospheric removal and emissivity normalization algorithms in Environment for Visualizing Images (ENVI), an image calibration and processing software package. Each ASTER scene was georectified to a Landsat TM 30 m orthorectified image with a root mean square error of less than 60 m.

    Rocks containing hydrous quartz, chalcedony, opal, and amorphous silica (hydrothermalsilicarich rocks), calcite-dolomite and epidote-chlorite (propylitic), alunite-pyrophyllite-kaolinite (argillic), and sericite-muscovite (phyllic) were mapped using Interactive Data Language (IDL) logical operators (Details in report table 1). The IDL logical operators consist of band thresholds and band ratios strung togetherto map spectral absorption features of minerals. All of the logical operator algorithms mask green vegetation using a ratio of VNIR band 3/band 2, which detectsthe chlorophyll absorption feature at 0.67 µm. In addition, a band 4 threshold is used to mask low signals, and thus noisy spectra.

    Hydrothermally altered phyllic and argillic rocks were mapped using ASTER VNIR and SWIR data at 30-m spatial resolution. SWIR band ratios were used in IDL logical operators to map Al-O-H spectral absorption features associated with alunite, kaolinite, and sericite-muscovite. The ratio of SWIR band 4/band 5 maps the 2. 165-µm spectral absorption feature associated with alunite, pyrophyllite, and kaolinite, and the ratios of SWIR band 4/band 6 and band 7/band 6 map the 2.2-µm spectral absorption feature exhibited by alunite, kaolinite, and sericite.

    Hydrothermal silica-rich rocks were mapped using ASTER SWIR and TIR band ratios at 90-m resolution. The ratio of SWIR band 4/band 7 is typically higher for hydrothermal silica-rich rocks, which have lower overall SWIR reflectance in the 2.0- to-2.4-µm region than nonhydrothermal silica-rich rocks because of residual molecular water or an O-H absorption feature spanning 2.26 to 2.4 µm. The ratio of TIR band 13/band 12 maps the 9.09-µm quartz reststrahlen absorption feature. Thus, silica-rich rocks were mapped using the TIR emissivity data and hydrothermal silica-rich rocks were discriminated from the non-hydrothermal silica-rich rocks using the corresponding SWIR reflectance data for each pixel.

    Hydrothermally altered propylitic rocks were mapped using ASTER SWIR and TIR band ratios at 90-m spatial resolution. Calcite, dolomite, epidote, and chlorite typically exhibit overlapping CO3 and Fe,Mg-O-H spectral absorption features at 2.31 to 2.33 µm and have been difficult to map separately using SWIR data in previous studies. In the TIR region, however, rocks containing calcite and dolomite exhibit an 11.2-µm spectral absorption feature, whereas epidote- and chlorite-rich rocks have TIR spectral absorption features centered at approximately 10.2 µm. Thus, TIR calcite- dolomite spectra exhibit higher band-13 emissivity and lower band- 14 emissivity, whereas epidote-chlorite TIR spectra exhibit lower band-13 emissivity and higher band-14 emissivity. The calcite- dolomite and epidote-chlorite logical operators use the ratio of SWIR band 6/band 8 to map the 2.31- to 2.33-µm absorption feature of both groups of minerals and the ratio of TIR band 13/band 14 set to greater than 1.005 to separate and map calcite-dolomite-rich rocks and less than 0.999 to separate and map epidote-chlorite-rich rocks.

    The argillic and phyllic units are mapped at 30-m spatial resolution and the hydrothermal silicarich rocks and propylitic units are mapped at 90-m spatial resolution. Each logical operator algorithm produces an image with pixel values of one or zero which is converted to a polygon-based shapefile for import into ArcGIS. The logical operators correctly mapped (with ±20 percent error) hydrothermal alteration at three calibration-validation test sites on the basis of field data and mineral maps compiled from previous studies.

  3. What similar or related data should the user be aware of?

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
  2. How accurate are the geographic locations?
    Each ASTER scene was georectified to a Landsat TM 30-m orthorectified image with a root mean square error of less than 60m.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    Unclassified areas within the study boundary are caused by gaps in the ASTER image coverage as well as pixels whose reflectance signatures do not indicate surficial mineralogy typical of hydrothermal alteration, or pixels that could not be reliably classified given the available data (for example, a shadow effect near some mountains causes reflectance in the shadow to be too noisy for reliable classification).
  5. How consistent are the relationships among the observations, including topology?
    The various mineral assemblages of interest here are indicated by different sets of reflectance characteristics, giving different spatial resolution for different assemblages.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints: none
Use_Constraints: none
  1. Who distributes the data set? (Distributor 1 of 1)
    Mineral Resources Program
    Attn: Peter N Schweitzer
    Geologist
    12201 Sunrise Valley Drive
    Reston, VA
    USA

    703-648-6533 (voice)
    703-648-6252 (FAX)
    askmrdata@usgs.gov
  2. What's the catalog number I need to order this data set? USGS Open-File Report 2013-1139
  3. What legal disclaimers am I supposed to read?
    Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 07-Dec-2016
Metadata author:
Peter N Schweitzer
USGS Eastern Mineral and Environmental Resources Science Center
Geologist
12201 Sunrise Valley Drive
Reston, VA
USA

703-648-6533 (voice)
703-648-6252 (FAX)
pschweitzer@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

This page is <https://mrdata.usgs.gov/metadata/ofr-2013-1139.faq.html>
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