Digital soils survey map of the Patagonia Mountains, Arizona

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What does this data set describe?

Title: Digital soils survey map of the Patagonia Mountains, Arizona
The Soil Survey of Santa Cruz and Parts of Cochise and Pima Counties, Arizona, a product of the USDA Soil Conservation Service and the Forest Service in cooperation with the Arizona Agricultural Experiment Station, released in 1979, was created according to the site conditions in 1971, when soil scientists identified soils types on aerial photographs. The scale at which these maps were published is 1:20,000.
These soil maps were automated for incorporation into the hydrologic modeling within a GIS. The aerial photos onto which the soils units were drawn had not been orthoganalized, and contained distortion. A total of 15 maps composed the study area. These maps were scanned into TIFF format using an 8-bit black and white drum scanner at 100 dpi. The images were imported into ERDAS IMAGINE and the white borders were removed through subset decollaring processes. Five CD-ROMs containing Digital Orthophoto Quarter Quads (DOQQs) were used to register and rectify the scanned soils maps. Polygonal data was then attributed according to the datasets.
  1. How might this data set be cited?
    Norman, Laura Margaret, 2002, Digital soils survey map of the Patagonia Mountains, Arizona: U.S. Geological Survey Open File Report 02-324, U.S. Geological Survey, Menlo Park, California.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -110.85575013
    East_Bounding_Coordinate: -110.61876161
    North_Bounding_Coordinate: 31.55456386
    South_Bounding_Coordinate: 31.33518703
  3. What does it look like? (JPEG)
    Reduced-size image of the map sheet, 720x593 pixels, 92k bytes
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2002
    publication date
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: map
  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):
      • Point (360)
      • String (1015)
      • GT-polygon composed of chains (361)
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      UTM_Zone_Number: 12
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -111
      Latitude_of_Projection_Origin: 0.0
      False_Easting: 500000
      False_Northing: 0.0
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 2.5
      Ordinates (y-coordinates) are specified to the nearest 2.5
      Planar coordinates are specified in Meters
      The horizontal datum used is North American Datum of 1983.
      The ellipsoid used is GRS1980.
      The semi-major axis of the ellipsoid used is 6378206.4.
      The flattening of the ellipsoid used is 1/294.98.
  7. How does the data set describe geographic features?
    Explanations of the user defined items listed below can be found in OFR text:
    1 	AREA                   8    18     F      5
    9  	PERIMETER              8    18     F      5
    17  	SCS_SOIL83#            4     5     B      -
    21  	SCS_SOIL83-ID          4     5     B      -
    25  	SOIL_SERIES            2     2     C      -
    27  	SLOPE                  2     2     C      -
    29  	IFERODED               2     2     I      -
    1  	FNODE#                 4     5     B      -
    5  	TNODE#                 4     5     B      -
    9  	LPOLY#                 4     5     B      -
    13  	RPOLY#                 4     5     B      -
    17  	LENGTH                 8    18     F      5
    25  	SCS_SOIL83#            4     5     B      -
    29  	SCS_SOIL83-ID          4     5     B      -
    soil unit
    Soil series designation (2-letter abbreviation)
    BaBarkerville-Gaddes Complex -- Gravelly sandy loam and sandy clay loam
    BgBarkerville-Gaddes Association -- Gravelly sandy loam and sandy clay loam
    BhBernadino-Hathaway Association -- Gravelly clay loam and cobbly sandy loam
    CaCalciorthids-Haplargids Association -- Properties too variable to be estimated
    CbCanelo Gravelly Sandy Loam -- Gravelly, very gravelly, or cobbly sandy loam
    CgCaralampi Gravelly Sandy Loam -- Gravelly sandy loam and very gravelly sandy clay loam
    CmCasto Very Gravelly Sandy Loam -- Gravelly and very gravelly sandy clay loam
    CoChiricahua Cobbly Sandy Loam -- Cobbly or gravelly heavy clay loam or clay
    CrChiricahua- Lampshire Association -- Cobbly or gravelly heavy clay loam or clay
    CsComoro Sandy Loam -- Sandy loam and gravelly sandy loam
    CtComoro Soils -- Sandy loam and gravelly sandy loam
    FrFaraway- Rock Outcrop Complex -- Very cobbly fine sandy loam
    GaGaddes Very Gravelly Sandy Loam -- Gravelly sandy loam, sandy loam, sandy clay loam, gravelly clay, and cobbly sandy clay loam
    GbGrabe- Comoro Complex -- Loam and sandy loam
    GeGrabe Soils -- Loam and sandy loam
    GhGraham Soils -- Very cobbly clay loam and clay
    GuGuest Soils -- Clay, gravelly clay and gravelly clay loam
    HOWater Bodies
    HaHathaway Gravelly Sandy Loam -- Gravelly sandy clay loam, gravelly and very gravelly sandy loam, and sandy loam
    LcLampshire-Chiricahua Association -- Very cobbly loam
    LgLampshire- Graham- Rock Outcrop Association -- Very cobbly loam
    LuLuzena Gravelly Loam, Deep Variant -- Gravelly clay loam and gravelly clay
    MgMartinez Gravelly Loam -- Loam or clay loam and clay
    NANot Available Undefined soil type
    PmPima Soils -- Clay loam
    RnRock Outcrop- Lithic Haplustolls Association Properties too variable to be estimated
    SoSonoita Gravelly Sandy Loam -- Gravelly sandy loam
    ThTorrifluvents and Haplustoils -- Properties too variable to be estimated
    TrTortugas- Rock Outcrop Complex -- Very cobbly loam
    WgWhite House Gravelly Loam -- Gravelly loam, clay loam, and clay
    WhWhite House Cobbly Sandy Loam -- Gravelly loam, clay loam, and clay
    WnWhite House- Bonita Complex -- Gravelly loam, clay loam, and clay
    WoWhite House- Caralampi Complex -- Gravelly loam, clay loam, and clay
    WtWhite House- Hathaway Association -- Gravelly loam, clay loam, and clay
    Categorized slope steepness (2-letter abbreviation)
    B0-5 percent slopes
    C1-10 percent slopes
    D0-20 percent slopes
    E20-40 percent slopes
    F1 -- 60 percent slopes
    Code indicating eroded slopes (integer)
    0not considered eroded at the time of the survey
    2eroded at the time of the survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Laura Margaret Norman
  2. Who also contributed to the data set?
    Coauthors helped to generate the final product map and ArcInfo coverage. Craig Wissler, professor at the University of Arizona, oversaw the automation and attribution of the actual data, while D.P. Guertin, also a professor at the University of Arizona, helped in assessing the final product and its further applications. Floyd Gray, geologist at the USGS, hired the work to be done as part of a 5-year project investigation of the fate and transport of minerals in the Patagonia Mountains in association with abandoned mine locations. Karen Bolm, also of the USGS, helped tremendously in the review of digital and manuscript data.
  3. To whom should users address questions about the data?
    Laura Margaret Norman
    US Geological Survey, GD
    Geographer, GIS Specialist
    520 North Park Avenue, Suite 355
    Tucson, Arizona

    (520) 670-5510 (voice)
    (fax) (520) 670-5571 (FAX)

Why was the data set created?

Beginning in March of 1997, the Preliminary Assessment of the Patagonia Mountains study area was undertaken. An integrated watershed analysis using Geographic Information Systems (GIS) based platform was undertaken to examine transport characteristics. The Universal Soil Loss Equation (USLE) and the Spatially Explicit Delivery MODel (SEDMOD) were chosen to assist in characterization of potential point and nonpoint source material yield within selected drainage systems.
This was done to provide information useful for defining areas of significant environmental impact and to shed some light on the most practical remediation strategies to be employed. Many studies have been conducted to determine different parameters, effects and contributions of human activity in the Patagonia and southern Santa Rita Mountains study area. Incorporation to a digital data model required acquisition of accurate geo-spatial digital soils data. This digital geospatial database is one of many being created by the U.S. Geological Survey as an ongoing effort to provide geologic information in a geographic information system (GIS) for use in spatial analysis.

How was the data set created?

  1. From what previous works were the data drawn?
    USDA survey (source 1 of 1)
    U.S. Department of Agriculture, 1979, Soil survey of Santa Cruz and parts of Cochise and Pima counties, Arizona: U.S. Government Printing Office, Washington, D.C..

    USDA Soil Conservation Service and Forest Service in cooperation with Arizona Agricultural Experiment Station
    Type_of_Source_Media: paper
    Source_Scale_Denominator: 20000
    Source_Contribution: soils map
  2. How were the data generated, processed, and modified?
    Date: 2000 (process 1 of 3)
    Known points were identified on the aerial photo and matched to points on the DOQQs, these were referred to as Ground Control Points (GCPs). This was the most time consuming portion of this project as the aerial photos were taken some 30 years prior to the DOQQs and buildings, trees, and waterways had changed considerably. The easiest and most accurate objects to identify were roads and intersections of roads with other features. These appeared to have the same shape throughout time, although some forest roads are out of use, or have been paved or widened. A 3rd order polynomial transformation requires a minimum of 10 GCPs to be identified. However, the level of accuracy increases as more points are entered and widely distributed. The GCP prediction tool within ERDAS IMAGINE uses the current transformation parameters to guess where the user will locate GCPs from the work in progress to source data, this enables the user to determine when enough points have been entered to ensure that the transformation is accurate. An average of 80 GCPs were identified on each aerial photo and cross-referenced with the source data for this study. The cubic convolution method of resampling was performed to effectively pierce the aerial photo with pinpoints to known real time coordinates and stretch or fold the picture to accurate proportions. This sampling method is suggested for aerial photos in which the cell size is dramatically changed. This transformed the image of an abstract piece of paper into an accurate representation of real time and space with registered known coordinates. The cubic convolution method resamples using an algorithm which recognizes the data files of 16 pixels in a 4 by 4 window, and this creates the most accurate output when ortho-rectifying aerial photos. Error still exists despite the high number of GCPs used to control the transformation. It is difficult to accurately fit images over mountainous terrain from aerial photos. Error existed in the DOQQs and new error was introduced in the resampling process. However, the photos edge-matched positively and roads, rivers, trees and soil polygons merged together seamlessly when mosaiced to create the big picture. The raster geometric correction was successful for use in this project.
    Date: 12-Mar-2002 (process 2 of 3)
    Fifth draft of metadata created by lmbrady using FGDCMETA.AML ver. 1.2 05/14/98 on ARC/INFO data set /bdr2/lmbrady/scs_soil
    Date: 12-Mar-2002 (process 3 of 3)
    Creation of original metadata record Person who carried out this activity:
    Attn: Laura Margaret Norman
    Geographer/ GIS Specialist
    520 North Park Avenue Suite #355
    Tucson, AZ

    (520) 670-5510 (voice)
    (fax)(520) 670-5571 (FAX)
  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?
    The final file was converted and compressed within ARCINFO to TIFF format and laid out onscreen with known vector coverages of digitized roads and rivers overlaid to check for accuracy and error. The most useful was the road coverage downloaded the AZGENREF library, which identified error to be within +/- 40 meters. This digital database is not meant to be used or displayed at any scale larger than 1:20,000.
  2. How accurate are the geographic locations?
    Identified error to be within +/- 40 meters
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    Only the maps that covered the study area of interest were digitized.
  5. How consistent are the relationships among the observations, including topology?
    Polygon topology present. All polygons are closed.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints: none
Anyone who uses these data must cite USGS. These data are not to be used at scales greater than 1:20,000.
  1. Who distributes the data set? (Distributor 1 of 1)
    USGS Information Services
    Box 25286 Denver Federal Center
    Denver, CO

    1-888-ASK-USGS (voice)
    303-202-4693 (FAX)
  2. What's the catalog number I need to order this data set?
  3. What legal disclaimers am I supposed to read?
    The U.S. Geological Survey (USGS) provides these geographic data "as is." The USGS makes no guarantee or warranty concerning the accuracy of information contained in the geographic data. The USGS further makes no warranties, either expressed or implied, as to any other matter whatsoever, including, but without limitation to, the condition of the product of its fitness for any particular purpose. The burden for determining fitness for use lies entirely with the user. Although these data have been processed successfully on computers with USGS, no warranty, expressed or implied, is made by the USGS regarding the use of these data on any other system, nor does the fact of distribution constitute or imply such warranty.
    In no event shall the USGS have any liability whatsoever for payment of any consequential, incidental, indirect, special, or tort damages of any kind, including, but not limited to, any loss of profits arising out of use of or reliance on the geographical data or arising out of the delivery, installation operation, or support by USGS.
    The digital geologic map GIS of the Patagonia Mountains area in Arizona is not meant to be used or displayed at any scale larger than 1:20,000 (for example, 1:12,000).
  4. How can I download or order the data?

Who wrote the metadata?

Last modified: 10-Jun-2016
Metadata author:
Peter N Schweitzer
USGS Midwest Area
Collection manager, USGS Geoscience Data Clearinghouse,
Mail Stop 954
12201 Sunrise Valley Dr
Reston, VA

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

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