Identification%20of%20Kleingrass%20in%20Gonzales%20Texas

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Identification of Kleingrass in Gonzales Texas

• Kevin Hankinson
• ES5053
• Fall 2004

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Panicum coloratum

• (Kleingrass)

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OBJECTIVE

• To determine the amount and relative distribution
  of Kleingrass (Panicum coloratum) in and around
  Gonzales, Texas.

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VEGETATION

• Panicum coloratum, also known as Kleingrass is
  not native to Texas. It was recommended for
  import from Africa in the 1950s by the Texas
  Agricultural Experimentation Station because of
  its ability to resist drought, survivability in
  variable soil conditions, and its tolerance to
  salt. Kleingrass makes excellent high quality hay
  and forage for cattle. Kleingrass is also used as
  a conservation tool to stabilize soils and
  promote revegitation of depleted range land.
  Kleingrass does however have several drawbacks.
  Saponins, glycosides with a distinctive foaming
  characteristic, in the grass have been found to
  cause liver damage in horses, sheep, and goats
  cattle are not affected. All things considered
  Kleingrass is still a very popular feed for
  cattle.

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LOCATION

• The study area is located approximately 75
  statute miles East of San Antonio, Texas on
  Interstate 10 and approximately 12 statue miles
  south on U.S. Highway 97. This area is located in
  the Gulf Coastal plains region of Texas. The soil
  in this area is usually sandy with a high
  concentration of iron, overall the soil is
  nutrient deficient. The study area encompasses
  approximately 144 square kilometers.

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DATA SOURCE

• The images for the study were downloaded from
  http//www.texasview.org/. which provided Landsat
  ETM images with 30 meter resolution for the
  study area for 12/16/1999, 4/25/2001, 11/6/2002,
  11/22/2002, and 3/30/2003. Unfortunately
  Texasview could not provide images for
  consecutive months or years. Each file contained
  approximately 48MB of information. Header files
  ranged in size from 7KB to 12KB.

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METHOD

• Each landsat ETM 742 composition image was first
  resized to a more manageable area. This resized
  image intentionally contained a known Kleingrass
  field of approximately 30 acres. To accomplish
  this resizing upper left and lower right points
  were determined and applied to the basic
  tools-resize-spatial subset-map feature which
  then performed the resizing operation. This new
  image was assigned to memory for later analysis.
  This process was repeated for each of the five
  images. Next, each image was viewed to determine
  visually that a difference existed between the
  Kleingrass field and adjacent fields of other
  types of vegetation. Only one of he five resized
  images raised any doubt that a distinction could
  not be made between the Kleingrass and other
  vegetation. This was the image from 12/16/1999,
  and the known (ground truth) Kleingrass field was
  strikingly similar to the adjacent field of
  Coastal Bermuda, therefore it was not used in the
  study.

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Resized ETM image
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Method continued

• Within the 144 square kilometer study area only
  28 of the 159600 pixels were defined as the
  region of interest (ROI), indicated in red, and
  used for the study. The ROI was defined by
  selecting the overlay-region of interest-zoom
  function in ENVI. Then, by use of the cross
  hair, the region was defined and saved to be
  applied to the other images. The ROI does not
  encompass the entire 30 acre Kleingrass field
  that was identified by field observation. The
  reason for this is that several trees located in
  the southern portion of the field contaminated
  the otherwise homogenous Kleingrass field.

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Zoom image of ground truth area
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Method continued

• After the ROI was defined it was used as a
  classification tool in order to determine the
  location of other areas of Kleingrass in the
  image. To determine the location and coverage of
  Kleingrass the supervised-classification-spectral
  angle mapper-import ROI function was used. The
  spectral angle mapper compares all available
  bands in each pixel with the ROI classification.
  An angular difference measure of .1 (radians) was
  initially used (0 being no difference and 1 being
  totally opposite). The results with the .1
  angular difference were stored in memory and used
  to open a new display that depicted any pixels
  that were similar as red and pixels that were the
  different as black. To verify that the results
  were valid the 742 composition resized image was
  linked to the newly generated spectral angle
  mapper image. By toggling between the two images
  it was possible to see if other pixels outside
  the ROI but within the 30 acre Kleingrass field
  would be displayed as red, indicating Kleingrass.
  If the red colored pixels roughly resembled the
  shape of the Kleingrass field the angular
  difference was considered to be valid. It was
  determined that the optimum angular difference to
  use was .03 radians. The process was repeated for
  each of the remaining images using the same ROI
  and angular difference.

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Spectral angular difference image
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Method continued

• To determine how much of the study area was
  populated with Kleingrass statistical
  calculations were performed on the newly created
  spectral angle mapper image (red or black pixels)
  which yielded the total number of pixels that had
  a digital number (DN) of 0 (black) or 1 (red) and
  the respective percentages.

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Statistical report
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RESULTS

• The statistical analysis for the spectral angular
  difference images indicated the percentage of
  Kleingrass within the study area ranged from a
  low of 1.95 (3117 pixels) from the data
  gathered on November 6th 2002 to a high of 10.77
  (17184 pixels) from the data gathered on March
  30th 2003.
• Date pixel with DN of 1 (red) percent
  coverage
• 4/25/2001 11627 7.29
• 11/6/2002 3117 1.95
• 11/22/2002 8395 5.26
• 3/30/2003 17184 10.77
• As of now no conclusive answer has been
  determined to be the cause of the substantial
  difference in the percent coverage of Kleingrass.
  However, some theories do come to mind. For
  instance the spectral signature of the Kleingrass
  does change from week to week and month to month.
  In order to compensate for this change a new
  spectral angle value must be chosen for each new
  image. Another theory that might explain this is
  that as the Kleingrass becomes active or dormant
  the spectral signature might change at varying
  rates and may at some point be to similar too
  other vegetation to be distinguished. To
  compensate for this the study would need to look
  out of the visible spectrum and into the IR bands
  for more refined results.