Identification%20of%20Kleingrass%20in%20Gonzales%20Texas - PowerPoint PPT Presentation
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Identification%20of%20Kleingrass%20in%20Gonzales%20Texas
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Panicum coloratum, also known as Kleingrass is not native to Texas. ... grass have been found to cause liver damage in horses, sheep, and goats' cattle ... – PowerPoint PPT presentation
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Title: 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.
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