Identification of Kleingrass in Gonzales Texas Kevin Hankinson ES5053 Fall 2004

Panicum coloratum (Kleingrass)

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

VEGETATION: Panicum coloratum, also known as Kleingrass is not native to Texas. It was recommended for import from Africa in the 1950’s 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.

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.

DATA SOURCE: The images for the study were downloaded from 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.

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.

Resized ETM+ image

Method continued Within the 144 square kilometer study area only 28 of the 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.

Zoom image of ground truth area

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.

Spectral angular difference image

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.

Statistical report

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 Date # pixel with DN of 1 (red)percent coverage 4/25/ /6/ /22/ /30/ 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.