Remote Sens. 2013, 5(2), 612-630; doi:10.3390/rs5020612
Article

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mustafa Mirik 1,* email, R. James Ansley 1 email, Karl Steddom 2 email, David C. Jones 3 email, Charles M. Rush 4 email, Gerald J. Michels Jr. 4 email and Norman C. Elliott 5 email
1 Texas A&M AgriLife Research and Extension Center, 11708 Highway 70 S., Vernon, TX 76385, USA 2 Texas A&M AgriLife Research and Extension Center, Overton, TX 75684, USA 3 Fort Hood ITAM, DPTMS Range Division, Fort Hood, TX 76544, USA 4 Texas A&M AgriLife Research and Extension Center, 6500 Amarillo Blvd. W., Amarillo, TX 79012, USA 5 USDA-ARS Plant Science Research Laboratory, 1301 N. Western St., Stillwater, OK 74075, USA
* Author to whom correspondence should be addressed.
Received: 18 November 2012; in revised form: 18 January 2013 / Accepted: 23 January 2013 / Published: 29 January 2013
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Abstract: Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery.
Keywords: accuracy assessment; invasive plant; weed management; weed infestation; remote sensing; geospatial data

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Cite This Article

MDPI and ACS Style

Mirik, M.; Ansley, R.J.; Steddom, K.; Jones, D.C.; Rush, C.M.; Michels, G.J., Jr.; Elliott, N.C. Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier. Remote Sens. 2013, 5, 612-630.

AMA Style

Mirik M, Ansley RJ, Steddom K, Jones DC, Rush CM, Michels GJ, Jr, Elliott NC. Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier. Remote Sensing. 2013; 5(2):612-630.

Chicago/Turabian Style

Mirik, Mustafa; Ansley, R. James; Steddom, Karl; Jones, David C.; Rush, Charles M.; Michels, Gerald J., Jr.; Elliott, Norman C. 2013. "Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier." Remote Sens. 5, no. 2: 612-630.

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