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Title: High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier

Author
item MIRIK, MUSTAFA - Texas A&M Agrilife
item ANSLEY, R - Texas A&M Agrilife
item STEDDOM, KARL - Texas A&M Agrilife
item RUSH, CHARLES - Texas A&M Agrilife
item MICHELS, GERALD - Texas A&M Agrilife
item WORKNEH, FEKEDE - Texas A&M Agrilife
item CUI, SONG - Middle Tennessee State University
item Elliott, Norman - Norm

Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/25/2014
Publication Date: 3/21/2014
Publication URL: http://handle.nal.usda.gov/10113/61774
Citation: Mirik, M., Ansley, R.J., Steddom, K., Rush, C.M., Michels, G.J., Workneh, F., Cui, S., Elliott, N.C. 2014. High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier. Journal of Applied Remote Sensing (JARS). 8(1):083661 14 p.

Interpretive Summary: The effects of insect infestation in agricultural crops are of major ecological and economic interest because of reduced yield, increased cost of pest control, and increased risk of environmental contamination from insecticide application. The Russian wheat aphid (RWA) is an insect pest that causes damage to wheat. We proposed that concentrated RWA feeding areas, referred to as "hot spots," could be identified and isolated from uninfested areas within a field for site specific aphid management using remotely sensed data. Our objectives were to (1) investigate the reflectance characteristics of infested and uninfested wheat by RWA and (2) evaluate utility of airborne hyperspectral imagery with 1-m spatial resolution for detecting, quantifying, and mapping RWA infested areas in commercial winter wheat fields using the constrained energy minimization classifier. Percent surface reflectance from uninfested wheat was lower in the visible and higher in the near infrared portions of the spectrum when compared with RWA-infested wheat. The overall classification accuracies of >89% for damage detection were achieved. These results indicate that hyperspectral imagery can be effectively used for accurate detection and quantification of RWA infestation in heat for site-specific aphid management. The significance of this result is that monitoring fields for damaging infestations of RWA could potentially done for less cost and with greater accuracy that current methods based on scouting wheat fields on foot and counting RWA infested wheat tillers.

Technical Abstract: The effects of insect infestation in agricultural crops are of major ecological and economic interest because of reduced yield, increased cost of pest control, and increased risk of environmental contamination from insecticide application. The Russian wheat aphid (RWA, Diuraphis noxia) is an insect pest that causes damage to wheat (Triticum aestivum L.). We proposed that concentrated RWA feeding areas, referred to as "hot spots," could be identified and isolated from uninfested areas within a field for site specific aphid management using remotely sensed data. Our objectives were to (1) investigate the reflectance characteristics of infested and uninfested wheat by RWA and (2) evaluate utility of airborne hyperspectral imagery with 1-m spatial resolution for detecting, quantifying, and mapping RWA infested areas in commercial winter wheat fields using the constrained energy minimization classifier. Percent surface reflectance from uninfested wheat was lower in the visible and higher in the near infrared portions of the spectrum when compared with RWA-infested wheat. The overall classification accuracies of >89% for damage detection were achieved. These results indicate that hyperspectral imagery can be effectively used for accurate detection and quantification of RWA infestation in heat for site-specific aphid management.