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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING AND GIS FOR DETECTING AND MAPPING INVASIVE WEEDS IN RIPARIAN AND WETLAND ECOSYSTEMS Title: Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot

Authors
item Yang, Chenghai
item Fernandez, Carlos -
item Everitt, James -

Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 29, 2010
Publication Date: September 22, 2010
Citation: Yang, C., Fernandez, C.J., Everitt, J.H. 2010. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystems Engineering. 107:131-139.

Interpretive Summary: Cotton root rot is a major cotton disease affecting cotton production in the southwestern and south central U.S. Accurate delineation of root rot infestations is necessary for cost-effective management of the disease. This study compared airborne multispectral and hyperspectral imagery for detecting and mapping root rot areas in cotton fields. Image classification and accuracy assessment of the root rot maps showed that both types of imagery accurately identified root rot areas within the fields. These results indicate that airborne imagery can be a useful tool for the detection and management of this destructive cotton disease.

Technical Abstract: Cotton root rot caused by the soilborne fungus, Phymatotrichum omnivorum, is a major cotton disease affecting cotton production in the southwestern and south central U.S. Accurate delineation of root rot infestations is necessary for cost-effective management of the disease. The objective of this study was to compare airborne multispectral and hyperspectral imagery for detecting and mapping root rot areas in cotton fields. Two center-pivot irrigated fields in south Texas were selected for this study. Airborne 3-band multispectral and 128-band hyperspectral imagery was taken from the two fields shortly before harvest when the infested areas were fully expressed for the season. Both types of imagery and the principal component images derived from the hyperspectral imagery were classified into 2-10 spectral classes using unsupervised classification techniques. The individual spectral classes were then grouped into infested and non-infested zones. Accuracy assessment on the two-zone classification maps showed that both types of imagery as well as the principal component imagery equally accurately identified root rot areas within the fields. These results indicate that both airborne multispectral and hyperspectral imagery can be used for assessing root rot infestations within cotton fields.

Last Modified: 10/30/2014
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