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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #321306
Title: Change detection of cotton root rot infection over a 10-year interval using airborne multispectral imagery

Author
item Yang, Chenghai
item ODVODY, GARY - Texas A&M Agrilife
item THOMASSON, JOHN - Texas A&M University
item ISAKEIT, THOMAS - Texas A&M University
item NICHOLS, ROBERT - Cotton, Inc

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/29/2016
Publication Date: 3/8/2016
Citation: Yang, C., Odvody, G., Thomasson, J., Isakeit, T., Nichols, R. 2016. Change detection of cotton root rot infection over a 10-year interval using airborne multispectral imagery. Computers and Electronics in Agriculture. 123:154-162.

Interpretive Summary: Accurate information regarding the spatial and temporal infections of cotton root rot within fields is important for effective management of the disease. This study examined the consistency and variation of cotton root rot infections within cotton fields over a long period of time using airborne multispectral imagery and assessed the feasibility to use historical imagery to create prescription maps for site-specific management of the disease. Change detection analysis on two cotton fields showed that the spatial patterns of the disease were similar over a 10-year interval, though temporal variations existed for each field. The results from this study demonstrate that cotton root rot maps derived from historical images with appropriate buffer zones added around the infected areas can be used as prescription maps for site-specific fungicide application for the control of cotton root rot.

Technical Abstract: Cotton root rot is a very serious and destructive disease of cotton grown in the southwestern and south central United States. Accurate information regarding the spatial and temporal infections of the disease within fields is important for effective management and control of the disease. The objectives of this study were to examine the consistency and variation of cotton root rot infections within cotton fields over a 10-year interval using airborne multispectral imagery and to assess the feasibility to use historical imagery to create prescription maps for site-specific management of the disease. Airborne multispectral images collected from a 102-ha cotton field in 2001 and 2011 and from a 97-ha field in 2002 and 2012 in south Texas were used in this study. The images were rectified and resampled to the same pixel size between the two years for each field. The normalized difference vegetation index (NDVI) images were generated and unsupervised classification was then used to classify the NDVI images into root rot-infected and non-infected zones. Change detection analysis was performed to detect the consistency and change in root rot infection between the two growing seasons for each field. Results indicate that the spatial patterns of the disease were similar between the two seasons, though variations existed for each field. To account for the potential expansion and temporal variation of the disease, buffer zones around the infected areas were created. The buffered maps between the two years agreed well. The results from this study demonstrate that classification maps derived from historical images in conjunction with appropriate buffer zones can be used as prescription maps for site-specific fungicide application to control cotton root rot.