Change detection of cotton root rot infection over 10-year intervals using airborne multispectral imagery

Abstract

Cotton root rot is a very serious and destructive disease of cotton grown in the southwestern United States. Accurate information regarding its spatial and temporal distribution within fields is important for effective management of the disease. The objectives of this study were to examine the consistency and variation of cotton root rot infections within cotton fields over 10-year intervals 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.