Diseases and insect pests area monitoring for winter wheat based on HJ-CCD imagery

Previous studies of remote sensing on monitoring crop diseases show that simple vegetation index threshold methods are not effective to discriminate stressed winter wheat from the healthy ones. In this paper, binary logistic regression was utilized to establish the relational model between field measured vegetation indexes and health status of winter wheat. In order to reduce the growing variations between different regions, an algorithm was implemented based on an assumption of neighborhood consistency among 3×3 pixels. Results indicate that renormalised difference vegetation index (RDVI) model and triangular vegetation index (TVI) model perform satisfactorily and have high reliability. These models were then applied to CCD image of the newly-launched environment monitoring and disaster forecasting microsatellite of China. Results showed that the area of stressed winter wheat detected by RDVI and TVI models were consistent with the census by the Zaoyang Plant Protection Station as well as the field investigation. Besides, TVI model has the accuracy of 76.47%, which can meet the needs of operational crop diseases and insect pests area monitoring.