Mapping wheat rust based on high spatial resolution satellite imagery

Abstract

Timely and accurate disease incidence monitoring and estimation using satellite imagery is critical for the effective control of wheat rust disease. Though studies have been conducted using different spectral and sensing technologies to detect and monitor the wheat rust disease, few studies have been conducted in the application of high resolution multi-spectral satellite imagery in the monitoring of wheat rust to facilitate an operational monitoring over large areas. Since there are not many options for the multispectral data than the hyperspectral data, it is more important to select appropriate vegetation indices for the classification model. However, there have been few literatures about the comparison of these feature selection methods on the application of wheat disease. We proposed a wheat rust disease mapping protocol including removing the non-vegetated and lodged area, calculating and filtering the vegetation indices and mapping the wheat rust. In the process of filtering the proper features, we applied wrapper feature selection instead of traditional filter feature selection combined with the classification methods (support vector machine and random forests). The experiment data was a scene of ZY-3 satellite image of the wheat field in Changge County in Henan Province with a certain portion of rust disease. The classification results can achieve overall accuracy of higher than 90%, ranging from 90.80% to 95.10%. The wrapper feature selection method with the overall accuracy of 93.60% is better than filters feature selection method with the overall accuracy of 92.65%. The random forests method with the overall accuracy of 94.80% is better than support vector machine method with the overall accuracy of 91.45%. The high accuracies thus justified the feasibility of using high-resolution multi-spectral satellite images for mapping wheat rust disease, which is promising for this technology to be applied in the practical wheat production management.