Discriminating the early stages of Sirex noctilio infestation using classification tree ensembles and shortwave infrared bands

Abstract

The woodwasp Sirex noctilio is causing extensive damage to Pinus patula trees in the summer rainfall areas of South Africa. The ability to remotely detect S. noctilio infestation remains crucial for monitoring purposes and for the effective deployment of suppression activities. In this study, we evaluated whether random forest and boosting trees can accurately discriminate between healthy trees and the early stages of S. noctilio infestation using reflectance measurements in the shortwave infrared (SWIR). Three variable selection methods, namely, a filter, the random forest out-of-bag samples and a wrapper algorithm, were used to select the smallest subset of SWIR bands. The results show that random forest produces better classification results than the competing boosting trees for all three variable selection methods, even when noise is introduced into the SWIR bands and class labels. The ability of the bands centred at 1990, 2009, 2028, 2047 and 2065 nm to discriminate between healthy trees and the early stages of infestation could be explained due to the rapid physiological changes that occur as a result of the toxic mucus and a fungus that S. noctilio injects into the tree. Overall, the results are encouraging and show that there is a link between the selected SWIR bands and existing physiological knowledge, thereby improving the chances of detecting the early stages of S. noctilio infestation at a canopy or landscape level.