Predicting Mycosphaerella leaf disease severity in a Eucalyptus globulus plantation using digital multi-spectral imagery

Abstract

Digital remote sensing is rapidly developing into an operational tool for forest health assessment at a range of scales. A key aspect of this development is the derivation of models relating spectral data contained in the images to the extent and severity of plant stress symptoms. This study assesses the utility of high spatial resolution (0.5m × 0.5m pixel size) airborne digital imagery to assess the presence and severity of defoliation and necrosis from Mycosphaerella leaf disease at the crown scale across a small plantation in north-western Tasmania, Australia. The best model of defoliation included the variance of reflectance at 780nm within the crown (r² = 0.5; p < 0.0001) and the best model of necrosis included the ratio of reflectance at 680nm/550nm (r² = 0.3; p < 0.0001). Maps derived from these linear models clearly illustrate the distribution and gradient of disease severity observed in the field. Error matrix analysis indicates moderate map accuracy for classified versions of both defoliation (OA = 71%; Kappa = 0.63) and necrosis (OA = 67%; Kappa = 0.54) but the maps reveal additional information about the distribution and severity of symptoms throughout the plantation. Methods such as those described in this paper may enable managers to more accurately target remedial actions. The transfer of this technology to space-borne platforms will potentially improve map accuracy and image availability, and reduce the cost of image collection. Together, these improvements increase the likelihood of remote sensing being incorporated into forest management strategies in future.