Quantification of forest canopy changes caused by spruce budworm defoliation using digital hemispherical imagery

Abstract

Detection of spruce budworm (Choristoneura fumiferana Clem.) defoliation is critical for forest protection strategies aimed at minimizing losses in growth and mortality. However current aerial and ground survey methods of detecting defoliation are imprecise and subjective, limiting their usefulness. We evaluated the use of hemispherical crown canopy images to quantify annual spruce budworm defoliation, comparing images taken before and after defoliation in each of two years in 75 sample plots in Québec, Canada. Gradient Boosting Machine analysis identified gap fraction change from May-October, gap fraction after defoliation, insecticide spraying, and % balsam fir (Abies balsamea (L.) Mill.) basal area as important explanatory variables of defoliation. Logistic Generalized Linear Model (GLM) and Random Forests (RF) models were trained on a random two-thirds of sample plots combining both years, and defoliation predictions were validated on the remaining one-third of plots. RF predictions consistently resulted in slightly higher correlations and lower root mean squared errors (RMSEs) than GLM predictions. Defoliation models including insecticide spraying, gap fraction change May-October, and % balsam fir basal area had RMSEs of 14–22%, whereas models excluding insecticide spraying had higher RMSEs of 18–24%. Model goodness-of-fit using two-sample Kolmogorov-Smirnov tests indicated that predicted and measured annual defoliation had similar distributions, with the exception of GLM and RF models excluding spray compared to ocular defoliation. Use of hemispherical images to quantify gap fraction change is a feasible, non-destructive, and objective method to assess canopy foliage changes caused by spruce budworm defoliation.