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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. [ABSTRACT FROM AUTHOR]

During spruce budworm (SBW; Choristoneura fumiferana (Clem.)) outbreaks, defoliation causes severe tree damage and mortality to spruce-balsam fir (Picea spp. - Abies balsamea (L.) Mill.) forests. Annual defoliation thematic maps provide vital information for forest protection planning against outbreaks, but such data are challenging to accurately collect over large areas during a short (2–3 week) period when damaged foliage peaks. Current operational methods for estimating annual defoliation are aerial surveys, through visual observations by professionals from small aircraft, defining broad defoliation classes which are subjective and spatially can be inaccurate. Remote sensing studies estimating annual defoliation are limited due to lack of ground verification data and difficulty of capturing spectral changes specific to the short detection period. Only one study has used satellite hyperspectral imagery to detect annual SBW defoliation, classifying just two classes of defoliation and using inaccurate aerial survey results for training and validation. In this paper, we introduce a new method to estimate annual SBW defoliation in three classes (light ≤ 30%, moderate 30–70%, and severe ≥ 70%), using change detection of vegetation indices (VIs) derived from satellite (EO-1 Hyperion) hyperspectral imagery acquired before and after annual defoliation. Random Forest and Support Vector Machine classifiers were used to classify defoliation, and intensive field measurements of branch-level defoliation per plot were used for accuracy assessment. Field plots, with spatially correlated defoliation within stands, were used to derive an additional 624 plots to obtain sufficient and independent datasets for training and validating the detection results. The top four VIs for detecting annual defoliation, based on Random Forest Total Variable Importance Score and Spearman's rank correlations, were Plant Senescence Reflectance Index, Normalized Difference Water Index, Moisture Stress Index, and Enhanced Vegetation Index. The spectral regions of VIs used to classify defoliation included visible, near-infrared, red-edge, and shortwave infrared. Accuracies of the light, moderate, and severe defoliation classes using our method ranged from 55–85%, 40–76%, and 40–80%, respectively, which were significantly higher than the 32% accuracy of aerial survey defoliation data for the plots. • Spruce budworm defoliation was detected using hyperspectral (EO-1 Hyperion) data. • Classified defoliation was validated with accurate branch defoliation measurements. • Multiple vegetation indices were selected using Random Forest variable importance. • Results were similar using Random Forest and Support Vector Machine classifiers. • Defoliation detection with hyperspectral data was superior to aerial surveys. [ABSTRACT FROM AUTHOR]