This research compares the capabilities of various Sentinel-2-derived spectral vegetation indices (SVIs) in particular red-edge SVIs to detect and classify spruce budworm (Choristoneura fumiferana) (SBW) defoliation using Support Vector Machine (SVM) and Random Forest (RF) models. The results showed the superiority of RF in model building for defoliation detection and classification into three classes (nil, light, and moderate) with overall errors of 17% and 32%, respectively. The most important variables for the best model were Enhanced Vegetation Index 7 (EVI7), Modified Chlorophyll Absorption in Reflectance Index (MCARI), Inverted Red-Edge Chlorophyll Index (IRECI), Normalized Difference Infrared Index 11 (NDII11) and Modified Simple Ratio (MSR). Red-edge SVIs were more effective variables for light defoliation detection compared to traditional SVIs such as Normalized Difference Vegetation Index (NDVI) and EVI8. These findings can help improve current remote sensing-based SBW defoliation detection and monitoring.

这项研究比较了各种Sentinel-2衍生的光谱植被指数（SVI），特别是红边SVI对云杉芽虫（Choristoneura fumiferana）进行检测和分类的能力。（SBW）使用支持向量机（SVM）和随机森林（RF）模型进行脱叶。结果表明，RF在模型创建中具有优越的性能，可将脱叶检测和分类为三类（零，轻和中度），总误差分别为17％和32％。对于最佳模型，最重要的变量是增强植被指数7（EVI7），修改后的反射率叶绿素吸收率（MCARI），倒置红边叶绿素指数（IRECI），归一化红外差异指数11（NDII11）和修改后的简单比率（ MSR）。与传统的SVI（如归一化植被指数（NDVI）和EVI8）相比，红边SVI是更有效的光脱叶检测变量。这些发现可以帮助改善当前基于遥感的SBW脱叶检测和监视。