•Trees affected by green attack were identified within a limited time window.•The detection rates increased from 0.3 to 0.8 two weeks before brood emergence began.•The highest separability was found in the green shoulder region (490–550 nm).•Green shoulder infection/curvature points shifted during vitality decline.•Indices were developed with higher accuracy and simplified with three bands. Forest stress monitoring and in-time identification of forest disturbances are important to improve forest resilience to climate change. Fast-developing drone techniques and hyperspectral imagery provide tools for understanding the forest decline process under stress and contribute to focused monitoring. This study explored and developed hyperspectral drone imagery for early detection of forest stress caused by European spruce bark beetle Ips typographus (L.), before offspring emergence, which is crucial in controlling the spread but has been shown to be challenging. This study challenges the highest possible detectability of infested trees using a hyperspectral drone system that provided images with very high spectral, spatial, and temporal resolutions in Southern Finland. Images were acquired bi-weekly, four times (T1, T2, T3, T4), covering 8 weeks from trees being attacked by the first filial generation (F1) to the beginning of second filial generation (F2) brood emergence. Very low separability was observed for the reflectance from healthy and attacked trees, but the first and second derivative reflectance captured vitality changes, with the green shoulder region (wavelengths 490–550 nm) exhibiting the highest separability of all wavelengths (400–1700 nm). We discovered that the peak and valley values of the first and second derivative curves in the green shoulder region consistently shifted with longer infestation time. Based on this finding, we developed green shoulder indices. The detection rates were 0.24–0.31 and 0.76–0.83 for T3 and T4, higher than commonly used VIs such as the Photochemical Reflectance Index and the Red Edge Inflection Position, with detection rates of 0.69 and 0.34 for T4, respectively. We also proposed simplified green shoulder indices using the reflectance from three bands that can be used with multispectral cameras and satellite images for large area monitoring of forest health. We concluded that the detectability of infestations was very low for the first month after attack, and then rapidly increased before brood emergence. We highlighted the great potential of green shoulder indices in quantifying the photochemical functioning of the vegetation under stress. The methodology can potentially be applied for early identification of forests with declining vitality caused by various sources of forest stress and disturbances, such as infestations, diseases and drought.