Kernel-Based Early Detection of Forest Bark Beetle Attack Using Vegetation Indices Time Series of Sentinel-2

The European spruce bark beetle (
Ips typographus
L.) is a biotic disturbance that devastates forest environmental services, and its activities are exacerbated due to climate change. Accordingly, researchers seek workflows using remote sensing imagery for bark beetle detection in the early stage of the attack, enabling proactive management. Most previous studies attempted to detect attacks with pixel-based approaches. This study explores the applicability of pixels’ spatial information, using kernels, for early bark beetle detection in south Sweden. Four vegetation indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Distance Red and SWIR (NDRS), and Chlorophyll Carotenoid Index (CCI), were derived from Sentinel-2 images and time-series of the coefficient of variation (CV) were calculated, followed by interpolation and smoothing to eliminate gaps and reduce noise. The CV time series were fed to a change detection algorithm called Detecting Breakpoints and Estimating Segments in Trend (DBEST). Detection accuracies ranged from 83.80% to 87.89%, with the highest related to NDVI, followed by NDRS. Detection dates mainly fell in June and July, 6–7 weeks after the bark beetle swarming. NDRS performed slightly better in detecting the attacks earlier, with an average detection date of 29th June. NDVI obtained higher detection accuracies for pine, spruce, and mixed conifer forests in nonwetland areas, dominating the study area. In general, the detection accuracies increased as the number of attacked trees and pixels increased in kernels. Results demonstrated the applicability of kernel-based early bark beetle attack detection, which can elucidate a new paradigm for bark beetle studies.