Uncovering Early Traces of Bark Beetle Induced Forest Stress via Semantically Enriched Sentinel-2 Data and Spectral Indices

Abstract

AbstractForest ecosystems are shaped by both abiotic and biotic disturbances. Unlike sudden disturbance agents, such as wind, avalanches and fire, bark beetle infestation progresses gradually. By the time infestation is observable by the human eye, trees are already in the final stages of infestation—the red- and grey-attack. In the relevant phase—the green-attack—biochemical and biophysical processes take place, which, however, are not or hardly visible. In this study, we applied a time series analysis based on semantically enriched Sentinel-2 data and spectral vegetation indices (SVIs) to detect early traces of bark beetle infestation in the Berchtesgaden National Park, Germany. Our approach used a stratified and hierarchical hybrid remote sensing image understanding system for pre-selecting candidate pixels, followed by the use of SVIs to confirm or refute the initial selection, heading towards a 'convergence of evidence approach’. Our results revealed that the near-infrared (NIR) and short-wave-infrared (SWIR) parts of the electromagnetic spectrum provided the best separability between pixels classified as healthy and early infested. Referring to vegetation indices, we found that those related to water stress have proven to be most sensitive. Compared to a SVI-only model that did not incorporate the concept of candidate pixels, our approach achieved distinctively higher producer’s accuracy (76% vs. 63%) and user’s accuracy (61% vs. 42%). The temporal accuracy of our method depends on the availability of satellite data and varies up to 3 weeks before or after the first ground-based detection in the field. Nonetheless, our method offers valuable early detection capabilities that can aid in implementing timely interventions to address bark beetle infestations in the early stage.