<inline-formula><tex-math notation="LaTeX">$\mathsf{SILVIA}$</tex-math></inline-formula>: An eXplainable Framework to Map Bark Beetle Infestation in Sentinel-2 Images

Abstract

Recent long spells of high temperatures and drought-hit summers have fostered the conditions for an unprecedented outbreak of bark beetles in Europe. This phenomenon has ruined vast swathes of European conifer forests creating a need among forest managers to find effective methods to gather information about the mapping of bark beetle infestation hotspots. Sentinel-2 data have been recently established as an alternative to field surveys for certain inventory tasks. Hence, this study explores the achievements of machine learning to perform the inventory mapping of bark beetle infestation hotspots in Sentinel-2 images. In particular, we investigate the accuracy performance of a spectral classifier that is learned for the study task by leveraging spectral vegetation indices and performing self-training. We use a dataset of Sentinel-2 images acquired in nonoverlapping forest scenes from the North-east of France acquired in October 2018. The selected scenes host bark beetle infestation hotspots of different sizes, which originate from the mass reproduction of the bark beetle in the 2018 infestation. We perform a learning stage by accounting for the ground-truth bark beetle infestation masks of a subset of images in the study imagery dataset (training imagery set). The goal is to produce a prediction of the bark beetle infestation masks for the remaining images in the study imagery dataset (working imagery set). Moreover, we use an explainable artificial intelligence technique to study the relevance of spectral information and explain the effect of both self-training and spectral vegetation indices on the mapping decisions.

Keywords

• Bark beetle infestation mapping
• explainable artificial intelligence (XAI)
• forest tree die-back
• self-training
• Sentinel-2 image processing
• spectral classification