Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three primary perspectives: bark beetle & host interactions, RS, and ML/DL. In contrast to prior efforts, this review encompasses all RS systems and emphasizes ML/DL methods to investigate their strengths and weaknesses. We parse existing literature based on multi- or hyper-spectral analyses and distill their knowledge based on: bark beetle species & attack phases with a primary emphasis on early stages of attacks, host trees, study regions, RS platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. Although DL-based methods and the random forest (RF) algorithm showed promising results, highlighting their potential to detect subtle changes across visible, thermal, and short-wave infrared (SWIR) spectral regions, they still have limited effectiveness and high uncertainties. To inspire novel solutions to these shortcomings, we delve into the principal challenges & opportunities from different perspectives, enabling a deeper understanding of the current state of research and guiding future research directions.