Seyed Mojtaba Marvasti-Zadeh, Devin Goodsman, Nilanjan Ray, Nadir Erbilgin
in ACM Computing Surveys.
Abstract: Bark beetle outbreaks can have serious consequences on forest ecosystem processes, biodiversity, forest structure and function, and economies. Thus, accurate and timely detection of bark beetle infestations in the early stage (known as green-attack detection) is crucial to mitigate the further impact, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that primarily rely on aerial surveys and field surveys, which can be impractical over vast areas. Existing approaches that exploit RS and ML/DL exhibit substantial diversity due to the wide range of factors involved. This paper provides a comprehensive review of past and current advances in green-attack detection 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, ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. Although DL-based methods and the random forest algorithm showed promising results, highlighting their potential to detect subtle changes across visible, thermal, and short-wave infrared spectral regions, their effectiveness remains limited, and high uncertainties persist due to the subtle distinctions between healthy and attacked trees. 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.