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.

翻译：本文从树皮甲虫与寄主相互作用、遥感以及机器学习/深度学习三个主要视角，全面综述了过去及当前在树皮甲虫致树木死亡早期探测方面的进展。与此前研究不同，本综述涵盖了所有遥感系统，并重点强调机器学习/深度学习方法的优势与局限。我们基于多光谱或高光谱分析对现有文献进行梳理，并根据以下要素提炼知识：树皮甲虫种类与侵袭阶段（重点关注侵袭初期）、寄主树木、研究区域、遥感平台及传感器、光谱/空间/时间分辨率、光谱特征、光谱植被指数、机器学习方法、学习模式、任务类别、模型、算法、类/聚类、特征，以及深度学习网络与架构。尽管基于深度学习的方法和随机森林算法展现出可喜结果，凸显其在可见光、热红外和短波红外光谱区域检测细微变化的潜力，但其有效性仍有限且存在高度不确定性。为激发针对这些不足的创新解决方案，我们从不同视角深入探讨主要挑战与机遇，从而加深对当前研究现状的理解，并指导未来研究方向。