Remote-sensing methods for mapping eucalypt dieback: A systematic review

Eucalypts dominate Australia's native forests and are important to global wood and fibre plantations. Both native and plantation eucalypt forests are subjected to diverse pests and diseases that act as proximal causes of canopy decline, known broadly as dieback. Remote sensing provides effective tools for monitoring tree health and mapping canopy dieback, offering valuable information for forest management. This review synthesises remote-sensing methods for mapping eucalypt dieback, with objectives to identify promising approaches and current research gaps. We assessed 32 studies from six countries and found research activity increased since 2016, with a transition of remote-sensing platforms from manned aircrafts to satellites and unmanned aerial vehicles (UAV). Insect pests specialised on eucalypts were the most frequently reported causes of dieback. Most studies mapped variables linked to overall tree health, defoliation, or damage. Multispectral and RGB sensors were most commonly used and both achieved high mean accuracy. Machine-learning and deep-learning algorithms were preferred analytical methods, but their accuracies are not significantly superior to parametric methods, especially for regression problems. Further studies are needed to evaluate hyperspectral and LiDAR sensors, especially when integrated with UAV-based high-resolution data. These are highly promising approaches that require further validation. Future research should also explore dense time series and individual tree-based approaches to strengthen the connection between remote-sensing signals and physiology-based understanding of dieback processes. This review synthesises current approaches and outlines key directions for advancing remote-sensing methods in mapping eucalypt dieback.