A systematic review of the application of remote sensing technologies in mapping forest insect pests and diseases at a tree-level

An increase in the frequency and severity of forest insect pest and disease (FIPD) outbreaks has drastically affected the health and functioning of many forest stands worldwide. This has led to an increased demand for enhanced monitoring techniques with the capabilities to identify individually infected trees before FIPD outbreaks have an opportunity to spread. In this regard, remote sensing has emerged as an indespensible tool with the capacity to map outbreaks at an individual tree level. As FIPD outbreaks have intensified, and with the advancement of monitoring capabilities, there has been a surge of interest within this field. In response to this rapid growth of interest, this review provides a comprehensive assessment of the recent advancements, challenges, and future prospects of the use of remote sensing in mapping FIPD at a tree-level. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol, we conducted a systematic review encompassing 87 studies published from 2000 to May 2023. Specifically, we examined various aspects, including taxonomic characteristics, sensor types, and the analytical methods applied. Our findings revealed a signficant increase in research activity in the last few years, with the majority of these studies conducted in Asia, North America, and Europe. The most extensively studied insect pest was the Bark beetle (Ips typographus), whilst Pine wilt disease was found to be the most researched disease. Unmanned aerial vehicles and hyperspectral sensors were favoured by researchers for the majority of monitoring tasks. In terms of analytical methods, random forest (84%), artificial neural network (83%), and convolutional neural networks (93%) were found to have produced the highest levels of model accuracy. Lastly, this review underscores the indispensable role of remote sensing in facilitating the monitoring of FIPD, and identifies specific limitations and potential research gaps that need to be addressed within the field.