Perspectives on the Future Roles of AI for Forest Health Monitoring

Global forest ecosystems face growing threats from land use change, climate and weather extremes, and insects and diseases. Managing these threats is difficult due to the time, cost, and human error associated with the quality and quantity of data required for research and assessment. While conventional analytical methods are being improved constantly, they are often slow in providing information needed to respond promptly to unprecedented changes driven by both natural and anthropogenic alterations to forest ecosystems. For this reason, potential applications of artificial intelligence (AI) have attracted increasing attention in the field. Here, we examine the benefits and challenges of using AI in near-term forest health monitoring (surveillance, mostly over small scales) and discuss the need for long-term and larger-scale assessment. Abundant evidence shows that existing AI methods already facilitate the rapid collection, compilation, and synthesis of available data from diverse sources. Furthermore, emerging technologies (e.g., agentic AI) are building these capabilities into autonomous systems. However, every AI tool has advantages and limitations. With constant improvements, integrative AI-driven approaches that simultaneously deal with multiple and cross-scale interacting factors are expected to deliver actionable insights about forest health better than any single AI tool. Consequently, they can enhance decision-making processes, reduce monitoring costs, and help mitigate the impacts of forest health threats. As AI continues to evolve, it is essential to circumscribe its role in forest health monitoring. Most importantly, AI should not define what humans value regarding forest health but instead should be applied to help us evaluate data about our chosen value targets.