Artificial Intelligence for Modelling Biophysical Elements of Forest Ecosystems: Opportunities and Challenges

Biophysical elements of forests are essential for tree health, biodiversity, and ecosystem services, with their effects increasingly influenced by climate change and global environmental dynamics. Understanding the interactions between biotic and abiotic components of forest ecosystems poses challenges due to the large spatial extent, size, and inaccessibility of tree massifs, along with the often complex relationships among various factors, including soil, hydrology, relief, meteorological conditions, and other ecosystem components. This review serves as an introduction to the field of forest biophysical elements, outlining significant challenges such as multifactorial links, and the responses of roots and vascular systems to soil quality. It highlights emerging issues related to environmental dynamics that complicate the study of these interactions. Furthermore, the paper explores the application of artificial intelligence (AI) in modeling the connections between biotic and abiotic components of forest ecosystems. This involves the integration of advanced techniques such as remote sensing, imaging, geospatial data analysis, and cartographic methods, which are employed across various spatial and temporal scales to enhance our understanding of these complex ecological relationships. Lessons from silviculture systems highlight tools and pitfalls for forestry, emphasising the necessity for interpretable models backed by machine learning (ML), the integration of ecological context, and validation of various algorithms such as random forest (RF) and Support vector machines (SVM). We conclude that coordinated data infrastructures are essential for ensuring that AI provides actionable insights and scalable solutions for monitoring complex forest ecosystems.