Empowering tree-scale monitoring over large areas: Individual tree delineation from high-resolution imagery

Accurate individual tree delineation (ITD) is essential for forest monitoring, biodiversity assessment, and ecological modeling. While remote sensing (RS) has significantly advanced forest ITD, challenges persist, especially in complex forest environments. The use of imagery data is compelling given the rapid increase in available high-resolution aerial and satellite imagery data, the increasing need for image-based analysis where reliable 3D data are unavailable, the widening gap between data supply and processing capabilities, and the limited validation of state-of-the-art (SOTA) methods across diverse real-world conditions. This study aims to advance ITD research by evaluating SOTA instance segmentation approaches, including both recently developed and established methods. The analysis evaluates ITD algorithm performance using the largest forest instance-segmentation imagery dataset to date and standardized evaluation protocols. This study identifies key factors affecting accuracy, reveals remaining challenges, and outlines future research directions. Findings in this study reveal that ITD accuracy is heavily influenced by image resolution, forest structure, and method design. Findings also reveal that, while algorithm innovations remain important, robustness and transferability that ensure generalization across diverse environments are what differentiate method performances. In addition, this study highlights that commonly used evaluation metrics may fail to adequately capture precise performance in specific applications, e.g., individual-tree-crown segmentation in this study. Assessment reliability can be strengthened through the adoption of stricter criteria. Future research should focus on expanding datasets, refining evaluation protocols, and developing adaptive models capable of handling varying canopy structures. These advancements will enhance ITD scalability and reliability, contributing to more effective forest research and management at a global scale.