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

作者：Xinlian Liang, Yinrui Wang, Jun Pan, Janne Heiskanen, Ningning Wang, Siyu Wu, Ilja Vuorinne, Jiaojiao Tian, Jonas Troles, Myriam Cloutier, Stefano Puliti, Aishwarya Chandrasekaran, James Ball, Xiangcheng Mi, Guochun Shen, Kun Song, Guofan Shao, Rasmus Astrup, Yunsheng Wang, Petri Pellikka, Mi Wang, Jianya Gong · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2026 · DOI：10.1016/j.isprsjprs.2025.12.022 · 被引用次数：4 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Satellite Image Processing and Photogrammetry

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 ca...