Individual Tree Segmentation Using multitemporal Airborne Laser Scanning (ALS) Data

With the increasing availability of multi-temporal ALS data, there is a growing interest in tracking individual trees over time. Although, consistent individual tree detection (ITD) across multiple time periods still remains a challenge due to segmentation errors and the complexity of forests.

This study evaluates the performance of three tree segmentation algorithms; Dalponte2016, Li2012 and Watershed, using multitemporal ALS dataset acquired in 2012, 2018 and 2022 within the Petawawa research forest, Ontario, Canada. Canopy heigh models (CHM) were generated from normalized point cloud data, and segmentation accuracy assessment using recall, precision and F1-score based on field measured stem data within the 14.1 m radius plots.