Estimating the leaf area index of urban trees using terrestrial LiDAR and the PATH method: sensitivity analysis and comparison with optical and direct methods

Abstract. Urban trees play a vital role in mitigating urban heat islands through shading and transpiration, processes directly governed by the Leaf Area Index (LAI). Accurately estimating LAI in complex urban environments remains a challenge. This study evaluates the PATH (Path Length Distribution) method for estimating the Plant Area Index (PAI) of individual urban trees using Terrestrial Laser Scanning (TLS). Focusing on three tree species in Strasbourg (France), we conduct a comprehensive sensitivity analysis of the geometric parameters of the PATH model, specifically the crown envelope reconstruction and the number of facets. A key contribution of this work is the validation of TLS-derived estimates against a direct destructive method and indirect optical measurements. Results indicate that a concave hull with approximately 3,000 facets provides a stable and reliable PAI estimation. Comparison with direct measurements shows that while the PATH method effectively captures seasonal leaf phenophases, it tends to underestimate PAI in high-density foliage conditions. This research also underlines the importance of geometric modeling in leaf area density estimation.