Bottom-Up Estimation of Stand Leaf Area Index From Individual Tree Measurement Using Terrestrial Laser Scanning Data.

Leaf area parameters are crucial in ecosystem studies. As ecophysiological models advance toward finer detail, accurately estimating LA at various scales becomes essential, particularly for diverse units like urban individual trees. Several algorithms based on terrestrial laser scanning (TLS) data have been developed to obtain the LA of individual trees. However, their use at the stand level needs further research. In this study, the comparative shortest-path algorithm (CSP) is introduced for the automatic individual tree segmentation, thereby facilitating the application of the path length distribution model (PATH) for leaf area estimation at the stand level. Using high-density TLS data, we presented a bottom-up estimation of stand leaf area index (LAI) from 50 individual tree measurements and validated the results at different scales. At the tree scale, the LA derived from TLS and allometric model were highly correlated, with an R-value of 0.83. At the stand scale, the proposed method provides consistent results with the allometric and TRAC instrument measurements, performing better than vertical upward photography. Generally, 23 shared stations under the forest are enough to accurately obtain the LA of 50 trees and the LAI in an urban forest stand. Sensitivity analysis shows that the method is not sensitive to TLS scan resolution and parameters used in tree crown envelope reconstruction. The proposed bottom-up approach provides a new way of estimating the LAI at stand level using TLS and has the advantage of providing multi-level leaf area information and avoiding the scale effect.