A LiDAR-Driven Effective Leaf Area Index Inversion Method of Urban Forests in Northeast China

Abstract

Leaf area index (LAI) stands as a pivotal parameter for the quantitative assessment of vegetation growth dynamics, and the rapid acquisition of the effective leaf area index (LAIe) in different scales is crucial for forest ecological monitoring. In this study, forest structure parameters were derived from fusion point cloud data obtained through Airborne Laser Scanning and Terrestrial Laser Scanning in three coniferous forests. The influence of point diameter on the extraction of different forest structure parameters was examined, and an in-depth analysis of the correlations between these parameters and measured LAIe was undertaken. The LAIe inversion model was constructed, and its performance for different forest types was studied. The results show that the precision of the extracted forest structure parameters was highest when the point diameter was set to 0.1 cm. Among the 10 forest structure parameters, internal canopy structures such as canopy openness (CO), gap fraction (GF) and canopy closure (CC) were significantly correlated with measured LAIe (p < 0.01), and the correlations between different forest types were significantly different. In addition, the multiparameter LAIe inversion model was able to distinguish forest type and thus better stimulate measured LAIe; also, it appeared closer to the 1:1 relationship line than the voxel model. This study made up for the inefficiency of LAIe measurement with optical instruments and the inaccuracy of passive remote sensing measurement and proved the possibility of LAIe extraction at a large scale via LiDAR in the future.