Quantitatively reconstructing the 3-D structure of individual leaves within tree canopies is critical for understanding forest function and environmental responses to climate change. While quantitative structure models (QSMs) using terrestrial laser scanning (TLS) effectively capture woody structures, they lack the capability to accurately reconstruct nonwoody leaf components. This study proposes accurate and detailed leaf (AdLeaf), a novel approach for fine-scale reconstruction of individual leaves using TLS point clouds. AdLeaf combines wood–leaf separation, individual leaf segmentation, detection and repair of incomplete leaves, explicit reconstruction, and parameter extraction. It automates semantic segmentation at the tree scale to separate woody and leafy components. Instance segmentation is refined through similarity graphs. Incomplete leaves are detected and repaired using shape concavity analysis and symmetry-based mirroring. AdLeaf enables direct measurement of leaf attributes, including count, area, inclination, volume, and azimuth. Validation using field scans, synthetic data, and both in situ and destructive measurements shows high accuracy: leaf counting errors ranged from 0.58% to 8.23% for trees with 201–4000 leaves. Reconstructed leaf geometries had mean and standard deviations (SDs) below 0.83 and 0.70 cm, respectively. Leaf area measurements (10–180 cm2) achieved a coefficient of determination (R2) of 0.95, a bias of −0.20 cm2, and a root-mean-square error of 5.63 cm2. Incomplete leaf detection errors were below 28%, with the repaired area relative root-mean-square error (rRMSE) reduced by 9.4%. By addressing QSM limitations, AdLeaf enables explicit 3-D leaf reconstructions that support detailed analysis of canopy light interception, spatial heterogeneity, and photosynthesis. It provides a robust framework for linking leaf structure to function at the tree level, advancing forest structure and radiative transfer research.

Complexity of forest structure is an important factor contributing to uncertainty in aboveground biomass estimates. In this study, we present a new method for reducing uncertainty in forest aboveground biomass (AGB) estimation based on plot-level terrestrial laser scanner (TLS) point clouds reconstruction. The method estimates the total AGB of plots with complex structures after automatically performing the steps of ground point filtering, single tree segmentation, and three-dimensional (3D) structure reconstruction. We used plot data from temperate and tropical forest ecosystems to verify the effectiveness of the method, reconstructing a 1300 m² temperate plantation plot and a 5000 m² mingled forest plot, respectively. The total biomass of 153 trees in the plantation plot was overestimated by 17.12 %, and the total biomass of 61 trees in the mingled forest plot was underestimated by 10.88 %. We found that the uncertainty of aboveground biomass estimation in tropical forests with more complex structures is not necessarily greater than in plantations. Therefore, in large-scale remote sensing observations of forest biomass, the number or area of plots can be increased to reduce the uncertainty of the results caused by the complex structure. The focus of this study is to explore TLS point clouds modeling methods to reduce the uncertainty in AGB estimation caused by the complexity of forest structures, and to provide reference cases for plot-level point clouds reconstruction methods. Forest ecologists can use this method to regularly observe forest growth and obtain indicators related to forest ecology without destroying trees.

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.

Tree-level information can be estimated based on light detection and ranging (LiDAR) point clouds. We propose to develop a quantitative structural model based on terrestrial laser scanning (TLS) point clouds to automatically and accurately estimate tree attributes and to detect real trees for the first time. This model is suitable for forest research where branches are involved in the calculation. First, the Adtree method was used to approximate the geometry of the tree stem and branches by fitting a series of cylinders. Trees were represented as a broad set of cylinders. Then, the end of the stem or all branches were closed. The tree model changed from a cylinder to a closed convex hull polyhedron, which was to reconstruct a 3D model of the tree. Finally, to extract effective tree attributes from the reconstructed 3D model, a convex hull polyhedron calculation method based on the tree model was defined. This calculation method can be used to extract wood (including tree stem and branches) volume, diameter at breast height (DBH) and tree height. To verify the accuracy of tree attributes extracted from the model, the tree models of 153 Chinese scholartrees from TLS data were reconstructed and the tree volume, DBH and tree height were extracted from the model. The experimental results show that the DBH and tree height extracted based on this model are in better consistency with the reference value based on field survey data. The bias, RMSE and R² of DBH were 0.38 cm, 1.28 cm and 0.92, respectively. The bias, RMSE and R² of tree height were-0.76 m, 1.21 m and 0.93, respectively. The tree volume extracted from the model is in better consistency with the reference value. The bias, root mean square error (RMSE) and determination coefficient (R²) of tree volume were-0.01236 m³, 0.03498 m³ and 0.96, respectively. This study provides a new model for nondestructive estimation of tree volume, above-ground biomass (AGB) or carbon stock based on LiDAR data.