Laser scanning technology has enabled to study three-dimensional (3D) structures in forests. For example, airborne laser scanning (ALS) point cloud has been applied to detect individual trees and segment tree crowns. However, the accuracy of such approach remains a challenge because of the intersected crowns and complicated understories. We developed a metabolic theory-based algorithm for individual tree detection and crown segmentation from ALS data. The algorithm is composed of two parts, of which one is an unscaled transporting distance-based top-to-bottom detection approach, and the other is a scaled transporting distance-based segmentation approach. The unscaled transporting distance for detection is the absolute distance from tree root to crown and then to ALS point based on the tree structure models, whereas the scaled transporting distance for segmentation is the unscaled distance that is scaled by an initial tree height obtained during detection. This is based on a basic metabolic theory that vascular plants tend to minimize the material transporting distance from root to leaves. Hence, seven types of materials transporting distance models were built based on monopodial branching structure or crown-centered structure. The performance of the proposed approach was then further examined and compared with two typical canopy height model-based approaches and one typical point cloud-based approach, taking forest in Oxfordshire, UK, as a case study. The results showed that our approach can reach a recall of 1.00, a precision of 0.96, and an F-score of 0.98 and can reach to much higher accuracy for tree height (R²  =  0.8045) than the comparison approaches (R²  <  0.2) in the study plot. One of the main reasons that led to such low accuracy of comparison approaches is much overestimation of understory height with a mean error that is 2.9 times higher than that of our approach on average. Furthermore, ALS point-to-point level accuracy assessment shows 9.7% more ALS points were truly assigned in our approach than that of comparison approaches. It is noticed that the algorithm presented is not sensitive to the two key parameters: p (a percentage determining the threshold of unscaled transporting distances) ranging from 32.0% to 34.0% and λ (the proportion between the assumed crown center height and tree height) ranging from 0.70 to 0.90 based on our data set. Such high accuracy of our approach can greatly improve detections of individual tree and crown segmentation, especially in delineating understories in complex-structured forest.