Higher Order Energy-Optimized Air–Ground Individual Tree Segmentation With Crown Morphology Fidelity

Accurate acquisition of carbon sequestration information at the individual tree scale is crucial for the refined assessment and management of carbon stocks in urban ecosystems. Traditional optical remote sensing methods, limited by 2-D spectral features, are more suitable for large-scale forest carbon stock estimation but struggle to achieve refined quantification at the individual tree level. Light detection and ranging (LiDAR) technology, which directly characterizes 3-D structural parameters of trees through point clouds, significantly outperforms optical methods in improving both segmentation quantity and morphological accuracy. To achieve a refined assessment and management of the urban carbon storage, on the one hand, we propose to integrate unmanned aerial vehicle (UAV)-borne and ground-based point clouds, thereby overcoming the observational limitations of single-source data. On the other hand, to address the problems of under- and over-segmentation and morphological distortion in individual tree segmentation. We have, respectively, proposed two extreme value segmentation models and a high-order energy morphological optimization model. Specifically, the canopy skyline extremum (CSE) model can solve the under-segmentation problem caused by the absence of canopy extreme points and the over-segmentation problem caused by pseudo-extreme points from the perspective of the canopy dimension. The vertical distribution extremum (VDE) model of a tree can solve the under-segmentation problem caused by the sparsity of trunk point clouds from the trunk dimension. For crown morphology fidelity, we construct, a voxel-based spatial correlation graph is constructed to characterize the distribution of branches and leaves. We use the Markov random field (MRF) energy optimization framework, integrate the prior knowledge of tree ecology, establish a high-order energy model that satisfies the objective cognitive morphology of trees, dynamically assign the membership relationship of branches and leaves, effectively solve the problem of interlacing and adhesion of branches and leaves between adjacent trees, overcome the problem of tree morphological distortion caused by the “one-size-fits-all” approach in traditional individual tree segmentation, improve the calculation accuracy of the tree crown diameter and canopy volume, and enhance the estimation accuracy of the carbon storage of individual trees to the decimeter level. Experiments were conducted on six datasets from two typical urban scenarios: street trees and landscaped gardens. Results validate the superior performance of our morphology-faithful segmentation. Quantitative comparisons with the state of the art show that our approach achieves optimal performance in both segmentation accuracy and stability. Further analysis confirms that the proposed morphological optimization preserves reasonable tree shapes, leading to more accurate individual tree attribute calculations. This study verifies that the proposed models significantly enhance the accuracy of urban individual tree segmentation (quantity and morphology), enabling decimeter-level urban carbon stock assessment, and provides a novel technical framework for precise ecosystem carbon sequestration monitoring.