The Novel Models for Identifying the Vertical Structure of Urban Vegetation from UAV LiDAR Data

Accurate quantification of vegetation vertical structure is crucial for analyzing the ecological functions of urban green spaces. However, constrained by the complexity of vegetation structure and spatial heterogeneity, current approaches for extracting vegetation vertical structure by airborne LiDAR have limitations in terms of layer boundary identification stability, threshold dependency, and ecological plausibility. This study developed two integrated UAV LiDAR-based stratification frameworks for identifying urban riparian vegetation vertical structure by combining established statistical modeling and signal processing techniques: (1) a Gaussian Mixture Model with Bayesian Information Criterion (GMM-BIC)-based probabilistic stratification framework; (2) a Savitzky–Golay filtering and Pruned Exact Linear Time (SG-PELT)-based change-point detection framework. Furthermore, the ecological height constraint was incorporated into the model to achieve biological adjustments. Two models were applied in the study area and compared using reference data. The results showed that the GMM-BIC method achieved an overall classification accuracy of 91.06%, with a macro-averaged F1-score of 87.77%, while the SG-PELT method attained an overall accuracy of 84.57%, with a macro-averaged F1-score of 79.20%. These results demonstrate that both models can effectively identify the vertical structure of urban vegetation. In particular, the two models exhibited distinct characteristics across different scenarios. The GMM-BIC model showed superior stratification accuracy in regions where vegetation height distribution displayed pronounced multi-peak characteristics and distinct differences among height segments. In comparison, the SG-PELT model demonstrated greater sensitivity in areas with significant height variation and clearly defined abrupt transitions between layers. These models could provide new methodologies for monitoring vegetation vertical structure and offer data support for biodiversity monitoring and ecological function assessment within urban ecosystems.