The Carbon Sequestration Benefits (CSB) of vegetation and forest have received more and more attention with the increase in CO₂ density in the atmosphere. The evaluation of the CSB of existing vegetation and forest has also become one of the important research topics in urban ecology. In the existing research, the evaluation of CSB methods can be categorized into a two-dimension index and three-dimension index. The two-dimension index mainly focuses on leaf area method and leaf area index method. Additionally, the three-dimension index mostly focuses on the Three-Dimension Green Quantity (3DGQ) method which further includes Approximate Geometry Model (AGM), Point Cloud (PC) method using 3D laser, and Point Cloud Convex Hull Slicing (PCCHS) method, etc. In this paper, we take Shanghai as the study area, and address the top 15 species with each species having 30 trees of the same age from Shanghai to calculate the average annual CSB of the 15 species using the two-dimension index and three-dimension index. Through this, we analyze the difference of the same species in different indexes in the same categories and in different categories. The research results provide a research basis for the in-depth exploration of the three-dimensional spatial pattern of urban green space and ecological benefit evaluation.

With the growing awareness of public health, urban parks have increasingly become popular venues for physical activities due to their accessibility and pleasant landscapes, among which walking, jogging, and running dominate. This study innovatively integrates exercise trajectory data from the Strava platform and semantic segmentation technology to analyze the interaction mechanisms among park view elements, physical activities, and physiological responses, based on empirical data from three representative parks in Shanghai. This study includes the following: (1) acquiring hotspot exercise paths and physiological data (heart rate and speed) of walking, jogging, and running users through the open Strava platform; (2) conducting semantic segmentation on real-word photos of three case parks to extract 17 types of park elements; (3) applying Spearman’s correlation analysis to reveal the differential impacts of park elements on physiological responses under walking, jogging, and running behaviors, demonstrating that combinations of elements such as trees, water bodies, fences, and sky influence exercise performance; and (4) constructing scene modules for site attraction, training improvement, and restorative relaxation for walking, jogging, and running, and proposing phased landscape scene sequence strategies to provide quantitative guidance for health-oriented park planning and design. This study breaks through the limitations of traditional subjective evaluations by coupling objective physiological data with spatial elements, offering novel insights for optimizing the exercise functionality of urban green spaces.

Accurate individual tree inventories are fundamental to urban forest management, yet automated delineation from Terrestrial Laser Scanning (TLS) data remains a challenge. This study presents a two-stage hybrid framework that combines a domain-adapted deep learning model (TreeLA-Net) with a geometric algorithm (SEGR) to solve this issue, aiming to reduce the need for instance-level annotations. TreeLA-Net first generates semantic labels, outperforming the baseline RandLA-Net by 2.5 percentage points in overall accuracy. Subsequently, SEGR leverages these priors to achieve a tree detection rate of 92.0% on our primary study site. To assess the framework’s transferability, an external validation was conducted on a new, independent site, where the model, without retraining, yielded a recall of 81.5%. These findings suggest that the framework is not strictly overfitted and possesses generalization capabilities. The proposed approach is offered as a potential tool to support data-driven urban forest management, particularly for automated tree mapping and inventory. We hope that this study may contribute to ongoing efforts to develop robust methods for characterizing complex urban forest structures.

Urban London plane (Platanus × acerifolia) street trees require efficient and interpretable methods for identifying individuals that warrant closer inspection within large roadside inventories. This study developed a two-layer screening framework for 2022 London plane trees along six urban roads in Nanjing, China. First, wind-tunnel tests of five standardized crown geometries were used to derive geometry-specific drag-moment coefficients and calculate aerodynamic crown-loading potential (ACLP), a relative indicator of crown-related aerodynamic demand. ACLP was calculated only for trees within the tested crown height-to-width range. Second, measurements from 19 windthrown London plane trees after Typhoon Bebinca were used to derive a local lower-quartile root-plate reference and calculate a root-plate reference aperture ratio (RPAR) from recorded tree-pit width. Of the 2022 inventoried trees, 1273 (63.0%) fell within the tested crown-geometry range, and 128 were identified as high-ACLP candidates using a 90th-percentile screening threshold. These candidates were strongly concentrated on two roads, and the principal road-level pattern remained stable across alternative percentile thresholds. The post-storm analysis provided a separate local reference for identifying tree-pit apertures that were comparatively small relative to observed root-plate dimensions. ACLP and RPAR should therefore be interpreted as distinct first-stage screening indicators rather than as predictors of windthrow probability or anchorage capacity. The framework provides a transparent basis for prioritizing follow-up field inspection and allocating limited urban tree-management resources.