The aboveground biomass (AGB) of individual trees is a critical indicator for assessing urban forest productivity and carbon storage. In the context of global warming, it plays a pivotal role in understanding urban forest carbon sequestration and regulating the global carbon cycle. Recent advances in light detection and ranging (LiDAR) have enabled the detailed characterization of three-dimensional (3D) structures, significantly enhancing the accuracy of individual tree AGB estimation. This review examines studies that use LiDAR-derived 3D structural metrics to model and estimate individual tree AGB, identifying key metrics that influence estimation accuracy. A bibliometric analysis of 795 relevant articles from the Web of Science Core Collection was conducted using R Studio (version 4.4.1) and VOSviewer 1.6.20 software, followed by an in-depth review of 80 papers focused on urban forests, published after 2010 and selected from the first and second quartiles of the Chinese Academy of Sciences journal ranking. The results show the following: (1) Dalponte2016 and watershed are more widely used among 2D raster-based algorithms, and 3D point cloud-based segmentation algorithms offer greater potential for innovation; (2) tree height and crown volume are important 3D structural metrics for individual tree AGB estimation, and biomass indices that integrate these parameters can further improve accuracy and applicability; (3) machine learning algorithms such as Random Forest and deep learning consistently outperform parametric methods, delivering stable AGB estimates; (4) LiDAR data sources, point cloud density, and forest types are important factors that significantly affect the accuracy of individual tree AGB estimation. Future research should emphasize deep learning applications for improving point cloud segmentation and 3D structure extraction accuracy in complex forest environments. Additionally, optimizing multi-sensor data fusion strategies to address data matching and resolution differences will be crucial for developing more accurate and widely applicable AGB estimation models.

The carbon mitigation benefits of urban parks are expanding beyond traditional biological carbon sequestration to encompass the regulation of urban energy demand. Direct carbon sequestration capacity (DCSC), resulting from vegetation carbon fixation, and indirect carbon-saving capacity (ICSC), arising from reductions in building cooling energy consumption induced by summer cooling effects, represent two important pathways for carbon mitigation by urban parks. However, how landscape patterns and morphological spatial patterns jointly regulate DCSC and ICSC, and what nonlinear responses and interactions exist among different spatial indicators remain to be further investigated. Focusing on urban parks in the main urban area of Harbin, this study quantified DCSC and ICSC using multisource remote sensing data and revealed the spatial differences between these two carbon mitigation functions. By developing an analytical framework integrating explainable machine learning and nonlinear response modeling, this study identified the key indicators affecting DCSC and ICSC, the directions of their contributions, and their interactions. It further characterized the nonlinear responses of key indicator combinations and identified favorable ranges for the synergistic enhancement in both carbon benefits. The results showed that: (1) DCSC ranged from 0.96 to 946.75 kg C·day⁻¹, while ICSC ranged from 0.10 to 9,828.50 kg C·day⁻¹. Total ICSC across all parks was approximately 16 times the total DCSC, and both DCSC and ICSC were generally lower in urban parks located within the inner ring. (2) The relative contributions of landscape metrics and MSPA classes to the two carbon benefits differed markedly: landscape metrics made a greater explanatory contribution to ICSC, whereas MSPA classes made a relatively greater explanatory contribution to DCSC. Mean shape index (SHAPE_MN), patch density (PD), and Perforation were identified as key indicators with the potential to enhance both DCSC and ICSC. (3) From a synergistic perspective, interactions involving SHAPE_MN were prominent, with SHAPE_MN × PD ranking among the strongest for both responses, highlighting the importance of boundary complexity and spatial fragmentation. In addition, coordinating green space coverage, spatial continuity, boundary morphology, and the integrity of core spaces can effectively promote the synergistic enhancement in both carbon benefits. These findings provide a new planning basis for optimizing urban park structures in cold-region cities under spatial constraints and advancing the goal of urban carbon neutrality.