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.

Land use affects ecosystem stability and agricultural ecological security in black soil regions. Additional attention is required regarding the impact of different land-use patterns on black soil. However, the construction of sustainable agricultural ecological security in black soil environments is a dynamic process that depends on the reviews of experts and statistical analyses of literature data. This study quantitatively reviewed the past 20 years of the literature regarding black soil. Using the superposition of the expert knowledge map and machine clustering, knowledge regarding land use in black soil fields was classified structurally. Further, studies directly related to the spatiotemporal pattern of land use were identified, and frequently cited works of the literature were screened to build a dynamic knowledge network of black soil research. The results show that (1) the cooperative relationship among China, the United States, and Canada is the strongest, but the density of cooperation networks between other countries is low; (2) land-use research regarding black soil is divided into four research areas: soil microbial community and activity, soil erosion and ecological processes, ecological management of land use, soil organic matter, and element cycling; (3) the monitoring and management mode of land use in black soil areas should be established to include information management that incorporates knowledge of the cultivated land factor potential, grain production capacity assessment, soil erosion evaluation and prediction, and farmland landscape planning.