Tree structural information is essential for studying forest ecosystem functions, driving mechanisms, and global change response mechanisms. Although current terrestrial laser scanning (TLS) can acquire high-precision 3D structural information of forests, mutual occlusion between trees, the scanner’s field of view, and terrain changes make the point clouds captured by laser scanning sensors incomplete, further hindering downstream tasks. This study proposes a skeleton-embedded tree point cloud completion method, termed SK-TreePCN, which recovers complete individual tree point clouds from incomplete scanning data in the field. SK-TreePCN employs a transformer trained on simulated point clouds generated by a 3D radiative transfer model. Unlike existing point cloud completion algorithms designed for regular shapes and simple structures, the SK-TreePCN method addresses structurally heterogeneous trees. The 3D radiative transfer model LESS, which can simulate various TLS data over highly heterogeneous scenes, is employed to generate massive point clouds with training labels. Among the various point cloud completion methods evaluated, SK-TreePCN exhibits outstanding performance regarding the Chamfer distance (CD) and $F1$ Score. The generated point clouds display a more natural appearance and clearer branches. The accuracy of tree height and diameter at breast height extracted from the recovered point cloud achieved ${\mathit{R}}^2$ values of 0.929 and 0.904, respectively. SK-TreePCN demonstrates applicability and robustness in recovering individual tree point clouds. It demonstrated great potential for TLS-based field measurements of trees, refining point cloud 3D reconstruction and tree information extraction and reducing field data collection labor while retaining satisfactory data quality.

Three-dimensional (3D) virtual trees play a vital role in modern forestry research, enabling the visualization of forest structures and supporting diverse simulations, including radiation transfer, climate change impacts, and dynamic forest management. Current virtual tree modeling primarily relies on 3D point cloud reconstruction from field survey data, and this approach faces significant challenges in scalability and structural diversity representation, limiting its broader applications in ecological modeling of forests. To address these limitations, we propose Diff-Tree, a novel diffusion model-based framework for generating diverse and realistic tree point cloud with reduced dependence on real-world data. The framework incorporates an innovative tree realism-aware filtering mechanism to ensure the authenticity of generated data while maintaining structural diversity. We validated Diff-Tree using two distinct datasets: one comprising five tree species from different families and genera, and another containing five Eucalyptus species from the same genus, demonstrating the method’s versatility across varying taxonomic levels. Quantitative evaluation shows that Diff-Tree successfully generates realistic tree point cloud while effectively enhancing structural diversity, achieving average ${MMD}_{CD}$ and ${COV}_{CD}$ values of (0.41, 65.78) and (0.56, 47.09) for the two datasets, respectively. The proposed method not only significantly reduces data acquisition costs but also provides a flexible, data-driven approach for virtual forest generation that adapts to diverse research requirements, offering a more efficient and practical solution for forestry research and ecological modeling.

In recent years, the increasing frequency of forest fires has threatened ecological and social security. Due to the risks of traditional fire drills, three-dimensional visualization technology has been adopted to simulate forest fire management. This paper presents an immersive decision-making framework for forest firefighting, designed to simulate the response of resources during fires. First, a fire resource scheduling optimization model for multiple fire stations is proposed. This model integrates the characteristics of fire spread with a mixed-integer linear programming (MILP) framework, aiming to minimize response time and firefighting costs. It enables flexible resource scheduling optimization under various fire spread scenarios and constraints on firefighting resources. Second, the ant lion optimization algorithm (ALO) is enhanced, incorporating multiple firefighting weighting factors such as the density, distance, and wind direction of burning trees. This improvement allows for the dynamic selection of priority firefighting targets, facilitating the precise allocation of resources to efficiently complete fire suppression tasks. Finally, a three-dimensional virtual forest environment is developed to simulate real-time actions and processes during firefighting operations. The proposed framework provides an immersive and visualized real-time fire simulation method, offering valuable support for decision-making in forest fire management.