There are three main types of forest fires: surface fires, tree crown fires, and underground fires. The frequency of surface fires and tree crown fires accounts for more than 90% of the overall frequency of forest fires. In order to construct an immersive three-dimensional visualization simulation of forest fires, various forest fire ignition methods, forest fire spread, and fire extinguishing simulation exercises are studied. This paper proposes a lightweight forest fire spread method based on cellular automata applied to the virtual 3D world. By building a plant model library using cells to express different plants, and by building a 3D geometric model of plants to truly capture the combustion process of a single plant, we can further simulate forest-scale fire propagation and analyze the factors that affect forest fire spread. In addition, based on the constructed immersive forest scene, this study explored various forms of fire extinguishing methods in the virtual environment, mainly liquid flame retardants such as water guns, helicopter-dropped flame retardants, or simulated rainfall. Therefore, the forest fire occurrence, spread, and fire extinguishing process can be visualized after the interactive simulation is designed and implemented. Finally, this study greatly enhanced the immersion and realism of the 3D forest fire scene by simulating the changes in plant materials during the spread of a forest fire.

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.