Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and Extraction of Individual Tree Parameters

Accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-Range Photogrammetry (CRP) is widely used in 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, Novel View Synthesis (NVS) technology such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using different imaging devices. We then performed dense reconstruction of forest stand using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. Photogrammetric method is less suited to more complex forest stands, resulting in tree point cloud models with issues such as excessive canopy noise, wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, especially in reconstructing canopy regions. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings suggest that NVS methods have significant potential for 3D reconstruction of urban forests, providing further technical support for forest resource visualization, inventory and management tasks.