A simple method for large-scale realistic mountain forest scenes reconstruction using visible stereoscopic imagery combined with unmanned aerial vehicle laser scanning data

Accurate 3D forest reconstruction is essential for quantifying biophysical processes and ecosystem functions. However, the limited spatial coverage of LiDAR systems constrains the generation of structurally detailed forest scenes over large landscapes. This study introduces a scalable data-fusion framework to generate realistic 3D mountainous forest scenes, integrating the extensive coverage of unmanned aerial vehicle stereoscopic imagery (SI) with the high-fidelity vertical structure of unmanned laser scanning (ULS). ULS-derived tree templates were scaled and oriented using structural attributes from SI. To ensure structural realism, topographic variables (elevation, slope, aspect) were incorporated as strict constraints during template matching. The resulting point clouds drove a voxel-based 3D reconstruction validated across 500 m × 500 m coniferous and broadleaf stands. The simulated models demonstrated high structural congruence with benchmark ULS data. Furthermore, radiative transfer simulations based on these 3D scenes showed strong consistency with Sentinel-2 observations. In the near-infrared band, normalized absolute bidirectional reflectance factor differences dropped to 8.89% (RMSE: 0.0277) for coniferous and 8.28% (RMSE: 0.0392) for broadleaf scenes. Ultimately, fusing SI with topographically constrained LiDAR templates provides a highly transferable, geo-data-intensive approach for generating precise 3D forest representations, advancing regional ecological modeling and Earth science applications.