Urban green spaces are important for residents’ physical and mental health, but green visibility is difficult to quantify in high-rise, high-density cities, especially across different height levels. To address this problem, this study proposes a stratified green visibility framework based on airborne LiDAR point clouds and a voxel model. Using the Dutch AHN5 dataset, the study area was converted into a unified 3D voxel space and classified into trees, grass, buildings, ground, and empty space. A voxel-level penetration probability model based on the Beer–Lambert law was introduced to represent the semi-transparent blocking effect of tree canopies, improving upon conventional binary visibility models. Multi-directional line-of-sight (LOS) tracing was then applied to calculate green visibility (GVI) and spatial openness (SOP) at different height layers. The results show that GVI is generally high around parks, large green spaces, and some enclosed courtyards, but its contribution from street trees is limited. Vertically, GVI decreases with height, while SOP tends to increase. Combining the two indicators helps identify different spatial types with distinct visual characteristics. The study demonstrates that airborne LiDAR, combined with voxelization and probabilistic 3D simulation, can effectively capture the vertical variation of urban GVI and support large-area assessment in high-density residential environments.