UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

As a key canopy structure parameter, the estimation method of Leaf Area Index (LAI) has always attracted attention. To explore potential to estimate forest LAI from 3D point cloud at low cost, we took photos different angles drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° 15°) OT30 30°)), which were used reconstruct based on photogrammetry. Subsequently, values leaf area distribution in vertical direction derived calculated voxelized model. Our results show that serious lack middle lower layers determines O is inaccurate. For oblique photogrammetry, with 30° provided better estimates than 15° (T30 T15, OT15), mainly reflected part canopy, particularly obvious low-LAI areas. The overall single-tilt angle scheme (T15, T30) was relatively complete, but rough details could not reflect actual situation well. Multi-angle (OT15, OT30) excellent (OT15: R2 = 0.8225, RMSE 0.3334 m2/m2; OT30: 0.9119, 0.1790 m2/m2). best accuracy sub-voxel size 0.09 m checkpoint (OT30: [H] 0.2917 m, [V] 0.1797 m). highlight coupling photography nadiral can be an effective solution LAI.