LiDAR and UAV Photogrammetry for Three-Dimensional Canopy Reconstruction: A Comparative Study for Precision Agriculture Under Mediterranean Conditions

When farmers and agronomists want to measure how tall a crop is, how much volume its canopy occupies, or how dense its foliage is, they increasingly turn to remote sensing tools rather than manual measurement. Two of the most promising technologies are LiDAR (Light Detection and Ranging, which fires laser pulses and measures how they bounce back) and UAV photogrammetry (drone-based photography stitched together into 3D models). Researchers in Sicily tested both technologies side by side on two very different tree species: Moringa oleifera, the fast-growing tropical tree valued for its nutritional leaves, and Ficus macrophylla, a large-canopied fig species with a far more complex internal structure. The results were clear: LiDAR outperformed drone photogrammetry on every structural measurement tested. For canopy height, LiDAR's error was roughly 0.19–0.21 metres compared with 0.52–0.60 metres for drone models — an error reduction of around 60–65%. For canopy volume, LiDAR's relative error was just 3.5–4.2%, while drone photogrammetry reached 13.7–16.1%. The gap between the two methods was larger for the structurally complex Ficus than for the more open Moringa canopy, suggesting that canopy architecture strongly influences which tool performs better. The study matters because accurate canopy measurements feed directly into decisions about irrigation, fertilisation, pruning, and yield prediction. For Moringa specifically, which is being trialled as a commercial crop in Mediterranean climates, having reliable, low-cost canopy monitoring tools could support more efficient cultivation at scale.