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

Abstract

This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture under Mediterranean conditions. Experiments were conducted in Sicily, Italy, on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV photogrammetric data were used to generate canopy models and estimate canopy height, canopy volume, and vegetation density distribution. A voxel-based approach was applied to LiDAR-derived point clouds to quantify internal canopy structure and vegetation density within the canopy volume. Accuracy was assessed by comparing remote sensing-derived canopy metrics with ground-truth field measurements. LiDAR outperformed UAV photogrammetry in canopy height estimation, achieving lower RMSE values than UAV-derived models (0.19–0.21 m vs. 0.52–0.60 m), corresponding to an approximate error reduction of 60–65%. LiDAR also provided more accurate canopy volume estimation, with lower relative errors than UAV photogrammetry (3.5–4.2% vs. 13.7–16.1%). The voxel-based LiDAR approach enabled the quantification of vegetation density distribution within the canopy volume, showing higher sensitivity to internal canopy layers compared with UAV photogrammetry, particularly in the structurally complex Ficus macrophylla canopy. UAV photogrammetry provided reliable estimates of the external canopy surface but underestimated structural parameters in dense vegetation due to canopy occlusion and limited penetration into inner canopy layers. Differences between the two methods were more pronounced in Ficus macrophylla than in Moringa oleifera, confirming the strong influence of canopy complexity on sensing performance. These findings demonstrate that LiDAR-derived structural and voxel-based metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, yield prediction, and canopy management in Mediterranean cropping systems.

Plant phenotyping relevance

LiDARとUAVフォトグラメトリによる植物キャノピーの3次元再構築・構造形質推定を比較検証し、地上計測との精度評価まで行っており、フェノタイピング手法が研究の中心である。

Code and data availability

The supplied blocks describe LiDAR/UAV canopy phenotyping methods but contain no public dataset, image, code, model, or supplement availability statement with an authors' URL. Only generic software (CloudCompare, Agisoft Metashape, Hovermap) is mentioned, which does not qualify.

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