Comparison of unmanned aircraft system-based photogrammetry and light detection and ranging for pecan tree height estimation

Authors: Yang, Chenghai; Hilton, Angelyn; Wang, Xinwang; Chatwin, Warren; Fritz, Bradley

Submitted to: Journal of the American Society for Horticultural Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/30/2026
Publication Date: 6/1/2026
Citation: Yang, C., Hilton, A.E., Wang, X., Chatwin, W.B., Fritz, B.K. 2026. Comparison of unmanned aircraft system-based photogrammetry and light detection and ranging for pecan tree height estimation. Journal of the American Society for Horticultural Science. 151(4):357-371. https://doi.org/10.21273/JASHS05620-26.
DOI: https://doi.org/10.21273/JASHS05620-26

Interpretive Summary: Accurate measurement of pecan tree height is important for monitoring growth and orchard health, but field measurements are slow and labor-intensive. Drone-based imaging and LiDAR are promising tools, yet their use in pecan orchards has been limited. This study evaluated drone imagery and LiDAR for estimating pecan tree height. Results showed that heights derived from drone-based 3D models were highly accurate and closely matched LiDAR and ground measurements, although insufficient image overlap at lower flight heights could reduce accuracy. These findings demonstrate that drone imaging can provide a reliable, lower-cost alternative to LiDAR, helping growers efficiently monitor tree growth and improve orchard management.

Technical Abstract: While unmanned aircraft system (UAS)-based photogrammetry and LiDAR are increasingly used for canopy height estimation in forestry and other orchard systems, their application to pecan orchards remains limited. Accurate measurements of tree height and canopy structure are essential in pecan production for assessing tree growth and health, and for supporting precision orchard management. This study provides one of the first systematic evaluations of UAS-based structure-from-motion (SfM) photogrammetry and UAS-mounted LiDAR for estimating pecan tree height. A rotary-wing UAS equipped with RGB and near-infrared (NIR) cameras collected imagery at 60 and 120 m above ground over two pecan orchards containing 480 and 308 trees, while LiDAR data were acquired at 70 m. UAS imagery was processed to generate 3D point clouds, digital surface models (DSMs), digital terrain models (DTMs), and orthomosaics. DTMs were derived using point cloud classification and DSM filtering, and tree heights were calculated relative to these terrain models using canopy height models (CHMs) and point cloud–based approaches. LiDAR data were processed to produce calibrated point clouds, DSMs, and DTMs, from which tree heights were extracted using comparable methods. Image-based tree heights showed strong agreement with manual measurements, with point cloud–derived high percentiles or maxima (R² = 0.982–0.996; RMSE = 14–25 cm) consistently outperforming CHM-based estimates across ground elevation methods, camera types, and flight altitudes. LiDAR-derived tree heights exhibited similarly high accuracy. Image-based and LiDAR-derived heights were strongly correlated across all trees at 120 m (R² = 0.982–0.995; RMSE = 18–25 cm), confirming the reliability of SfM photogrammetry. However, incomplete canopy reconstruction in some 60'm datasets led to underestimation, highlighting the importance of sufficient image overlap for accurate 3D canopy modeling. These results demonstrate that UAS image-based point clouds can provide pecan tree heights comparable to LiDAR, offering a cost-effective approach for tree growth monitoring, orchard management, and precision agriculture applications.