Impact of UAV flight parameters and acquisition context for canopy height and trunk diameter measurement using LiDAR
Abstract
Key messageDrone-borne LiDAR can measure forest structure quickly and accurately. In a poplar plantation, tree height was retrieved reliably under almost any flight setting, whereas trunk diameter proved far more demanding: accurate values required flying in winter (leafless trees), at low altitude, with triple returns and generous plot margins, and along the planting rows. These practical guidelines make drone-based forest monitoring more reliable and reproducible.ContextAccurate tree height and trunk diameter measurements underpin forest inventory, biomass estimation, and sustainable management. UAV-borne LiDAR is a promising tool for tree-level inventory, yet how flight parameters and acquisition context affect the accuracy of derived structural metrics remains insufficiently documented.AimsWe quantified how UAV flight altitude, speed, plot margins, number of returns, flight plan geometry, and seasonal conditions influence canopy height and trunk diameter estimation.MethodsTwenty-two UAV flights were conducted over a managed 0.23-ha poplar plantation in southwestern France (2024–2025) using a DJI Matrice 300 with a Zenmuse L1 sensor and different varied parameters. Tree-level metrics were compared with field measurements, terrestrial and airborne LiDAR, and satellite-derived canopy height maps.ResultsCanopy height was retrieved robustly (best RMSE = 0.33 m, best R2 = 0.99 versus terrestrial LiDAR), requiring roughly 1000 pts·m⁻2 and altitudes below 60 m above the canopy. Trunk diameter was far more sensitive: accurate retrieval (best RMSE = 3.2 cm; best R2 = 0.90) required leaf-off conditions, low altitude, triple returns, generous margins, and flight lines aligned with planting rows.ConclusionUAV LiDAR can replace labor-intensive field measurements for canopy height under almost any flight setting, but reliable trunk diameter requires leaf-off, low-altitude flights aligned with planting rows; notably, generous plot margins allow flying higher and faster without loss of accuracy, reducing acquisition time and data volume.
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BibTeX
@article{battista2026impact,
title = {{Impact of UAV flight parameters and acquisition context for canopy height and trunk diameter measurement using LiDAR}},
author = {Clément Battista and Frédéric Frappart and Martin Schwartz and Frédéric Baup},
journal = {Annals of Forest Science},
year = {2026},
month = oct,
doi = {10.1186/s13595-026-01371-5},
url = {https://hal.science/hal-05804899v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Impact of UAV flight parameters and acquisition context for canopy height and trunk diameter measurement using LiDAR
AU - Clément Battista
AU - Frédéric Frappart
AU - Martin Schwartz
AU - Frédéric Baup
PY - 2026
DA - 2026/10/13
JO - Annals of Forest Science
DO - 10.1186/s13595-026-01371-5
UR - https://hal.science/hal-05804899v1
AB - Key messageDrone-borne LiDAR can measure forest structure quickly and accurately. In a poplar plantation, tree height was retrieved reliably under almost any flight setting, whereas trunk diameter proved far more demanding: accurate values required flying in winter (leafless trees), at low altitude, with triple returns and generous plot margins, and along the planting rows. These practical guidelines make drone-based forest monitoring more reliable and reproducible.ContextAccurate tree height and trunk diameter measurements underpin forest inventory, biomass estimation, and sustainable management. UAV-borne LiDAR is a promising tool for tree-level inventory, yet how flight parameters and acquisition context affect the accuracy of derived structural metrics remains insufficiently documented.AimsWe quantified how UAV flight altitude, speed, plot margins, number of returns, flight plan geometry, and seasonal conditions influence canopy height and trunk diameter estimation.MethodsTwenty-two UAV flights were conducted over a managed 0.23-ha poplar plantation in southwestern France (2024–2025) using a DJI Matrice 300 with a Zenmuse L1 sensor and different varied parameters. Tree-level metrics were compared with field measurements, terrestrial and airborne LiDAR, and satellite-derived canopy height maps.ResultsCanopy height was retrieved robustly (best RMSE = 0.33 m, best R2 = 0.99 versus terrestrial LiDAR), requiring roughly 1000 pts·m⁻2 and altitudes below 60 m above the canopy. Trunk diameter was far more sensitive: accurate retrieval (best RMSE = 3.2 cm; best R2 = 0.90) required leaf-off conditions, low altitude, triple returns, generous margins, and flight lines aligned with planting rows.ConclusionUAV LiDAR can replace labor-intensive field measurements for canopy height under almost any flight setting, but reliable trunk diameter requires leaf-off, low-altitude flights aligned with planting rows; notably, generous plot margins allow flying higher and faster without loss of accuracy, reducing acquisition time and data volume.
SN - 1286-4560
SN - 1297-966X
ER -