The use of UAV-LiDAR time series to monitor spring and fall phenology of a beech provenance experiment

Understanding how tree provenances respond to local climate conditions is essential for predicting forest resilience under climate change. To investigate the performance of different European beech (Fagus sylvatica) provenances in the Dutch climate, a provenance trial was established in 1998 in Wageningen, the Netherlands. In this study, we evaluate the ability of UAV-borne LiDAR time series to capture temporal differences in spring and autumn leaf phenology among provenances.

Weekly UAV surveys were conducted from March to June and from October to December 2024 and 2025, with two additional flights during the summer period, over a 0.9 ha beech provenance trial consisting of 29 European provenances planted in three blocks (plot size 10 × 10 m). Data were acquired using a DJI M300 UAV equipped with a DJI L1 LiDAR sensor. From the LiDAR data, structural and radiometric canopy metrics were derived. These time series were compared with dendrometer measurements and physiological information related to the geographic origin of the provenances.

UAV-LiDAR structural metrics, such as canopy cover and height distribution, showed stable and consistent temporal patterns and were generally less sensitive to illumination and calibration effects than multispectral indices. However, LiDAR-derived metrics were highly sensitive to flight altitude, highlighting the importance of maintaining consistent acquisition settings throughout a time series. Differences in the onset and senescence of leaf phenology between provenances were observed from the LiDAR data, but clear relationships with provenance origin and dendrometer data are not yet conclusive.