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Unmanned aerial vehicles (UAV) provide a new platform for monitoring crown-level leaf phenology due to the ability to cover a vast area while offering branch-level image resolution. However, below-crown vegetation, e.g., understory vegetation, subcanopy trees, and the branches of neighboring trees, along with the multi-layered structure of the target crown may significantly reduce the accuracy of UAV-based estimates of crown leaf phenology. To test this hypothesis, we compared UAV-derived crown leaf phenology results against those based on ground observations at the individual tree scale for 19 deciduous broad-leaved species (55 individuals in total) characterized by different crown structures. The mean crown-level green chromatic coordinate derived from UAV images poorly explained inter- and intra-species variations in spring leaf phenology, most probably due to the consistently early leaf emergence in the below-crown vegetation. The start dates for leaf expansion and end dates for leaf falling could be estimated with an accuracy of &lt;1-week when the influence of below-crown vegetation was removed from the UAV images through visual interpretation. However, a large discrepancy between the phenological metrics derived from UAV images and ground observations was still found for the end date of leaf expansion (EOE) and start date of leaf falling (SOF). Bayesian modeling revealed that the discrepancy for EOE increased as crown length and volume increased. The crown structure was not found to contribute to the discrepancy in SOF value. Our study provides evidence that crown structure is a pivotal factor to consider when using UAV photography to reliably estimate crown leaf phenology at the individual tree-scale.

forests

Crown Structure Explains the Discrepancy in Leaf Phenology Metrics Derived from Ground- and UAV-Based Observations in a Japanese Cool Temperate Deciduous Forest