The linkage between functional traits and drone-derived phenology of 74 Northern Hemisphere tree species

Tree phenology is a major component of the global carbon and water cycle, serving as a fingerprint of climate change, and exhibiting significant variability both within and between species. In the emerging field of drone monitoring, it remains unclear whether this phenological variability can be effectively captured across numerous tree species. Additionally, the drivers behind interspecific variations in the phenology of deciduous trees are poorly understood, although they may be linked to plant functional traits. In this study, we derived the start of season (SOS), end of season (EOS), and length of season (LOS) for 3099 individuals from 74 deciduous tree species of the Northern Hemisphere at a unique study site in southeast Germany using drone imagery. We validated these phenological metrics with in-situ data and analyzed the interspecific variability in terms of plant functional traits. The drone-derived SOS and EOS showed high agreement with ground observations of leaf unfolding (R2 = 0.49) and leaf discoloration (R2 = 0.79), indicating that this methodology robustly captures phenology at the individual level with low temporal and human effort. Both intra- and interspecific phenological variability were high in spring and autumn, leading to differences in the LOS of up to two months under almost identical environmental conditions. Functional traits such as seed dry mass, chromosome number, and continent of origin played significant roles in explaining interspecific phenological differences in SOS, EOS, and LOS, respectively. In total, 55 %, 39 %, and 45 % of interspecific variation in SOS, EOS, and LOS could be explained by the Boosted Regression Tree (BRT) models based on functional traits. Our findings encourage new research avenues in tree phenology and advance our understanding of the growth strategies of key tree species in the Northern Hemisphere.

Variation in the timing and duration of autumn leaf phenology among temperate deciduous trees, native shrubs and non-native shrubs

The timing and duration of autumn leaf phenology marks important transitions in temperate deciduous forests, such as, start of senescence, declining productivity and changing nutrient cycling. Phenological research on temperate deciduous forests typically focuses on upper canopy trees, overlooking the contribution of other plant functional groups like shrubs. Yet shrubs tend to remain green longer than trees, while non-native shrubs, in particular, tend to exhibit an extended growing season that confers a competitive advantage over native shrubs. We monitored leaf senescence and leaf fall (2017-2020) of trees and shrubs (native and non-native) in an urban woodland fragment in Wisconsin, USA. Our findings revealed that, the start of leaf senescence did not differ significantly between vegetation groups, but leaf fall started (DOY 273) two weeks later in shrubs. Non-native shrubs exhibited a considerably delayed start (DOY 262) and end of leaf senescence (DOY 300), with leaf-fall ending (DOY 315) nearly four weeks later than native shrubs and trees. Overall, the duration of the autumn phenological season was longer for non-native shrubs than either native shrubs or trees. Comparison of the timing of spring phenophases with the start and end of leaf senescence revealed that when spring phenology in trees starts later in the season senescence also starts later and ends earlier. The opposite pattern was observed in native shrubs. In conclusion, understanding the contributions of plant functional groups to overall forest phenology requires future investigation to ensure accurate predictions of future ecosystem productivity and help address discrepancies with remote sensing phenometrics.

Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.