关键词: 数码重复摄影, 无人机, 绿色坐标指数, 森林, 林冠层, 林下层, 物候

Abstract: Understanding leaf phenology is essential for capturing forest dynamics, yet traditional monitoring methods fail to resolve vertically stratified phenology due to canopy occlusion and limited spatial coverage. To address this gap, we developed an integrated unmanned aerial vehicle and ground-fixed camera system enabling simultaneous monitoring of forest overstory and understory phenology. Deployed in a subtropical forest during 2017–2023, this system archived 0.075 m × 0.075 m resolution aerial imagery and hourly ground photography, tracking vegetation dynamics across community and species scales. Our system-derived Green Chromatic Coordinate was strongly correlated with Normalized Difference Vegetation Index (r = 0.82), Enhanced Vegetation Index (r = 0.91), Gross Primary Productivity (r = 0.95), and Leaf Area Index (r = 0.79 for overstory; r = 0.92 for understory) validating its effectiveness as a phenological proxy in subtropical forests. Critically, the understory exhibited delayed leaf maturation (16.23 days) and senescence (61.19 and 11.62 days for start and end of leaf falling, respectively) compared to the overstory, revealing a vertical “phenological escape” phenomenon. These phenological mismatches buffered seasonal productivity fluctuates, by sustaining carbon uptake during overstory senescence. Our approach overcomes the limitations of fixed observation towers and satellite imagery by offering flexible, scalable, and cost-effective monitoring of vertical stratification in forests. By quantifying vertical layer interactions, our approach advances predictive modeling of ecosystem-climate feedback and guides forest management under climate change.

Key words: digital repeat photography, unmanned aerial vehicle, Green Chromatic Coordinates, forest, overstory, understory, phenology