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Detection of flowering and quantification of flowering phenology are key to monitoring the reproduction of tropical trees and their response to global change. However, effective monitoring of flowering over various scales from individuals to forest ecosystem levels is lacking due to the relatively small sizes of flowers, diverse flowering strategies across species, and the short duration of flowering, making accurate flower detection difficult. Drone-based surveys require less time and human resources than traditional ground-based flower surveys and thus may be able to help address this in a cost-effective manner but remain underexplored in species-rich tropical forest ecosystems. Here, we developed a method that integrated the Residual Networks 50 (ResNet50) deep learning algorithm with high resolution imagery (c. 0.05 m) from monthly drone surveys done in a 50-ha tropical forest plot on Barro Colorado Island (BCI), Panamá, over 2018–2020 to detect a diversity of flowering species in this tree community and to track the timing of flowering throughout the year. We built a comprehensive training library of canopy components (flower, leaf, branch, and shade) from this forest plot throughout the study period, trained a single deep learning model across all drone imagery, and validated it using five-fold cross validation at the pixel scale. We further generated image- and tree-crown-specific supervised classifications to evaluate the deep learning model at the tree-crown scale. Our deep learning method accurately classified flowers (User's accuracy = 95.3 %, Producer's accuracy = 85.8 %) while maintaining high predictive power for the other three classes (Overall accuracy = 98.4 %). Our results also demonstrated high consistency against tree-crown-specific supervised classifications for flower (r2 = 0.85), leaf (r2 = 0.84), and branch (r2 = 0.92) components, with lower agreement observed for the shade component (r2 = 0.59). These results demonstrate the effectiveness of our method in advancing fine-scale flower monitoring in the tropics, with potential to be extended to other regions or other remote sensing platforms with frequent high-resolution monitoring. The method will allow us to better monitor flowering in tropical forests and improve our understanding of how phenology and reproductive success may be affected by climate change. [ABSTRACT FROM AUTHOR]

Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams. [ABSTRACT FROM AUTHOR]