Abstract: Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity, shaping the interactions between species and their environment. To gain a deeper understanding of the mechanisms driving climate change, phenological monitoring is essential. Traditional methods of defining phenological phases often rely on fixed thresholds. However, with the development of technology, deep learning-based classification models are now able to more accurately delineate phenological phases from images, enabling phenological monitoring. Despite the significant advancements these models have made in phenological monitoring, they still face challenges in fully capturing the complexity of biotic-environmental interactions, which can limit the fine-grained accuracy of phenological phase identification. To address this, we propose a novel deep learning model, RESformer, designed to monitor tree phenology at a fine-grained level using PhenoCam images. RESformer features a lightweight structure, making it suitable for deployment in resource-constrained environments. It incorporates a dual-branch routing mechanism that considers both global and local information, thereby improving the accuracy of phenological monitoring. To validate the effectiveness of RESformer, we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin, focusing on the phenology of Acer. The images were classified into seven distinct phenological stages, with RESformer achieving an overall monitoring accuracy of 96.02%. Furthermore, we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate (GCC) index and ten popular classification models. The results showed that RESformer excelled in fine-grained monitoring, effectively capturing and identifying changes in phenological stages. This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.

Key words: Fine-grained phenological period, Acer phenological monitoring, Green chromatic coordinate, PhenoCam