Tracking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Models
This work addresses the challenge of individual-level phenological monitoring in tropical environments, providing a method for ecologists to uncover fine-grained trends that coarser sampling methods miss.
This project deployed low-cost, animal-triggered camera traps in a Hawaiian cloud forest to track plant phenology and flora-faunal interactions. By combining foundation vision models and traditional computer vision, the researchers measured phenological trends from images that were comparable to on-the-ground observations, without needing supervised learning.
Plant phenology, the study of cyclical events such as leafing out, flowering, or fruiting, has wide ecological impacts but is broadly understudied, especially in the tropics. Image analysis has greatly enhanced remote phenological monitoring, yet capturing phenology at the individual level remains challenging. In this project, we deployed low-cost, animal-triggered camera traps at the Pu'u Maka'ala Natural Area Reserve in Hawaii to simultaneously document shifts in plant phenology and flora-faunal interactions. Using a combination of foundation vision models and traditional computer vision methods, we measure phenological trends from images comparable to on-the-ground observations without relying on supervised learning techniques. These temporally fine-grained phenology measurements from camera-trap images uncover trends that coarser traditional sampling fails to detect. When combined with detailed visitation data detected from images, these trends can begin to elucidate drivers of both plant phenology and animal ecology.