用低成本相机捕捉夏威夷云林植物物候与动植物互动
Tracking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Models
- 结合视觉大模型与传统算法分析相机图像
- 实现无需标注的个体级物候变化监测
- 适合生态学、气候变化研究者参考
植物物候学研究如萌芽、开花和结果等周期性事件，具有广泛生态影响，但在热带地区仍研究不足。图像分析极大提升了远程物候监测能力，但个体水平的物候追踪仍具挑战。本项目在夏威夷普乌马卡阿拉自然保护区部署低成本动物触发相机陷阱，同步记录植物物候变化与动植物相互作用。通过融合视觉基础模型与传统计算机视觉方法，从图像中提取出与实地观测相当的物候趋势，且不依赖监督学习。基于相机图像的高时间分辨率物候测量揭示了传统粗粒度采样难以发现的趋势。结合图像中检测到的访客数据，这些趋势开始揭示植物物候与动物生态的驱动因素。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。