One standard method to capture data for phenological studies is with digital cameras, taking periodic pictures of vegetation. The large volume of digital images introduces the opportunity to enrich these studies by incorporating big data techniques. The new challenges, then, are to efficiently process large datasets and produce insightful information by controlling noise and variability. On these grounds, the contributions of this paper are the following. (a) A histogram-based visualization for large scale phenological data. (b) Phenological metrics based on the HSV color space, that enhance such histogram-based visualization. (c) A mathematical model to tackle the natural variability and uncertainty of phenological images. (d) The implementation of a parallel workflow to process a large amount of collected data efficiently. We validate these contributions with datasets taken from the Phenological Eyes Network (PEN), demonstrating the effectiveness of our approach. The experiments presented here are reproducible with the provided companion material.

一种用于物候研究的数据采集标准方法是使用数码相机，对植被进行定期拍照。大量的数字图像为通过整合大数据技术丰富这些研究提供了机会。因此，新的挑战是如何有效地处理大型数据集并通过控制噪声和可变性来产生具有洞察力的信息。基于这些理由，本文的贡献如下。（a）基于直方图的可视化的大规模物候数据。（b）基于HSV颜色空间的物候指标，可增强基于直方图的可视化效果。（c）解决物候图像自然变化和不确定性的数学模型。（d）实施并行工作流程以有效处理大量收集的数据。我们使用从“物候眼网”（PEN）收集的数据集来验证这些贡献，证明了我们方法的有效性。此处提供的实验可通过提供的伴随材料进行重现。