SEV LTER: Tracking Vegetation Phenology Using PhenoCam Imagery at the Sevilleta National Wildlife Refuge, New Mexico, 2014-2024

As of 03/03/2024, the Sevilleta Long-Term Ecological Research Program is equipped with a total of 65 digital RGB cameras, or PhenoCams, across the Sevilleta National Wildlife Refuge. These cameras are installed on eddy covariance flux towers and at a number of precipitation manipulation experiments to track vegetation phenology and productivity across dryland ecotones. PhenoCams have been paired with eddy covariance flux tower data at the site since 2014, while some Mean-Variance Experiment PhenoCams were installed as recently as June 2023. For information on PhenoCam data processing and formatting, see Richardson et al., 2018, Scientific Data (https://doi.org/10.1038/sdata.2018.28), Seyednasrollah et al., 2019, Scientific Data (https://doi.org/10.1038/s41597-019-0229-9), and the PhenoCam Network web page (https://phenocam.nau.edu/webcam/). The PhenoCam Network uses imagery from digital cameras to track vegetation phenology and seasonal changes in vegetation activity in diverse ecosystems across North America and around the world. Imagery is uploaded to the PhenoCam server hosted at Northern Arizona University, where it is made publicly available in near-real time, every 30 minutes from sunrise to sunset, 365 days a year. The data are processed using simple image analysis tools to yield a measure of canopy greenness, from which phenological metrics are extracted, characterizing the start and end of the growing season. These transition dates have been shown to align well with on-the-ground observations at various research sites. Long-term PhenoCam data can be used to track the impact of climate variability and change on the rhythm of the seasons.截至2024年3月3日，塞维利塔长期生态研究项目（Sevilleta Long-Term Ecological Research Program）在塞维利塔国家野生动物保护区内共部署了65台数字RGB相机，即物候相机（PhenoCams）。这些相机被安装于涡度协方差通量塔（eddy covariance flux towers）以及多个降水调控实验站点，用于追踪旱地生态交错带的植被物候与生产力。自2014年起，该站点的物候相机便已与涡度协方差通量塔数据进行配对，而部分均值方差实验（Mean-Variance Experiment）专用的物候相机直至2023年6月才完成部署。若需了解物候相机数据的处理与格式化规范，可参考Richardson等人2018年发表于《Scientific Data》的研究（https://doi.org/10.1038/sdata.2018.28）、Seyednasrollah等人2019年发表于《Scientific Data》的研究（https://doi.org/10.1038/s41597-019-0229-9），以及物候相机网络（PhenoCam Network）官网（https://phenocam.nau.edu/webcam/）。 物候相机网络（PhenoCam Network）依托数码相机影像，追踪北美及全球多样生态系统中的植被物候与植被活动的季节动态。影像会被上传至北亚利桑那大学（Northern Arizona University）托管的物候相机服务器，全年365天从日出至日落每30分钟更新一次，以近实时的方式向公众开放。 研究人员通过简易图像分析工具处理这些影像数据，得到冠层绿度指标，并从中提取物候参数，以表征生长季的起始与结束时间。已有研究证实，这些物候转换日期与多个研究站点的实地观测结果高度契合。长期物候相机数据集可用于追踪气候变率与气候变化对季节节律的影响。