School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, 86011, USA.
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, 86011, USA.
New Phytol. 2019 Jun;222(4):1742-1750. doi: 10.1111/nph.15591. Epub 2018 Dec 14.
Contents Summary I. Introduction II. Evolving modes of phenological study III. The phenocam approach IV. Applications of the phenocam method V. Looking forward Acknowledgements References SUMMARY: Global change is shifting the seasonality of vegetation in ecosystems around the globe. High-frequency digital camera imagery, and vegetation indices derived from that imagery, is facilitating better tracking of phenological responses to environmental variation. This method, commonly referred to as the 'phenocam' approach, is well suited to several specific applications, including: close-up observation of individual organisms; long-term canopy-level monitoring at individual sites; automated phenological monitoring in regional-to-continental scale observatory networks; and tracking responses to experimental treatments. Several camera networks are already well established, and some camera records are a more than a decade long. These data can be used to identify the environmental controls on phenology in different ecosystems, which will contribute to the development of improved prognostic phenology models.
内容摘要 I. 引言 II. 物候研究模式的演变 III. 物候相机方法 IV. 物候方法的应用 V. 展望 致谢 参考文献 摘要:全球变化正在改变全球生态系统中植被的季节性。高频数字相机图像及其衍生的植被指数,使人们能够更好地跟踪对环境变化的物候响应。这种方法通常被称为“物候相机”方法,非常适合几种特定的应用,包括:个体生物的近距离观察;单个地点的长期冠层水平监测;区域到大陆尺度观测网络中的自动物候监测;以及跟踪对实验处理的响应。一些相机网络已经建立得很好,有些相机记录的时间超过了十年。这些数据可用于确定不同生态系统中物候的环境控制因素,这将有助于开发改进的预测物候模型。