Northern Arizona University, School of Informatics, Computing and Cyber Systems, Flagstaff, AZ, 86011, USA.
Northern Arizona University, Center for Ecosystem Science and Society, Flagstaff, AZ, 86011, USA.
Sci Rep. 2018 Apr 9;8(1):5679. doi: 10.1038/s41598-018-23804-6.
Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both "greenness rising" and "greenness falling" transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.
物候学是评估生态系统健康的一个有价值的指标,在环境监测和管理方面有广泛的应用。在这里,我们使用来自近地表 PhenoCam 图像和 MODIS 卫星遥感的物候转换日期进行了对比分析。我们使用了大约 600 个站点年的数据,来自覆盖广泛植被类型和气候带的 128 个相机站点。在“绿色度上升”和“绿色度下降”的转换阶段,我们发现 PhenoCam 和 MODIS 的转换日期在农业、落叶林和草原站点之间通常具有较好的一致性,前提是相机视场内的植被能够代表更广泛的景观。对于常绿林站点,PhenoCam 和 MODIS 转换日期之间的相关性较差。我们讨论了潜在的原因(包括亚像元空间异质性、转换日期提取方法的灵活性、常绿系统中植被指数的敏感性以及 PhenoCam 地理位置不确定性),这些原因导致了源自 PhenoCam 和 MODIS 图像的植被指数时间序列之间的不一致。这项分析增加了我们对卫星遥感准确描述一系列生态系统季节性动态的能力的信心,并为在地面上发生的实际物候变化背景下解释这些动态提供了基础。