PDF(Pubmed)
Abstract：
Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. In each image, three regions of interest (ROIs) were selected to represent different types of vegetation (scrub, arbor, and grassland), and five vegetation indexes were calculated within each ROI. The results showed that the green chromatic coordinate (GCC), excess green index (ExG), and vegetation contrast index (VCI) all well-captured seasonal changes in vegetation greenness. The PhenoCam captured seasonal trajectories of different vegetation that reflect differences in vegetation growth. Such differences may be influenced by external abiotic environmental factors. We analyzed the nonlinear response of the GCC series to environmental variables with the generalized additive model (GAM). Our results suggested that soil temperature was an important driver affecting plant phenology in the Loess gully region, especially the scrub showed a significant nonlinear response to soil temperature change. Since in situ phenology monitoring experiments of the small-scale on the Loess Plateau are still relatively rare, our work provides a reference for further understanding of vegetation phenological variations and ecosystem functions on the Loess Plateau.
摘要：
植物物候是反映气候变化对生态系统影响的重要指标。我们利用近地表遥感持续监测植被物候,即，2020年3月至2022年11月，中国黄土高原沟壑地区的PhenoCam。在每个图像中，选择了三个感兴趣的区域(ROI)来代表不同类型的植被(灌木丛，Arbor,和草原)，并在每个ROI内计算了五个植被指数。结果表明，绿色色坐标(GCC)，超额绿色指数(ExG)，和植被对比指数(VCI)都很好地捕捉到了植被绿度的季节性变化。PhenoCam捕获了不同植被的季节性轨迹，反映了植被生长的差异。这种差异可能受到外部非生物环境因素的影响。我们用广义加性模型（GAM）分析了GCC系列对环境变量的非线性响应。我们的结果表明，土壤温度是影响黄土沟壑地区植物物候的重要驱动因素，特别是灌丛对土壤温度变化表现出显著的非线性响应。由于黄土高原小尺度的原位物候监测实验还比较少见,为进一步了解黄土高原植被物候变化和生态系统功能提供了参考。
