Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA), Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari (CREA-IT), Monterotondo, Rome, Italy.
Consiglio per la Ricerca in Agricoltura e L'analisi Dell'economia Agraria (CREA), Centro di ricerca Agricoltura e Ambiente (CREA-AA), Rome, Italy.
Int J Biometeorol. 2019 Aug;63(8):1039-1049. doi: 10.1007/s00484-019-01718-w. Epub 2019 May 7.
Weather extremes and extreme climate events, like late spring frosts, are expected to increase in frequency and duration during the next decades. Although spring phenology of European beech is well adapted to escape freeze damages on longer time scales, the effects of occasional late spring frosts (LSF) are among the main climatic damages to these forests to such an extent that they limit beech distribution and elevation range, especially at its southern margin. The aim of this work was to evaluate the short-term effects of two consecutive LSF events occurred in 2016 and 2017 in Italy on the beech forest vegetation activity. Remotely sensed land surface temperature (LST) data were used to detect the pixels where LSF occurred, while enhanced vegetation index (EVI) data were used to quantify LSF effects by computing a spring vegetation activity anomaly index (sAI). In 2016 and 2017, the LSF covered, respectively, about 29% and 32% of the total Italian beech-dominated area. The two LSF widely differed in their spatial patterns and their effects. In 2016, the pixels belonging to the sAI classes with the highest spring anomalies were also those where prolonged LSF occur, while, in 2017, the pixels belonging to the highest sAI classes were those that underwent the shorter (but probably more intense) LSF events. Under scenarios of increased frequency risk of repeated LSF, the proposed methodology may represent an automatic and low-cost tool both for monitoring and predicting European beech growth patterns.
在未来几十年,极端天气和极端气候事件(如晚春霜冻)预计将更加频繁和持久。尽管欧洲山毛榉的春季物候期非常适应长时间尺度的冻害,但偶尔发生的晚春霜冻(LSF)的影响是这些森林的主要气候损害之一,以至于它们限制了山毛榉的分布和海拔范围,尤其是在其南部边缘。本研究的目的是评估 2016 年和 2017 年意大利连续两次 LSF 事件对山毛榉林植被活动的短期影响。利用遥感地表温度(LST)数据来检测发生 LSF 的像素,同时利用增强植被指数(EVI)数据来量化 LSF 效应,通过计算春季植被活动异常指数(sAI)来实现。在 2016 年和 2017 年,LSF 分别覆盖了意大利山毛榉主导地区的约 29%和 32%。这两次 LSF 在空间模式和影响方面存在很大差异。在 2016 年,属于 sAI 类中春季异常值最高的像素也是那些发生长时间 LSF 的像素,而在 2017 年,属于 sAI 类中最高的像素是那些经历了较短(但可能更强烈)LSF 事件的像素。在 LSF 重复发生频率风险增加的情景下,所提出的方法可能是一种自动的、低成本的监测和预测欧洲山毛榉生长模式的工具。