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A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests
Biogeosciences ( IF 3.9 ) Pub Date : 2021-06-07 , DOI: 10.5194/bg-18-3391-2021
Kamel Soudani , Nicolas Delpierre , Daniel Berveiller , Gabriel Hmimina , Jean-Yves Pontailler , Lou Seureau , Gaëlle Vincent , Éric Dufrêne

Tree phenology is a major driver of forest–atmosphere mass and energy exchanges. Yet, tree phenology has rarely been monitored in a consistent way throughout the life of a flux-tower site. Here, we used seasonal time series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (greenness chromatic coordinate), broadband NDVI, LAI (leaf area index), fAPAR (fraction of absorbed photosynthetic active radiation), CC (canopy closure), fRvis (fraction of reflected radiation) and GPP (gross primary productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest using an asymmetric double sigmoid function (ADS) fitted to the time series. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured the interannual variability of spring phenology very well (R2>0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) of less than 4 d. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 d. The ADS method used to derive the phenological markers provides the most biased estimates for the GPP and GCC. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2=0.63 and 0.80 for NDVI and CC, respectively), with an MAD of less than 7 d. Our results show that proximal-sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at eddy covariance (EC) flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

中文翻译:

温带落叶林叶片物候监测近端方法研究

树木物候是森林-大气质量和能量交换的主要驱动力。然而,在通量塔站点的整个生命周期中,很少以一致的方式监测树木物候。在这里,我们使用了地面 NDVI(归一化差异植被指数)、RGB 相机 GCC(绿度色度坐标)、宽带 NDVI、LAI(叶面积指数)、f APAR(吸收光合有效辐射的分数)、CC 的季节性时间序列(天篷关闭), f R vis(反射辐射的分数)和 GPP(总初级生产力)来预测六个物候标记,使用适合时间序列的不对称双 sigmoid 函数 (ADS) 来检测温带落叶林中萌芽和叶片衰老的开始、中间和结束. 我们将它们与田间物候学家在 13 年间观察到的萌芽和叶片衰老进行了比较。GCC、NDVI 和 CC 很好地捕捉了春季物候的年际变化 ( R 2 > 0.80) 并提供了观察到的萌芽日期的最佳估计,平均绝对偏差 (MAD) 小于 4 d。对于 CC 和 GCC 方法,春季物候过渡期间的中幅度 (50%) 阈值日期与观察到的物候日期非常吻合。对于基于 NDVI 的方法,平均观测日期与 NDVI 达到其年变化幅度的 25% 的日期重合。对于其他方法,MAD 的范围为 6 至 17 天。用于推导物候标记的 ADS 方法为 GPP 和 GCC 提供了最有偏差的估计。在叶片衰老阶段,NDVI 和 CC 衍生的日期与观测日期显着相关(R 2 =0.63NDVI 和 CC 分别为 0.80),MAD 小于 7 d。我们的结果表明,近端传感方法可用于推导出稳健的物候指标。它们可用于在涡流协方差 (EC) 通量测量站点检索长期物候序列,并帮助解释质量和能量交换的年际变化和趋势。
更新日期:2021-06-07
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