Remote sensing tracks daily radial wood growth of evergreen needleleaf trees
Remote sensing tracks daily radial wood growth of evergreen needleleaf trees
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DOI：
10.1111/gcb.15112
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发表时间：
2020-05-05
影响因子：
11.6
通讯作者：
Jennewein, Jyoti S.
中科院分区：
文献类型：
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作者：
Eitel, Jan U. H.;Griffin, Kevin L.;Jennewein, Jyoti S.
总初级生产力（GPP）和遥感光化学反射指数（PRI）之间的关系表明，叶片PRI的时间序列可以提供洞察气候变化对碳循环的影响。然而，由于通过GPP吸收的大部分碳通过呼吸作用迅速返回到大气中，我们提出了一个关键问题-PRI时间序列可以提供有关地上碳储量长期收益的信息吗？在这里，我们研究PRI时间序列的适用性，以了解在世界上最大的陆地碳库之一-北方森林的年内树干生长动态。我们假设PRI时间序列可以用来确定径向生长的开始（假设1）和停止（假设2），并能够跟踪一年内树木的生长动态（假设3）。在2018年和2019年收集了树木水平的测量结果，以将高度时间分辨的PRI观测结果与通过点式树木生长计收集的每日径向树木生长信息明确地联系起来。我们发现，由PRI时间序列确定的光合作用活性的季节性发作是显着早于（p <0.05）比从点测树仪时间序列，不支持我们的第一个假设确定的径向树生长的发作。相反，光合活性的季节性下降和停止径向树生长没有显着差异（p > 0.05）时，来自PRI和测树仪时间序列，分别支持我们的第二个假设。混合效应模型的结果支持我们的第三个假设，表明PRI是一个统计学上显著的（p < .0001）预测年内径向树生长动态，并跟踪这些每日径向树生长动态的显着细节，条件和边际决定系数为0.48和0.96（2018年）和0.43和0.98（2019年）。我们的研究结果表明，PRI可以通过减轻与年内植被对气候变化的响应相关的重要不确定性，为碳循环动态的细微差别提供新的见解。
Relationships between gross primary productivity (GPP) and the remotely sensed photochemical reflectance index (PRI) suggest that time series of foliar PRI may provide insight into climate change effects on carbon cycling. However, because a large fraction of carbon assimilated via GPP is quickly returned to the atmosphere via respiration, we ask a critical question-can PRI time series provide information about longer term gains in aboveground carbon stocks? Here we study the suitability of PRI time series to understand intra-annual stem-growth dynamics at one of the world's largest terrestrial carbon pools-the boreal forest. We hypothesized that PRI time series can be used to determine the onset (hypothesis 1) and cessation (hypothesis 2) of radial growth and enable tracking of intra-annual tree growth dynamics (hypothesis 3). Tree-level measurements were collected in 2018 and 2019 to link highly temporally resolved PRI observations unambiguously with information on daily radial tree growth collected via point dendrometers. We show that the seasonal onset of photosynthetic activity as determined by PRI time series was significantly earlier (p < .05) than the onset of radial tree growth determined from the point dendrometer time series which does not support our first hypothesis. In contrast, seasonal decline of photosynthetic activity and cessation of radial tree growth was not significantly different (p > .05) when derived from PRI and dendrometer time series, respectively, supporting our second hypothesis. Mixed-effects modeling results supported our third hypothesis by showing that the PRI was a statistically significant (p < .0001) predictor of intra-annual radial tree growth dynamics, and tracked these daily radial tree-growth dynamics in remarkable detail with conditional and marginal coefficients of determination of 0.48 and 0.96 (for 2018) and 0.43 and 0.98 (for 2019), respectively. Our findings suggest that PRI could provide novel insights into nuances of carbon cycling dynamics by alleviating important uncertainties associated with intra-annual vegetation response to climate change.