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Tower‐Based Remote Sensing Reveals Mechanisms Behind a Two‐phased Spring Transition in a Mixed‐Species Boreal Forest
Journal of Geophysical Research: Biogeosciences ( IF 3.7 ) Pub Date : 2021-04-08 , DOI: 10.1029/2020jg006191
Zoe Pierrat 1 , Magali F. Nehemy 2 , Alexandre Roy 3 , Troy Magney 4 , Nicholas C. Parazoo 5 , Colin Laroque 2 , Christoforos Pappas 6 , Oliver Sonnentag 6 , Katja Grossmann 7 , David R. Bowling 8 , Ulli Seibt 1 , Alexandra Ramirez 1 , Bruce Johnson 2 , Warren Helgason 2 , Alan Barr 2 , Jochen Stutz 1
Affiliation  

The boreal forest is a major contributor to the global climate system, therefore, reducing uncertainties in how the forest will respond to a changing climate is critical. One source of uncertainty is the timing and drivers of the spring transition. Remote sensing can provide important information on this transition, but persistent foliage greenness, seasonal snow cover, and a high prevalence of mixed forest stands (both deciduous and evergreen species) complicate interpretation of these signals. We collected tower‐based remotely sensed data (reflectance‐based vegetation indices and Solar‐Induced Chlorophyll Fluorescence [SIF]), stem radius measurements, gross primary productivity, and environmental conditions in a boreal mixed forest stand. Evaluation of this data set shows a two‐phased spring transition. The first phase is the reactivation of photosynthesis and transpiration in evergreens, marked by an increase in relative SIF, and is triggered by thawed stems, warm air temperatures, and increased available soil moisture. The second phase is a reduction in bulk photoprotective pigments in evergreens, marked by an increase in the Chlorophyll‐Carotenoid Index. Deciduous leaf‐out occurs during this phase, marked by an increase in all remotely sensed metrics. The second phase is controlled by soil thaw. Our results demonstrate that remote sensing metrics can be used to detect specific physiological changes in boreal tree species during the spring transition. The two‐phased transition explains inconsistencies in remote sensing estimates of the timing and drivers of spring recovery. Our results imply that satellite‐based observations will improve by using a combination of vegetation indices and SIF, along with species distribution information.

中文翻译:

混合物种北方森林中基于塔的遥感揭示了两阶段春季过渡之后的机制

北方森林是全球气候系统的主要贡献者,因此,减少森林应对气候变化的不确定性至关重要。不确定因素之一是弹簧过渡的时间和驱动因素。遥感可以提供有关这一转变的重要信息,但是持久的树叶绿色,季节性的积雪和高比例的混交林分(落叶和常绿树种)使这些信号的解释变得复杂。我们收集了基于塔的遥感数据(基于反射的植被指数和太阳诱导的叶绿素荧光[SIF]),茎半径的测量值,总初级生产力以及北方混交林林分的环境条件。对该数据集的评估显示出两阶段的弹簧过渡。第一阶段是常绿植物的光合作用和蒸腾作用的重新激活,其特征是相对SIF的增加,这是由茎的解冻,温暖的空气温度和可利用的土壤水分增加引起的。第二阶段是常绿植物中大量光防护色素的减少,其特征是叶绿素-类胡萝卜素指数的增加。在此阶段发生落叶,以所有遥感指标的增加为标志。第二阶段由土壤融化控制。我们的结果表明,遥感指标可用于检测春季过渡期间北方树种的特定生理变化。两阶段过渡解释了春季恢复时间和驱动因素的遥感估算中的不一致。
更新日期:2021-05-15
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