Tower-based remote sensing for understanding boreal forest carbon dynamics

June 1, 2022 3:00pm to 4:00pm
MSA 7124/Zoom

 

Speaker: Zoe Pierrat

Institution: UCLA 

Abstract: The largest source of uncertainty in future climate projections is the terrestrial carbon cycle. Within the terrestrial carbon cycle, one of the most poorly understood ecosystems is the boreal forest which stores a significant amount of carbon and is highly sensitive to environmental change. To better understand boreal forest carbon dynamics, we need more accurate quantification of boreal forest carbon uptake by way of photosynthesis, also known as gross primary production (GPP). Of particular importance is the onset of carbon uptake in spring which has implications for growing season length. Remote sensing is a powerful and necessary tool for tracking photosynthesis across a variety of ecosystems. In particular, solar-induced chlorophyll fluorescence (SIF) is able to track changes in GPP more effectively than traditional greenness-based vegetation indices (VIs) in evergreen ecosystems such as the boreal. Studies at the leaf, tower, and satellite scales have all highlighted a nuanced relationship between SIF and gross primary production (GPP) leaving questions over why, when, and how remote sensing can be used for approximating GPP. My research clarifies the relationships between remote sensing metrics and carbon dynamics by using high spatio-temporal data from a tower-based spectrometer system. In this talk I present 2+ years of tower-based remote sensing data to answer 1) how remote sensing can be used to detect ecophysiological change during the spring transition, and 2) what are the relationships between SIF, VIs and GPP at varying temporal resolutions and why? My results shed light on the fine-scale mechanisms driving the diurnal and seasonal patterns of remote sensing data, link these mechanisms to environmental controls, and inform modeling efforts to constrain GPP in boreal ecosystems.