Linking stand architecture with canopy reflectance to estimate vertical patterns of light-use efficiency

Abstract

Remote sensing of plant carbon uptake, or gross primary production (GPP), in a repeatable and consistent manner remains a key element of a comprehensive understanding of the role of vegetation within the global carbon cycle. To further this understanding at a landscape level or global scale, accurate remote sensing of photosynthetic light-use efficiency (LUE) is required to understand photosynthetic down-regulation and environmental constraints to plant photosynthesis. The past decade has seen advances in detecting both leaf- and canopy-level physiological stress behaviours using the photochemical reflectance index (PRI), a narrow-waveband normalized difference index that relates LUE to a xanthophyll-induced absorption feature at 531nm. To date, however, much of this research has occurred using top of canopy measurements, while our understanding of the vertical distribution of LUE within the crown is limited. In this study, we demonstrate an approach which could be used to scale photosynthetic behaviour of vegetation vertically and horizontally using estimates of vertical canopy structure obtained from terrestrial Light Detection and Ranging (LiDAR) data to predict proportions of shaded and sunlit canopy which are then linked to predictions of LUE. We apply the approach over a mature Aspen study site located in central Saskatchewan, Canada utilising full-waveform LiDAR data provided by the ground-based laser scanner system and canopy spectra obtained by the AMSPEC II spectro-radiometer. Agreement between predictions of Gross Primary Productivity (GPP) using the developed approach compared to independent observations was highly significant at hourly intervals (R2=0.80, p<0.01) under clear sky conditions. A range of LUE vertical profiles for different stand structures across the growing season were developed providing estimations of how crown structure can impact LUE vertically in the crown. We conclude with a recommendation for ongoing research to verify these types of trends using concurrently acquired, independently derived leaf LUE from photosynthesis light-response curves, and forest structure variation from LiDAR, to provide a more exact quantification of these patterns.