Using spectroscopy and digital camera to track leaf trait seasonality in a northern temperate forest

Xi Yang, Brown university, geoxiyang@gmail.com
Jianwu Tang, Marine Biological Laboratory, jtang@mbl.edu (Presenter)
John Mustard, Brown university, john_mustard@brown.edu

Plant photosynthesis is a key component of the global carbon cycle. Understanding the seasonality of photosynthesis and its driving factors is critical to assess the global change impact on ecosystem carbon cycles. Leaf traits such as pigments (chlorophyll, carotenoids and anthocyanin), nitrogen concentration and leaf area index (LAI) are considered as good indicators of plant photosynthetic capacity. Recently, analyzing the spectroscopic features found in plants’ reflectance/transmittance/absorptance and developing a non-destructive way to estimate the concentration of pigments, nitrogen content and leaf water content emerged. However, the robustness of this spectroscopy-leaf traits relationship has not been tested throughout the growing season, during which these leaf traits change along with phenological changes. Meanwhile, digital cameras provided an inexpensive way to track vegetation growth. But the physiological meaning of the index derived from the time-series pictures (e.g., relative greenness) is still not clear. To test the hypothesis that spectroscopy measurements could be used to estimate leaf traits, and the spectroscopy-leaf traits relationships vary with seasonality, and to explain how the observed changes in the index characterize the vegetation growth, we measured the spectra of a white oak forest leaves weekly in the growing season, in combination with the daily images acquired from the digital camera installed on four towers. We found that using spectra to predict leaf traits throughout the growing season is a robust method, but the strength of this method varies throughout the time. We also found the index derived from digital camera was mainly controlled by several leaf traits such as chlorophyll and LMA. Our finding provides a mechanistic understanding of satellite-based remote sensing data.

Presentation Type:  Poster

Session:  Global Change Impact & Vulnerability   (Tue 11:30 AM)

Associated Project(s): 

• Mustard, John: Rates and Drivers of Land Use Land Cover Change in the Agricultural Frontier of Mato Grosso, Brazil ...details

Poster Location ID: 289