Hyperspectral derived traits and vegetation indices predict rates of carbon cycling in alpine meadows
Abstract
Developing models to predict carbon exchange, the balance between photosynthetic uptake and release of carbon dioxide, is critical for the terrestrial carbon cycle. Alpine meadows are ecosystems where soils are carbon-rich, and show sharp transitions in ecosystem structure due to the extreme climatic conditions such as snowmelt date, temperature seasonality, and length of the growing season. Alpine plant communities are strongly associated with high heterogeneity in microclimatic conditions and topographic variability, but the consequences of this variation for ecosystem processes remains unclear. Thus, identifying indicators of vegetation change in alpine meadows is essential to understand how community changes affect rates of carbon cycling. We quantified above- and below-ground carbon fluxes along an elevation gradient (2700-3500 m) in 38 plots (1.3 m2) in the East River Watershed in Colorado. We measured reflectance of plant communities at leaf- and plot-level, to evaluate whether reflectance, trait spectra or vegetation indices are good predictors of net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem (Reco) and soil respiration (SoilR). We found that at leaf-level, hyperspectral-derived traits such as specific leaf area and lead dry matter content explained the most variation of above-ground fluxes (NEE and GPP), while at plot-level vegetation indices were the strongest predictors and explained over 70% of the variation in the rates of NEE and GPP. Overall, changes in below-ground fluxes were poorly explained by either trait spectra or vegetation indices at leaf- and plot-level, but at plot-level the reflectance alone explained more than 50% of the variation in the rates of Reco and SoilR, which suggest a potential to predict below-ground fluxes. Our findings highlight the potential of vegetation indices to predict carbon fluxes in alpine meadows, and improve our predictions of how alpine communities will respond to current and predicted climate changes. Future analyses will evaluate the effect of trait spectra and vegetation indices measured from airborne hyperspectral imaging to evaluate the consistency of these results and evaluate the potential of scaling-up models to predict carbon fluxes as well as evapotranspiration in alpine meadows along the elevation gradient.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMB035.0002D
- Keywords:
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- 0439 Ecosystems;
- structure and dynamics;
- BIOGEOSCIENCES;
- 0466 Modeling;
- BIOGEOSCIENCES;
- 0476 Plant ecology;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES