- Related Research Areas
- Carbon Cycle & Ecosystems
Spectroscopic approaches are effective for detecting important biochemical traits of leaves, such as nitrogen and lignin concentration. At present, we are using imaging spectroscopy to characterize key forest functional traits including leaf structure (essentially cell volume), shade tolerance (~ratio of chlorophyll content to leaf volume) and recalcitrance (lignin concentration). In a new pilot study, we have tested the capacity of green-leaf spectroscopy to measure other important physiological traits needed to estimate canopy photosynthesis and respiration following the Farquhar model. Because metabolic rates vary with foliar nitrogen and ambient temperature, our initial studies have been conducted experimentally on aspen and cottonwood trees under three temperature regimes and two N fertilization scenarios. Using interval-PLS, we have found strong relationships (R^2 > 0.68) between green leaf spectra and: (a) warm leaf respiration rate (normalized to 23C), (b) cold leaf respiration rate (13C), (c) CO2 assimilation rate (on an area and mass basis), (d) specific leaf area, (e) leaf nitrogen, (f) maximum rate of carboxylation, V(c)max, and (g) maximum rate of electron transport, Jmax. Further, evaluation of spectral loadings indicate that the wavelengths important to prediction correspond to known sensitivities within spectral data, e.g. wavebands related to N, water, and chlorophyll content, and structural characteristics of the spongy mesophyll. We propose to test the potential for extending our spectroscopic measurements to hyperspectral imaging. Using existing AVIRIS and Hyperion imagery with our field data and the SAILH radiative transfer model, we will demonstrate the scaling of these variables to the canopy for aspen forests in the Lake States. In addition, we will employ thermal imagery in conjunction with surface temperature data to estimate leaf temperatures that are critical to regulating metabolic rates. This effort will greatly enhance our ability to use anticipated HyspIRI data to concurrently map key variables associated with photosynthesis in forests.
Project PI: Philip Townsend/University of Wisconsin-Madison
Russell Labs 1630 Linden Drive Madison, WI 53706
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