Seasonal change in understory reflectance of boreal forests and influence on canopy vegetation indices
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abstract
One objective of the Boreal Ecosystem‐Atmospheric Study (BOREAS) is to increase our understanding of the nature of canopy spectral bidirectional reflectance in the visible/near‐infrared regimes for open canopies typical of boreal forest stands. For such stands, the need to characterize the reflectance of the sunlit and shaded vegetated understory is critical. These variables are subject to temporal variability due to differences in species phenology and foliar display as well as diurnal and seasonal changes in solar illumination through a seasonally varying upper canopy foliar area. To provide for this need, a multiteam field effort was mounted to measure the nadir midday understory reflectance for the flux tower sites during 1994 BOREAS field campaigns between February and October, specifically during the winter focused field campaign (FFC‐W), the spring thaw focused field campaign (FFC‐T), and the three intensive field campaigns (IFC‐1, IFC‐2, and IFC‐3) between June and September, which sample vegetation phenological change. This was accomplished by measuring at near‐solar noon the sunlit and shaded nadir reflectance of the understory along a surveyed leaf area index (LAI) transect line at each flux tower site. Site‐to‐site comparisons of understory reflectance spectra reveal stand differences that become more significant as the season progresses. Mean midday understory reflectance spectra were observed to be remarkably consistent over the season for young jack pine stands, followed by somewhat increased variability for mature jack pine, and significant seasonal variability for black spruce stands. Derived vegetation indices for understories are generally consistent with extrapolations of previous relationships of canopy spectral vegetation indices (VIs) versus leaf area index to zero LAI. Inclusion of these “zeroLAI” understory‐derived indices significantly enhance the correlation in the linear VI‐LAI relationships.
