Arctic tundra and boreal forest environments account for a large proportion of Canada's land surface and are important systems within the context of global climate change research. These northern environments are thought to be particularly sensitive to changes in climate, yet it remains unclear as to how these environments will respond. It is expected that any alterations in arctic tundra and boreal forest ecosystem function associated with increased temperatures will be expressed through shifts in plant phenology (i.e., vegetation growth patterns with season), species composition, and abundance. Remote sensing provides a means for monitoring these shifts, with the potential to characterize biophysical variables that control carbon fluxes over landscapes or regions.
Our research focuses on the estimation and mapping of biophysical variables for northern terrestrial landscapes traversing tundra and boreal forest ecosystems. We are currently involved in varied remote sensing research in the high Arctic using both optical and SAR imagery to model soil moisture, vegetation cover, biomass, and carbon exchange. Vegetation strata in tundra environments are not well defined and the vertical component is limited. Hence, vegetation indices derived from spectral data from satellites may provide accurate estimates of biophysical variables for arctic ecosystems. However, we need to develop methods that are sensitive to variable chlorophyll content, exposed soil/gravel tills, and variable moisture regimes, all of which affect plant communities, biomass accumulation, biodiversity and productivity.
Estimating biophysical variables for forested ecosystems represents a three-dimensional problem since structural components of a forest ecosystem are distributed vertically as well as horizontally. Our current emphasis is on the modelling of biophysical variables (e.g. volume, biomass) for forest ecosystems using airborne light detection and ranging (LiDAR). LiDAR captures three-dimensional information on forest structure (e.g., tree and canopy height) and provides significant potential for volume and biomass estimation using forest allometry (e.g., the relationship of tree height to other structural parameters such as tree volume). Analysis of LiDAR height metrics is being conducted to determine how three-dimensional surfaces of the forest canopy and terrain can be created and utilized to predict the structural (and functional) nature of forest stands.