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Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require "scaling-up" from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas. [ABSTRACT FROM AUTHOR]

Carbon dioxide, water vapour, and energy fluxes vary spatially and temporally within forested environments. However, it is not clear to what extent they vary as a result of variability in the spatial distribution of biomass and elevation. The following study presents a new methodology for extracting changes in the structural characteristics of vegetation and elevation within footprint areas, for direct comparison with eddy covariance (EC) CO2 flux concentrations. The purpose was to determine whether within-site canopy structure and local elevation influenced CO2 fluxes in a mature jack pine (Pinus banksiana Lamb.) forest located in Saskatchewan, Canada. Airborne light detection and ranging (lidar) was used to extract tree height, canopy depth, foliage cover, and elevation within 30 min flux footprints. Within-footprint mean structural components and elevation were related to 30 min mean net ecosystem productivity (NEP) and gross ecosystem production (GEP). NEP and GEP were modeled using multiple regression, and when compared with measured fluxes, almost all periods showed improvements in the prediction of flux concentration when canopy structure and elevation were included. Increased biomass was related to increased NEP and GEP in June and August when the ecosystem was not limited by soil moisture. On a daily basis, fractional cover and elevation had varying but significant influences on CO2 fluxes. [ABSTRACT FROM AUTHOR]