The use of remote sensing to characterize forest structure and improve the modeling of snow processes in extensively disturbed watersheds

The lodgepole pine (Pinus contorta) forests of British Columbia have been recently affected by mountain pine beetle (MPB) (Dendroctonus ponderosae), constituting one of the most destructive insect outbreaks in North America. In such a snow-dominated environment, a receding forest cover is associated with increases in snow accumulation during winter, enhancements of snowmelt rates and suppression of spring transpiration. These changes can elevate flooding risk and thus threaten society. However, the unprecedented extent of the disturbance and particular nature of the beetles’ severe but gradual effect on the forests’ integrity have challenged scientists aiming to quantify the real ecological impacts. Even though hydrologic models remain as the only tool currently available to evaluate the effects of MPB on hydrologic dynamics, they are impaired in their present form for relying on coarse and oversimplified characterizations of forest structure. Remote sensing technologies such as Airborne Laser Scanning (ALS), which provides detailed three-dimensional representations of canopy structure, offer a remarkable alternative to fill this knowledge gap. The main objective of this thesis is to determine how hydrologic modeling can be improved by remote sensing through a better characterization of forest structure. Given the variety and complexity of hydrologic models, the same research question is applied independently to the simplest forms of plot-level univariate empirical models and complex physically-based simulators operating at the watershed level. It was found that remotely-sensed forest metrics are better predictors of snow accumulation and ablation at the plot level than traditional ground-based variables, and that the accurate estimation of maximum snow accumulation and snow ablation with ultrasonic range devices significantly increases the quality of simple empirical models. It was also shown that a novel method, which minimizes the geometrical differences between ALS and traditional ground instruments’ data, was fundamental to obtain accurate plot-level estimates of forest structure metrics identified as primary drivers of snow processes. Wall-to-wall watershed-level coverage of hydrologically-relevant forest variables was successfully achieved by integrating ALS and Landsat metrics. The methods developed will result in better inputs for hydrologic models with the potential to improve the quality of snow process and streamflow predictions.Forestry, Faculty ofGraduat