Franklin, Steven E

Understanding snowpack variability is important as it plays an imperative role in environmental, hydrologic, and atmospheric systems. Research questions related to three linked areas were investigated in this thesis: 1) scaling issues in snow hydrology, 2) forest-snowpack relationships, and 3) methods of integrating snow water equivalent (SWE) into a hydrologic model for a large, forested drainage basin in central Ontario. The first study evaluated differences in SWE across process, measurement, and model scales. Point scale snowpack measurements could be bias corrected using scaling factors derived from a limited number of transect measurements and appropriately stratified point scale measurements may be suitable for replacing transect scale mean SWE when transect data are not possible to collect. Comparison of modelled products to measurements highlighted the importance of understanding the spatial representativeness of in-situ measurements and the processes those measurements represent when validating snow products or assimilating data into models.The second study investigated the efficacy of field-based, and remotely sensed datasets to describe forest structure and resolve forest-snowpack relationships. Canopy cover was highly correlated with melt rate and timing at the site scale however, significant correlations were present in 2016 but not 2017, which was attributed to interannual differences in climate. Peak SWE metrics did not correlate well with forest metrics. This was likely due to mid-winter melt events throughout both study years, where a mix of accumulation and melt processes confounded forest-snowpack relationships. The third study evaluated the accuracy of the Copernicus SWE product and assessed the impact of calibrating and assimilating SWE data on model performance. The bias corrected Copernicus product agreed with measured data and provided a good estimate of mean basin SWE. Calibration of a hydrologic model to subbasin SWE substantially improved modelled SWE performance. Modelled SWE skill was not improved by assimilating SWE into the calibrated model. All models evaluated had similar streamflow performance, indicating streamflow in the study basin can be accurately estimated using a model with a poor representation of SWE. The findings from this work improved knowledge and understanding of snow processes in the hydrologically significant Great Lakes-St Lawrence Forest region of central Ontario.

Author Keywords: data assimilation, hydrologic model, multi-objective calibration, remote sensing, scale, snow

Currently, UAVs are deployed to measure crop health in a timely manner by mapping vegetation indices. A study using two different fields was conducted in order to search for a relationship that may exist between crop health and soil fertility. A UAV equipped with sensor technology was used for mapping of vegetation indices which were then statistically compared to soil nutrient data collected via soil sampling. Elevation data was also collected which was then statistically compared to soil nutrients as well as crop health. Results of this study were unfortunately impacted by variables outside of the researcher's control. Moisture became the greatest limiting factor in 2016 followed by an excess of rain in 2017. Results did not show any promising correlations as moisture uncontrollably became the defining variable. Further research in a more controlled setting will need to be conducted in order to explore this potential relationship.

Author Keywords: Agriculture, Multispectral Imagery, Precision Agriculture, Proximal Soil Sensing, Remote Sensing, Unmanned Aerial Vehicle

The forestry sector has a well-developed history of using remote sensing to identify structural characteristics of forests and to detect and attribute changes that occur in forested landscapes. Monitoring the recovery of disturbed forests is an important factor in long term forest management. However, forest that is recovered spectrally may not be recovered when considered in terms of a Free to Grow assessment. A Free to Grow assessment is used in Ontario to determine whether a disturbed site will likely achieve a desired future state, i.e., is recovered according to a forestry perspective. The objective of this research was to determine the relationship between a pixel-based Landsat Time Series of spectral recovery and the results of Free to Grow assessments. Spectral trajectories were generated from representative pixels within known harvested forest areas. Results indicate that while Free to Grow sites often achieve spectral recovery (>90%), many non-Free to Grow sites were classified as spectrally recovered, suggesting that improved methods of spectral recovery monitoring are needed.

Author Keywords: forest recovery, Free to Grow, Landsat Time Series, LandTrendr, Pixel-based, spectral recovery