Remote Sensing to Advance Understanding of Snow-Vegetation Relationships and Quantify Snow Depth and Snow Water Equivalent

Abstract

Snowpack is an important source of freshwater in mountainous regions. Understanding the role of different controls on snow properties (depth, distribution, and snow water equivalent (SWE)) and processes (accumulation and ablation) is important to predict available freshwater. Snow processes vary with respect to the predominant local controls in different landscapes. In many mountainous landscapes, controls on snow properties and processes are highly correlated with vegetation properties. In this dissertation, to elucidate the relationships between snow and vegetation, I use terrestrial laser scanning to explore how forest canopy structure affects snow depth distribution. In addition, I examine different vegetation metrics to find what measure of vegetation best describes snow under the canopy. By leveraging airborne lidar and deep learning, I investigate vegetation and topographical descriptors and their scale of influence on snow depth and pattern. Finally, I use radar remote sensing and machine learning techniques to estimate snow density and snow water equivalent in a mountainous western watershed.