Snowpack is one of the main freshwater supplies 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 stream flow. Snow processes vary in 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, I have three primary studies. First, to elucidate the snow and vegetation relationships, 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 propose using UAVSAR (radar remote sensing data) and machine learning techniques to estimate snow density and snow water equivalent in Grand Mesa, Colorado.