High school students revolutionizing avalanche risk prediction using drones with mounted GPR, LiDAR and multispectral cameras

Current avalanche risk assessment relies heavily on manual snowpack observations and point-based meteorological data. Traditional forecasting requires personnel to physically enter high-risk terrain to perform stability tests and snow pit analyses, a process that is inherently dangerous, time-consuming, and limited by low spatial resolution. These manual "in-situ" measurements often fail to capture the complex stratigraphy and spatial variability of snow stability across entire slopes, leading to potential gaps in regional safety models.

To address these limitations, this research presents an integrated remote sensing framework utilizing Unmanned Aerial Systems (UAS) equipped with a multi-sensor payload. By synthesizing data from Ground Penetrating Radar (GPR), LiDAR, and multispectral cameras, we developed a non-invasive methodology for comprehensive snowpack characterization. The LiDAR sensors provide high-precision surface topography and snow depth measurements, while the GPR allows for the identification of internal stratigraphic boundaries and the estimation of snow density through electromagnetic wave propagation analysis. Concurrently, multispectral imaging assesses surface albedo and moisture content, offering insights into thermal degradation and surface hoar development.

The results of this integration are high-resolution 3D snow profile maps that allow for the quantitative assessment of snow hardness and density across broad, inaccessible slopes. By digitizing the snowpack structure at a granular level, this system provides forecasters with the data density required for accurate stability modeling without the necessity of human exposure to avalanche-prone zones. Ultimately, this UAS-based approach represents a paradigm shift in mountain safety, transitioning from discrete, high-risk manual sampling to continuous, remote, and data-driven hazard mitigation.