Calculating LiDAR Point Cloud Uncertainty and Propagating Uncertainty to Snow-Water Equivalent Data Products

Abstract:

The use of high-resolution topographic data in the form of three-dimensional point clouds obtained from laser scanning systems (LiDAR) is becoming common across scientific disciplines.
However little consideration has typically been given to the accuracy and the precision of LiDAR-derived measurements at the individual point scale.
Numerous disparate sources contribute to the aggregate precision of each point measurement, including uncertainties in the range measurement, measurement of the attitude and position of the LiDAR collection platform, uncertainties associated with the interaction between the laser pulse and the target surface, and more.
We have implemented open-source software tools to calculate per-point stochastic measurement errors for a point cloud using the general LiDAR georeferencing equation.
We demonstrate the use of these propagated uncertainties by applying our methods to data collected by the Airborne Snow Observatory ALS, a NASA JPL project using a combination of airborne hyperspectral and LiDAR data to estimate snow-water equivalent distributions over full river basins.
We present basin-scale snow depth maps with associated uncertainties, and demonstrate the propagation of those uncertainties to snow volume and snow-water equivalent calculations.