New opportunities for low-cost LiDAR-derived snow depth estimates from a consumer drone-mounted smartphone

Abstract

Snow has significant impacts on springtime flooding, water resource management practices and the regional water-energy budget. In situ observations are considered some of the highest quality measurements of snow depth available, and are useful constraints for numerical weather prediction models and reanalysis system estimates of snow water equivalent. The application of laser altimetry (LiDAR-Light Detection and Ranging) for measuring snow depth has proven an effective method for quickly observing large areas, however this technique is expensive to perform due to the high cost of the necessary equipment coupled with required operator training. In this work, we examine the capabilities of the iPhone 12 Pro LiDAR (iLiDAR) when attached to a consumer-grade DJI Phantom 4 quadcopter in estimating snow depth at three study sites in southern Ontario, Canada. Initial comparisons between drone iLiDAR depth estimates and collocated snow ruler measurements demonstrate good agreement, with a root-mean-square error of 3 cm and absolute mean error of 2.5 cm. The intersection of these two technologies defines a novel, low-cost alternative to traditional LiDAR-based snow depth measurement systems, while maintaining a high level of observational accuracy and precision.