Item: Lidar measurement of snow depth: accuracy and error sources
Title: Lidar measurement of snow depth: accuracy and error sources
Proceedings: Proceedings of the 2006 International Snow Science Workshop, Telluride, Colorado
Authors: Jeffrey S. Deems, Watershed Science, Colorado State University, Thomas H. Painter, National Snow and Ice Data Center, University of Colorado
Abstract: Airborne laser altimetry (lidar) is a remote sensing technology that holds tremendous promise for mapping snow depth in snow hydrology and avalanche applications. In recent years lidar has seen a dramatic widening of applications in the natural sciences, resulting in technological improvements and an increase in the availability of sensors. Modern sensors allow recording of multiple pulse returns, which allows mapping of vegetation heights and surface elevations below forest canopies. Typical reported vertical accuracies are on the order of 15 cm with an average ground point spacing of 1.5 m. However many parameters in the lidar acquisition process, such as laser scan angle, laser pulse rate, and flight geometry relative to terrain gradients require consideration to ensure adequate point coverage in forested and/or mountainous terrain. Additionally, laser light interaction with the snow surface has a significant volumetric scattering component, requiring different considerations for surface height error estimation than for other earth surface materials. The penetration depth of the laser pulse (NIR wavelength of 1064 nm) is dependent primarily on grain size, liquid water content, and the angle of incidence. Using published estimates of penetration depth, we estimate radiative transfer contribution to depth measurement errors to be on the order of 1 cm. In this paper, we present a review of lidar altimetry procedures and error sources, investigate potential errors unique to snow surface remote sensing in the NIR wavelengths, and make recommendations for projects using lidar for snow depth mapping.
Keywords: lidar, snow depth, remote sensing, spatial variability
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