Comparing Mountain Snowpack Depth Model Results from Different Airborne Laser Scanning Flight Path Samples

Abstract

Abstract The objective of this study is to evaluate the performance of an Airborne Laser Scanning (ALS) snow sampling strategy using two distinct flight paths within a mountainous watershed. Drivers of snow depth variability (canopy, elevation, topographic position index, aspect) were used to generate a classified snow accumulation unit (SAU) raster for the Westcastle watershed, Alberta (103 km2). A “Least Cost Path” (LCP) analysis and an “expert” three-transect selection (T3) were used to create two flight path scenarios that each sampled <18% of the watershed area and maximized the number of represented SAUs. Watershed “wall-to-wall” snow depth was predicted from the T3, LCP, and combined T3 + LCP sampling data using ESRI’s Forest Based Regression. The variance was ∼ 83% for each of the three FBR scenarios. However, validation of the watershed-wide observed versus FBR predicted snow depth at watershed-scale produced R2 = 0.72 and RMSE = 0.38 m for the combined T3 + LCP flight line and R 2 = 0.66 (RMSE = 0.43 m) for T3 alone. The LCP sampling did not perform as well (R 2 = 0.34, RMSE = 0.61 m), indicating grid cell-level SAU attributes need to be supplemented by latitudinal and longitudinal sampling that captures beyond grid cell-level hydro-climatological trends across the watershed. By flying sampling corridors, that capture land surface attributes representative of the spatial variability of snow depth, watershed-scale snow volumes can be predicted.