Comparing Mountain Snowpack Depth Model Results from Different Airborne Laser Scanning Flight Path Samples. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Barnes, Celeste; Hopkinson, Chris
• Is Part Of: Canadian journal of remote sensing. Volume 48:Issue 1(2022)
• Page Start: 81
• Page End: 92
• Publication Date: 2022-01-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2021.1999797
• Languages: English
• ISSNs: 0703-8992

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 km 2 ). 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 R 2 = 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.