Comparing alpine watershed attributes from LiDAR, Photogrammetric, and Contour‐based Digital Elevation Models

Abstract

AbstractAs part of an alpine hydrological study in the Canadian Rocky Mountains, three digital elevation model (DEM) data sets were obtained for the purpose of watershed characterization. The data sources were: (1) archived public access BC TRIM (Terrain Resource Information Management) 1:20 000 contour vectors; (2) stereo aerial photography DEM with a derived point spacing between 5 m and 20 m; (3) airborne LiDAR (light detection and ranging) with point spacing from 1 m to 4 m. GIS layers of terrain and watershed attributes were created for each of the three DEM data sets at grid cell resolutions of 5 m and 25 m. Watershed attributes investigated were: DEM elevation, area, hypsometry, and stream network topology. In areas of lower relief and forest cover, the TRIM contour DEM contained topological errors at both 5 m and 25 m resolutions due to the poor representation of terrain from widely spaced contours. The photo DEM introduced obvious stream topology errors at 25 m due to the inability of the photo DEM to discern subtle terrain beneath forest canopies. The photo and TRIM DEMs overestimated basin hypsometry relative to the LiDAR watersheds at highest elevations due, in part, to their inability to represent the inside of gullies and steps associated with geological strata. In the case of the photo DEM, selectively digitizing break lines such as cliff edges, while missing shadowed areas, led to the creation of an interpolated surface that was biased towards the outer extremities of the terrain. Conversely, relative to the photo‐based datasets, the LiDAR DEM better captured the inside of gullies and steps while under‐sampling break line features, leading to a bias in the interpolated surface towards internal terrain extremities. As would be expected, the quality and resolution of the terrain data increased from BC TRIM to photo to LiDAR. If modelling watersheds within the Canadian Rockies at the meso scale and above, BC TRIM (or equivalent) 1:20 000 contour vectors would be most appropriate given availability and cost considerations. The benefits of LiDAR are apparent if higher resolution and more accurate watershed attribute information is needed detailing first‐order hydrological channel features on steep shadowed mountain slopes or zero‐order hill‐slope depressions beneath forest canopies. Such landscape features provide preferential storages for winter snowpack in mountainous watersheds, suggesting that in the future LiDAR might be a tool of choice for snowpack resource monitoring in these regions. Copyright © 2008 John Wiley & Sons, Ltd.