Abstract

ABSTRACT The current provincial-extent digital elevation model (DEM) and corresponding hydrological maps for Ontario have been produced using traditional photogrammetry and aerial photograph interpretation. This process is labour-intensive and requires visual interpretation of stereo image pairs. The ground surface and small hydrological features may be inaccurately delineated in areas where vegetation is dense or the ground is otherwise shielded from aerial view. In an effort to improve and automate delineation of hydrological features, we examined the behaviour and final products of the D8 flowrouting algorithm in 2 software environments (TAS and TauDEM for ArcGIS) operating on a high spatial resolution DEM derived using canopy-penetrating light detection and ranging (LiDAR) technology in a pilot study in the Romeo Malette Forest (41.25°N, 81.50°W). Filtered LiDAR data points (5-m spacing) were interpolated using IDW, TIN, and splines, each resulting in a 2.5-m spatial resolution DEM. Results demonstrate improved realism in the characterization of surficial hydrology by LIDAR derived products as compared to applying identical algorithms on existing coarser provincial data. Benefits include the ability to represent streams of lower Strahler order to define crisp watershed boundaries, and the more accurate identification of local depressions that form potentially wet sites. This approach identifies wet sites that should be avoided during forest operations (e.g., skidder traffic) and can provide additional information for trail layout, road planning, and water crossings. By increasing the number of uses of LiDAR, the capital investment in these data becomes increasingly palatable for forest companies interested in obtaining detailed plans of their forest holdings.