High-resolution topographic data have the potential to differentiate the main morphological features of a landscape. This paper analyses the capability of airborne LiDAR-derived data in the recognition of channel-bed morphology. For the purpose of this study, 0.5 m and 1 m resolution Digital Terrain Models (DTMs) were derived from the last pulse LiDAR data obtained by filtering the vegetation points. The analysis was carried out both at 1-D scale, i.e. along the longitudinal channel profile, and at 2-D scale, taking into account the whole extent of the channel bed. The 1-D approach analyzed the residuals of elevations orthogonal to the regression line drawn along the channel profile and the standard deviation of local slope. The 2-D analysis was based on two roughness indexes, consisting on the local variability of the elevation and slope of the channel bed. The study was conducted in a headwater catchment located in the Eastern Italian Alps. The results suggested a good capability of LiDAR data in the recognition of river morphology giving the potential to distinguish the riffle-pool and step-pool reaches.
The effectiveness of airborne LiDAR data in the recognition of channel-bed morphology
TAROLLI, PAOLO;DALLA FONTANA, GIANCARLO
2008
Abstract
High-resolution topographic data have the potential to differentiate the main morphological features of a landscape. This paper analyses the capability of airborne LiDAR-derived data in the recognition of channel-bed morphology. For the purpose of this study, 0.5 m and 1 m resolution Digital Terrain Models (DTMs) were derived from the last pulse LiDAR data obtained by filtering the vegetation points. The analysis was carried out both at 1-D scale, i.e. along the longitudinal channel profile, and at 2-D scale, taking into account the whole extent of the channel bed. The 1-D approach analyzed the residuals of elevations orthogonal to the regression line drawn along the channel profile and the standard deviation of local slope. The 2-D analysis was based on two roughness indexes, consisting on the local variability of the elevation and slope of the channel bed. The study was conducted in a headwater catchment located in the Eastern Italian Alps. The results suggested a good capability of LiDAR data in the recognition of river morphology giving the potential to distinguish the riffle-pool and step-pool reaches.Pubblicazioni consigliate
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