Since the texture of surfaces plays a key role in the shaping of many environmental processes, high resolution measurements are important to study these phenomena. Specifically, 3-D point cloud data is desirable to document river shape and evolution, surface roughness, and erosion-sedimentation processes. The best method of obtaining these measurements is using a terrestrial laser scanner. However, these are too expensive for limited-use experiments. Therefore, I developed a simple, affordable, and robust system used to acquire high resolution data relating to hydraulic and fluvial environments.

Stream morphology is an important aspect of many hydrological and ecological applications such as stream restoration design (SRD) and estimating sediment loads for total maximum daily load (TMDL) development. Surveying of stream morphology traditionally involves point measurement tools, such as total stations, or remote sensing technologies, such as aerial laser scanning (ALS), which have limitations in spatial resolution. Terrestrial laser scanning (TLS) can potentially offer improvements over other surveying methods by providing greater resolution and accuracy. The first two objectives were to quantify the measurement and interpolation errors from total station surveying using TLS as a reference dataset for two fluvial applications: 1) measuring streambank retreat (SBR) for sediment load calculations; and 2) measuring topography for habitat complexity quantification. The third objective was to apply knowledge uncertainties and stochastic variability to the application of SRD. A streambank on Stroubles Creek in Blacksburg, VA was surveyed six times over two years to measure SBR. Both total station surveying and erosion pins overestimated total volumetric retreat compared to TLS by 32% and 17%, respectively. The error in SBR using traditional methods would be significant when extrapolating to reach-scale estimates of sediment load. TLS allowed for collecting topographic data over the entire streambank surface and provides small-scale measurements on the spatial variability of SBR. The topography of a reach on the Staunton River in Shenandoah National Park, VA was measured to quantify habitat complexity. Total station surveying underestimated the volume of in-stream rocks by 55% compared to TLS. An algorithm was developed for delineating in-stream rocks from the TLS dataset. Complexity metrics, such as percent in-stream rock cover and cross-sectional heterogeneity, were derived and compared between both methods. TLS quantified habitat complexity in an automated, unbiased manner at a high spatial resolution. Finally, a two-phase uncertainty analysis was performed with Monte Carlo Simulation (MCS) on a two-stage channel SRD for Stroubles Creek. Both knowledge errors (Manning's <i>n</i> and Shield's number) and natural stochasticity (bankfull discharge and grain size) were incorporated into the analysis. The uncertainty design solutions for possible channel dimensions varied over a range of one to four times the magnitude of the deterministic solution. The uncertainty inherent in SRD should be quantified and used to provide a range of design options and to quantify the level of risk in selected design outcomes. / Ph. D.