An algorithm to generate 2D bathymetry of an Alpine river for habitat suitability assessment

Here we present an original approach to generate 2D high detail riverbed based on a drone photogrammetric survey, and RTK bathymetry measurements for Mera river in the Italian Alps. The aim is to better represent macro-roughness and riverbed structure of the river, also extending it to an ungauged area. Specifically, we apply a step-by-step approach. I) Depth and average slope of the riverbed were calculated from bathymetry data. II) Thus, a trapezoidal channel with constant slope and variable width was defined using the drone images. III) Riffle-pool sequence was assessed as a function of river width and applied to the generated channel. IV) Finally, the semi-random Perlin Noise was added to recreate riverbed irregularities in the natural stream. HEC-RAS 2D hydraulic software was then implemented to assess spatialized water depth and velocity. The proposed methodology could be quite relevant in river hydraulics to decouple roughness coefficient from water submergence, and in Physical Habitat Simulation Model (PHABSIM), where the dependency of the output is not linear with hydraulic parameters (i.e. water depth and velocity). Indeed, we apply PHABSIM for a case study of a stretch of the river and results are compared with a previous environmental study for Mera river.

Simulation of Laser Profilometer Measurements in the Presence of Speckle Using Perlin Noise

In the manufacturing industry, inspection systems play a crucial role in ensuring product quality. High-resolution profilometric sensors have become increasingly popular for inspection due to their ability to provide detailed surface information. However, the development and testing of inspection systems can be costly and time-consuming. This paper presents the development of a simulation of an inspection system using a high-resolution profilometric sensor. A geometrical and noise model is proposed to simulate the readings of any actual profilometric sensor. The model replicates the sensor's movement on the CAD model of the inspected part. The model incorporates the physical properties of the sensor and combines noise sources from sensor uncertainty and speckle noise induced by the roughness of the material. Our contribution lies in noise modeling. This work proposes a combination of Perlin noise to simulate the speckle noise and Gaussian noise for the uncertainty-related noise. Perlin noise is generated based on the surface roughness parameters of the inspected part. The accuracy of the simulation system is evaluated by comparing the simulated scans with real scans. The results highlight the ability to simulate real scans of different parts, using commercial sensor specifications and the CAD model of the inspected part.