In this thesis, a model for air flow and temperature in urban areas is studied. In particular, the influence of vegetation in a street canyon is investigated. This includes the effect of vegetation on air flow and the cooling property of vegetation to lower temperature in street canyons. Computational Fluid Dynamics simulations with vegetation in a street canyon (green facades and trees) are performed. To model this, the Reynolds Averaged Navier Stokes equations in 3D are closed using the k-e turbulence model with source and sink terms to account for the effects of vegetation on air flow. In the thermal equation, the Simple Gradient Diffusion Hypothesis is used to close the equations and a cooling power term is introduced to account for the transpirational cooling property of vegetation. The results regarding the vertical velocities in the street canyon are compared with earlier simulations by Gromke et al. The simulations involving temperature showed significant effects of the vegetation in the street canyon. At street level, the green facades yielded a stronger cooling effect than trees. However, the cooling effect of trees is stronger halfway the canyon and just above the canyon in comparison with green facades. An important note is that the temperature drop inside the tree canopy strongly suggests that the cooling power in the simulation is modelled stronger than one would expect in reality. Another part of this research is the implementation of a Higher Order Scheme. This is done with the Van Leer limiter. The simulations with this new numerical method yielded very similar results compared with the UDS simulations. One outlier is noticed in the velocity in the 푦-direction for the empty street canyon. A possible explanation could be the sensitivity of the method near the ground. The study of the effect of vegetation in a street canyon could be extended in the future by comparing combinations of vegetation and adjusting the parameters of the green facades. Furthermore, the Van Leer limiter could be extended to the temperature part of the simulations in the future, since sharper gradients are expected here, so the improvement with respect to UDS and QUDS could be more significant. Finally, also other flux limiters, such as the Koren scheme, could be tested.

The Catterline Bay, located in North East Scotland, is prone to erosion processes. Using Terrestrial Laser Scanning (TLS) two point clouds were obtained in 2019 and 2022 from this area of ca. 0.2 km2. Here, a methodology was proposed and tested to assess how erosion processes can be monitored from high resolution point clouds in small study areas. Filtering of ground points from the point clouds was done using a full 3D approach. The local dimensionality on multiple scales was used to generate features for the classification, for which Linear Discriminant Analysis was used. The algorithm was able to precisely separate non ground points from ground points with a precision on test data sets of 94.7% on ground points and 94.3% on non-ground points. The ground points provided a basis for the Digital Terrain Model (DTM) raster. Several geomorphological quantities could be derived from this DTM. Next to these quantities, also information about vegetation height and 3D change detection using the Multi-scale Model to Model Cloud Comparison (M3C2) was extracted. From a combination of the Terrain Ruggedness Index (TRI), change detection using the Multiscale Model to Model Cloud Comparison (M3C2) technique and photos, erosion zones were derived. Also stable, non-erosion, zones were identified. Various statistics from the geomorphological quantities were analysed for both zone types. Erosion zones have clear edges of high TRI values, indicating the scarps of landslides. The erosion zones also contain groups of negative M3C2 distances, indicating depletion zones. Not only the TRI and M3C2 distances have significantly different statistics for erosion and non-erosion zones, also the slope is steeper in these zones and the Topographic Wetness Index is smaller. The mean slope for erosion zones is 39.3° compared to 30.9° for non-erosion zones. To capture the behaviour of the TRI in the defined zones, pTRI was introduced which gives a rate of how many cells have a high TRI value in a defined zone. The pTRI, the rate of TRI values above 0.06 m, is more than 4 times larger for erosion zones (0.17) than for non-erosion zones (0.04). Changes in vegetation height could be linked to locations of implemented Nature Based Solutions (NBS). The results were in agreement with changes in NDVI, which were calculated from optical satellite imagery. In conclusion, the new methodology has great potential to identify erosion zones in complex sloped terrain and monitor changes in vegetation.