WOOD-LEAF UNSUPERVISED CLASSIFICATION OF SILVER BIRCH TREES FOR BIOMASS ASSESSMENT USING OBLIQUE POINT CLOUDS

Atmospheric new particle formation and cluster growth to aerosol particles is an important field of research, in particular due to the climate change phenomenon. Evaporation rates are very difficult to account for but they are important to explain the formation and growth of particles. Different quantum chemistry (QC) methods produce substantially different values for the evaporation rates. We propose a novel approach for inferring evaporation rates of clusters from available measurements.

We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. The relatively high density of manual data (135 pt over 6700 m², i.e., 2 pt/100 m²) enables to assess the performance of UAS in capturing the marked spatial variability of snow. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.