SPECIFIC ALPINE ENVIRONMENT LAND COVER CLASSIFICATION METHODOLOGY: GOOGLE EARTH ENGINE PROCESSING FOR SENTINEL-2 DATA

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.