Classifying Sparse Vegetation in a Proglacial Valley Using UAV Imagery and Random Forest Algorithm

Abstract

Extreme hydro-meteorological events become an increasing risk in high mountain environments, resulting in erosion events that endanger human infrastructure and life. Vegetation is known to be an important stabilizing factor; however, little is known about the spatial patterns of species composition in glacial forelands. This investigation aims to differentiate sparse vegetation in a steep alpine environment in the Austrian part of the Central Eastern Alps using low-cost multispectral cameras on an unmanned aerial vehicle (UAV). Highly resolved imagery from a consumer-grade UAV proved an appropriate basis for the SfM-based modeling of the research area as well as for vegetation mapping. Consideration must be paid to changing light conditions during data acquisition, especially with multispectral sensors. Different approaches were tested, and the best results were obtained using the Random Forest (RF) algorithm with the target class discrimination based on the RGB orthomosaic and the DEM as supplementary dataset. Our work contributes to the field of biogeomorphic research in proglacial areas as well as to the field of small-scale remote sensing and vegetation measuring. Our findings show that the occurrence of vegetation patches differs in terms of density and diversity within this relatively recent deglaciated environment.

Keywords

• UAV remote sensing
• vegetation mapping
• structure from motion