Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation

Abstract

More accurate snow quality predictions are needed to economically and socially support communities in a changing Arctic environment. This contrasts with the current availability of affordable and efficient snow monitoring methods. In this study, a novel approach is presented to determine spatial snow depth distribution in challenging alpine terrain that was tested during a field campaign performed in the Tarfala valley, Kebnekaise mountains, northern Sweden, in April 2019. The combination of a multispectral camera and an Unmanned Aerial Vehicle (UAV) was used to derive three-dimensional (3D) snow surface models via Structure from Motion (SfM) with direct georeferencing. The main advantage over conventional photogrammetric surveys is the utilization of accurate Real-Time Kinematic (RTK) positioning which enables direct georeferencing of the images, and therefore eliminates the need for ground control points. The proposed method is capable of producing high-resolution 3D snow-covered surface models (<7 cm/pixel) of alpine areas up to eight hectares in a fast, reliable and affordable way. The test sites’ average snow depth was 160 cm with an average standard deviation of 78 cm. The overall Root-Mean-Square Errors (RMSE) of the snow depth range from 11.52 cm for data acquired in ideal surveying conditions to 41.03 cm in aggravated light and wind conditions. Results of this study suggest that the red components in the electromagnetic spectrum, i.e., the red, red edge, and near-infrared (NIR) band, contain the majority of information used in photogrammetric processing. The experiments highlighted a significant influence of the multi-spectral imagery on the quality of the final snow depth estimation as well as a strong potential to reduce processing times and computational resources by limiting the dimensionality of the imagery through the application of a Principal Component Analysis (PCA) before the photogrammetric 3D reconstruction. The proposed method is part of closing the scale gap between discrete point measurements and regional-scale remote sensing and complements large-scale remote sensing data and snow model output with an adequate validation source.