ASSESSING THE MEASUREMENT QUALITY OF UAV-BORNE LASER SCANNING IN STEEP AND SNOW-COVERED AREAS

Abstract

Avalanche monitoring in the Norwegian mountains has potential for preventing disasters and informing hikers. Using a Unmanned Aerial Vehicle (UAV)-borne laser scanner, this work proposes a protocol to assess the quality of point cloud data for monitoring avalanche risks. Roof models are used as control planes to analyze the collected points. The distances to the control planes are used to investigate error measures, and inliers and plane equations are compared with the roof model dimensions. These parameters provide insights into the reliability of the point cloud data. The comparison shows the impact of flight speed and altitude on accuracy. While varying flight speed does not affect error measures, both speed and altitude significantly affect the number of collected points. Point coverage is concentrated near the top of the roof models, resulting in a calculated model volume that is only 50% of the expected value. Comparing the Random Sample Consensus (RANSAC)-derived plane equations with the roof model planes reveals that more points are collected above the roof model planes. The standard deviations of the inliers range from 0.011 m to 0.023 m, and the root mean square error (RMSE) ranges from 0.060 m to 0.019 m. These findings indicate the reliability of UAVs for monitoring steep and snow-covered areas without the need for reference points to correct positions.