Monitoring solifluction movement in space and time: A semi-automated high-resolution approach

Abstract

Solifluction is the slow downslope movement of soil mass due to freeze-thaw processes. It is widespread on hillslopes in Polar and Alpine regions and contributes substantially to sediment transport. As solifluction lobe movement is in the order of millimeters to centimeters per year, it is difficult to measure with high spatial and temporal resolution and accuracy. In this study we developed a semi-automated approach to monitor movement using unmanned aerial vehicles, image co-alignment, and COSI-Corr (Co-registration of Optically Sensed Images and Correlation) to track slope movement from orthophotos. The method was applied on yearly images acquired between 2017 and 2021 of three solifluction lobes with different degrees of vegetation cover along an elevational gradient in Turtmann Valley, Swiss Alps. We found movement patterns across all three lobes with highest movement rates at the solifluction lobes center and lowest rates at lobe fronts. Overall, at the highest elevations (2560 m) lobe movement rates were highest with up to 14.0 cm yr−1 and intermediate elevations (2417 m) had the lowest values up to 2.9 cm yr−1. The lobe at the lowest elevation (2170 m) showed intermediate movement rates with up to 4.9 cm yr−1 for single years. Our monitoring approach provides yearly, spatially extensive movement estimates across the complete spatial extent of a lobe for each 1 cm2 of its surface, strongly increasing measurement resolution in comparison to traditional solifluction monitoring approaches using point measurements. In comparison to previous close-range remote sensing approaches, the use of a co-alignment procedure for the acquired drone data enabled a time-saving field setup without Ground Control Points (GCPs). The resulting high co-registration accuracy enabled us to detect solifluction movement if it exceeds 5 mm with sparse vegetation cover. Dense vegetation cover limited feature-tracking but detected movement rates and patterns are in the same order of magnitude and matched previous measurements using classical total station measurements at the lowest, mostly vegetated lobe. This study demonstrates the use of drone-based Digital Elevation Models (DEMs) and orthophotos in a semi-automated method which reaches the high spatiotemporal resolution necessary to detect subtle movements of solifluction lobes at yearly intervals at the sub-centimeter scale. This provides new insights into solifluction movement and how much it contributes to sediment transport. Therefore, our semi-automated approach has a great potential to uncover the fundamental processes and better understand solifluction movement.