Evaluating river ice elevation, thickness, and roughness using airborne LiDAR

Abstract

Monitoring river ice processes is essential for understanding ice-related hydraulic dynamics and potential flood risks. In this study, high-resolution top-of-ice elevation was surveyed using airborne LiDAR, providing fundamental information for characterizing the ice cover. This elevation data alone serves as a critical metric for river ice monitoring. Building on this foundation, two critical river ice parameters, ice thickness and ice surface roughness, were analyzed using a novel integrative approach. To evaluate ice thickness in a fluvial setting, field-measured water level data were combined with remotely sensed data, specifically a high-resolution digital elevation model (DEM) of a fluvial ice cover generated from LiDAR measurements. This approach enabled estimation of ice thickness by leveraging the synergy between in situ observations and advanced remote sensing techniques. Additionally, the study evaluated the performance and capabilities of the remotely piloted aircraft (RPA) Matrice-350 equipped with the DJI L2 LiDAR payload. The RPA platform demonstrated high precision in capturing both elevation data and surface imagery, making it an effective tool for surveying ice-covered rivers in challenging environments. The results highlight the potential of RPA-mounted LiDAR systems for advancing river ice monitoring and analysis. Finally, the surface characteristics of river ice were systematically analyzed and classified. By applying statistical metrics, the ice surface roughness was categorized into two distinct groups: smooth and rough. This classification provides valuable insights into the spatial heterogeneity of river ice, with implications for hydraulic modeling and ice-related engineering applications. This study demonstrates the effectiveness of integrating remote sensing techniques with advanced RPA technology to comprehensively evaluate critical river ice parameters. The resulting dataset significantly enhances the understanding of the field of river ice engineering and improves the accuracy of numerical modeling by providing essential inputs for ice jam simulations.