Abstract
The calving, drifting, and melting of icebergs has local, regional, and global implications. Besides the impacts to local ecosystems due to changes in seawater salinity and temperature, the freshwater influx and transport can have significant regional effects related to the ocean circulation. The increased influx of freshwater ice due to increase calving from ice shelves and the destabilization of the continental ice sheet will affect sea levels globally. In addition, drifting icebergs pose threats to offshore operations because they could damage offshore installations, e.g., pipelines and subsea manifolds, and interrupt marine transportation. Iceberg drift and deterioration models have been developed to better predict climate change and protect offshore operations. Iceberg shape is one of the most critical parameters in these models, but it is challenging to obtain because of iceberg movement caused by winds, waves, and currents. In this paper, we present an algorithm for iceberg motion estimation and shape reconstruction based on in-situ point cloud measurements. The algorithm is developed based on point cloud matching strategies, policy-based optimization, and Kalman filtering. A down-sampling method is also integrated to reduce the processing time for possible real-time applications. The motion estimation algorithm is applied to a simulated data set and field measurements collected by an Unmanned Surface Vehicle (USV) on a free-floating, translating, and rotating, iceberg. In the field data, the above-water iceberg surface was measured with a scanning LIDAR, while the below-water portion (0–50 m) was profiled using a side-looking multi-beam sonar. When applying the motion estimation algorithm to these two independent point cloud measurements collected by the two sensing modalities, consistent iceberg motion estimates are obtained. The resulting motion estimates are then used to reconstruct the iceberg shape. During the field experiment, additional oceanographic measurements, such as temperature, ocean current, and wind, were collected simultaneously by the USV. We have observed water upwelling and a colder and fresher water plume at the sea surface downstream the iceberg. Combining the iceberg shape rendering and the surrounding environmental measurements, we estimated the iceberg melting parameters due to the sensible heat flux and surface wave erosion at different iceberg sections.
Highlights
Icebergs calve off glaciers in cold polar regions
To apply the algorithm to iceberg mapping data collected at higher sea states, one could extend the point cloud matching into 3D and include iceberg heave motion into the estimation
Comparing the results obtained from the LIDAR and sonar dataset, we found that (1) both estimations show a decrease in the iceberg drift, and (2) more valid estimates and smaller uncertainty are obtained from the LIDAR point cloud because LIDAR data has less noise and higher data density than the sonar measurements
Summary
Icebergs calve off glaciers in cold polar regions. During their life span, they drift and rotate due to forces from winds, waves and ocean currents. Using unmanned systems could reduce the risk to personnel for operations in the harsh environments around icebergs These unmanned platforms could accommodate a suite of sensors, providing high quality multi-modal spatial-temporal measurements (Zhou et al, 2014; Kimball and Rock, 2015), with the potential to improve our knowledge about the iceberg mechanisms, i.e., drift and deterioration. We present a new approach, using an Unmanned Surface Vehicle for iceberg mapping, in which multimodal measurements on iceberg shape and surrounding water parameters are collected to provide detailed iceberg shape and melting information.
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