ABSTRACT

The ice sheet mass loss being observed in Greenland and Antarctica directly contributes to global Sea Level Rise (SLR). By the end of this century, scientists predict that changes in the polar ice sheets could contribute anywhere from tens of centimeters to almost two meters in SLR. This large uncertainty in future SLR predictions is due, in part, to insufficient measurements of bedrock topography and surface crevasses in the most critical regions of the ice sheets. These measurements are used by scientists in ice sheet models to predict contributions to SLR. The observational gaps in bedrock topography and surface crevasses limit scientists? abilities to accurately model changes in the dynamic ice sheets. This project addresses this data need by equipping a small drone helicopter with a radar suite to produce fine-grid measurements of ice thickness, bed topography, and crevasses in critical regions of the ice sheet. Rising seas will have huge social and economic impacts on the entire global population ? especially to the estimated 150 million people living in coastal regions at elevations within 1 m of current sea level. The uncertainties in SLR predictions greatly inhibit our ability to properly plan for and adapt to our changing climate. The broader impacts of this work are not limited to reducing uncertainty in SLR predictions. This project also involves the training of post-secondary students in developing next-generation remote sensing technologies to better prepare them for 21st century careers. By integrating research and education, post-secondary students will gain practical experience via classroom design, build, and test projects. Through these projects, students will be exposed to the environmental and social issues that are driving the need for this new technology.

The intellectual merits of this work encompass both the technological development of the new sensor-platform and the glaciological studies this tool will enable. The primary technological research goal is to extend the application of drones in environmental remote sensing by: 1) using a novel approach for antenna integration and multi-pass distributed array processing that overcomes major payload limitations of small drones, and 2) demonstrating an autonomous platform that is easier to operate yet has sufficient payload capabilities and is robust enough to conduct measurements in polar environments. The vehicle?s flight capabilities will enable crevasse mapping and bed topography data collection with a combined spatial extent and resolution that will allow scientists to study: 1) the effects of measurement resolution on modeling ice sheet dynamic processes; 2) the significance of bed topography on glacial behavior at multiple time scales; and, 3) crevassing mechanisms and correlating crevasse attributes to calving events.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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