GIS-Supported Airfield Selection near Zhongshan Station, East Antarctica, based on Multi-Mission Remote Sensing Data

Abstract

As Antarctica attracts increasing attention in global climate change studies, the demand for field expeditions has increased in recent decades. Aircraft has become the most efficient mode of transportation because of the advantages of short travel times over long distances, access to unreachable locations and capability to carry different types of sensors to obtain large areal coverages. However, few studies have been published regarding Antarctic airfield site selection for heavy-wheeled aircraft. In this paper, we present methods and results of blue-ice airfield preliminary selection near Zhongshan station, East Antarctica. The geographic information system (GIS)-based method is supported by multi-mission high-resolution images from the ZY-3, WorldView-2 and Landsat-8 satellites along with existing remote sensing products. Ground truth observations were integrated with satellite panchromatic and spectral information to identify runway candidate areas. The information inferred by remote sensing data, including firn type, ice movement, surface slope and ice fracture, is used for evaluation of the airfield selection rules. Finally, the rankings and recommendations of runway candidates were performed in a GIS analysis environment. The site selection approach developed in this paper can be applied in preconstruction studies of other similar cryosphere environments to provide appropriate candidates for a final stage field investigation.

Keywords:

• Airfield
• blue ice
• remote sensing
• site selection

Acknowledgements

We would like to acknowledge both reviewers for their constructive and helpful comments and suggestions for improving the quality of our manuscript. This research was supported by the National Key Research and Development Program of China (2017YFA0603102), the National Science Foundation of China (41730102, 41776186), and the Fundamental Research Funds for the Central Universities (22120170152). Data from the National Ice Data Center (NSIDC) in USA and Beijing Normal University in China are greatly appreciated.