Landfast sea ice monitoring using multisensor fusion in the Antarctic

Abstract

Landfast sea ice (fast ice) means sea ice that is attached to the shoreline with little or no motion in contrast to pack ice which drifts on the sea. As fast ice plays an important role in the environmental and biological systems of the Antarctic, it is crucial to accurately monitor the spatiotemporal distribution of fast ice. Previous studies on fast ice using satellite remote sensing were mostly focused on the Arctic and near-Arctic areas, whereas few studies were conducted over the Antarctic, especially the West Antarctic region. This research mapped fast ice using multisensor data from 2003 to 2008 based on machine learning approaches – decision trees (DTs) and random forest (RF). A total of seven satellite-derived products, including Advanced Microwave Scanning Radiometer for the Earth observing system brightness temperatures and sea ice concentration, Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) and Special Sensor Microwave/Imager ice velocity, were used as input variables for identifying fast ice. RF resulted in better performance than that of DT for fast ice classification. Visual comparison of the fast ice classification results with 250-m MODIS images for selected areas also revealed that RF outperformed DT. Ice velocity and IST were identified as the most contributing variables to classify fast ice. Spatiotemporal variations of fast ice in the East and West Antarctic were also examined using the time series of the fast ice maps produced by RF. The residence time of fast ice was much shorter in the West Antarctic than in the East.

Keywords:

• landfast sea ice
• Antarctic
• random forest
• decision trees

Acknowledgment

Special thanks to Dr Alexander Fraser for sharing the fast ice maps of the East Antarctic for reference.

Additional information

Funding

This research was supported by the project titled “SaTellite remote sensing on set Antarctic ocean Research (STAR) [KOPRI, PE15040]”, funded by the Korea Polar Research Institute, South Korea, and was partially supported by the Space Core Technology Development program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (2014M1A3A3A03034799).