Shore Zone Classification from ICESat-2 Data over Saint Lawrence Island

Abstract

The shore zone is the most active zone in the atmosphere, hydrosphere, biosphere and lithosphere of nature, and has the environmental characteristics of both ocean and land. The ICESat-2 satellite provides height measurements of shore zone using a photon-counting LiDAR. The purpose of this study is to explore the application potential of ICESat-2 satellite data in shore zone classification. Saint Lawrence Island, Alaska, was chosen as the study area. Firstly, in this study, the upper and lower boundaries of the shore zone of the study area were extracted based on Google Earth images. The slope and width between the two boundaries were then calculated according to the formula. Secondly, six statistical indicators (standard deviation, relative standard deviation, average absolute deviation, relative average deviation, absolute median error and quartile deviation) related to the substrate and sediment classification that could reflect the characteristics of the shore zone profile were extracted, and the statistical indicators were used as input parameters of the softmax regression model for classification. Finally, the accuracy of the shore zone classification was validated using the ShoreZone classification system. The results show that, among the 246 shore zone sections in the study area, 86% (212) has been correctly classified. The results therefore indicate that ICESat-2 data can be used to support the characterization of shore zone morphology.

Keywords:

• ATL03
• ICESat-2
• Saint Lawrence Island
• shore zone classification

Acknowledgements

The authors would like to thank the National Snow and Ice Data Center (NSIDC) for providing the ICESat-2 data.

Declaration of interest statement

No potential competing interest was reported by the authors.

Additional information

Funding

This research was funded by the National Natural Science Foundation of China under grant 41822106; the National High Resolution Ground Observation System of China under grant 11-Y20A12-9001-17/18; the Shanghai Science and Technology Innovation Action Plan Program under grant 18511102100; the Dawn Scholar of Shanghai Program under grant 18SG22; the State Key Laboratory of Disaster Reduction in Civil Engineering under grant SLDRCE19-B-35; and the Fundamental Research Funds for the Central Universities of China.