Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data

Changda Liu, Jie Li, Qiuhua Tang, Jiawei Qi and Xinghua Zhou
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Changda Liu: The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Jie Li: The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Qiuhua Tang: The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Jiawei Qi: College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Xinghua Zhou: The First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China

Land, 2022, vol. 11, issue 2, 1-15

Abstract: Shore zone information is essential for coastal habitat assessment, environmental hazard monitoring, and resource conservation. However, traditional coastal zone classification mainly relies on in situ measurements and expert knowledge interpretation, which are costly and inefficient. This study classifies a shore zone area using satellite remote sensing data only and investigates the effect of the statistical indicators from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) information with the Sentinel-2 data-derived spectral variables on the prediction results. Google Earth Engine was used to synthesize long time-series Sentinel-2 images, and different features were calculated for this synthetic image. Then, statistical indicators reflecting the characteristics of the shore zone profile were extracted from ICESat-2. Finally, a random forest algorithm was used to develop characteristics and shore zone classification. Comparing the results with the data measured shows that the proposed method can effectively classify the shore zone; it has an accuracy of 83.61% and a kappa coefficient of 0.81.

Keywords: shore zone classification; ICESat-2; Sentinel-2; Google Earth Engine; random forest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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