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

Author

Listed:
  • 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
    Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, Qingdao 266590, 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
    College of Marine Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

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.

Suggested Citation

  • Changda Liu & Jie Li & Qiuhua Tang & Jiawei Qi & Xinghua Zhou, 2022. "Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data," Land, MDPI, vol. 11(2), pages 1-15, February.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:2:p:240-:d:742940
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    References listed on IDEAS

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