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Assessment of vegetation damage by three typhoons (Bavi, Maysak, and Haishen) in Northeast China in 2020

Author

Listed:
  • Guannan Dong

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zhengjia Liu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Guoming Du

    (Northeast Agricultural University)

  • Jinwei Dong

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Kai Liu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Typhoons usually bring natural disasters and economic losses. Satellite-based vegetation monitoring approaches largely improve our understanding of monitoring and assessing the area damaged by typhoons at high temporal and spatial scales, but it is still unclear which approach could achieve a greater robustness in assessing typhoon damages. From August 27 to September 8, 2020, three typhoons of Bavi, Maysak, and Haishen successively passed through the Northeast China, covering Liaoning, Jilin, and Heilongjiang provinces, and caused great damages to local vegetation and crops. Here, we employed two top-recognized approaches, i.e., the Normalized Difference Infrared Index (NDII) and the Disturbance Index (DI) derived from the moderate-resolution imaging spectroradiometer, to assess impacts three typhoons of Northeast China on the local vegetation and crops. With the help of Google Earth high-resolution images, this study demonstrated that the DI-based assessment gave a more accurate performance with an overall accuracy of 83% compared with NDII in typhoon-induced damages. DI was therefore used for spatial monitoring and assessment of three typhoon impacts. The DI-based results revealed that the damaged area of vegetation and crops in Northeast China was over 1.24 × 105 km2, including croplands, forests, and grasslands with the damaged area of 4.74 × 104 km2 (38.23% of total damaged area), 3.41 × 104 km2 (27.5%), and 0.12 × 104 km2 (0.97%), respectively. The damaged proportions were 14.13%, 13.19%, and 3.11% accounting for croplands, forests, and grasslands, respectively, of entire Northeast China. This study proves that DI-based vegetation damage assessment has more potential in large-scale monitoring of typhoon damages.

Suggested Citation

  • Guannan Dong & Zhengjia Liu & Guoming Du & Jinwei Dong & Kai Liu, 2022. "Assessment of vegetation damage by three typhoons (Bavi, Maysak, and Haishen) in Northeast China in 2020," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2883-2899, December.
    • Handle: RePEc:spr:nathaz:v:114:y:2022:i:3:d:10.1007_s11069-022-05497-3
    DOI: 10.1007/s11069-022-05497-3
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    References listed on IDEAS

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    1. Meiya Wang & Hanqiu Xu, 2018. "Remote sensing-based assessment of vegetation damage by a strong typhoon (Meranti) in Xiamen Island, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(3), pages 1231-1249, September.
    2. Mohammad Shamim Hasan Mandal & Tetsuro Hosaka, 2020. "Assessing cyclone disturbances (1988–2016) in the Sundarbans mangrove forests using Landsat and Google Earth Engine," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 133-150, May.
    3. Chen Jun & Yifang Ban & Songnian Li, 2014. "Open access to Earth land-cover map," Nature, Nature, vol. 514(7523), pages 434-434, October.
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