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<p>Super Typhoon Doksuri caused severe damage to Fujian Province, a core region of subtropical forest distribution along the southeastern coast of China. This study utilized MODIS reflectance data and the Google Earth Engine platform to quantify the extent of forest damage and post-typhoon recovery. Results showed that approximately 38.93% of forests were affected to varying degrees. After controlling for seasonal and interannual variability, NDVI during the typhoon period declined by 0.0241 compared to the same period in 2022 and by 0.0282 compared to the multi-year average. Considering the spatial heterogeneity of wind effects, regression models combined with Shapley Additive Explanations were used to analyze the regulatory roles of topography, precipitation, runoff, and forest type. The results indicate that elevation and runoff are key factors influencing damage severity, with high-elevation and low-runoff areas suffering the most. This remote sensing-based framework aids forest vulnerability assessment and supports disaster management and ecological conservation.</p>

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Remote sensing and statistical assessment of the impact of typhoon Doksuri on subtropical forests in China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

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  • Xu Zhang,
  • Xiang Zhang,
  • Chao Yang,
  • Aminjon Gulakhmadov,
  • Wenying Du,
  • Xihui Gu,
  • Guiyu Li,
  • Panda Rabindra Kumar,
  • Veber Afonso Figueiredo Costa,
  • Mahlatse Kganyago,
  • Won-Ho Nam,
  • Wei Qi,
  • Qian Yu,
  • Nengcheng Chen

摘要

Super Typhoon Doksuri caused severe damage to Fujian Province, a core region of subtropical forest distribution along the southeastern coast of China. This study utilized MODIS reflectance data and the Google Earth Engine platform to quantify the extent of forest damage and post-typhoon recovery. Results showed that approximately 38.93% of forests were affected to varying degrees. After controlling for seasonal and interannual variability, NDVI during the typhoon period declined by 0.0241 compared to the same period in 2022 and by 0.0282 compared to the multi-year average. Considering the spatial heterogeneity of wind effects, regression models combined with Shapley Additive Explanations were used to analyze the regulatory roles of topography, precipitation, runoff, and forest type. The results indicate that elevation and runoff are key factors influencing damage severity, with high-elevation and low-runoff areas suffering the most. This remote sensing-based framework aids forest vulnerability assessment and supports disaster management and ecological conservation.