Remote sensing and statistical assessment of the impact of typhoon Doksuri on subtropical forests in China
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.
Authors
- Praveen Kumar (ORCID: https://orcid.org/0000-0001-8458-9614)
- Mahlatse Kganyago (ORCID: https://orcid.org/0000-0001-9553-0378)
- Guiyu Li
- Veber Afonso Figueiredo Costa (ORCID: https://orcid.org/0000-0002-3848-2098)
- Aminjon Gulakhmadov (ORCID: https://orcid.org/0000-0002-2366-2491)
- Chao Yang (ORCID: https://orcid.org/0000-0001-7724-0385)
- Won‐Ho Nam (ORCID: https://orcid.org/0000-0002-9671-6569)
- Nengcheng Chen (ORCID: https://orcid.org/0000-0002-3521-9972)
- Wenying Du (ORCID: https://orcid.org/0000-0002-3202-5128)
- Xiang Zhang (ORCID: https://orcid.org/0000-0002-1017-742X)
- Wei Dong Qi (ORCID: https://orcid.org/0000-0002-7378-1392)
- Qian Yu (ORCID: https://orcid.org/0000-0003-1953-1170)
- Xu Zhang (ORCID: https://orcid.org/0009-0006-0447-0474)
- Xihui Gu (ORCID: https://orcid.org/0000-0002-7414-5371)
Institutions
- Universidade Federal de Minas Gerais (BR)
- Hankyong National University (KR)
- Siksha O Anusandhan University (IN)
- Academy of Sciences of the Republic of Tajikistan (TJ)
- University of Johannesburg (ZA)
- Beijing Normal University (CN)
- China University of Geosciences (CN)
- Shenzhen Research Institute of China University of Geosciences (CN)
- Xinjiang Institute of Ecology and Geography (CN)
- Traffic Management Research Institute (CN)
- Fujian University of Technology (CN)
Publication Details
- Journal
- npj natural hazards.
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1038/s44304-025-00151-x
- Primary Topic
- Remote Sensing and Land Use
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Hubei Provincial Department of Education
- National Natural Science Foundation of China
- Beijing Normal University