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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 4, pp. 236-252

Satellite-based mapping of forest vulnerability to wind impact (by an example of Perm Krai)

A.V. Semakina 1 , A.N. Shikhov 1 , E.A. Klimina 1 
1 Perm State University, Perm, Russia
Accepted: 20.05.2025
DOI: 10.21046/2070-7401-2025-22-4-236-252
Windthrow is the most significant type of natural disturbance for dark-coniferous and mixed forests covering vast areas in the European part of Russia, and Perm Krai in particular. Its risk assessment involves determination of the hazard, exposure and vulnerability and has not been previously performed for large regions of Russia. In this study, a training sample of over 102,000 features was compiled using freely available satellite data, their processing products and digital elevation models. The Random Forest Regressor model was trained on its basis to assess the vulnerability of forests to wind impact. The predictors were 16 characteristics of forest cover (dominant tree species, age, growing stock, stand height) and topography. The highest importance and strongest correlation with the dependent variable had growing stock, according to the GlobBiomass dataset, mean height of trees, and the proportion of pine and dark coniferous forests area. The Random Forest model was used to calculate the vulnerability of forests Perm Krai to wind impact and to compile the corresponding map. It was shown that the model does not allow correct estimation of spatial distribution of forest damage at the local level (for an individual windthrow), as it disregards the local distribution of wind speed. However, for the entire territory of Perm Krai, the obtained estimate agrees well with the observed spatial distribution of windthrow events occurred over the past 40 years. The model can also be used to predict forest vulnerability to wind impact for other regions with similar forest characteristics, primarily in the forest zone of the European part of Russia.
Keywords: windthrow, vulnerability, exposure, risk, predictors, forest stand characteristics, mapping, machine learning, Random Forest
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