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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. 3, pp. 171-181

Predicting post-fire forest mortality using remote sensing data and machine learning

E.G. Shvetsov 1 
1 Krasnoyarsk Science Center SB RAS, Krasnoyarsk, Russia
Accepted: 21.03.2025
DOI: 10.21046/2070-7401-2025-22-3-171-181
Large number of wildfires and burned area that occur annually in Siberia determine the need to develop tools for assessing and predicting the effects of wildfires on forests. The study uses open access data sets and machine learning methods to predict areas of post-fire forest mortality, as well as to assess the significance of features that determine the amount of tree mortality. Using a set of thematic satellite products and the Random Forest method for the territory of Krasnoyarsk Krai, Republics of Khakassia and Tyva, the significance of a set of features in predicting the proportion of forest stand loss after fire was assessed, and a Random Forest model was developed. The work used features describing both forest and topographic conditions of the territory, moisture content of forest fuels, as well as characteristics of wildfires and magnitude of changes in spectral properties of the surface caused by wildfires. The overall accuracy of the model was 0.84, and the F1-score was 0.76. The dNBR index showed the greatest relative importance in predicting the proportion of post-fire tree mortality. In general, the features characterizing changes in the surface reflective properties after fire (spectral indices dNBR, dBAI, as well as reflectivity in several spectral ranges) determined more than 50 % of variability in the estimates of post-fire tree mortality.
Keywords: wildfires, remote sensing, machine learning, Random Forest, Siberia
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