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

, , . , , . RF (.Random Forest, ) , , . , , , , . 0,84, F 1 -score -0,76. dNBR (.Differenced Normalized Burn Ratio). , , ( dNBR, dBAI (.Differenced Burn Area Index), ), 50 % .

Authors

Publication Details

Journal
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
Published
2025-01-01
DOI
https://doi.org/10.21046/2070-7401-2025-22-3-171-181
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Е. Г. Швецов
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
Fire effects on ecosystems
article

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

Е. Г. Швецов
article en

Abstract

, , . , , . RF (.Random Forest, ) , , . , , , , . 0,84, F 1 -score -0,76. dNBR (.Differenced Normalized Burn Ratio). , , ( dNBR, dBAI (.Differenced Burn Area Index), ), 50 % .

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosaVol. 22(3)
Climate action
Openalex Percentile: Top 85%
Fire effects on ecosystems
DOIOpen Access PDFOpenAlex
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Predicting post-fire forest mortality using remote sensing data and machine learning — Е. Г. Швецов · Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa (2025) | TGRS Research Map | TGRS