Preprint Article Version 1 This version is not peer-reviewed

Detecting trends in post-fire forest recovery in Middle Volga from 2000 to 2023

Eldar Kurbanov
* ORCID logo ,
Ludmila Tarasova
* ORCID logo ,
Aydin Yakhyayev
,
Oleg Vorobev
ORCID logo ,
Siyavush Gozalov
ORCID logo ,
Sergei Lezhnin
* ORCID logo ,
Jinliang Wang
ORCID logo ,
Jinming Sha
,
Denis Dergunov
ORCID logo ,
Anna Yastrebova
Version 1 : Received: 23 September 2024 / Approved: 23 September 2024 / Online: 24 September 2024 (05:14:01 CEST)

How to cite: Kurbanov, E.; Tarasova, L.; Yakhyayev, A.; Vorobev, O.; Gozalov, S.; Lezhnin, S.; Wang, J.; Sha, J.; Dergunov, D.; Yastrebova, A. Detecting trends in post-fire forest recovery in Middle Volga from 2000 to 2023. Preprints 2024, 2024091781. https://doi.org/10.20944/preprints202409.1781.v1 Kurbanov, E.; Tarasova, L.; Yakhyayev, A.; Vorobev, O.; Gozalov, S.; Lezhnin, S.; Wang, J.; Sha, J.; Dergunov, D.; Yastrebova, A. Detecting trends in post-fire forest recovery in Middle Volga from 2000 to 2023. Preprints 2024, 2024091781. https://doi.org/10.20944/preprints202409.1781.v1

Abstract

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.

Keywords

forest ecosystems; wildfire; burnt area; post-fire forest recovery; Landsat; time-series; GEE; LandTrendr; trend analyses; climatic factors

Subject

Environmental and Earth Sciences, Remote Sensing

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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