Temporal and Spatial Analyses of Forest Burnt Area in the Middle Volga Region Based on Satellite Imagery and Climatic Factors

Author:

Kurbanov Eldar1ORCID,Vorobev Oleg1,Lezhnin Sergei1ORCID,Dergunov Denis1,Wang Jinliang2ORCID,Sha Jinming3,Gubaev Aleksandr1,Tarasova Ludmila1,Wang Yibo1

Affiliation:

1. Center for Sustainable Forest Management and Remote Sensing, Volga State University of Technology, Yoshkar-Ola 424000, Russia

2. Faculty of Geography, Yunnan Normal University, Chenggong District, Kunming 650500, China

3. School of Geographical Science, Fujian Normal University, Fuzhou 350007, China

Abstract

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen’s slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (>84%) and F-score value (>82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed.

Funder

Russian Science Foundation

Publisher

MDPI AG

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