Assessment of post-fire vegetation recovery in Southern Siberia using remote sensing observations

Abstract

Abstract Wildfire is one of the main disturbances affecting forest dynamics, succession, and the carbon cycle in Siberian forests. The Zabaikal region in southern Siberia is characterized by one of the highest levels of fire activity in Russia. Time series of Landsat data and field measurements of the reforestation state were analyzed in order to estimate post-fire vegetation recovery. The results showed that the normalized burn ratio time series can be used to estimate forest recovery in the pine- and larch-dominated forests of the Zabaikal region. Multiple factors determine a forest’s recovery rate after a wildfire, including fire severity, tree species characteristics, topography, hydrology, soil properties, and climate. Assessing these factors is important if we are to understand the effects of fire on forest succession and to implement sustainable forest management strategies. In this work we used the field data and Landsat data to estimate post-fire vegetation dynamics as a function of several environmental factors. These factors include fire severity, pre-fire forest state, topography, and positive surface temperature anomalies. A regression model showed that fire frequency, fire severity, and surface temperature anomalies are the primary factors, explaining about 58% of the variance in post-fire recovery. High frequency of fire and positive surface temperature anomalies hamper the post-fire reforestation process, while more severe burns are followed by higher recovery rates. Further studies are necessary to consider other important factors such as soil properties, moisture, and precipitation, for better explanation of post-fire vegetation recovery.