Effect of Fire on Aboveground Carbon Pools Dynamic in the Boreal Forests of Eastern Eurasia: Analysis of Field and Remote Data

Author:

Ivanov Aleksandr1,Masyutina Yulia1,Susloparova Elizaveta1,Danilov Aleksandr1,Zenevskaya Evgenia2,Bryanin Semyon1

Affiliation:

1. Institute of Geology and Nature Management, Far East Branch, Russian Academy of Sciences, Blagoveshchensk 675000, Russia

2. Primorskaya State Agricultural Academy, Ussuriisk 692510, Russia

Abstract

The forests of the boreal biome, which perform an important climate-regulating function, are the most susceptible to forest fires. An important task is to obtain quantitative estimates of carbon (C) losses of forest ecosystems under different fire damage scenarios, as well as the possibility of such estimates based on remote sensing data. Our study provides comprehensive field data on C stocks in pools of plant phytomass and necromass, forest litter, and ground cover for a vast area of boreal forests in the Russian Far East. We studied forests of the larch formation that have been affected by fires of varying intensity. The severity of the fires was assessed based on differenced Normalized Burn Ratio (dNBR). The variation in C pools depending on the strength of the fire is shown. We did not find a relationship of C stocks with the dNBR in the forests in the south of the study area that might have caused the rapid change of species during post-fire recovery. In the northern part of the area, there is a trend of a decrease in plant phytomass with an increase in dNBR (R2 = 0.78). The proportion of dead standing wood share in the total C stock increases with increasing fire severity (R2 = 0.63). The maximum and average C stocks in the litter were 10.6 and 3.9 t C ha−1, respectively; coarse woody debris contained 8.7 and 2.0 t C ha−1; carbon stocks in living ground cover were 1.2 on average and reached 4.7 t C ha−1. Our study shows that dNBR can serve as a good predictor of the C stock of phytomass after a fire in the northern part of the Far East region, which opens up opportunities for approximate quantitative remote estimates of C losses.

Funder

Russian Science Foundation

Publisher

MDPI AG

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