Smoke from wildfires in Siberia often affects air quality over vast territories of the Northern hemisphere during the summer. Increasing fire emissions also affect regional and global carbon balance. To estimate annual carbon emissions from wildfires in Siberia from 2002–2020, we categorized levels of fire intensity for individual active fire pixels based on fire radiative power data from the standard MODIS product (MOD14/MYD14). For the last two decades, estimated annual direct carbon emissions from wildfires varied greatly, ranging from 20–220 Tg C per year. Sporadic maxima were observed in 2003 (>150 Tg C/year), in 2012 (>220 Tg C/year), in 2019 (~180 Tg C/year). However, the 2020 fire season was extraordinary in terms of fire emissions (~350 Tg C/year). The estimated average annual level of fire emissions was 80 ± 20 Tg C/year when extreme years were excluded from the analysis. For the next decade the average level of fire emissions might increase to 250 ± 30 Tg C/year for extreme fire seasons, and to 110 ± 20 Tg C/year for moderate fire seasons. However, under the extreme IPCC RPC 8.5 scenario for Siberia, wildfire emissions might increase to 1200–1500 Tg C/year by 2050 if there were no significant changes in patterns of vegetation distribution and fuel loadings.

We investigated changes in the temperature regime of post-fire and post-technogenic cryogenic soils of Central Siberia using remote sensing data and results of numerical simulation. We have selected the time series of satellite data for two variants of plots with disturbed vegetation and on-ground cover: natural ecosystems of post-fire plots and post-technogenic plots with reclamation as well as dumps without reclamation. Surface thermal anomalies and temperature in soil horizons were evaluated from remote data and numerical simulation and compared with summarized experimental data. We estimated the influence of soil profile disturbances on the temperature anomalies forming on the surface and in soil horizons based on the results of heat transfer modeling in the soil profile. According to remote sensing data, within 20 years, the thermal insulation properties of the vegetation cover restore in the post-fire areas, and the relative temperature anomaly reaches the level of background values. In post-technogenic plots, conditions are more “contrast” comparing to the background, and the process of the thermal regime restoration takes a longer time (>60 years). Forming “neo-technogenic ecosystems” are distinct in special thermal regimes of soils that differ from the background ones both in reclamated and in non-reclamated plots. An assumption was made of the changes in the moisture content regime as the main factor causing the long-term existence of thermal anomalies in the upper soil horizons of disturbed plots. In addition, we discussed the formation of transition zones (“ecotones”) along the periphery of the disturbed plots due to horizontal heat transfer.

The paper presents the results of an analysis of defoliation features in the dark coniferous tree stands after the impact of the Siberian silkmoth (Dendrolimus sibiricus) in the mountain dark coniferous taiga of the Irbey forestry of Krasnoyarsk krai of Central Siberia in 2018–2020. Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) materials were used, as well as information about forest stands and forest inventory characteristics in the format of a vector layer for taxons within the zone of Siberian silkmoth infestation. The degree of forest stand disturbance was assessed in terms of the relative anomaly of the vegetation index (Δ_rel, %) compared with the characteristic background values for three variants of the dominant dark coniferous stands: fir (Abies sibirica), Siberian pine (Pinus sibirica), and spruce (Picea obovata). A transition was made from pixel-by-pixel data to values averaged over taxons and related to dominant tree stands. It is shown that the threshold values of the relative anomaly Δ_rel at the level of 10, 25, and 50% make it possible to detail the degree of defoliation according to 5 classes of the state of damaged areas. A conjugated analysis involving forest inventory data made it possible to emphasize the differences in the rate of defoliation features on satellite imagery, as well as to indirectly assess the resistance of the dominant dark coniferous (fir, Siberian pine and spruce stands) to the silk moth impact, in terms of the ratio of the area, with signs of extreme defoliation on final stage of outbreak development in 2020.

A method for recovery monitoring in post-fire and post-technogenic landscapes was proposed based on satellite data in a wide spectral range, including the infrared band data. A decrease in the spectral surface albedo in post-fire areas, caused by the destruction of on-ground vegetation, provokes excessive heating of the surface and upper soil layer. Surface thermal anomalies were evaluated under conditions of changes in the heat-insulating properties of vegetation and ground cover. The relative temperature anomalies in post-fire plots (overestimation up to 30% compared to non-disturbed territory) are typical for permafrost conditions of Siberia. Similar process was recorded for both natural (post-fire) and post-technogenic landscapes. Within 22 years after the fire, thermal insulation properties of the vegetation cover were restored. Thus, the relative temperature anomaly (of 3±1%) has reached the background value. In post-technogenic plots, conditions are more “contrast” compared to the background, and restoration of the thermal regime takes significantly longer (>60 years). “Neo-technogenic ecosystems” with specific soil thermal regimes compared to the background ones are formed both for reclaimed and for non-reclaimed post-technogenic plots. On average, surface temperature has overestimated at least by 10–15% in post-technogenic plots compared to non-disturbed territory.

In this study, we have analyzed tree stand disturbance by hthe Siberian Silk Moth (Dendrolimus sibiricus Tschetverikov (Lepidoptera: Lasiocampidae)) in Central Siberia (Krasnoyarsk region, Russia) in 2015–2020. We considered two plots that experienced silk moth outbreaks in 2015–2018 and 2018–2020 and used satellite data (Terra/MODIS, Landsat/ETM/OLI), field forest inventory data, a meteorological data set, and a vegetation cover vector layer. Silk moth-disturbed areas were classified using NDVI, which was calculated for each 15-day period during the growing season (April–September). We obtained formalized descriptions of the temporal dynamics of the disturbed area. Next, we classified the degree of disturbance of the forest stand after the impact of the silk moth by the threshold method according to the ranges of NDVI anomalies. Based on the generalized data from the forest inventory, we performed a correlation analysis of the relationship between the main characteristics of forests and the classes of disturbance. Finally, using a series of regression equations, we described a procedure for predicting the degree of impact on the stand during the time of silk moth outbreaks in the dark-needle coniferous stands of Central Siberia.

The impact of fire on the boreal forests of eastern Siberia was analyzed using the normalized burn ratio (NBR) and its pre- versus post-fire difference (dNBR) applied to Landsat-8/OLI data. We provided the classification of fire impact in relation to dominant tree stands and vegetation types. For post-fire areas of different disturbance levels, we evaluated the 5-year dynamics of restoration processes in terms of anomalies in the surface temperature (ΔT/T_bg, %) and in the Normalized Difference Vegetation Index (ΔNDVI/NDVI_bg, %), which can indirectly characterize the post-fire state of vegetation and recovery.