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The effect of climate change on forest dynamics is likely to increase in importance in the forthcoming decades. For this reason, it is essential to predict the extent to which changes in temperature, precipitation, and atmospheric CO2 might affect the development of forest ecosystems and successional pathways. The gap model ZELIG-CFS was used to simulate the potential long-term effects of climate change on species-specific annual change in mean basal area and stand density under two scenarios of representative concentration pathways (RCP), 4.5 and 8.5, for the boreal forest region of Ontario, Canada, where mean temperature, precipitation, and atmospheric CO2 are expected to increase. Forest ecosystems in this boreal region included pure and mixed stands of black spruce (Picea mariana [Mill.] B.S.P.), paper birch (Betula papyrifera Marsh.), balsam fir (Abies balsamea [L.] Mill.), jack pine (Pinus banksiana Lamb.), trembling aspen (Populus tremuloides Michx.), white spruce (Picea glauca [Moench] Voss), northern white cedar (Thuja occidentalis L.), American larch (Larix laricina [Du Roi] K. Koch), and balsam poplar (Populus balsamifera L.). Simulation results under climate change generally predicted a decline in the basal area and stand density for black spruce, balsam fir, jack pine, and white spruce, but an increase for paper birch, trembling aspen, American larch, and balsam poplar. However, the extent of change differed regionally among species. Forest composition is expected to change over the long term. Simulation results indicated that shade-intolerant deciduous and conifer species will increase their dominance over the 100-year time horizon. This transition toward the increasing presence of deciduous forests is likely explained by more favorable temperature conditions for their growth and development. [ABSTRACT FROM AUTHOR]

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099. [ABSTRACT FROM AUTHOR]

The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the first study to compare the performance of several laser scanning systems for the direct measurement of stem curve in difficult forest conditions. To automatically detect tree stems and to calculate their attributes, we utilized our previously developed algorithm integrated with a novel bias compensation method to reduce the overestimation of stem diameter arising from finite laser beam divergence. The bias compensation method reduced the absolute value of the diameter bias by 55–99%. The most accurate laser scanning systems were equipped with a Velodyne VLP-16 sensor, which has a relatively low beam divergence, on a handheld or UAV platform. In easy plots, these systems found a root-mean-square error (RMSE) of below 10% for DBH and stem curve estimates and approximately 10% for stem volume. With the handheld system in difficult plots, the DBH and stem curve estimates had an RMSE under 10%, and the stem volume RMSE was below 20%. Even though bias compensation reduced the difference in bias and RMSE between laser scanners with high and low beam divergence, the RMSE remained higher for systems with a high beam divergence. The airborne laser scanner operating above the forest canopy provided tree attribute estimates close to the accuracy of the under-canopy laser scanners, but with a significantly lower completeness rate for stem detection, especially in difficult forest conditions. [ABSTRACT FROM AUTHOR]

The effect of climate change on forest dynamics is likely to increase in importance in the forthcoming decades. For this reason, it is essential to predict the extent to which changes in temperature, precipitation, and atmospheric CO2 might affect the development of forest ecosystems and successional pathways. The gap model ZELIG-CFS was used to simulate the potential long-term effects of climate change on species-specific annual change in mean basal area and stand density under two scenarios of representative concentration pathways (RCP), 4.5 and 8.5, for the boreal forest region of Ontario, Canada, where mean temperature, precipitation, and atmospheric CO2 are expected to increase. Forest ecosystems in this boreal region included pure and mixed stands of black spruce (Picea mariana [Mill.] B.S.P.), paper birch (Betula papyrifera Marsh.), balsam fir (Abies balsamea [L.] Mill.), jack pine (Pinus banksiana Lamb.), trembling aspen (Populus tremuloides Michx.), white spruce (Picea glauca [Moench] Voss), northern white cedar (Thuja occidentalis L.), American larch (Larix laricina [Du Roi] K. Koch), and balsam poplar (Populus balsamifera L.). Simulation results under climate change generally predicted a decline in the basal area and stand density for black spruce, balsam fir, jack pine, and white spruce, but an increase for paper birch, trembling aspen, American larch, and balsam poplar. However, the extent of change differed regionally among species. Forest composition is expected to change over the long term. Simulation results indicated that shade-intolerant deciduous and conifer species will increase their dominance over the 100-year time horizon. This transition toward the increasing presence of deciduous forests is likely explained by more favorable temperature conditions for their growth and development.

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099.

Alaska's boreal forests are experiencing rapid changes in climate that may favor deciduous-dominated systems, with important implications for global biogeochemical and energy cycles. However, aspen (Populus tremuloides Michx.) has experienced substantial defoliation from the aspen leaf miner (Phyllocnistis populiella Cham., hereafter ALM) in Alaska, resulting in significant growth reductions. We conducted a tree-ring and Δ13C study to test the hypothesis that moisture limitation may have predisposed aspen to leaf miner damage. Contrary to our hypothesis, differences in climate-growth correlations between relatively severely and lightly affected trees were negligible during the pre-outbreak decades. Stands with greater summer precipitation had more limited ALM impact, however differences among models were small and multiple climate variables were suitable predictors of ALM impact. The strong negative relationship we detected between tree-ring Δ13C and basal area increment (BAI) suggested that interannual variation in Δ13C was driven primarily by variation in photosynthesis, limiting the utility of Δ13C as a tool to detect stomatal responses to moisture-limitation. Instead, we found that larger, faster-growing individuals on gentler slopes showed a stronger absolute reduction in BAI (pre-ALM BAIffpost-ALM BAI), but were similar in relative BAI reduction (pre-ALM BAI/post-ALM BAI), with smaller, slower growing trees. Older trees and stands with greater relative abundance of white spruce [Picea glauca (Moench) Voss] had greater relative ALM impact whereas slower growing trees on steeper slopes were less affected. The significant effect of white spruce abundance on ALM impact was likely due to favorable leaf miner overwintering habitat provided beneath white spruce trees, which can lead to increased leaf miner survival and thus greater reductions in aspen growth. Our results illustrate the subtle but complex biotic interaction between microclimate and pest physiology in determining ALM-induced aspen growth reductions, adding important nuance to a hypothesized increase in deciduous tree cover in Alaska's boreal forest. [ABSTRACT FROM AUTHOR]

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s. [ABSTRACT FROM AUTHOR]

Only a few palaeo-records extend beyond the Holocene in Yakutia, eastern Siberia, since most of the lakes in the region are of Holocene thermokarst origin. Thus, we have a poor understanding of the long-term interactions between terrestrial and aquatic ecosystems and their response to climate change. The Lake Khamra region in southwestern Yakutia is of particular interest because it is in the transition zones from discontinuous to sporadic permafrost and from summergreen to evergreen boreal forests. Our multiproxy study of Lake Khamra sediments reaching back to the Last Glacial Maximum 21 cal ka BP, includes analyses of organic carbon, nitrogen, XRF-derived elements, sedimentary ancient DNA amplicon sequencing of aquatic and terrestrial plants and diatoms, as well as classical counting of pollen and non-pollen palynomorphs (NPP). The palaeogenetic approach revealed 45 diatom, 191 terrestrial plant, and 65 aquatic macrophyte taxa. Pollen analyses identified 34 pollen taxa and 28 NPP taxa. The inferred terrestrial ecosystem of the Last Glacial comprises tundra vegetation dominated by forbs and grasses, likely inhabited by megaherbivores. By 18.4 cal ka BP a lake had developed with a high abundance of macrophytes and dominant fragilarioid diatoms, while shrubs expanded around the lake. In the Bolling-Allerod at 14.7 cal ka BP both the terrestrial and aquatic systems reflect climate amelioration, alongside lake water-level rise and woodland establishment, which was curbed by the Younger Dryas cooling. In the Early Holocene warmer and wetter climate led to taiga development and lake waterlevel rise, reflected by diatom composition turnover from only epiphytic to planktonic diatoms. In the Mid-Holocene the lake water level decreased at ca. 8.2 cal ka BP and increased again at ca. 6.5 cal ka BP. At the same time mixed evergreen-summergreen forest expanded. In the Late Holocene, at ca. 4 cal ka BP, vegetation cover similar to modern conditions established. This study reveals the long-term shifts in aquatic and terrestrial ecosystems and a comprehensive understanding of lake development and catchment history of the Lake Khamra region. [ABSTRACT FROM AUTHOR]

In the context of global warming, the study of the long-term spatial change characteristics of boreal forest cover is not only important for global climate change and sustainable development research but can also provide support for further research on the response of boreal forest changes to climate change. Using Landsat TM/OLI images from 1980 to 2020 as the data source and Google Earth Engine (GEE) as the platform, Finland was selected as the study area of boreal forests, and typical sample points of different features were chosen to classify forested and non-forested land using the random forest algorithm combined with spectral indices and classified feature sets of tasseled cap transform to obtain the four-phase forest cover change maps of the region. GEE test sample points and random selection points of images from the GF-2 and GF-7 satellites were used for verification. The classification accuracy was 97.17% and 88.9%. The five-phase forest cover images were segmented by a 2° latitude zone, and the spatial and temporal dynamic changes in forest cover in the whole area and each latitude zone were quantified by pixel superposition analysis. The results showed that, in the past 40 years, the boreal forest cover in Finland changed significantly, and the forest cover decreased from 75.79% to 65.36%, by 10.43%. Forest change mainly occurs in coniferous forests, whereas broadleaf forests are more stable. The forest coverage in each latitude zone decreased to varying degrees, with higher changes occurring in high-latitude areas above 64° N between 1980 and 2000, and higher and more severe changes occurring in low-latitude areas below 64° N between 2000 and 2020. Coniferous forests are the dominant type of forest in Finland, and the degradation of coniferous forests in the south is likely to become more severe, whereas the north and above is likely to become more favorable for coniferous forests. More monitoring and research are needed to follow up on the very different changes in the north and south regions. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is increasing the occurrence of wildfires, especially in northern high latitudes, leading to a shift in land surface climate. This study aims to determine the predominant climatic effects of fires in boreal forests to assess their impact on vegetation composition, surface albedo, and snow dynamics. The influence of fire-induced changes on Earth's radiative forcing is investigated, while considering variations in burn severity and postfire vegetation structure. Six burn sites are explored in central Alaska's boreal region, alongside six control sites, by utilizing Moderate Resolution Imaging Spectroradiometer (MODIS)-derived albedo, Leaf Area Index (LAI), snowmelt timing data, AmeriFlux radiation, National Land Cover Database (NLCD) land cover, and Monitoring Trends in Burn Severity (MTBS) data. Key findings reveal significant postfire shifts in land cover at each site, mainly from high- to low-stature vegetation. A continuous increase in postfire surface albedo and negative surface shortwave forcing was noted even after 12 years postfire, particularly during the spring and at high-severity burn areas. Results indicate that the cooling effect from increased albedo during the snow season may surpass the warming effects of earlier snowmelt. The overall climate impact of fires depends on burn severity and vegetation composition. [ABSTRACT FROM AUTHOR]

Boreal forests nowadays act as a sink for atmospheric carbon dioxide; however, their sequestration capacity is highly sensitive to weather conditions and, specifically to ongoing climate warming. Extreme weather events such as heavy rainfalls or, conversely, heat waves during the growing season might perturb the ecosystem carbon balance and convert them to an additional CO2 source. Thus, there is an urgent need to revise ecosystem carbon fluxes in vast Siberian taiga ecosystems as influenced by extreme weather events. In this study, we focused on the soil CO2 pulses appearing after the rainfall events and quantification of their input to the seasonal cumulative CO2 efflux in the boreal forests in Central Siberia. Seasonal measurements of soil CO2 fluxes (both soil respiration and net soil exchange) were conducted during three consecutive frost-free seasons using the dynamic chamber method. Seasonal dynamics of net soil exchange fluxes demonstrated positive values, reflecting that soil respiration rates exceeded CO2 uptake in the forest floor vegetation layer. Moreover, the heavy rains caused a rapid pulse of soil emissions and, as a consequence, the release of additional amounts of CO2 from the soil into the atmosphere. A single rain event may cause a 5–11-fold increase of the NSE flux compared to the pre-rainfall values. The input of CO2 pulses to the seasonal cumulative efflux varied from near zero to 39% depending on precipitation patterns of a particular season. These findings emphasize the critical need for more frequent measurements of soil CO2 fluxes throughout the growing season which capture the CO2 pulses induced by rain events. This approach has inevitable importance for the accurate assessment of seasonal CO2 soil emissions and adequate predictions of response of boreal pine forests to climatic changes. [ABSTRACT FROM AUTHOR]

Roads are known to alter environmental conditions and the composition of road edge plant communities, particularly when exogenous materials are used as road surfacing. In this study, we evaluate the impact of gravel roads on the organic layer thickness (OLT) and aspen distribution in a boreal forest landscape of Eastern Canada. The OLT and aspen distribution were compared at different distances from the roads (0 m, 10 m, and >10 m) to determine whether a reduction in the OLT along the roads could explain the distribution of aspen along the road network, and in particular the role of the roads as habitat corridors. In addition, germination tests were carried out to determine whether mineral soil from the roads could promote aspen establishment, by comparing the germination rate of substrates consisting only of mineral soil or mosses, and substrates consisting of mosses covered with 0.5 cm or 2 cm of mineral soil. The presence of aspen in the study landscape is limited by thick organic deposits (≥50 cm). However, the thickness of these deposits is reduced to approximately 10 cm at the edges of gravel roads, in part by the transport of mineral soil from the roads. This reduction in the OLT facilitates the establishment of aspen and helps explain its distribution along the road network. [ABSTRACT FROM AUTHOR]

Boreal forests are one of the most important biomes storing carbon (C). Wildfires burn yearly on average more than 1% of the boreal forest, and it is expected that the fire return intervals will shorten due to climate change. Fire is one of the most influential factors affecting soil organic matter quantity and quality, soil C pools, and presumably also the time C resides in the soil (soil C turnover time in years). We compared the potential effects of forest fire through post-fire succession on soil carbon dioxide (CO2) emission rates and soil C turnover time in two fire chronosequences, one with underlying permafrost soil and the other without permafrost. We found that fire had a significant effect on potential soil C turnover times, but surprisingly there was no significant difference in soil C turnover times between the permafrost and non-permafrost areas, although the soil CO2 emissions rates in permafrost areas are approximately three times higher compared to non-permafrost areas. In recently burned areas the potential soil C turnover times were two times longer compared to control areas located in forests burned more than 100 years ago. The longest potential soil C turnover times were recorded in mineral soil layers (30 cm) of permafrost soils, and the shortest potential soil C turnover times were recorded in humus layers of non-permafrost areas. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine whether the iPad Pro 12th generation LiDAR sensor is useful to measure tree diameter at breast height (DBH) in natural boreal forests. This is a follow-up to a previous study that was conducted in a research forest and identified the optimal method for (DBH) estimation as a circular scanning and fitting ellipses to 4 cm stem cross-sections at breast height. The iPad Pro LiDAR scanner was used to acquire point clouds for 15 sites representing a range of natural boreal forest conditions in Ontario, Canada, and estimate DBH. The secondary objective was to determine if tested stand (species composition, age, density, understory) or tree (species, DBH) factors affected the accuracy of estimated DBH. Overall, estimated DBH values were within 1 cm of actual DBH values for 78 of 133 measured trees (59%). An RMSE of 1.5 cm (8.6%) was achieved. Stand age had a large effect (>0.15) on the accuracy of estimated DBH values, while density, understory, and DBH had moderate effects (0.05–0.14). No trend was identified between accuracy and stand age. Accuracy improved as understory density decreased and as tree DBH increased. Inertial measurement unit (IMU) and positional accuracy errors with the iPad Pro scanner limit the feasibility of using this device for forest inventories. [ABSTRACT FROM AUTHOR]

Data on the main properties of Histic Podzols in the pine forests of semi-hydromorphic landscapes in the middle taiga of the Komi Republic after forest fires are presented. A decrease in topsoil horizon thickness by more than 7.6 times, an increase in litter density by 6 times, and a decrease in litter stock by 4 times were observed in postfire soil. There was an increase in carbon content in the pyrogenic horizon (48%) and in the upper part of the podzolic horizon—from 0.49 at the control plot to 1.16% after the fire. The accumulation of all studied trace metals (Cu—from 2.5 to 6.8 mg × kg−1; Zn—from 35.7 to 127.4 mg × kg−1; Ni—from 2.2 to 8.1 mg × kg−1; Pb—from 1.4 to 28.3 mg × kg−1; Cd—from 0.3 to 1.1 mg × kg−1) in soils after wildfires was recorded. The effect of the fire can be traced to a depth of approximately 20–30 cm. A significant influence of the pyrogenic factor on the alpha and beta bacterial diversity was noted. The bacterial response to a forest fire can be divided into an increased proportion of spore-forming and Gram-negative species with complex metabolism as well. [ABSTRACT FROM AUTHOR]

Soil shear strength (SSS) is an important soil attribute that is influenced by vegetation. If aboveground biomass estimates can be used to predict soil shear strength, it would greatly enhance our ability to estimate SSS across large areas. Using data collected from 24 plots in Alaska, we analyzed the relationship between soil shear strength and ground-collected vegetation attributes and remotely sensed (RS) variables. We constructed both univariate and multivariate models to assess the predictive capabilities of the vegetation and RS variables. Total trees and total conifers were significant predictors of SSS, with a negative relationship existing between total trees/total conifers and SSS. Graminoid cover (%) was positively correlated with soil shear strength and was also a significant predictor of SSS. Of the RS variables, the bands B1 (0.443 µm), B2 (0.490 µm), and B3 (0.560 µm) from the Sentinel 2 satellite system were all significant predictors of SSS. A multivariate model improved model fit over the simple univariate models, with an R² = 0.46. We have both demonstrated a connection between SSS and aboveground vegetation attributes for areas within interior Alaska and that it is possible to link SSS to RS variables using a multivariate model. [ABSTRACT FROM AUTHOR]

The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the first study to compare the performance of several laser scanning systems for the direct measurement of stem curve in difficult forest conditions. To automatically detect tree stems and to calculate their attributes, we utilized our previously developed algorithm integrated with a novel bias compensation method to reduce the overestimation of stem diameter arising from finite laser beam divergence. The bias compensation method reduced the absolute value of the diameter bias by 55–99%. The most accurate laser scanning systems were equipped with a Velodyne VLP-16 sensor, which has a relatively low beam divergence, on a handheld or UAV platform. In easy plots, these systems found a root-mean-square error (RMSE) of below 10% for DBH and stem curve estimates and approximately 10% for stem volume. With the handheld system in difficult plots, the DBH and stem curve estimates had an RMSE under 10%, and the stem volume RMSE was below 20%. Even though bias compensation reduced the difference in bias and RMSE between laser scanners with high and low beam divergence, the RMSE remained higher for systems with a high beam divergence. The airborne laser scanner operating above the forest canopy provided tree attribute estimates close to the accuracy of the under-canopy laser scanners, but with a significantly lower completeness rate for stem detection, especially in difficult forest conditions.