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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.

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.

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.

The paper is aimed at assessing the associations between the road networks geography and dynamics of wildfire events in the East Siberian boreal forest. We examined the relationship between the function of roads, their use, and management and the wildfire ignition, propagation, and termination during the catastrophic fire season of 2016 in the Irkutsk Region of Russia. Document analysis and interviews were utilized to identify main forest users and road infrastructure functional types and examine wildfire management practices. We combined community observations and satellite remotely sensed data to assess relationships between the location, extent, and timing of wildfires and different types of roads as fire sources, barriers, and suppression access points. Our study confirms a strong spatial relationship between the wildfire ignition points and roads differentiated by their types with the highest probability of fire ignition near forestry roads and the lowest near subsistence roads. Roads also play an important role in wildfire suppression, working as both physical barriers and access points for firefighters. Our research illustrates the importance of local and Indigenous observations along the roads for monitoring and understanding wildfires, including “zombie fires”. It also has practical implications for fire management collectively developed by authorities and local communities.

Abstract Herbivores shape vegetative communities via numerous mechanisms, including browse. We used vegetation monitoring data from Isle Royale National Park to examine woody species change across a nine‐year interval, coinciding with herbivore escalation. Here, moose and snowshoe hare are the dominant herbivores, while the gray wolf is the apex predator. Our initial sampling period (2010) followed six years of low moose abundance, while our second sampling event (2019) followed a nine‐year escalation in moose density. We tested for change in both saplings and shrubs and compared diameter size distributions of common tree species in three island sections. We found a decline in large saplings of sugar maple, a species limited to the west section of the island. We also saw declines in small saplings of sugar maple, paper birch, and trembling aspen. For some species, including black spruce and white spruce, taxa that are unpalatable to moose, diameter distributions were proportionally larger (i.e., indicative of fewer small individuals) in 2019 than in 2010. In contrast, diameter distributions of black ash in the central section and trembling aspen in the central and east sections of the island were proportionally smaller (indicative of more small individuals) in 2019 than in 2010. We found that ~50% of common shrub taxa were more abundant during the second sampling event, while none declined. Our work here brings to light several unanticipated results. While we demonstrated partial recovery of trembling aspen, likely originating during the earlier period of low moose abundance, our results suggest further changes may be attributable to other island herbivores. The response of black ash, a hydric species, may be stemming from a sixfold increase in active beaver sites, while that of spruce may be attributable to a historic spike in snowshoe hare density. We currently have only a limited understanding of the impacts of the island's lesser studied herbivores on overstory regeneration. Further research on these linkages can inform decisions addressing controls on forest structure.

Abstract Ecosystems across the United States are changing in complex and unpredictable ways and analysis of these changes requires coordinated, long‐term research. This paper is a product of a synthesis effort of the U.S. National Science Foundation funded Long‐Term Ecological Research (LTER) network addressing the LTER core research area of “populations and communities.” This analysis revealed that each LTER site had at least one compelling “story” about what their site would look like in 50–100 yr. As the stories were prepared, themes emerged, and the stories were group into papers along five themes: state change, connectivity, resilience, time lags, and cascading effects. This paper addresses the cascading effects theme and includes stories from the Bonanza Creek (boreal), Kellogg Biological Station (agricultural and freshwater), Palmer (Antarctica), and Harvard Forest (temperate forest) LTER sites. We define cascading effects very broadly to include a wide array of unforeseen chains of events that result from a variety of actions or changes in a system. While climate change is having important direct effects on boreal forests, indirect effects mediated by fire activity—severity, size, and return interval—have large cascading effects over the long term. In northeastern temperate forests, legacies of human management and disturbance affect the composition of current forests, which creates a cascade of effects that interact with the climate‐facilitated invasion of an exotic pest. In Antarctica, declining sea ice creates a cascade of effects including declines in Adèlie and increases in Gentoo penguins, changes in phytoplankton, and consequent changes in zooplankton populations. An invasion of an exotic species of lady beetle is likely to have important future effects on pest control and conservation of native species in agricultural landscapes. New studies of zebra mussels, a well‐studied invader, have established links between climate, the heat tolerance of the mussels, and harmful algal blooms. Collectively, these stories highlight the need for long‐term studies to sort out the complexities of different types of ecological cascades. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about trophic interactions as an important driver of ecosystem structure, function, services, and futures.

Abstract Background Protected areas (PA) are central to biodiversity, but their efficiency is challenged by human-induced habitat loss and fragmentation. In the Fennoscandian boreal region, forestry with clearcutting is a threat to biodiversity causing the loss of mature forest elements and deterioration of ecological processes in forest landscapes, ultimately affecting PAs via declined structural connectivity. This paper aims to (1) determine PAs with high, red-listed species concentrations; (2) estimate the change in forest habitat around these PAs on different spatial scales; and (3) determine if forest management intensity is higher around biologically most valuable PAs. Occurrences of red-listed forest-dwelling species in Finland were used to identify PAs harbouring these species and to produce site-specific importance indices. CORINE landcover data was used as a baseline for the distribution of forests to assess the cover of clear-cuttings from 2001 to 2019 with the Global Forest Change (GFC) data set in three buffer areas around the PAs with occurrences of red-listed species. Results The largest proportion of clear-cuts occurred in 1 km and 10 km buffers around the PAs in the southern and middle boreal zones, being ca. 20%. This indicates that the forest habitat is degrading fast at regional and landscape levels. On the positive side, the change in forest cover was lower around the biologically most important PAs compared to other PAs with red-listed species. Conclusions Open and free satellite-data based assessments of the cover and change of forests provide reliable estimates about the rates at which mature and old-growth forests are being converted into young managed ones in Finland mainly via clear-cuts on different scales around PAs. The rate of clear-cuts was lowest in adjacent buffer areas next to the most species-rich PAs, which provides opportunities for biodiversity conservation efforts to be targeted to the remaining mature and old-growth forests found in the vicinity of these areas.

Northern boreal forests are characterized by accumulation of accumulation of peat (e.g., known as paludification). The functioning of northern boreal forest species and their capacity to adapt to environmental changes appear to depend on soil conditions. Climate warming is expected to have particularly pronounced effects on paludified boreal ecosystems and can alter current forest species composition and adaptation by changing soil conditions such as moisture, temperature regimes, and soil respiration. In this paper, we review and synthesize results from various reported studies (i.e., 88 research articles cited hereafter) to assess the effects of climatic warming on soil conditions of paludified forests in North America. Predictions that global warming may increase the decomposition rate must be considered in combination with its impact on soil moisture, which appears to be a limiting factor. Local adaptation or acclimation to current climatic conditions is occurring in boreal forests, which is likely to be important for continued ecosystem stability in the context of climate change. The most commonly cited response of boreal forest species to global warming is a northward migration that tracks the climate and soil conditions (e.g., temperature and moisture) to which they are adapted. Yet, some constraints may influence this kind of adaptation, such as water availability, changes in fire regimes, decomposer adaptations, and the dynamic of peat accumulation. In this paper, as a study case, we examined an example of potential effects of climatic warming on future paludification changes in the eastern lowland region of Canada through three different combined hypothetical scenarios based on temperature and precipitation (e.g., unchanged, increase, or decrease). An increase scenario in precipitation will likely favor peat accumulation in boreal forest stands prone to paludification and facilitate forested peatland expansion into upland forest, while decreased or unchanged precipitation combined with an increase in temperature will probably favor succession of forested peatlands to upland boreal forests. Each of the three scenarios were discussed in this study, and consequent silvicultural treatment options were suggested for each scenario to cope with anticipated soil and species changes in the boreal forests. We concluded that, despite the fact boreal soils will not constrain adaptation of boreal forests, some consequences of climatic warming may reduce the ability of certain species to respond to natural disturbances such as pest and disease outbreaks, and extreme weather events.

This paper presents the development and application of a deep learning-based approach for semi-automated detection of tar production kilns using new Finnish high-density Airborne Laser Scanning (ALS) data in the boreal taiga forest zone. The historical significance of tar production, an important livelihood for centuries, has had extensive environmental and ecological impacts, particularly in the thinly inhabited northern and eastern parts of Finland. Despite being one of the most widespread archaeological features in the country, tar kilns have received relatively little attention until recently. The authors employed a Convolutional Neural Networks (CNN) U-Net-based algorithm to detect these features from the ALS data, which proved to be more accurate, faster, and capable of covering systematically larger spatial areas than human actors. It also produces more consistent, replicable, and ethically sustainable results. This semi-automated approach enabled the efficient location of a vast number of previously unknown archaeological features, significantly increasing the number of tar kilns in each study area compared to the previous situation. This has implications also for the cultural resource management in Finland. The authors’ findings have influenced the preparation of the renewal of the Finnish Antiquities Act, raising concerns about the perceived impacts on cultural heritage management and land use sectors due to the projected tenfold increase in archaeological site detection using deep learning algorithms. The use of environmental remote sensing data may provide a means of examining the long-term cultural and ecological impacts of tar production in greater detail. Our pilot studies suggest that artificial intelligence and deep learning techniques have the potential to revolutionize archaeological research and cultural resource management in Finland, offering promising avenues for future exploration.

Fires are one of the most widespread factors of changes in the ecosystems of boreal forests. The paper presents the results of a study of the morphological and physicochemical properties and soil organic matter (SOM) of Albic Podzols under pine forests (Pinus sylvestris L.) of the middle taiga zone of Siberia (Krasnoyrsky kray) with various time passed after a surface fire (from 1 to 121 years ago). The influence of forest fires in the early years on the chemical properties of Albic Podzols includes a decrease in acidity, a decrease in the content of water-soluble compounds of carbon and nitrogen and an increase in the content of light polycyclic aromatic hydrocarbons (PAHs) in organic and upper mineral horizons. Podzols of pine forests that were affected by fires more than forty-five years ago are close to manure forest soils according to most physical and chemical properties. Significant correlations were found between the thickness (r = 0.75, p < 0.05), the moisture content (r = 0.90, p < 0.05) of organic horizons and the content of ∑PAHs in the organic horizon (r = −0.71, p < 0.05) with the time elapsed after the fire (i.e., from 1 to 121 years). The index of the age of pyrogenic activity (IPA) calculated as the ratio of ∑ PAHs content in the organic horizon to ∑ PAHs at the upper mineral horizon is significantly higher in forests affected by fires from 1 to 23 years than for plots with «older» fires (45–121 years). Thus, the article presents the conserved and most changing factors under the impact of fires in the boreal forests of Russia.

Fires are a naturally cyclical factor regulating ecosystems’ function and forming new postfire ecosystems. Peat soils are unique archives that store information about ecological and climatic changes and the history of past fires during the Holocene. The paper presents a reconstruction of the dynamics of fires in the subzone of the middle taiga of Western Siberia in the Holocene. Data on fires were obtained based on the results of a study of the content of macroscopic coal particles and radiocarbon dating. The effect of fires on soil organic matter (SOM) was estimated using 13C NMR spectroscopy and the content of polyaromatic hydrocarbons (PAHs). It is shown that throughout the Holocene, the peatlands studied were prone to fires. The conducted analyses show that the maximum content of charcoal particles is observed in the Atlantic (~9100–5800 cal. B.P.) and Subatlantic (~3100 cal. B.P. to the present) periods. The high correlation dependence of the content of coals with the content of PAHs (r = 0.56, p < 0.05) and aromatic structures of SOM (r = 0.61, p < 0.05) in peat horizons is shown, which can characterize these parameters as a reliable marker of pyrogenesis.

Wildfires in permafrost areas, including smoldering fires (e.g., “zombie fires”), have increasingly become a concern in the Arctic and subarctic. Their detection is difficult and requires ground truthing. Local and Indigenous knowledge are becoming useful sources of information that could guide future research and wildfire management. This paper focuses on permafrost peatland fires in the Siberian subarctic taiga linked to local communities and their infrastructure. It presents the results of field studies in Evenki and old-settler communities of Tokma and Khanda in the Irkutsk region of Russia in conjunction with concurrent remote sensing data analysis. The study areas located in the discontinuous permafrost zone allow examination of the dynamics of wildfires in permafrost peatlands and adjacent forested areas. Interviews revealed an unusual prevalence and witness-observed characteristics of smoldering peatland fires over permafrost, such as longer than expected fire risk periods, impacts on community infrastructure, changes in migration of wild animals, and an increasing number of smoldering wildfires including overwintering “zombie fires” in the last five years. The analysis of concurrent satellite remote sensing data confirmed observations from communities, but demonstrated a limited capacity of satellite imagery to accurately capture changing wildfire activity in permafrost peatlands, which may have significant implications for global climate.

Remote sensing is a powerful tool for understanding and scaling measurements of plant carbon uptake via photosynthesis, gross primary productivity (GPP), across space and time. The success of remote sensing measurements can be attributed to their ability to capture valuable information on plant structure (physical) and function (physiological), both of which impact GPP. However, no single remote sensing measure provides a universal constraint on GPP and the relationships between remote sensing measurements and GPP are often site specific, thereby limiting broader usefulness and neglecting important nuances in these signals. Improvements must be made in how we connect remotely sensed measurements to GPP, particularly in boreal ecosystems which have been traditionally challenging to study with remote sensing. In this paper we improve GPP prediction by using random forest models as a quantitative framework that incorporates physical and physiological information provided by solar-induced fluorescence (SIF) and vegetation indices (VIs). We analyze 2.5 years of tower-based remote sensing data (SIF and VIs) across two field locations at the northern and southern ends of the North American boreal forest. We find (a) remotely sensed products contain information relevant for understanding GPP dynamics, (b) random forest models capture quantitative SIF, GPP, and light availability relationships, and (c) combining SIF and VIs in a random forest model outperforms traditional parameterizations of GPP based on SIF alone. Our new method for predicting GPP based on SIF and VIs improves our ability to quantify terrestrial carbon exchange in boreal ecosystems and has the potential for applications in other biomes.

Wildfire in Siberia is extensive, affecting up to 15 Mha annually. The proportion of the vegetation affected by severe fires is yet unknown, and it is a problem that requires a solution because post-fire mortality of tree stands in Siberian taiga has a strong effect on the global budget of carbon. The impact of fire in our area of interest in eastern Siberia was analyzed using the normalized burn ratio (NBR) and its pre- versus post-fire difference (dNBR) applied to Landsat-8 (OLI) collected in 2020–2021. In this paper, we present the classification of fire impact in relation to dominant tree stands and vegetation types in boreal forests of eastern Siberia. The dNBR of post-fire plots ranged widely (0.30–0.60) in homogeneous larch (Larix sibirica, L. gmelinii) forests, pine (Pinus sylvestris) forests, dark coniferous stands (Pinus sibirica, Abies sibirica, Picea obovata), sparse larch stands, and Siberian dwarf pine (Pinus pumila) stands. We quantified the proportions of low, moderate, and high fire severity (37%, 39%, and 24% of the total area burned, respectively) in dense tree stands, which were varied to 30%, 57%, and 13%, respectively, for sparse stands and tundra vegetation dominated in the north of eastern Siberia. The proportion of severe fires varied according to the transition from dominant larch stands (33.2% of the area burned) to pine (12.6%) and dark coniferous (up to 26.4%). The current proportion of stand-replacement fires in eastern Siberia is 12–33%, depending on vegetation type and tree density, which is about 2500 thousand hectares in 2021 in the region. According to our findings, the “healthy/unburned vegetation” class was quantified as well at least 700 thousand hectares in 2021.

Environmental conditions and seasonal variations affect the backscattered radar signal from a forest. This potentially causes errors in a biomass retrieval scheme using data from the synthetic aperture radar (SAR) data. A better understanding of these effects and the electromagnetic scattering mechanisms in forests is required to improve biomass estimation algorithms for current and upcoming P- and L-band SAR missions. In this paper, temporal changes in HH-, VV-, and HV-polarized P- and L-band radar backscatter and temporal coherence from a boreal forest site are analyzed in relation to environmental parameters. The radar data were collected from a stand of mature Norway spruce ( Picea abies (L.) Karst.) with an above-ground biomass of approximately 250 tons/ha at intervals of 5 min from January to August 2017 using the BorealScat tower-based scatterometer. It was observed that subzero temperatures during the winters cause large variations (4 to 10 dB) in P- and L-band backscatter, for which the HH/VV backscatter ratio offered some mitigation. High wind speeds were also seen to cause deviations in the average backscatter at P-band due to decreased double-bounce scattering. Severe temporal decorrelation was observed at L-band over timescales of days or more, whereas the P-band temporal coherence remained high (>0.9) for at least a month neglecting windy periods. Temporal coherence at P-band was highest during night times when wind speeds are low.

In the face of dramatic climate change and human pressure acting on remaining forest areas across tropical, temperate and boreal biomes, there has emerged a coordinated effort to identify and protect forests that are currently considered “intact”. These forests are hypothesized to be more resilient to future abiotic perturbations than fragmented or degraded forests, and therefore, will provide more reliable carbon storage and/or biodiversity services into an uncertain future. Research in the fields of contemporary and paleoecology can offer valuable insights to enhance our ability to assess resilience of forests and whether these would be comparable across forest biomes. Contemporary ecological monitoring has been able to capture processes acting over the short-to-medium term, while paleoecological methods allow us to derive insights of the long-term processes affecting forest dynamics. Recent efforts to both identify intact forests, based on area definitions, and assess vegetation climate sensitivity globally have relied on satellite imagery analysis for the time period 2000–2013. In this paper, we compare these published datasets and do find that on average intact forests in boreal and tropical biomes are less sensitive to temperature and water availability, respectively; however, the patterns are less clear within biomes (e.g., across continents). By taking a longer perspective, through paleoecology, we present several studies that show a range of forest responses to past climatic and human disturbance, suggesting that short-term trends may not be reliable predictors of long-term resilience. We highlight that few contemporary and paleoecology studies have considered forest area when assessing resilience and those that have did find that smaller forest areas exhibited greater dynamism in species composition, which could be a proxy for declining resilience. Climatic conditions in the Anthropocene will be pushing forest systems across biomes into novel climates very rapidly and with current knowledge it is difficult to predict how forests will react in the immediate term, which is the most relevant timeframe for global efforts to reduce carbon emissions.

In this review and synthesis paper, we review the resilience of secondary forests to climate change through the lenses of ecosystem legacies and landscape diversity. Ecosystem legacy of secondary forests was categorized as continuous forest, non-continuous forest, reassembled after conversion to other land uses, and novel reassembled forests of non-native species. Landscape diversity, including landforms that create varied local climatic and soil conditions, can buffer changing climate to some extent by allowing species from warmer climates to exist on warm microsites, while also providing refugial locations for species that grow in cool climates. We present five frames that allow forest managers to visualize a trajectory of change in the context of projected regional climate change, which are: Frame 1 (persistence), keep the same dominant tree species with little change; Frame 2 (moderate change), keep the same tree species with large changes in relative abundance; Frame 3 (forest biome change), major turnover in dominant tree species to a different forest biome; Frame 4 (forest loss), change from a forest to a non-forest biome; and Frame 5 (planted novel ecosystem), establish a novel ecosystem to maintain forest. These frames interact with ecosystem legacies and landscape diversity to determine levels of ecosystem resilience in a changing climate. Although forest readiness to adapt to Frame 1 and 2 scenarios, which would occur with reduced greenhouse gas emissions, is high, a business as usual climate change scenario would likely overwhelm the capacity of ecosystem legacies to buffer forest response, so that many forests would change to warmer forest biomes or non-forested biomes. Furthermore, the interactions among frames, legacies, and landscape diversity influence the transient dynamics of forest change; only Frame 1 leads to stable endpoints, while the other frames would have transient dynamics of change for the remainder of the 21st century.

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.

This paper presents an original approach to characterizing historical fire regimes for regions with limited fire data. Fire variables were derived from satellite datasets and regional fire occurrence statistics. They defined the integral elements of a fire regime such as historical trends, spatiotemporal evolution, fire seasonality and causes. Temporal evolution was investigated based on a regime shift detection method developed by Rodionov while changes in the fire regime were analyzed for statistical significance using the Mann–Kendall trend test and Sen’s slope estimator. A descriptive analysis was performed to assess fire seasonality, causes, and together formed the basis for this methodology. We validated the proposed approach by assessing historical fire activity in the Sakha Republic (Yakutia), which is one of the most fire-prone regions of Russia. The assessment was conducted with data from the period of 1996–2018. We detected increases in historical fire activity as well as thresholds of change in the fire regime. Changes during the analysis period included lengthening of fire season, increased burned area extent, and extension of peak fire period. Overall, significant changes in the fire regime were detected in the regions strongly affected by warming and increasing anthropogenic alteration.

Stand age distribution is an important descriptor of boreal forest structure, which is directly linked to many ecosystem processes including the carbon cycle, the land–atmosphere interaction and ecosystem services, among others. Almost half of the global boreal biome is located in Russia. The vast extent, remote location, and limited accessibility of Russian boreal forests make remote sensing the only feasible approach to characterize these forests to their full extent. A wide variety of satellite observations are currently available to monitor forest change and infer its structure; however, the period of observations is mostly limited to the 2000s era. Reconstruction of wall-to-wall maps of stand age distribution requires merging longer-term site observations of forest cover change available at the Landsat scale at a subset of locations in Russia with the wall-to-wall coverage available from coarse resolution satellites since 2000. This paper presents a dataset consisting of a suite of multi-year forest disturbance samples and samples of undisturbed forests across Russia derived from Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images from 1985 to 2000. These samples provide crucial information regarding disturbance history in selected regions across the Russian boreal forest and are designed to serve as a training and/or validation dataset for coarse resolution data products. The overall accuracy and Kappa coefficient for the entire sample collection was found to be 83.98% and 0.83%, respectively. It is hoped that the presented dataset will benefit subsequent studies on a variety of aspects of the Russian boreal forest, especially in relation to the carbon budget and climate.

Numerous studies demonstrated the potential of the magnitude of interferometric coherence |γ| for forest growing stock volume (GSV) estimation in boreal forests. Coherence derived from images acquired under frozen conditions proved to be of specific interest. This also applies to PALSAR coherence, although affected by a comparatively large temporal baseline of at least 46 days. However, when working with spaceborne L-band data, acquired under unfrozen conditions, a large spread of |γ| was observed at all GSV levels. This scatter negatively affects the correlation of GSV and |γ|. So far, the impact of tree species on |γ| has rarely been studied in this context, although the different tree geometries are likely to have an impact on volumetric decorrelation. This paper presents the results of a study investigating the impact of tree species on PALSAR coherence employing 36 interferograms. The observations show only a small impact of the tree species on |γ| during frozen conditions. At unfrozen conditions, the impact is about three times larger. Deciduous species (aspen, birch, larch) exhibit the lowest |γ|, while coniferous species (fir, pine) feature the highest |γ|. For example, at unfrozen conditions, the |γ| of fir is 0.15 greater than the |γ| of larch, while the mean |γ| of dense forest is 0.38. Accordingly, the impact of tree species on |γ| under unfrozen conditions causes a portion of the observed spread of the GSV-|γ| relationship. Consequently, when aiming at |γ| based GSV assessment using L-band SAR data acquired during unfrozen conditions, the impact of the species on |γ| needs to be considered. For studies aiming at |γ| based GSV estimation across species, PALSAR data acquired at frozen conditions is preferable.

This paper presents a scaling approach for estimating sapwood area at the stand level using knowledge obtained for individual trees of five boreal species: Populus tremuloides (Michx.), Pinus contorta (Doug. ex Loud. var. latifolia Engelm.), Pinus banksiana (Lamb.), Picea mariana (Mill.) BSP, and Picea glauca (Moench) Voss. Previously developed allometric models for sapwood depth and diameter at breast height for individual tree species were used to build stand level sapwood area estimates as well as stand level leaf area estimates, in pure and mixed vascular vegetation stands. A stand’s vegetation heterogeneity is considered in the scaling approach by proposing regression models for each species. The new combined scaling approach drew strong linear correlations at the stand scale between sapwood area and leaf area using observations taken in mixed stands of Southern Alberta, Canada. This last outcome suggests a good linear relationship between stand sapwood area and stand leaf area. The accuracy of the results was tested by observing each regression model’s adequacy and by estimating the error propagated through the whole scaling process.

Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote sensors are key to analyzing those spatial and temporal patterns of fire occurrence. This paper compiles four burned area (BA) time series based on remote sensing imagery for the Alaska region between 1982−2015: Burned Areas Boundaries Dataset-Monitoring Trends in Burn Severity (BABD-MTBS) derived from Landsat sensors, Fire Climate Change Initiative (Fire_CCI) (2001−2015) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Direct Broadcast Monthly Burned Area Product (MCD64A1) (2000−2015) with MODIS data, and Burned Area-Long-Term Data Record (BA-LTDR) using Advanced Very High Resolution Radiometer LTDR (AVHRR-LTDR) dataset. All products were analyzed and compared against one another, and their accuracy was assessed through reference data obtained by the Alaskan Fire Service (AFS). The BABD-MTBS product, with the highest spatial resolution (30 m), shows the best overall estimation of BA (81%), however, for the years before 2000 (pre-MODIS era), the BA sensed by this product was only 44.3%, against the 55.5% obtained by the BA-LTDR product with a lower spatial resolution (5 km). In contrast, for the MODIS era (after 2000), BABD-MTBS virtually matches the reference data (98.5%), while the other three time series showed similar results of around 60%. Based on the theoretical limits of their corresponding Pareto boundaries, the lower resolution BA products could be improved, although those based on MODIS data are currently limited by the algorithm’s reliance on the active fire MODIS product, with a 1 km nominal spatial resolution. The large inter-annual variation found in the commission and omission errors in this study suggests that for a fair assessment of the accuracy of any BA product, all available reference data for space and time should be considered and should not be carried out by selective sampling.

This paper describes the long-term effects on vegetation following the catastrophic fire in 1987 on the northern Great Xing’an Mountain by analyzing the AVHRR GIMMS 15-day composite normalized difference vegetation index (NDVI) dataset. Both temporal and spatial characteristics were analyzed for natural regeneration and tree planting scenarios from 1984 to 2006. Regressing post-fire NDVI values on the pre-fire values helped identify the NDVI for burnt pixels in vegetation stands. Stand differences in fire damage were classified into five levels: Very High (VH), High (H), Moderate (M), Low (L) and Slight (S). Furthermore, intra-annual and inter-annual post-fire vegetation recovery trajectories were analyzed by deriving a time series of NDVI and relative regrowth index (RRI) values for the entire burned area. Finally, spatial pattern and trend analyses were conducted using the pixel-based post-fire annual stands regrowth index (SRI) with a nonparametric Mann-Kendall (MK) statistics method. The results show that October was a better period compared to other months for distinguishing the post- and pre-fire vegetation conditions using the NDVI signals in boreal forests of China because colored leaves on grasses and shrubs fall down, while the leaves on healthy trees remain green in October. The MK statistics method is robustly capable of detecting vegetation trends in a relatively long time series. Because tree planting primarily occurred in the severely burned area (approximately equal to the Medium, High and Very High fire damage areas) following the Daxing’anling fire in 1987, the severely burned area exhibited a better recovery trend than the lightly burned regions. Reasonable tree planting can substantially quicken the recovery and shorten the restoration time of the target species. More detailed satellite analyses and field data will be required in the future for a more convincing validation of the results.

Leaf Area Index (LAI) is an important input variable for forest ecosystem modeling as it is a factor in predicting productivity and biomass, two key aspects of forest health. Current in situ methods of determining LAI are sometimes destructive and generally very time consuming. Other LAI derivation methods, mainly satellite-based in nature, do not provide sufficient spatial resolution or the precision required by forest managers for tactical planning. This paper focuses on estimating LAI from: (i) height and density metrics derived from Light Detection and Ranging (LiDAR); (ii) spectral vegetation indices (SVIs), in particular the Normalized Difference Vegetation Index (NDVI); and (iii) a combination of these methods. For the Hearst Forest of Northern Ontario, in situ measurements of LAI were derived from digital hemispherical photographs (DHPs) while remote sensing variables were derived from low density LiDAR (i.e., 1 m−2) and high spatial resolution WorldView-2 data (2 m). Multiple Linear Regression (MLR) models were generated using these variables. Results from these analyses demonstrate: (i) moderate explanatory power (i.e., R2 = 0.53) for LiDAR height and density metrics that have proven to be related to canopy structure; (ii) no relationship when using SVIs; and (iii) no significant improvement of LiDAR models when combining them with SVI variables. The results suggest that LiDAR models in boreal forest environments provide satisfactory estimations of LAI, even with narrow ranges of LAI for model calibration. Models derived from low point density LiDAR in a mixedwood boreal environment seem to offer a reliable method of estimating LAI at high spatial resolution for decision makers in the forestry community. This method can be easily incorporated into simultaneous modeling efforts for forest inventory variables using LiDAR.

Current British Columbia forest regulations drive the regeneration management towards pure conifer stands rather than remaining in a mixed-species condition. This approach may result in unnecessary vegetation control. The main objective of this investigation was to study the impact of variable paper birch densities on white spruce growth in 15-20 years old stands for management implications. Regression analysis was used to examine the effect of birch density and two competition indices to predict spruce growth. A mixed model ANOVA showed that spruce mean annual DBH and basal area increment differed significantly among sites and density. From the regression analysis it appears that birch density up to 4,000 stems.ha-1 had no significant influence on spruce growth which is much higher than the current BC reforestation guideline (1,000 stems.ha-1 broadleaves). Similarly, birch relative density index (RDI) had to exceed 3 to affect spruce DBH growth significantly on all sites except one. On most sites, spruce had a larger DBH than birch. Our results also suggest that rather than following the current broadcast approach to vegetation management, a targeted approach could enhance forest productivity and stand diversity.

The likelihood of addressing the complex environmental, economic, and social/cultural issues associated with local climate change impacts is enhanced when collaborative partnerships with local people are established. Using a community-centered approach in the Teslin region of Canada’s Yukon Territory, we utilized our research skills to respond to local needs for information by facilitating both an internal community process to clarify traditional and local knowledge, values, and perceptions on locally identified priorities, while gathering external information to enable local people to make sound decisions. Specifically, we sought to clarify local perceptions surrounding climate change impacts on fire risk and wildlife habitat, and the potential adaptation strategies appropriate and feasible within the Teslin Tlingit Traditional Territory. This paper provides a characterization of the study region and our project team; provides background on the interview and data collection process; presents our key results; and discusses the importance of our findings and charts a way forward for our continued work with the people in the Teslin region. This approach presents an excellent opportunity to help people holistically connect a range of local values, including fire risk mitigation, habitat enhancement, economic development, and enhanced social health.

While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In this paper, we make use of L- and P-band airborne SAR and in situ data measured in the field together with laser scanning data acquired over one hemi-boreal (Remningstorp) and one boreal (Krycklan) forest study area in Sweden. We fit statistical models to different combinations of topographic-corrected SAR backscatter and forest heights estimated from PolInSAR for the biomass estimation, and evaluate retrieval performance in terms of R2 and using 10-fold cross-validation. The study shows that specific combinations of radar observables from L- and P-band lead to biomass predictions that are more accurate in comparison with single-band retrievals. The correlations and accuracies between the combinations of SAR features and aboveground biomass are consistent across the two study areas, whereas the retrieval performance varied for individual bands. P-band-based retrievals were more accurate than L-band for the hemi-boreal Remningstorp site and less accurate than L-band for the boreal Krycklan site. The aboveground biomass levels as well as the ground topography differ between the two sites. The results suggest that P-band is more sensitive to higher biomass and L-band to lower biomass forests. The forest height from PolInSAR improved the results at L-band in the higher biomass substantially, whereas no improvement was observed at P-band in both study areas. These results are relevant in the context of combining information over boreal forests from future low-frequency SAR missions such as the European Space Agency (ESA) BIOMASS mission, which will operate at P-band, and future L-band missions planned by several space agencies.

The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22–45 g C·m−2·month−1 (30–80%) and 0.13–0.35 kg C·m−2·year−1 (20–60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests’ carbon reserves.

Glyphosate herbicide is widely used to control bluejoint reedgrass (Calamagrostis canadensis (Michx.) Beauv.), trembling aspen (Populus tremuloides Michx.), and other competing species in regenerating white spruce (Picea glauca (Moench) Voss) plantations in Alberta, Canada. In 2004, we initiated a study to examine the effects of the aerial application of glyphosate herbicide on plant community diversity and tree growth near Calling Lake, Alberta. Four treatments were applied: (a) no treatment (control); (b) herbicide application in the first growing season after harvesting; (c) herbicide application in the third growing season after harvesting; and (d) herbicide application in the second and fourth growing seasons after harvesting (two treatments). After 11 growing seasons, species richness was not significantly affected by treatment, while Shannon and Simpson index values were highest in areas treated with herbicide in the first growing season. Herbicide treatment did not have a significant effect on the cover of bluejoint reedgrass after 11 growing seasons, but did significantly reduce trembling aspen and paper birch cover and height. Application of glyphosate in the second and fourth growing seasons resulted in the greatest reductions to aspen cover and height, as well as significant increases in spruce diameter at age 11. Simulations with the Mixedwood Growth Model indicate that all tested herbicide treatments will reduce aspen volume while increasing spruce volume at age 90, with the largest impacts evident where two treatments were applied.

Leaf area index (LAI) is one of the most commonly used ecological variables in describing forests. Since 2000, 1-km resolution Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composites of LAI have been operationally available from the National Aeronautics and Space Administration (NASA), USA, at no cost to the user. In this paper, we present a simple protocol to enhance the spatial resolution of NASA-produced LAI composites to 250-m resolution. This is done by fusing MODIS-based estimates of enhanced vegetation index (EVI), consisting of 16-day 250-m resolution composites (also from NASA), with estimates of LAI. We apply the protocol to derive 250-m resolution maps of LAI for the boreal forest region of northern Alberta, Canada. Data fusion was possible in this study because of the inherent linear correlation that exists between EVI and LAI for the April to October growing period of 2005–2008, producing r2-values of 0.85–0.95 and p-values < 0.0001. Comparison of MODIS-based LAI with field-based measurements using the Tracing Radiation and Architecture of Canopies (TRAC) sensor and LAI-2000 Plant Canopy Analyzer showed reasonable agreement across values; statistical comparison of LAI data points produced an r2-value of 0.71 and a p-value < 0.0001. Seventy one percent of MODIS-based LAI were within ±20% of field estimates.

In the short term, trees rely on the internal storage of water because it affects their ability to sustain photosynthesis and growth. However, water is not rapidly available for transpiration from all the compartments of the plant and the living tissues of the stem act as a buffer to preclude low water potentials during peaks of transpiration. In this paper, electronic dendrometers were used from mid-June to mid-September 2008 to compare the radius variations in stem and roots of black spruce [Picea mariana (Mill.) B.S.P.] in two sites of the boreal forest of Quebec, Canada, with different soil characteristics and water retention. The duration of the daily cycles was similar between sites and measurement heights but greater amplitudes of contraction and expansion were observed on the stem and in the site with the shallowest soil organic layer. The expansion phase had higher amplitudes and lasted longer than contraction. On average, the contraction phase occurred between 07:00 and 16:30 (legal time), while expansion lasted 14.5 h. The roots in the site with the deepest organic layer showed a wider variation in the onset of contraction, which could be as late as 13:00. The probability of observing the contraction phase depended on precipitation. With a precipitation 60% probability of observing contraction between 05:00 and 21:00, decreasing to 20% with precipitation >1.1 mm h-1. These findings demonstrated that the depth of the organic layer plays an important role in maintaining the internal water reserve of trees. The dynamics of water depletion and replenishment can modify the water potential of xylem and cell turgor during the enlargement phase, thus affecting radial growth. Changes in temperature and precipitation regime could influence the dynamics of internal water storage in trees growing on shallower and drier soils.

We present a focus issue of Environmental Research Letters on the ‘Recent dynamics of arctic and sub-arctic vegetation’. The focus issue includes three perspective articles (Verbyla 2011 Environ. Res. Lett. 6 041003, Williams et al 2011 Environ. Res. Lett. 6 041004, Loranty and Goetz 2012 Environ. Res. Lett. 7 011005) and 22 research articles. The focus issue arose as a result of heightened interest in the response of high-latitude vegetation to natural and anthropogenic changes in climate and disturbance regimes, and the consequences that these vegetation changes might have for northern ecosystems. A special session at the December 2010 American Geophysical Union Meeting on the ‘Greening of the Arctic’ spurred the call for papers. Many of the resulting articles stem from intensive research efforts stimulated by International Polar Year projects and the growing acknowledgment of ongoing climate change impacts in northern terrestrial ecosystems.

Economics and ecology are often presented as opposing disciplines. Both fields have strengths and weaknesses. A new transdisciplinary field, ecological economics, attempts to bring together the strengths of both disciplines with a vision for a sustainable future. In this paper, we focus on one particular concept championed by ecological economists, natural capital. In particular, our interest is on the institutionalization of this concept through the United Nation's Satellite System for Integrated Environmental and Economic Accounting (SEEA). SEEA is an international convention that incorporates natural resource accounting as a complement to the traditional System of National Accounts (SNA). In the case of boreal forests, the stocks and flows of forest resources can be assessed to determine prospects for sustainability. To provide a context for how natural resource accounting may be applied to boreal forests, we review the origin and purpose of natural resource accounting, summarize several cases in which natural resource accounting has been applied, and present an example of stocks and flows from Michigan's (United States) boreal forest resources. Natural resource accounting work from Canada, Finland, Norway, and other countries with boreal forests should be compiled and analyzed to provide more insights regarding circumpolar forest conditions.