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The monitoring of forest ecosystems is significantly affected by the lack of consistent historical data of low-severity (forest partially disturbed) or gradual disturbance (e.g. eastern spruce budworm epidemic). The goal of this paper is to explore the use of a subset of Landsat time series and deep learning models to identify both the type and the year of disturbances, including low-severity and gradual disturbances, in the boreal forest of eastern Canada at the pixel level. Remote sensing data such as the spectral information from Landsat time series are the best available option for large scale observations of disturbances that go back decades. Traditional modeling approaches, like LandTrendr, require substantial handcrafted pre-processing to remove noise and to extract temporal features from the image sequences before using them as input to a classical machine-learning model. Deep-learning models can autonomously discern which features are relevant within the coarse temporal and spectral information from the Landsat annual dense time series. We evaluated the performance of TempCNN and Transformer model in detecting and classifying the type and the year of the forest disturbance using Landsat time series subsequences. Our findings resulted in the generation of four disturbance maps outlining the forest history from 1986 to 2021 within the eastern Canadian boreal forest. Our experimental outcomes demonstrate several significant benefits of employing deep learning models. Firstly, using noisy Landsat time series they achieve comparable accuracy for classifying fire and total harvesting than existing publicly available disturbance maps. Secondly, the use of shorter time series subsequence with deep learning models enables to map adequately different overlapping disturbances occurring in the complete time series. Finally, they increase the number of distinguishable disturbance classes by adding partial harvesting, gradual disturbances, and forest recovery from older events, making them useful approaches for obtaining the first remote sensing-based map for areas affected by the eastern spruce budworm. • TempCNN, Transformer tested on Landsat time series for disturbance classification. • The type and the year of disturbance events were classified with good accuracy. • Deep learning effective for identifying partial and progressive disturbances. • Landsat subsequences extract overlapping disturbances. [ABSTRACT FROM AUTHOR]

Forest management is an important tool for GHG mitigation by representing three carbon pools: living biomass, forest soil, and wood-based products. Additionally, increasing attention has been given to the potential for wood products to substitute fossil-intensive products as a climate mitigation strategy. The goal of this paper is to analyse the theoretical GHG effects of fully replacing four common non-wood products with wood-based products of 'low' and 'high' technology options that have a similar functionality: (1) Spruce particle board substituting polyurethane (PU) foam insulation board; (2) spruce cross-laminated timber beam (CLT) substituting steel beam; (3) birch energy wood substituting electric heating; and (4) birch plywood substituting plaster board. The analysis was based on forestry in Western Norway as a case study, where forests typically consist of naturally generated birch and expanding areas of planted Norway spruce. In this study we compare wood products derived from paired stands of Norway spruce and downy birch. The analysis showed that spruce gave a higher theoretical substitution effect relative to birch for the selected pairs of woody and non-woody products. CLT substituting steel beam gave the highest substitution effect, approximately 15% higher than particle board substituting PU foam board. The theoretical substitution effect in mass units of carbon per kg wood product for the two spruce wood products was approximately 17 times higher relative to substituting Norwegian hydro energy-based electric heating, whereas plywood substituting plaster board may in fact increase GHG emissions. As the gross emissions were relatively similar for the birch plywood and the spruce particle board, the major substitution effect was related to the avoided emission of the non-woody product rather than to the tree species per se. The paper concludes that the choice of product to be substituted was the key factor that determined the final substitution effects. Furthermore, the study showed that transportation was the single most important factor that affected the emissions between planting and delivery of the timber at production gate. The analysis enables informed decisions related to CO 2 -emissions at the various steps from tree planting to wood conversion, and underline the importance of informed decision related to the choice of substitution products. • We compared the theoretical substitution effect of four wood products (spruce particle board, spruce cross-laminated timber beam, birch energy wood, and birch plywood) for the case of Western Norway. • The spruce products gave a better substitution effect relative to birch, and cross laminated beam from spruce substituting steel beam had the highest substitution effect amongst the four selected alternatives. • Avoided emission was the major determining factor for the calculated substitution effect, thus, the estimated substitution factors were mainly related to the selected product to be substituted rather than to the tree species used as raw material. • Emissions related to transportation was the single most important emission factor of all forestry activities, amounting to approximately 48% of the estimated emissions. Export by boat suggested a potential increase in the transport emissions of 62%. [ABSTRACT FROM AUTHOR]

Highlights • Canopy solar radiation transmittance (τ) was measured at 20 sub-canopy stations. • Plant area index (PAI) values from 1.11 to 6.29 m2 m−2 were obtained at the sites. • PAI was an adequate proxy for τ given the frequent cloudiness at the site. • Canadian Land Surface Scheme was used to model water budget with each PAI value. • Evapotranspiration and its components reacts strongly to increasing PAI. Abstract Forest canopies act as permeable barriers between the atmosphere and the ground, reflecting and absorbing solar radiation. In the boreal forest, the large number of gaps and heterogeneities further complicates these processes. Several studies have adequately measured and modeled the transmittance of solar radiation through forest canopies in western North America and Scandinavia, but few have addressed those of Eastern North America. Furthermore, most of these studies have assessed the effects of solar radiation transmittance on snowpack energetics, but few have focused on the hydrological impacts during the growing season. This paper addresses this knowledge gap with precise measurements of sub-canopy solar radiation in a juvenile balsam fir forest located in the Montmorency Forest, Quebec, Canada. Twenty (20) sub-canopy stations were deployed in a 200 m by 150 m gridded box around a flux tower measuring above canopy radiation and eddy covariance fluxes during late summer and early fall 2016. Results show that the heterogeneous forest has substantial spatial variability of transmittance, with site-specific seasonal averages ranging between 0.07 and 0.69. Canopy gaps of size relative to tree height (H) between 0.1 H and H had a temporal influence on solar radiation transmittance in canopy gaps at the sub-daily scale, but do not influence seasonal trends. This is attributed to very frequent cloudiness at the site, which renders the solar radiation mostly diffuse. As a result, a Beer-Lambert extinction law proved adequate at modeling site-specific or spatially averaged transmittance on a seasonal basis. We complement the observations by modeling canopy and soil moisture balances at 20 sites using the Canadian Land Surface Scheme (CLASS). The modeling results exhibit the following trend: a thicker (thinner) vegetation leads to more (less) evapotranspiration, because there is more (less) evaporation of intercepted precipitation and more (less) transpiration, but less (more) ground evaporation. During drier periods, the latter leads to wetter soil conditions for the thicker vegetation. These modeling results of sensitivity to vegetation density, while informative, still need to be confirmed with observations. [ABSTRACT FROM AUTHOR]

The Siberian larch forests are a major and unique component of the circumpolar boreal biome. However, because of their large extent, remote location, and lack of consistent satellite data coverage prior to 2000, comparatively little is known about their disturbance and recovery patterns at the regional scale beyond the past 15 years. Spatial distribution of recovering forests in the boreal zone is highly important for quantifying the feedback of forest cover to the climate system through carbon cycling and surface albedo which are strongly related to stand age. This paper presents a new method to map stand age distribution of young recovering forests that are primarily dominated by larch in Siberia using Landsat data acquired during the data-rich post-2000 era. This approach is based on the assumption that the relationship between the spectral signature of a forest stand acquired by Landsat ETM + and TM sensors and stand age before and after the year 2000 is similar thus allowing for training the algorithm on the data from the post-2000 era and applying the algorithm to infer stand age for the pre-2000 era. The output map combines the modeled forest disturbances before 2000 (56% producer's and 81% user's accuracy for disturbances) and direct observations of forest loss after 2000 (80% producer's and 92% user's accuracy for disturbances) to deliver a 24-year stand age distribution map at 30-m resolution for the Siberian larch forests. This is the first dataset that focuses specifically on the mid-term stand age dynamics of these forests at the regional scale. Our results show that > 10% of the forested area in the Siberian larch forests has experienced stand-replacing fires in the past 24 years. [ABSTRACT FROM AUTHOR]

Abstract: ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~1.5millionkm2) to map four stem volume classes (0–20, 20–50, 50–80, and >80m3/ha). [ABSTRACT FROM AUTHOR]

A variety of evidence suggests that the boreal forests of Canada are responding to climate change. Specifically, several studies have inferred that widespread browning trends detected in time series of the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) reflect the response of boreal forests to longer growing seasons, increased summer drought stress, and higher frequency of fires. Data from the Thematic Mapper (TM5) and Enhanced Thematic Mapper Plus (ETM+) sensors onboard Landsat 5 and 7, respectively, span essentially the same time period as the AVHRR record, but provide data with substantially higher radiometric and spatial fidelity, and by extension, a much improved basis for evaluating decadal-scale trends in spectral vegetation indices such as the NDVI. However, detection of trends, which are often subtle, requires careful attention to ensure that artifacts associated with the quality and stability of inter- and intra-sensor calibration do not lead to spurious conclusions in results from time series analyses. In this paper, we use time series of TM5 and ETM+ images for fifteen sites distributed across the Canadian boreal forest zone to explore if and how sensor geometry and inter- and intra-sensor calibration affect trends in spectral vegetation indices derived from multi-decadal Landsat time series. To do this, we created annual cloud-free composites for each Landsat spectral band based on peak summer NDVI at each site from 1984 to 2011 using all available TM5 and ETM+ data. To distinguish trends arising from long term climate change from those related to disturbance, we isolated areas within each site that were undisturbed during the Landsat record, and used these locations to analyze sources of variance in time series of red reflectance, near-infrared (NIR) reflectance, the NDVI, and the Enhanced Vegetation Index (EVI). Our results highlight the challenges involved in distinguishing trends in surface properties from data artifacts caused by undetected atmospheric effects, changes in sensor view angles, and subtle radiometric differences between the TM5 and ETM+ sensors. In particular, differences in sensor view geometry across adjacent overlapping Landsat scenes cause vegetated pixels in the eastern portion of Landsat scenes to have higher reflectances in the red and NIR bands (by 5 and 6 percent, respectively) than pixels in the western portion of scenes. While this effect does not significantly change NDVI values, it does affect EVI values. We also found modest, but potentially significant, differences between the red band reflectance of each sensor, with TM5 data having 14 percent higher red reflectance on average for vegetated pixels, which can introduce spurious trends in time series that combine TM5 and ETM+ data. More generally, the results from this work demonstrate that while the 30 + year Landsat archive provides unprecedented opportunities for studying changes to the Earth's terrestrial biosphere over the last three decades, care must be taken when inferring trends in these data without considering how sources of variance unrelated to surface processes affect the integrity of Landsat time series. [ABSTRACT FROM AUTHOR]

The western Canadian mixedwood boreal forests were projected to be significantly affected by regional drought. However, drought degrees were spatially different across elevations, longitudes and latitudes, which might cause different tree growth responses to climate change in different sub-regions within western Canada. In this way, regional classification of western Canadian boreal forests and understanding spatial tree growth responses to climate might be necessary for future forest management and monitoring. In this paper, tree-ring chronologies of two dominant tree species, trembling aspen ( Populus tremuloides Michx.) and white spruce ( Picea glauca (Moench.) Voss), were obtained from mixed forest stands distributed across western Canada to study spatial tree growth response to climate based on three regional classification schemes (a phytogeographic sub-region classification, a natural sub-region classification and non-classification). Phytogeographic sub-region classification was estimated based on tree ring samples we collected in this study, while natural sub-region classification was previously developed based on analysis of regional differences in vegetation, soil, site and climate conditions. Results showed that air temperature did not significantly increase, while drought stress became more severe between 1985 to 2010. Relationships between trembling aspen growth and temperature differed between north and south parts of the study area, resulting from spatial difference in water supply. Trembling aspen growth was influenced by temperature or moisture variables of the previous years. White spruce growth was influenced primarily by moisture variables (current or previous year), and response coefficients between white spruce and drought conditions (represented by drought code) were negative in all phytogeographic sub-regions, suggesting that white spruce was more sensitive to drought stress under climate change. As a late-successional dominant species, increasingly drought stress on white spruce might cause significant alteration in forest composition of western Canadian boreal forest. [ABSTRACT FROM AUTHOR]

Fire activity in boreal forests will substantially increase with prolonged growing seasons under a warming climate. This trend poses challenges to managing fires in boreal forest landscapes. A fire environment zone map offers a basis for evaluating these fire-related problems and designing more effective fire management plans to improve the allocation of management resources across a landscape. Toward that goal, we identified three fire environment zones across boreal forest landscapes in northeastern China using analytical methods to identify spatial clustering of the environmental variables of climate, vegetation, topography, and human activity. The three fire environment zones were found to be in strong agreement with the spatial distributions of the historical fire data (occurrence, size, and frequency) for 1966–2005. This paper discusses how the resulting fire environment zone map can be used to guide forest fire management and fire regime prediction. [ABSTRACT FROM AUTHOR]

Optical remote sensing methods for mapping of the seasonal snow cover are often obstructed by the masking effect of forest canopy. Therefore, optical algorithms tend to underestimate the amount of snow cover in forested regions. In this paper, we investigate the influence of boreal forest stand characteristics on the observed scene reflectance under full dry snow cover conditions by applying an advantageous experimental setup combining airborne hyperspectral imaging and LIDAR data sets from a test region in Sodankylä, northern Finland. This is particularly useful to the understanding of the composition of the mixed satellite scene reflectance behavior and it is relation to the natural ground targets’ spectral signatures. At first, we demonstrate the effects of varying forest stand characteristics, including Canopy Cover (CC), Tree Height (TH) and the product of the these parameters referred to as CCxTH, on the reflectance measured by airborne imaging spectrometer AisaDUAL. Then, we analyze the effects of the presence of snow on forest canopy on the observed AisaDUAL data. The analysis of the effects of canopy was enabled by the high resolution LIDAR measurements which provide reference information on forest canopy characteristics. According to the results the change in Canopy Cover, as well as in CCxTH, is related to the observed change in reflectance, as well as to changes in such spectral indices as Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI). Additionally, NDSI was found to vary extensively particularly in dense forests (CC > 85%), where the relative variation was over 100%. This should be considered when applying NDSI-based snow mapping methods in the case of forested areas. One notable finding was that the relation between the forest characteristics and reflectance was nearly exponential, while with reflectance indices it was linear. Besides, the results show that NDSI was a more effective parameter in detecting snow on canopy (values deviated 0.3 on average) than NDVI (values deviated 0.3 on average) in all Canopy Cover classes. The difference in NDSI between these two cases, snow-covered and snow-free canopy, increased when the canopy coverage increased. [ABSTRACT FROM AUTHOR]

Abstract: The estimates of above-ground forest biomass (AGB) in Northern Eurasia are highly uncertain, despite the global importance of AGB for ecosystem services and its role as carbon stores. In this paper, we demonstrate the potential of ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar), for the estimation of AGB in the range of 10–190tons (dry matter)/ha in mixed and deciduous forests at the southern edge of the boreal region in Western Siberia. Various regression models were tested to determine the relationship between forest biomass derived from field measurements and radar backscatter. The best results were obtained using HV-polarized backscatter with the Water Cloud model, giving estimation errors in terms of root mean square errors (RMSE) between 25% and 32% of the mean biomass, and coefficient of determination (R 2) between 0.35 and 0.49 for the whole range of SAR backscatter used in the analysis. The method displayed a higher prediction accuracy with RMSE of 15%, and the R 2 between 0.55 and 0.72 when restricted to SAR backscatter (σ 0)<−12.6dB where the model was clearly defined. The SAR-based estimates offer a potential of rapid, high resolution and low cost mapping of the lower biomass woody vegetation (sparse or young forests on shallow peat) in Siberia, the area where more accurate national or large scale forest inventories hardly exist. [Copyright &y& Elsevier]

To mitigate greenhouse gas and comply with cap-and-trade systems, the carbon capture and storage (CCS) is presently unviable for industrials dealing with low concentration of CO2 emissions. Alternatively, a new offset opportunity is being analysed in Canada: the afforestation of open woodlands (OWs) in the boreal territory. The results obtained from model simulations (with CBM-CFS3) showed that afforestation of boreal OWs can be a low C-intensive mitigation activity, in particular when understory planting is the chosen silvicultural approach, so that only 8-12 years are needed to reach a net positive C balance with the afforestation of OWs. A large-scale afforestation of boreal OWs - scheduled at 20kha per year during 20 years for a maximum of 400 kha - could provide capped industrials with a significant offset potential, for instance up to nearly 8% offset of all Quebec industrial process emissions (2009 data) after 45 years. In spite of a certain number of issues that can contribute to the uncertainty of the real environmental and economical benefits from the afforestation of OWs as a mitigation activity - most of which issues are discussed in this paper - this study presented a first glimpse at the extent to which the afforestation of boreal OWs in Quebec can provide large emitters with eventually substantial and efficient GHG offset potential, especially those emitters tied up with incompressible GHG emissions. [ABSTRACT FROM AUTHOR]

Abstract: Methods for the estimation of forest growing stock volume (GSV) are a major topic of investigation in the remote sensing community. The boreal zone contains almost 30% of global forest by area but measurements of forest resources are often outdated. Although past and current spaceborne synthetic aperture radar (SAR) backscatter data are not optimal for forest-related studies, a multi-temporal combination of individual GSV estimates can improve the retrieval as compared to the single-image case. This feature has been included in a novel GSV retrieval approach, hereafter referred to as the BIOMASAR algorithm. One innovative aspect of the algorithm is its independence from in situ measurements for model training. Model parameter estimates are obtained from central tendency statistics of the backscatter measurements for unvegetated and dense forest areas, which can be selected by means of a continuous tree canopy cover product, such as the MODIS Vegetation Continuous Fields product. In this paper, the performance of the algorithm has been evaluated using hyper-temporal series of C-band Envisat Advanced SAR (ASAR) images acquired in ScanSAR mode at 100m and 1km pixel size. To assess the robustness of the retrieval approach, study areas in Central Siberia (Russia), Sweden and Québec (Canada) have been considered. The algorithm validation activities demonstrated that the automatic approach implemented in the BIOMASAR algorithm performed similarly to traditional approaches based on in situ data. The retrieved GSV showed no saturation up to 300m3/ha, which represented almost the entire range of GSV at the study areas. The relative root mean square error (RMSE) was between 34.2% and 48.1% at 1km pixel size. Larger errors were obtained at 100m because of local errors in the reference datasets. Averaging GSV estimates over neighboring pixels improved the retrieval statistics substantially. For an aggregation factor of 10×10pixels, the relative RMSE was below 25%, regardless of the original resolution of the SAR data. [Copyright &y& Elsevier]

This paper proposes a method of controlled trend surface to simultaneously account for large-scale spatial trends and non-spatial local effects. With this method, a geospatial model of forest dynamics was developed for the Alaska boreal forest from 446 constantly monitored permanent sample plots. The geospatial component of this model represented large-scale spatial trends in recruitment, diameter growth, and mortality. The model was tested on two sets of validation plots which represented temporal and spatial extensions of the current sample coverage. The results suggest that the controlled trend surface model was generally more accurate than both the non-spatial and conventional trend surface models. With this model, we mapped the forest dynamics of the entire Alaska boreal region by aggregating predicted stand states across the region. It was predicted that under current conditions of climate and natural disturbances, most of the Alaska boreal forest region may undergo a major shift from deciduous-dominant to conifer-dominant, with an average increase of 0.33m2 hayear−1 in basal area over the Twenty-First Century. [Copyright &y& Elsevier]

Across forest ecosystems worldwide, alternative silvicultural practices such as partial or “structural retention” harvesting are gaining increasing use as means to balance economic and ecological management goals. Recent studies have documented highly elevated rates of residual-tree mortality and growth, both of which are likely to influence post-harvest stand development. Explicit consideration of residual-tree responses to partial harvest is therefore required to make accurate growth and yield predictions. In this paper we incorporate empirically based, spatially explicit tree growth and mortality predictors into a forest simulation model in order to quantify stand dynamics following a range of simulated partial harvests. We consider lowland black spruce boreal forests and use a common strip-cutting technique, Harvest with Advance Regeneration Protection, as a reference against which to compare alternative scenarios. Predicted stand development proceeded most rapidly in scenarios with reduced skidding impacts and relatively high levels of retention, since these treatments were associated with lower residual-tree mortality rates and larger growing stocks respectively. Stand development was also influenced on a proportional basis by residual-tree growth rates, which increased with declining neighbourhood competition. Across a range of harvest scenarios with equivalent retention levels (20%), differences in residual-tree responses among treatments led to a 35-year range in projected basal area return intervals (70–105 years). Our results demonstrate that short-term responses of residual trees affect stand development rates over long (>100-year) time scales, and show that post-harvest stand recovery rates are influenced not only by the retention level but also by the specific parameters of partial harvest prescriptions, including the layout of skid trails and the spatial arrangement of residual trees. Results from this study provide essential information for evaluating partial harvests in the black spruce boreal forest and supply modelling tools for assessing alternative silvicultural treatments in other forest ecosystems. [Copyright &y& Elsevier]

Fire regimes result from reciprocal interactions between vegetation and fire that may be further affected by other disturbances, including climate, landform, and terrain. In this paper, we describe fire and fuel extensions for the forest landscape simulation model, LANDIS-II, that allow dynamic interactions among fire, vegetation, climate, and landscape structure, and incorporate realistic fire characteristics (shapes, distributions, and effects) that can vary within and between fire events. We demonstrate the capabilities of the new extensions using two case study examples with very different ecosystem characteristics: a boreal forest system from central Labrador, Canada, and a mixed conifer system from the Sierra Nevada Mountains (California, USA). In Labrador, comparison between the more complex dynamic fire extension and a classic fire simulator based on a simple fire size distribution showed little difference in terms of mean fire rotation and potential severity, but cumulative burn patterns created by the dynamic fire extension were more heterogeneous due to feedback between fuel types and fire behavior. Simulations in the Sierra Nevada indicated that burn patterns were responsive to topographic features, fuel types, and an extreme weather scenario, although the magnitude of responses depended on elevation. In both study areas, simulated fire size and resulting fire rotation intervals were moderately sensitive to parameters controlling the curvilinear response between fire spread and weather, as well as to the assumptions underlying the correlation between weather conditions and fire duration. Potential fire severity was more variable within the Sierra Nevada landscape and also was more sensitive to the correlation between weather conditions and fire duration. The fire modeling approach described here should be applicable to questions related to climate change and disturbance interactions, particularly within locations characterized by steep topography, where temporally or spatially dynamic vegetation significantly influences spread rates, where fire severity is variable, and where multiple disturbance types of varying severities are common. [Copyright &y& Elsevier]

Abstract: An existing forest reflectance model based on photon recollision probability is extended to be applicable also in the case of snow covered forest floors. In summer conditions (small forest floor albedo), as is normally assumed, the part of the total forest albedo originating from the forest floor is mainly from bidirectional transmission through gaps in the canopy (photons transmitted without interaction through the canopy first downwards and then upwards after being reflected at the forest floor). The extended model developed in this paper additionally takes into account multiple scattered photons, which have interacted within the canopy prior to or/and after being reflected by the forest floor. The bidirectional reflectance distribution function (BRDF) of the forest floor is assumed to be a mixture of a Lambertian and a purely forward/backward scattering surface. Results show that snow covered forest floors affect the total canopy albedo to a large extent via this multiple scattered component included in the model. If it is not taken into account the canopy albedo estimate may be up to 0.2 units too small. Lambertian and purely forward/backscattering forest floors cause practically similar total forest albedo values for moderate sun zenith angle values. The black and white sky albedo values are very similar. The developed albedo model was successfully used to simulate the main features of measured albedo values. [Copyright &y& Elsevier]

Statistical models relating habitat characteristics to species occurrences are increasingly used to evaluate the consequences of forest management activities and conservation plans over large spatial and temporal scales. In practice, such models do not always generalize to other locations, hence, they should be validated using independent data. In this paper, we assess the predictive ability of 16 songbird habitat models developed in the Calling Lake region of the boreal mixedwood forest of Alberta using both internal and external validation approaches. Internal validation relied on the same dataset used to develop the models while external validation utilised independent data collected within four validation landscapes in the same ecological region. Two aspects of predictive accuracy were evaluated: the agreement between observations and predicted values (calibration) and the models’ ability to classify locations into those in which species are present or absent (discrimination). Calibration was assessed using the Hosmer–Lemeshow (H–L) statistic and plots showing predicted versus observed probabilities of occurrence. Discrimination was assessed using receiver operating characteristic (ROC) curves and the associated area under the ROC curve. With internal validation, the calibration plots were reasonable for all species, however the H–L statistic indicated a good fit for only eight species. Model discrimination between occupied and unoccupied sites, on the other hand, was reasonable for 14 of 16 models. With external validation, both the calibration plots and the H–L statistic identified only three species with a reasonable fit. The remaining models generally over- or under-predicted probability of occurrence. External model discrimination was reasonable for 10 of 16 species. When we re-estimated the models using all available data, the calibration plots were again reasonable for all species, with the H–L statistic indicating good fit for 12 species. Similarly, model discrimination was reasonable for 14 of 16 species. Several factors may help to explain why model performance using external data was generally poorer and more variable than when using internal data. These include differences in landscape structure and disturbance history as well as differences in the frequency of occurrence of individual species. Overall, our analyses identified several species whose habitat models degraded considerably, especially as measured by calibration. By re-estimating the models, we increased the range of variation in our covariates which likely led to an improvement in model calibration. We discuss the importance of ongoing evaluation and refinement of habitat models that will be used in conservation planning or to evaluate management scenarios at large spatial and temporal scales. [Copyright &y& Elsevier]

Abstract: This paper describes the activity of a small meandering stream and the development of its floodplain during the last 4600 years (calendar years BP) in the northern boreal zone of Québec. Three trenches were excavated across the floodplain''s full width and permitted the interpretation of morphosedimentary units in relation to modern analogs. Chronological controls within trenches was provided by the dendrochronological and radiocarbon dating of buried tree trunks. From 4600 to 2900 cal. BP and from 1000 to 120 cal. BP, the channel migrated and constructed its floodplain at very slow rates, mostly because of low flow velocities, vegetated streambanks and the cohesive texture of marine sediments reworked by the channel. Vertical accretion rates were extremely variable on the floodplain, with high rates proximal to the modern channel and low rates over distal (also older) portions of the floodplain. Following a major channel shift (meander cut-off or avulsion) around 2900 cal. BP, channel migration appears to have been constrained to a narrow zone adjacent to the modern channel. Within this constrained zone, the migrating channel has reworked its own sediments leading to a marked unconformity between 2900–1000 cal. BP. It is thought that underlying marine sediments protrusions, and perhaps the forested banks, protected older alluvial sediments from being eroded during the last three millenniums. Our study shows that small boreal floodplains may contain, in a very small area, abundant and diversified archives of their evolution. [Copyright &y& Elsevier]

Abstract: Carbon budget of Siberian boreal forests is one of the largest uncertainties in the global carbon cycle under changing environment and then climate prediction. A coupled carbon cycle and fire regime model, which is applicable to simulate carbon budget of a fire-prone boreal forest in eastern Siberia, was developed in this paper. The carbon model consists of 10 pools and represents carbon dynamics including biomass burning, on the basis of an ecophysiological carbon cycle model (Sim-CYCLE). The fire model simulates fire behavior (ignition, expansion, and extinction) at a landscape scale with a spatially explicit cellular automaton, in a stochastic manner. Fire spread and burning intensity are parameterized as functions of temperature, moisture, and fuel availability, and ground fire and crown fire are dealt with separately. Interactions between the carbon cycle and fire behavior models are included, such that the carbon model simulates fuel dynamics and post-fire vegetation growth and the fire model simulates biomass burning (carbon dioxide emission, detritus production, and charcoal formation). The model was calibrated at a deciduous needle-leaved (larch) forest near Yakutsk, Russia (62°N, 130°E), in which Japanese researchers conducted surveys of carbon dynamics. The long-term simulation (1200 years) indicated that the mean fire interval of the larch forest was as short as 64 years, and most fires (94% of burnt area) occurred as ground fires. Although crown fires were less frequent (6% of burnt area), they exerted more severe impacts on carbon storage than ground fires. On average, the biomass burning released about 12% of carbon fixed by plant photosynthesis directly and resulted in acceleration of carbon cycling of the boreal forest. Young post-fire forests exhibited higher photosynthetic activity and compensated for the loss of old stands. Sensitivity of the model simulation to variation in parameter values and environmental conditions was examined. Finally, the simulation analysis confirmed that the fire regime is an important ecosystem attribute, and that a coupled carbon and fire model is useful to investigate the carbon budget of boreal forest in eastern Siberia. [Copyright &y& Elsevier]

The Boreal Ecosystem Atmosphere Study (BOREAS) was a large, multiyear internationally supported study designed to improve our understanding of the boreal forest biome and its interactions with the atmosphere, biosphere, and the carbon cycle in the face of global climate change. In the initial phase of this study (early 1990s), remote sensing played a key role by providing products needed for planning and modeling. During and after the main BOREAS field campaigns (1994 and 1996), innovative remote sensing approaches and analyses expanded our understanding of the boreal forest in four key areas: (1) definition of vegetation structure, (2) land-cover classification, (3) assessment of the carbon balance, and (4) links between surface properties, weather, and climate. In addition to six BOREAS special issues and over 500 journal papers, a principal legacy of BOREAS is its well-documented and publicly available database, which provides a lasting scientific resource and opportunity to further advance our understanding of this critical northern biome. [Copyright &y& Elsevier]

This brief paper indicates that forest fires may have short and longer term effects on runoff and thus, can influence trend studies on the response of watersheds to climate change. Twenty-two watersheds at the Experimental Lakes Area in northwestern Ontario were studied to view the impacts of climatic variability and forest fires on runoff. A roughly 30 year database demonstrated few trends in climatological variables and even fewer trends in runoff data at the 5% significance level. Daily maximum temperature increased by 0.053 °C per year, while precipitation in the months of February and March showed significant decreases. Total snow showed a significant decrease over a 30 year period at the 8% significance level. The Mann Kendall test for trend was applied to the runoff indices of 19 watersheds and it was revealed that only six exhibited trends. Of these, five had been burned during the test period. Virtually all burned watersheds showed initial increases in runoff, however, long term runoff trended lower in the burned watersheds, while the one watershed that was not burned showed an increasing trend. Forest fires alter the age distribution of trees with subsequent impacts on water yields in the short and longer term. [Copyright &y& Elsevier]

Satellite remote sensing has been widely used for the evaluation of wildfire burn severity in various ecosystems. While a variety of remote sensing-based burn severity indices have been developed, the Landsat-based differenced Normalized Burn Ratio (dNBR) presents the most widely-used approach to burn severity assessment for fire research and management. Although dNBR-based approaches have been continuously updated, including the development of the relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), one key obstacle for the reliable applications of the burn severity indices in the high northern latitudes has not been adequately addressed. Specifically, optimal Landsat image pairs are very rarely available for the calculation of the burn severity indices in certain regions of the high northern latitudes (HNL), resulting in the burn severity index outputs calculated for a large number of wildfires being considerably affected by non-fire-related factors. The suboptimal selection of image pairs may have partially contributed to the lack of consistency in the performance of the burn severity indices in the HNL regions. In this paper, we systematically evaluated the impacts of suboptimal image pairs on signal stability through two sets of analyses conducted at different spatial scales. First, at the regional scale, we examined the burn severity indices calculated for a selection of wildfires in Alaska. Here, we demonstrate the inconsistent performance of the same index calculated based on Landsat data that are typically considered as "optimal". In addition, we show that the calculated indices may be more prone to the negative impacts of these inconsistencies, thus leading to reduced reliability, when there is limited Landsat data availability. Second, at the continental scale, we show that certain areas in the HNL, especially Alaska during the pre-2000 era, are subject to the potentially strong negative impact associated with the limited data availability. Through systematically analyzing this issue, we hope to not only divert more attention to it but also to provide potential solutions, based on which further improvements that may be of particular importance for research on Arctic wildfires could be made. • Examines a limitation of burn severity indices that is specific to the Arctic region • Takes advantage both local and cloud-based platforms for more robust analyses • Utilizes full Landsat archive over North America in the spatio-temporal analysis • Offers potential solutions to address the revealed limitation [ABSTRACT FROM AUTHOR]

In this paper we describe how forest management practices in Fennoscandian countries, namely Sweden and Finland, expose streams to multiple stressors over space and time. In this region, forestry includes several different management actions and we explore how these may successively disturb the same location over 60–100 year long rotation periods. Of these actions, final harvest and associated road construction, soil scarification, and/or ditch network maintenance are the most obvious sources of stressors to aquatic ecosystems. Yet, more subtle actions such as planting, thinning of competing saplings and trees, and removing logging residues also represent disturbances around waterways in these landscapes. We review literature about how these different forestry practices may introduce a combination of physicochemical stressors, including hydrological change, increased sediment transport, altered thermal and light regimes, and water quality deterioration. We further elaborate on how the single stressors may combine and interact and we consequently hypothesise how these interactions may affect aquatic communities and processes. Because production forestry is practiced on a large area in both countries, the various stressors appear multiple times during the rotation cycles and potentially affect the majority of the stream network length within most catchments. We concluded that forestry practices have traditionally not been the focus of multiple stressor studies and should be investigated further in both observational and experimental fashion. Stressors accumulate across time and space in forestry dominated landscapes, and may interact in unpredictable ways, limiting our current understanding of what forested stream networks are exposed to and how we can design and apply best management practices. Unlabelled Image • Streams in Fennoscandian production forests are subject to number of disturbances. • Operations during the forestry rotation cycle introduce stressors to small streams. • Individual stressors interact in time and space causing multiple stressor phenomena. • Aquatic multiple stressors are not well understood in forestry dominated landscapes. • Future research should focus on multiple stressors using experimental approaches. [ABSTRACT FROM AUTHOR]

This paper investigates the potential for the Soil Moisture Active Passive (SMAP) L-band radiometer to estimate the percentage of frozen soil inside a pixel during fall periods. To evaluate the spatial and temporal variability of the autumn freeze in northeastern Canada boreal forest, a network of compact and self-recording temperature sensors (iButton) along transects averaging 10 km was deployed on the soil surface at two different sites. Results show important spatial variability in soil freezing timing with maximums, for the two sites, spanning from 7.5 to 9.5 weeks to reach a frozen state. It is known that L-band radiometry such as Soil Moisture Active Passive (SMAP) can monitor freeze/thaw (F/T) seasonality at low spatial resolution (~40 km). However, the possible strong surface variability within a 40 km × 40 km pixel can lead to spatial delay in fall freezing. Simulations of brightness temperature (TB) weighted by spatially distributed iButton F/T records and using the ω-τ vegetation model show good agreement with SMAP satellite observations during the fall transition period with an average RMSE of 3.5 K. A new intra-pixel frozen soil percentage retrieval algorithm using only SMAP observations and air temperature is proposed. The percentage of frozen soil in falls 2015 and 2016 from the new algorithm and the in situ measurements are in good agreement with a coefficient of determination (R2) ranging between 0.63 and 0.88. • Substantial spatial variability of soil freezing timing in boreal forest • L-band SMAP radiometer sensitivity to spatial variability of soil freezing • A new SMAP algorithm to estimate the intra-pixel fraction of frozen soil [ABSTRACT FROM AUTHOR]