In old-growth forests, natural disturbances form a complex mosaic of structures, providing a wide diversity of habitats and functions of great importance. Old-growth forests are still often seen as a homogeneous whole and few remote-sensing approaches have been tested to identify their structural diversity, especially in boreal forests. The aim of this study is to use a combination of airborne LiDAR and satellite imagery to identify and discriminate old-growth forest structures resulting from different disturbance histories. The study area, which was located in the mixed boreal forest of Quebec (Canada), is Monts Valin National Park and adjacent managed territories. Balsam fir (Abies balsamea (L.) Mill) is the dominant species in the study area, but hardwood species such as white birch (Betula papyrifera Marsh.) and trembling aspen (Populus tremuloides Michx.) can also be abundant in the early succession stages. Four forest classes were studied: second-growth (logged between 1970 and 1980); transition old-growth (burned in 1920); undisturbed old-growth (unburned for at least 125 years); and disturbed old-growth forest (unburned for at least 125 years, but severely disturbed by an insect outbreak around 1980). A multivariate Random Forest model was used to discriminate the classes on 6466 1 ha tiles, based on 11 complementary LiDAR and satellite-derived indices describing stand vertical and horizontal structure, together with “greenness” and disturbance history over the last 30 years. This model had high predictive efficiency (AUC = 94.2), with 81.8% of the tiles accurately classified. Interestingly, undisturbed old-growth forests exhibited intermediate characteristics compared to transition and disturbed old-growth forests. This emphasizes that some structural attributes recognized as important for the classification of temperate and tropical old-growth forests, such as high vertical complexity, are of lesser relevance for boreal old-growth forests. In comparison to undisturbed old-growth forests, transition old-growth forests had a taller canopy of high “greenness” due to a greater hardwood abundance; disturbed old-growth forests had a higher gap fraction and heterogeneity in tree size; second-growth forests exhibited a lower and more even canopy. Misclassified tiles were explained by spatial variation in disturbance severity or different levels of forest resistance and resilience to disturbance. These misclassifications are also of ecological interest, as they highlight the nuances in structural diversity that are rarely identified by disturbance mapping. A reasonable combination of LiDAR and satellite indices was effective not only in discriminating old-growth forests from second-growth forests, but also identifying their different structures, which result from specific disturbance histories. This method could contribute to effective monitoring of changes in the areas and characteristics old-growth forest that are caused by anthropogenic and natural disturbances.

In eastern Canada, harvesting practices and spatial organization of harvested sites are modulated according to ecosystem forest management objectives. We determined how spatial organization affects efficiency by evaluating wood procurement costs. A comparative analysis of efficiency was presented using a non-parametric technique, i.e., data envelopment analysis (DEA), which allows multiple variable analyses of different factors. A database of 50 harvested sites during the period 2015–2018, located along a north-south latitudinal gradient between 46° to 50°, was constructed with variables describing spatial organization (roads and dispersion of patches) and operational aspects (wood procurement costs). The evaluated financial efficiencies show high values greater than 70%. The causes of inefficiency were dispersion of the patches, distance to the mill, and the number of kilometers of built roads. When efficiency values were arranged by latitudinal location, northern sites exhibited a lower value of overall and scale efficiency due to the high values in the wood harvested, and developed road density of the zone.

In areas sensitive to forest management, paludification and successive disturbances in boreal forest can affect forest regeneration negatively, sometimes resulting in stand opening. As these negative effects on forest productivity are not fully considered in strategic management planning, a new landscape dynamics model integrating fire, paludification, forest harvesting, and regeneration failure was used to assess these impacts in a large forest management unit (10,828 km2) of northwestern Québec. Two reforestation scenarios, one based on the accessibility of the areas to be treated and the other aimed at restoring all burned and paludified areas to production were compared to one with no intervention. The success of the scenarios was evaluated using the predicted volume harvested, the proportion of closed or opened stands areas, an indicator of productivity; the cost of reforestation and the royalties associated with harvesting. Harvesting the paludified areas without reforesting would lead to a sharp increase in open stands areas (+17.3%). The strategy of reforesting accessible areas is the most promising for achieving sustainable forest management targets. The monitoring of maximum potential volume (MPV) and the closed forest area as indicators of landscape productivity provides the ability to anticipate problems earlier than with the conventional forest planning indicators.

Eastern Canadian boreal forests are mainly influenced by natural wildfires and forest management activities. To evaluate forest dynamics under possible interactions among fire and timber harvest in a future climate warming scenario (RCP 2.6, RCP 4.5 and RCP 8.5) the forest landscape model Landis II was used to simulate the dynamics of the 78000 km2 of boreal forests in eastern Canada. Forest management intensity scenarios were modeled considering the changes in the annual harvested area (0.5%, 1%, and 2%) and the age that conifers and hardwoods can be harvested (50 and 30 years, 70 and 50 years, and 90 and 70 years). The results of the 300-year model projections implied that both forest management intensity and climatic scenarios explained most of the variability in aboveground biomass, aboveground net primary productivity and forest composition. Forest management seems to be the most important factor that modified the landscape in the southern forests because there were scheduled stands with the age and composition required by each harvesting prescription to deal with the annual allowable cut volume. On the contrary, in the northern forests there was a mixed effect of climate change and forest management because many of the areas suitable for harvesting were previously burned limiting the amount of area available for harvesting. Thus, although it is expected an increase in wildfire area burned due to climate change, the intensification of forest management seems to be the most important driver of the increase of hardwoods and mixed stands and the decrease of conifers stands on the mixedwood boreal landscape, mainly in the southern forests. These results suggest that timber supply would be at risk in the Abitibi Plain, therefore, some strategies should be applied to adapt forest management to climate change.

Forest fires are a key driver of boreal landscape dynamics and are expected to increase with climate change in the coming decades. A profound understanding of the effects of fire upon boreal forest dynamics is thus critically needed for our ability to manage these ecosystems and conserve their services. In the present study, we investigate the long-term post-fire forest dynamics in the southern boreal forests of western Quebec using historical aerial photographs from the 1930s, alongside with modern aerial photographs from the 1990s. We quantify the changes in forest cover classes (i.e., conifers, mixed and broadleaved) for 16 study sites that were burned between 1940 and 1970. We then analyzed how interactions between pre-fire forest composition, site characteristics and a fire severity weather index (FSWI) affected the probability of changes in forest cover. In the 1930s, half of the cover of sampled sites were coniferous while the other half were broadleaved or mixed. Between the 1930s and the 1990s, 41% of the areas maintained their initial cover while 59% changed. The lowest probability of changes was found with initial coniferous cover and well drained till deposits. Moreover, an important proportion of 1930s broadleaved/mixed cover transitioned to conifers in the 1990s, which was mainly associated with high FSWI and well-drained deposits. Overall, our results highlight a relatively high resistance and resilience of southern boreal coniferous forests to fire, which suggest that future increase in fire frequency may not necessarily result in a drastic loss of conifers. View Full-Text

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. View Full-Text

Bryophytes represent an essential component of global biodiversity and play a significant role in many ecosystems, including boreal forests. In Canadian boreal forests, industrial exploitation of natural resources threatens bryophyte species and the ecological processes and services they support. However, the consideration of bryophytes in conservation issues is limited by current knowledge gaps on their distribution and diversity patterns. This is mainly due to the ineffectiveness of traditional field surveys to acquire information over large areas. Using remote sensing data in combination with species distribution models (SDMs), we aim to predict and map diversity patterns (in terms of richness) of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) in 28,436 km2 of boreal forests of Quebec (Canada). A bryophyte presence/absence database was used to develop four response variables: total bryophyte richness, moss richness, liverwort richness and sphagna richness. We pre-selected a group of 38 environmental predictors including climate, topography, soil moisture and drainage as well as vegetation. Then a final set of predictors was selected individually for each response variable through a two-step selection procedure. The Random Forest (RF) algorithm was used to develop spatially explicit regression models and to generate predictive cartography at 30 m resolution for the study area. Predictive mapping-associated uncertainty statistics were provided. Our models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), outperforming models from previous studies. Vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze–thaw events) consistently appeared among the most important predictors for all bryophyte groups modeled. However, guild-level models identified differences in important factors determining the richness of each of the guilds and, therefore, in their predicted richness patterns. For example, the predictor number of days > 30 °C was especially relevant for liverworts, while drainage class, topographic position index and PALSAR HH-polarized L-band were identified among the most important predictors for sphagna. These differences have important implications for management and conservation strategies for bryophytes. This study provides evidence of the potential of remote sensing for assessing and making predictions on bryophyte diversity across the landscape. © 2020 Elsevier Ltd

In this study, we used data from multiple sensors onboard NASA Aqua satellite to conduct a 10?year (2002–2011) remote sensing of microwave emissivity difference vegetation index (EDVI) over China. We investigated the spatial and temporal variations of EDVI in tropical and subtropical evergreen forest, deciduous forest, rice and wheat farmlands, grassland, and montane vegetation regions. The average of China's EDVI is positive in dense vegetation regions and negative in sparse vegetation regions, depending on the proportion of bare soil and open water. In all selected studying regions, the seasonal variation of EDVI follows the trend of vegetation phenology, even in regions with large proportion of open water. EDVI is positively correlated to the greenness of vegetation (normalized difference vegetation index [NDVI]) with certain phase difference in their seasonal cycle. In autumn, EDVI begins to decline earlier and faster than NDVI. In tropical rainforest, EDVI also starts to increase earlier than NDVI in spring. The large?scale spatial distribution of EDVI under clear sky and cloudy sky is similar. In montane vegetation regions, EDVI under heavy clouds (90% fraction) conditions is significantly greater than that under clear sky (10% fraction), indicating a possible cloud induced enhancement of vegetation water content. In forests and croplands in the plains, such effect is not remarkable.

The accumulation of organic material on top of the mineral soil over time (a process called paludification) is common in Northern Boreal coniferous forests. This natural process leads to a marked decrease in forest productivity overtime. Topography both at the surface of the forest floor (i.e., above ground) and the subsurface (i.e., top of mineral soil which is underground) is known to play a critical role in the paludification process. Until recently, the availability of more accurate topographic information regarding the surface and subsurface was a limiting factor for land management and modeling of spatial organic layer thickness (OLT) variability, a proxy for paludification. However so far, no research has assessed which of these two topographic variables has the greatest influence on paludification. This study aims to assess which topographic variable (surface or subsurface) better explains paludification, using high-resolution remote sensing technology (i.e., Light Detection and Ranging: LiDAR and Ground Penetrating Radar: GPR). To this end, field soil measurements were made in over 1614 sites distributed throughout the reference Valrennes Experimental site in Canadian northern coniferous forests. Then, a machine learning model (i.e., Random Forest, RF) was implemented to rank a set of selected predictor topographic variables (i.e., slope, aspect, mean curvature, plan curvature, profile curvature, and topographic wetness index) using the Mean Decrease Gini (MDG) index as an indicator of importance. Results showed that overall 83% of the overall variance was explained by the RF selected model, while the derived subsurface topography predictors had the lowest MDGs for predicting paludification. On the other hand, the surface slope predictor had the highest MDGs and better explained paludification. This finding would be particularly useful for implanting sustainable management strategies based on the surface variables of paludified northern boreal forests. This study has also highlighted the potential of LiDAR data to provide surface topographic spatial detail information for planning and optimizing forest management activities in paludified boreal forests. This is even of great importance when we know that LiDAR variables are easier to obtain compared to GPR derived variables (subsurface topographic variables).

In eastern Canada, spruces (Picea spp.) and pines (Pinus spp.) are among the main commercial species being logged for their lumber or wood fiber. Annually, about 175 million seedlings are planted in areas totaling ~100,000 ha. Appropriate microsite selection is essential during reforestation operations, given that it can improve the chances of survival and initial growth of the seedlings. In fir (Abies spp.) and spruce forests of eastern Canada, the optimal characteristics of establishment microsites have yet to be identified; these would be determined by different physical and climatic variables operating at several scales. Our study determined the influence of climatic (regional-scale), edaphic (stand-scale), local (microsite-scale) and planting conditions on the establishment substrate and initial growth of black spruce (Picea mariana Britton, Sterns and Poggenb.) and jack pine (Pinus banksiana Lamb.). Substrate characterization and growth monitoring (three growing seasons) for the two species were conducted on 29 planted cutblocks that were distributed over an east–west climatic gradient (precipitation and temperature) in the balsam fir and black spruce–feather moss forests of Quebec (Canada). Linear mixed models and multivariate analyses (PCAs) determined the effects of climatic, edaphic and micro-environmental variables and their interactions on the establishment substrate and seedling initial growth. The predictive models explained, respectively, 61% and 75% of the growth variability of black spruce and jack pine. Successful establishment of black spruce and jack pine depended upon regional conditions of precipitations and temperature, as well as on their interactions with stand-scale edaphic variables (surface deposit, drainage and slope) and local variables (micro-environmental) at the microsite-scale (establishment substrate types and substrate temperature). Mineral, organo-mineral and organic establishment substrates exerted mixed effects on seedling growth according to regional precipitation and temperature conditions, as well as their interactions with edaphic and local variables at the stand and microsite-scales, respectively. View Full-Text