Alexandre Morin-Bernard

Although climate-driven growth decline is well documented from tree-ring studies, translating this knowledge into spatially continuous information on forest vulnerability remains a challenge. To address this limitation, we developed a scalable approach integrating tree-ring data, climate variables, and Landsat-derived metrics to estimate severe growth-decline probability as a spatially explicit indicator of reduced tree vigor and vulnerability in black spruce (Picea mariana) across the Canadian boreal forest from 1988 to 2019. Severe declines were defined from multi-year tree-ring growth change using regionally calibrated thresholds. We then modelled severe growth-decline probability as a function of climate and Landsat-derived predictors, evaluated performance using repeated site-level cross-validation, and generated annual, spatially continuous predictions to identify recurrent high-risk areas.We found that severe growth-decline probability was highest in warm, moisture-limited climates, whereas the marginal effects of climate variation were stronger and more consistent under intermediate to moist conditions and weaker or contrasting in drier regimes. Repeated cross-validation showed moderate but consistent ability of the model to rank locations by severe growth decline probability (mean Spearman’s ρ = 0.40; mean AUC = 0.70). Out-of-fold calibration indicated that observed severe-decline frequency increased monotonically with predicted-probability classes, supporting model calibration. Landsat-derived metrics improved predictive performance beyond climate alone (Δ Spearman’s ρ = +0.08; Δ AUC = +0.08), indicating that spectral trajectories helped refine spatial discrimination of decline probability.These results demonstrate the capacity of the approach to generate spatially explicit estimates of severe growth-decline probability across broad boreal landscapes, supporting wall-to-wall assessment of forest vulnerability to changing climate.

Spatially accurate forestry road networks are essential for effective forestry operations, sustainable resource management, and conservation. Current forestry road databases in Ontario have significant location errors due to limitations and human errors associated with conventional road delineation approaches such as GPS-based field surveys and photointerpretation. A previously developed algorithm, which used airborne laser scanning (ALS) data, successfully corrected road locations in Quebec. However, its design limited its application in other landscapes, ALS instruments, and road construction and maintenance practices. This study advances that algorithm by integrating a deep learning component to improve its robustness and scalability for diverse forest conditions. A hybrid workflow combines the original friction-based conductivity surface with a road probability surface generated by an Attention Residual U-Net model trained on 11 LiDAR-derived features using road segments from five forest sites in Quebec. The enhanced workflow was applied to two forest management units in Ontario: Nipissing and Dryden. The results showed significant improvement in road alignment when compared to the existing provincial data and the outputs from the earlier automated approach. The deep learning-enhanced algorithm lowered mean positional error by 78% (from 9.36 m to 2.07 m) and increased the proportion of road centerline points within 3 m of the reference from 66.7% to 87.2%. These improved centerline accuracies will further support a scalable tool for rapid and accurate forestry road network mapping, which in turn will aid sustainable forest management and conservation planning at both provincial and national scales.

Rapid changes in climate and disturbance regimes are increasing uncertainty regarding the future vigour and productivity of boreal forests. This challenge is particularly relevant in Canada, where boreal forests cover more than 5.5 million km² and are predominantly composed of black spruce (Picea mariana Mill.) a species of central ecological and economic importance that appears increasingly sensitive to interacting climatic and biotic stressors. Drought, anomalous temperature extremes, frost damage and insect outbreaks can alter growth trajectories at annual to multi-decadal timescales. Quantifying the magnitude and spatial distribution of these growth changes is therefore crucial for anticipating impacts on timber supply, ecosystem service provision, and forest carbon balance.Tree-ring data provide annual-resolution records of growth and have been instrumental in characterizing climate–growth relationships across the boreal forest. However, dendrochronological networks remain spatially sparse and often capture generalized sensitivities that do not fully reflect local growth responses driven by fine-scale environmental conditions, stand structure, and disturbance legacies. Critically, they do not provide a spatially continuous and regularly updated assessment of changes in forest productivity, nor do they readily identify the regions most vulnerable to emerging stressors. Time series of satellite observations offer a complementary and scalable perspective by providing spatially explicit, long-term measurements of canopy dynamics. In particular, Landsat imagery enables direct observation of forest canopy trajectories, capturing realized responses to multiple, interacting stressors and providing critical information to refine spatial assessments of growth dynamics beyond relationships based solely on climatic variability. Integrating Earth observation data with climate and tree-ring information therefore offers a powerful opportunity to leverage their complementary strengths and deliver timely, decision-relevant information for the stewardship of forest ecosystems.In this study, we modelled the annual probability of severe growth decline in black spruce–dominated forests across Canada from 1988 to 2020 by integrating broad-scale climate data and Landsat time series with tree-ring–derived growth information from the CFS-TRenD repository. Tree-ring width series from 3,125 trees across 648 sites were used to characterize growth decline events and to train a probabilistic modelling framework that accounts for temporal dependence in growth responses and spatial heterogeneity in climate–growth relationships. The resulting model was then applied across key regions of Canada to examine spatiotemporal patterns in growth decline likelihood over recent decades and among major boreal ecozones. Results show that changes in the temporal trajectories of Landsat-derived spectral indices and forest structural attributes, together with indicators of climate extremes, were among the strongest predictors of growth decline probability, with spatial patterns and temporal trends in predicted likelihood consistent with observed growth variability in independent tree-ring series.Although the mapped probabilities do not represent direct observations of severe growth decline, they provide continuous, spatially explicit information that is critical for identifying vulnerable regions, guiding targeted monitoring efforts, and anticipating future changes in boreal forest productivity under ongoing environmental change. More broadly, this study demonstrates how freely available climatic and satellite-derived datasets can be integrated with tree-ring information to extend growth-related insights to continental scales and support spatially explicit assessments of forest productivity and vulnerability.

Terrestrial and arboreal lichen are the primary winter food source of the threatened boreal ecotype of woodland caribou (Rangifer tarandus caribou). Despite its essential role, lichen biomass, a key factor of caribou habitat suitability, remains poorly quantified in forested landscapes, limiting effective habitat management and recovery planning. Our objective was to estimate lichen volume across two Newfoundland (Canada) boreal caribou herd ranges (Gros Morne and Gaff Topsails) using vegetation-specific models that integrate 1984–2022 Landsat Best Available Pixel composites with annual vegetation classifications from the National Terrestrial Ecosystem Monitoring System. Field data from 66 plots informed zero-inflated gamma models for coniferous forests, wetlands, barren land and shrublands. Predictors included spectral reflectance, vegetation indices, and vegetation type interactions. Model predictions closely matched observed lichen volume (pooled R2 = 0.77, RMSE = 8.58 dm3 m−2). Spatial outputs revealed widespread low lichen volume, with the highest volume in coniferous forests. Temporal trends showed a general increase in lichen volume since the early 1990s, with faster recovery across Gros Morne—potentially aided by the protected national park—compared to the more disturbed Gaff Topsails, where historical disturbances could have slowed early recovery. Estimated lichen biomass showed that patches reaching ~3,000 kg·ha−1—a level typical of stands preferentially used by caribou in winter—were rare but widely distributed across the landscape. These vegetation-stratified, long-term estimates of lichen volume provide a key tool for assessing habitat suitability and guiding conservation strategies aimed at supporting woodland caribou recovery across Newfoundland.

In times of unprecedented global change, forest management education, especially in silviculture, must evolve to prepare future forest managers with relevant skills and a comprehensive view of adaptive silviculture. As a data-driven science, silviculture must now integrate multidisciplinary technical and professional expertise to shape the forests of tomorrow—by planning and assessing treatment impacts while also meeting socioeconomic needs. As a result, silviculture education, particularly in undergraduate programs, needs to advance to meet these future challenges to ensure students have the essential tools and analytical skills required to undertake holistic, field-based silviculture practice. To support this advancement, we propose a vision for silviculture education that emphasizes fundamental knowledge (e.g., forest ecology, mensuration, governance), while also adapting to evolving concepts driven by socioeconomic factors, new silvicultural systems, a focus on ecosystem services, and the availability of new geospatial technologies. This new vision calls for strong leadership in experiential education to foster active learning through innovative tools, work-integrated experiences, and interdisciplinary collaboration. Such a curriculum will equip students with the skills required to integrate knowledge and adapt holistically, preparing them to meet the evolving demands of modern silviculture.

Increasing temperature and changes in water dynamics are bringing uncertainty regarding the future productivity of boreal forests, even in the absence of stand-replacing disturbances. There is accumulating evidence that water deficits caused by warmer summer temperatures are linked to decreases in the growth rate of boreal tree species in some regions. In this context, it is essential to provide forest professionals with a means of monitoring net forest growth rates in undisturbed areas and at the scale of a management unit in order to determine where and when changes in growth are taking place. This is challenging using conventional forest inventory approaches. In this study, we use Landsat time series and data from permanent sample plots (PSP) to develop spatially explicit estimates of annual net basal area growth at a 30-m spatial resolution for a forest management unit in Canada. An ordinary least square regression model was developed using data from 120 PSPs and validated on an independent set of 60 PSPs, with R2 values of 0.61 and 0.58, respectively. Applying the model over a 586 607-ha study area revealed considerable temporal and spatial variability in the predicted growth rates and their evolution through time. There was an overall decline in predicted growth rates over time, with this trend corroborated by the PSP data and attributed to the ageing demographics of the forests in the study area. This variability was related to forest development stage, species composition, and structural attributes derived from light detection and ranging (LiDAR). The information generated by the suggested approach can help to improve yield predictions, optimize rotation lengths, and allow for the identification of target areas where silvicultural interventions aimed at maintaining or enhancing growth could be conducted.