Mapping growth decline probability and trends across Canada’s black spruce forests from tree-ring, Landsat, and climate data

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.