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Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.

 

Changes in vegetation distribution are underway in Arctic and boreal regions due to climate warming and associated fire disturbance. These changes have wide ranging downstream impacts—affecting wildlife habitat, nutrient cycling, climate feedbacks and fire regimes. It is thus critical to understand where these changes are occurring and what types of vegetation are affected, and to quantify the magnitude of the changes. In this study, we mapped live aboveground biomass for five common plant functional types (PFTs; deciduous shrubs, evergreen shrubs, forbs, graminoids and lichens) within Alaska and northwest Canada, every five years from 1985 to 2020. We employed a multi-scale approach, scaling from field harvest data and unmanned aerial vehicle-based biomass predictions to produce wall-to-wall maps based on climatological, topographic, phenological and Landsat spectral predictors. We found deciduous shrub and graminoid biomass were predicted best among PFTs. Our time-series analyses show increases in deciduous (37%) and evergreen shrub (7%) biomass, and decreases in graminoid (14%) and lichen (13%) biomass over a study area of approximately 500 000 km². Fire was an important driver of recent changes in the study area, with the largest changes in biomass associated with historic fire perimeters. Decreases in lichen and graminoid biomass often corresponded with increasing shrub biomass. These findings illustrate the driving trends in vegetation change within the Arctic/boreal region. Understanding these changes and the impacts they in turn will have on Arctic and boreal ecosystems will be critical to understanding the trajectory of climate change in the region.

 

Abstract Widespread changes in the distribution and abundance of plant functional types (PFTs) are occurring in Arctic and boreal ecosystems due to the intensification of disturbances, such as fire, and climate-driven vegetation dynamics, such as tundra shrub expansion. To understand how these changes affect boreal and tundra ecosystems, we need to first quantify change for multiple PFTs across recent years. While landscape patches are generally composed of a mixture of PFTs, most previous moderate resolution (30 m) remote sensing analyses have mapped vegetation distribution and change within land cover categories that are based on the dominant PFT; or else the continuous distribution of one or a few PFTs, but for a single point in time. Here we map a 35 year time-series (1985–2020) of top cover (TC) for seven PFTs across a 1.77 × 10 6 km 2 study area in northern and central Alaska and northwestern Canada. We improve on previous methods of detecting vegetation change by modeling TC, a continuous measure of plant abundance. The PFTs collectively include all vascular plants within the study area as well as light macrolichens, a nonvascular class of high importance to caribou management. We identified net increases in deciduous shrubs (66 × 10 3 km 2 ), evergreen shrubs (20 × 10 3 km 2 ), broadleaf trees (17 × 10 3 km 2 ), and conifer trees (16 × 10 3 km 2 ), and net decreases in graminoids (−40 × 10 3 km 2 ) and light macrolichens (−13 × 10 3 km 2 ) over the full map area, with similar patterns across Arctic, oroarctic, and boreal bioclimatic zones. Model performance was assessed using spatially blocked, nested five-fold cross-validation with overall root mean square errors ranging from 8.3% to 19.0%. Most net change occurred as succession or plant expansion within areas undisturbed by recent fire, though PFT TC change also clearly resulted from fire disturbance. These maps have important applications for assessment of surface energy budgets, permafrost changes, nutrient cycling, and wildlife management and movement analysis. 

Alaska’s Yukon-Kuskokwim Delta (YKD) is one of the warmest parts of the Arctic tundra biome and tundra fires are common in its upland areas. Here, we combine field measurements, Landsat observations, and quantitative cover maps for tundra plant functional types (PFTs) to characterize multi-decadal succession and landscape change after fire in lichen-dominated upland tundra of the YKD, where extensive wildfires occurred in 1971–1972, 1985, 2006–2007, and 2015. Unburned tundra was characterized by abundant lichens, and low lichen cover was consistently associated with historical fire. While we observed some successional patterns that were consistent with earlier work in Alaskan tussock tundra, other patterns were not. In the landscape we studied, a large proportion of pre-fire moss cover and surface peat tended to survive fire, which favors survival of existing vascular plants and limits opportunities for seed recruitment. Although shrub cover was much higher in 1985 and 1971–1972 burns than in unburned tundra, tall shrubs (>0.5 m height) were rare and the PFT maps indicate high landscape-scale variability in the degree and persistence of shrub increase after fire. Fire has induced persistent changes in species composition and structure of upland tundra on the YKD, but the lichen-dominated fuels and thick surface peat appear to have limited the potential for severe fire and accompanying edaphic changes. Soil thaw depths were about 10 cm deeper in 2006–2007 burns than in unburned tundra, but were similar to unburned tundra in 1985 and 1971–1972 burns. Historically, repeat fire has been rare on the YKD, and the functional diversity of vegetation has recovered within several decades post-fire. Our findings provide a basis for predicting and monitoring post-fire tundra succession on the YKD and elsewhere.

 

Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.