Alaska’s boreal-tundra resolved with vegetation height from commercial imagery and a foundation model

A reconfiguration of vegetation structure patterns in the boreal forest-tundra ecotone and Low Arctic tundra is underway, driven by shrubification, tree expansion, permafrost thaw, and disturbance regimes linked to rapid warming. The short-statured vegetation in these regions presents challenges for resolving the heterogeneity of plant functional types (PFTs), even when very high resolution spaceborne imagery is used with earth observation artificial intelligence (AI EO ) models. In this study, we resolve height variation of high northern latitude PFTs by fine-tuning an AI EO model built for global applications using a large variety of domain-specific airborne training and validation data, and field observations. We map, validate, and quantify vegetation heights at 2 m horizontal and decimeter vertical scales, and estimate the area of height classes across 40% of Alaska’s boreal-tundra ecotone. Results show most of this area is low stature vegetation <0.35 m in height (63.4% ± 5.2%, 95% confidence interval), while various low shrub (11.9% ± 1.2%, 6.3%± 1.1%) and tall shrub and forest (10.9% ± 1.9%, 3.1% ± 0.6%, 1.8 ± 0.5%, 2.6 ± 0.8%) classes distinguish height intervals useful for vegetation structure and plant functional type monitoring in Alaska. Together, canopies <1.5 m tall comprise approximately 82% of the mapped landscape, nearly 18% of which are some form of low shrubs 0.35–1.5 m in height, while height classes ⩾1.5 m occupy the remaining ∼18% of the mapped area. Validation reveals that predictions underestimate the tallest surfaces of the vegetation canopy. This work addresses a gap in resolving the fine-scaled vegetation heterogeneity associated with key PFTs, leveraging the complementarity of commercial and public remote sensing data, to provide a basis for assessing the variation in the routes and rates of Arctic and sub-Arctic vegetation change.