AimTo evaluate current and future dynamics of 25 tree species spanning United States and Canada.LocationUnited States and Canada.MethodsWe combine, for the first time, the species compositions from relative importance derived from the USA’s Forest Inventory Analysis (FIA) with gridded estimates based on Canada's National Forest Inventory (NFI‐kNN))‐based photo plot data to evaluate future habitats and colonization potentials for 25 tree species. Using 21 climatic variables under RCP 4.5 and RCP 8.5, we model climatic habitat suitability (HQ) within a consensus‐based multimodel ensemble regression approach. A migration model is used to assess colonization likelihoods (CL) for ~100 years and combined with HQ to evaluate the various combinations of HQ + CL outcomes for the 25 species.ResultsAt a continental scale, many species in the conterminous United States lose suitable climatic habitat (especially under RCP 8.5) while Canada and USA’s Alaska gain climate habitat. For most species, even under optimistic migration rates, only a small portion of overall future suitable habitat is projected to be naturally colonized in ~100 years, although considerable variation exists among species.Main conclusionsFor the species examined here, habitat losses were primarily experienced along southern range limits, while habitat gains were associated with northern range limits (especially under RCP 8.5). However, for many species, southern range limits are projected to remain relatively intact, albeit with reduced habitat quality. Our models predict that only a small portion of the climatic habitat generated by climate change will be colonized naturally by the end of the current century—even with optimistic tree migration rates. However, considerable variation among species points to the need for significant management efforts, including assisted migration, for economic or ecological reasons. Our work highlights the need to employ range‐wide data, evaluate colonization potentials and enhance cross‐border collaborations.

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The Landsat program has long supported pioneering research on the recovery of forest information by remote sensing technologies for several decades, and efforts to improve the thematic resolution and accuracy of forest compositional products remains an area of continued innovation. Recent development and application of Landsat time series analysis offers unique opportunities for quantifying seasonality and trend components among different forest types for developing alternative feature sets for forest vegetation mapping. Within a large forested landscape in Southeastern Ohio, USA, we examined the use of harmonic metrics developed from time series of all available Landsat-8 observations (2013–2019) relative to seasonal image composites, including accompanying spectral components and vegetation indices. A reference dataset among three sources was integrated and used to categorize forest inventory data into seven forest type classes and gradient compositional response. Results showed that the combination of harmonic metrics and topographic variables achieved an accuracy agreement with the reference data of 74.9% relative to seasonal composites (71.6%) and spectral indices (70.3%). Differences in agreement were attributed to improved discrimination of three heterogeneous upland hardwood classes and an early-successional, young forest class, all forest types of primary interest among managers across the region. Variable importance metrics often identified the cosine and sine terms that quantify the seasonality in spectral values in the harmonic feature space, suggesting these aspects best support the characterization of forest types at greater thematic detail than seasonal compositing procedures. This study demonstrates how advanced time series metrics can improve forest type modeling and forest gradient quantifications, thus showcasing a need for continued exploration of such approaches across different forest types.

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