This data publication contains a set of 30m resolution raster files representing Canadian wall-to-wall maps, at 5-year intervals from 1985 to 2025, of broad landcover type, forest canopy height, degree of crown closure and aboveground tree biomass, along with species composition of several major tree species. The Spatialized CAnadian National Forest Inventory data product (SCANFI) was developed using the newly updated National Forest Inventory photo-plot dataset, which consists of a regular sample grid of photo-interpreted high-resolution imagery covering all of Canada’s non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a novel k-nearest neighbours (k-NN) and random forest imputation method.

SCANFI is not meant to replace nor ignore provincial inventories which could include better and more regularly updated inputs, training data and local knowledge. Instead, SCANFI was developed to provide a current, spatially-explicit estimate of forest attributes, using a consistent data source and methodology across all provincial boundaries and territories. SCANFI is the first coherent 30m Canadian wall-to-wall map of tree structure and species composition and opens novel opportunities for a wide range of studies in a number of areas, such as forest economics, fire science and ecology.

SCANFI v2 is described here. The main methodological and performance differences between SCANFI v1 (https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc) and SCANFI v2 are briefly outlined below. For a more detailed description of these changes, please consult the accompanying report. For the scientific publication about SCANFI v1, see Guindon et al. (2024).

SCANFI version 2 main updates

1. Transition to Landsat Collection 2 Updated all Landsat time series inputs from Collection 1 to Collection 2, providing improved radiometric calibration and geometric consistency across sensors and years.

2. SCANFI time series with LandTrendr-smoothed winter imagery Introduced LandTrendr-smoothed winter imagery, enabling a full 5-year interval time series from 1985 to 2025 with dual-season inputs.

3. Updated climate normals Replaced previous climate layers with smoother 1991–2020 normals (MacDonald et al. 2024), eliminating tiling artifacts.

4. Increased sample selection density of NFI training data Shifted from a photoplot-level 250m systematic grid to a denser sampling strategy: 4 pixels/hectare per polygon (max 100 points, excluding border pixels). This sampling strategy greatly increased the number of training points available, improving representation of inter- and intra-polygon variability and strengthening model performance.

5. More accurate water prediction and delineation of urban areas and croplands Water and cropland prediction is now done using a soon-to-be-published landcover time series layer in order to: (i) prevent the misclassification of recently disturbed areas as water in SCANFI v1; and (ii) convert urban areas and croplands to forested land before 2020 when the Agriculture and Agri-Food Canada (AAFC) land use map used in the original study is not available (Agriculture and Agri-Food Canada, 2022).

6. Forest age map SCANFI v2 introduces new national forest age maps by combining recent disturbance history data with spatialized NFI age estimates for undisturbed stands, producing consistent stand age attribution across Canada from 1985 to 2025, at 5-year intervals. The resulting approach reduces noise relative to single-year estimates and yields spatially complete, temporally stable forest age maps.

7. Modified response and target variable sets Water is no longer used as a target variable, whereas land position (alpine, wetland or upland) was added to the target variable list and is now an output layer. The selection of Landsat bands and indices used in modeling was also refined, improving sensitivity to forest structure, composition, and disturbance. Finally, SCANFI now relies on the recently published Canadian Medium Resolution Digital Elevation Model (Government of Canada, Natural Resources Canada, Strategic Policy and Innovation Sector, 2025). See update report for more details.

8. Improved extrapolation of arctic ecozones The tile-level approach used to extrapolate arctic ecozones outside the sampling range of the NFI photoplots was replaced by the use of two training sets to predict two distinct geographical northern areas: (1) northern Quebec; and (2) northern Northwest Territories and Nunavut. This approach considerably improved model reliability and consistency in the arctic ecozones.

9. Updated time-since-disturbance layers Integrated complete 1965-2025 harvest and fire records (Correia et al. 2024; Perbet et al. 2025), improving disturbance history initialization and enhancing stand-structure attribution.

10. Modified tree species cover layers In order to avoid interpretation errors and facilitate user analyses, SCANFI v2 tree species layers now represent the corresponding tree species crown closure, instead of the proportion of the overall crown closure that is represented by that species. Unlike SCANFI v1, users no longer need to multiply overall crown closure by tree species proportion (and divide by 100) to obtain tree species crown closure. Examples include:

    • A pixel with 30% total crown closure composed of 50% black spruce and 50% jack pine will have 15% black spruce crown closure and 15% jack pine crown closure in SCANFI v2.
    • A pixel with 40% total crown closure entirely dominated by balsam fir will have 40% balsam fir crown closure in SCANFI v2.
    • A pixel with 100% total crown closure composed of 60% black spruce and 40% balsam fir will have 60% black spruce and 40% balsam fir crown closure in both SCANFI v1 and SCANFI v2.

Tree species aboveground biomass can still be obtained by estimating the proportion of the corresponding tree species relative to pixel-level crown closure and multiplying that value by pixel-level biomass.

Validation summary and comparison with version 1

External validation results show that SCANFI v2 consistently outperforms SCANFI v1 across all independent datasets used in Guindon et al. (2024). Structural attributes validated with Global Ecosystem Dynamics Investigation (GEDI), Potapov et al. (2021), and airborne lidar exhibit R² gains of approximately 2-3 points and modest reductions in RMSE and MAE, indicating improved estimates of height and crown closure.

Species composition shows the largest improvements, with R² increases of roughly 4 to more than 15 points for key species such as black spruce, jack pine, tamarack, and the broadleaf grouping (see the update report for more details). Biomass estimates also improve, with smaller but consistent gains in explained variance and reduced error across ground-plot datasets. In contrast, internal cross-validation metrics are slightly lower for v2, reflecting the effect of a complex training dataset with much more forested samples rather than reduced real-world predictive performance.

SCANFI time series

SCANFI v2 provides a publicly available 5-year interval national time series (1985–2025) based on LandTrendr-smoothed summer and winter Landsat imagery with cross-sensor harmonization. Pixel-level temporal inconsistencies remain by design to preserve traceability and consistency across attributes, making the time series unsuitable for plot-level analyses but reliable for large-scale regional and national assessments where noise averages out. Although not intended for detailed biomass trend analysis, the estimated national biomass increase of approximately 4.8% between 1985 and 2025 suggests ecologically plausible values, pending further evaluation.

Limitations

1. The spectral disturbances of some areas disturbed by pests are not comprehensively represented in the training set, thus making it impossible to predict all defoliation cases. One such area, severely impacted by the recent eastern spruce budworm outbreak, is located on the North Shore of the St. Lawrence River. These forests are misrepresented in our training data; therefore, our estimates in these areas are imprecise.

2. Attributes of open stand classes, namely shrub, herb, rock and bryoid, are more difficult to estimate through the photointerpretation of aerial images. Therefore, these estimates could be less reliable than the forest attribute estimates.

3. As reported in the manuscript, the uncertainty of tree species cover predictions is relatively high. This is particularly true for less abundant tree species, such as ponderosa pine and tamarack. The tree species layers are therefore suitable for regional and coarser scale studies.

4. Our validation indicates that the areas in Yukon exhibit a notably lower R² value. Consequently, estimates within these regions are less dependable.

5. Urban areas and roads are classified as rock, according to the 2020 Agriculture and Agri-Food Canada land-use classification map (Agriculture and Agri-Food Canada, 2022). Even though those areas contain mostly buildings and infrastructure, they may also contain trees. Forested urban parks are usually classified as forested areas. Vegetation attributes are also predicted for forested areas in agricultural regions.

Data download

The data can be downloaded from the FTP server (ftp.maps.canada.ca/pub/nrcan_rncan/Forests_Foret/SCANFI/v2/), referenced in the “Data and Resources” section, using a browser download manager or an external client such as FileZilla.

Dataset citation

• Luc Guindon, David L.P. Correia, Francis Manka and Byron Smiley. 2026. SCANFI v2: Spatialized CAnadian National Forest Inventory data product v2. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/07653869-f303-46c2-a04e-9ab479b73cbf

References

• Agriculture and Agri-Food Canada. (2022). 2020 AAFC Land Use [Map]. Retrieved from https://open.canada.ca/data/en/dataset/7a098ea9-cc31-4d79-b326-89f6cd1fbb7d.

• Correia, D.L., Guindon, L. and Parisien, M.A., 2024. Extending Canadian forest disturbance history maps prior to 1985. Ecosphere, 15(8), p.e4956.

• Guindon, L., Manka, F., Correia, D.L., Villemaire, P., Smiley, B., Bernier, P., Gauthier, S., Beaudoin, A., Boucher, J. and Boulanger, Y., 2024. A new approach for Spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series. Canadian Journal of Forest Research, 54(7), pp.793-815.

• MacDonald, H., McKenney, D.W., Pedlar, J., Lawrence, K., de Boer, K. and Hutchinson, M.F., 2024. Spatial datasets of 30-year (1991–2020) average monthly total precipitation and minimum/maximum temperature for Canada and the United States. Data in Brief, 55, p.110561.

• Government of Canada, Natural Resources Canada, Strategic Policy and Innovation Sector. (2025, August 28). Medium Resolution Digital Elevation Model (MRDEM) - CanElevation Series. Natural Resources Canada, Federal Geospatial Platform. https://osdp-psdo.canada.ca/dp/en/search/metadata/NRCAN-FGP-1-18752265-bda3-498c-a4ba-9dfe68cb98da

• Perbet, P., Guindon, L., Correia, D.L., Gahrouei, O.R., Côté, J.F. and Béland, M., 2025. Historical insect disturbance maps from 1985 onwards for Canadian forests derived using earth observation data. Scientific Data.

• Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M.C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C.E. and Armston, J., 2021. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 253, p.112165.