Satellite-based forest area consistent with FAO definitions for Canada. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The forest area is based on the Food and Agricultural Organization of the United Nations (FAO) definition. The FAO definition incorporates land use, whereby trees removed by fire and harvesting for instance, remain forest as the trees will return. The included map displays the current forest cover for year as noted (i.e. 2022), plus the satellite-based temporally informed forest area where tree cover has been temporarily lost due to stand replacing disturbances (i.e., fire, harvest). For an overview of the methods, data, image processing, as well as information on accuracy assessment see Wulder et al. (2020). Open Access: Wulder, M.A., T. Hermosilla, G. Stinson, F.A. Gougeon, J.C. White, D.A. Hill, B.P. Smiley. (2020). Satellite-based time series land cover and change information to map forest area consistent with national and international reporting requirements. Forestry: An International Journal of Forest Research 93(3), 331-34, https://doi.org/10.1093/forestry/cpaa0063 . ( Wulder et al. 2020)

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

FCOVER corresponds to the amount of the ground surface that is covered by vegetation, including the understory, when viewed vertically (from nadir). FCOVER is an indicator of the spatial extent of vegetation independent of land cover class. It is a dimensionless quantity that varies from 0 to 1, and as an intrinsic property of the canopy, is not dependent on satellite observation conditions. This product consists of a national scale coverage (Canada) of monthly maps of FCOVER indicator during a growing season (May-June-July-August-September) at 20m resolution. References: L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617 Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests, Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600. https://www.sciencedirect.com/science/article/pii/S0034425723001517

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per ground surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. This product consists of a national scale coverage (Canada) of monthly maps of the maximum LAI during a growing season (May-June-july-August-September) at 20m. References: L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617 Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests, Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600. https://www.sciencedirect.com/science/article/pii/S0034425723001517

Fraction of absorbed photosynthetically active radiation (fAPAR) quantified the absorbed by green foliage. fAPAR has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. This product consists of a national scale coverage (Canada) of monthly maps of fAPAR during a growing season (May-June-July-August-September) at 20m resolution. References: L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617 Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests, Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600. https://www.sciencedirect.com/science/article/pii/S0034425723001517

Vegetation biophysical parameters correspond to physical properties of vegetation structure (e.g. density, height, biomass), biochemistry (e.g. chlorophyll and water content) or energy exchange (e.g. albedo, temperature). These parameters have been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. The Canada wide products are derived from systematically acquired satellite imagery with spatial resolution from 10m to 30m and provided as monthly temporal or peak-season composites due to cloud cover. Products are derived applying algorithms developed at Canada Centre for Remote Sensing (NRCan) to Copernicus Sentinel 2 satellite imagery. Select a related product first to view content.

FCOVER corresponds to the amount of the ground surface that is covered by vegetation, including the understory, when viewed vertically (from nadir). FCOVER is an indicator of the spatial extent of vegetation independent of land cover class. It is a dimensionless quantity that varies from 0 to 1, and as an intrinsic property of the canopy, is not dependent on satellite observation conditions.This product consists of FCOVER indicator during peak-season (June-July-August) at 100m resolution covering Canada's land mass.

This data publication contains a set of 30-m resolution raster files representing annual (1985–2025) wall-to-wall maps of fractional landcover across Canada originally described in (Guindon et al., 2026a). The dataset provides pixel-level percentage estimates of water, rock, soil, burn scars, lichen, herbaceous vegetation, low shrubs, and tall shrubs, as well as broadleaf and coniferous treed crown closure layers derived from SCANFI v2. A summary discrete landcover classification layer is also provided. The Spatialized CAnadian National Forest Inventory (SCANFI) version 3 extends the existing SCANFI framework to include both treed and non-treed surface components, enabling a comprehensive vertical surface projection where all fractional cover components within each 30-m pixel sum to 100%. **Relationship with SCANFI v2** SCANFI v3 is fully harmonized with the SCANFI v2 dataset (Guindon et al., 2026b) https://doi.org/10.23687/07653869-f303-46c2-a04e-9ab479b73cbf). Coniferous and broadleaf treed crown closure values are extracted directly from SCANFI v2, ensuring consistency across treed structural and species attributes. The novel non-treed fractional cover layers introduced in SCANFI v3 are designed to be compatible with existing SCANFI v2 layers (canopy height, aboveground biomass, tree species composition), extending the product's utility without replacing previous outputs. **Methodology overview** The methodology, along with exhaustive validation analyses, are described in detail in the official publication (Guindon et al., 2026a). **Landcover classification methodology** - Non-vegetated pixels (water, rock, soil, burn scars) were classified by the cover type with the highest fractional proportion. - Non-treed vegetation was split into shrub and non-shrub (herbaceous/lichen) classes, based on the highest fractional proportion. - Shrub pixels were further classified as low or tall shrub, based on the highest fractional proportion. - Treed pixels were classified as coniferous or broadleaf if crown closure exceeded 75%, otherwise as mixed. - Non-treed pixels with 10-50% coniferous crown closure were reclassified as "treed coniferous," noting the dominant ground cover (e.g., treed coniferous with lichen). **Validation summary** - Treed cover (from SCANFI v2): - Internal leave-one-plot-out validation: overall crown closure R² = 0.82, RMSE = 13.95; broadleaf cover R² = 0.70, RMSE = 19.82. - External validation of crown closure with ABoVE airborne lidar: R² = 0.73, RMSE = 28.55. - Additional external validation with 48,255 MAGPlots: broadleaf R² = 0.77, RMSE = 18.38; coniferous R² = 0.75, RMSE = 19.36. - Water cover: - Landsat random forest classifier (detection of mixed water pixels): accuracy = 93%, Kappa = 0.89. - XGBoost regression for continuous water cover: R² = 0.92, RMSE = 12.01, MAE = 5.48 (all pixels); R² = 0.48, RMSE = 17.68, MAE = 11.38 (mixed water pixels only). - Landsat non-treed fractional cover (30-m): Stratified leave-one-plot-out cross-validation R² ranged from 0.22 (low shrubs) to 0.58 (treed broadleaf). Intermediate performance for herbaceous, lichen, and burn scars (R² = 0.55). - External validation: - Comparison with the Quebec northern ecological map (n = 1,242,423 polygons) showed overall agreement across landcover strata. - NASA ABoVE lidar vertical profiles were consistent with predicted landcover classes. - Field validation (342 plots across five provinces and territories) confirmed internal metrics for rock, soil, lichen, and low shrubs, with improved treed class accuracy (broadleaf R² = 0.65; coniferous R² = 0.69). **Dataset description** Annual (1985–2025) fractional landcover is represented as single-band GeoTIFF files with the following naming convention: SCANFI_XXX_YYY_ZZZ_[version_number]_[YYYYMMDD].tif, where: - XXX = landcover type: - nonTreed for all novel non-treed landcover classes introduced in this study - treed for SCANFI treed species crown closure - landcover for the summary discrete landcover layer - YYY = fractional component class: water, rock, soil, burnScars, lichen, herbaceous, lowShrubs, tallShrubs, broadleaf, or coniferous - ZZZ = target year: 1985 onwards - version_number = v3 - YYYYMMDD = date of file creation, provided for update tracking All GeoTIFF files are Cloud Optimized GeoTIFFs (COGs). Pixel values (except for the landcover layer) represent the percentage of the corresponding class, ranging from 0 to 100. The SCANFI_landcover GeoTIFF files contain integer codes that identify different landcover types. The codes are as follows: 1,Water 2,Rock 3,Soil 4,Burn scars 5,Lichen 6,Herbaceous 7,Low shrubs 8,Tall shrubs 9,Treed broadleaf 10,Treed mixed 11,Treed coniferous 12,Treed coniferous with lichen 13,Treed coniferous with rock/soil 14,Treed coniferous with herbs 15,Treed coniferous with low shrubland 16,Treed coniferous with tall shrubland 17,Cropland 18,Urban 19,Road 20,Snow/Ice **Data download** The data can be downloaded from the FTP server on the Open Data portal presented here, preferably using a browser download manager or an external client such as FileZilla. The data will also be available as Cloud Optimized GeoTiffs (COGs) on the Canada Centre for Mapping and Earth Observation Data Cube Platform (https://datacube.services.geo.ca/en/index.html) and on the Laurentian Forestry Center Remote Sensing lab’s Google Earth Engine data catalog (https://developers.google.com/earth-engine/datasets/publisher/gcpm041u-lemur?hl=en). **Known limitations and usage notes** **1.** Wetland environments remain particularly challenging to map, as exposed wet soils or mud surfaces may occasionally be confused with rock or bare soil. **2.** The separation between tall shrubs (>1 m) and young deciduous broadleaf stands can be difficult in regenerating forests, where structural and spectral differences are gradual. This can also be challenging in northern areas with low productivity. **3.** Lichen cover may still be predicted along small roads, trails, or disturbed surfaces not identified in the Statistics Canada or OpenStreetMap masks, as well as within some unmapped urban areas. **4.** Some regions of Yukon remain challenging, as previously reported in SCANFI v1 (Guindon et al. 2024). These errors are mainly related to limitations in the NFI training dataset available for these remote northern environments. Future NFI photo acquisitions are expected to address this issue. **5.** Understory predictions are expected to be more reliable in open stands than in dense forested environments. **6.** The lichen layer represents the vertical fractional presence of lichen cover and should not be interpreted directly as lichen biomass, as factors such as lichen thickness are not explicitly represented. **7.** The product is based on a vertical land-surface projection approach, representing what optical satellite sensors observe from above. Vegetation located beneath dense forest canopies cannot be directly mapped. However, where local field plot data are available, the fractional layers may serve as predictors for deriving additional site-specific ecosystem attributes (e.g. moss cover). **8.** In tall and dense forested areas, ground observations remain limited and the near-vertical Landsat observation geometry reduces sensitivity to understory conditions. This dataset was primarily designed for open northern environments and post-disturbance landscapes. **Mapping information** - projection: epsg 3979 - resolution: 30m **License** The data are licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0). **Dataset citation** - Guindon, L., Correia, D.L.P., Gahrouei, O.R., Perbet, P., Manka, F., Villemaire, P. and Lacarte, S. 2026. SCANFI v3: Canadian fractional landcover maps from 1985 onward. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/50f132f9-f312-4951-bb9f-9ea99580f29f **References** - Guindon, L., Correia, D.L.P., Gahrouei, O.R., Perbet, P., Smiley, B., Stralberg, D., Collins, L., Parisien, M.-A., Wang, X., Manka, F. and Boucher, J. 2026a. Canadian fractional landcover maps from 1985 onward. Scientific Data [In press] - Guindon, L., Correia, D.L.P., Manka, F., and Smiley, B. 2026b. SCANFI v2: Spatialized CAnadian National Forest Inventory data product v2. Natural Resources Canada. https://doi.org/10.23687/07653869-f303-46c2-a04e-9ab479b73cbf - Guindon, L., Manka, F., Correia, D.L.P., 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. https://doi.org/10.1139/cjfr-2023-0118

2 Billion Trees Program Forests and trees sustain life on Earth. Beyond the jobs that our sustainably managed forests provide, people living in Canada rely on forests for a wealth of benefits. Healthy forest ecosystems sustain thousands of living organisms, supply us with food, provide shelter and shade on a sunny day, clean the air we breathe and the water we drink, and hold spiritual significance for many, particularly within Indigenous cultures. The following 10 tree planting projects (out of 72 projects funded by the 2 Billion Trees program in 2021) are being showcased to highlight the diversity of projects funded across Canada. From the application of traditional ecological knowledge, habitat restoration, increasing tree biodiversity in urban centres, or engaging local residents in an effort to educate people on the importance of nature-based climate solutions, these projects demonstrate that planting trees brings a wealth of benefits for all Canadians, from coast to coast.

Leaf area index (LAI) quantifies the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of time series of LAI observed between 2016 and 2025 over reclamation sites in Alberta, Canada May and October. The temporal frequency depends on cloud cover.