Canada Base Map Elevation (CBME) consists of a simple light hillshade for use as a background for mapping application. The Canada Atlas Lambert (EPSG:3978) is used and only covers the extents of Canada.

The Canada Centre for Mapping and Earth Observation (CCMEO) has created a 30m resolution radar mosaic of Canada's landmass from the RADARSAT Constellation Mission (RCM). This product highlights different types of radar interaction with the surface, which can assist the interpretation and study of land cover on a national scale. The national mosaic is made up of 3222 RCM images acquired between August 2023 and February 2024. (Credit: RADARSAT Constellation Mission imagery © Government of Canada [2024]. RADARSAT is an official mark of the CSA.)

Satellite-Based Forest Inventory (SBFI) informing on Canada's forested land cover, disturbance recovery, structure, species, stand age from 2020, and stand-replacing disturbances from 1985-2020. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The SBFI polygons represent homogeneous forest conditions akin to those of stands delineated in a strategic forest inventory. Over 25 million SBFI polygons were delineated using a multiresolution segmentation algorithm applied to the 2020 Landsat surface-reflectance BAP image composite (30-m spatial resolution), fire year, and harvest year layers derived from Landsat with the C2C approach. A minimum map unit of 0.45 ha (5 pixels) was used to define polygons. The entirety of Canada's forest ecosystems were mapped using the same data, attributes, and temporal representation, resulting in a common vegetation inventory system of Canada's ~650 Mha forested ecosystems. Given the large and diverse forest area of Canada, the strength of an SBFI lies in its use of a consistent data source and methodology across jurisdictional boundaries, and across managed and unmanaged forest areas, enabling consistently generated synoptic, spatially explicit information outputs. The data included herein are based upon free and open satellite data and information products following established and communicated approaches. When using this data, please cite as: Wulder, M.A., Hermosilla, T., White, J.C., Hobart, G.W., Bater, C.W., Bronson, S.C., 2024. Development and implementation of a stand-level Satellite-Based Forest Inventory for Canada. Forestry: An International Journal of Forest Research 97(4), 546-563. (Wulder et al. 2024).

GeoSearch is an interactive mapping application that makes it easy to find places in Canada, see them on a map, and get basic geographic and demographic data for them.

CanLaBS v2 is an update to the Canada Landsat Burned Severity (CanLaBS) data product, available at https://doi.org/10.23687/b1f61b7e-4ba6-4244-bc79-c1174f2f92cd, builds upon the methodology originally described in Guindon et al. (2021), entitled “Trends in wildfire burn severity across Canada, 1985 to 2015” and published in the Canadian Journal of Forest Research (https://doi.org/10.1139/cjfr-2020-0353). CanLaBS v2 introduces several important improvements to input data sources, temporal coverage, and modeling approaches. **1. Key Updates in CanLaBS v2** **1.1 Transition to Landsat Collection 2** All Landsat inputs used to derive burn severity metrics have been updated from Landsat Collection 1 to Landsat Collection 2 (Earth Resources Observation and Science (EROS) Center, 2020a, 2020b, 2020c). Landsat Collection 2 provides improved radiometric calibration, refined atmospheric correction, and enhanced geometric accuracy, resulting in greater temporal consistency and more reliable spectral change detection across sensors and years. **1.2. Expanded fire perimeter coverage (NBAC 1986–2024)** The updated product now covers all fire perimeters included in the National Burn Area Composite (NBAC; Skakun et al., 2022) from 1986 to 2024. This substantially extends the temporal range of the dataset relative to the original release and ensures consistency with the most up-to-date national fire perimeter record used in Canada-wide disturbance analyses. **1.3. Improved random forest model for salvage logging detection** Salvage logging detection has been updated using an improved random forest (RF) classification model trained on 3614 photo-interpreted reference points. The model uses a refined set of spectral predictors derived from Landsat imagery, including pre- and post-fire band 3, post-fire bands 4, 5 and 7 (according to the Landsat 7 nomenclature), inter-annual spectral differences (ΔB3, ΔB4, ΔB5), and pre- and post-fire Normalized Difference Vegetation Index (NDVI). Model performance was evaluated using a train-test split (80%, 20%, respectively). This analysis revealed an overall accuracy of 90.6% and Cohen’s kappa of 0.87 (See **Table 1 in the update report**, available in the download section). Some confusion occurred between low-vegetation fires and salvage logging (the primary class of interest), but overall performance was strong, with 95.49% precision, 75.6% recall, and an F1-score of 84.39%. **1.4. Revised gapfilling strategy** As in the original product, gapfilling of pre-fire Landsat data is retained to ensure complete characterization of pre-disturbance conditions. However, post-fire Landsat gapfilling is no longer applied in this version. This results in some missing data but avoids the introduction of uncertainty associated with radiometric regression-based gapfilling. A total of 6.9% of all NBAC burnt pixels are missing data. This proportion decreased over time due to improved Landsat data coverage, from 12.7% for fires before 2000 (pre-Landsat 7) to 2.59% for fires after 2012 (post-Landsat 8 launch). **1.5. Removal of pre-fire forest attribute layers** Pre-fire forest attribute layers (e.g., canopy density, live aboveground dry biomass, species composition) are no longer included in this version of CanLaBS. These attributes are now provided through the Spatialized Canadian National Forest Inventory (SCANFI v2; Guindon et al., 2026 ), which offers a more comprehensive, internally consistent, and regularly updated source of pre-disturbance forest information. Users are encouraged to combine CanLaBS with SCANFI v2 (Guindon et al., 2026) for their analyses. Users should use forest attributes from 2 years before the fire to avoid over-smoothed data that artificially underestimate pre-fire forest vegetation when pre-fire year Landsat data are unavailable. The fire start dates can be accessed via NBAC (https://cwfis.cfs.nrcan.gc.ca/datamart). **2. Use limitations** **2.1.** This database is not designed to study a single fire or a limited number of fires but rather to study large areas with several fires. No radiometric correction or change was made per fire such as the offset method, or a mean, or median approach for pixels of the same year (see cjfr-2020-0353supplb at https://doi.org/10.1139/cjfr-2020-0353). Even if surface reflectance images were used, there may be radiometric differences within the same fire due to the use of different Landsat scenes. Differences in atmospheric correction between adjacent scenes may therefore be perceptible. The primary reason for not applying additional corrections in these cases is the insufficient number of pixels available per fire during July and August, particularly in certain regions and specific time periods.To achieve a spatially and temporally consistent database, a uniform processing approach was applied to all pixels. These points are discussed in the article and in the supplementary material (see cjfr-2020-0353supplb at https://doi.org/10.1139/cjfr-2020-0353). **2.2.** Burnt areas that have undergone salvage logging were detected using a classification approach. This is not an exhaustive mapping of all areas that were salvage logged beyond one year after the fire, the goal was to eliminate these areas from the analyses, as the post-fire values (NBRpost) would be biased by the absence of trees and by the presence of soil disturbed by scarification. **2.3.** Fires occurring in forests heavily affected by the mountain pine beetle (Dendroctonus ponderosae), spruce budworm (Choristoneura fumiferana), or other defoliators should ideally be excluded from analyses, as pre-fire NBR values are inherently low, potentially biasing dNBR-based assessments. CanLaD (Perbet et al., 2025) now provides identification of these affected areas (available at https://doi.org/10.23687/902801fd-4d9d-4df4-9e95-319e429545cc). **2.4.** The 1985 and 2024 fires represent the beginning and end years of the time series, it is possible that some fires are incomplete for these years, and perhaps to a lesser extent for the 1986 and 2023 fires. **3. Summary** Overall, this update improves the precision and temporal coverage of the CanLaBS data product by leveraging Landsat Collection 2 with updated national fire perimeter polygons and a refined salvage detection method. These changes enhance the suitability of the dataset for national-scale analyses of fire effects, post-fire management, and long-term disturbance dynamics in Canadian forests. **4. Layers description** There are 3 layers: - CanLaBS_1985_2024_v20260121.tif - dNBR values for all burnt pixels according to NBAC - CanLaBS_salvageMask_1985_2024_v20260121.tif - Binary layer where '1' identifies pixels where salvage logging occurred - NBAC_MRB_1972to2024_reproj.tif - NBAC fire year **5. Data download** The data can be downloaded from the FTP server (ftp.maps.canada.ca/pub/nrcan_rncan/Forest-fires_Incendie-de-foret/CanLaBS_v2-Burned_Severity-Severite_des_feux), referenced in the “Data and Resources” section, using a browser download manager, such as DownThemAll, or an external client such as FileZilla. **6. Dataset citation** - Guindon L., Correia D., Perbet P. 2026. Canada Landsat Burned Severity (CanLaBS v2): a Canada-wide Landsat-based 30-m resolution product of burned severity since 1985. https:/doi.org/10.23687/2af751e7-79f9-4da8-9b45-14688818dca3 **7. References** - Earth Resources Observation and Science (EROS) Center. 2020a. Landsat 4–5 Thematic Mapper Level-2, Collection 2. Dataset. U.S. Geological Survey. https://doi.org/10.5066/P9IAXOVV - Earth Resources Observation and Science (EROS) Center. 2020b. Landsat 7 Enhanced Thematic Mapper Plus Level-2, Collection 2. Dataset. U.S. Geological Survey. https://doi.org/10.5066/P9C7I13B - Earth Resources Observation and Science (EROS) Center. 2020c. Landsat 8–9 Operational Land Imager / Thermal Infrared Sensor Level-2, Collection 2. Dataset. U.S. Geological Survey. https://doi.org/10.5066/P9OGBGM6 - Guindon, L., S. Gauthier, F. Manka, M. A. Parisien, E. Whitman, P. Bernier, A. Beaudoin, P. Villemaire, and R. Skakun. 2021. “Trends in Wildfire Burn Severity across Canada, 1985 to 2015.” Canadian Journal of Forest Research 51 (9): 1230–1244. https://doi.org/10.1139/cjfr-2020-0353 - Guindon, L., P. Villemaire, D. L. P. Correia, F. Manka, S. Lacarte, and B. Smiley. 2023. SCANFI: Spatialized CAnadian National Forest Inventory Data Product. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/18e6a919-53fd-41ce-b4e2-44a9707c52dc - Guindon, L., D. L. P. Correia, F. Manka, and B. Smiley. 2026. SCANFI v2: Spatialized Canadian National Forest Inventory Data Product. Quebec, Canada: Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre. https://doi.org/10.23687/07653869-f303-46c2-a04e-9ab479b73cbf - Perbet, P., L. Guindon, D. L. P. Correia, et al. 2025. “Historical Insect Disturbance Maps from 1985 Onwards for Canadian Forests Derived Using Earth Observation Data.” Scientific Data 12: 2012. https://doi.org/10.1038/s41597-025-06269-x - Perbet, P., L. Guindon, D. L. P. Correia, P. Villemaire, O. Reisi Gahrouei, and R. St-Amant. Canada Landsat Disturbance with Pest (CanLaD): A Canada-Wide Landsat-Based 30-m Resolution Product of Fire, Harvest and Pest Outbreak Detection and Attribution since 1987. https://doi.org/10.23687/902801fd-4d9d-4df4-9e95-319e429545cc - Skakun, R., G. Castilla, J. Metsaranta, E. Whitman, S. Rodrigue, J. Little, K. Groenewegen, and M. Coyle. 2022. “Extending the National Burned Area Composite Time Series of Wildfires in Canada.” Remote Sensing 14 (13): 3050.

**1. Overview** Pre-CanLaD v2 updates Canada's 1965-1984 forest disturbance history dataset initially published by Correia et al. (2024) with enhanced detection methods and improved temporal accuracy and coverage. **2. Key Updates in pre-CanLaD v2** **2.1. Enhanced Detection** - Fractional land-cover change analysis identifies post-disturbance forest regeneration using 1985-2020 vegetation cover differences - Dual detection approach combines automated polygon-based classification with manual photo-interpretation - Incorporates National Burn Area Composite (NBAC; Skakun et al., 2022) 1972-1984 fire perimeters for improved fire year attribution **2.2. Detection Results** See **Table 1 in the update report**, available in the download section below. This table presents the total disturbed areas, by disturbance type, for the periods 1950–1964 and 1965–1984. The “New detection” column represents disturbed areas for which no reliable previous data were available. The “Previously recorded” column represents disturbed areas already documented in the datasets described in Table 3 of Correia et al. (2024) and NBAC, for which the predicted disturbance year was subsequently adjusted. **3. Dataset description** Four raster layers available: - preCanLaD_disturbanceType_v2_20251126.tif - Pixel values are disturbance type: - 2 = Fire - 3 = Harvest - 4 = Insect - preCanLaD_disturbanceYear_v2_20251126.tif - Pixel values are disturbance year: - 1950-1984 = years - 999 = unknown (user should convert this value to 1955) - preCanLaD_correctionMask_v2_20251126.tif - Pixel values are data provenance codes: - 0 = Pre-CanLaD-only disturbance - 1 = Confirmed fire (manual or confirmed by pre-CanLaD v1 in the original publication) - 11 = Canadian National Fire Database (CNFDB) corrected fire - 12 = NBAC-corrected fire - 2 = Confirmed harvest - preCanLaD_updateMask_v2_20251126.tif - Pixel values are version change indicator: - 1 = v1 retained - 2 = v2 new/updated **4. Key Improvements** - Improved fire-harvest discrimination through provincial harvest polygon integration - Hierarchical dating system prioritizes NBAC > CNFDB polygons > CNFDB points > model-based dates > manual interpretation and unknown dates - Better harmonization with current historic disturbance records - Quality controls: 11-pixel minimum patch size, 1985-2020 disturbance masking **5. Known Limitations** - Some temporal uncertainty remains; grouping disturbance years into age classes can help mitigate its effects.High-severity burns preferentially detected; low-severity fires may be missed, along with fires in non-forested and open forest areas with low forest recovery rates - Some fire-harvest confusion in transition zones - Harvest detections may be biased toward provinces with publicly available forest inventories because the pre-CanLaD v2 method relies on these inventories to identify additional harvested areas - No pixel-level dates for insect outbreaks **6. Data download** The data can be downloaded from the FTP server (ftp.maps.canada.ca/pub/nrcan_rncan/Forests_Foret/canada_disturbances_1965to1984/v2/), referenced in the “Data and Resources” section, using a browser download manager, such as DownThemAll, or an external client such as FileZilla. **7. Dataset citation** - Guindon L., Correia D.L.P. and S. Brisson. 2026. Pre-CanLaD v2: Canada Landsat Disturbance (CanLaD) 30-m resolution disturbance detection prior to 1984. https://doi.org/10.23687/8d49698f-40f9-40da-b097-a3f4c90adf5a **8. Reference** - Correia, David L., Luc Guindon, and Marc-André Parisien. 2024. “Extending Canadian Forest Disturbance History Maps Prior to 1985.” Ecosphere 15 (8): e4956. - Natural Resources Canada. 2023. Canadian National Fire Database Natural Resources Canada. Edmonton: Canadian Forest Service, Northern Forestry Centre. https://cwfis.cfs.nrcan.gc.ca. - Skakun, R., G. Castilla, J. Metsaranta, E. Whitman, S. Rodrigue, J. Little, K. Groenewegen, and M. Coyle. 2022. “Extending the National Burned Area Composite Time Series of Wildfires in Canada.” Remote Sensing 14: 3050. ___ For detailed methodology, see download links, file “Pre-CanLaD_v2_Update_report_EN.pdf”

Deforestation in Canada is estimated with the National Deforestation Monitoring System (NDMS). Details describing this process are published here: https://cfs.nrcan.gc.ca/publications?id=36042. Deforestation is the direct human-induced conversion of forested land to non-forested land use. Canada’s National Deforestation Monitoring System (NDMS) was designed and implemented to provide information needed by Canada to meet its obligation under the United Nations Framework Convention on Climate Change (UNFCCC) to report the areas affected annually by deforestation. It also provides important information for the public, government policy makers, and scientists. To provide information about the amount of deforestation and why, where, and when it occurred in Canada, the NDMS uses deforestation mapped on a system of sample areas. The mapping is based on visual interpretation of satellite imagery supported by available ancillary information, such as high resolution imagery, forest inventory, and industrial databases, and informed by records-based information and expert knowledge. Accurate detection and mapping of deforestation events involves manual interpretation of satellite remote sensing imagery by specialized analysts. A key factor in the mapping is to distinguish deforestation from other forest cover losses that occur in Canada. The NDMS was designed to make use of all available lines of evidence and be flexible to accommodate variable resourcing levels. This system has been producing national deforestation monitoring results annually since 2006. The flexibility of the NDMS’s design makes it possible to adapt to future changes in data and resource availability, and positions the program well for sustained operational delivery into the future.

This map is the base for a series of maps that depict Canada's involvement in a range of topics, including international trade and Canada's participation in various international partnerships and organizations. The dataset includes countries, major rivers, lakes and oceans, pacific island groupings, marine annotations, and country annotations, which are available for download in both English and French. Additionally, static maps of the world as well as each individual continent will be available for download in both English and French.

The Common Attribute Schema for Forest Resource Inventories (CASFRI) is a Canadian forest resource inventory data repository. Forest resource inventory datasets in CASFRI are harmonized to a common data model so that data collected by different agencies following different standards can be used together. Participating provincial, territorial and federal government departments and agencies share current and historical map-based forest resource inventory datasets through CASFRI so that their data are available to users who’s areas of interest span multiple jurisdictions. CASFRI was originally developed by academic researchers (Cumming et al., https://doi.org/10.1139/cjfr-2014-0102). This flavour of CASFRI (CASFRIv5) was developed anew in collaboration with academic researchers at the University of Laval to provide a government version of CASFRI that is findable, accessible, interoperable, and reusable. It uses the most up-to-date forest inventory data provided by participating provincial, territorial, and federal government departments and agencies. CASFRIv5 is hosted on the Canadian Council of Forest Ministers’ data portal, the National Forest Information System (http://nfis.org).

There are six official time zones in Canada: Pacific, Mountain, Central, Eastern, Newfoundland, and Atlantic. All time zone boundaries in Canada are regulated by the provinces and territories. This record contains maps and data for both legislated Standard Time and Daylight Saving Time boundaries. Daylight Saving Time occurs every year; it starts on the second Sunday in March and ends on the first Sunday in November. However, the Yukon, Saskatchewan and parts of British Columbia, Quebec and Nunavut do not observe Daylight Saving Time. The changes to Pacific time announced on March 2, 2026, by the Government of British Columbia are not reflected in this dataset. Please consult the [Government of British Columbia's announcement](https://news.gov.bc.ca/releases/2026AG0013-000209#:~:text=B.C.%27s%20new%20time%20zone,%20Pacific%20time,%20will,and%20other%20regions%20observing%20mountain%20standard%20time) for more information. The maps and data were produced by the Canada Centre for Mapping and Earth Observation (CCMEO) at Natural Resources Canada. The time zone boundary lines were created from information provided by the National Research Council who are responsible for official time in Canada.