High-resolution false-color Landsat image composite of Canada's forested ecosystems (2022). This national image product represents the Composite to Change (C2C) proxy composite image derived from thousands of Landsat images acquired between July 1 and August 30, 2022. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The overall process followed is described in (Hermosilla et al. 2016 ) with details on the generation of gap-free surface reflectance composites in ( Hermosilla et al. 2015). Following the motivation and rationale presented in White et al. (White et al. 2014), Landsat imagery is subjected to a series of processing steps to remove clouds and shadows as well as haze and other unwanted atmospheric effects. Year-on-year time series of Landsat imagery are interrogated to avoid missing values, and to ensure exhaustive spatial coverage of the national surface reflectance composites. False-colour 3-channel image (bands: shortwave infrared, SWIR1; near infrared; red)When using these data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054 (Hermosilla et al. 2016 ).

Land Features entities are: Island, Shoreline, Wooded Area, Saturated soil, Landform Feature (esker, sand\...), and Cut Line. CanVec is a digital cartographic reference product of Natural Resources Canada (NRCan). It originates from the best available data sources covering Canadian territory, offers quality topographical information in vector format, and complies with international geomatics standards. CanVec is a multi-source product coming mainly from the National Topographic Data Base (NTDB), the Mapping the North process conducted by the Canada Center for Mapping and Earth Observation (CCMEO), the Atlas of Canada data, the GeoBase initiative, and the data update using satellite imagery coverage (e.g. Landsat 7, Spot, Radarsat, etc.).Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) sensors were used to generate the circa 2010 Mosaic of Canada at 30 m spatial resolution. All scenes were processed to Standard Terrain Correction Level 1T by the United States Geological Survey (USGS). Further processing performed by the Canada Centre for Remote Sensing included conversion of sensor measurements to top of atmosphere reflectance, cloud and cloud shadow detection, re-projection, selection of best measurements, mosaic generation ,noise removal and quality control. To provide a clear sky measurement for each location in Canada, data from the years 2009, 2010, and 2011 were used, but 2010 was preferentially selected. Bands 3 (0.63-0.69 µm), 4 (0.76-0.90 µm), 5 (1.55-1.75 µm), and 7 (2.08-2.35 µm) are provided in this version as significant atmosphere effects strongly limit the quality of the blue (0.45-0.52 µm) and green (0.52-0.60 µm) bands. Multi-criteria compositing was used for the selection of the most representative pixel. For ETM+ onboard Landsat 7 a scan line malfunction caused missing lines of data in all scenes collected after May 2003. Atmosphere and target variability between scenes cause these lines to have significant radiometric differences in some cases. A Fourier transformation approach was applied to correct this occurrence. This mosaic was developed for land cover and biophysical mapping applications across Canada. Other applications of these data are also possible, but should consider the temporal and spectral limitations of the product. Research to enhance the spatial, spectral and temporal aspects are in development for future versions of moderate resolution products from historical Landsat sensors, Landsat 8, and Sentinel 2 data.

This data publication contains a set of files in which areas affected by fire or by harvest from 1984 to 2015 are identified at the level of individual 30m pixels on the Landsat grid. Details of the product development can be found in Guindon et al (2018). The change detection is based on reflectance-corrected yearly summer (July and August) Landsat mosaics from 1984 to 2015 created from individual scenes developed from USGS reflectance products (Masek et al, 2006; Vermote et al, 2006). Briefly, the change detection method uses a six-year temporal signature centered on the disturbance year to identify fire, harvest and no change. The signatures were derived from visually-interpreted disturbance or no-change polygons that were used to fit a decision tree model. The method detects about 91% of the areas harvested and 85% of the areas burned across Canada’s forests over the study period, but overestimates areas disturbed in the two initial and mostly in the two final years of the 1985 to 2015 time series. This is caused by the absence of appropriate pre-disturbance and post-disturbance data for the model-based detection and attribution. Disturbance coverage in those four years should therefore be used with caution. As in Guindon et al (2014), the method was designed to minimize commission errors and has a disturbance class attribution success rate of about 98%. The attribution success rate of disturbance year for fire is of about 69% for the exact year and of about 99% when attribution to the following year is also considered as a success. This common one-year lag is mostly due to the use of mid-summer Landsat mosaics for the analysis that will cause spring and fall events of the same year to be attributed to successive years. For example, a fire that occurred in the fall of 2004 (after July and August), will be detected and attributed to 2005, while for a fire that occurred in the spring of 2004 will be detected and attributed to 2004. The presence of clouds and shadows or image availability causes 10% of missing data annually and therefore can too delay the capture of events. The data provides uniform spatial and temporal information on fire and harvest across all provinces and territories of Canada and is intended for strategic-level analysis. Since no attention was given to other minor disturbances such as mining, road or flooding, the product should not be used for their identification. Finally, calibration datasets were developed for only three major forest pests (mountain pine beetle, eastern spruce budworm and forest tent caterpillar), but were folded within the “no-change” class in order to minimize commission errors for fire and harvest . Less common pests for which validation datasets are hard to develop were not considered and therefore could in rare circumstances generate false fire events. Considering that area having two (3.3%) to three disturbances (less than 1%) events are not common, only the most recent disturbance is provided, overlapping older disturbances in these rare case. ## Please cite this dataset as: Guindon, L., P. Villemaire, R. St-Amant, P.Y. Bernier, A. Beaudoin, F. Caron, M. Bonucelli and H. Dorion. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad ## Scientific article citation: The creation, validation and limitations of the CanLaD product are described in the Supplementary Material file associated with the following article: Guindon, L.; Bernier, P.Y.; Gauthier, S.; Stinson, G.; Villemaire, P.; Beaudoin, A. 2018. Missing forest cover gains in boreal forests explained. Ecosphere, 9 (1) Article e02094. doi:10.1002/ecs2.2094. ## Cited references: Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.

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.

In 2013, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from the BC Ministry of Agriculture and our regional AAFC colleagues.

In 2016, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) repeated the process of generating annual crop inventory digital maps using satellite imagery to for all of Canada, in support of a national crop inventory. A Decision Tree (DT) based methodology was applied using optical (Landsat-8, Sentinel-2, Gaofen-1) and radar (RADARSAT-2) based satellite images, and having a final spatial resolution of 30m. In conjunction with satellite acquisitions, ground-truth information was provided by: provincial crop insurance companies in Alberta, Saskatchewan, Manitoba, & Quebec; point observations from the BC Ministry of Agriculture, & the Ontario Ministry of Agriculture, Food and Rural Affairs; and data collection supported by our regional AAFC Research and Development Centres in St. John’s, Kentville, Charlottetown, Fredericton, Guelph, and Summerland.

In 2014, the Alberta Energy Regulator (AER) initiated a Play-Based Regulation (PBR) pilot project as a step towards implementation of the Unconventional Regulatory Framework. One of the goals of the PBR pilot is to encourage companies in the unconventional play area to work together on plans for surface development to minimize the numbers of facilities and surface impacts. This dataset is one of a series created using earth observation imagery to assess surface change caused by energy exploration. The PBR area extends from Twp. 52, Rge. 7, W 5th Mer. to Twp. 70, Rge. 5, W 6th Mer., covering the towns of Edson, Fox Creek, Mayerthorpe, Whitecourt, Swan Hills, and Valleyview. For this digital data release, a land use and land cover classification dataset was derived from 2005 Landsat multispectral imagery for the PBR pilot area. The classification contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 4 - mixed developed areas, 5 - developed areas, 6 - shoal, 7 - shrub land, 8 - grassland, 9 - agriculture areas, 10 - coniferous forest, 11 - broadleaf forest, and 12 - mixed forest. These categories can be used as baseline data for planning, managing and monitoring surface infrastructure needs and impacts.

In 2014, the Alberta Energy Regulator (AER) initiated a Play-Based Regulation (PBR) pilot project as a step towards implementation of the Unconventional Regulatory Framework. One of the goals of the PBR pilot is to encourage companies in the unconventional play area to work together on plans for surface development to minimize the numbers of facilities and surface impacts. This dataset is one of a series created using earth observation imagery to assess surface change caused by energy exploration. The PBR area extends from Twp. 52, Rge. 7, W 5th Mer. to Twp. 70, Rge. 5, W 6th Mer., covering the towns of Edson, Fox Creek, Mayerthorpe, Whitecourt, Swan Hills, and Valleyview. For this digital data release, a land use and land cover classification dataset was derived from 2005 Landsat multispectral imagery for the PBR pilot area. The classification contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 4 - mixed developed areas, 5 - developed areas, 6 - shoal, 7 - shrub land, 8 - grassland, 9 - agriculture areas, 10 - coniferous forest, 11 - broadleaf forest, and 12 - mixed forest. These categories can be used as baseline data for planning, managing and monitoring surface infrastructure needs and impacts.

In 2014, the Alberta Energy Regulator (AER) initiated a Play-Based Regulation (PBR) pilot project as a step towards implementation of the Unconventional Regulatory Framework. One of the goals of the PBR pilot is to encourage companies in the unconventional play area to work together on plans for surface development to minimize the numbers of facilities and surface impacts. This dataset is one of a series created using earth observation imagery to assess surface change caused by energy exploration. The PBR area extends from Twp. 52, Rge. 7, W 5th Mer. to Twp. 70, Rge. 5, W 6th Mer., covering the towns of Edson, Fox Creek, Mayerthorpe, Whitecourt, Swan Hills, and Valleyview. For this digital data release, a land use and land cover classification dataset was derived from 2005 Landsat multispectral imagery for the PBR pilot area. The classification contains 13 classes: 0 - unclassified, 1 - exposed land/cut blocks/harvested areas, 2 - water bodies, 3 - transitional bare surfaces, 4 - mixed developed areas, 5 - developed areas, 6 - shoal, 7 - shrub land, 8 - grassland, 9 - agriculture areas, 10 - coniferous forest, 11 - broadleaf forest, and 12 - mixed forest. These categories can be used as baseline data for planning, managing and monitoring surface infrastructure needs and impacts.