This collection is a legacy product that is no longer supported. It may not meet current government standards.This inventory presents chronologically the satellite images acquired, orthorectified and published over time by Natural Resources Canada. It is composed of imagery from the Landsat7 (1999-2003) and RADARSAT-1 (2001-2002) satellites, as well as the CanImage by-product and the control points used to process the images.Landsat7 Orthorectified Imagery: The orthoimage dataset is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and created with the most accurate control data available at the time of creation.RADARSAT-1 Orthorectified Imagery: The 5 RADARSAT-1 images (processed and distributed by RADARSAT International (RSI) complete the landsat 7 orthoimagery coverage. They are stored as raster data produced from SAR Standard 7 (S7) beam mode with a pixel size of 15 m. They have been produced in accordance with NAD83 (North American Datum of 1983) using the Universal Transverse Mercator (UTM) projection. RADARSAT-1 orthoimagery were produced with the 1:250 000 Canadian Digital Elevation Data (CDED) and photogrammetric control points generated from the Aerial Survey Data Base (ASDB).CanImage -Landsat7 Orthoimages of Canada,1:50 000: CanImage is a raster image containing information from Landsat7 orthoimages that have been resampled and based on the National Topographic System (NTS) at the 1:50 000 scale in the UTM projection. The product is distributed in datasets in GeoTIFF format. The resolution of this product is 15 metres.Landsat7 Imagery Control Points: the control points were used for the geometric correction of Landsat7 satellite imagery. They can also be used to correct vector data and for simultaneously displaying data from several sources prepared at different scales or resolutions.

This collection is a legacy product that is no longer supported. It may not meet current government standards. Canada's Orthoimages 2005-2010 is the national medium-resolution imagery coverage of Canada. These digital raster data acquired by the Spot4 and Spot5 satellites comprise five spectral bands, namely: a panchromatic band having 10 m pixels and four multispectral bands having pixels of 20 m. These orthoimages were produced according to the 1983 North American Reference System (NAD83SCRS) according to the Universal Transverse Mercator (UTM) and Lambert Conformal Conic (LCC) mapping.The set of orthoimages was created with the most accurate control data available at the time of its creation: Landsat7 Imagery Control Points, National Road Network (NRN) ) and the Landsat7 Orthorectified Imagery.

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.

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 ).

The Moderate Resolution Imaging Spectroradiometer (MODIS ) is one of the most sophisticated sensors that is used in a wide range of applications related to land, ocean and atmosphere. It has 36 spectral channels with spatial resolution varying between 250 m and 1 km at nadir. MODIS channels 1 (B1, visible) and 2 (B2, near infrared) are available at 250 m spatial resolution, an additional five channels for terrestrial applications (bands B3 to B7) are available at 500 m spatial resolution, the other twenty-nine channels not included in this data set capture images with a spatial resolution of 1 km. The MODIS record begins in March 2000 and extends to present with daily measurements over the globe. This level 3 product for Canada was created from the following original Level 1 (1B) MODIS data (collection 5): a) MOD02QKM - Level 1B 250 m swath data, 5 min granules; b ) MOD02HKM - level 1B , 500 m swath data, 5 min granules; c) MOD03 - level 1 geolocation information, 1 km swath data, 5 min granules. All these data are available from the DAAC Earth Observing System Data Gateway (NASA http://ladsweb.nascom.nasa.gov/data/search.html). The terrestrial channels MODIS (B3 to B7) at 500 m spatial resolution were reduced to 250 m with an adaptive regression system and normalization described in Trishchenko et al. (2006, 2009), and the data were mapped using a Lambert Conformal Conic (LCC ) projection (Khlopenkov et al., 2008). These data were combined to form pan-Canadian images using a technique for detection of clear sky, clouds and cloud shadows with a maximum interval of 10 days (Luo et al., 2008). Atmospheric and sun-sensor geometry corrections have not been applied. For each date, data include forward and backward scattering observations as separate files. This allows data to be optimized for a given application. For general use, data from either forward or backward scattering or both should be used. Future release of the MODIS time series will correct the forward and backward scattering geometry to provide a single best observation for each pixel.

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)

In 2009 the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop inventory digital maps using satellite imagery. Focusing on the Prairie Provinces, a Decision Tree (DT) based methodology was applied using both optical (AWiFS, Landsat-5) and radar (RADARSAT-2) based satellite imagery, and having a final spatial resolution of 56m. Methods were also developed to enhance the optical classification with RADARSAT-2 imagery, addressing issues associated with cloud cover. In conjunction with satellite acquisitions, ground-truth information was provided by provincial crop insurance companies and point observations from our regional AAFC colleagues. The overall process for Crop Inventory Map includes: satellite data acquisition; field data acquisition for classification training and accuracy assessment; and, operational implementation of the classification methodology.The initial methodology was developed in partnership with AAFC Research Branch, and supported in part by the Canadian Space Agency. The long-term objective of this endeavour is to expand from the Prairies and produce an annual crop inventory of the entire agricultural extent of Canada.

The ASTER instrument that was launched onboard NASA’s Terra spacecraft in December 1999 has an along-track stereoscopic capability using two telescopes in its near infrared spectral band to acquire data from nadir and backward views. Over 1.2 million scenes (level-1A products) acquired between March 2000 and August 2008 were used to generate the ASTER Global DEM (ASTGTM) collection.  For more information, please see the metadata link above.

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 (Table 1). 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%.**Table 1.** Test set confusion matrix of the salvage logging detection random forest model. (CSV format)Observed / Predicted , No Fire , Fire , Low vegetation fire , Salvage loggingNo Fire , 160 , 1 , 0 , 1Fire , 4 , 148 , 7 , 2Low vegetation fire , 0 , 9 , 220 , 3Salvage logging , 4 , 8 , 29 , 127**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, 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.

A spatial dataset of all Crown lands in Nova Scotia. Crown lands are all or any part of the land under the administration and control of the Minister of Natural Resources and Renewables as per the Crown Lands Act. The dataset includes land in which the Nova Scotia Department of Natural Resources and Renewables has full or partial interest. Data download also available via GeoNova: https://nsgi.novascotia.ca/WSF_DDS/DDS.svc/DownloadFile?tkey=fhrTtdnDvfytwLz6&id=87 Map service view also available via GeoNova: https://www.arcgis.com/home/webmap/viewer.html?url=https%3A%2F%2Fnsgiwa.novascotia.ca%2Farcgis%2Frest%2Fservices%2FPLAN%2FPLANCrownLandsWM84V1%2FMapServer&source=sd