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.

| Observed / Predicted | No Fire | Fire | Low vegetation fire | Salvage logging |

|------------------------------|-----------:|----------:|--------------------------:|-----------------------:|

| No Fire | 160 | 1 | 0 | 1 |

| Fire | 4 | 148 | 7 | 2 |

| Low vegetation fire | 0 | 9 | 220 | 3 |

| Salvage 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.