Canada Landsat Burned Severity product 1985-2015 (CanLaBS)

This data publication contains a set of files in which different variables related to fire burned severity (Canada Landsat Burned Severity, CanLaBS) were computed for all events in Canada between 1985 and 2015 as detected by the Canada Landsat Disturbance (CanLaD (Guindon et al. 2017 and 2018) product. Details on the creation of this product are available in Guindon et al. 2020 (https://doi.org/10.1139/cjfr-2020-0353) and in supplementary materials accompanying the publication. The current document is therefore a complement to the article and supplementary materials. The supplementary materials are referenced in the publication (cjfr-2020-0353suppla, cjfr-2020-0353supplb etc.).

This is the first Canada-wide product that aims to promote nationwide research on fire severity by making available the data used in the article. The data is in the form of grids composed of pixels at a resolution of 30m. To simplify the distribution and manipulation of the data and considering that two or three fire occurrences within a given location is rare (respectively 2.3% and less than 0.01%), only the most recent fire data are considered in the final product. For these very rare cases, from 2015 to 1985, the most recent burned areas overlap the older data. Overlapping fire count can be found in layer “CanLaBS_Nbdisturb_v0”, multiple fire events in same areas have values equal to or greater than two.

Landsat radiometric values for calculating the NBR index were derived from summer Landsat mosaics (July and August), for years 1984 to 2015 (Guindon et al. 2018). These mosaics were developed from individual USGS Landsat scenes with surface reflectance correction (Masek et al., 2006; Vermote et al., 2006). For each annual compound, the pixel with the less atmospheric opacity was selected. An algorithm was also developed to remove clouds that were not detected by the cloud masks provided with the USGS data.

Here is a general description of the layers provided and a more technical description can be found in Table 1 (see "Ressources" section below):

NBR and dNBR. All these values are multiplied by 1000. The value of dNBR represents the value obtained for NBRpre - NBRpost. It is calculated for each pixel that was classified as a fire in CanLaD, according to the corrected year (see cjfr-2020-0353suppla).
Year of fire. The fire years detected in CanLaD (Guindon et al. 2018) was corrected using different fire databases, this layer contains the correct year. (see cjfr-2020-0353suppla)
Julian Days of the Fire, based on various high-resolution products. However, this variable is only available from 1989 onwards.
Presence of salvage logging one year after the fire. Classification of satellite images detecting scarified soils (see cjfr-2020-0353suppld).
Pre-fire forest attributes: Pre-fire forest attributes values were calculated for median mosaics, from 1985 to 2000. These attributes values were derived from NFI (national forest inventory) photo-plot attributes and were spatialized. Pre-fire attribute values were created to stratify the analyses (see cjfr-2020-0353supplc). The predicted variables are as follows:

• Canopy density in percent.

• Predicted living biomass in tonnes per hectare.

• Percentage coniferous biomass proportion of total biomass.

• Percentage hardwood biomass proportion of total biomass.

• Percentage unknown species biomass proportion of total biomass. Note, as unknown species are found especially in northern areas, they are considered coniferous for the purpose of the article.

Missing remote sensing data, one year after the fire. The estimation of burned severity needs NBR data (NBRpost) in the next year after fire occurrences. NBRpost is available for 91% of the cases, but for the remaining 9%, no data were available due to the presence of clouds. For these cases, satellite data from the years following the fire were used with a regression radiometry correction. This gives values to missing data for year following the fire. This layer flags the areas that have derived data. The values of 1= one year after the fire (no regression), 2= two years after the fire (regression), 3= three years after the fire (regression) and 4= four years after the fire (no regression, set as missing data). (see cjfr-2020-0353supplb).
Areas with more than one fire disturbance between 1985 and 2015 (1=one single disturbance, 2=two or more, 3=three or more).

## Data citation:

Guindon, L., Villemaire P., Manka F., Dorion H. , Skakun R., St-Amant R., Gauthier S. : Canada Landsat Burned Severity (CanLaBS): a Canada-wide Landsat-based 30-m resolution product of burned severity since 1985

https://doi.org/10.23687/b1f61b7e-4ba6-4244-bc79-c1174f2f92cd

The creation, the validation and the limits of the CanLaBS product are describe in the text and supplementary material: Guindon, L., Gauthier, S., Manka, F., Parisien, MA, Whitman, E., Bernier, P., Beaudoin, A., Villemaire P., Skakun R. Trends in wildfire burn severity across Canada, 1985 to 2015

https://doi.org/10.1139/cjfr-2020-0353

## References cited:

Guindon, L., Villemaire, P., St-Amant, R., Bernier, P.Y., Beaudoin, A., Caron, F., Bonucelli, M., and

Dorion, H. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30m

resolution product of fire and harvest detection and attribution since 1984.

https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad

Guindon, L., Bernier, P., Gauthier, S., Stinson, G., Villemaire, P., & Beaudoin, A. (2018). Missing forest cover gains in boreal forests explained. Ecosphere, 9(1), e02094.

https://doi.org//10.1002/ecs2.2094

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.

Datasets available for download

Additional Info

Field	Value
Last Updated	October 20, 2025, 03:57 (UTC)
Created	October 20, 2025, 03:57 (UTC)
Domain / Topic Domain or topic of the dataset being cataloged.	Environment, Geoscientific Information, Imagery Base Maps Earth Cover
Title Title for the Dataset.	Canada Landsat Burned Severity product 1985-2015 (CanLaBS)
Description A description of the dataset.	This data publication contains a set of files in which different variables related to fire burned severity (Canada Landsat Burned Severity, CanLaBS) were computed for all events in Canada between 1985 and 2015 as detected by the Canada Landsat Disturbance (CanLaD (Guindon et al. 2017 and 2018) product. Details on the creation of this product are available in Guindon et al. 2020 (https://doi.org/10.1139/cjfr-2020-0353) and in supplementary materials accompanying the publication. The current document is therefore a complement to the article and supplementary materials. The supplementary materials are referenced in the publication (cjfr-2020-0353suppla, cjfr-2020-0353supplb etc.). This is the first Canada-wide product that aims to promote nationwide research on fire severity by making available the data used in the article. The data is in the form of grids composed of pixels at a resolution of 30m. To simplify the distribution and manipulation of the data and considering that two or three fire occurrences within a given location is rare (respectively 2.3% and less than 0.01%), only the most recent fire data are considered in the final product. For these very rare cases, from 2015 to 1985, the most recent burned areas overlap the older data. Overlapping fire count can be found in layer “CanLaBS_Nbdisturb_v0”, multiple fire events in same areas have values equal to or greater than two. Landsat radiometric values for calculating the NBR index were derived from summer Landsat mosaics (July and August), for years 1984 to 2015 (Guindon et al. 2018). These mosaics were developed from individual USGS Landsat scenes with surface reflectance correction (Masek et al., 2006; Vermote et al., 2006). For each annual compound, the pixel with the less atmospheric opacity was selected. An algorithm was also developed to remove clouds that were not detected by the cloud masks provided with the USGS data. Here is a general description of the layers provided and a more technical description can be found in Table 1 (see "Ressources" section below): NBR and dNBR. All these values are multiplied by 1000. The value of dNBR represents the value obtained for NBRpre - NBRpost. It is calculated for each pixel that was classified as a fire in CanLaD, according to the corrected year (see cjfr-2020-0353suppla). Year of fire. The fire years detected in CanLaD (Guindon et al. 2018) was corrected using different fire databases, this layer contains the correct year. (see cjfr-2020-0353suppla) Julian Days of the Fire, based on various high-resolution products. However, this variable is only available from 1989 onwards. Presence of salvage logging one year after the fire. Classification of satellite images detecting scarified soils (see cjfr-2020-0353suppld). Pre-fire forest attributes: Pre-fire forest attributes values were calculated for median mosaics, from 1985 to 2000. These attributes values were derived from NFI (national forest inventory) photo-plot attributes and were spatialized. Pre-fire attribute values were created to stratify the analyses (see cjfr-2020-0353supplc). The predicted variables are as follows: • Canopy density in percent. • Predicted living biomass in tonnes per hectare. • Percentage coniferous biomass proportion of total biomass. • Percentage hardwood biomass proportion of total biomass. • Percentage unknown species biomass proportion of total biomass. Note, as unknown species are found especially in northern areas, they are considered coniferous for the purpose of the article. Missing remote sensing data, one year after the fire. The estimation of burned severity needs NBR data (NBRpost) in the next year after fire occurrences. NBRpost is available for 91% of the cases, but for the remaining 9%, no data were available due to the presence of clouds. For these cases, satellite data from the years following the fire were used with a regression radiometry correction. This gives values to missing data for year following the fire. This layer flags the areas that have derived data. The values of 1= one year after the fire (no regression), 2= two years after the fire (regression), 3= three years after the fire (regression) and 4= four years after the fire (no regression, set as missing data). (see cjfr-2020-0353supplb). Areas with more than one fire disturbance between 1985 and 2015 (1=one single disturbance, 2=two or more, 3=three or more). ## Data citation: Guindon, L., Villemaire P., Manka F., Dorion H. , Skakun R., St-Amant R., Gauthier S. : Canada Landsat Burned Severity (CanLaBS): a Canada-wide Landsat-based 30-m resolution product of burned severity since 1985 https://doi.org/10.23687/b1f61b7e-4ba6-4244-bc79-c1174f2f92cd The creation, the validation and the limits of the CanLaBS product are describe in the text and supplementary material: Guindon, L., Gauthier, S., Manka, F., Parisien, MA, Whitman, E., Bernier, P., Beaudoin, A., Villemaire P., Skakun R. Trends in wildfire burn severity across Canada, 1985 to 2015 https://doi.org/10.1139/cjfr-2020-0353 ## References cited: Guindon, L., Villemaire, P., St-Amant, R., Bernier, P.Y., Beaudoin, A., Caron, F., Bonucelli, M., and Dorion, H. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad Guindon, L., Bernier, P., Gauthier, S., Stinson, G., Villemaire, P., & Beaudoin, A. (2018). Missing forest cover gains in boreal forests explained. Ecosphere, 9(1), e02094. https://doi.org//10.1002/ecs2.2094 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.
Tags / Keywords Keywords/tags categorizing the dataset.	Forest fires Form Descriptors Nature and Environment Remote sensing Science and Technology forest wildfire...
Format (CSV, XLS, TXT, PDF, etc) File format of the dataset.
Dataset Size Dataset size in megabytes.
Metadata Identifier Metadata identifier – can be used as the unique identifier for catalogue entry
Published Date Published date of the dataset.	2020-01-01
Time Period Data Span (start date) Start date of the data in the dataset.
Time Period Data Span (end date) End date of time data in the dataset.
GeoSpatial Area Data Span A spatial region or named place the dataset covers.

Field	Value
Access category Type of access granted for the dataset (open, closed, service, etc).
License License used to access the dataset.	Open Government Licence - Canada
Limits on use Limits on use of data.
Location Location of the dataset.	https://open.canada.ca/data/en/dataset/b1f61b7e-4ba6-4244-bc79-c1174f2f92cd
Data Service Data service for accessing a dataset.
Owner Owner of the dataset.	Natural Resources Canada \| Ressources naturelles Canada
Contact Point Who to contact regarding access?	Government of Canada;Natural Resources Canada;Canadian Forest Service, voice; 1 (418) 648-4492, [email protected]
Contact Point Email The email to contact regarding access?
Publisher Publisher of the dataset.
Publisher Email Email of the publisher.	[email protected]
Author Author of the dataset.
Author Email Email of the author.
Accessed At Date the data and metadata was accessed.

Field	Value
Identifier Unique identifier for the dataset.
Language Language(s) of the dataset
Link to dataset description A URL to an external document describing the dataset.
Persistent Identifier Data is identified by a persistent identifier.
Globally Unique Identifier Data is identified by a persistent and globally unique identifier.
Contains data about individuals Does the data hold data about individuals?
Contains data about identifiable individuals Does the data hold identifiable data about individual?
Contains Indigenous Data Does the data hold data about Indigenous communities?
Portal Type Platform type of the source portal.

Field	Value
Version Version of the datatset	None
Source Source of the dataset.	None
Version notes Version notes about the dataset.
Is version of another dataset Link to dataset that it is a version of.
Other versions Link to datasets that are versions of it.
Provenance Text Provenance Text of the data.
Provenance URL Provenance URL of the data.
Temporal resolution Describes how granular the date/time data in the dataset is.
GeoSpatial resolution in meters Describes how granular (in meters) geospatial data is in the dataset.
GeoSpatial resolution (in regions) Describes how granular (in regions) geospatial data is in the dataset.

Field	Value
Indigenous Community Permission Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
Community Permission Community permission (who gave permission).
The Indigenous communities the dataset is about Indigenous communities from which data is derived.

Field	Value
Number of data rows If tabular dataset, total number of rows.
Number of data columns If tabular dataset, total number of unique columns.
Number of data cells If tabular dataset, total number of cells with data.
Number of data relations If RDF dataset, total number of triples.
Number of entities If RDF dataset, total number of entities.
Number of data properties If RDF dataset, total number of unique properties used by the triples.
Data quality Describes the quality of the data in the dataset.
Metric for data quality A metric used to measure the quality of the data, such as missing values or invalid formats.

Canada Landsat Burned Severity product 1985-2015 (CanLaBS)

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