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.

A national map of Canadian Fire Behaviour Prediction (FBP) Fuel Types (FT) developed from public data sources. The resolution of the raster grid is 30m, classified from the Spatialized Canadian National Forest Inventory (SCANFI) dataset, ecozones of Canada, and the National Burned Area Composite (NBAC). The purpose of the dataset is to characterize Canadian forests into fuel types for use in Fire Behaviour Prediction calculations as well as for situational awareness of national fire potential.

This data product aimed to extend the existing pre-1985 disturbance history record by mapping wildfire, harvest, and insect outbreaks in Canadian forests between 1965 and 1984. Our geospatial data processing methodology relied on multi-layer perceptrons (MLP) trained on spectral recovery signatures to map and age these disturbances. Specific years were not assigned to insect outbreaks due to the lack of dependable training and validation data. In order to provide a more accurate data product that is compatible with existing datasets (e.g. provincial forest inventories), we used these reliable, but incomplete datasets to correct our predictions of disturbance type and year whenever they were available. Coupled with the updated Canada Landsat Disturbance (CanLaD) data product (Guindon et al. 2017), we are thus able to obtain a pan-Canadian 30m resolution disturbance history record from 1965 until 2023. The full description of the methodology and the exhaustive validation analyses are described in detail in Correia et al. (2024). The following limitations should be taken into account when using this dataset: • It is recommended to group disturbance age predictions into age classes, as this should reduce the noise present in the disturbance age estimation models. • Fire-harvest misclassification seems to be particularly common in transition zones like the southern edge of the Boreal Shield, where fires and harvest are both relatively common. • There seems to be an overestimation of 1965 fires due to a misclassification of burnt areas older than 1965 in northern, less productive areas as belonging to the beginning of our time series. • We likely detected mostly high-severity burnt areas that depict complete mortality, since the faster recovery of low-severity burns makes them more challenging to detect. • Insect outbreak detections were mostly associated with the historic eastern spruce budworm outbreak of the 1970s. Even though pixel-level insect disturbance year was not predicted, realistic estimates can be obtained by cross-checking our data product with historic reports. The following raster layers are available: • canlad_1965_1984_disturbanceType: Estimated disturbance type o 2 = Fire o 3 = Harvest o 4 = Insect • canlad_1965_1984_disturbanceYear: Estimated disturbance year o Numeric value from 1965 to 1984 • canlad_1965_1984_correctionMask: Raster indicating which predictions have been corrected with external datasets o 0 = Unconfirmed disturbance o 1 = Confirmed fire o 2 = Confirmed harvest Please cite this data product as: Correia, D. L. P., L. Guindon, and M. A. Parisien. 2024. Canada-wide Landsat-based 30-m resolution product of disturbance detection prior to 1984. https://doi.org/10.23687/660b7c6a-cdec-4c02-90c7-d63e91825c42 References: Correia, D. L. P., L. Guindon, and M. A. Parisien. 2024. Extending Canadian forest disturbance history maps prior to 1985. Ecosphere [in press]. 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

Canada's National Forest Inventory ground plots are a stratified random sample of select Canadian terrestrial ecozones. Ground plots are remeasured periodically. Data from ground plots include field measurements, compiled attributes and descriptions made at a point inside or adjacent to a photo plot. Ground plot data include individual large-tree and small-tree measurements, shrub tallies, understory vegetation sampling, vegetation cover, stump assessments, woody debris data, surface substrates, site descriptions and soil measurements.

This publication contains thirteen (13) maps of different biogeochemical and soil properties of forest ecosystems of Canada’s managed forest. A scientific article gives additional details on the methodology: Paré, D., Manka, F., Barrette, J., Augustin, F., Beguin, J. 2021. Indicators of site sensitivity to the removal of forest harvest residues at the sub-continental scale: mapping, comparisons, and challenges. Ecol. Indicators. https://dx.doi.org/10.1016/j.ecolind.2021.107516

The SMTC map and database provides the location of, and information about completed, under construction, or planned mass timber projects and manufacturing facilities in Canada. Project information includes building size, height, occupancy, mass timber materials used, year of construction, and other criteria. Manufacturer information includes facility location, materials produced, and production capacity.

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): 1. 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). 2. 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) 3. Julian Days of the Fire, based on various high-resolution products. However, this variable is only available from 1989 onwards. 4. Presence of salvage logging one year after the fire. Classification of satellite images detecting scarified soils (see cjfr-2020-0353suppld). 5. 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. 6. 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). 7. 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: 1. 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 2. 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: 1. 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 2. 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 3. 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. 4. 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.