**Attention: there is a new version of this product (Pre-CanLaD v2)** Pre-CanLaD v2 can be found here: https://doi.org/10.23687/8d49698f-40f9-40da-b097-a3f4c90adf5a 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

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 product is a 1km resolution composite over the North American domain, which, for areas with radar coverage, can distinguish the occurrence, type and intensity of precipitation. This product uses two 1km radar composites as input: a North American composite cleaned using dual polarization technology, another particle classification radar composite (precipitation) and surface temperature from the High Resolution Deterministic Prediction System (HRDPS). The SPTP product is produced every 6 minutes.

Satellite-based forest area consistent with FAO definitions for Canada. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The forest area is based on the Food and Agricultural Organization of the United Nations (FAO) definition. The FAO definition incorporates land use, whereby trees removed by fire and harvesting for instance, remain forest as the trees will return. The included map displays the current forest cover for year as noted (i.e. 2022), plus the satellite-based temporally informed forest area where tree cover has been temporarily lost due to stand replacing disturbances (i.e., fire, harvest). For an overview of the methods, data, image processing, as well as information on accuracy assessment see Wulder et al. (2020). Open Access: Wulder, M.A., T. Hermosilla, G. Stinson, F.A. Gougeon, J.C. White, D.A. Hill, B.P. Smiley. (2020). Satellite-based time series land cover and change information to map forest area consistent with national and international reporting requirements. Forestry: An International Journal of Forest Research 93(3), 331-34, https://doi.org/10.1093/forestry/cpaa0063 . ( Wulder et al. 2020)

This dataset provides wall-to-wall maps of forest structure across Canada's 650 million hectare forested ecosystems for the year 2022, generated at a spatial resolution of 30 m. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Structure estimates include key attributes such as canopy height, canopy cover, and aboveground biomass, derived using a combination of airborne lidar and Landsat-based spectral composites. Structure models were trained using the - lidar-plot framework - (Wulder et al. 2012), which integrates co-located airborne lidar data and ground plot measurements with Landsat time-series composites (Hermosilla et al. 2016). A Nearest Neighbour imputation approach was applied to estimate structural attributes across the full extent of Canada's forested area. These nationally consistent products are intended to support strategic-level forest monitoring and assessment and are not designed for operational forest management. For further details on the methods, accuracy assessment, and source data, see Matasci et al. (2018). Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment, 216, 697-714. https://doi.org/10.1016/j.rse.2018.07.024 (Matasci et al. 2018)

This dataset provides wall-to-wall maps of forest structure across Canada's 650 million hectare forested ecosystems for the year 2022, generated at a spatial resolution of 30 m. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Structure estimates include key attributes such as canopy height, canopy cover, and aboveground biomass, derived using a combination of airborne lidar and Landsat-based spectral composites. Structure models were trained using the - lidar-plot framework - (Wulder et al. 2012), which integrates co-located airborne lidar data and ground plot measurements with Landsat time-series composites (Hermosilla et al. 2016). A Nearest Neighbour imputation approach was applied to estimate structural attributes across the full extent of Canada's forested area. These nationally consistent products are intended to support strategic-level forest monitoring and assessment and are not designed for operational forest management. For further details on the methods, accuracy assessment, and source data, see Matasci et al. (2018). Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment, 216, 697-714. https://doi.org/10.1016/j.rse.2018.07.024 (Matasci et al. 2018)

High-resolution map of leading tree species distribution for Canada’s forested ecosystems (2019). Leading tree species map produced from a 2019 Landsat image composite, geographic and climate data, elevation derivatives, and remote sensing derived phenology following the framework described in Hermosilla et al. (2022). Regional classification models were generated based on Canada’s National Forest Inventory using a 150x150 km tiling system. The leading tree species are defined by representing the most voted tree species from the Random Forests classification models (i.e. the class with the highest class membership probability).The data represents leading tree species of Canada's forested ecosystems in 2019. An image compositing window of August 1 ± 30 days was used to generate the best-available-pixel (BAP) image composites utilized as source data for the classification. The science and methods developed to generate the information outcomes shown here, that track and characterize the history of Canada’s forests, were led by Canadian Forest Service of Natural Resources Canada, developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS), partnered with the University of British Columbia, augmented by processing capacity from Digital Research Alliance of Canada. For an overview on the data, image processing, and methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2022) https://doi.org/10.1016/j.rse.2022.113276 When using this data, please cite as: Hermosilla, T., Bastyr, A., Coops, N.C., White, J.C., Wulder, M.A., 2022. Mapping the presence and distribution of tree species in Canada’s forested ecosystems. Remote Sensing of Environment 282, 113276.

Forest Percent Above Mean 2015 Percentage of first returns above the mean height (%). It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Represents the canopy cover above mean canopy height. Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from lidar plots (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. Matasci et al. 2018) Geographic extent: Canada's forested ecosystems (~ 650 Mha) Time period: 1985–2011

High-resolution annual forest land cover maps for Canada's forested ecosystems (1984-2022). It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The annual time series of forest land cover maps are national in scope (entire 650 million hectare forested ecosystem) and represent a wall-to-wall land cover characterization yearly from 1984 to 2022. These time-series land cover maps were produced from annual time-series of Landsat image composites, forest change information, and ancillary topographic and hydrologic data following the framework described in Hermosilla et al. (2022), which builds upon the approach introduced in Hermosilla et al. (2018). The methodological innovations included (i) a refined training pool derived from existing land cover products using airborne and spaceborne measures of forest structure; (ii) selection of training samples proportionally to the land cover distribution using a distance-weighted approach; and (iii) generation of regional classification models using a 150x150 km tiling system. Maps are post-processed using disturbance information to ensure logical class transitions over time using a Hidden Markov Model. Hidden Markov Models assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., 2022. Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes. Remote Sensing of Environment. Vol. 268, No. 112780. https://doi.org/10.1016/j.rse.2021.112780. (Hermosilla et al. 2022) Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2018). Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing. 44(1) 67-87.DOI: 10.1080/07038992.2018.1437719 (Hermosilla et al. 2018).