Our Imagery Base Maps and Mosaics of a number of Raster Datasets.  This includes the ASTER DEM, CDED and Shaded Relief Datasets.  As well as a number of mosaics, including SPOT, RapidEye, Landsat, and MVI Landcover data.

This layer represents Land use polygons as determined by a combination of analytic techniques, mostly using Landsat 5 image mosaics . BTM 1 was done on a federal satellite image base that was only accurate to about 250m. The images were geo-corrected, not ortho-corrected, so there is distortion in areas of high relief. This is not a multipart feature

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 ).

Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) sensors were used to generate the circa 2010 Mosaic of Canada at 30 m spatial resolution. All scenes were processed to Standard Terrain Correction Level 1T by the United States Geological Survey (USGS). Further processing performed by the Canada Centre for Remote Sensing included conversion of sensor measurements to top of atmosphere reflectance, cloud and cloud shadow detection, re-projection, selection of best measurements, mosaic generation ,noise removal and quality control. To provide a clear sky measurement for each location in Canada, data from the years 2009, 2010, and 2011 were used, but 2010 was preferentially selected. Bands 3 (0.63-0.69 µm), 4 (0.76-0.90 µm), 5 (1.55-1.75 µm), and 7 (2.08-2.35 µm) are provided in this version as significant atmosphere effects strongly limit the quality of the blue (0.45-0.52 µm) and green (0.52-0.60 µm) bands. Multi-criteria compositing was used for the selection of the most representative pixel. For ETM+ onboard Landsat 7 a scan line malfunction caused missing lines of data in all scenes collected after May 2003. Atmosphere and target variability between scenes cause these lines to have significant radiometric differences in some cases. A Fourier transformation approach was applied to correct this occurrence. This mosaic was developed for land cover and biophysical mapping applications across Canada. Other applications of these data are also possible, but should consider the temporal and spectral limitations of the product. Research to enhance the spatial, spectral and temporal aspects are in development for future versions of moderate resolution products from historical Landsat sensors, Landsat 8, and Sentinel 2 data.

The annual Landsat mosaics offer a unique portrait of the whole of Quebec. They make it possible to analyze environmental and territorial changes over more than 40 years, in particular the evolution of natural environments, urban sprawl, agricultural and forestry activities, the transformations of protected areas as well as the modifications of water networks, such as reservoirs and dams.## #Principales characteristics of mosaics###* 30m resolution* GeoTIFF format* Images acquired between July and August * Time series covering 1984 to the present * Production based on the selection of the best optical quality pixel* Designed to provide a consistent representation from year to yearThese mosaics are produced in collaboration with the Laurentian Forestry Center of the Canadian Forest Service (CFS). The SCF produces the mosaics and the Directorate-General for Geospatial Information of the Ministry of Natural Resources and Forests prepares them for dissemination.The main components are as follows:### #Images GeoTIFF by spectral bands####* Unenhanced mosaics available by spectral bands in 16-bit GeoTIFF format (ex.: SR_L5789_HARM_BP_2013_B1_gapfill_mrnf_masked.tif). Each mosaic includes bands 1 (Blue), 2 (green), 2 (green), 3 (red), 3 (red), 4 (near infrared), 5 (medium infrared) and 7 (medium infrared);* Matrix showing the year of the images where the highest quality pixels were extracted (ex.: SR_L5789_HARM_BP_2013_year_gapfill_mrnf_masked.tif);* Matrix showing the Julian days of the highest quality pixels extracted. Julian days start on January 1, 1970, hence the JJ70 label (e.g.: SR_L5789_HARM_BP_2013_JJ70_gapfill_mrnf_masked.tif). They make it possible to find the exact date when the pixels were captured in each mosaic;* Matrix showing which Landsat satellite corresponds to each higher-quality pixel extracted (ex.: SR_L5789_HARM_BP_2013_sensor_gapfill_mrnf_masked.tif).### #Images enhanced mosaics####GeoTIFF mosaics bringing together the six spectral bands enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) for each year (e.g.: Mosaique_Landsat_2013_CLAHE.tif). Notes to the user: 1. Some artifacts may be present where no Landsat images acquired during the same year were free of clouds, airveils, or shadows. 2. Mosaics from 1984 to 1989 include certain areas where there is no data due to limited acquisition capacity. The number of Landsat satellites in orbit at that time was not sufficient to ensure complete imaging coverage during the months of July and August.3. Use of images: This is not a requirement, but the following source citation may be used in publications or presentation materials to recognize the United States Geological Survey. Landsat Collection 2 Surface Reflectance (SR) courtesy of the U.S. Geological Survey.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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.

These three satellite mosaics cover the entire territory of Quebec and include images taken in 2018, 2019 and 2020. The spectral bands are blue (band 2), near infrared (band 8), and short wave infrared (band 11).The Copernicus Sentinel-2 mission includes a constellation of two satellites in orbit that are in tandem and 180° apart from each other. The orbital configuration allows coverage with a revisit rate varying from two to ten days depending on the latitude. The Sentinel-2 constellation captures multispectral satellite images at a resolution of 10 m for the next generation of operational products, such as land use maps, land change detection maps, and geophysical variables. [Product Technical Specifications] (https://diffusion.mern.gouv.qc.ca/diffusion/RGQ/Matriciel/Satellite/Regional/Mosaiques_Sentinel-2/Document/Sentinel-2_User_Handbook.pdf)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Wildfire change magnitude 1985-2022. Spectral change magnitude for wildfires that occurred from 1985 to 2022. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The wildfire change magnitude included in this product is expressed via differenced Normalized Burn Ratio (dNBR), computed as the variation between the spectral values before and after a given change event. Higher dNBR values are related to higher burn severity. Time series of Landsat data with 30-m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by wildfire for the period 1985-2022 for Canada's 650 million-hectare forested ecosystems.When using this 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).See references below for an overview on the data processing, metric calculation, change attribution and time series change detection methods applied, as well as information on independent accuracy assessment of the data.Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. (Hermosilla et al. 2015a).Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. (Hermosilla et al. 2015b).

Wildfire change year 1985-2022.Wildfire changes occurred from 1985 to 2022 displaying the year of greatest wildfire disturbance. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The information outcomes represent 38 years of wildfires in Canada's forests, derived from a single, consistent, spatially explicit data source in a fully automated manner. Time series of Landsat data with 30 m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by wildfire for the period 1985-2022 for Canada's 650-million-hectare forested ecosystems.When using this 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. https://doi.org/10.1080/17538947.2016.1187673 (Hermosilla et al. 2016).See references below for an overview on the data processing, metric calculation, change attribution, and time series change detection methods applied, as well as information on independent accuracy assessment of the data..Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., (2015). An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. https://doi.org/10.1016/j.rse.2014.11.005 (Hermosilla et al. 2015a).Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., (2015). Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. https://doi.org/10.1016/j.rse.2015.09.004 (Hermosilla et al. 2015b).Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111. https://doi.org/10.1016/j.jag.2017.07.013 (Hermosilla et al. 2017).

Harvest changes occurred from 1985 to 2022 displaying the year of greatest harvest disturbance. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The information outcomes represent 38 years of harvest activity in Canada's forests, derived from a single, consistent, spatially explicit data source in a fully automated manner. Time series of Landsat data with 30 m spatial resolution were used to characterize national trends in stand replacing forest disturbances caused by harvest for the period 1985-2022 for Canada's 650-million-hectare forested ecosystems.When using this 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. https://doi.org/10.1080/17538947.2016.1187673 ( Hermosilla et al. 2016).See references below for an overview on the data processing, metric calculation, change attribution, and time series change detection methods applied, as well as information on independent accuracy assessment of the data.Hermosilla, T., Wulder, M. A., White, J. C., Coops, N.C., Hobart, G.W., (2015). An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234. ( Hermosilla et al. 2015a).Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., (2015). Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment 170, 121-132. ( Hermosilla et al. 2015b).Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G. W. Hobart, (2017). Updating Landsat time series of surface-reflectance composites and forest change products with new observations. International Journal of Applied Earth Observation and Geoinformation. 63,104-111. https://doi.org/10.1016/j.jag.2017.07.013 (Hermosilla et al. 2017)