Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as well as this 2015 land cover map. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2015 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This land cover dataset for Canada is produced using observation from Operational Land Imager (OLI) Landsat sensor. An accuracy assessment based on 806 randomly distributed samples shows that land cover data produced with this new approach has achieved 79.90% accuracy with no marked spatial disparities.- [Land Cover of Canada - Cartographic Product Collection](https://open.canada.ca/data/en/dataset/11990a35-912e-4002-b197-d57dd88836d7)

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as the first of a planned series of maps to be updated every five years, or more frequently. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2010 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This land cover dataset for Canada is produced using observation from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) Landsat sensors. An accuracy assessment based on 2811 randomly distributed samples shows that land cover data produced with this new approach has achieved 76.60% accuracy with no marked spatial disparities.- [Land Cover of Canada - Cartographic Product Collection](https://open.canada.ca/data/en/dataset/11990a35-912e-4002-b197-d57dd88836d7)

This publication contains a raster maps at 250 m resolution of the merchantable volume (m3/ha) of the mature Canadian forest available for harvesting in the next 20 years (2011 to 2031). The maps were produced from remote sensing products at a spatial resolution of 250 m on the MODIS pixel grid and 30 m on the Landsat pixel grid. More specifically, we used forest attribute data at the 250 m pixel for the years 2001 and 2011 (Beaudoin et al 2014 and 2018) combined with forest cover changes for the years 1985 to 2015 at 30 m (Guindon et al. 2017 and 2018). The map of mature forests in Canada was prepared at the forest management unit (FMU) level and therefore exclude private lands. To be considered mature (i.e. available for cutting in the next 20 years), the forest pixels of Beaudoin et al. (2018) was to have a merchantable volume per ha equal to or greater than 80% of the average merchantable volume of the pixels that were harvested between 2001 and 2011 per forest management unit. A scientific article gives additional details on the methodology: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Can. J. For. Res. 48: http://www.nrcresearchpress.com/doi/10.1139/cjfr-2018-0080Reference for this dataset: Barrette J, Paré D, Manka F, Guindon L, Bernier P, Titus B. 2018. Forecasting the spatial distribution of logging residues in Canada’s managed forests. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/dd94871a-9a20-47f5-825b-768518140f35

Wall-to-wall map of water bodies across Canada's forested ecosystems for the year 2022, derived from the "water" class of the annual Virtual Land Cover of Engine (VLCE) product. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The VLCE maps are based on Landsat image time-series composites and represent annual land cover classifications from 1984 to 2022 at a spatial resolution of 30 m. The classification process integrates forest change information and ancillary topographic and hydrologic variables, applying a regional modeling framework based on a 150x150 km tiling system ( Hermosilla et al., 2022). Training data are drawn from multiple land cover sources and selected proportionally to land cover distributions using a distance-weighted approach. Classifications are refined over time using a Hidden Markov Model to ensure consistency and reduce classification noise between years.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. 268, 112780. https://doi.org/10.1016/j.rse.2021.112780. ( Hermosilla et al., 2022)Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W. 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. https://doi.org/10.1080/07038992.2018.1437719.( Hermosilla 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)

This dataset provides a Canada-wide map of vegetation height and the delineation of the northern forest limit. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Vegetation height estimates were derived from ICESat-2 LiDAR observations, integrated with Landsat time series and topographic variables to model spatial patterns. The northern forest limit represents the transition between boreal forest and tundra, an ecologically significant zone for monitoring climate change impacts and biodiversity. Vegetation height was modeled for six time-periods including 1985-1995, 1990-2000, 1995-2005, 2000-2010, 2005-2015 and 2010-2021. Predictions for each time period represent the median conditions for that period. Predictions of height and the probability of canopy presence were generated using Random Forests models trained on spaceborne-lidar data collected by ICESat-2 from 2019-2021 and overlapping Landsat satellite imagery from 2010-2021. These Random Forests models were then applied to the entire archive of Landsat imagery, representing a period of ~35 years. This dataset provides spatially explicit prediction of vegetation height (m) along the Canadian northern forest limit at 30 m spatial resolution. Pixels with a low (< 50 %) probability of containing a vegetation canopy have been assigned a height of 0 m. The science and methods for this dataset were the result of a collaboration between the Canadian Forest Service of Natural Resources Canada, partnered with the Integrated Remote Sensing Studio (IRSS) in the Faculty of Forestry at the University of British Columbia. When using this data, please cite: Travers-Smith, H., Coops, N. C., Mulverhill, C., Wulder, M. A., Ignace, D., Lantz, T. C. (2024). Mapping vegetation height and identifying the northern forest limit across Canada using ICESat-2, Landsat time series and topographic data. Remote Sensing of Environment, 305, 114097. https://doi.org/10.1016/j.rse.2024.114097 (Travers-Smith et al. 2024). Additional details outlining application of the model to the time-series of Landsat data can be found here: Travers-Smith, H., Coops, N., Mulverhill, C., Wulder, M. A., Lantz, T. C., Ignace, D. (2025). Satellite observations reveal stable forest limits and shrub expansion across the Canadian forest-tundra ecotone. Environmental Research Letters, 20(10). https://doi.org/10.1088/1748-9326/adfc7f (Travers-Smith et al. 2025).

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).**

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).

Important noteThe AQRéseau+ product, distributed in the Adresses Québec range, is evolving and is now available under the name of [Quebec Land Transport Repository (RQTT)] (https://www.donneesquebec.ca/recherche/dataset/referentiel-quebecois-du-transport-terrestre). Consult the product sheet for up-to-date data. The web service associated with AQRéseau+ remains available in this sheet until the RQTT web service is online.The transition of Adresses Québec products will continue until the end of 2026. For more information, see the [Frequently Asked Questions (FAQ)] document (https://diffusion.mern.gouv.qc.ca/diffusion/RGQ/Vectoriel/Documentation/AdressesQuebec/FAQ_Fin_diffusion_AQ.pdf).Adresses Quebec is a road geobase that covers all of Quebec. It offers a complete road network including official names, addresses by slices and points, information for route management, postal codes (MOs) and a cartographic context.The user guide is available at ** [next link] (https://diffusion.mern.gouv.qc.ca/diffusion/RGQ/Vectoriel/Documentation/AdressesQuebec/guide_utilisateurs.pdf) **.For more information on the full range of products, refer to the ** [directory of web services and geographic data] (https://mrnf.gouv.qc.ca/ministere/cartes-information-geographique/repertoire-services-web-donnees-geographiques/) ** of the MRNF.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Data represents surface water occurrence frequency (percentage), which describes the frequency for each grid appeared as water in the 30 years time period of 1991 to 2020. The data covers Canada’s entire landmass including all transboundary watersheds, and is at 30-meter spatial resolution. The surface water occurrence frequency is derived using the surface water model of Wang et al. (2023) from all-available monthly water data observed by the Landsat satellites (Pekel et al., 2016). In this data set, permanent waters are represented by 100%, and permanent land surfaces by 0%, of water occurrence for a 30-meter by 30-meter grid. **This third party metadata element follows the Spatio Temporal Asset Catalog (STAC) specification.**