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 years 2010, 2015 as well as this 2020 land cover map. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2020 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 832 randomly distributed samples shows that land cover data produced with this new approach has achieved 86.9% 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)

Ice maps produced for the prevention of flooding by ice jams and the monitoring of river ice during spring floods, winter temperatures or even during problems with ice jams. The maps are derived from radar satellite images, therefore available regardless of cloud cover, from several different sources, using algorithms to classify pixels into types of ice cover. Data is only processed and displayed on the main rivers at risk. The date the image was taken and the approximate region covered by the data is shown in the layer name. Data is added several times a week, but the frequency of revisits to each river can vary between 2 days and 2 weeks. __ | Name | Period | Satellite | Resolution | Algorithm | __ | R2 | 2018 - 2022 | Radarsat 2 | 7m | 7m | 7m | Icemap-r | | IceMap-r | | R | R | R | R | R | R | R | R | R | R | 2 times a week, 7 m | 7 m | IceMap-r | 7 m | | IceMap-r | 7 m | | IceMap-r | 7 m | | Icemap-r | | R | R | R | R | R | R | R | R | R | R | R | R | 2, 8, 7 m | | IceMap-r | 7 m | | IceMap-r 12.5m | owner DGI | The different classes in the legend make it possible to differentiate the following types of ice: * __Water (dark blue) __: open water * __Water/Smooth ice (blue) __: combination of water on ice, or spaced rafts of frasil * __Smooth ice (cyan) __: or black ice, the exact term for this type of ice is “columnar ice”, due to the vertical and elongated shape of the crystals that compose it. Black ice is generally transparent because it contains few or no air bubbles. It is formed by cooling, in fairly calm water, which is why it is sometimes called “thermal ice”. Its surface is very smooth. * __Consolidated ice (light pink) __: it includes Frasil ice or snow ice. Frasil ice forms in turbulent and very cold water. Composed of fine rounded crystals. These grains accumulate and rise to the surface to form moving ice rafts. These rafts end up close enough to freeze together (agglomerated ice). It contains a lot of air bubbles. Its surface is slightly to moderately rough. * __Consolidated ice with accumulations (dark pink) __: ice cover formed by the stacking and freezing of various forms of moving ice. blocks that are superimposed or pieces of ice that are detached in one place and that are piled up in another. Moderately rough to very rough surface. The images from Radarsat-2 and RCM are obtained through a partnership between Public Safety Canada and the MSP. The ICEMAP-R algorithm developed by INRS makes it possible to identify the type of ice according to the internal roughness of the ice (presence of air bubbles) and the roughness of the surface of the ice cover (presence of blocks and accumulations). The initial version was usable for Radarsat 2. The 2022 and 2023 RCM ice maps are given as an indication (new algorithm in process), only the 2024 data are processed with the Icemap-R algorithm adapted to RCM. Since 2018, the MSP has also used images from Sentinel-1, a radar satellite from the European Space Agency with a resolution of 10 m, resampled to 12.5m for ice maps. The images are then processed by the firm Dromedaire Géo-Innovation, which uses a proprietary algorithm. The output of the various algorithms has been reclassified to obtain a comparable legend. Historical data may have presented an alternative classification. Until 2022, the legend varied between winter and thaw. LIMITATIONS: the ice map is the result of an automated radar satellite image processing process. This process involves interpretation uncertainties that may be caused by the climatic conditions that prevailed when the image was acquired (melt, presence of water on the ice) or by physical characteristics of the watercourse (presence of shoals, islands or rapids). They also depend on the resolution of the initial images. Thus, although the ice map created is representative of reality, there may be some errors in identifying ice conditions at the local level. The use of the product is optimal when combined with field observations. The web service also contains visible satellite images from Landsat (L8, L9) or Sentinel 2 (S2); in this case colored compounds (false colors to benefit from the infrared bands in particular) are used to best visualize the presence of ice.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This national map of flood susceptibility or flood prone areas is based on patterns of historic flood events as predicted by an ensemble machine learning model. The recommended use is national, provincial or regional scale and can be used as a guide for identifying areas for further investigation. The Flood Susceptibility Index (FSI) Dataset, while processed and available at 30m cell size, is not recommended for use at the pixel or street level, given the uncertainty in the modelling process and the variability of results as discussed in https://www.mdpi.com/2673-4931/25/1/18 . For additional details on the methods, tests, models and datasets used to generate this data layer, please see https://geoscan.nrcan.gc.ca/starweb/geoscan/servlet.starweb?path=geoscan/fulle.web&search1=R=329493

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). Here, permanent waters are represented by 100%, and permanent land surfaces by 0%, of water occurrence for a 30-meter by 30-meter grid. References: Pekel, J.-F., A. Cottam, N. Gorelick, A.S. Belward, 2016, High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418-422. Wang, S., J. Li, and H. A. J. Russell, 2023, Methods for Estimating Surface Water Storage Changes and Their Evaluations. Journal of Hydrometeorology, DOI: https://doi.org/10.1175/JHM-D-22-0098.1.

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per ground surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. This product consists of a national scale coverage (Canada) of monthly maps of the maximum LAI during a growing season (May-June-july-August-September) at 20m. References: L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617 Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests, Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600. https://www.sciencedirect.com/science/article/pii/S0034425723001517

FCOVER corresponds to the amount of the ground surface that is covered by vegetation, including the understory, when viewed vertically (from nadir). FCOVER is an indicator of the spatial extent of vegetation independent of land cover class. It is a dimensionless quantity that varies from 0 to 1, and as an intrinsic property of the canopy, is not dependent on satellite observation conditions.This product consists of FCOVER indicator during peak-season (June-July-August) at 100m resolution covering Canada's land mass.

Leaf area index (LAI) quantified the density of vegetation irrespective of land cover. LAI quantifies the total foliage surface area per groud surface area. LAI has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem,weather and climate modelling and monitoring. This product consists of annual maps of the maximum LAI during a grownig season (June-July-August) at 100m resolution covering Canada's land mass.

Fraction of absorbed photosynthetically active radiation (fAPAR) quantified the absorbed by green foliage. fAPAR has been identified by the Global Climate Observing System as an essential climate variable required for ecosystem, weather and climate modelling and monitoring. This product consists of a national scale coverage (Canada) of monthly maps of fAPAR during a growing season (May-June-July-August-September) at 20m resolution. References: L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617 Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests, Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600. https://www.sciencedirect.com/science/article/pii/S0034425723001517

FCOVER corresponds to the amount of the ground surface that is covered by vegetation, including the understory, when viewed vertically (from nadir). FCOVER is an indicator of the spatial extent of vegetation independent of land cover class. It is a dimensionless quantity that varies from 0 to 1, and as an intrinsic property of the canopy, is not dependent on satellite observation conditions. This product consists of a national scale coverage (Canada) of monthly maps of FCOVER indicator during a growing season (May-June-July-August-September) at 20m resolution. References: L. Brown, R. Fernandes, N. Djamai, C. Meier, N. Gobron, H. Morris, C. Canisius, G. Bai, C. Lerebourg, C. Lanconelli, M. Clerici, J. Dash. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States IISPRS J. Photogramm. Remote Sens., 175 (2021), pp. 71-87, https://doi.org/10.1016/j.isprsjprs.2021.02.020. https://www.sciencedirect.com/science/article/pii/S0924271621000617 Richard Fernandes, Luke Brown, Francis Canisius, Jadu Dash, Liming He, Gang Hong, Lucy Huang, Nhu Quynh Le, Camryn MacDougall, Courtney Meier, Patrick Osei Darko, Hemit Shah, Lynsay Spafford, Lixin Sun, 2023. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests, Remote Sensing of Environment, Volume 293, https://doi.org/10.1016/j.rse.2023.113600. https://www.sciencedirect.com/science/article/pii/S0034425723001517

The High Resolution Digital Elevation Model Mosaic provides a unique and continuous representation of the high resolution elevation data available across the country. The High Resolution Digital Elevation Model (HRDEM) product used is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The mosaic is available for both the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) from web mapping services. It is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This strategy aims to increase Canada's coverage of high-resolution elevation data and increase the accessibility of the products. Unlike the HRDEM product in the same series, which is distributed by acquisition project without integration between projects, the mosaic is created to provide a single, continuous representation of strategy data. The most recent datasets for a given territory are used to generate the mosaic. This mosaic is disseminated through the Data Cube Platform, implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The mosaic is available from Web Map Services (WMS), Web Coverage Services (WCS) and SpatioTemporal Asset Catalog (STAC) collections. Accessible data includes the Digital Terrain Model (DTM), the Digital Surface Model (DSM) and derived products such as shaded relief and slope. The mosaic is referenced to the Canadian Height Reference System 2013 (CGVD2013) which is the reference standard for orthometric heights across Canada. Source data for HRDEM datasets used to create the mosaic is acquired through multiple projects with different partners. Collaboration is a key factor to the success of the National Elevation Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.