The mapping depicts a first-order estimate of the combined volumetric percentage of excess ice in the top 5 m of permafrost from segregated, wedge, and relict ice. The estimates for the three ice types are based on modelling by O'Neill et al. (2019) (https://doi.org/10.5194/tc-13-753-2019), and informed by available published values of ground ice content and expert knowledge. The mapping offers an improved depiction of ground ice in Canada at a broad scale, incorporating current knowledge on the associations between geological and environmental conditions and ground ice type and abundance. It provides a foundation for hypothesis testing related to broad-scale controls on ground ice formation, preservation, and melt.

In 2018, Fisheries and Oceans Canada initiated the Multidisciplinary Arctic Program (MAP) – Last Ice, the first ecosystem study of the poorly characterized region of Tuvaijuittuq, where multiyear ice still resides in the Arctic Ocean. The program MAP-Last Ice takes a coordinated approach to integrate the physical, biochemical, and ecological components of the sea ice-ocean connected ecosystem and its response to climate and ocean forcings. This program provides baseline ecological knowledge for Tuvaijuittuq and, in particular, for its unique multiyear ice ecosystem. The database provides baseline data on fatty acid composition and stable isotopes signatures of sea ice communities in multi- and first-year ice in Tuvaijuittuq. The data were collected during the 2018 spring field campaign of the MAP-Last Ice Program, offshore of Canadian Forces Station (CFS) Alert, in the Lincoln Sea.

Snow and ice are important hydrological resources. Their minimum spatial extent here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. The MSI probability is derived from sequence of seventeen 10-day clear-sky composites corresponding to April, 1 to September, 20 warm period for each year since 2000. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period since 2000 have been processed with the special technology developed at the Canada Centre for Remote Sensing (CCRS) as described in Trishchenko, 2016; Trishchenko et al., 2016; 2009, 2006, Trishchenko and Ungureanu, 2021, Khlopenkov and Trishchenko, 2008, Luo et al., 2008. The presence of snow or ice is determined for each pixel of the image based on snow/ice scene identification procedure and the probability if computed for the entire warm season as a ratio of number of snow/ice flags to the total number of pixels available (less or equal to 17). The minimum snow and ice extent can be derived from the probability map by applying a certain threshold. New data version V5.0 replaces previous version V4.0 for all data available since 2000. All MSI files were reprocessed for all MODIS input data based on collection 6.1. The output format has not changed since previous version. It is described in Trishchenko (2024). The impact of input data change is small and can be detected only for time interval 2000-2015. Data starting 2016 has been already derived using MODIS collection 6.1 input.The differences between the MSI data based on MODIS Collection 5 (i.e. MSI V4) versus MODIS Collection 6.1 (i.e. MSI V5), on average, are quite small. The region-wide relative difference in the MSI extent varies from -3.97% to +1.75%. The mean value is -0.14%, the median value is 0.18% and standard deviation is 1.83%. As such, we do not expect any sizeable impact of the version change on our previous conclusions regarding trends and climate variations, except for refining the relative values of statistical parameters within the range of a few percents. References:TRISHCHENKO, A.P., 2024: Probability maps of the annual minimum snow and ice (MSI) presence over April,1 to September, 20 period since 2000 derived from MODIS 250m imagery over Canada and neighbouring regions. Data format description. CCRS, NRCan. 4pp.

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. The satellites and algorithms used according to the periods are summarized in this list:* __Image prefix__: period covered; source satellite; resolution of maps produced; algorithm used * __R2__: 2018 - 2022; Radarsat 2; 7m; ICEMAP-r* __RCM__: 2024 - now; Radarsat Constellation Mission; 5m; ICEMAP-r version 2024 * __S1__: 2018 - now; Sentinel 1; 12.5m; Arctus proprietary algorithm 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 frasil rafts* __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) __: 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 clusters (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 progress), only data since 2024 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 Arctus, 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. The web service also contains visible satellite images from Landsat satellites (the image prefixes are then L8, L9) or Sentinel 2 (prefix S2). In this case, colored compounds (false colors to benefit from infrared bands in particular) are used to best visualize the presence of ice. From 2024, the colored compound S2 used is as follows:* Red: 8A band (Near Infrared - VNIR) 20m (resampled to 10m)* Green: band 3 (Green) 10m * Blue: band 2 (Blue) 10m**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in sea ice concentration based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Sea ice concentration is represented as the percentage (%) of grid cell area. Therefore, projected change in sea ice concentration is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensembles of sea ice concentration change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in sea ice concentration (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

The Annual Minimum Snow and Ice (MSI) Extent of the Atlas of Canada National Scale Data, are data sets compiled containing annual data from 2000 to present. The data sets were derived from research published by the Canada Centre for Remote Sensing which classified satellite imagery over Canada and neighbouring regions for the continued presence or absence of snow and ice from April 1 to September 20 each year. The Atlas of Canada MSI products consist of a vector dataset and a raster time-series animation application.VECTOR DATASETThe vector dataset has been generalized to display at the scale of 1:1,000,000.TIME-SERIES ANIMATION APPLICATIONThe time-series animation application has not been generalized from its original scale (250 m pixels).The application is disseminated through the Data Cube Platform, implemented by the Canada Centre for Mapping and Earth Observation, Natural Resources Canada using geospatial big data management techniques. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The time-series is also available as a Web Map Service (WMS) and Web Coverage Service (WCS).CREDITSource data provided by Alexander P. Trishchenko, Canada Centre for Remote Sensing, Natural Resources Canada Metadata record: https://open.canada.ca/data/en/dataset/808b84a1-6356-4103-a8e9-db46d5c20fcf

IBL - Imagery, basemaps, and land cover (imageryBaseMapsEarthCover) Basemaps. For example, resources describing land cover, topographic maps, and classified and unclassified images

RESULTS opening's forest cover polygons with inventory component provided. Current forest cover submissions into RESULTS must contain attribute and map information. However, there are historical forest cover polygon information where maps are not available. Forest Cover is provided at three critical milestones of at harvesting, at achieved regeneration and at free growing. This is part of the Silviculture and Land status Tracking dataset, which includes tracking achievement of silviculture obligations on Crown Land

RESULTS opening's forest cover poylgons with silviculture component provided. Current forest cover subimssion into RESULTS must contain attribute and map information. However, there are historical forest cover polygon infomration where maps are not available. Forest Cover is provided at three critical milestones of at harvesting, at regeneration, and at free growing. This is a part o fthe Silviculture and Land Status Tracking dataset, which includes tracking achievement of silviculture obligations on Crown Land

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)