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)

Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis. - [Landcover of Canada 2010](https://open.canada.ca/data/en/dataset/c688b87f-e85f-4842-b0e1-a8f79ebf1133) - [Landcover of Canada 2015](https://open.canada.ca/data/en/dataset/4e615eae-b90c-420b-adee-2ca35896caf6) - [Landcover of Canada 2020](https://open.canada.ca/data/en/dataset/ee1580ab-a23d-4f86-a09b-79763677eb47)

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 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)

This collection is a legacy product that is no longer supported. It may not meet current government standards. This inventory presents chronologically the satellite images acquired, orthorectified and published over time by Natural Resources Canada. It is composed of imagery from the Landsat7 (1999-2003) and RADARSAT-1 (2001-2002) satellites, as well as the CanImage by-product and the control points used to process the images. Landsat7 Orthorectified Imagery: The orthoimage dataset is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and created with the most accurate control data available at the time of creation. RADARSAT-1 Orthorectified Imagery: The 5 RADARSAT-1 images (processed and distributed by RADARSAT International (RSI) complete the landsat 7 orthoimagery coverage. They are stored as raster data produced from SAR Standard 7 (S7) beam mode with a pixel size of 15 m. They have been produced in accordance with NAD83 (North American Datum of 1983) using the Universal Transverse Mercator (UTM) projection. RADARSAT-1 orthoimagery were produced with the 1:250 000 Canadian Digital Elevation Data (CDED) and photogrammetric control points generated from the Aerial Survey Data Base (ASDB). CanImage -Landsat7 Orthoimages of Canada,1:50 000: CanImage is a raster image containing information from Landsat7 orthoimages that have been resampled and based on the National Topographic System (NTS) at the 1:50 000 scale in the UTM projection. The product is distributed in datasets in GeoTIFF format. The resolution of this product is 15 metres. Landsat7 Imagery Control Points: the control points were used for the geometric correction of Landsat7 satellite imagery. They can also be used to correct vector data and for simultaneously displaying data from several sources prepared at different scales or resolutions.

__The link: *Access the data directory* is available in the section*Dataset Description Sheets; Additional Information*__. Satellite image mosaics are products designed by combining several adjacent tiles of satellite images from the Landsat or Sentinel sensor. The coverage of the mosaics varies according to the years of acquisition, ranging from southern Quebec to all of Quebec. These mosaics are designed to identify land use classes, including forest environments, agricultural environments, wetlands, and environments modified by humans. They also offer an overview of the various natural disturbances that occur on the territory. In the end, they offer easy monitoring of the evolution of forest cover and natural disturbances across territory and time. These mosaics are primarily used to support planning, monitoring, and land use planning. The resolution of the mosaics ranges from 10 to 30 meters.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Knowledge of the location of Earth’s surface water in time and space is critical to inform policy decisions on environment, wildlife, and human security. Dynamic surface water maps generally represent water occurrence, also referred to as inundation frequency, depicting the percentage of valid observations when water is detected at the surface. The location and duration of surface water varies from areas of permanent water with 100% inundation frequency where water is always observed, to areas of permanent land with 0% inundation where water never occurs. Between these two extremes are areas of ephemeral water that experience periodic flooding with inundation frequencies between 0-100%. National-scale dynamic surface water information was generated for years 1984-2023 at 30m spatial resolution from the historical Landsat archive over Canada. The complete dataset consists of annual water maps and derived products including inundation frequency and inter-annual wetting and drying trends calculated using per-pixel logistic regression. The complete dataset enables an assessment of the timing, duration, and trends towards wetting or drying at regional to national scales. Associated publication: https://www.sciencedirect.com/science/article/pii/S0034425722002358