20191115 14184000 1.0 INSPIRE Metadata Directive TRUE 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. Mid-latidude Western Hemisphere Northern Hemisphere North America Canada Land Cover Remote Sensing Landsat Reflectance This land cover dataset is the Canadian contribution to the 30 m spatial resolution 2010 Land Cover Map of North America. Government of Canada; Natural Resources Canada; Canada Centre for Remote Sensing 2010 Land Cover of Canada 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